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power-system-simulation-toolbox/psst | docs/notebooks/validation/Validation07-Timeseries.ipynb | 2 | 120012 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Validation 07 - Timeseries"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import psst"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from psst.case import read_matpower\n",
"from psst.network import create_network\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Validation of case 1"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"case = read_matpower('./cases/case7.m')"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"case.load = pd.read_csv('./cases/case7.csv', index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
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BBRA9sHtE7Fw97i8i4i9bnmduRPy65ftzI+LhqqZbImJfgMy8KTO/QZljXVIb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\nDG9JkhrG8JYkqWH+P4mNiZQ3YPBjAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110e51950>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"network = create_network(case, prog='neato')\n",
"network.draw()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"<psst.case.PSSTCase(name=casematpower, Generators=2, Buses=2, Branches=1)>"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"case"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TYPE</th>\n",
" <th>PD</th>\n",
" <th>QD</th>\n",
" <th>GS</th>\n",
" <th>BS</th>\n",
" <th>AREA</th>\n",
" <th>VM</th>\n",
" <th>VA</th>\n",
" <th>BASEKV</th>\n",
" <th>ZONE</th>\n",
" <th>VMAX</th>\n",
" <th>VMIN</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Bus1</th>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>131.47</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>230</td>\n",
" <td>1</td>\n",
" <td>1.1</td>\n",
" <td>0.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Bus2</th>\n",
" <td>2</td>\n",
" <td>100</td>\n",
" <td>0.00</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>230</td>\n",
" <td>1</td>\n",
" <td>1.1</td>\n",
" <td>0.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" TYPE PD QD GS BS AREA VM VA BASEKV ZONE VMAX VMIN\n",
"Bus1 3 0 131.47 0 0 1 1 0 230 1 1.1 0.9\n",
"Bus2 2 100 0.00 0 0 1 1 0 230 1 1.1 0.9"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"case.bus"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>F_BUS</th>\n",
" <th>T_BUS</th>\n",
" <th>BR_R</th>\n",
" <th>BR_X</th>\n",
" <th>BR_B</th>\n",
" <th>RATE_A</th>\n",
" <th>RATE_B</th>\n",
" <th>RATE_C</th>\n",
" <th>TAP</th>\n",
" <th>SHIFT</th>\n",
" <th>BR_STATUS</th>\n",
" <th>ANGMIN</th>\n",
" <th>ANGMAX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Bus1</td>\n",
" <td>Bus2</td>\n",
" <td>0.00281</td>\n",
" <td>0.0281</td>\n",
" <td>0.00712</td>\n",
" <td>800</td>\n",
" <td>800</td>\n",
" <td>800</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>-360</td>\n",
" <td>360</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" F_BUS T_BUS BR_R BR_X BR_B RATE_A RATE_B RATE_C TAP SHIFT \\\n",
"0 Bus1 Bus2 0.00281 0.0281 0.00712 800 800 800 0 0 \n",
"\n",
" BR_STATUS ANGMIN ANGMAX \n",
"0 1 -360 360 "
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"case.branch"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GEN_BUS</th>\n",
" <th>PG</th>\n",
" <th>QG</th>\n",
" <th>QMAX</th>\n",
" <th>QMIN</th>\n",
" <th>VG</th>\n",
" <th>MBASE</th>\n",
" <th>GEN_STATUS</th>\n",
" <th>PMAX</th>\n",
" <th>PMIN</th>\n",
" <th>PC1</th>\n",
" <th>PC2</th>\n",
" <th>QC1MIN</th>\n",
" <th>QC1MAX</th>\n",
" <th>QC2MIN</th>\n",
" <th>QC2MAX</th>\n",
" <th>RAMP_AGC</th>\n",
" <th>RAMP_10</th>\n",
" <th>RAMP_30</th>\n",
" <th>RAMP_Q</th>\n",
" <th>APF</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>GenCo0</th>\n",
" <td>Bus1</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>-30</td>\n",
" <td>1</td>\n",
" <td>100</td>\n",
" <td>1</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GenCo1</th>\n",
" <td>Bus2</td>\n",
" <td>500</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>-30</td>\n",
" <td>1</td>\n",
" <td>100</td>\n",
" <td>1</td>\n",
" <td>500</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GEN_BUS PG QG QMAX QMIN VG MBASE GEN_STATUS PMAX PMIN PC1 \\\n",
"GenCo0 Bus1 200 0 30 -30 1 100 1 200 0 0 \n",
"GenCo1 Bus2 500 0 30 -30 1 100 1 500 0 0 \n",
"\n",
" PC2 QC1MIN QC1MAX QC2MIN QC2MAX RAMP_AGC RAMP_10 RAMP_30 \\\n",
"GenCo0 0 0 0 0 0 0 0 0 \n",
"GenCo1 0 0 0 0 0 0 0 0 \n",
"\n",
" RAMP_Q APF \n",
"GenCo0 0 0 \n",
"GenCo1 0 0 "
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"case.gen"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>MODEL</th>\n",
" <th>STARTUP</th>\n",
" <th>SHUTDOWN</th>\n",
" <th>NCOST</th>\n",
" <th>COST_1</th>\n",
" <th>COST_0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>GenCo0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GenCo1</th>\n",
" <td>1</td>\n",
" <td>5000</td>\n",
" <td>1000</td>\n",
" <td>2</td>\n",
" <td>14</td>\n",
" <td>2000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" MODEL STARTUP SHUTDOWN NCOST COST_1 COST_0\n",
"GenCo0 1 0 0 2 10 0\n",
"GenCo1 1 5000 1000 2 14 2000"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"case.gencost"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"image/png": 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xJEmVGcaSJFVmGEuSVJlhLElSZYaxJEmVGcaSJFVmGEuSVJlhLElSZYaxJEmVGcaSJFVm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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111a41790>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axs = plt.subplots(1, 1, figsize=(8, 5))\n",
"ax = axs\n",
"case.load['Bus2'].plot.bar(ax=ax)\n",
"ax.set_ylim(0, 500);"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from psst.model import build_model"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"model = build_model(case)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"<psst.model.PSSTModel(status=None)>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Welcome to the CBC MILP Solver \n",
"Version: 2.9.6 \n",
"Build Date: May 27 2016 \n",
"\n",
"command line - /usr/local/bin/cbc -mipgap 0.01 -printingOptions all -import /var/folders/wk/lcf0vgd90bx0vq1873tn04knk_djr3/T/tmpjB6l4V.pyomo.lp -import -stat=1 -solve -solu /var/folders/wk/lcf0vgd90bx0vq1873tn04knk_djr3/T/tmpjB6l4V.pyomo.soln (default strategy 1)\n",
"No match for mipgap - ? for list of commands\n",
"No match for 0.01 - ? for list of commands\n",
"Option for printingOptions changed from normal to all\n",
"Current default (if $ as parameter) for import is /var/folders/wk/lcf0vgd90bx0vq1873tn04knk_djr3/T/tmpjB6l4V.pyomo.lp\n",
"Presolve 262 (-637) rows, 383 (-370) columns and 901 (-1386) elements\n",
"Statistics for presolved model\n",
"Original problem has 48 integers (48 of which binary)\n",
"Presolved problem has 24 integers (24 of which binary)\n",
"==== 190 zero objective 3 different\n",
"190 variables have objective of 0\n",
"49 variables have objective of 1\n",
"144 variables have objective of 1e+06\n",
"==== absolute objective values 3 different\n",
"190 variables have objective of 0\n",
"49 variables have objective of 1\n",
"144 variables have objective of 1e+06\n",
"==== for integers 24 zero objective 1 different\n",
"24 variables have objective of 0\n",
"==== for integers absolute objective values 1 different\n",
"24 variables have objective of 0\n",
"===== end objective counts\n",
"\n",
"\n",
"Problem has 262 rows, 383 columns (193 with objective) and 901 elements\n",
"There are 193 singletons with objective \n",
"Column breakdown:\n",
"287 of type 0.0->inf, 48 of type 0.0->up, 0 of type lo->inf, \n",
"24 of type lo->up, 0 of type free, 0 of type fixed, \n",
"0 of type -inf->0.0, 0 of type -inf->up, 24 of type 0.0->1.0 \n",
"Row breakdown:\n",
"24 of type E 0.0, 0 of type E 1.0, 0 of type E -1.0, \n",
"48 of type E other, 0 of type G 0.0, 0 of type G 1.0, \n",
"0 of type G other, 119 of type L 0.0, 0 of type L 1.0, \n",
"71 of type L other, 0 of type Range 0.0->1.0, 0 of type Range other, \n",
"0 of type Free \n",
"Continuous objective value is 46700 - 0.00 seconds\n",
"Cgl0003I 0 fixed, 0 tightened bounds, 23 strengthened rows, 0 substitutions\n",
"Cgl0004I processed model has 238 rows, 382 columns (24 integer (24 of which binary)) and 760 elements\n",
"Cbc0038I Initial state - 3 integers unsatisfied sum - 0.8\n",
"Cbc0038I Pass 1: suminf. 0.00000 (0) obj. 6.00037e+08 iterations 12\n",
"Cbc0038I Solution found of 6.00037e+08\n",
"Cbc0038I Relaxing continuous gives 6.00037e+08\n",
"Cbc0038I Before mini branch and bound, 21 integers at bound fixed and 264 continuous\n",
"Cbc0038I Full problem 238 rows 382 columns, reduced to 14 rows 13 columns\n",
"Cbc0038I Mini branch and bound improved solution from 6.00037e+08 to 54700 (0.02 seconds)\n",
"Cbc0038I Round again with cutoff of 53900\n",
"Cbc0038I Pass 2: suminf. 0.79999 (3) obj. 53900 iterations 7\n",
"Cbc0038I Pass 3: suminf. 0.20000 (3) obj. 53900 iterations 12\n",
"Cbc0038I Pass 4: suminf. 3.08000 (7) obj. 53900 iterations 11\n",
"Cbc0038I Pass 5: suminf. 3.08000 (7) obj. 53900 iterations 10\n",
"Cbc0038I Pass 6: suminf. 3.08000 (7) obj. 53900 iterations 2\n",
"Cbc0038I Pass 7: suminf. 3.08000 (7) obj. 53900 iterations 4\n",
"Cbc0038I Pass 8: suminf. 3.92727 (8) obj. 53900 iterations 6\n",
"Cbc0038I Pass 9: suminf. 3.92727 (8) obj. 53900 iterations 6\n",
"Cbc0038I Pass 10: suminf. 0.79999 (3) obj. 53900 iterations 46\n",
"Cbc0038I Pass 11: suminf. 0.20000 (3) obj. 53900 iterations 28\n",
"Cbc0038I Pass 12: suminf. 0.20000 (3) obj. 53900 iterations 9\n",
"Cbc0038I Pass 13: suminf. 0.20000 (3) obj. 53900 iterations 12\n",
"Cbc0038I Pass 14: suminf. 0.20000 (3) obj. 53900 iterations 4\n",
"Cbc0038I Pass 15: suminf. 0.20000 (3) obj. 53900 iterations 21\n",
"Cbc0038I Pass 16: suminf. 1.50000 (5) obj. 53900 iterations 12\n",
"Cbc0038I Pass 17: suminf. 1.50000 (5) obj. 53900 iterations 5\n",
"Cbc0038I Pass 18: suminf. 2.26667 (6) obj. 53900 iterations 18\n",
"Cbc0038I Pass 19: suminf. 2.26667 (6) obj. 53900 iterations 9\n",
"Cbc0038I Pass 20: suminf. 0.79999 (3) obj. 53900 iterations 33\n",
"Cbc0038I Pass 21: suminf. 1.36000 (5) obj. 53900 iterations 24\n",
"Cbc0038I Pass 22: suminf. 1.36000 (5) obj. 53900 iterations 7\n",
"Cbc0038I Pass 23: suminf. 0.20000 (3) obj. 53900 iterations 17\n",
"Cbc0038I Pass 24: suminf. 0.80000 (4) obj. 53900 iterations 7\n",
"Cbc0038I Pass 25: suminf. 0.80000 (4) obj. 53900 iterations 8\n",
"Cbc0038I Pass 26: suminf. 0.80000 (4) obj. 53900 iterations 0\n",
"Cbc0038I Pass 27: suminf. 0.80000 (4) obj. 53900 iterations 11\n",
"Cbc0038I Pass 28: suminf. 0.80000 (4) obj. 53900 iterations 9\n",
"Cbc0038I Pass 29: suminf. 0.79999 (3) obj. 53900 iterations 23\n",
"Cbc0038I Pass 30: suminf. 0.90000 (4) obj. 53900 iterations 19\n",
"Cbc0038I Pass 31: suminf. 0.90000 (4) obj. 53900 iterations 2\n",
"Cbc0038I No solution found this major pass\n",
"Cbc0038I Before mini branch and bound, 12 integers at bound fixed and 259 continuous\n",
"Cbc0038I Full problem 238 rows 382 columns, reduced to 4 rows 4 columns\n",
"Cbc0038I Mini branch and bound did not improve solution (0.05 seconds)\n",
"Cbc0038I After 0.05 seconds - Feasibility pump exiting with objective of 54700 - took 0.03 seconds\n",
"Cbc0012I Integer solution of 54700 found by feasibility pump after 0 iterations and 0 nodes (0.05 seconds)\n",
"Cbc0038I Full problem 238 rows 382 columns, reduced to 124 rows 242 columns - 3 fixed gives 118, 233 - still too large\n",
"Cbc0038I Full problem 238 rows 382 columns, reduced to 118 rows 233 columns - too large\n",
"Cbc0006I The LP relaxation is infeasible or too expensive\n",
"Cbc0013I At root node, 0 cuts changed objective from 46700 to 54700 in 1 passes\n",
"Cbc0014I Cut generator 0 (Probing) - 4 row cuts average 2.0 elements, 1 column cuts (1 active) in 0.001 seconds - new frequency is 1\n",
"Cbc0014I Cut generator 1 (Gomory) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100\n",
"Cbc0014I Cut generator 2 (Knapsack) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100\n",
"Cbc0014I Cut generator 3 (Clique) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100\n",
"Cbc0014I Cut generator 4 (MixedIntegerRounding2) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100\n",
"Cbc0014I Cut generator 5 (FlowCover) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100\n",
"Cbc0014I Cut generator 6 (TwoMirCuts) - 0 row cuts average 0.0 elements, 0 column cuts (0 active) in 0.000 seconds - new frequency is -100\n",
"Cbc0001I Search completed - best objective 54700, took 4 iterations and 0 nodes (0.05 seconds)\n",
"Cbc0035I Maximum depth 0, 0 variables fixed on reduced cost\n",
"Cuts at root node changed objective from 46700 to 54700\n",
"Probing was tried 1 times and created 5 cuts of which 0 were active after adding rounds of cuts (0.001 seconds)\n",
"Gomory was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"Knapsack was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"Clique was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"MixedIntegerRounding2 was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"FlowCover was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"TwoMirCuts was tried 0 times and created 0 cuts of which 0 were active after adding rounds of cuts (0.000 seconds)\n",
"\n",
"Result - Optimal solution found\n",
"\n",
"Objective value: 54700.00000000\n",
"Enumerated nodes: 0\n",
"Total iterations: 4\n",
"Time (CPU seconds): 0.06\n",
"Time (Wallclock seconds): 0.06\n",
"\n",
"Total time (CPU seconds): 0.08 (Wallclock seconds): 0.09\n",
"\n"
]
}
],
"source": [
"model.solve(solver='cbc', verbose=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Input data"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>PMAX</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>GenCo0</th>\n",
" <td>200</td>\n",
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" <td>500</td>\n",
" </tr>\n",
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"text/plain": [
" PMAX\n",
"GenCo0 200\n",
"GenCo1 500"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(case.gen['PMAX'])"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
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"outputs": [
{
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"text/plain": [
" Bus1 Bus2\n",
"0 0.0 100.0\n",
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"2 0.0 100.0\n",
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]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"case.load"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"### Model Results"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
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"11 1 0\n",
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"16 1 0\n",
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"18 1 0\n",
"19 1 0\n",
"20 1 0\n",
"21 1 0\n",
"22 1 0\n",
"23 1 0"
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},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
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"source": [
"model.results.unit_commitment"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
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"21 150 0\n",
"22 100 0\n",
"23 100 0"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.results.power_generated"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
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"deletable": true,
"editable": true,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"6000"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.results.commitment_cost"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"42700"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.results.production_cost"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"6000.0"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.results.noload_cost"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"text/plain": [
" 0\n",
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.results.line_power"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from psst.plot import line_power, stacked_power_generation"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
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dlJNOOinXX3/9E36ttZYzzzwzp5xySj7/+c/nvvvuy3333Ze/+qu/ygMPPHDcZ5988sm5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\njgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110ab7b10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stacked_power_generation(model.results, legend=True)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true,
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x110db4310>"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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kSVqi4onODqkkSW3zE+AjtZUUJUnSSlDxPC1JUseoLct/KXAKcFrZ9UiSBEDPsgtYMXZI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\nJElSKyxIJUmSJEmt+P//Vq85Y23hyQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x110b23cd0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"line_power(network, model.results, hour=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
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}
| mit |
grfiv/MNIST | knn/knn.ipynb | 3 | 160186 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#KNN"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import division\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from operator import itemgetter\n",
"from tabulate import tabulate\n",
"\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.decomposition import PCA \n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.pipeline import Pipeline\n",
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.grid_search import RandomizedSearchCV\n",
"from sklearn.cross_validation import StratifiedKFold\n",
"from sklearn.cross_validation import ShuffleSplit\n",
"from sklearn.metrics import confusion_matrix, classification_report\n",
"\n",
"import sys, math, time\n",
"\n",
"# private functions\n",
"sys.path.append('/home/george/Dropbox/MNIST/src') \n",
"from MNIST_utilities import load_all_MNIST, \\\n",
" plot_confusion_matrix, \\\n",
" print_imgs, \\\n",
" plot_learning_curve\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#%qtconsole"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Load the MNIST data"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"trainX shape: (120000, 784)\n",
"trainY shape: (120000,)\n",
"\n",
"testX shape: (10000, 784)\n",
"testY shape: (10000, 1)\n"
]
}
],
"source": [
"# load MNIST here\n",
"trainX, trainY, testX, testY = \\\n",
" load_all_MNIST(portion=1.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Note that distance measures can be significantly affected by variable scale"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# ... but in this case the test-set predictions were much worse\n",
"#scaler = StandardScaler()\n",
"#trainX = scaler.fit_transform(trainX)\n",
"#testX = scaler.transform(testX)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Perform a grid search to find the best KNN parameters"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total points in the search grid: 8\n",
"Fitting 5 folds for each of 8 candidates, totalling 40 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 1 jobs | elapsed: 102.4min\n",
"[Parallel(n_jobs=-1)]: Done 32 out of 40 | elapsed: 422.3min remaining: 105.6min\n",
"[Parallel(n_jobs=-1)]: Done 40 out of 40 | elapsed: 520.3min finished\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"time in minutes 521.09\n"
]
}
],
"source": [
"t0 = time.time()\n",
"\n",
"knn = KNeighborsClassifier(weights='distance', p=2)\n",
"\n",
"pipe = Pipeline(steps=[ ('knn', knn)])\n",
"\n",
"#Parameters of pipelines can be set using ‘__’ separated parameter names:\n",
"search_grid = dict(knn__n_neighbors = np.arange(1,8+1))\n",
"\n",
"# ----------------------------------------------------------------------------\n",
"# you can't randomize if the number of grid points is less than the iterations\n",
"n_iter = 100\n",
"grid_points = 1\n",
"for value in search_grid.itervalues():\n",
" grid_points *= len(value)\n",
"print(\"Total points in the search grid: {}\".format(grid_points))\n",
"\n",
"if grid_points <= n_iter:\n",
" estimator = GridSearchCV(estimator = pipe, param_grid = search_grid,\n",
" cv = StratifiedKFold(y = trainY, n_folds = 5),\n",
" n_jobs=-1, pre_dispatch=10, verbose=1)\n",
" \n",
"else:\n",
" estimator = RandomizedSearchCV(estimator = pipe, param_distributions = search_grid, n_iter = n_iter,\n",
" cv = StratifiedKFold(y = trainY, n_folds = 5),\n",
" n_jobs=-1, pre_dispatch=10, verbose=1)\n",
"# ----------------------------------------------------------------------------\n",
"\n",
"\n",
"estimator.fit(trainX, trainY)\n",
"\n",
"print(\"\\ntime in minutes {0:.2f}\".format((time.time()-t0)/60))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Analyze the results of the grid search"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Proportion of random scores below 98%: 0.00\n",
"\n",
"mean: 0.98671, std: 0.00057, params: {'knn__n_neighbors': 1}\n",
"mean: 0.98671, std: 0.00057, params: {'knn__n_neighbors': 2}\n",
"mean: 0.98528, std: 0.00072, params: {'knn__n_neighbors': 4}\n",
"mean: 0.98500, std: 0.00077, params: {'knn__n_neighbors': 3}\n",
"mean: 0.98355, std: 0.00075, params: {'knn__n_neighbors': 5}\n",
"mean: 0.98347, std: 0.00075, params: {'knn__n_neighbors': 6}\n",
"mean: 0.98205, std: 0.00075, params: {'knn__n_neighbors': 8}\n",
"mean: 0.98193, std: 0.00070, params: {'knn__n_neighbors': 7}\n"
]
}
],
"source": [
"# what proportion of parameter combinations\n",
"# had an accuracy below 98% (anything 98% or below is not a contender)\n",
"# --------------------------------------------------------------------\n",
"\n",
"mean_score_list = [score.mean_validation_score for score in estimator.grid_scores_]\n",
"print(\"\\nProportion of scores below 98%: {0:.2f}\\n\". \\\n",
" format(sum(np.array(mean_score_list)<0.98)/len(mean_score_list)))\n",
" \n",
"\n",
"# what do the top 10 parameter combinations look like?\n",
"# ----------------------------------------------------\n",
"\n",
"for score in sorted(estimator.grid_scores_, key=itemgetter(1), reverse=True)[:10]:\n",
" print score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Predict the test data"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 974 1 1 1 0 1 2 0 0 0]\n",
" [ 0 1128 3 2 0 0 1 1 0 0]\n",
" [ 7 2 1002 3 2 0 1 12 3 0]\n",
" [ 2 0 2 986 0 8 0 3 6 3]\n",
" [ 0 2 1 0 959 0 4 2 0 14]\n",
" [ 3 1 1 7 2 863 9 2 2 2]\n",
" [ 5 3 2 0 1 2 945 0 0 0]\n",
" [ 2 7 5 0 0 0 0 1004 0 10]\n",
" [ 5 0 4 6 3 4 5 2 938 7]\n",
" [ 1 1 0 3 12 4 1 9 3 975]]\n",
"\n",
"Model accuracy: 0.9774, model misclass rate: 0.0226\n"
]
},
{
"data": {
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kSUPOSlmSNNS2WNC94evZZlKWJA21QRq+NilLkoZaNyd6zbbB+fggSdKQs1KWJA21QaqU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\nS5LUJ6yUJUlDzeF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X1Vrg/cCLkjylnVD1A+B84A97GNdVVXVDVf2G5tPmPZN8DiDJHkl2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\nCAEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0e9fa50750>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"target_names = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]\n",
"\n",
"predicted_values = estimator.predict(testX)\n",
"y_true, y_pred = testY, predicted_values\n",
"\n",
"#print(classification_report(y_true, y_pred, target_names=target_names))\n",
"\n",
"cm = confusion_matrix(y_true, y_pred) \n",
"\n",
"print(cm)\n",
"model_accuracy = sum(cm.diagonal())/len(testY)\n",
"model_misclass = 1 - model_accuracy\n",
"print(\"\\nModel accuracy: {0}, model misclass rate: {1}\".format(model_accuracy, model_misclass))\n",
"\n",
"plot_confusion_matrix(cm, target_names)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"##Print a random sample of predictions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f0ec08c98d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print_imgs(images = testX, \n",
" actual_labels = y_true, \n",
" predicted_labels = y_pred,\n",
" starting_index = np.random.randint(0, high=testY.shape[0]-36, size=1)[0],\n",
" size = 6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Learning Curves \n",
"\n",
"1. do we predict the training data well? (flat red line hugs the 1.0 line)\n",
"2. does the prediction improve with more data? (green line increases from left to right)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"t0 = time.time()\n",
"\n",
"parm_list = \"\"\n",
"for i, param in enumerate(estimator.best_params_):\n",
" if i % 3 == 0: parm_list += \"\\n\"\n",
" param_val = estimator.best_params_[param]\n",
" parm_list += param + \"=\" + str(param_val) + \" \"\n",
"\n",
"\n",
"plot_learning_curve(estimator = estimator.best_estimator_, \n",
" title = \"KNN\" + parm_list, \n",
" X = trainX, \n",
" y = trainY, \n",
" ylim = (0.85, 1.01), \n",
" cv = ShuffleSplit(n = trainX.shape[0], \n",
" n_iter = 5, \n",
" test_size = 0.2, \n",
" random_state = 0), \n",
" n_jobs = -1)\n",
"\n",
"plt.show()\n",
"\n",
"print(\"\\ntime in minutes {0:.2f}\".format((time.time()-t0)/60))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
tschinz/iPython_Workspace | 02_WP/Admin/example_py3/Winpython_checker.ipynb | 1 | 27074 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Winpython Default checker"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
"warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
"warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
"# warnings.filterwarnings(\"ignore\") # would silence all warnings"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Compilers: Numba and Cython\n",
"\n",
"##### Requirement\n",
"To get Cython working, Winpython 3.5 users should install \"Microsoft Visual C++ Build Tools 2015\" (visualcppbuildtools_full.exe, a 4 Go installation) at https://beta.visualstudio.com/download-visual-studio-vs/\n",
"\n",
"To get Numba working, not-windows10 users may have to install \"Microsoft Visual C++ 2015 Redistributable\" (vc_redist) at <https://beta.visualstudio.com/download-visual-studio-vs/>\n",
"\n",
"#### Compiler toolchains"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking Numba JIT toolchain\n",
"import numpy as np\n",
"image = np.zeros((1024, 1536), dtype = np.uint8)\n",
"\n",
"from pylab import imshow, show\n",
"from timeit import default_timer as timer\n",
"\n",
"def create_fractal(min_x, max_x, min_y, max_y, image, iters , mandelx):\n",
" height = image.shape[0]\n",
" width = image.shape[1]\n",
" pixel_size_x = (max_x - min_x) / width\n",
" pixel_size_y = (max_y - min_y) / height\n",
" \n",
" for x in range(width):\n",
" real = min_x + x * pixel_size_x\n",
" for y in range(height):\n",
" imag = min_y + y * pixel_size_y\n",
" color = mandelx(real, imag, iters)\n",
" image[y, x] = color"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Numba (a JIT Compiler)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from numba import autojit\n",
"\n",
"@autojit\n",
"def mandel(x, y, max_iters):\n",
" c = complex(x, y)\n",
" z = 0.0j\n",
" for i in range(max_iters):\n",
" z = z*z + c\n",
" if (z.real*z.real + z.imag*z.imag) >= 4:\n",
" return i\n",
" return max_iters\n",
"\n",
"start = timer()\n",
"create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20 , mandel) \n",
"dt = timer() - start\n",
"\n",
"print (\"Mandelbrot created by numba in %f s\" % dt)\n",
"imshow(image)\n",
"show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Cython (a compiler for writing C extensions for the Python language)\n",
"WinPython 3.5 and 3.6 users may not have mingwpy available, and so need \"VisualStudio C++ Community Edition 2015\" https://www.visualstudio.com/downloads/download-visual-studio-vs#d-visual-c "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Cython + Mingwpy compiler toolchain test\n",
"%load_ext Cython"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%cython -a\n",
"# with %%cython -a , full C-speed lines are shown in white, slowest python-speed lines are shown in dark yellow lines \n",
"# ==> put your cython rewrite effort on dark yellow lines\n",
"def mandel_cython(x, y, max_iters):\n",
" cdef int i \n",
" cdef double cx, cy , zx, zy\n",
" cx , cy = x, y \n",
" zx , zy =0 ,0 \n",
" for i in range(max_iters):\n",
" zx , zy = zx*zx - zy*zy + cx , zx*zy*2 + cy\n",
" if (zx*zx + zy*zy) >= 4:\n",
" return i\n",
" return max_iters"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"start = timer()\n",
"create_fractal(-2.0, 1.0, -1.0, 1.0, image, 20 , mandel_cython) \n",
"dt = timer() - start\n",
"\n",
"print (\"Mandelbrot created by cython in %f s\" % dt)\n",
"imshow(image)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Graphics: Matplotlib, Pandas, Seaborn, Holoviews, Bokeh, bqplot, ipyleaflet, plotnine"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Matplotlib\n",
"# for more examples, see: http://matplotlib.org/gallery.html\n",
"from mpl_toolkits.mplot3d import axes3d\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import cm\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.gca(projection='3d')\n",
"X, Y, Z = axes3d.get_test_data(0.05)\n",
"ax.plot_surface(X, Y, Z, rstride=8, cstride=8, alpha=0.3)\n",
"cset = ax.contourf(X, Y, Z, zdir='z', offset=-100, cmap=cm.coolwarm)\n",
"cset = ax.contourf(X, Y, Z, zdir='x', offset=-40, cmap=cm.coolwarm)\n",
"cset = ax.contourf(X, Y, Z, zdir='y', offset=40, cmap=cm.coolwarm)\n",
"\n",
"ax.set_xlabel('X')\n",
"ax.set_xlim(-40, 40)\n",
"ax.set_ylabel('Y')\n",
"ax.set_ylim(-40, 40)\n",
"ax.set_zlabel('Z')\n",
"ax.set_zlim(-100, 100)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Seaborn\n",
"# for more examples, see http://stanford.edu/~mwaskom/software/seaborn/examples/index.html\n",
"import seaborn as sns\n",
"sns.set()\n",
"df = sns.load_dataset(\"iris\")\n",
"sns.pairplot(df, hue=\"species\", size=1.5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# altair-2.0.0 example\n",
"import altair as alt\n",
"\n",
"# Uncomment/run this line to enable Altair in JupyterLab/nteract:\n",
"# alt.enable_mime_rendering() # api_v1\n",
"#alt.renderers.enable('default') # api_v2\n",
"alt.renderers.enable('notebook') # api_v2,if in Notebook\n",
"alt.Chart(df).mark_bar().encode(\n",
" x=alt.X('sepal_length', bin=alt.Bin(maxbins=50)),\n",
" y='count(*):Q',\n",
" color='species:N',\n",
" #column='species',\n",
").interactive() # api_v1 .configure_cell(width=200)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Holoviews\n",
"# for more example, see http://holoviews.org/Tutorials/index.html\n",
"import numpy as np\n",
"import holoviews as hv\n",
"hv.extension('matplotlib')\n",
"dots = np.linspace(-0.45, 0.45, 11)\n",
"fractal = hv.Image(image)\n",
"\n",
"layouts = {y: (fractal * hv.Points(fractal.sample([(i,y) for i in dots])) +\n",
" fractal.sample(y=y) )\n",
" for y in np.linspace(0, 0.45,11)}\n",
"\n",
"hv.HoloMap(layouts, kdims=['Y']).collate().cols(2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Bokeh 0.12.5 \n",
"import numpy as np\n",
"from six.moves import zip\n",
"from bokeh.plotting import figure, show, output_notebook\n",
"N = 4000\n",
"x = np.random.random(size=N) * 100\n",
"y = np.random.random(size=N) * 100\n",
"radii = np.random.random(size=N) * 1.5\n",
"colors = [\"#%02x%02x%02x\" % (int(r), int(g), 150) for r, g in zip(50+2*x, 30+2*y)]\n",
"\n",
"output_notebook()\n",
"TOOLS=\"hover,crosshair,pan,wheel_zoom,box_zoom,reset,tap,save,box_select,poly_select,lasso_select\"\n",
"\n",
"p = figure(tools=TOOLS)\n",
"p.scatter(x,y, radius=radii, fill_color=colors, fill_alpha=0.6, line_color=None)\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Datashader (holoviews+Bokeh)\n",
"import numpy as np\n",
"import pandas as pd\n",
"import holoviews as hv\n",
"import datashader as ds\n",
"from holoviews.operation.datashader import aggregate, shade, datashade, dynspread\n",
"from bokeh.models import DatetimeTickFormatter\n",
"hv.extension('bokeh')\n",
"\n",
"def time_series(T = 1, N = 100, mu = 0.1, sigma = 0.1, S0 = 20): \n",
" \"\"\"Parameterized noisy time series\"\"\"\n",
" dt = float(T)/N\n",
" t = np.linspace(0, T, N)\n",
" W = np.random.standard_normal(size = N) \n",
" W = np.cumsum(W)*np.sqrt(dt) # standard brownian motion\n",
" X = (mu-0.5*sigma**2)*t + sigma*W \n",
" S = S0*np.exp(X) # geometric brownian motion\n",
" return S\n",
"\n",
"def apply_formatter(plot, element):\n",
" plot.handles['xaxis'].formatter = DatetimeTickFormatter()\n",
" \n",
"drange = pd.date_range(start=\"2014-01-01\", end=\"2016-01-01\", freq='1D') # or '1min'\n",
"dates = drange.values.astype('int64')/10**6 # Convert dates to ints\n",
"curve = hv.Curve((dates, time_series(N=len(dates), sigma = 1)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%opts RGB [finalize_hooks=[apply_formatter] width=800]\n",
"%%opts Overlay [finalize_hooks=[apply_formatter] width=800] \n",
"%%opts Scatter [tools=['hover', 'box_select']] (line_color=\"black\" fill_color=\"red\" size=10)\n",
"\n",
"from holoviews.operation.timeseries import rolling, rolling_outlier_std\n",
"smoothed = rolling(curve, rolling_window=50)\n",
"outliers = rolling_outlier_std(curve, rolling_window=50, sigma=2)\n",
"datashade(curve, cmap=[\"blue\"]) * dynspread(datashade(smoothed, cmap=[\"red\"]),max_px=1) * outliers"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#bqplot\n",
"from IPython.display import display\n",
"from bqplot import (Figure, Map, Mercator, Orthographic, ColorScale, ColorAxis,\n",
" AlbersUSA, topo_load, Tooltip)\n",
"def_tt = Tooltip(fields=['id', 'name'])\n",
"map_mark = Map(scales={'projection': Mercator()}, tooltip=def_tt)\n",
"map_mark.interactions = {'click': 'select', 'hover': 'tooltip'}\n",
"fig = Figure(marks=[map_mark], title='Interactions Example')\n",
"display(fig)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ipyleaflet (javascript library usage)\n",
"from ipyleaflet import (\n",
" Map, Marker, TileLayer, ImageOverlay, Polyline, Polygon,\n",
" Rectangle, Circle, CircleMarker, GeoJSON, DrawControl\n",
")\n",
"from traitlets import link\n",
"center = [34.6252978589571, -77.34580993652344]\n",
"m = Map(center=[34.6252978589571, -77.34580993652344], zoom=10)\n",
"dc = DrawControl()\n",
"\n",
"def handle_draw(self, action, geo_json):\n",
" print(action)\n",
" print(geo_json)\n",
"m\n",
"m"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"dc.on_draw(handle_draw)\n",
"m.add_control(dc)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# plotnine: giving a taste of ggplot of R langage (formerly we were using ggpy)\n",
"from plotnine import ggplot, aes, geom_blank, geom_point, stat_smooth, facet_wrap, theme_bw\n",
"from plotnine.data import mtcars\n",
"ggplot(mtcars, aes(x='hp', y='wt', color='mpg')) + geom_point() +\\\n",
"facet_wrap(\"~cyl\") + theme_bw()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Ipython Notebook: Interactivity & other"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import IPython;IPython.__version__"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Audio Example : https://github.com/ipython/ipywidgets/blob/master/examples/Beat%20Frequencies.ipynb\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from ipywidgets import interactive\n",
"from IPython.display import Audio, display\n",
"def beat_freq(f1=220.0, f2=224.0):\n",
" max_time = 3\n",
" rate = 8000\n",
" times = np.linspace(0,max_time,rate*max_time)\n",
" signal = np.sin(2*np.pi*f1*times) + np.sin(2*np.pi*f2*times)\n",
" print(f1, f2, abs(f1-f2))\n",
" display(Audio(data=signal, rate=rate))\n",
" try:\n",
" plt.plot(signal); #plt.plot(v.result);\n",
" except:\n",
" pass\n",
" return signal\n",
"v = interactive(beat_freq, f1=(200.0,300.0), f2=(200.0,300.0))\n",
"display(v)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Networks graph Example : https://github.com/ipython/ipywidgets/blob/master/examples/Exploring%20Graphs.ipynb\n",
"%matplotlib inline\n",
"from ipywidgets import interact\n",
"import matplotlib.pyplot as plt\n",
"import networkx as nx\n",
"# wrap a few graph generation functions so they have the same signature\n",
"\n",
"def random_lobster(n, m, k, p):\n",
" return nx.random_lobster(n, p, p / m)\n",
"\n",
"def powerlaw_cluster(n, m, k, p):\n",
" return nx.powerlaw_cluster_graph(n, m, p)\n",
"\n",
"def erdos_renyi(n, m, k, p):\n",
" return nx.erdos_renyi_graph(n, p)\n",
"\n",
"def newman_watts_strogatz(n, m, k, p):\n",
" return nx.newman_watts_strogatz_graph(n, k, p)\n",
"\n",
"@interact(n=(2,30), m=(1,10), k=(1,10), p=(0.0, 1.0, 0.001),\n",
" generator={'lobster': random_lobster,\n",
" 'power law': powerlaw_cluster,\n",
" 'Newman-Watts-Strogatz': newman_watts_strogatz,\n",
" u'Erdős-Rényi': erdos_renyi,\n",
" })\n",
"def plot_random_graph(n, m, k, p, generator):\n",
" g = generator(n, m, k, p)\n",
" nx.draw(g)\n",
" plt.title(generator.__name__)\n",
" plt.show()\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mathematical: statsmodels, lmfit, "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking statsmodels\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"plt.style.use('ggplot')\n",
"import statsmodels.api as sm\n",
"data = sm.datasets.anes96.load_pandas()\n",
"party_ID = np.arange(7)\n",
"labels = [\"Strong Democrat\", \"Weak Democrat\", \"Independent-Democrat\",\n",
" \"Independent-Independent\", \"Independent-Republican\",\n",
" \"Weak Republican\", \"Strong Republican\"]\n",
"plt.rcParams['figure.subplot.bottom'] = 0.23 # keep labels visible\n",
"plt.rcParams['figure.figsize'] = (6.0, 4.0) # make plot larger in notebook\n",
"age = [data.exog['age'][data.endog == id] for id in party_ID]\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"plot_opts={'cutoff_val':5, 'cutoff_type':'abs',\n",
" 'label_fontsize':'small',\n",
" 'label_rotation':30}\n",
"sm.graphics.beanplot(age, ax=ax, labels=labels,\n",
" plot_opts=plot_opts)\n",
"ax.set_xlabel(\"Party identification of respondent\")\n",
"ax.set_ylabel(\"Age\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# lmfit test (from http://nbviewer.ipython.org/github/lmfit/lmfit-py/blob/master/examples/lmfit-model.ipynb)\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"def decay(t, N, tau):\n",
" return N*np.exp(-t/tau)\n",
"t = np.linspace(0, 5, num=1000)\n",
"data = decay(t, 7, 3) + np.random.randn(*t.shape)\n",
"\n",
"from lmfit import Model\n",
"\n",
"model = Model(decay, independent_vars=['t'])\n",
"result = model.fit(data, t=t, N=10, tau=1)\n",
"plt.plot(t, data) # data\n",
"plt.plot(t, decay(t=t, **result.values), color='orange', linewidth=5) # best-fit model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DataFrames: Pandas, Dask"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Pandas \n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"idx = pd.date_range('2000', '2005', freq='d', closed='left')\n",
"datas = pd.DataFrame({'Color': [ 'green' if x> 1 else 'red' for x in np.random.randn(len(idx))], \n",
" 'Measure': np.random.randn(len(idx)), 'Year': idx.year},\n",
" index=idx.date)\n",
"datas.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Split / Apply / Combine \n",
" Split your data into multiple independent groups.\n",
" Apply some function to each group.\n",
" Combine your groups back into a single data object.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"datas.query('Measure > 0').groupby(['Color','Year']).size().unstack()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Web Scraping: Beautifulsoup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking Web Scraping: beautifulsoup and requests \n",
"import requests\n",
"from bs4 import BeautifulSoup\n",
"\n",
"URL = 'http://en.wikipedia.org/wiki/Franklin,_Tennessee'\n",
"\n",
"req = requests.get(URL, headers={'User-Agent' : \"Mining the Social Web\"})\n",
"soup = BeautifulSoup(req.text, \"lxml\")\n",
"\n",
"geoTag = soup.find(True, 'geo')\n",
"\n",
"if geoTag and len(geoTag) > 1:\n",
" lat = geoTag.find(True, 'latitude').string\n",
" lon = geoTag.find(True, 'longitude').string\n",
" print ('Location is at', lat, lon)\n",
"elif geoTag and len(geoTag) == 1:\n",
" (lat, lon) = geoTag.string.split(';')\n",
" (lat, lon) = (lat.strip(), lon.strip())\n",
" print ('Location is at', lat, lon)\n",
"else:\n",
" print ('No location found')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Operations Research: Pulp"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Pulp example : minimizing the weight to carry 99 pennies\n",
"# (from Philip I Thomas)\n",
"# see https://www.youtube.com/watch?v=UmMn-N5w-lI#t=995\n",
"# Import PuLP modeler functions\n",
"from pulp import *\n",
"# The prob variable is created to contain the problem data \n",
"prob = LpProblem(\"99 pennies Problem\",LpMinimize)\n",
"\n",
"# Variables represent how many of each coin we want to carry\n",
"pennies = LpVariable(\"Number of pennies\",0,None,LpInteger)\n",
"nickels = LpVariable(\"Number of nickels\",0,None,LpInteger)\n",
"dimes = LpVariable(\"Number of dimes\",0,None,LpInteger)\n",
"quarters = LpVariable(\"Number of quarters\",0,None,LpInteger)\n",
"\n",
"# The objective function is added to 'prob' first\n",
"\n",
"# we want to minimize (LpMinimize) this \n",
"prob += 2.5 * pennies + 5 * nickels + 2.268 * dimes + 5.670 * quarters, \"Total coins Weight\"\n",
"\n",
"# We want exactly 99 cents\n",
"prob += 1 * pennies + 5 * nickels + 10 * dimes + 25 * quarters == 99, \"\"\n",
"\n",
"# The problem data is written to an .lp file\n",
"prob.writeLP(\"99cents.lp\")\n",
"prob.solve()\n",
"\n",
"# print (\"status\",LpStatus[prob.status] )\n",
"print (\"Minimal Weight to carry exactly 99 pennies is %s grams\" % value(prob.objective))\n",
"# Each of the variables is printed with it's resolved optimum value\n",
"for v in prob.variables():\n",
" print (v.name, \"=\", v.varValue)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Deep Learning: see tutorial-first-neural-network-python-keras"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Symbolic Calculation: sympy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking sympy \n",
"import sympy\n",
"a, b =sympy.symbols('a b')\n",
"e=(a+b)**5\n",
"e.expand()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## SQL tools: sqlite, Ipython-sql, sqlite_bro, baresql, db.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking Ipython-sql, sqlparse, SQLalchemy\n",
"%load_ext sql"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%sql sqlite:///.baresql.db\n",
"DROP TABLE IF EXISTS writer;\n",
"CREATE TABLE writer (first_name, last_name, year_of_death);\n",
"INSERT INTO writer VALUES ('William', 'Shakespeare', 1616);\n",
"INSERT INTO writer VALUES ('Bertold', 'Brecht', 1956);\n",
"SELECT * , sqlite_version() as sqlite_version from Writer order by Year_of_death"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking baresql\n",
"from __future__ import print_function, unicode_literals, division # line needed only if Python2.7\n",
"from baresql import baresql\n",
"bsql = baresql.baresql(connection=\"sqlite:///.baresql.db\")\n",
"bsqldf = lambda q: bsql.df(q, dict(globals(),**locals()))\n",
"\n",
"users = ['Alexander', 'Billy', 'Charles', 'Danielle', 'Esmeralda', 'Franz', 'Greg']\n",
"# We use the python 'users' list like a SQL table\n",
"sql = \"select 'Welcome ' || c0 || ' !' as say_hello, length(c0) as name_length from users$$ where c0 like '%a%' \"\n",
"bsqldf(sql)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Transfering Datas to sqlite, doing transformation in sql, going back to Pandas and Matplotlib\n",
"bsqldf('''\n",
"select Color, Year, count(*) as size \n",
"from datas$$ \n",
"where Measure > 0 \n",
"group by Color, Year'''\n",
" ).set_index(['Year', 'Color']).unstack().plot(kind='bar')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking db.py\n",
"from db import DB\n",
"db=DB(dbtype=\"sqlite\", filename=\".baresql.db\")\n",
"db.query(\"select sqlite_version() as sqlite_version ;\") "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"db.tables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# checking sqlite_bro: this should lanch a separate non-browser window with sqlite_bro's welcome\n",
"!cmd start cmd /C sqlite_bro"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# pyodbc \n",
"import pyodbc\n",
"\n",
"# look for pyodbc providers\n",
"sources = pyodbc.dataSources()\n",
"dsns = list(sources.keys())\n",
"sl = [' %s [%s]' % (dsn, sources[dsn]) for dsn in dsns]\n",
"print(\"pyodbc Providers: (beware 32/64 bit driver and python version must match)\\n\", '\\n'.join(sl))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# pythonnet\n",
"import clr\n",
"clr.AddReference(\"System.Data\")\n",
"import System.Data.OleDb as ADONET\n",
"import System.Data.Odbc as ODBCNET\n",
"import System.Data.Common as DATACOM\n",
"\n",
"table = DATACOM.DbProviderFactories.GetFactoryClasses()\n",
"print(\"\\n .NET Providers: (beware 32/64 bit driver and python version must match)\")\n",
"for row in table.Rows:\n",
" print(\" %s\" % row[table.Columns[0]])\n",
" print(\" \",[row[column] for column in table.Columns if column != table.Columns[0]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Qt libraries Demo\n",
"\n",
" \n",
"#### See [Dedicated Qt Libraries Demo](Qt_libraries_demo.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Wrap-up"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# optional scipy full test (takes up to 10 minutes)\n",
"#!cmd /C start cmd /k python.exe -c \"import scipy;scipy.test()\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip list"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
},
"widgets": {
"state": {
"056d32c70f644417b86a152d3a2385bd": {
"views": [
{
"cell_index": 14
}
]
},
"2307e84bf81346d49818eef8862360ca": {
"views": [
{
"cell_index": 22
}
]
},
"4e7a6f5db8e74905a08d4636afa3b82f": {
"views": [
{
"cell_index": 15
}
]
},
"e762d7875083491eb2933958cc3331a9": {
"views": [
{
"cell_index": 21
}
]
}
},
"version": "1.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-2.0 |
ageek/pynotebooks | python-for-DA/pandas-ch02.ipynb | 1 | 219156 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"cd \"/home/bakuda/pandas-book/\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/home/bakuda/pandas-book\n"
]
}
],
"prompt_number": 88
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"path = 'ch02/usagov_bitly_data2012-03-16-1331923249.txt'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 89
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"open(path).readline()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 90,
"text": [
"'{ \"a\": \"Mozilla\\\\/5.0 (Windows NT 6.1; WOW64) AppleWebKit\\\\/535.11 (KHTML, like Gecko) Chrome\\\\/17.0.963.78 Safari\\\\/535.11\", \"c\": \"US\", \"nk\": 1, \"tz\": \"America\\\\/New_York\", \"gr\": \"MA\", \"g\": \"A6qOVH\", \"h\": \"wfLQtf\", \"l\": \"orofrog\", \"al\": \"en-US,en;q=0.8\", \"hh\": \"1.usa.gov\", \"r\": \"http:\\\\/\\\\/www.facebook.com\\\\/l\\\\/7AQEFzjSi\\\\/1.usa.gov\\\\/wfLQtf\", \"u\": \"http:\\\\/\\\\/www.ncbi.nlm.nih.gov\\\\/pubmed\\\\/22415991\", \"t\": 1331923247, \"hc\": 1331822918, \"cy\": \"Danvers\", \"ll\": [ 42.576698, -70.954903 ] }\\n'"
]
}
],
"prompt_number": 90
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import json\n",
"records = [json.loads(line) for line in open(path)]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 91
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"records[:1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 92,
"text": [
"[{u'a': u'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11',\n",
" u'al': u'en-US,en;q=0.8',\n",
" u'c': u'US',\n",
" u'cy': u'Danvers',\n",
" u'g': u'A6qOVH',\n",
" u'gr': u'MA',\n",
" u'h': u'wfLQtf',\n",
" u'hc': 1331822918,\n",
" u'hh': u'1.usa.gov',\n",
" u'l': u'orofrog',\n",
" u'll': [42.576698, -70.954903],\n",
" u'nk': 1,\n",
" u'r': u'http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/wfLQtf',\n",
" u't': 1331923247,\n",
" u'tz': u'America/New_York',\n",
" u'u': u'http://www.ncbi.nlm.nih.gov/pubmed/22415991'}]"
]
}
],
"prompt_number": 92
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"records[0]['tz']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"u'America/New_York'"
]
}
],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tzs = [rec['tz'] for rec in records if 'tz' in rec]\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tzs[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"[u'America/New_York',\n",
" u'America/Denver',\n",
" u'America/New_York',\n",
" u'America/Sao_Paulo',\n",
" u'America/New_York']"
]
}
],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(tzs)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 19,
"text": [
"list"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(tzs)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 20,
"text": [
"3440"
]
}
],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(set(tzs))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"97"
]
}
],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%rmagic"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"ERROR: Line magic function `%rmagic` not found.\n"
]
}
],
"prompt_number": 22
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tzs[:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 23,
"text": [
"[u'America/New_York',\n",
" u'America/Denver',\n",
" u'America/New_York',\n",
" u'America/Sao_Paulo',\n",
" u'America/New_York',\n",
" u'America/New_York',\n",
" u'Europe/Warsaw',\n",
" u'',\n",
" u'',\n",
" u'']"
]
}
],
"prompt_number": 23
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from collections import Counter"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 25
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"counts = Counter(tzs)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 26
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"counts.most_common(10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 28,
"text": [
"[(u'America/New_York', 1251),\n",
" (u'', 521),\n",
" (u'America/Chicago', 400),\n",
" (u'America/Los_Angeles', 382),\n",
" (u'America/Denver', 191),\n",
" (u'Europe/London', 74),\n",
" (u'Asia/Tokyo', 37),\n",
" (u'Pacific/Honolulu', 36),\n",
" (u'Europe/Madrid', 35),\n",
" (u'America/Sao_Paulo', 33)]"
]
}
],
"prompt_number": 28
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 93
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.DataFrame(records)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 95,
"text": [
"(3560, 18)"
]
}
],
"prompt_number": 95
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['tz'][:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 96,
"text": [
"0 America/New_York\n",
"1 America/Denver\n",
"2 America/New_York\n",
"3 America/Sao_Paulo\n",
"4 America/New_York\n",
"5 America/New_York\n",
"6 Europe/Warsaw\n",
"7 \n",
"8 \n",
"9 \n",
"Name: tz, dtype: object"
]
}
],
"prompt_number": 96
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tz_counts = df['tz'].value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 35
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tz_counts[:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 36,
"text": [
"America/New_York 1251\n",
" 521\n",
"America/Chicago 400\n",
"America/Los_Angeles 382\n",
"America/Denver 191\n",
"Europe/London 74\n",
"Asia/Tokyo 37\n",
"Pacific/Honolulu 36\n",
"Europe/Madrid 35\n",
"America/Sao_Paulo 33\n",
"dtype: int64"
]
}
],
"prompt_number": 36
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clean_tz = df['tz'].fillna('Missing')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 37
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"clean_tz[clean_tz=='']='Unknown' "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tz_counts = clean_tz.value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tz_counts[:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 40,
"text": [
"America/New_York 1251\n",
"Unknown 521\n",
"America/Chicago 400\n",
"America/Los_Angeles 382\n",
"America/Denver 191\n",
"Missing 120\n",
"Europe/London 74\n",
"Asia/Tokyo 37\n",
"Pacific/Honolulu 36\n",
"Europe/Madrid 35\n",
"dtype: int64"
]
}
],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tz_counts[:10].plot(kind='bar', rot=0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 41,
"text": [
"<matplotlib.axes.AxesSubplot at 0xb273d6c>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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V1dVISkpCaGhoR5RmjDFmgA5ZVjI1NcXatWsxfvx41NbWYvbs2Rg8eHBHlGaM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\nYII=\n",
"text": [
"<matplotlib.figure.Figure at 0xb27348c>"
]
}
],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"tz_counts[:10].plot(kind='barh', rot=0)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 42,
"text": [
"<matplotlib.axes.AxesSubplot at 0xb228cac>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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qC/4eK2OMMWaluLDaoMpLKsppatr0GQDyH6eRc345Zwc4v9Tknl9MfFawTZJf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\nIi6sjDHGmIj+P4WjBwlmNooIAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0xb21558c>"
]
}
],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['a'][:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 43,
"text": [
"0 Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...\n",
"1 GoogleMaps/RochesterNY\n",
"2 Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...\n",
"3 Mozilla/5.0 (Macintosh; Intel Mac OS X 10_6_8)...\n",
"4 Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...\n",
"Name: a, dtype: object"
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"s = df['a'][0]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"s"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 45,
"text": [
"u'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/535.11 (KHTML, like Gecko) Chrome/17.0.963.78 Safari/535.11'"
]
}
],
"prompt_number": 45
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"s.split()[0]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 47,
"text": [
"u'Mozilla/5.0'"
]
}
],
"prompt_number": 47
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"results = pd.Series([x.split()[0] for x in df['a'].dropna()])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#df.tz\n",
"# same as below\n",
"df['tz'][:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 52,
"text": [
"0 America/New_York\n",
"1 America/Denver\n",
"2 America/New_York\n",
"3 America/Sao_Paulo\n",
"4 America/New_York\n",
"Name: tz, dtype: object"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"results[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 54,
"text": [
"0 Mozilla/5.0\n",
"1 GoogleMaps/RochesterNY\n",
"2 Mozilla/4.0\n",
"3 Mozilla/5.0\n",
"4 Mozilla/5.0\n",
"dtype: object"
]
}
],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%load_ext rmagic"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"install.packages(\"nycflights13\", repos='http://cran.us.r\u2010project.org')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/usr/local/lib/python2.7/dist-packages/ipython-1.1.0-py2.7.egg/IPython/extensions/rmagic.py:194: UserWarning: Installing package into \u2018/home/bakuda/R/i686-pc-linux-gnu-library/3.2\u2019\n",
"(as \u2018lib\u2019 is unspecified)\n",
"\n",
" value = ri.baseenv['eval'](ri.parse(line))\n"
]
}
],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"library(nycflights13)\n",
"write.csv(flights, \"flights.csv\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flights = pd.read_csv(\"flights.csv\", index_col=0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 59
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"flights.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 60,
"text": [
"(336776, 16)"
]
}
],
"prompt_number": 60
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"head(flights)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 62
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"search()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 63
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#flights.head()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 100
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.loc[:2,:]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>_heartbeat_</th>\n",
" <th>a</th>\n",
" <th>al</th>\n",
" <th>c</th>\n",
" <th>cy</th>\n",
" <th>g</th>\n",
" <th>gr</th>\n",
" <th>h</th>\n",
" <th>hc</th>\n",
" <th>hh</th>\n",
" <th>kw</th>\n",
" <th>l</th>\n",
" <th>ll</th>\n",
" <th>nk</th>\n",
" <th>r</th>\n",
" <th>t</th>\n",
" <th>tz</th>\n",
" <th>u</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td> Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi...</td>\n",
" <td> en-US,en;q=0.8</td>\n",
" <td> US</td>\n",
" <td> Danvers</td>\n",
" <td> A6qOVH</td>\n",
" <td> MA</td>\n",
" <td> wfLQtf</td>\n",
" <td> 1331822918</td>\n",
" <td> 1.usa.gov</td>\n",
" <td> NaN</td>\n",
" <td> orofrog</td>\n",
" <td> [42.576698, -70.954903]</td>\n",
" <td> 1</td>\n",
" <td> http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/...</td>\n",
" <td> 1331923247</td>\n",
" <td> America/New_York</td>\n",
" <td> http://www.ncbi.nlm.nih.gov/pubmed/22415991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td> GoogleMaps/RochesterNY</td>\n",
" <td> NaN</td>\n",
" <td> US</td>\n",
" <td> Provo</td>\n",
" <td> mwszkS</td>\n",
" <td> UT</td>\n",
" <td> mwszkS</td>\n",
" <td> 1308262393</td>\n",
" <td> j.mp</td>\n",
" <td> NaN</td>\n",
" <td> bitly</td>\n",
" <td> [40.218102, -111.613297]</td>\n",
" <td> 0</td>\n",
" <td> http://www.AwareMap.com/</td>\n",
" <td> 1331923249</td>\n",
" <td> America/Denver</td>\n",
" <td> http://www.monroecounty.gov/etc/911/rss.php</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td> Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ...</td>\n",
" <td> en-US</td>\n",
" <td> US</td>\n",
" <td> Washington</td>\n",
" <td> xxr3Qb</td>\n",
" <td> DC</td>\n",
" <td> xxr3Qb</td>\n",
" <td> 1331919941</td>\n",
" <td> 1.usa.gov</td>\n",
" <td> NaN</td>\n",
" <td> bitly</td>\n",
" <td> [38.9007, -77.043098]</td>\n",
" <td> 1</td>\n",
" <td> http://t.co/03elZC4Q</td>\n",
" <td> 1331923250</td>\n",
" <td> America/New_York</td>\n",
" <td> http://boxer.senate.gov/en/press/releases/0316...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows \u00d7 18 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 66,
"text": [
" _heartbeat_ a \\\n",
"0 NaN Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKi... \n",
"1 NaN GoogleMaps/RochesterNY \n",
"2 NaN Mozilla/4.0 (compatible; MSIE 8.0; Windows NT ... \n",
"\n",
" al c cy g gr h hc hh \\\n",
"0 en-US,en;q=0.8 US Danvers A6qOVH MA wfLQtf 1331822918 1.usa.gov \n",
"1 NaN US Provo mwszkS UT mwszkS 1308262393 j.mp \n",
"2 en-US US Washington xxr3Qb DC xxr3Qb 1331919941 1.usa.gov \n",
"\n",
" kw l ll nk \\\n",
"0 NaN orofrog [42.576698, -70.954903] 1 \n",
"1 NaN bitly [40.218102, -111.613297] 0 \n",
"2 NaN bitly [38.9007, -77.043098] 1 \n",
"\n",
" r t \\\n",
"0 http://www.facebook.com/l/7AQEFzjSi/1.usa.gov/... 1331923247 \n",
"1 http://www.AwareMap.com/ 1331923249 \n",
"2 http://t.co/03elZC4Q 1331923250 \n",
"\n",
" tz u \n",
"0 America/New_York http://www.ncbi.nlm.nih.gov/pubmed/22415991 \n",
"1 America/Denver http://www.monroecounty.gov/etc/911/rss.php \n",
"2 America/New_York http://boxer.senate.gov/en/press/releases/0316... \n",
"\n",
"[3 rows x 18 columns]"
]
}
],
"prompt_number": 66
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(records)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 67,
"text": [
"list"
]
}
],
"prompt_number": 67
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(records[0])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 68,
"text": [
"dict"
]
}
],
"prompt_number": 68
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 69,
"text": [
"Index([u'_heartbeat_', u'a', u'al', u'c', u'cy', u'g', u'gr', u'h', u'hc', u'hh', u'kw', u'l', u'll', u'nk', u'r', u't', u'tz', u'u'], dtype='object')"
]
}
],
"prompt_number": 69
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 74,
"text": [
"(3560, 18)"
]
}
],
"prompt_number": 74
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.to_csv('/home/bakuda/pandas-book/ch02/bitly-data-for-R.csv', encoding='utf8', header=True, sep=',')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.DataFrame(records)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#df.loc[10:18]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 99
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"results.value_counts()[:10].plot(kind='barh')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 103,
"text": [
"<matplotlib.axes.AxesSubplot at 0xe0531ec>"
]
},
{
"metadata": {},
"output_type": "display_data",
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rr+jZsye6d++OgoIC9OrVq8Y+/fbbb0L5t99+M5iBM8YYez7wwP2U0tPTERISIuSmc3Nz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\nwU1ERKQjHNxEREQ68jdlyLykjOF/1gAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0xd43db6c>"
]
}
],
"prompt_number": 103
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cdf = df[df['a'].notnull()]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 104
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cdf.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 105,
"text": [
"(3440, 18)"
]
}
],
"prompt_number": 105
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cdf.groupby('a')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 106,
"text": [
"<pandas.core.groupby.DataFrameGroupBy object at 0xd43caec>"
]
}
],
"prompt_number": 106
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"results.value_counts()[:20].plot(kind='barh')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 111,
"text": [
"<matplotlib.axes.AxesSubplot at 0x1dbed58c>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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vbw8DAwO0adMG/v7+MDU1BQDY29ujsLBQxdRFJBKhrKyMW8fOyMjAwoULIRAI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\n3BwOh8Ph6BH/B+awi8RwuG0pAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x1e8e56ec>"
]
}
],
"prompt_number": 111
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"os_types = np.where(cdf['a'].str.contains('Windows'), 'Windows', 'Not Windows')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 112
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"os_types[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 114,
"text": [
"array(['Windows', 'Not Windows', 'Windows', 'Not Windows', 'Windows'], \n",
" dtype='|S11')"
]
}
],
"prompt_number": 114
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"by_tz_os = cdf.groupby(['tz',os_types])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 116
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"by_tz_os.size()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 118,
"text": [
"tz \n",
" Not Windows 245\n",
" Windows 276\n",
"Africa/Cairo Windows 3\n",
"Africa/Casablanca Windows 1\n",
"Africa/Ceuta Windows 2\n",
"Africa/Johannesburg Windows 1\n",
"Africa/Lusaka Windows 1\n",
"America/Anchorage Not Windows 4\n",
" Windows 1\n",
"America/Argentina/Buenos_Aires Not Windows 1\n",
"America/Argentina/Cordoba Windows 1\n",
"America/Argentina/Mendoza Windows 1\n",
"America/Bogota Not Windows 1\n",
" Windows 2\n",
"America/Caracas Windows 1\n",
"...\n",
"Europe/Skopje Windows 1\n",
"Europe/Sofia Windows 1\n",
"Europe/Stockholm Not Windows 2\n",
" Windows 12\n",
"Europe/Uzhgorod Windows 1\n",
"Europe/Vienna Not Windows 3\n",
" Windows 3\n",
"Europe/Vilnius Windows 2\n",
"Europe/Volgograd Windows 1\n",
"Europe/Warsaw Not Windows 1\n",
" Windows 15\n",
"Europe/Zurich Not Windows 4\n",
"Pacific/Auckland Not Windows 3\n",
" Windows 8\n",
"Pacific/Honolulu Windows 36\n",
"Length: 149, dtype: int64"
]
}
],
"prompt_number": 118
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"agg_counts = by_tz_os.size().unstack().fillna(0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 119
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"agg_counts[:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Not Windows</th>\n",
" <th>Windows</th>\n",
" </tr>\n",
" <tr>\n",
" <th>tz</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th></th>\n",
" <td> 245</td>\n",
" <td> 276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Cairo</th>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Casablanca</th>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Ceuta</th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Johannesburg</th>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Lusaka</th>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Anchorage</th>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Argentina/Buenos_Aires</th>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Argentina/Cordoba</th>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Argentina/Mendoza</th>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows \u00d7 2 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 120,
"text": [
" Not Windows Windows\n",
"tz \n",
" 245 276\n",
"Africa/Cairo 0 3\n",
"Africa/Casablanca 0 1\n",
"Africa/Ceuta 0 2\n",
"Africa/Johannesburg 0 1\n",
"Africa/Lusaka 0 1\n",
"America/Anchorage 4 1\n",
"America/Argentina/Buenos_Aires 1 0\n",
"America/Argentina/Cordoba 0 1\n",
"America/Argentina/Mendoza 0 1\n",
"\n",
"[10 rows x 2 columns]"
]
}
],
"prompt_number": 120
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# Unstack is like R::tidyr:spread()\n",
"by_tz_os.size().unstack()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Not Windows</th>\n",
" <th>Windows</th>\n",
" </tr>\n",
" <tr>\n",
" <th>tz</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th></th>\n",
" <td> 245</td>\n",
" <td> 276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Cairo</th>\n",
" <td> NaN</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Casablanca</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Ceuta</th>\n",
" <td> NaN</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Johannesburg</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Lusaka</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Anchorage</th>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Argentina/Buenos_Aires</th>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Argentina/Cordoba</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Argentina/Mendoza</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Bogota</th>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Caracas</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Chicago</th>\n",
" <td> 115</td>\n",
" <td> 285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Chihuahua</th>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Costa_Rica</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Denver</th>\n",
" <td> 132</td>\n",
" <td> 59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Edmonton</th>\n",
" <td> 2</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Guayaquil</th>\n",
" <td> 2</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Halifax</th>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Indianapolis</th>\n",
" <td> 8</td>\n",
" <td> 12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/La_Paz</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Lima</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Los_Angeles</th>\n",
" <td> 130</td>\n",
" <td> 252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Managua</th>\n",
" <td> NaN</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Mazatlan</th>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Mexico_City</th>\n",
" <td> 7</td>\n",
" <td> 8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Monterrey</th>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Montevideo</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Montreal</th>\n",
" <td> 3</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/New_York</th>\n",
" <td> 339</td>\n",
" <td> 912</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Phoenix</th>\n",
" <td> 3</td>\n",
" <td> 17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Puerto_Rico</th>\n",
" <td> 9</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Rainy_River</th>\n",
" <td> 10</td>\n",
" <td> 15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Recife</th>\n",
" <td> NaN</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Santo_Domingo</th>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Sao_Paulo</th>\n",
" <td> 13</td>\n",
" <td> 20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/St_Kitts</th>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Tegucigalpa</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Vancouver</th>\n",
" <td> 6</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Winnipeg</th>\n",
" <td> 3</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Amman</th>\n",
" <td> NaN</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Bangkok</th>\n",
" <td> NaN</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Beirut</th>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Calcutta</th>\n",
" <td> 2</td>\n",
" <td> 7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Dubai</th>\n",
" <td> 2</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Harbin</th>\n",
" <td> NaN</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Hong_Kong</th>\n",
" <td> 4</td>\n",
" <td> 6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Istanbul</th>\n",
" <td> NaN</td>\n",
" <td> 9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Jakarta</th>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Jerusalem</th>\n",
" <td> 2</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Karachi</th>\n",
" <td> NaN</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Kuala_Lumpur</th>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Kuching</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Manila</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Nicosia</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Novosibirsk</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Pontianak</th>\n",
" <td> 1</td>\n",
" <td> NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Riyadh</th>\n",
" <td> NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Seoul</th>\n",
" <td> 4</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Tokyo</th>\n",
" <td> 2</td>\n",
" <td> 35</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>97 rows \u00d7 2 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 121,
"text": [
" Not Windows Windows\n",
"tz \n",
" 245 276\n",
"Africa/Cairo NaN 3\n",
"Africa/Casablanca NaN 1\n",
"Africa/Ceuta NaN 2\n",
"Africa/Johannesburg NaN 1\n",
"Africa/Lusaka NaN 1\n",
"America/Anchorage 4 1\n",
"America/Argentina/Buenos_Aires 1 NaN\n",
"America/Argentina/Cordoba NaN 1\n",
"America/Argentina/Mendoza NaN 1\n",
"America/Bogota 1 2\n",
"America/Caracas NaN 1\n",
"America/Chicago 115 285\n",
"America/Chihuahua 1 1\n",
"America/Costa_Rica NaN 1\n",
"America/Denver 132 59\n",
"America/Edmonton 2 4\n",
"America/Guayaquil 2 NaN\n",
"America/Halifax 1 3\n",
"America/Indianapolis 8 12\n",
"America/La_Paz NaN 1\n",
"America/Lima NaN 1\n",
"America/Los_Angeles 130 252\n",
"America/Managua NaN 3\n",
"America/Mazatlan 1 NaN\n",
"America/Mexico_City 7 8\n",
"America/Monterrey 1 NaN\n",
"America/Montevideo NaN 1\n",
"America/Montreal 3 6\n",
"America/New_York 339 912\n",
"America/Phoenix 3 17\n",
"America/Puerto_Rico 9 1\n",
"America/Rainy_River 10 15\n",
"America/Recife NaN 2\n",
"America/Santo_Domingo 1 NaN\n",
"America/Sao_Paulo 13 20\n",
"America/St_Kitts 1 NaN\n",
"America/Tegucigalpa NaN 1\n",
"America/Vancouver 6 6\n",
"America/Winnipeg 3 1\n",
"Asia/Amman NaN 2\n",
"Asia/Bangkok NaN 6\n",
"Asia/Beirut 1 3\n",
"Asia/Calcutta 2 7\n",
"Asia/Dubai 2 2\n",
"Asia/Harbin NaN 3\n",
"Asia/Hong_Kong 4 6\n",
"Asia/Istanbul NaN 9\n",
"Asia/Jakarta 2 1\n",
"Asia/Jerusalem 2 1\n",
"Asia/Karachi NaN 3\n",
"Asia/Kuala_Lumpur 1 2\n",
"Asia/Kuching NaN 1\n",
"Asia/Manila NaN 1\n",
"Asia/Nicosia NaN 1\n",
"Asia/Novosibirsk NaN 1\n",
"Asia/Pontianak 1 NaN\n",
"Asia/Riyadh NaN 1\n",
"Asia/Seoul 4 1\n",
"Asia/Tokyo 2 35\n",
" ... ...\n",
"\n",
"[97 rows x 2 columns]"
]
}
],
"prompt_number": 121
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cdf.groupby(['tz',os_types]).size().unstack().fillna(0)[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Not Windows</th>\n",
" <th>Windows</th>\n",
" </tr>\n",
" <tr>\n",
" <th>tz</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th></th>\n",
" <td> 245</td>\n",
" <td> 276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Cairo</th>\n",
" <td> 0</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Casablanca</th>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Ceuta</th>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Africa/Johannesburg</th>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 2 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 123,
"text": [
" Not Windows Windows\n",
"tz \n",
" 245 276\n",
"Africa/Cairo 0 3\n",
"Africa/Casablanca 0 1\n",
"Africa/Ceuta 0 2\n",
"Africa/Johannesburg 0 1\n",
"\n",
"[5 rows x 2 columns]"
]
}
],
"prompt_number": 123
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"indexer = agg_counts.sum(1).argsort()\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 124
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"indexer[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 125,
"text": [
"tz\n",
" 24\n",
"Africa/Cairo 20\n",
"Africa/Casablanca 21\n",
"Africa/Ceuta 92\n",
"Africa/Johannesburg 87\n",
"dtype: int64"
]
}
],
"prompt_number": 125
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"count_subset = agg_counts.take(indexer)[-10:]"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 126
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"count_subset"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Not Windows</th>\n",
" <th>Windows</th>\n",
" </tr>\n",
" <tr>\n",
" <th>tz</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>America/Sao_Paulo</th>\n",
" <td> 13</td>\n",
" <td> 20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Europe/Madrid</th>\n",
" <td> 16</td>\n",
" <td> 19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pacific/Honolulu</th>\n",
" <td> 0</td>\n",
" <td> 36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Asia/Tokyo</th>\n",
" <td> 2</td>\n",
" <td> 35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Europe/London</th>\n",
" <td> 43</td>\n",
" <td> 31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Denver</th>\n",
" <td> 132</td>\n",
" <td> 59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Los_Angeles</th>\n",
" <td> 130</td>\n",
" <td> 252</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/Chicago</th>\n",
" <td> 115</td>\n",
" <td> 285</td>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <td> 245</td>\n",
" <td> 276</td>\n",
" </tr>\n",
" <tr>\n",
" <th>America/New_York</th>\n",
" <td> 339</td>\n",
" <td> 912</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows \u00d7 2 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 127,
"text": [
" Not Windows Windows\n",
"tz \n",
"America/Sao_Paulo 13 20\n",
"Europe/Madrid 16 19\n",
"Pacific/Honolulu 0 36\n",
"Asia/Tokyo 2 35\n",
"Europe/London 43 31\n",
"America/Denver 132 59\n",
"America/Los_Angeles 130 252\n",
"America/Chicago 115 285\n",
" 245 276\n",
"America/New_York 339 912\n",
"\n",
"[10 rows x 2 columns]"
]
}
],
"prompt_number": 127
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"count_subset.plot(kind='barh', stacked=True)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 130,
"text": [
"<matplotlib.axes.AxesSubplot at 0x1cd3e0cc>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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GXnvtNaDkPyEsLIwNGzbQvXt3YmNjcXFxscmxdOlSIiIiKCws5MSJEyxevJix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\nGRdjxhhjTGRcjBljjDGR/QNLOBxqCm3WIQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1df7992c>"
]
}
],
"prompt_number": 130
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cd \"/home/bakuda/pandas-book/ch02/movielens/\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/home/bakuda/pandas-book/ch02/movielens\n"
]
}
],
"prompt_number": 146
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"unames = ['user_id', 'gender', 'age', 'occupation', 'zip']\n",
"users = pd.read_table('./users.dat', sep='::', header=None,\n",
"names=unames)\n",
"rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
"ratings = pd.read_table('./ratings.dat', sep='::', header=None,\n",
"names=rnames)\n",
"mnames = ['movie_id', 'title', 'genres']\n",
"movies = pd.read_table('./movies.dat', sep='::', header=None,\n",
"names=mnames)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 148
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"users[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>occupation</th>\n",
" <th>zip</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> F</td>\n",
" <td> 1</td>\n",
" <td> 10</td>\n",
" <td> 48067</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> M</td>\n",
" <td> 56</td>\n",
" <td> 16</td>\n",
" <td> 70072</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> M</td>\n",
" <td> 25</td>\n",
" <td> 15</td>\n",
" <td> 55117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> M</td>\n",
" <td> 45</td>\n",
" <td> 7</td>\n",
" <td> 02460</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> M</td>\n",
" <td> 25</td>\n",
" <td> 20</td>\n",
" <td> 55455</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 5 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 149,
"text": [
" user_id gender age occupation zip\n",
"0 1 F 1 10 48067\n",
"1 2 M 56 16 70072\n",
"2 3 M 25 15 55117\n",
"3 4 M 45 7 02460\n",
"4 5 M 25 20 55455\n",
"\n",
"[5 rows x 5 columns]"
]
}
],
"prompt_number": 149
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ratings[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>movie_id</th>\n",
" <th>rating</th>\n",
" <th>timestamp</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> 1193</td>\n",
" <td> 5</td>\n",
" <td> 978300760</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 1</td>\n",
" <td> 661</td>\n",
" <td> 3</td>\n",
" <td> 978302109</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 1</td>\n",
" <td> 914</td>\n",
" <td> 3</td>\n",
" <td> 978301968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 1</td>\n",
" <td> 3408</td>\n",
" <td> 4</td>\n",
" <td> 978300275</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 1</td>\n",
" <td> 2355</td>\n",
" <td> 5</td>\n",
" <td> 978824291</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 4 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 153,
"text": [
" user_id movie_id rating timestamp\n",
"0 1 1193 5 978300760\n",
"1 1 661 3 978302109\n",
"2 1 914 3 978301968\n",
"3 1 3408 4 978300275\n",
"4 1 2355 5 978824291\n",
"\n",
"[5 rows x 4 columns]"
]
}
],
"prompt_number": 153
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"movies[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>movie_id</th>\n",
" <th>title</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> Toy Story (1995)</td>\n",
" <td> Animation|Children's|Comedy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> Jumanji (1995)</td>\n",
" <td> Adventure|Children's|Fantasy</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> Grumpier Old Men (1995)</td>\n",
" <td> Comedy|Romance</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> Waiting to Exhale (1995)</td>\n",
" <td> Comedy|Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> Father of the Bride Part II (1995)</td>\n",
" <td> Comedy</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 3 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 152,
"text": [
" movie_id title genres\n",
"0 1 Toy Story (1995) Animation|Children's|Comedy\n",
"1 2 Jumanji (1995) Adventure|Children's|Fantasy\n",
"2 3 Grumpier Old Men (1995) Comedy|Romance\n",
"3 4 Waiting to Exhale (1995) Comedy|Drama\n",
"4 5 Father of the Bride Part II (1995) Comedy\n",
"\n",
"[5 rows x 3 columns]"
]
}
],
"prompt_number": 152
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = pd.merge(pd.merge(ratings, users), movies)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 154
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data.shape"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 155,
"text": [
"(1000209, 10)"
]
}
],
"prompt_number": 155
},
{
"cell_type": "code",
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"input": [
"data[:10]"
],
"language": "python",
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"outputs": [
{
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" <th></th>\n",
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" <th>movie_id</th>\n",
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" <th>timestamp</th>\n",
" <th>gender</th>\n",
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" <th>occupation</th>\n",
" <th>zip</th>\n",
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" <td> 978156168</td>\n",
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" <td> 1193</td>\n",
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" <td> 982730936</td>\n",
" <td> M</td>\n",
" <td> 1</td>\n",
" <td> 10</td>\n",
" <td> 48073</td>\n",
" <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td> Drama</td>\n",
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" <td> 24</td>\n",
" <td> 1193</td>\n",
" <td> 5</td>\n",
" <td> 978136709</td>\n",
" <td> F</td>\n",
" <td> 25</td>\n",
" <td> 7</td>\n",
" <td> 10023</td>\n",
" <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td> Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td> 28</td>\n",
" <td> 1193</td>\n",
" <td> 3</td>\n",
" <td> 978125194</td>\n",
" <td> F</td>\n",
" <td> 25</td>\n",
" <td> 1</td>\n",
" <td> 14607</td>\n",
" <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td> Drama</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td> 33</td>\n",
" <td> 1193</td>\n",
" <td> 5</td>\n",
" <td> 978557765</td>\n",
" <td> M</td>\n",
" <td> 45</td>\n",
" <td> 3</td>\n",
" <td> 55421</td>\n",
" <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td> Drama</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows \u00d7 10 columns</p>\n",
"</div>"
],
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"output_type": "pyout",
"prompt_number": 156,
"text": [
" user_id movie_id rating timestamp gender age occupation zip \\\n",
"0 1 1193 5 978300760 F 1 10 48067 \n",
"1 2 1193 5 978298413 M 56 16 70072 \n",
"2 12 1193 4 978220179 M 25 12 32793 \n",
"3 15 1193 4 978199279 M 25 7 22903 \n",
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"5 18 1193 4 978156168 F 18 3 95825 \n",
"6 19 1193 5 982730936 M 1 10 48073 \n",
"7 24 1193 5 978136709 F 25 7 10023 \n",
"8 28 1193 3 978125194 F 25 1 14607 \n",
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"\n",
" title genres \n",
"0 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"1 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"2 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"3 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"4 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"5 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"6 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"7 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"8 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"9 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"\n",
"[10 rows x 10 columns]"
]
}
],
"prompt_number": 156
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data[:1]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>user_id</th>\n",
" <th>movie_id</th>\n",
" <th>rating</th>\n",
" <th>timestamp</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" <th>occupation</th>\n",
" <th>zip</th>\n",
" <th>title</th>\n",
" <th>genres</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> 1193</td>\n",
" <td> 5</td>\n",
" <td> 978300760</td>\n",
" <td> F</td>\n",
" <td> 1</td>\n",
" <td> 10</td>\n",
" <td> 48067</td>\n",
" <td> One Flew Over the Cuckoo's Nest (1975)</td>\n",
" <td> Drama</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows \u00d7 10 columns</p>\n",
"</div>"
],
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" user_id movie_id rating timestamp gender age occupation zip \\\n",
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"\n",
" title genres \n",
"0 One Flew Over the Cuckoo's Nest (1975) Drama \n",
"\n",
"[1 rows x 10 columns]"
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}
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"input": [
"data.ix[0]"
],
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{
"metadata": {},
"output_type": "pyout",
"prompt_number": 159,
"text": [
"user_id 1\n",
"movie_id 1193\n",
"rating 5\n",
"timestamp 978300760\n",
"gender F\n",
"age 1\n",
"occupation 10\n",
"zip 48067\n",
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]
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}
],
"metadata": {}
}
]
} | gpl-2.0 |
JifuZhao/Poisson-Kriging | old/D3S_Anomaly_Detection.ipynb | 1 | 2417768 | null | mit |
tsivula/becs-114.1311 | demos_ch11/demo11_2.ipynb | 2 | 927497 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Bayesian data analysis\n",
"## Chapter 11, demo 2\n",
"\n",
"Metropolis sampling demonstration"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First we import required libraries and tune some settings"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy import linalg, stats\n",
"\n",
"%matplotlib inline\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib import animation, rc\n",
"from IPython.display import HTML\n",
"import matplotlib\n",
"import arviz as az"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [],
"source": [
"import os, sys\n",
"# add utilities directory to path\n",
"util_path = os.path.abspath(os.path.join(os.path.pardir, 'utilities_and_data'))\n",
"if util_path not in sys.path and os.path.exists(util_path):\n",
" sys.path.insert(0, util_path)\n",
"\n",
"# import from utilities\n",
"import plot_tools"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"# edit default plot settings\n",
"plt.rc('font', size=12)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 1\n",
"\n",
"We create a two dimensional normal distribution to be used as an example target distribution. We also define the starting values for the Metropolis sampling here."
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [],
"source": [
"# parameters of a two dimensional Normal distribution used as a toy target distribution\n",
"y1 = 0 # mean of the first dimension\n",
"y2 = 0 # mean of the second dimension\n",
"r = 0.8 # covariance between the first and second dimension\n",
"S = np.array([[1.0, r], [r, 1.0]]) # covariance within both dimensions is 1.0\n",
"\n",
"# starting value of the chain\n",
"t1 = -2.5 # first dimension\n",
"t2 = 2.5 #second dimension\n",
"# number of iterations.\n",
"M = 5000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we apply Metropolis sampling to sample the generated two dimensional normal distribution. We sample from the toy distribution to visualize 90% HPD (Highest Posterior Density) interval. See BDA3 p. 85 for how to compute HPD for a multivariate normal distribution. In 2d-case contour for 90% HPD is an ellipse, whose semimajor axes can be computed from the eigenvalues of the covariance matrix scaled by a value selected to get ellipse match the density at the edge of 90% HPD. Angle of the ellipse could be computed from the eigenvectors, but since the marginals are same we know that angle is pi/4."
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loaded pre-computed values in variable `tt`\n",
"shape:(5000, 2), dtype:float64\n"
]
}
],
"source": [
"# Metropolis sampling here\n",
"\n",
"# allocate memory for the samples\n",
"tt = np.empty((M, 2))\n",
"tt[0] = [t1, t2] # Save starting point\n",
"\n",
"# For demonstration, load pre-computed values.\n",
"# Replace this with your algorithm!\n",
"# tt is a M x 2 array, with M samples of both theta_1 and theta_2\n",
"res_path = os.path.abspath(\n",
" os.path.join(\n",
" os.path.pardir,\n",
" 'utilities_and_data',\n",
" 'demo11_2a.csv'\n",
" )\n",
")\n",
"res = np.loadtxt(res_path, skiprows=1, usecols = (1,2), delimiter = ',')\n",
"tt = res\n",
"print('loaded pre-computed values in variable `tt`')\n",
"print('shape:{}, dtype:{}'.format(tt.shape, tt.dtype))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The rest is just for illustration. We first calculate the 90% HPD using the covariance matrix."
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"# plotting grid\n",
"Y1 = np.linspace(-4.5, 4.5, 150)\n",
"Y2 = np.linspace(-4.5, 4.5, 150)\n",
"\n",
"# number of samples to discard from the beginning\n",
"burnin = 500\n",
"\n",
"# Plot 90% HPD.\n",
"# In 2d-case contour for 90% HPD is an ellipse, whose semimajor\n",
"# axes can be computed from the eigenvalues of the covariance\n",
"# matrix scaled by a value selected to get ellipse match the\n",
"# density at the edge of 90% HPD. Angle of the ellipse could be \n",
"# computed from the eigenvectors, but since marginals are same\n",
"# we know that angle is 45 degrees.\n",
"q = np.sort(np.sqrt(linalg.eigh(S, eigvals_only=True)) * 2.147) # 2.147 is the value to get the ellipse match the \n",
" # density at the edge of 90% HPD\n",
"\n",
"def add90hpd(ax):\n",
" \"\"\"Plot 90hpd region into the given axis\"\"\"\n",
" el = mpl.patches.Ellipse(\n",
" xy = (y1,y2), #center point of the ellipse\n",
" width = 2 * q[1], # q[1] is larger semimajor axis of the ellipse. Scaling by two gives the larger \n",
" # major axis (diameter in the wider direction) \n",
" height = 2 * q[0], # q[0] is smaller semimajor axis of the ellipse. Scaling by two gives the smaller \n",
" # major axis (diameter in the narrower direction) \n",
" angle = 45, #angle of the ellipse is 45 degrees (pi/4) as mentioned earlier\n",
" facecolor = 'none',\n",
" edgecolor = 'C1'\n",
" )\n",
" ax.add_artist(el)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We show the sequential progress of the sampling for the first 500 samples. These 500 samples are then removed as burnin from the final samples. Finally we also plot the rest of the samples that remain after the burnin has been removed."
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1152x864 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# create the plots\n",
"subplotshape = (2, 3)\n",
"fig, axes = plt.subplots(\n",
" subplotshape[0], subplotshape[1], sharex=True, sharey=True,\n",
" figsize=(16,12), subplot_kw=dict(aspect='equal')\n",
")\n",
"\n",
"# set limits for axes\n",
"axes[0,0].set_xlim([-4.5, 4.5])\n",
"axes[0,0].set_ylim([-4.5, 4.5])\n",
"\n",
"# set labels for x- and y-axes\n",
"for i in range(subplotshape[0]):\n",
" axes[i,0].set_ylabel(r'$\\theta_2$')\n",
"for j in range(subplotshape[1]):\n",
" axes[-1,j].set_xlabel(r'$\\theta_1$')\n",
"\n",
"# add a shared legend\n",
"axes[0,0].legend(\n",
" ( mpl.lines.Line2D([], [], color='C1'), \n",
" mpl.lines.Line2D(\n",
" [], [], linestyle='', marker='o', markersize=10,\n",
" markerfacecolor='none', markeredgecolor='C0'\n",
" ),\n",
" mpl.lines.Line2D(\n",
" [], [], color='C0', marker='o', markersize=10,\n",
" markerfacecolor='none', markeredgecolor='C0'\n",
" ),\n",
" mpl.lines.Line2D(\n",
" [], [], color='m', marker='o', markersize=10,\n",
" markerfacecolor='none', markeredgecolor='C2'\n",
" )\n",
" ),\n",
" ( '90% HPD interval',\n",
" 'samples',\n",
" 'Markov chain'\n",
" ),\n",
" numpoints=1,\n",
" loc='lower left',\n",
" bbox_to_anchor=(0., 1.02, 1., .102),\n",
" fontsize=16\n",
")\n",
"\n",
"\n",
"# FIRST SUBPLOT\n",
"ax = axes[0,0]\n",
"add90hpd(ax)\n",
"i = 5\n",
"line, = ax.plot(tt[:i+1,0], tt[:i+1,1], color='C0') # plot the line between samples\n",
"\n",
"# plot only every other sample as a circle marker\n",
"line, = ax.plot(\n",
" tt[:i+1:2,0], tt[:i+1:2,1], 'o', markersize=10,\n",
" markerfacecolor='none', markeredgecolor='C0')\n",
"ax.text(-3.5, 3.5, '5 samples', fontsize=16)\n",
"\n",
"# SECOND SUBPLOT\n",
"ax = axes[0,1]\n",
"add90hpd(ax)\n",
"i = 20\n",
"ax.plot(tt[:i+1,0], tt[:i+1,1], color='C0')\n",
"ax.plot(\n",
" tt[:i+1,0], tt[:i+1,1], 'o', markersize=10,\n",
" markerfacecolor='none', markeredgecolor='C0'\n",
")\n",
"ax.text(-3.5, 3.5, '20 samples', fontsize=16)\n",
"\n",
"# THIRD SUBPLOT\n",
"ax = axes[0,2]\n",
"add90hpd(ax)\n",
"i = 1000\n",
"ax.plot(tt[:i+1,0], tt[:i+1,1], color='C0', alpha=0.5)\n",
"ax.scatter(tt[:i+1,0], tt[:i+1,1], 18, color='C0')\n",
"ax.text(-3.5, 3.0, 'first 1000 samples\\nincluding warm-up', fontsize=16)\n",
"\n",
"#FOURTH SUBPLOT\n",
"ax = axes[1,0]\n",
"add90hpd(ax)\n",
"# plotting warm-up\n",
"ax.plot(tt[:burnin,0], tt[:burnin,1], color='C2', alpha=0.5)\n",
"ax.scatter(tt[:burnin,0], tt[:burnin,1], 18, color='C2')\n",
"\n",
"ax.text(-3.5, 3.5, 'warm-up of 500 samples', fontsize=16)\n",
"\n",
"# FIFTH SUBPLOT\n",
"ax = axes[1,1]\n",
"add90hpd(ax)\n",
"i = 999\n",
"ax.scatter(\n",
" tt[burnin:i+1,0], tt[burnin:i+1,1],\n",
" 20, color='C0'\n",
")\n",
"ax.text(-3.5, 3.5, 'first 1000 samples with\\nwarm-up removed', fontsize=16)\n",
"\n",
"# SIXTH SUBPLOT\n",
"ax = axes[1,2]\n",
"add90hpd(ax)\n",
"ax.scatter(\n",
" tt[burnin:,0], tt[burnin:,1], 20,\n",
" color='C0', alpha=0.3\n",
")\n",
"ax.text(-3.5, 3.5, '4500 samples remain after\\nremoving warm-up', fontsize=16)\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking at the last sublot showing the 4500 samples after removing warm-up, we can see that the sampling seems to have sampled the target distribution quite well. All of the 90% HPD area is sampled and also about 10% of the samples seem to fall outside of the 90% HPD. Notice, that for example in the first subplot it looks like only 3 samples are shown. This is because in Metropolis sampling the previous sample is taken as a new sample if the other candidate for the new sample is not accepted. In the first subplot we have accepted the new sample twice and taken the old sample as the new sample twice. Overlapping samples can't be seen in the plot, so this leads to the plot showing only two new samples after the starting point."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll next plot an animation showing the first 500 samples of the Metropolis sampling. This animation requires ffmpeg in order to work properly. If you need to install ffmpeg, the instructions can be found at https://www.wikihow.com/Install-FFmpeg-on-Windows. If the animation doesn't play correctly, you can also find the animation from the same folder as this demo with the filename \"metropolissampler1.mp4\". Notice that the animation seems to sometimes pause at some samples for a small time. This is because the previous sample has been sampled again when the candidate sample was rejected."
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# First set up the figure, the axis and the axis labels\n",
"fig = plt.figure(figsize=(5,5))\n",
"ax = plt.axes(xlim=(-4.5, 4.5), ylim=(-4.5, 4.5))\n",
"ax.set_ylabel(r'$\\theta_2$')\n",
"ax.set_xlabel(r'$\\theta_1$')\n",
"\n",
"# Plot a legend\n",
"ax.legend(\n",
" ( mpl.lines.Line2D([], [], color='C1'), \n",
" mpl.lines.Line2D(\n",
" [], [], linestyle='', marker='o', markersize=5,\n",
" markerfacecolor='C0', markeredgecolor='C0'\n",
" ),\n",
" mpl.lines.Line2D([], [], color='C0', alpha=0.5),\n",
" ),\n",
" ( '90% HPD interval',\n",
" 'Draws',\n",
" 'Steps of the sampler'\n",
" ),\n",
" numpoints=1,\n",
" loc='lower left',\n",
" bbox_to_anchor=(1.2, 0.5, 0, 0),\n",
" fontsize=16\n",
")\n",
"\n",
"# Initialize styles for the lines and points to be plotted. At this point the lines and points have no data.\n",
"line, = ax.plot([], [], color='C0', alpha=0.5)\n",
"point, = ax.plot([], [],'.', markersize=10, lw=0, markerfacecolor='C0', markeredgecolor='C0')\n",
"\n",
"# initialization function: plot the background of each frame\n",
"def init():\n",
" line.set_data([], [])\n",
" point.set_data([], [])\n",
" return (line,point,)\n",
"\n",
"\n",
"add90hpd(ax) #draw the 90% HPD\n",
"\n",
"#choose only the warmup samples\n",
"warmup1 = tt[:burnin,0]\n",
"warmup2 = tt[:burnin,1]\n",
"\n",
"\n",
"# animation function. This is called sequentially\n",
"def animate(i):\n",
" x = warmup1[:i] #choose points until i\n",
" y = warmup2[:i] #choose points until i\n",
" line.set_data(x, y) #draw lines between all points \n",
" point.set_data(x, y) #draw points for every other sample\n",
" return (line,point,)\n",
"\n",
"# choose the animation writer. If you don't have ffmpeg installed you can change this to some writer that you already have.\n",
"matplotlib.rcParams['animation.writer'] = 'ffmpeg'\n",
"\n",
"# call the animator. blit=True means only re-draw the parts that have changed.\n",
"anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
" frames=500, interval=100, blit=True)\n",
"\n",
"# show the animation as a html5 video to allow showing it in an iPython notebook\n",
"HTML(anim.to_html5_video())\n",
"\n",
"# Uncomment below to save the animation as an mp4-file\n",
"\n",
"# #Save the movie file\n",
"#Writer = animation.writers['ffmpeg']\n",
"#writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)\n",
"#anim.save('metropolissampler1.mp4', writer=writer)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visual convergence diagnostics\n",
"\n",
"Here we plot the behavior of theta1 and theta2 separately. We also plot the cumulative average and autocorrelation for theta1 and theta2 samples separately to visually see the convergence."
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x1152 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.style.use(plot_tools.custom_styles['gray_background'])\n",
"\n",
"fig = plt.figure(figsize=(8, 16))\n",
"\n",
"indexes = np.arange(burnin, M)\n",
"samps = tt[indexes] # choose only samples after burnin\n",
"\n",
"# Plotting trends for theta1 and theta2 behavior separately. Only 4500 samples after warm-up included.\n",
"ax1 = fig.add_subplot(3, 1, 1)\n",
"ax1.axhline(y=0, color='gray')\n",
"line1, line2, = ax1.plot(indexes, samps, linewidth=0.8) # create lines for both theta1 and theta2 samples\n",
"ax1.legend((line1, line2), (r'$\\theta_1$', r'$\\theta_2$'))\n",
"ax1.set_xlabel('iteration')\n",
"ax1.set_title('trends')\n",
"ax1.set_xlim([burnin, 5000])\n",
"\n",
"# Plotting cumulative averages for theta1 and theta2 behavior separately. Only 4500 samples after warm-up included.\n",
"ax2 = fig.add_subplot(3, 1, 2)\n",
"ax2.axhline(y=0, color='gray')\n",
"ax2.plot(\n",
" indexes,\n",
" np.cumsum(samps, axis=0)/np.arange(1,len(samps)+1)[:,None] # cumulative sum divided by the number of samples\n",
")\n",
"ax2.set_xlabel('iteration')\n",
"ax2.set_title('cumulative average')\n",
"ax2.set_xlim([burnin, 5000])\n",
"\n",
"# Plotting estimated autocorrelation for theta1 and theta2 behavior separately. Only 4500 samples after warm-up included.\n",
"ax3 = fig.add_subplot(3, 1, 3)\n",
"maxlag = 20 # maximum lag for autocorrelation\n",
"sampsc = samps - np.mean(samps, axis=0) # scale the samples by deducting the mean\n",
"acorlags = np.arange(maxlag+1) # lags from 0 to maxlag\n",
"ax3.axhline(y=0, color='gray')\n",
"# calculate autocorrelation for all different lags\n",
"for i in [0,1]: # loop for theta1 and theta2\n",
" t = np.correlate(sampsc[:,i], sampsc[:,i], 'full') # autocorrelation with full range of lags\n",
" t = t[-len(sampsc):-len(sampsc)+maxlag+1] / t[-len(sampsc)] # choose only the lags that we want to use\n",
" ax3.plot(acorlags, t)\n",
"ax3.set_xlabel('lag')\n",
"ax3.set_title('estimate of the autocorrelation function')\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking at the trends we can see that the sampling moves around the distribution quite rapidly. Looking at the cumulative average we can see that the chains seem to converge. Looking at the estimated autocorrelation function we can see that there's not too much autocorrelation once the lag is increased."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we visualize the estimate of the Monte Carlo error estimates for the cumulative average plots."
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(8, 8))\n",
"\n",
"indexes = np.arange(burnin, M)\n",
"samps = tt[indexes]\n",
"nsamps = np.arange(1, len(samps)+1)\n",
"\n",
"# Plotting cumulative averages for theta1 and theta2 behavior separately similarly as earlier.\n",
"ax1 = fig.add_subplot(1, 1, 1)\n",
"ax1.axhline(y=0, color='gray')\n",
"line1, line2, = ax1.plot(\n",
" indexes,\n",
" np.cumsum(samps, axis=0) / nsamps[:,None],\n",
" linewidth=1\n",
")\n",
"\n",
"# Plotting 95% interval for MCMC error\n",
"er1, = ax1.plot(\n",
" indexes, 1.96/np.sqrt(nsamps/4), 'k--', linewidth=1)\n",
"ax1.plot(indexes, -1.96/np.sqrt(nsamps/4), 'k--', linewidth=1)\n",
"\n",
"# Plotting 95% interval for independent MC\n",
"er2, = ax1.plot(\n",
" indexes, 1.96/np.sqrt(nsamps), 'k:', linewidth=1)\n",
"ax1.plot(indexes, -1.96/np.sqrt(nsamps), 'k:', linewidth=1)\n",
"\n",
"# axis label and title\n",
"ax1.set_xlabel('iteration')\n",
"ax1.set_title('cumulative average')\n",
"\n",
"# Plotting legend\n",
"ax1.legend(\n",
" (line1, line2, er1, er2),\n",
" (r'$\\theta_1$', r'$\\theta_2$',\n",
" '95% interval for MCMC error',\n",
" '95% interval for independent MC'\n",
" )\n",
")\n",
"ax1.set_xlim([burnin, 5000])\n",
"ax1.set_ylim([-1.5, 1.5])\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The analysis and the generated samples both show that Metropolis sampling worked quite well for the target distribution that we used. Next we'll test the same Metropolis sampling for a more narrow two dimensional normal distribution."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 2\n",
"\n",
"Now our target distribution will be a two dimensional normal distribution with r = 0.99. This makes the distribution much narrower."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"# parameters of a two dimensional Normal distribution used as a toy target distribution\n",
"y1 = 0 # mean of the first dimension\n",
"y2 = 0 # mean of the second dimension\n",
"r = 0.99 # covariance between the first and second dimension\n",
"S = np.array([[1.0, r], [r, 1.0]]) # covariance within both dimensions is 1.0\n",
"\n",
"# starting value of the chain\n",
"t1 = -2.5 # first dimension\n",
"t2 = 2.5 #second dimension\n",
"# number of iterations.\n",
"M = 5000"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we apply Metropolis sampling to sample the generated two dimensional normal distribution. We sample from the toy distribution to visualize 90% HPD (Highest Posterior Density) interval. See BDA3 p. 85 for how to compute HPD for a multivariate normal distribution. In 2d-case contour for 90% HPD is an ellipse, whose semimajor axes can be computed from the eigenvalues of the covariance matrix scaled by a value selected to get ellipse match the density at the edge of 90% HPD. Angle of the ellipse could be computed from the eigenvectors, but since the marginals are same we know that angle is pi/4."
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"loaded pre-computed values in variable `tt`\n",
"shape:(5000, 2), dtype:float64\n"
]
}
],
"source": [
"# Metropolis sampling here\n",
"\n",
"# allocate memory for the samples\n",
"tt = np.empty((M, 2))\n",
"tt[0] = [t1, t2] # Save starting point\n",
"\n",
"# For demonstration, load pre-computed values.\n",
"# Replace this with your algorithm!\n",
"# tt is a M x 2 array, with M samples of both theta_1 and theta_2\n",
"res_path = os.path.abspath(\n",
" os.path.join(\n",
" os.path.pardir,\n",
" 'utilities_and_data',\n",
" 'demo11_2b.csv'\n",
" )\n",
")\n",
"res = np.loadtxt(res_path, skiprows=1, usecols = (1,2), delimiter = ',')\n",
"tt = res\n",
"print('loaded pre-computed values in variable `tt`')\n",
"print('shape:{}, dtype:{}'.format(tt.shape, tt.dtype))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The rest is just for illustration. We first calculate the 90% HPD using the covariance matrix."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"# plotting grid\n",
"Y1 = np.linspace(-4.5, 4.5, 150)\n",
"Y2 = np.linspace(-4.5, 4.5, 150)\n",
"\n",
"# number of samples to discard from the beginning\n",
"burnin = 500\n",
"\n",
"# Plot 90% HPD.\n",
"# In 2d-case contour for 90% HPD is an ellipse, whose semimajor\n",
"# axes can be computed from the eigenvalues of the covariance\n",
"# matrix scaled by a value selected to get ellipse match the\n",
"# density at the edge of 90% HPD. Angle of the ellipse could be \n",
"# computed from the eigenvectors, but since marginals are same\n",
"# we know that angle is 45 degrees.\n",
"q = np.sort(np.sqrt(linalg.eigh(S, eigvals_only=True)) * 2.147) # 2.147 is the value to get the ellipse match the \n",
" # density at the edge of 90% HPD\n",
"\n",
"def add90hpd(ax):\n",
" \"\"\"Plot 90hpd region into the given axis\"\"\"\n",
" el = mpl.patches.Ellipse(\n",
" xy = (y1,y2), #center point of the ellipse\n",
" width = 2 * q[1], # q[1] is larger semimajor axis of the ellipse. Scaling by two gives the larger \n",
" # major axis (diameter in the wider direction) \n",
" height = 2 * q[0], # q[0] is smaller semimajor axis of the ellipse. Scaling by two gives the smaller \n",
" # major axis (diameter in the narrower direction) \n",
" angle = 45, #angle of the ellipse is 45 degrees (pi/4) as mentioned earlier\n",
" facecolor = 'none',\n",
" edgecolor = 'C1'\n",
" )\n",
" ax.add_artist(el)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We show the sequential progress of the sampling for the first 500 samples. These 500 samples are then removed as burnin from the final samples. Finally we also plot the rest of the samples that remain after the burnin has been removed."
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x864 with 6 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# create the plots\n",
"subplotshape = (2, 3)\n",
"fig, axes = plt.subplots(\n",
" subplotshape[0], subplotshape[1], sharex=True, sharey=True,\n",
" figsize=(16,12), subplot_kw=dict(aspect='equal')\n",
")\n",
"\n",
"# set limits for axes\n",
"axes[0,0].set_xlim([-4.5, 4.5])\n",
"axes[0,0].set_ylim([-4.5, 4.5])\n",
"\n",
"# set labels for x- and y-axes\n",
"for i in range(subplotshape[0]):\n",
" axes[i,0].set_ylabel(r'$\\theta_2$')\n",
"for j in range(subplotshape[1]):\n",
" axes[-1,j].set_xlabel(r'$\\theta_1$')\n",
"\n",
"# add a shared legend\n",
"axes[0,0].legend(\n",
" ( mpl.lines.Line2D([], [], color='C1'), \n",
" mpl.lines.Line2D(\n",
" [], [], linestyle='', marker='o', markersize=10,\n",
" markerfacecolor='none', markeredgecolor='C0'\n",
" ),\n",
" mpl.lines.Line2D(\n",
" [], [], color='C0', marker='o', markersize=10,\n",
" markerfacecolor='none', markeredgecolor='C0'\n",
" ),\n",
" mpl.lines.Line2D(\n",
" [], [], color='m', marker='o', markersize=10,\n",
" markerfacecolor='none', markeredgecolor='C2'\n",
" )\n",
" ),\n",
" ( '90% HPD interval',\n",
" 'samples',\n",
" 'Markov chain'\n",
" ),\n",
" numpoints=1,\n",
" loc='lower left',\n",
" bbox_to_anchor=(0., 1.02, 1., .102),\n",
" fontsize=16\n",
")\n",
"\n",
"\n",
"# FIRST SUBPLOT\n",
"ax = axes[0,0]\n",
"add90hpd(ax)\n",
"i = 5\n",
"line, = ax.plot(tt[:i+1,0], tt[:i+1,1], color='C0') # plot the line between samples\n",
"\n",
"# plot only every other sample as a circle marker\n",
"line, = ax.plot(\n",
" tt[:i+1:2,0], tt[:i+1:2,1], 'o', markersize=10,\n",
" markerfacecolor='none', markeredgecolor='C0')\n",
"ax.text(-3.5, 3.5, '5 samples', fontsize=16)\n",
"\n",
"# SECOND SUBPLOT\n",
"ax = axes[0,1]\n",
"add90hpd(ax)\n",
"i = 20\n",
"ax.plot(tt[:i+1,0], tt[:i+1,1], color='C0')\n",
"ax.plot(\n",
" tt[:i+1,0], tt[:i+1,1], 'o', markersize=10,\n",
" markerfacecolor='none', markeredgecolor='C0'\n",
")\n",
"ax.text(-3.5, 3.5, '20 samples', fontsize=16)\n",
"\n",
"# THIRD SUBPLOT\n",
"ax = axes[0,2]\n",
"add90hpd(ax)\n",
"i = 1000\n",
"ax.plot(tt[:i+1,0], tt[:i+1,1], color='C0', alpha=0.5)\n",
"ax.scatter(tt[:i+1,0], tt[:i+1,1], 18, color='C0')\n",
"ax.text(-3.5, 3.0, 'first 1000 samples\\nincluding warm-up', fontsize=16)\n",
"\n",
"#FOURTH SUBPLOT\n",
"ax = axes[1,0]\n",
"add90hpd(ax)\n",
"# plotting warm-up\n",
"ax.plot(tt[:burnin,0], tt[:burnin,1], color='C2', alpha=0.5)\n",
"ax.scatter(tt[:burnin,0], tt[:burnin,1], 18, color='C2')\n",
"\n",
"ax.text(-3.5, 3.5, 'warm-up of 500 samples', fontsize=16)\n",
"\n",
"# FIFTH SUBPLOT\n",
"ax = axes[1,1]\n",
"add90hpd(ax)\n",
"i = 999\n",
"ax.scatter(\n",
" tt[burnin:i+1,0], tt[burnin:i+1,1],\n",
" 20, color='C0'\n",
")\n",
"ax.text(-3.5, 3.5, 'first 1000 samples with\\nwarm-up removed', fontsize=16)\n",
"\n",
"# SIXTH SUBPLOT\n",
"ax = axes[1,2]\n",
"add90hpd(ax)\n",
"ax.scatter(\n",
" tt[burnin:,0], tt[burnin:,1], 20,\n",
" color='C0', alpha=0.3\n",
")\n",
"ax.text(-3.5, 3.5, '4500 samples remain after\\nremoving warm-up', fontsize=16)\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking at the last sublot showing the 4500 samples after removing warm-up, we can see that the sampling seems to have sampled the target distribution at least quite well. There are samples all around the 90% HPD. We need further diagnostics to actually see if the sampling is good. Notice, that for example in the first subplot it looks like only 3 samples are shown. This is because in Metropolis sampling the previous sample is taken as a new sample if the other candidate for the new sample is not accepted. In the first subplot we have accepted the new sample twice and taken the old sample as the new sample twice. Overlapping samples can't be seen in the plot, so this leads to the plot showing only two new samples after the starting point."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We'll next plot an animation showing the first 500 samples of the Metropolis sampling. This animation requires ffmpeg in order to work properly. If you need to install ffmpeg, the instructions can be found at https://www.wikihow.com/Install-FFmpeg-on-Windows. If the animation doesn't play correctly, you can also find the animation from the same folder as this demo with the filename \"metropolissampler2.mp4\". Notice that the animation seems to sometimes pause at some samples for a small time. This is because the previous sample has been sampled again when the candidate sample was rejected."
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# First set up the figure, the axis and the axis labels\n",
"fig = plt.figure(figsize=(5,5))\n",
"ax = plt.axes(xlim=(-4.5, 4.5), ylim=(-4.5, 4.5))\n",
"ax.set_ylabel(r'$\\theta_2$')\n",
"ax.set_xlabel(r'$\\theta_1$')\n",
"\n",
"# Plot a legend\n",
"ax.legend(\n",
" ( mpl.lines.Line2D([], [], color='C1'), \n",
" mpl.lines.Line2D(\n",
" [], [], linestyle='', marker='o', markersize=5,\n",
" markerfacecolor='C0', markeredgecolor='C0'\n",
" ),\n",
" mpl.lines.Line2D([], [], color='C0', alpha=0.5),\n",
" ),\n",
" ( '90% HPD interval',\n",
" 'Draws',\n",
" 'Steps of the sampler'\n",
" ),\n",
" numpoints=1,\n",
" loc='lower left',\n",
" bbox_to_anchor=(1.2, 0.5, 0, 0),\n",
" fontsize=16\n",
")\n",
"\n",
"# Initialize styles for the lines and points to be plotted. At this point the lines and points have no data.\n",
"line, = ax.plot([], [], color='C0', alpha=0.5)\n",
"point, = ax.plot([], [],'.', markersize=10, lw=0, markerfacecolor='C0', markeredgecolor='C0')\n",
"\n",
"# initialization function: plot the background of each frame\n",
"def init():\n",
" line.set_data([], [])\n",
" point.set_data([], [])\n",
" return (line,point,)\n",
"\n",
"\n",
"add90hpd(ax) #draw the 90% HPD\n",
"\n",
"#choose only the warmup samples\n",
"warmup1 = tt[:burnin,0]\n",
"warmup2 = tt[:burnin,1]\n",
"\n",
"\n",
"# animation function. This is called sequentially\n",
"def animate(i):\n",
" x = warmup1[:i] #choose points until i\n",
" y = warmup2[:i] #choose points until i\n",
" line.set_data(x, y) #draw lines between all points \n",
" point.set_data(x, y) #draw points for every other sample\n",
" return (line,point,)\n",
"\n",
"# choose the animation writer. If you don't have ffmpeg installed you can change this to some writer that you already have.\n",
"matplotlib.rcParams['animation.writer'] = 'ffmpeg'\n",
"\n",
"# call the animator. blit=True means only re-draw the parts that have changed.\n",
"anim = animation.FuncAnimation(fig, animate, init_func=init,\n",
" frames=500, interval=100, blit=True)\n",
"\n",
"# show the animation as a html5 video to allow showing it in an iPython notebook\n",
"HTML(anim.to_html5_video())\n",
"\n",
"# Uncomment below to save the animation as an mp4-file\n",
"\n",
"# #Save the movie file\n",
"#Writer = animation.writers['ffmpeg']\n",
"#writer = Writer(fps=15, metadata=dict(artist='Me'), bitrate=1800)\n",
"#anim.save('metropolissampler2.mp4', writer=writer)\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visual convergence diagnostics\n",
"\n",
"Here we plot the behavior of theta1 and theta2 separately. We also plot the cumulative average and autocorrelation for theta1 and theta2 samples separately to visually see the convergence."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x1152 with 3 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.style.use(plot_tools.custom_styles['gray_background'])\n",
"\n",
"fig = plt.figure(figsize=(8, 16))\n",
"\n",
"indexes = np.arange(burnin, M)\n",
"samps = tt[indexes] # choose only samples after burnin\n",
"\n",
"# Plotting trends for theta1 and theta2 behavior separately. Only 4500 samples after warm-up included.\n",
"ax1 = fig.add_subplot(3, 1, 1)\n",
"ax1.axhline(y=0, color='gray')\n",
"line1, line2, = ax1.plot(indexes, samps, linewidth=0.8) # create lines for both theta1 and theta2 samples\n",
"ax1.legend((line1, line2), (r'$\\theta_1$', r'$\\theta_2$'))\n",
"ax1.set_xlabel('iteration')\n",
"ax1.set_title('trends')\n",
"ax1.set_xlim([burnin, 5000])\n",
"\n",
"# Plotting cumulative averages for theta1 and theta2 behavior separately. Only 4500 samples after warm-up included.\n",
"ax2 = fig.add_subplot(3, 1, 2)\n",
"ax2.axhline(y=0, color='gray')\n",
"ax2.plot(\n",
" indexes,\n",
" np.cumsum(samps, axis=0)/np.arange(1,len(samps)+1)[:,None] # cumulative sum divided by the number of samples\n",
")\n",
"ax2.set_xlabel('iteration')\n",
"ax2.set_title('cumulative average')\n",
"ax2.set_xlim([burnin, 5000])\n",
"\n",
"# Plotting estimated autocorrelation for theta1 and theta2 behavior separately. Only 4500 samples after warm-up included.\n",
"ax3 = fig.add_subplot(3, 1, 3)\n",
"maxlag = 20 # maximum lag for autocorrelation\n",
"sampsc = samps - np.mean(samps, axis=0) # scale the samples by deducting the mean\n",
"acorlags = np.arange(maxlag+1) # lags from 0 to maxlag\n",
"ax3.axhline(y=0, color='gray')\n",
"# calculate autocorrelation for all different lags\n",
"for i in [0,1]: # loop for theta1 and theta2\n",
" t = np.correlate(sampsc[:,i], sampsc[:,i], 'full') # autocorrelation with full range of lags\n",
" t = t[-len(sampsc):-len(sampsc)+maxlag+1] / t[-len(sampsc)] # choose only the lags that we want to use\n",
" ax3.plot(acorlags, t)\n",
"ax3.set_xlabel('lag')\n",
"ax3.set_title('estimate of the autocorrelation function')\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Looking at the trend curves we can see that the sampling moves around the target distribution quite slowly. Looking at the cumulative average we can see that the chains only start to converge very late. We need at least the 5000 samples to have a relatively good convergence. Looking at the estimated autocorrelation function we can see that significant autocorrelation remains until lag 20."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next we visualize the estimate of the Monte Carlo error estimates for the cumulative average plots."
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(8, 8))\n",
"\n",
"indexes = np.arange(burnin, M)\n",
"samps = tt[indexes]\n",
"nsamps = np.arange(1, len(samps)+1)\n",
"\n",
"# Plotting cumulative averages for theta1 and theta2 behavior separately similarly as earlier.\n",
"ax1 = fig.add_subplot(1, 1, 1)\n",
"ax1.axhline(y=0, color='gray')\n",
"line1, line2, = ax1.plot(\n",
" indexes,\n",
" np.cumsum(samps, axis=0) / nsamps[:,None],\n",
" linewidth=1\n",
")\n",
"\n",
"# Plotting 95% interval for MCMC error\n",
"er1, = ax1.plot(\n",
" indexes, 1.96/np.sqrt(nsamps/4), 'k--', linewidth=1)\n",
"ax1.plot(indexes, -1.96/np.sqrt(nsamps/4), 'k--', linewidth=1)\n",
"\n",
"# Plotting 95% interval for independent MC\n",
"er2, = ax1.plot(\n",
" indexes, 1.96/np.sqrt(nsamps), 'k:', linewidth=1)\n",
"ax1.plot(indexes, -1.96/np.sqrt(nsamps), 'k:', linewidth=1)\n",
"\n",
"# axis label and title\n",
"ax1.set_xlabel('iteration')\n",
"ax1.set_title('cumulative average')\n",
"\n",
"# Plotting legend\n",
"ax1.legend(\n",
" (line1, line2, er1, er2),\n",
" (r'$\\theta_1$', r'$\\theta_2$',\n",
" '95% interval for MCMC error',\n",
" '95% interval for independent MC'\n",
" )\n",
")\n",
"ax1.set_xlim([burnin, 5000])\n",
"ax1.set_ylim([-1.5, 1.5])\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The analysis and the generated samples both show that Metropolis sampling didn't work very well for the chosen target distribution. For this target distribution we should consider some other sampling method or at least generate a very large number of samples to have a correct distribution of samples."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
UCIDataScienceInitiative/IntroToJulia | Notebooks/PackageEcosystem.ipynb | 1 | 6875 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Overview of the Package Ecosystem\n",
"\n",
"Julia's package ecosystem is organized in terms of Github organizations. While this is informal, many of the main packages (but not all!) can be found in the various organizations.\n",
"\n",
"http://julialang.org/community/\n",
"\n",
"A useful source on the the changing package ecosystem (might be) found here:\n",
"\n",
"http://www.pkgupdate.com/"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## A Quick Look At Some Organizations\n",
"\n",
"Let's take a quick look at some organizations which provide important functionality to Julia. I will go through some of the most well-developed and \"ready for use orgs\". Of course, there are more that I will be leaving off the list.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### JuliaLang\n",
"\n",
"- JuliaLang is the Base organization\n",
"- It holds the Julia language itself\n",
"- Other core pacakges exist in JuliaLang\n",
" - PkgDev for package development\n",
" - IJulia\n",
" - Compat for version compatibility\n",
"- There is a general trend of \"slimming Base\" to lower the Travis load on JuliaLang"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"### JuliaStats\n",
"\n",
"- Hosts Dataframes.jl, the data frame implementation of Julia\n",
"- Distributions.jl holds probability distributions and methods for generating random numbers according to specific distributions\n",
"- The standard regression and hypothesis testing libraries are held here\n",
"- Klara.jl is a native MCMC engine\n",
"- One of the main R linear model library developers, Douglas Bates, is a heavy contributor\n",
"\n",
">As some of you may know, I have had a (rather late) mid-life crisis and run off with another language called Julia. (http://julialang.org)\n",
"\n",
"Note, Dataframes used to be slow. A very large change is coming in the next week. To understand it in detail, read: http://www.johnmyleswhite.com/notebook/2015/11/28/why-julias-dataframes-are-still-slow/"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## JuliaOpt\n",
"\n",
"- Julia for Mathematical Programming (JuMP) is one of the premire Julia libraries. It implements a DSL for interfacing with many commercial and non-commercial mathematical optimization (linear, mixed-integer, conic, semidefinite, nonlinear) algorithms. Most of JuliaOpt can be used through JuMP\n",
"- Optim.jl are a set of native Julia optimization algorithms\n",
"- An interesting fact is that the creator of NLopt is a heavy contributor to Julia and JuliaOpt"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## JuliaParallel\n",
"\n",
"Bindings to many popular parallel libraries / APIs are found in JuliaParallel:\n",
"\n",
"- DistributedArrays.jl: A distributed array implmentation\n",
"- PETSc.jl\n",
"- MPI.jl\n",
"- ScaLAPACK.jl"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## JuliaGPU\n",
"\n",
"Bindings for common GPU libraries:\n",
"\n",
"- ArrayFire.jl\n",
"- CUDArt.jl\n",
"- CUSPARSE.jl\n",
"- CUDNN.jl\n",
"- CUFFT.jl\n",
"- CUBLAS.jl\n",
"\n",
"JuliaGPU is also developing a framework for easy GPU usage:\n",
"\n",
"- CUDAnative.jl\n",
"- GPUArrays.jl"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## JuliaDiff\n",
"\n",
"JuliaDiff holds libraries for differentiation in Julia\n",
"\n",
"- ForwardDiff.jl: A robust implementation of forward-mode autodifferentiation\n",
"- ReverseDiffSource.jl: A newer library for reverse-mode autodifferentiation (backwards propogation)"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## JuliaGraphs\n",
"\n",
"JuliaGraphs is built around LightGraphs.jl, a fast and performant implementation of graph algorithms in Julia"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## JuliaMath\n",
"\n",
"JuliaMath holds basic mathematical libraries.\n",
"\n",
"- IterativeSolvers.jl: Iterative methods for `Ax=b`, Krylov subspace methods, etc.\n",
"- Roots.jl: Root-finding algorithms"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## JuliaDiffEq\n",
"\n",
"JuliaDiffEq holds the packages for solving differential equations.\n",
"\n",
"- DifferentialEquations.jl: The core package for solving ODEs, SDEs, PDEs, DAEs, DDEs, jump problems, etc.\n",
"- Sundials.jl: Wrappers for the Sundials ODE/DAE solvers"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## JuliaInterop\n",
"\n",
"Interoperability of Julia with other languages.\n",
"\n",
"- MATLAB.jl\n",
"- RCall.jl\n",
"- Mathematica.jl\n",
"- JavaCall.jl\n",
"- CxxWrap.jl\n",
"- ObjectiveC.jl"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## JuliaPy\n",
"\n",
"Julia interop with Python\n",
"\n",
"- PyPlot.jl: A wrapper for the Python matplotlib library\n",
"- SymPy.jl\n",
"- PyCall.jl\n",
"- pyjulia\n",
"- Pandas.jl"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Misc\n",
"\n",
"- JLD.jl: An HDF5-based saving format for Julia\n",
"- Bio.jl: A huge library for bioinformatics in Julia"
]
}
],
"metadata": {
"anaconda-cloud": {},
"celltoolbar": "Slideshow",
"kernelspec": {
"display_name": "Julia 1.0.0",
"language": "julia",
"name": "julia-1.0"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.0.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
pacoqueen/ginn | extra/install/ipython2/ipython-5.10.0/tools/tests/heartbeat/gilsleep.ipynb | 2 | 1642 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Holding the GIL for too long could disrupt the heartbeat due to non-copying sends.\n",
"\n",
"The following cell repeatedly calls a function that holds the GIL for five seconds.\n",
"\n",
"The heartbeat will fail after a few iterations prior to fixing Issue [#1260](https://github.com/ipython/ipython/issues/1260)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import sys\n",
"import time\n",
"\n",
"from cython import inline\n",
"\n",
"def gilsleep(t):\n",
" \"\"\"gil-holding sleep with cython.inline\"\"\"\n",
" code = '\\n'.join([\n",
" 'from posix cimport unistd',\n",
" 'unistd.sleep(t)',\n",
" ])\n",
" while True:\n",
" inline(code, quiet=True, t=t)\n",
" print time.time()\n",
" sys.stdout.flush() # this is important\n",
"\n",
"gilsleep(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
mshayeb/BigDataSpark | spark_tutorial_student.ipynb | 9 | 53415 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# + \n",
"# **Spark Tutorial: Learning Apache Spark**\n",
"#### This tutorial will teach you how to use [Apache Spark](http://spark.apache.org/), a framework for large-scale data processing, within a notebook. Many traditional frameworks were designed to be run on a single computer. However, many datasets today are too large to be stored on a single computer, and even when a dataset can be stored on one computer (such as the datasets in this tutorial), the dataset can often be processed much more quickly using multiple computers. Spark has efficient implementations of a number of transformations and actions that can be composed together to perform data processing and analysis. Spark excels at distributing these operations across a cluster while abstracting away many of the underlying implementation details. Spark has been designed with a focus on scalability and efficiency. With Spark you can begin developing your solution on your laptop, using a small dataset, and then use that same code to process terabytes or even petabytes across a distributed cluster.\n",
"#### **During this tutorial we will cover:**\n",
"#### *Part 1:* Basic notebook usage and [Python](https://docs.python.org/2/) integration\n",
"#### *Part 2:* An introduction to using [Apache Spark](https://spark.apache.org/) with the Python [pySpark API](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD) running in the browser\n",
"#### *Part 3:* Using RDDs and chaining together transformations and actions\n",
"#### *Part 4:* Lambda functions\n",
"#### *Part 5:* Additional RDD actions\n",
"#### *Part 6:* Additional RDD transformations\n",
"#### *Part 7:* Caching RDDs and storage options\n",
"#### *Part 8:* Debugging Spark applications and lazy evaluation\n",
"#### The following transformations will be covered:\n",
"* #### `map()`, `mapPartitions()`, `mapPartitionsWithIndex()`, `filter()`, `flatMap()`, `reduceByKey()`, `groupByKey()`\n",
"#### The following actions will be covered:\n",
"* #### `first()`, `take()`, `takeSample()`, `takeOrdered()`, `collect()`, `count()`, `countByValue()`, `reduce()`, `top()`\n",
"#### Also covered:\n",
"* #### `cache()`, `unpersist()`, `id()`, `setName()`\n",
"#### Note that, for reference, you can look up the details of these methods in [Spark's Python API](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **Part 1: Basic notebook usage and [Python](https://docs.python.org/2/) integration **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **(1a) Notebook usage**\n",
"#### A notebook is comprised of a linear sequence of cells. These cells can contain either markdown or code, but we won't mix both in one cell. When a markdown cell is executed it renders formatted text, images, and links just like HTML in a normal webpage. The text you are reading right now is part of a markdown cell. Python code cells allow you to execute arbitrary Python commands just like in any Python shell. Place your cursor inside the cell below, and press \"Shift\" + \"Enter\" to execute the code and advance to the next cell. You can also press \"Ctrl\" + \"Enter\" to execute the code and remain in the cell. These commands work the same in both markdown and code cells."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# This is a Python cell. You can run normal Python code here...\n",
"print 'The sum of 1 and 1 is {0}'.format(1+1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Here is another Python cell, this time with a variable (x) declaration and an if statement:\n",
"x = 42\n",
"if x > 40:\n",
" print 'The sum of 1 and 2 is {0}'.format(1+2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **(1b) Notebook state**\n",
"#### As you work through a notebook it is important that you run all of the code cells. The notebook is stateful, which means that variables and their values are retained until the notebook is detached (in Databricks Cloud) or the kernel is restarted (in IPython notebooks). If you do not run all of the code cells as you proceed through the notebook, your variables will not be properly initialized and later code might fail. You will also need to rerun any cells that you have modified in order for the changes to be available to other cells."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# This cell relies on x being defined already.\n",
"# If we didn't run the cells from part (1a) this code would fail.\n",
"print x * 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **(1c) Library imports**\n",
"#### We can import standard Python libraries ([modules](https://docs.python.org/2/tutorial/modules.html)) the usual way. An `import` statement will import the specified module. In this tutorial and future labs, we will provide any imports that are necessary."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Import the regular expression library\n",
"import re\n",
"m = re.search('(?<=abc)def', 'abcdef')\n",
"m.group(0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Import the datetime library\n",
"import datetime\n",
"print 'This was last run on: {0}'.format(datetime.datetime.now())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **Part 2: An introduction to using [Apache Spark](https://spark.apache.org/) with the Python [pySpark API](https://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD) running in the browser**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **Spark Context**\n",
"#### In Spark, communication occurs between a driver and executors. The driver has Spark jobs that it needs to run and these jobs are split into tasks that are submitted to the executors for completion. The results from these tasks are delivered back to the driver.\n",
"#### In part 1, we saw that normal python code can be executed via cells. When using Databricks Cloud this code gets executed in the Spark driver's Java Virtual Machine (JVM) and not in an executor's JVM, and when using an IPython notebook it is executed within the kernel associated with the notebook. Since no Spark functionality is actually being used, no tasks are launched on the executors.\n",
"#### In order to use Spark and its API we will need to use a `SparkContext`. When running Spark, you start a new Spark application by creating a [SparkContext](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.SparkContext). When the `SparkContext` is created, it asks the master for some cores to use to do work. The master sets these cores aside just for you; they won't be used for other applications. When using Databricks Cloud or the virtual machine provisioned for this class, the `SparkContext` is created for you automatically as `sc`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **(2a) Example Cluster**\n",
"#### The diagram below shows an example cluster, where the cores allocated for an application are outlined in purple.\n",
"\n",
"#### You can view the details of your Spark application in the Spark web UI. The web UI is accessible in Databricks cloud by going to \"Clusters\" and then clicking on the \"View Spark UI\" link for your cluster. When running locally you'll find it at [localhost:4040](http://localhost:4040). In the web UI, under the \"Jobs\" tab, you can see a list of jobs that have been scheduled or run. It's likely there isn't any thing interesting here yet because we haven't run any jobs, but we'll return to this page later.\n",
"#### At a high level, every Spark application consists of a driver program that launches various parallel operations on executor Java Virtual Machines (JVMs) running either in a cluster or locally on the same machine. In Databricks Cloud, \"Databricks Shell\" is the driver program. When running locally, \"PySparkShell\" is the driver program. In all cases, this driver program contains the main loop for the program and creates distributed datasets on the cluster, then applies operations (transformations & actions) to those datasets.\n",
"#### Driver programs access Spark through a SparkContext object, which represents a connection to a computing cluster. A Spark context object (`sc`) is the main entry point for Spark functionality. A Spark context can be used to create Resilient Distributed Datasets (RDDs) on a cluster.\n",
"#### Try printing out `sc` to see its type."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Display the type of the Spark Context sc\n",
"type(sc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **(2b) `SparkContext` attributes**\n",
"#### You can use Python's [dir()](https://docs.python.org/2/library/functions.html?highlight=dir#dir) function to get a list of all the attributes (including methods) accessible through the `sc` object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# List sc's attributes\n",
"dir(sc)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **(2c) Getting help**\n",
"#### Alternatively, you can use Python's [help()](https://docs.python.org/2/library/functions.html?highlight=help#help) function to get an easier to read list of all the attributes, including examples, that the `sc` object has."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Use help to obtain more detailed information\n",
"help(sc)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# After reading the help we've decided we want to use sc.version to see what version of Spark we are running\n",
"sc.version"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Help can be used on any Python object\n",
"help(map)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### **Part 3: Using RDDs and chaining together transformations and actions**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **Working with your first RDD**\n",
"#### In Spark, we first create a base [Resilient Distributed Dataset](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD) (RDD). We can then apply one or more transformations to that base RDD. *An RDD is immutable, so once it is created, it cannot be changed.* As a result, each transformation creates a new RDD. Finally, we can apply one or more actions to the RDDs. Note that Spark uses lazy evaluation, so transformations are not actually executed until an action occurs.\n",
"#### We will perform several exercises to obtain a better understanding of RDDs:\n",
"* ##### Create a Python collection of 10,000 integers\n",
"* ##### Create a Spark base RDD from that collection\n",
"* ##### Subtract one from each value using `map`\n",
"* ##### Perform action `collect` to view results\n",
"* ##### Perform action `count` to view counts\n",
"* ##### Apply transformation `filter` and view results with `collect`\n",
"* ##### Learn about lambda functions\n",
"* ##### Explore how lazy evaluation works and the debugging challenges that it introduces"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **(3a) Create a Python collection of integers in the range of 1 .. 10000**\n",
"#### We will use the [xrange()](https://docs.python.org/2/library/functions.html?highlight=xrange#xrange) function to create a [list()](https://docs.python.org/2/library/functions.html?highlight=list#list) of integers. `xrange()` only generates values as they are needed. This is different from the behavior of [range()](https://docs.python.org/2/library/functions.html?highlight=range#range) which generates the complete list upon execution. Because of this `xrange()` is more memory efficient than `range()`, especially for large ranges."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = xrange(1, 10001)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Data is just a normal Python list\n",
"# Obtain data's first element\n",
"data[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# We can check the size of the list using the len() function\n",
"len(data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **(3b) Distributed data and using a collection to create an RDD**\n",
"#### In Spark, datasets are represented as a list of entries, where the list is broken up into many different partitions that are each stored on a different machine. Each partition holds a unique subset of the entries in the list. Spark calls datasets that it stores \"Resilient Distributed Datasets\" (RDDs).\n",
"#### One of the defining features of Spark, compared to other data analytics frameworks (e.g., Hadoop), is that it stores data in memory rather than on disk. This allows Spark applications to run much more quickly, because they are not slowed down by needing to read data from disk.\n",
"#### The figure below illustrates how Spark breaks a list of data entries into partitions that are each stored in memory on a worker.\n",
"\n",
"#### To create the RDD, we use `sc.parallelize()`, which tells Spark to create a new set of input data based on data that is passed in. In this example, we will provide an `xrange`. The second argument to the [sc.parallelize()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.SparkContext.parallelize) method tells Spark how many partitions to break the data into when it stores the data in memory (we'll talk more about this later in this tutorial). Note that for better performance when using `parallelize`, `xrange()` is recommended if the input represents a range. This is the reason why we used `xrange()` in 3a.\n",
"#### There are many different types of RDDs. The base class for RDDs is [pyspark.RDD](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD) and other RDDs subclass `pyspark.RDD`. Since the other RDD types inherit from `pyspark.RDD` they have the same APIs and are functionally identical. We'll see that `sc.parallelize()` generates a `pyspark.rdd.PipelinedRDD` when its input is an `xrange`, and a `pyspark.RDD` when its input is a `range`.\n",
"#### After we generate RDDs, we can view them in the \"Storage\" tab of the web UI. You'll notice that new datasets are not listed until Spark needs to return a result due to an action being executed. This feature of Spark is called \"lazy evaluation\". This allows Spark to avoid performing unnecessary calculations."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Parallelize data using 8 partitions\n",
"# This operation is a transformation of data into an RDD\n",
"# Spark uses lazy evaluation, so no Spark jobs are run at this point\n",
"xrangeRDD = sc.parallelize(data, 8)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's view help on parallelize\n",
"help(sc.parallelize)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's see what type sc.parallelize() returned\n",
"print 'type of xrangeRDD: {0}'.format(type(xrangeRDD))\n",
"\n",
"# How about if we use a range\n",
"dataRange = range(1, 10001)\n",
"rangeRDD = sc.parallelize(dataRange, 8)\n",
"print 'type of dataRangeRDD: {0}'.format(type(rangeRDD))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Each RDD gets a unique ID\n",
"print 'xrangeRDD id: {0}'.format(xrangeRDD.id())\n",
"print 'rangeRDD id: {0}'.format(rangeRDD.id())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# We can name each newly created RDD using the setName() method\n",
"xrangeRDD.setName('My first RDD')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's view the lineage (the set of transformations) of the RDD using toDebugString()\n",
"print xrangeRDD.toDebugString()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's use help to see what methods we can call on this RDD\n",
"help(xrangeRDD)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's see how many partitions the RDD will be split into by using the getNumPartitions()\n",
"xrangeRDD.getNumPartitions()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **(3c): Subtract one from each value using `map`**\n",
"#### So far, we've created a distributed dataset that is split into many partitions, where each partition is stored on a single machine in our cluster. Let's look at what happens when we do a basic operation on the dataset. Many useful data analysis operations can be specified as \"do something to each item in the dataset\". These data-parallel operations are convenient because each item in the dataset can be processed individually: the operation on one entry doesn't effect the operations on any of the other entries. Therefore, Spark can parallelize the operation.\n",
"#### `map(f)`, the most common Spark transformation, is one such example: it applies a function `f` to each item in the dataset, and outputs the resulting dataset. When you run `map()` on a dataset, a single *stage* of tasks is launched. A *stage* is a group of tasks that all perform the same computation, but on different input data. One task is launched for each partitition, as shown in the example below. A task is a unit of execution that runs on a single machine. When we run `map(f)` within a partition, a new *task* applies `f` to all of the entries in a particular partition, and outputs a new partition. In this example figure, the dataset is broken into four partitions, so four `map()` tasks are launched.\n",
"\n",
"#### The figure below shows how this would work on the smaller data set from the earlier figures. Note that one task is launched for each partition.\n",
"\n",
"#### When applying the `map()` transformation, each item in the parent RDD will map to one element in the new RDD. So, if the parent RDD has twenty elements, the new RDD will also have twenty items.\n",
"#### Now we will use `map()` to subtract one from each value in the base RDD we just created. First, we define a Python function called `sub()` that will subtract one from the input integer. Second, we will pass each item in the base RDD into a `map()` transformation that applies the `sub()` function to each element. And finally, we print out the RDD transformation hierarchy using `toDebugString()`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create sub function to subtract 1\n",
"def sub(value):\n",
" \"\"\"\"Subtracts one from `value`.\n",
"\n",
" Args:\n",
" value (int): A number.\n",
"\n",
" Returns:\n",
" int: `value` minus one.\n",
" \"\"\"\n",
" return (value - 1)\n",
"\n",
"# Transform xrangeRDD through map transformation using sub function\n",
"# Because map is a transformation and Spark uses lazy evaluation, no jobs, stages,\n",
"# or tasks will be launched when we run this code.\n",
"subRDD = xrangeRDD.map(sub)\n",
"\n",
"# Let's see the RDD transformation hierarchy\n",
"print subRDD.toDebugString()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (3d) Perform action `collect` to view results **\n",
"#### To see a list of elements decremented by one, we need to create a new list on the driver from the the data distributed in the executor nodes. To do this we call the `collect()` method on our RDD. `collect()` is often used after a filter or other operation to ensure that we are only returning a *small* amount of data to the driver. This is done because the data returned to the driver must fit into the driver's available memory. If not, the driver will crash.\n",
"#### The `collect()` method is the first action operation that we have encountered. Action operations cause Spark to perform the (lazy) transformation operations that are required to compute the RDD returned by the action. In our example, this means that tasks will now be launched to perform the `parallelize`, `map`, and `collect` operations.\n",
"#### In this example, the dataset is broken into four partitions, so four `collect()` tasks are launched. Each task collects the entries in its partition and sends the result to the SparkContext, which creates a list of the values, as shown in the figure below.\n",
"\n",
"#### The above figures showed what would happen if we ran `collect()` on a small example dataset with just four partitions.\n",
"#### Now let's run `collect()` on `subRDD`."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's collect the data\n",
"print subRDD.collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (3d) Perform action `count` to view counts **\n",
"#### One of the most basic jobs that we can run is the `count()` job which will count the number of elements in an RDD using the `count()` action. Since `map()` creates a new RDD with the same number of elements as the starting RDD, we expect that applying `count()` to each RDD will return the same result.\n",
"#### Note that because `count()` is an action operation, if we had not already performed an action with `collect()`, then Spark would now perform the transformation operations when we executed `count()`.\n",
"#### Each task counts the entries in its partition and sends the result to your SparkContext, which adds up all of the counts. The figure below shows what would happen if we ran `count()` on a small example dataset with just four partitions.\n",
""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print xrangeRDD.count()\n",
"print subRDD.count()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (3e) Apply transformation `filter` and view results with `collect` **\n",
"#### Next, we'll create a new RDD that only contains the values less than ten by using the `filter(f)` data-parallel operation. The `filter(f)` method is a transformation operation that creates a new RDD from the input RDD by applying filter function `f` to each item in the parent RDD and only passing those elements where the filter function returns `True`. Elements that do not return `True` will be dropped. Like `map()`, filter can be applied individually to each entry in the dataset, so is easily parallelized using Spark.\n",
"#### The figure below shows how this would work on the small four-partition dataset.\n",
"\n",
"#### To filter this dataset, we'll define a function called `ten()`, which returns `True` if the input is less than 10 and `False` otherwise. This function will be passed to the `filter()` transformation as the filter function `f`.\n",
"#### To view the filtered list of elements less than ten, we need to create a new list on the driver from the distributed data on the executor nodes. We use the `collect()` method to return a list that contains all of the elements in this filtered RDD to the driver program."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Define a function to filter a single value\n",
"def ten(value):\n",
" \"\"\"Return whether value is below ten.\n",
"\n",
" Args:\n",
" value (int): A number.\n",
"\n",
" Returns:\n",
" bool: Whether `value` is less than ten.\n",
" \"\"\"\n",
" if (value < 10):\n",
" return True\n",
" else:\n",
" return False\n",
"# The ten function could also be written concisely as: def ten(value): return value < 10\n",
"\n",
"# Pass the function ten to the filter transformation\n",
"# Filter is a transformation so no tasks are run\n",
"filteredRDD = subRDD.filter(ten)\n",
"\n",
"# View the results using collect()\n",
"# Collect is an action and triggers the filter transformation to run\n",
"print filteredRDD.collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ** Part 4: Lambda Functions **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (4a) Using Python `lambda()` functions **\n",
"#### Python supports the use of small one-line anonymous functions that are not bound to a name at runtime. Borrowed from LISP, these `lambda` functions can be used wherever function objects are required. They are syntactically restricted to a single expression. Remember that `lambda` functions are a matter of style and using them is never required - semantically, they are just syntactic sugar for a normal function definition. You can always define a separate normal function instead, but using a `lambda()` function is an equivalent and more compact form of coding. Ideally you should consider using `lambda` functions where you want to encapsulate non-reusable code without littering your code with one-line functions.\n",
"#### Here, instead of defining a separate function for the `filter()` transformation, we will use an inline `lambda()` function."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lambdaRDD = subRDD.filter(lambda x: x < 10)\n",
"lambdaRDD.collect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's collect the even values less than 10\n",
"evenRDD = lambdaRDD.filter(lambda x: x % 2 == 0)\n",
"evenRDD.collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ** Part 5: Additional RDD actions **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (5a) Other common actions **\n",
"#### Let's investigate the additional actions: [first()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.first), [take()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.take), [top()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.top), [takeOrdered()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.takeOrdered), and [reduce()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.reduce)\n",
"#### One useful thing to do when we have a new dataset is to look at the first few entries to obtain a rough idea of what information is available. In Spark, we can do that using the `first()`, `take()`, `top()`, and `takeOrdered()` actions. Note that for the `first()` and `take()` actions, the elements that are returned depend on how the RDD is *partitioned*.\n",
"#### Instead of using the `collect()` action, we can use the `take(n)` action to return the first n elements of the RDD. The `first()` action returns the first element of an RDD, and is equivalent to `take(1)`.\n",
"#### The `takeOrdered()` action returns the first n elements of the RDD, using either their natural order or a custom comparator. The key advantage of using `takeOrdered()` instead of `first()` or `take()` is that `takeOrdered()` returns a deterministic result, while the other two actions may return differing results, depending on the number of partions or execution environment. `takeOrdered()` returns the list sorted in *ascending order*. The `top()` action is similar to `takeOrdered()` except that it returns the list in *descending order.*\n",
"#### The `reduce()` action reduces the elements of a RDD to a single value by applying a function that takes two parameters and returns a single value. The function should be commutative and associative, as `reduce()` is applied at the partition level and then again to aggregate results from partitions. If these rules don't hold, the results from `reduce()` will be inconsistent. Reducing locally at partitions makes `reduce()` very efficient."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's get the first element\n",
"print filteredRDD.first()\n",
"# The first 4\n",
"print filteredRDD.take(4)\n",
"# Note that it is ok to take more elements than the RDD has\n",
"print filteredRDD.take(12)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Retrieve the three smallest elements\n",
"print filteredRDD.takeOrdered(3)\n",
"# Retrieve the five largest elements\n",
"print filteredRDD.top(5)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Pass a lambda function to takeOrdered to reverse the order\n",
"filteredRDD.takeOrdered(4, lambda s: -s)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Obtain Python's add function\n",
"from operator import add\n",
"# Efficiently sum the RDD using reduce\n",
"print filteredRDD.reduce(add)\n",
"# Sum using reduce with a lambda function\n",
"print filteredRDD.reduce(lambda a, b: a + b)\n",
"# Note that subtraction is not both associative and commutative\n",
"print filteredRDD.reduce(lambda a, b: a - b)\n",
"print filteredRDD.repartition(4).reduce(lambda a, b: a - b)\n",
"# While addition is\n",
"print filteredRDD.repartition(4).reduce(lambda a, b: a + b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (5b) Advanced actions **\n",
"#### Here are two additional actions that are useful for retrieving information from an RDD: [takeSample()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.takeSample) and [countByValue()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.countByValue)\n",
"#### The `takeSample()` action returns an array with a random sample of elements from the dataset. It takes in a `withReplacement` argument, which specifies whether it is okay to randomly pick the same item multiple times from the parent RDD (so when `withReplacement=True`, you can get the same item back multiple times). It also takes an optional `seed` parameter that allows you to specify a seed value for the random number generator, so that reproducible results can be obtained.\n",
"#### The `countByValue()` action returns the count of each unique value in the RDD as a dictionary that maps values to counts."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# takeSample reusing elements\n",
"print filteredRDD.takeSample(withReplacement=True, num=6)\n",
"# takeSample without reuse\n",
"print filteredRDD.takeSample(withReplacement=False, num=6)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Set seed for predictability\n",
"print filteredRDD.takeSample(withReplacement=False, num=6, seed=500)\n",
"# Try reruning this cell and the cell above -- the results from this cell will remain constant\n",
"# Use ctrl-enter to run without moving to the next cell"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create new base RDD to show countByValue\n",
"repetitiveRDD = sc.parallelize([1, 2, 3, 1, 2, 3, 1, 2, 1, 2, 3, 3, 3, 4, 5, 4, 6])\n",
"print repetitiveRDD.countByValue()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ** Part 6: Additional RDD transformations **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (6a) `flatMap` **\n",
"#### When performing a `map()` transformation using a function, sometimes the function will return more (or less) than one element. We would like the newly created RDD to consist of the elements outputted by the function. Simply applying a `map()` transformation would yield a new RDD made up of iterators. Each iterator could have zero or more elements. Instead, we often want an RDD consisting of the values contained in those iterators. The solution is to use a [flatMap()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.flatMap) transformation, `flatMap()` is similar to `map()`, except that with `flatMap()` each input item can be mapped to zero or more output elements.\n",
"#### To demonstrate `flatMap()`, we will first emit a word along with its plural, and then a range that grows in length with each subsequent operation."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Let's create a new base RDD to work from\n",
"wordsList = ['cat', 'elephant', 'rat', 'rat', 'cat']\n",
"wordsRDD = sc.parallelize(wordsList, 4)\n",
"\n",
"# Use map\n",
"singularAndPluralWordsRDDMap = wordsRDD.map(lambda x: (x, x + 's'))\n",
"# Use flatMap\n",
"singularAndPluralWordsRDD = wordsRDD.flatMap(lambda x: (x, x + 's'))\n",
"\n",
"# View the results\n",
"print singularAndPluralWordsRDDMap.collect()\n",
"print singularAndPluralWordsRDD.collect()\n",
"# View the number of elements in the RDD\n",
"print singularAndPluralWordsRDDMap.count()\n",
"print singularAndPluralWordsRDD.count()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"simpleRDD = sc.parallelize([2, 3, 4])\n",
"print simpleRDD.map(lambda x: range(1, x)).collect()\n",
"print simpleRDD.flatMap(lambda x: range(1, x)).collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (6b) `groupByKey` and `reduceByKey` **\n",
"#### Let's investigate the additional transformations: [groupByKey()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.groupByKey) and [reduceByKey()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.reduceByKey).\n",
"#### Both of these transformations operate on pair RDDs. A pair RDD is an RDD where each element is a pair tuple (key, value). For example, `sc.parallelize([('a', 1), ('a', 2), ('b', 1)])` would create a pair RDD where the keys are 'a', 'a', 'b' and the values are 1, 2, 1.\n",
"#### The `reduceByKey()` transformation gathers together pairs that have the same key and applies a function to two associated values at a time. `reduceByKey()` operates by applying the function first within each partition on a per-key basis and then across the partitions.\n",
"#### While both the `groupByKey()` and `reduceByKey()` transformations can often be used to solve the same problem and will produce the same answer, the `reduceByKey()` transformation works much better for large distributed datasets. This is because Spark knows it can combine output with a common key on each partition *before* shuffling (redistributing) the data across nodes. Only use `groupByKey()` if the operation would not benefit from reducing the data before the shuffle occurs.\n",
"#### Look at the diagram below to understand how `reduceByKey` works. Notice how pairs on the same machine with the same key are combined (by using the lamdba function passed into reduceByKey) before the data is shuffled. Then the lamdba function is called again to reduce all the values from each partition to produce one final result.\n",
"\n",
"#### On the other hand, when using the `groupByKey()` transformation - all the key-value pairs are shuffled around, causing a lot of unnecessary data to being transferred over the network.\n",
"#### To determine which machine to shuffle a pair to, Spark calls a partitioning function on the key of the pair. Spark spills data to disk when there is more data shuffled onto a single executor machine than can fit in memory. However, it flushes out the data to disk one key at a time, so if a single key has more key-value pairs than can fit in memory an out of memory exception occurs. This will be more gracefully handled in a later release of Spark so that the job can still proceed, but should still be avoided. When Spark needs to spill to disk, performance is severely impacted.\n",
"\n",
"#### As your dataset grows, the difference in the amount of data that needs to be shuffled, between the `reduceByKey()` and `groupByKey()` transformations, becomes increasingly exaggerated.\n",
"#### Here are more transformations to prefer over `groupByKey()`:\n",
" + #### [combineByKey()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.combineByKey) can be used when you are combining elements but your return type differs from your input value type.\n",
" + #### [foldByKey()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.foldByKey) merges the values for each key using an associative function and a neutral \"zero value\".\n",
"#### Now let's go through a simple `groupByKey()` and `reduceByKey()` example."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pairRDD = sc.parallelize([('a', 1), ('a', 2), ('b', 1)])\n",
"# mapValues only used to improve format for printing\n",
"print pairRDD.groupByKey().mapValues(lambda x: list(x)).collect()\n",
"\n",
"# Different ways to sum by key\n",
"print pairRDD.groupByKey().map(lambda (k, v): (k, sum(v))).collect()\n",
"# Using mapValues, which is recommended when they key doesn't change\n",
"print pairRDD.groupByKey().mapValues(lambda x: sum(x)).collect()\n",
"# reduceByKey is more efficient / scalable\n",
"print pairRDD.reduceByKey(add).collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (6c) Advanced transformations ** [Optional]\n",
"#### Let's investigate the advanced transformations: [mapPartitions()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.mapPartitions) and [mapPartitionsWithIndex()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.mapPartitionsWithIndex)\n",
"#### The `mapPartitions()` transformation uses a function that takes in an iterator (to the items in that specific partition) and returns an iterator. The function is applied on a partition by partition basis.\n",
"#### The `mapPartitionsWithIndex()` transformation uses a function that takes in a partition index (think of this like the partition number) and an iterator (to the items in that specific partition). For every partition (index, iterator) pair, the function returns a tuple of the same partition index number and an iterator of the transformed items in that partition."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# mapPartitions takes a function that takes an iterator and returns an iterator\n",
"print wordsRDD.collect()\n",
"itemsRDD = wordsRDD.mapPartitions(lambda iterator: [','.join(iterator)])\n",
"print itemsRDD.collect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"itemsByPartRDD = wordsRDD.mapPartitionsWithIndex(lambda index, iterator: [(index, list(iterator))])\n",
"# We can see that three of the (partitions) workers have one element and the fourth worker has two\n",
"# elements, although things may not bode well for the rat...\n",
"print itemsByPartRDD.collect()\n",
"# Rerun without returning a list (acts more like flatMap)\n",
"itemsByPartRDD = wordsRDD.mapPartitionsWithIndex(lambda index, iterator: (index, list(iterator)))\n",
"print itemsByPartRDD.collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ** Part 7: Caching RDDs and storage options **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (7a) Caching RDDs **\n",
"#### For efficiency Spark keeps your RDDs in memory. By keeping the contents in memory, Spark can quickly access the data. However, memory is limited, so if you try to keep too many RDDs in memory, Spark will automatically delete RDDs from memory to make space for new RDDs. If you later refer to one of the RDDs, Spark will automatically recreate the RDD for you, but that takes time.\n",
"#### So, if you plan to use an RDD more than once, then you should tell Spark to cache that RDD. You can use the `cache()` operation to keep the RDD in memory. However, if you cache too many RDDs and Spark runs out of memory, it will delete the least recently used (LRU) RDD first. Again, the RDD will be automatically recreated when accessed.\n",
"#### You can check if an RDD is cached by using the `is_cached` attribute, and you can see your cached RDD in the \"Storage\" section of the Spark web UI. If you click on the RDD's name, you can see more information about where the RDD is stored."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Name the RDD\n",
"filteredRDD.setName('My Filtered RDD')\n",
"# Cache the RDD\n",
"filteredRDD.cache()\n",
"# Is it cached\n",
"print filteredRDD.is_cached"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (7b) Unpersist and storage options **\n",
"#### Spark automatically manages the RDDs cached in memory and will save them to disk if it runs out of memory. For efficiency, once you are finished using an RDD, you can optionally tell Spark to stop caching it in memory by using the RDD's `unpersist()` method to inform Spark that you no longer need the RDD in memory.\n",
"#### You can see the set of transformations that were applied to create an RDD by using the `toDebugString()` method, which will provide storage information, and you can directly query the current storage information for an RDD using the `getStorageLevel()` operation.\n",
"#### ** Advanced: ** Spark provides many more options for managing how RDDs are stored in memory or even saved to disk. You can explore the API for RDD's [persist()](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD.persist) operation using Python's [help()](https://docs.python.org/2/library/functions.html?highlight=help#help) command. The `persist()` operation, optionally, takes a pySpark [StorageLevel](http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.StorageLevel) object."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Note that toDebugString also provides storage information\n",
"print filteredRDD.toDebugString()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# If we are done with the RDD we can unpersist it so that its memory can be reclaimed\n",
"filteredRDD.unpersist()\n",
"# Storage level for a non cached RDD\n",
"print filteredRDD.getStorageLevel()\n",
"filteredRDD.cache()\n",
"# Storage level for a cached RDD\n",
"print filteredRDD.getStorageLevel()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ** Part 8: Debugging Spark applications and lazy evaluation **"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** How Python is Executed in Spark **\n",
"#### Internally, Spark executes using a Java Virtual Machine (JVM). pySpark runs Python code in a JVM using [Py4J](http://py4j.sourceforge.net). Py4J enables Python programs running in a Python interpreter to dynamically access Java objects in a Java Virtual Machine. Methods are called as if the Java objects resided in the Python interpreter and Java collections can be accessed through standard Python collection methods. Py4J also enables Java programs to call back Python objects.\n",
"#### Because pySpark uses Py4J, coding errors often result in a complicated, confusing stack trace that can be difficult to understand. In the following section, we'll explore how to understand stack traces."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (8a) Challenges with lazy evaluation using transformations and actions **\n",
"#### Spark's use of lazy evaluation can make debugging more difficult because code is not always executed immediately. To see an example of how this can happen, let's first define a broken filter function.\n",
"#### Next we perform a `filter()` operation using the broken filtering function. No error will occur at this point due to Spark's use of lazy evaluation.\n",
"#### The `filter()` method will not be executed *until* an action operation is invoked on the RDD. We will perform an action by using the `collect()` method to return a list that contains all of the elements in this RDD."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def brokenTen(value):\n",
" \"\"\"Incorrect implementation of the ten function.\n",
"\n",
" Note:\n",
" The `if` statement checks an undefined variable `val` instead of `value`.\n",
"\n",
" Args:\n",
" value (int): A number.\n",
"\n",
" Returns:\n",
" bool: Whether `value` is less than ten.\n",
"\n",
" Raises:\n",
" NameError: The function references `val`, which is not available in the local or global\n",
" namespace, so a `NameError` is raised.\n",
" \"\"\"\n",
" if (val < 10):\n",
" return True\n",
" else:\n",
" return False\n",
"\n",
"brokenRDD = subRDD.filter(brokenTen)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Now we'll see the error\n",
"brokenRDD.collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (8b) Finding the bug **\n",
"#### When the `filter()` method is executed, Spark evaluates the RDD by executing the `parallelize()` and `filter()` methods. Since our `filter()` method has an error in the filtering function `brokenTen()`, an error occurs.\n",
"#### Scroll through the output \"Py4JJavaError Traceback (most recent call last)\" part of the cell and first you will see that the line that generated the error is the `collect()` method line. There is *nothing wrong with this line*. However, it is an action and that caused other methods to be executed. Continue scrolling through the Traceback and you will see the following error line:\n",
" NameError: global name 'val' is not defined\n",
"#### Looking at this error line, we can see that we used the wrong variable name in our filtering function `brokenTen()`."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (8c) Moving toward expert style **\n",
"#### As you are learning Spark, I recommend that you write your code in the form:\n",
" RDD.transformation1()\n",
" RDD.action1()\n",
" RDD.transformation2()\n",
" RDD.action2()\n",
"#### Using this style will make debugging your code much easier as it makes errors easier to localize - errors in your transformations will occur when the next action is executed.\n",
"#### Once you become more experienced with Spark, you can write your code with the form:\n",
" RDD.transformation1().transformation2().action()\n",
"#### We can also use `lambda()` functions instead of separately defined functions when their use improves readability and conciseness."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Cleaner code through lambda use\n",
"subRDD.filter(lambda x: x < 10).collect()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Even better by moving our chain of operators into a single line.\n",
"sc.parallelize(data).map(lambda y: y - 1).filter(lambda x: x < 10).collect()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### ** (8d) Readability and code style **\n",
"#### To make the expert coding style more readable, enclose the statement in parentheses and put each method, transformation, or action on a separate line."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Final version\n",
"(sc\n",
" .parallelize(data)\n",
" .map(lambda y: y - 1)\n",
" .filter(lambda x: x < 10)\n",
" .collect())"
]
}
],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
ttinoco/PSCHUB | notebooks/Welcome.ipynb | 1 | 1307 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Power Systems Computing Hub\n",
"\n",
"## Navigation\n",
"\n",
"* [Home](../tree)\n",
"* [Workspace](../tree/workspace)\n",
"* [Examples](../tree/examples)\n",
"\n",
"## Resources\n",
"\n",
"* [GRIDOPT](http://gridopt.readthedocs.io)\n",
"* [PFNET](http://pfnet-python.readthedocs.io)\n",
"* [OPTALG](http://optalg.readthedocs.io)\n",
"* [Jupyter](http://jupyter.org/)\n",
"* [Python](https://www.python.org/)\n",
"* [Markdown](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)\n",
"* [Latex](https://tobi.oetiker.ch/lshort/lshort.pdf)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
OSU-CS-325/Project_One_MSS | better-enumeration/betterEnumeration.ipynb | 1 | 3708 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Better Enumeration"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Pseudo-Code\n",
"The \"Better Enumeration\" maximum sub-array algorithm is described by the following pseudo-code:\n",
"~~~~\n",
"BETTER-ENUMERATION-MAX-SUBARRAY(A[1, ..., N])\n",
"\tmaximum sum = -Infinity\n",
"\tfor i from 1 to N\n",
"\t\tcurrent sum = 0\n",
"\t\tfor j from i to N\n",
"\t\t\tcurrent sum = current sum + A[j]\n",
"\t\t\tif current sum > maximum sum\n",
"\t\t\t\tmaximum sum = current sum\n",
"\t\t\t\tstart index = i\n",
"\t\t\t\tend index = j\n",
"\n",
"\treturn maximum sum, A[start index, ..., end index]\n",
"~~~~"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Theoretical Run-time Analysis\n",
"The outer $i$ loop runs from $1$ to $N$, and the inner $j$ loop runs from $i$ to $N$. Inside the inner loop are constant time operations. We can compute the number of iterations of these constant time operations as:\n",
"\n",
"\\begin{equation}\n",
"\\begin{split}\n",
"\\sum_{i=1}^N \\sum_{j=i}^N \\Theta(1) & = \\sum_{i=1}^N (N + 1 - i)\\cdot \\Theta(1) =N(N+1)\\cdot \\Theta(1) -\\frac{1}{2}N(N+1)\\cdot \\Theta(1) \\\\\n",
"& = \\frac{1}{2}N(N+1)\\cdot \\Theta(1) = \\Theta(N^2)\n",
"\\end{split}\n",
"\\end{equation}\n",
"\n",
"Thus, the theoritical run-time is $\\Theta(N^2)$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Experimental Analysis\n",
"For a series of array sizes $N$, 10 random arrays were generated and run through the \"Better Enumeration\" algorithm. The CPU clock time was recorded for each of the 10 random array inputs, and an average run time was computed. Below is the plot of average run time versus $N$ for the \"Better Enumeration\" algorithm."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center><font color='red' size=\"6\"><img src=\"./img/better-enumeration.png\" /></font></center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A curve fit was applied to the average run time data, which resulted in the following fit equation as a function of $x$ standing in for $N$:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$y = 5.48722 * 10^{-8} * x^{2} + 1.42659 * 10^{-4} * x - 7.776 * 10^{-1}$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The fit curve for the plotted data has the same degree as the theoretical runtime of $\\theta(N^{2})$ so the experimental appears to match the theoretical runtime. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Based on the average run time data curve fit, we would expect the \"Better Enumeration\" to be able to process the following number of elements in the given amount of time:\n",
"\n",
"| Time | Max Input Size |\n",
"|:----------:|:--------------:|\n",
"| 10 seconds | 12775 |\n",
"| 30 seconds | 22419 |\n",
"| 60 seconds | 32006 |"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
the-deep-learners/TensorFlow-LiveLessons | notebooks/tensor-fied_intro_to_tensorflow.ipynb | 1 | 24433 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to TensorFlow, now leveraging tensors!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this notebook, we modify our [intro to TensorFlow notebook](https://github.com/the-deep-learners/TensorFlow-LiveLessons/blob/master/notebooks/point_by_point_intro_to_tensorflow.ipynb) to use tensors in place of our *for* loop. This is a derivation of Jared Ostmeyer's [Naked Tensor](https://github.com/jostmey/NakedTensor/) code. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### The initial steps are identical to the earlier notebook"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"np.random.seed(42)\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import tensorflow as tf\n",
"tf.set_random_seed(42)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"xs = [0., 1., 2., 3., 4., 5., 6., 7.] \n",
"ys = [-.82, -.94, -.12, .26, .39, .64, 1.02, 1.] "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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a4Mk5PmOyeX8syZeBTcBngFVJLmrOGi4HJuepYw+wB6DX680ZIJKkbroOJR0AtjfL24HP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\nJKnl/wHDphZCP1Xq2gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d90705dd8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"_ = ax.scatter(xs, ys)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"m = tf.Variable(-0.5)\n",
"b = tf.Variable(1.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Define the cost as a tensor -- more elegant than a *for* loop and enables distributed computing in TensorFlow"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ys_model = m*xs+b\n",
"total_error = tf.reduce_sum((ys-ys_model)**2) # use an op to calculate SSE across all values instead of one by one"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### The remaining steps are also identical to the earlier notebook!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"optimizer_operation = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(total_error)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"initializer_operation = tf.global_variables_initializer()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with tf.Session() as session:\n",
" \n",
" session.run(initializer_operation)\n",
" \n",
" n_epochs = 1000 # 10, then 1000\n",
" for iteration in range(n_epochs):\n",
" session.run(optimizer_operation)\n",
" \n",
" slope, intercept = session.run([m, b])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.29314372"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"slope"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-0.84175235"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"intercept"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_hat = intercept + slope*np.array(xs)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>y</th>\n",
" <th>y_hat</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.82</td>\n",
" <td>-0.841752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>-0.94</td>\n",
" <td>-0.548609</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>-0.12</td>\n",
" <td>-0.255465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.26</td>\n",
" <td>0.037679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.39</td>\n",
" <td>0.330823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.64</td>\n",
" <td>0.623966</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1.02</td>\n",
" <td>0.917110</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1.00</td>\n",
" <td>1.210254</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" y y_hat\n",
"0 -0.82 -0.841752\n",
"1 -0.94 -0.548609\n",
"2 -0.12 -0.255465\n",
"3 0.26 0.037679\n",
"4 0.39 0.330823\n",
"5 0.64 0.623966\n",
"6 1.02 0.917110\n",
"7 1.00 1.210254"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.DataFrame(list(zip(ys, y_hat)), columns=['y', 'y_hat'])"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f1d4650e0f0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"\n",
"ax.scatter(xs, ys)\n",
"x_min, x_max = ax.get_xlim()\n",
"y_min, y_max = intercept, intercept + slope*(x_max-x_min)\n",
"\n",
"ax.plot([x_min, x_max], [y_min, y_max])\n",
"_ = ax.set_xlim([x_min, x_max])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
JaviMerino/lisa | ipynb/android/workloads/Android_YouTube.ipynb | 1 | 16914 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# EAS Testing - YouTube on Android"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The goal of this experiment is to run Youtube videos on a Nexus N5X running Android with an EAS kernel and collect results. The Analysis phase will consist in comparing EAS with other schedulers, that is comparing *sched* governor with:\n",
"\n",
" - interactive\n",
" - performance\n",
" - powersave\n",
" - ondemand"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import logging\n",
"reload(logging)\n",
"log_fmt = '%(asctime)-9s %(levelname)-8s: %(message)s'\n",
"logging.basicConfig(format=log_fmt)\n",
"\n",
"# Change to info once the notebook runs ok\n",
"logging.getLogger().setLevel(logging.INFO)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline\n",
"\n",
"import os\n",
"import pexpect as pe\n",
"from time import sleep\n",
"\n",
"# Support to access the remote target\n",
"import devlib\n",
"from env import TestEnv\n",
"\n",
"from devlib.utils.android import adb_command\n",
"\n",
"# Support for trace events analysis\n",
"from trace import Trace\n",
"\n",
"# Suport for FTrace events parsing and visualization\n",
"import trappy"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Set it to your local CATAPULT home folder\n",
"CATAPULT_HOME = \"/home/pippo/work/catapult\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Test Environment set up"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In case more than one Android device are conencted to the host, you must specify the ID of the device you want to target in `my_target_conf`. Run `adb devices` on your host to get the ID."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Setup a target configuration\n",
"my_target_conf = {\n",
" \n",
" # Target platform and board\n",
" \"platform\" : 'android',\n",
"\n",
" # Add target support\n",
" \"board\" : 'n5x',\n",
" \n",
" # Device ID\n",
" #\"device\" : \"00b1346f0878ccb1\",\n",
" \n",
" # Define devlib modules to load\n",
" \"modules\" : [\n",
" 'cpufreq' # enable CPUFreq support\n",
" ],\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"my_tests_conf = {\n",
"\n",
" # Folder where all the results will be collected\n",
" \"results_dir\" : \"Android_Youtube\",\n",
"\n",
" # Platform configurations to test\n",
" \"confs\" : [\n",
" {\n",
" \"tag\" : \"youtube\",\n",
" \"flags\" : \"ftrace\", # Enable FTrace events\n",
" \"sched_features\" : \"ENERGY_AWARE\", # enable EAS\n",
" },\n",
" ],\n",
" \n",
" # FTrace events to collect for all the tests configuration which have\n",
" # the \"ftrace\" flag enabled\n",
" \"ftrace\" : {\n",
" \"events\" : [\n",
" \"sched_switch\",\n",
" \"sched_load_avg_cpu\",\n",
" \"cpu_frequency\",\n",
" \"cpu_capacity\"\n",
" ],\n",
" \"buffsize\" : 10 * 1024,\n",
" },\n",
" \n",
" # Tools required by the experiments\n",
" \"tools\" : [ 'trace-cmd' ],\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"adbd is already running as root\r\n"
]
}
],
"source": [
"# Ensure ADB has root priviledges, which are required by systrace\n",
"!adb root"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2016-04-12 15:20:10,656 INFO : Target - Using base path: /home/pippo/work/lisa\n",
"2016-04-12 15:20:10,657 INFO : Target - Loading custom (inline) target configuration\n",
"2016-04-12 15:20:10,658 INFO : Target - Loading custom (inline) test configuration\n",
"2016-04-12 15:20:10,659 INFO : Target - Devlib modules to load: ['bl', 'cpufreq']\n",
"2016-04-12 15:20:10,660 INFO : Target - Connecting Android target [DEFAULT]\n",
"2016-04-12 15:20:11,142 INFO : Target - Initializing target workdir:\n",
"2016-04-12 15:20:11,144 INFO : Target - /data/local/tmp/devlib-target\n",
"2016-04-12 15:20:13,757 INFO : Target - Topology:\n",
"2016-04-12 15:20:13,759 INFO : Target - [[0, 1, 2, 3], [4, 5]]\n",
"2016-04-12 15:20:14,399 INFO : FTrace - Enabled tracepoints:\n",
"2016-04-12 15:20:14,400 INFO : FTrace - sched_switch\n",
"2016-04-12 15:20:14,401 INFO : FTrace - sched_load_avg_cpu\n",
"2016-04-12 15:20:14,402 INFO : FTrace - cpu_frequency\n",
"2016-04-12 15:20:14,402 INFO : FTrace - cpu_capacity\n",
"2016-04-12 15:20:14,403 WARNING : TestEnv - Wipe previous contents of the results folder:\n",
"2016-04-12 15:20:14,404 WARNING : TestEnv - /home/pippo/work/lisa/results/Android_Youtube\n",
"2016-04-12 15:20:14,434 INFO : TestEnv - Set results folder to:\n",
"2016-04-12 15:20:14,435 INFO : TestEnv - /home/pippo/work/lisa/results/Android_Youtube\n",
"2016-04-12 15:20:14,435 INFO : TestEnv - Experiment results available also in:\n",
"2016-04-12 15:20:14,435 INFO : TestEnv - /home/pippo/work/lisa/results_latest\n"
]
}
],
"source": [
"# Initialize a test environment using:\n",
"# the provided target configuration (my_target_conf)\n",
"# the provided test configuration (my_test_conf)\n",
"te = TestEnv(target_conf=my_target_conf, test_conf=my_tests_conf)\n",
"target = te.target"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Support Functions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This set of support functions will help us running the benchmark using different CPUFreq governors."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def set_performance():\n",
" target.cpufreq.set_all_governors('performance')\n",
"\n",
"def set_powersave():\n",
" target.cpufreq.set_all_governors('powersave')\n",
"\n",
"def set_interactive():\n",
" target.cpufreq.set_all_governors('interactive')\n",
"\n",
"def set_sched():\n",
" target.cpufreq.set_all_governors('sched')\n",
"\n",
"def set_ondemand():\n",
" target.cpufreq.set_all_governors('ondemand')\n",
" \n",
" for cpu in target.list_online_cpus():\n",
" tunables = target.cpufreq.get_governor_tunables(cpu)\n",
" target.cpufreq.set_governor_tunables(\n",
" cpu,\n",
" 'ondemand',\n",
" **{'sampling_rate' : tunables['sampling_rate_min']}\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# CPUFreq configurations to test\n",
"confs = {\n",
" 'performance' : {\n",
" 'label' : 'prf',\n",
" 'set' : set_performance,\n",
" },\n",
" #'powersave' : {\n",
" # 'label' : 'pws',\n",
" # 'set' : set_powersave,\n",
" #},\n",
" 'interactive' : {\n",
" 'label' : 'int',\n",
" 'set' : set_interactive,\n",
" },\n",
" #'sched' : {\n",
" # 'label' : 'sch',\n",
" # 'set' : set_sched,\n",
" #},\n",
" #'ondemand' : {\n",
" # 'label' : 'odm',\n",
" # 'set' : set_ondemand,\n",
" #}\n",
"}\n",
"\n",
"# The set of results for each comparison test\n",
"results = {}"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"YOUTUBE_CMD = 'shell dumpsys gfxinfo com.google.android.youtube > {}'\n",
"\n",
"def youtube_run(exp_dir, video_url, video_duration_s):\n",
" # Unlock device screen (assume no password required)\n",
" target.execute('input keyevent 82')\n",
" # Press Back button to be sure we run the video from the start\n",
" target.execute('input keyevent KEYCODE_BACK')\n",
"\n",
" # Start YouTube video on the target device\n",
" target.execute('am start -a android.intent.action.VIEW \"{}\"'.format(video_url))\n",
" # Allow the activity to start\n",
" sleep(3)\n",
" # Reset framestats collection\n",
" target.execute('dumpsys gfxinfo --reset')\n",
" # Wait until the end of the video\n",
" sleep(video_duration_s)\n",
" \n",
" # Get frame stats\n",
" framestats_file = os.path.join(exp_dir, \"framestats.txt\")\n",
" adb_command(target.adb_name, YOUTUBE_CMD.format(framestats_file))\n",
"\n",
" # Close application\n",
" target.execute('am force-stop com.google.android.youtube')\n",
"\n",
" # Clear application data\n",
" target.execute('pm clear com.google.android.youtube')\n",
"\n",
" return framestats_file"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"SYSTRACE_CMD = CATAPULT_HOME + \"/systrace/systrace/systrace.py -o {} gfx view sched freq idle -t {}\"\n",
"\n",
"def experiment(governor, exp_dir, collect='ftrace', trace_time=30):\n",
" os.system('mkdir -p {}'.format(exp_dir));\n",
"\n",
" logging.info('------------------------')\n",
" logging.info('Run workload using %s governor', governor)\n",
" confs[governor]['set']()\n",
"\n",
" # Start the required tracing command\n",
" if 'ftrace' in collect:\n",
" # Start FTrace and Energy monitoring\n",
" te.ftrace.start()\n",
" elif 'systrace' in collect:\n",
" # Start systrace\n",
" trace_file = os.path.join(exp_dir, 'trace.html')\n",
" trace_cmd = SYSTRACE_CMD.format(trace_file, trace_time)\n",
" logging.info('SysTrace: %s', trace_cmd)\n",
" systrace_output = pe.spawn(trace_cmd)\n",
"\n",
" ### Run the benchmark ###\n",
" framestats_file = youtube_run(exp_dir, \"https://youtu.be/XSGBVzeBUbk?t=45s\", trace_time)\n",
"\n",
" # Stop the required trace command\n",
" if 'ftrace' in collect:\n",
" te.ftrace.stop()\n",
" # Collect and keep track of the trace\n",
" trace_file = os.path.join(exp_dir, 'trace.dat')\n",
" te.ftrace.get_trace(trace_file)\n",
" elif 'systrace' in collect:\n",
" logging.info('Waiting systrace report [%s]...', trace_file)\n",
" systrace_output.wait() \n",
"\n",
" # Parse trace\n",
" tr = Trace(te.platform, trace_file,\n",
" events=my_tests_conf['ftrace']['events'])\n",
"\n",
" # return all the experiment data\n",
" return {\n",
" 'dir' : exp_dir,\n",
" 'framestats_file' : framestats_file,\n",
" 'trace' : trace_file,\n",
" 'ftrace' : tr.ftrace\n",
" }"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Run experiments and collect traces"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2016-04-12 15:20:15,080 INFO : ------------------------\n",
"2016-04-12 15:20:15,081 INFO : Run workload using performance governor\n",
"2016-04-12 15:20:15,219 INFO : SysTrace: /home/pippo/work/catapult/systrace/systrace/systrace.py -o /home/pippo/work/lisa/results/Android_Youtube/performance/trace.html gfx view sched freq idle -t 15\n",
"2016-04-12 15:20:37,068 INFO : Waiting systrace report [/home/pippo/work/lisa/results/Android_Youtube/performance/trace.html]...\n",
"2016-04-12 15:20:42,981 INFO : Parsing SysTrace format...\n",
"2016-04-12 15:20:44,844 INFO : Collected events spans a 3.673 [s] time interval\n",
"2016-04-12 15:20:44,845 INFO : Set plots time range to (0.000000, 3.672542)[s]\n",
"2016-04-12 15:20:45,134 INFO : ------------------------\n",
"2016-04-12 15:20:45,135 INFO : Run workload using interactive governor\n",
"2016-04-12 15:20:45,177 INFO : SysTrace: /home/pippo/work/catapult/systrace/systrace/systrace.py -o /home/pippo/work/lisa/results/Android_Youtube/interactive/trace.html gfx view sched freq idle -t 15\n",
"2016-04-12 15:21:08,083 INFO : Waiting systrace report [/home/pippo/work/lisa/results/Android_Youtube/interactive/trace.html]...\n",
"2016-04-12 15:21:25,486 INFO : Parsing SysTrace format...\n",
"2016-04-12 15:21:27,296 INFO : Collected events spans a 3.450 [s] time interval\n",
"2016-04-12 15:21:27,296 INFO : Set plots time range to (0.000000, 3.449909)[s]\n"
]
}
],
"source": [
"# Run the benchmark in all the configured governors\n",
"for governor in confs:\n",
" test_dir = os.path.join(te.res_dir, governor)\n",
" results[governor] = experiment(governor, test_dir,\n",
" collect='systrace', trace_time=15)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# UI Performance Analysis"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Frame Statistics for PERFORMANCE governor\n",
"Stats since: 6429956328009ns\n",
"Total frames rendered: 549\n",
"Janky frames: 65 (11.84%)\n",
"90th percentile: 17ms\n",
"95th percentile: 34ms\n",
"99th percentile: 89ms\n",
"\n",
"Frame Statistics for INTERACTIVE governor\n",
"Stats since: 6429956328009ns\n",
"Total frames rendered: 665\n",
"Janky frames: 88 (13.23%)\n",
"90th percentile: 18ms\n",
"95th percentile: 36ms\n",
"99th percentile: 89ms\n",
"\n"
]
}
],
"source": [
"for governor in confs:\n",
" framestats_file = results[governor]['framestats_file']\n",
" print \"Frame Statistics for {} governor\".format(governor.upper())\n",
" !sed '/Stats since/,/99th/!d;/99th/q' $framestats_file\n",
" print \"\""
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trace_file = results['interactive']['trace']\n",
"!xdg-open {trace_file}"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
},
"toc": {
"toc_cell": false,
"toc_number_sections": true,
"toc_threshold": 6,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
pranavgirish/Performance-Analysis-of-ML-Algorithms | format_data.ipynb | 1 | 734798 | {
"cells": [
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from pandas import DataFrame\n",
"from sklearn import svm, linear_model, neural_network, naive_bayes, neighbors, tree, ensemble, linear_model\n",
"from sklearn.model_selection import StratifiedKFold\n",
"from sklearn.metrics import accuracy_score, f1_score\n",
"from sklearn import preprocessing\n",
"from tqdm import tqdm_notebook as tqdm\n",
"import time\n",
"import os\n",
"import glob\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from itertools import product"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"kval = [1, 3, 5, 9, 17, 33, 65, 129, 257, 513]\n",
"w = ['uniform', 'distance']\n",
"conf=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]\n",
"ridge=[0.00000001, 0.000001, 0.0001, 0.01, 1, 10, 100, 1000, 10000, 100000]\n",
"kval_knn = [1, 3, 5, 9, 17, 33, 65, 129, 257, 313]\n",
"file = ['electricity-normalized','pc4','MagicTelescope','irish','pc1','tic-tac-toe','ionosphere','diabetes']"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'0.00000001'"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p='{0:.8f}'.format(ridge[0])\n"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/pranav/Project/Result/C45/*.csv\n"
]
}
],
"source": [
"files = os.path.join(os.getcwd(),'Result/C45', \"*.csv\")\n",
"print(files)\n",
"data = glob.glob(files)\n",
"len(data)\n",
"data.sort()\n",
"#data"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for j in range(len(data)):\n",
" #print(data[j])\n",
" df = pd.read_csv(data[j])\n",
" df1 = df.as_matrix()\n",
" #print(df)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/pranav/Project/Result/C45/MagicTelescopeCONF0.1KVAL1.csv\n"
]
},
{
"data": {
"text/plain": [
"0.1"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"p1=str(0.1)\n",
"p2=str(1)\n",
"s='/home/pranav/Project/Result/C45/'+file[2]+'CONF'+p1+'KVAL'+p2+'.csv'\n",
"\n",
"print(s)\n",
"dfq = pd.read_csv(s)\n",
"#dfq\n",
"len(file)\n",
"conf[0]"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"#C45\n",
"n_datasets = len(file)\n",
"p1_c45 = 9\n",
"p2_c45 = 10\n",
"shape2 = (n_datasets, p1_c45, p2_c45)\n",
"accuracies_c45 = np.zeros(shape2)\n",
"f1_scores_c45 = np.zeros(shape2)\n",
"build_time_c45 = np.zeros(shape2)\n",
"for k in range(len(file)):\n",
" for i in range(p1_c45):\n",
" for j in range(p2_c45):\n",
" \n",
" #print(s)\n",
" \n",
" p1=str(conf[i])\n",
" p2=str(kval[j])\n",
" s='/home/pranav/Project/Result/C45/'+file[k]+'CONF'+p1+'KVAL'+p2+'.csv'\n",
" df = pd.read_csv(s)\n",
" df1 = df.as_matrix()\n",
" check1=0\n",
" check2=0\n",
" for q in range(len(df1)):\n",
" df2 = str(df1[q,0])\n",
" if 'Correctly' in df2:\n",
" check1=check1+1\n",
" #print(check1)\n",
" if check1 == 2:\n",
" #print(df2[57:64])\n",
" x=(float(df2[57:64])/100)\n",
" #x=float(x)\n",
" #print(x)\n",
" if 'Weighted' in df2:\n",
" check2=check2+1\n",
" #print(check2)\n",
" if check2 == 2:\n",
" y=float(df2[55:60])\n",
" #print(y)\n",
" \n",
" if 'Time taken to build model' in df2:\n",
" z=float(df2[27:31])\n",
" #print(z)\n",
" \n",
" \n",
" accuracies_c45[k,i,j] = x\n",
" f1_scores_c45[k,i,j] = y\n",
" build_time_c45[k,i,j] = z"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for d in range(len(file)):\n",
" sf1 = pd.DataFrame(accuracies_c45[d], columns=kval, index=conf)\n",
" sf2 = pd.DataFrame(f1_scores_c45[d], columns=kval, index=conf)\n",
" sf3 = pd.DataFrame(build_time_c45[d], columns=kval, index=conf)\n",
" y=str(d)\n",
" path1 = '/home/pranav/Project/results_weka/c45/d_' + y + '_' +file[d] + '_acc_c45' \n",
" sf1.to_csv(path_or_buf=path1)\n",
" path2 = '/home/pranav/Project/results_weka/c45/d_' + y + '_' +file[d] + '_fm_c45' \n",
" sf2.to_csv(path_or_buf=path2)\n",
" path3 = '/home/pranav/Project/results_weka/c45/d_' + y + '_' +file[d] + '_bt_c45' \n",
" sf3.to_csv(path_or_buf=path3)"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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BHDhwgODgYPz8/MxoUkREnJSzXyM1bUIGb29vPDw8bP/u1asXfn5+bNy40awmRUTECZm1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\nelAiFRERqYf/Bycx5CxePXQSAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430c31160>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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LLbcwbdo0oqOjcXV1tW1/4IEHTE1MRERaCLur4jdPdgtsYWEhHh4eZGdn19qu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\niIiICVRgRURETKACKyIiYoL/B3Qbo+qoBej/AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430969550>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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tRXx8PF544YU6S5CIiBo4mbeklRZSd3d3AMC6deuwdu1azXYvLy9MmjRJ+syI\niKhRaPCTjR48eID//Oc/mue3b99GWlqapEkREVHjoVDU/FEfaJ1s9Omnn2LChAl48uQJFAoFDA0N\nERISUhe5ERFRI9Dg19r19PSEp6cnsrKyIISAubl5XeRFREQkC5UW0tWrV+OTTz7Bv/71rwrHr6Oi\noiRNjIiISA4qLaSOjo4AgFdffbXOkiEiosanvlzrrKlKC+nmzZvRp08fHD58GCtWrKjLnIiIqBGR\n+6zdSgtpamoqxo4di5s3b2L8+PHl9vOeokREVBtkXkcrL6SbNm3C1atXsWDBAkybNq0ucyIiokak\nwXakLVq0QK9evbBp0yaYmJhACAEhRF3mRkREVO9p/fjLTz/9hJUrVyI/Px8AIISAQqHAlStXJE+O\niIiovtNaSLdv347du3ejbdu2dZEPERE1MjIf2dVeSDt27MgiSkREkmmw10if69y5M2bOnAk3NzcY\nGhpqto8ePVrSxIiIqJHQuup7/aa1kKanp8PY2BgXLlwos52FlIiIakOD70gXLVoEAMjKyoJCoYCZ\nmVmNAi1evBizZ8+u0bFERET1ldZCeu7cOQQFBSE/Px9CCLRq1QpLlixBt27dKj3Gz8+vzF8YQghc\nuXIFly9fBgBs2LChFlInIiLSP62FdOnSpfjuu+/g4OAAALh8+TIiIiKqXNnI2dkZZ8+exYwZM9C2\nbVsIITBlyhRNd0tERPSczEd2tRdSAwMDTREFgC5dupSZdFSRmTNn4saNG1i8eDFefvllvP/++2ja\ntCnatWune8ZERNSgyP0aqda5UgYGBti/fz/y8vKQl5eHvXv3ai2kAGBvb481a9bA0tISEyZMQF5e\nXq0kTEREDYtCUfNHfaC1I503bx7mz5+Pzz//HAYGBnBycsK8efOqHWDkyJHw8PDAqVOndEqUiIga\nqPpSEWtIayH9xz/+geXLl6NFixYAAJVKhdatW/+fgpiZmcHb2xsAEBcXB09PzxqkSkREVP9oHdqN\niYnBrFmzNM+nT5+OjRs3Vuvk+fn5SE1NRWpqKgoKCgAAubm5NUyViIgaIoWBosaP+kBrR7p79+4y\nM3R/+OEH+Pr6wtfXt9JjkpOTERERgZycHJibm0MIgfT0dFhbW2Pu3Lm1kzkREVE9oLWQqtVqGBn9\nv5cZGGhfy2nhwoWIiIiAvb19me0pKSkIDw/nTcGJiEhD5pdItRdSDw8P+Pj4oGfPnigtLcWpU6fg\n5eVV5TEKGboCAAAXDElEQVRCiHJFFAAcHR2hVqtrni0RETU4cv/4i9ZCGhAQADc3NyQlJUGhUCA0\nNBTOzs5VHuPk5AR/f394enrCwsICwLNJSrGxsXBzc6udzImIqEGQeR3VXkgBoFevXujVq1e1Txoc\nHIzExEQkJCQgKSkJAKBUKhEYGAgXF5eaZUpERFQPVauQ1oSrqytcXV2lOj0RETUUMm9JJSukRERE\n1VFfPsZSUzK/nSoREZF+sSMlIiK9kvnILgspERHpmcwrKYd2iYiIdMCOlIiI9ErmDSkLKRER6Zfc\nZ+2ykBIRkV7JfYlAXiMlIiLSATtSIiLSL3k3pOxIiYio4Vq4cCHGjh0LHx8fzdrvz8XFxWHUqFF4\n5513sHHjRs323bt34/XXX8dbb72FI0eOaI3BjpSIiPRKqmukZ86cQWpqKrZs2YIbN24gJCQEW7Zs\nAQCUlpZi/vz5+Pnnn9GqVSt89NFH8PT0RNOmTfHtt99ix44dKCgoQHR0NPr3719lHBZSIiLSK6kK\naUJCAjw9PQEA9vb2yM7ORl5eHkxNTZGZmYmWLVtqbvX5yiuvID4+Hs2aNYO7uztMTU1hamqK+fPn\na43DoV0iItIvAx0eVVCpVDA3N9c8t7CwQEZGhubf+fn5uHXrFoqLi3H69GmoVCrcuXMHRUVF8Pf3\nx7hx45CQkKA1fXakRESkV3X18RchRJmYixcvRkhICFq0aAFbW1vNvqysLHzzzTe4d+8e3n33XRw+\nfLjKHNmREhFRg6RUKqFSqTTP09PTYWVlpXnu5uaGTZs2YfXq1WjRogXatWsHS0tLuLi4wMjICB06\ndEDz5s3x+PHjKuOwkBIRUYPUu3dvxMbGAgBSUlKgVCphamqq2f/hhx/i0aNHKCgowOHDh+Hu7o4+\nffrg1KlTKC0tRWZmJgoKCsoMD1eEQ7tERKRXUg3t9ujRA46OjvDx8YFCoUBoaCh27tyJFi1aYNCg\nQRgzZgwmTpwIhUKBjz/+WDPxyNvbG2PGjAEAzJkzBwYGVfecCvHXQeN6ZH/QSn2nQERE/+UVNUmy\nc9/Y/HONj7X3GVmLmdSMZB3phQsX0Lp1a9ja2uL8+fM4d+4c7Ozs4OHhIVVIIiKSIS5aX4Hw8HDc\nuHEDeXl5GDJkCI4dO4a+ffti165dOHbsGMLCwqQIS0REciTzReslKaR//PEHNm3ahMLCQnh5eeHg\nwYMwNjYGAPj4+EgRkoiISC8kmbWrVqtRWlqKF154AX5+fpoiWlhYiJKSEilCEhER6YUkhXTUqFH4\n4IMPAAAff/wxAODs2bMYMWIEfH19pQhJREQypVDU/FEfSDK0O2bMGLz++utltnXq1AlbtmyBpaWl\nFCGJiEimeGPvSjRr1qzMczMzM1haWiIuLk6qkEREJEcGipo/6gFJF2TIz8/XLM9kZWUFExMT5Obm\nShmSiIhkRu4dqSSFNDk5GREREcjJyYG5uTmEEEhPT4e1tTXmzp0rRUgiIiK9kKSQLly4EBEREbC3\nty+zPSUlBeHh4YiJiZEiLBERyZG8G1JprpEKIcoVUQBwdHSEWq2WIiQREZFeSNKROjk5wd/fH56e\nnppFgFUqFWJjY+Hm5iZFSCIikileI61AcHAwEhMTkZCQgKSkJADP7gsXGBgIFxcXKUISEZFMca3d\nSri6usLV1VWq0xMRUUPBjpSIiKjm5D60K9mCDERERI0BO1IiItIveTek7EiJiIh0wY6UiIj0irN2\niYiIdCHzyUYspEREpFectUtERNSIsSMlIiL94jVSIiKimuPQLhERUSPGjpSIiPRL3g0pCykREekX\nh3aJiIgaMXakRESkX5y1S0REVHNyH9plISUiIv2SeSHlNVIiIiIdsCMlIiK9kvvQrmQd6bFjx7B7\n925kZ2eX2b5t2zapQhIREdU5SQrp559/jh07duD8+fMYM2YMEhISNPt++eUXKUISEZFcGShq/qgH\nJBna/c9//oNNmzYBANLT0zFp0iTMmDEDvXv3hhBCipBERCRTch/alaSQqtVqpKenQ6lUQqlU4vvv\nv8dHH32Ex48fy/4bRkREtUzmdUGSod3p06fDz88P+fn5AABLS0ts2LABp0+fxoULF6QISUREMqUw\nUNT4UR8oRB2PtRYVFaFZs2ZaX7c/aGUdZENERNXhFTVJsnOrEuNrfGxr11drMZOaqfPPkZ44caKu\nQxIREUlG0s+R5ufnQ6VSAQCsrKxgYmKC3NxcKUMSEZHcyPwaqSSFNDk5GREREcjJyYG5uTmEEEhP\nT4e1tTXmzp0rRUgiIpIpuU9ClaSQLly4EBEREbC3ty+zPSUlBeHh4YiJiZEiLBERyRELaXlCiHJF\nFAAcHR2hVqulCElERDJVX2bf1pQkhdTJyQn+/v7w9PSEhYUFAEClUiE2NhZubm5ShCQiItILSQpp\ncHAwEhMTkZCQgKSkJACAUqlEYGAgXFxcpAhJRESkF5LN2nV1dYWrq6tUpyciooaC10iJiIh0wEJK\nRERUc/z4CxERkS5kPmu3zpcIJCIiakjYkRIRkV4pFNL1dAsXLsTFixehUCgQEhKC7t27a/bFxcVh\n5cqVMDY2xrBhw+Dr6wsAiIqKwu+//46SkhJ88skn8PLyqjIGCykRETVIZ86cQWpqKrZs2YIbN24g\nJCQEW7ZsAQCUlpZi/vz5+Pnnn9GqVSt89NFH8PT0xK1bt/Dnn39iy5YtyMzMxMiRI1lIiYionpNo\nslFCQgI8PT0BAPb29sjOzkZeXh5MTU2RmZmJli1bahYNeuWVVxAfH4833nhD07W2bNkShYWFUKvV\nMDQ0rDQOr5ESEZFeKRSKGj+qolKpYG5urnluYWGBjIwMzb/z8/Nx69YtFBcX4/Tp01CpVDA0NISJ\niQkAYPv27ejbt2+VRRRgR0pERPpWR7N2hRCafysUCixevBghISFo0aIFbG1ty7w2Li4O27dvxw8/\n/KD1vCykRETUICmVSs09sQEgPT0dVlZWmudubm7YtGkTAGDp0qVo164dAOD48eNYtWoV1q5dixYt\nWmiNw6FdIiLSK6mGdnv37o3Y2FgAz27jqVQqYWpqqtn/4Ycf4tGjRygoKMDhw4fh7u6O3NxcREVF\nYfXq1WjVqlW18mdHSkRE+iXRZKMePXrA0dERPj4+UCgUCA0Nxc6dO9GiRQsMGjQIY8aMwcSJE6FQ\nKPDxxx/DwsJCM1v3008/1ZwnMjISbdu2rTx98ddB43pkf9BKfadARET/5RU1SbJz51xPqfGxLf8/\nx1rMpGbYkRIRkX5JuCBDXWAhJSIivVJwrV0iIqLGix0pERHpF2+jRkREVHO8HykREZEuZD7ZSN7Z\nExER6VmdF9I9e/bUdUgiIqrHFAaKGj/qgzovpM/vBUdERNQQSHKNdNSoURVePBZC4NatW1KEJCIi\nueJko/I6deqEl156SXND1eeEEJg5c6YUIYmISKY4a7cC4eHhiIqKgrm5ueYGqc/Z2NhIEZKIiORK\n5rN2uWg9ERFpJeWi9QUPb9f4WBPrDrWYSc3U+Z8BcXFxdR2SiIhIMpIuyJCfn6+5O7mVlRVMTEyQ\nm5srZUgiIqI6JUkhTU5ORkREBHJycmBubg4hBNLT02FtbY25c+dKEZKIiGSKk40qsHDhQkRERMDe\n3r7M9pSUFISHhyMmJkaKsEREJEcyn2wkSSEVQpQrogDg6OgItVotRUgiIpIpdqQVcHJygr+/Pzw9\nPWFhYQEAUKlUiI2NhZubmxQhiYhIrtiRlhccHIzExEQkJCQgKSkJAKBUKhEYGAgXFxcpQhIREemF\nZLN2XV1d4erqKtXpiYiI6gXej5SIiPSqvtzFpaZYSImISL842YiIiKjmFJxsREREpAOZd6T1dtF6\nIiIiOZB3P01ERKRnLKREREQ6YCElIiLSAQspERGRDlhIiYiIdMBCSkREpIMG/TnSa9euISAgABMm\nTICvr6/e8igsLMTs2bPx6NEjPHnyBAEBARgwYIDe8jl9+jSmTZuGTp06AQAcHBzwxRdf6C2f0tJS\nhIaG4s8//0STJk0QFhZW4W34pPb398vUqVORmZkJAMjKyoKzszPmz58veR4VvV9atWqFqKgoGBkZ\nwdjYGEuWLNHcWaku7N69G2vXroWRkRGmTp2Kffv2ISUlBa1atQIAfPDBB+jfv7+kOfz953P//n0E\nBwejpKQERkZGWLJkCaysrLB582Zs27YNTZo0wfvvvw9vb29J8omKisLvv/+OkpISfPLJJzh06FC5\n70nr1q0RGRmpOeb69ev49ttv0aNHj1rLo7Lf5w0bNiAyMhJnzpxB8+bNAQDffPMNjh8/DiEE+vfv\nj4CAgFrLo1ETDVR+fr7w9fUVc+bMET/++KNec9mzZ4/4/vvvhRBC3LlzR3h5eek1n1OnTokpU6bo\nNYe/2r9/v5g2bZoQQojU1FTx8ccf13kO2t4vs2fPFhcvXqyTXCp6v0yZMkXcvn1bCCFEdHS0WLly\nZZ3kIoQQjx8/Fl5eXiI3N1c8fPhQzJkzR8yaNUscOnSoznKo6OcTFBQk9uzZI4QQYuPGjSIyMlKo\nVCoxaNAgUVRUJIqKisTYsWNFYWFhreeTkJAgPvzwQyHEs+9Pv379tH5PsrOzxfjx44Vara7VXCr6\nff7555/FsmXLRP/+/UVeXp4QQoi0tDTN60pKSsSgQYPEgwcPajWXxqrBDu0aGxtjzZo1UCqV+k4F\nQ4cOxUcffQQAuH//PqytrfWcUf1y69YtdO/eHQDQoUMH3Lt3r85vAF/V++XmzZvIzc3V5Ci1it4v\nK1asQPv27SGEwMOHD2FjY1MnuQBAQkIC3N3dYWpqCqVSWSdd+d9V9PMJDQ3VdJvm5ubIysrC3bt3\n8c9//hNNmzZF06ZN8eKLL+LixYu1no+rqyu+/vprAEDLli1RWFio9T27bt06vPfeezAwkP7/dj09\nPTF9+vQyN8y2tbXFihUrAADZ2dlQKBQwNTWVPJfGoMEWUiMjIzRr1kzfaZTh4+ODzz77DCEhIfpO\nBdevX4e/vz/eeecdnDx5Uq+5ODg44MSJE1Cr1bh58ybS0tI0Q6p1par3y4YNG/RyaeDv75djx45h\n8ODBUKlUeP311+ssjzt37qCoqAj+/v4YN24cEhISAAAbN27Eu+++i+nTp+Px48eS5lDRz8fExASG\nhoZQq9XYtGkTRowYgQ4dOuDatWt4/Pgx8vPzcf78eTx69KjW8zE0NISJiQkAYPv27ejbty8MDQ0r\n/Z4UFRXhxIkTGDhwYK3nApT/fa6qQC5YsADDhw9HQECAZsiXdKTvllhqK1as0PvQ7l9dvnxZDB8+\nXJSWluothwcPHog9e/aI0tJSkZqaKvr16yeePHmit3yEEGLZsmVi7NixYu7cuWLkyJEiPT1dL3n8\n/f3y5MkTMXz4cL3kIkT590tpaamIioqq06Hd1atXi08++UQUFxdr3i/x8fHi8uXLmv3z5s2rk1z+\n/vMpKSkRM2bMENHR0Zpte/fuFWPHjhWBgYFixowZ4tdff5UsnwMHDojRo0eLnJycKr8nv/zyi1ix\nYoUkOVT1+zxgwADN0O5fZWVliREjRmguF5BuGmxHWp9cunQJ9+/fBwC89NJLUKvVkv8FXxVra2sM\nHToUCoUCHTp0QOvWrfHw4UO95QMA06dPx+bNmzFv3jzk5OTA0tJSr/k8l5iYWGdDus9V9H757bff\nAAAKhQLe3t74/fff6ywfS0tLuLi4wMjICB06dEDz5s3h4OCAl156CQDg4eGBa9eu1Vk+fxUcHIyO\nHTsiMDBQs23IkCHYvHkzoqOjIYRAu3btJIl9/PhxrFq1CmvWrEGLFi3g7u5e6ffk8OHDcHd3lySP\n6v4+379/H8nJyQAAMzMz9OjRQ/OcdMNCWgfOnj2LH374AQCgUqlQUFAAc3NzveWze/durFu3DgCQ\nkZGBR48e6fW67R9//IHg4GAAz4Yvu3TpUifXkaojOTkZL774Yp3GrOj9snLlSly5cgUAcPHiRdjZ\n2dVZPn369MGpU6dQWlqKzMxMFBQUYO7cuUhLSwPwbNbo8xmjdWn37t1o0qQJpk6dqtlWUlICPz8/\nPHnyBBkZGbhy5Qq6du1a67Fzc3MRFRWF1atXa2bpTpkypdLvyaVLlyR7H1X39/nx48cICwtDSUkJ\n1Go1UlJS6vR91JA12Lu/XLp0CZGRkbh79y6MjIxgbW2N6OhozZu+LhUVFeHzzz/H/fv3UVRUhMDA\nQHh4eNR5Hs/l5eXhs88+Q05ODoqLixEYGIh+/frpLZ/S0lKEhITg+vXraNq0Kb788ku0adOmTnOo\n7P0SHR2Nnj17YujQoXWWS0XvFysrK0RERMDQ0BDNmjVDVFRUnXbtmzdvxvbt2wEAkyZNQvPmzbFk\nyRK88MILMDExwaJFiyTNp6Kfz6NHj9C0aVPN9UB7e3uEhYUhJiYG27Ztg0KhQFBQkCSd4JYtWxAd\nHV2mEL311lvYuHFjhd8Td3d3zbXl2lbR7/Ply5cRHx+PCxcuoFu3bnB2dkZQUBBWr16NuLg4zcdf\n/trJU8012EJKRERUF+rH+BkREZFMsZASERHpgIWUiIhIByykREREOmAhJSIi0gELKREAPz+/Ol/f\n9+/Wr18Pb29vHD58uMx2Dw8PpKam1uickZGRGD58OD94TyShBn0bNaLq+vHHH/WdAg4dOoSQkJBa\n/UzvgQMHsHr1ar3clo6osWAhpQbt9OnTWLVqFWxsbJCcnAwnJyd07twZBw4cQFZWFtasWQMbGxt0\n7twZKSkpWLlyJbKysvDgwQOkpqbi5ZdfLnev1p07dyI+Ph6lpaX4z3/+g3bt2iE6OhoKhQLfffcd\njhw5AiMjI3Tq1Alz5sxBkyZNyhy/fft2bN68GS+88AIsLS2xYMEC7Nq1CykpKVi6dClKSkoqXdx8\n2bJlOHfuHIqKiuDq6oqgoCAIIRAaGoqbN2/i6dOncHJywpw5c/DVV1/h4cOHmD17Nr744os6X+qQ\nqNHQ4zq/RJI7deqU6NGjh8jMzBRFRUWiW7du4ueffxZCCDFr1izx73//WwghhIODgyguLhYrVqwQ\nPj4+oqSkRBQWFgpnZ2eRlZVV5pw7duwQHh4eorCwUJSWloqBAweKlJQUce7cOfHGG2+Ip0+fCiGE\nmDJliti5c2eZY+/evSv69u0rcnNzhRBCLF68WLPguq+vrzh58mS5r2HAgAHi1q1bYu/evSIoKEiz\nPSAgQBw8eFA8fvy4zELu3t7e4urVq2WOJSLpsCOlBs/e3l6zNGSrVq3g4uIC4Nli33l5eeVe37Nn\nTxgaGsLQ0BDm5ubIzs6GmZlZmdd0795dc1uvNm3aIDs7G1evXoWrq6umA3Vzc0NycjJGjhypOe7y\n5ctwdHTULGvn5uaGzZs3V+vrOH36NC5cuAA/Pz8Az9Z7vXPnDvr164f79+9j7NixMDY2RkZGRp3f\nho6oMWMhpQbP0NCw0ueighUy//766r7mrzdRrmzb31XnNc8ZGxtjzJgx+OCDD8ps3717N5KTkxET\nEwMjIyO89dZb1TofEdUOztolqiXOzs44ffo0iouLAQAJCQlwcnIq85quXbsiJSVF0wnHx8eXe01l\nevbsiQMHDqCkpAQA8M033+DWrVt49OgR7OzsYGRkhEuXLuH27dt4+vRpLX5lRFQVdqREtcTJyQnD\nhg3D+PHjYWBgAEdHRwwfPrzMa2xsbDBt2jS8//77MDY2ho2NDWbMmFGt83t5eeHChQvw8fGBoaEh\nunTpgvbt22Pw4MHw9/eHr68vevTogYkTJ2LBggXYunWrFF8mEf0N7/5CRESkAw7tEhER6YCFlIiI\nSAcspERERDpgISUiItIBCykREZEOWEiJiIh0wEJKRESkAxZSIiIiHfz/Q6pJjN+UY6IAAAAASUVO\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f14303a4a90>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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+tGtx1m5lZSXGjh2LPn36oG/fvnj22Wdx5cqVRo+ZPXs2Vq5cCQBYsmQJPv30UwDA119/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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f14303fcd68>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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cuXPIzc0FAPTr1w8GgwHXr18HcHMRpZ49eyI0NLRRn4sFloiIFCVJTX80ZPjw4UhKSgJw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\nEeSXS1Wkj3MAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f14306cf278>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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jPz8fAODm5obJkydDq9Vi0KBBCAwMRNeuXes8pjZ8HikREcnKyU5zjf66fG1paSlWrVqF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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f14309f9fd0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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UVOV5R0dHbNq06S/1waFdIiIiC7AiJSIiRXHReiIiIguoPI9yaJeIiMgSrEiJiEhRHNol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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1431f4e470>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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ZwsPDERwcjL179+L06dNITk5Gfn4+Ro4cySAlIiL7JlWPND09HSEhIQAAPz8/FBQUoLi4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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430e79ac8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for d in range(len(file)):\n",
" ax = sns.heatmap(accuracies_c45[d], xticklabels=kval, yticklabels=conf[::-1])\n",
" plt.xlabel('min no of leaf')\n",
" plt.ylabel('confidence factor')\n",
" plt.title(file[d])\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#KNN\n",
"n_datasets = len(file)\n",
"p1_knn = 2\n",
"p2_knn = 10\n",
"shape2 = (n_datasets, p1_knn, p2_knn)\n",
"accuracies_knn = np.zeros(shape2)\n",
"f1_scores_knn = np.zeros(shape2)\n",
"build_time_knn = np.zeros(shape2)\n",
"for k in range(len(file)):\n",
" for i in range(p1_knn):\n",
" for j in range(p2_knn):\n",
" \n",
" #print(s)\n",
" \n",
" #p1=str(conf[i])\n",
" p2=str(kval[j])\n",
" if i == 0:\n",
" s='/home/pranav/Project/Result/KNN/'+file[k]+'FOR_'+'KVAL'+p2+'.csv'\n",
" if i == 1:\n",
" s='/home/pranav/Project/Result/KNN/'+file[k]+'INVERSE_FOR_'+'KVAL'+p2+'.csv'\n",
" df = pd.read_csv(s)\n",
" df1 = df.as_matrix()\n",
" check1=0\n",
" check2=0\n",
" for q in range(len(df1)):\n",
" df2 = str(df1[q,0])\n",
" if 'Correctly' in df2:\n",
" check1=check1+1\n",
" #print(check1)\n",
" if check1 == 2:\n",
" #print(df2[57:64])\n",
" x=(float(df2[57:64])/100)\n",
" #x=float(x)\n",
" #print(x)\n",
" if 'Weighted' in df2:\n",
" check2=check2+1\n",
" #print(check2)\n",
" if check2 == 2:\n",
" y=float(df2[55:60])\n",
" #print(y)\n",
" \n",
" if 'Time taken to build model' in df2:\n",
" z=float(df2[27:31])\n",
" #print(z)\n",
" \n",
" \n",
" accuracies_knn[k,i,j] = x\n",
" f1_scores_knn[k,i,j] = y\n",
" build_time_knn[k,i,j] = z"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"for d in range(len(file)):\n",
" sf1 = pd.DataFrame(accuracies_knn[d], columns=kval, index=w)\n",
" sf2 = pd.DataFrame(f1_scores_knn[d], columns=kval, index=w)\n",
" sf3 = pd.DataFrame(build_time_knn[d], columns=kval, index=w)\n",
" x=str(d)\n",
" path1 = '/home/pranav/Project/results_weka/knn/d_' + x + '_' +file[d] + '_acc_knn' \n",
" sf1.to_csv(path_or_buf=path1)\n",
" path2 = '/home/pranav/Project/results_weka/knn/d_' + x + '_' +file[d] + '_fm_knn' \n",
" sf2.to_csv(path_or_buf=path2)\n",
" path3 = '/home/pranav/Project/results_weka/knn/d_' + x + '_' +file[d] + '_bt_knn' \n",
" sf3.to_csv(path_or_buf=path3)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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Ul5enwYMHKywsTJ6envrrX/9q33/dunX2440ZM0ahoaHX3L6pCbZBgwYaPXq0mU0AAFCk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\nBP8P+tj/d4SAmhEAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f14309322b0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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77z4zhgMAoMUxJWDnzp2r559/XsOHD//ZHLnD4XAfdAIAALsyJWB37dqlW265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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430c06358>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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sea/aIZjp4t5a7RBk3R9yVTsEM52DGtekKs6PPqbYsZMXflDn9/oZQusxkvrF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\nwARLRESkACZYIiIiBfwve9JvnpUWLTYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f143092ab38>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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fX2lpadq8ebPPcwcOHNCAAQN0zz33aMqUKd79q1at0p133qm77rpLOTk5tZf/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\nIAUAwA8EKQAAfiBIAQDwA0EKAIAf/h+RvEqAy7NohgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430a32630>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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84NYjOf1kwN52220aMmSIZs2apcmTJ3tu9/LyUmhoaJMUBwBwP5fm67lXEXt7eysxMVFf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\nIGABADCAgAUAwID/B27/IShVGahaAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f14309e28d0>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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FhYWKj4/Xe++9p+HDh+uee+5RSUmJlUMCABzGMIw63+oDS4O0oqJCmzZt0urV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\nwAMEKQAAHiBIAQDwwP8Dechk5x8UKA8AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f14318ebba8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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eIrBPnz7auHGjiouLVVpa6r4BAHC5MLUj/fvf/67KyuqHjjgcDm3YsMHMYQEA8BpTg7Si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\nIEgBAPAAQQoAgAcIUgAAPPD/AK10gz/1DHoIAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f143097b5f8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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UVOE4LDYCAFjLTSdkMAyj1NcZGRkaPHiwGjZsqOHDhzv3h166dElLlixRenq6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\nABcQpAAAuIAgBQDABf8PcIxTGS4TUgsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430491f98>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for d in range(len(file)):\n",
" ax = sns.heatmap(accuracies_knn[d], xticklabels=kval, yticklabels=w)\n",
" #plt.xticks(np.arange(accuracies[0].shape[1]), kval)\n",
" plt.xlabel('neighbours')\n",
" plt.ylabel('weight')\n",
" plt.title(file[d])\n",
" plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/pranav/Project/Result/RandomForest/diabetesNTREE257KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE1KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE1KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE1KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE1KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE1KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE1KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE1KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE1KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE1KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE1KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE3KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE3KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE3KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE3KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE3KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE3KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE3KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE3KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE3KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE3KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/electricity-normalizedNTREE5KVAL1.csv\n",
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"/home/pranav/Project/Result/RandomForest/tic-tac-toeNTREE129KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/tic-tac-toeNTREE129KVAL129.csv\n",
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"/home/pranav/Project/Result/RandomForest/tic-tac-toeNTREE257KVAL5.csv\n",
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"/home/pranav/Project/Result/RandomForest/tic-tac-toeNTREE257KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/tic-tac-toeNTREE257KVAL129.csv\n",
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"/home/pranav/Project/Result/RandomForest/tic-tac-toeNTREE513KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/tic-tac-toeNTREE513KVAL9.csv\n",
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"/home/pranav/Project/Result/RandomForest/tic-tac-toeNTREE513KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/tic-tac-toeNTREE513KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/tic-tac-toeNTREE513KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/tic-tac-toeNTREE513KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE1KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE1KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE1KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE1KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE1KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE1KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE1KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE1KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE1KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE1KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE3KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE3KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE3KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE3KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE3KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE3KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE3KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE3KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE3KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE3KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE5KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE5KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE5KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE5KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE5KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE5KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE5KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE5KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE5KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE5KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE9KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE9KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE9KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE9KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE9KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE9KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE9KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE9KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE9KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE9KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE17KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE17KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE17KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE17KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE17KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE17KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE17KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE17KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE17KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE17KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE33KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE33KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE33KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE33KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE33KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE33KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE33KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE33KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE33KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE33KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE65KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE65KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE65KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE65KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE65KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE65KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE65KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE65KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE65KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE65KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE129KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE129KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE129KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE129KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE129KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE129KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE129KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE129KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE129KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE129KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE257KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE257KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE257KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE257KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE257KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE257KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE257KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE257KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE257KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE257KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE513KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE513KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE513KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE513KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE513KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE513KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE513KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE513KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE513KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/ionosphereNTREE513KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE1KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE1KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE1KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE1KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE1KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE1KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE1KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE1KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE1KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE1KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE3KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE3KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE3KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE3KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE3KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE3KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE3KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE3KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE3KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE3KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE5KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE5KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE5KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE5KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE5KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE5KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE5KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE5KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE5KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE5KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE9KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE9KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE9KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE9KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE9KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE9KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE9KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE9KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE9KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE9KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE17KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE17KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE17KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE17KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE17KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE17KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE17KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE17KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE17KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE17KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE33KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE33KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE33KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE33KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE33KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE33KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE33KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE33KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE33KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE33KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE65KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE65KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE65KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE65KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE65KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE65KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE65KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE65KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE65KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE65KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE129KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE129KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE129KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE129KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE129KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE129KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE129KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE129KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE129KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE129KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE257KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE257KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE257KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE257KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE257KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE257KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE257KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE257KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE257KVAL257.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE257KVAL513.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE513KVAL1.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE513KVAL3.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE513KVAL5.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE513KVAL9.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE513KVAL17.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE513KVAL33.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE513KVAL65.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE513KVAL129.csv\n",
"/home/pranav/Project/Result/RandomForest/diabetesNTREE513KVAL257.csv\n"
]
}
],
"source": [
"#RandomForest\n",
"n_datasets = len(file)\n",
"p1_rf = 10\n",
"p2_rf = 10\n",
"shape2 = (n_datasets, p1_rf, p2_rf)\n",
"accuracies_rf = np.zeros(shape2)\n",
"f1_scores_rf = np.zeros(shape2)\n",
"build_time_rf = np.zeros(shape2)\n",
"for k in range(len(file)):\n",
" for i in range(p1_rf):\n",
" for j in range(p2_rf):\n",
" \n",
" print(s)\n",
" \n",
" p1=str(kval[i])\n",
" p2=str(kval[j])\n",
" s='/home/pranav/Project/Result/RandomForest/'+file[k]+'NTREE'+p1+'KVAL'+p2+'.csv'\n",
" df = pd.read_csv(s)\n",
" df1 = df.as_matrix()\n",
" check1=0\n",
" check2=0\n",
" for q in range(len(df1)):\n",
" df2 = str(df1[q,0])\n",
" if 'Correctly' in df2:\n",
" check1=check1+1\n",
" #print(check1)\n",
" if check1 == 2:\n",
" #print(df2[57:64])\n",
" x=(float(df2[57:64])/100)\n",
" #x=float(x)\n",
" #print(x)\n",
" if 'Weighted' in df2:\n",
" check2=check2+1\n",
" #print(check2)\n",
" if check2 == 2:\n",
" y=float(df2[55:60])\n",
" #print(y)\n",
" \n",
" if 'Time taken to build model' in df2:\n",
" z=float(df2[27:31])\n",
" #print(z)\n",
" \n",
" \n",
" accuracies_rf[k,i,j] = x\n",
" f1_scores_rf[k,i,j] = y\n",
" build_time_rf[k,i,j] = z"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for d in range(len(file)):\n",
" sf1 = pd.DataFrame(accuracies_rf[d], columns=kval, index=kval)\n",
" sf2 = pd.DataFrame(f1_scores_rf[d], columns=kval, index=kval)\n",
" sf3 = pd.DataFrame(build_time_rf[d], columns=kval, index=kval)\n",
" z=str(d)\n",
" path1 = '/home/pranav/Project/results_weka/randomforest/d_' + z + '_' +file[d] + '_acc_rf' \n",
" sf1.to_csv(path_or_buf=path1)\n",
" path2 = '/home/pranav/Project/results_weka/randomforest/d_' + z + '_' +file[d] + '_fm_rf' \n",
" sf2.to_csv(path_or_buf=path2)\n",
" path3 = '/home/pranav/Project/results_weka/randomforest/d_' + z + '_' +file[d] + '_bt_rf' \n",
" sf3.to_csv(path_or_buf=path3)"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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q1Srt13PmzIGTkxNiY2Ph6uqK6OhoKZokIiKSheTXSNVqNRYtWgSgejRvcnKyFE0SERHJ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\nIiUiItIDEykREZEe/h+okUmjwXA/RQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f14306117b8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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kpITS0lIAvL298fb2pqysDLvdTnl5OYGBgRw8eNDR5V577bX88MMPVFdX8+mnnzJ06FAA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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430bf2320>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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HdHQ0Hh4e+Pn52bbFxsbSrFkz23JwcDANGzYEbuW4kydPFnpepiTYt956i48++oh9+/bR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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f143061f518>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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LFy/C29sbNTU18PX1xerVq+Hu7i5FWCIiUiCF11FprpHGxsbixRdfxP79+/HB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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430a49588>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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W1li5cqWIkERE1EaJWuxfbUIK7D/+8Q/cddddCAgIwKZNm7B582ZlW1NWvyAiovZDkpp/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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f14309c8748>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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RkZEAgMmTJ+OFF16Qo0kiIlIpLuxdh7CwMAQHByMrKwudO3dG69atAdzp8rW2tpajSSIi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\nRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f14303ca940>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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iKYmiqH8uCALWrVsHjUYDR0dHtG/fvtFj7kWSQrp9+3bs2LEDNjY2uHXrFpYsWYLNmzcj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\nAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430533c50>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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IkCH3NYGQLI+/7Ny5s9FjeXl5cjRJRERk4OzZs8jKykJKSgquXbuG2NhYpKSkAAC8vLyw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\nA3rOeWKHV+ImAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f143089d160>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for d in range(len(file)):\n",
" ax = sns.heatmap(accuracies_rf[d][::-1], yticklabels=kval[::-1], xticklabels=kval)\n",
" plt.ylabel('no of trees')\n",
" plt.xlabel('max depth')\n",
" plt.title(file[d])\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER5R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER1R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER1R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER1R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER1R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER1R1.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER1R10.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER1R100.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER1R1000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER1R10000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER1R100000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER3R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER3R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER3R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER3R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER3R1.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER3R10.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER3R100.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER3R1000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER3R10000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER3R100000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER5R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER5R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER5R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER5R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER5R1.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER5R10.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER5R100.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER5R1000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER5R10000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER5R100000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER9R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER9R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER9R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER9R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER9R1.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER9R10.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER9R100.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER9R1000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER9R10000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER9R100000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER17R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER17R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER17R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER17R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER17R1.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER17R10.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER17R100.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER17R1000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER17R10000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER17R100000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER33R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER33R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER33R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER33R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER33R1.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER33R10.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER33R100.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER33R1000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER33R10000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER33R100000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER65R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER65R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER65R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER65R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER65R1.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER65R10.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER65R100.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER65R1000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER65R10000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER65R100000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER129R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER129R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER129R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER129R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER129R1.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER129R10.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER129R100.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER129R1000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER129R10000.csv\n",
"/home/pranav/Project/Result/Logistic/electricity-normalizedITER129R100000.csv\n",
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"/home/pranav/Project/Result/Logistic/ionosphereITER9R100000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER17R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER17R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER17R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER17R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER17R1.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER17R10.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER17R100.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER17R1000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER17R10000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER17R100000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER33R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER33R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER33R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER33R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER33R1.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER33R10.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER33R100.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER33R1000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER33R10000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER33R100000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER65R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER65R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER65R0.0001.csv\n",
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"/home/pranav/Project/Result/Logistic/ionosphereITER65R1.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER65R10.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER65R100.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER65R1000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER65R10000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER65R100000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER129R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER129R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER129R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER129R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER129R1.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER129R10.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER129R100.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER129R1000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER129R10000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER129R100000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER257R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER257R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER257R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER257R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER257R1.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER257R10.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER257R100.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER257R1000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER257R10000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER257R100000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER513R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER513R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER513R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER513R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER513R1.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER513R10.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER513R100.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER513R1000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER513R10000.csv\n",
"/home/pranav/Project/Result/Logistic/ionosphereITER513R100000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER1R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER1R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER1R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER1R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER1R1.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER1R10.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER1R100.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER1R1000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER1R10000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER1R100000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER3R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER3R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER3R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER3R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER3R1.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER3R10.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER3R100.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER3R1000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER3R10000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER3R100000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER5R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER5R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER5R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER5R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER5R1.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER5R10.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER5R100.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER5R1000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER5R10000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER5R100000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER9R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER9R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER9R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER9R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER9R1.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER9R10.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER9R100.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER9R1000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER9R10000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER9R100000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER17R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER17R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER17R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER17R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER17R1.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER17R10.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER17R100.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER17R1000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER17R10000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER17R100000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER33R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER33R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER33R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER33R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER33R1.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER33R10.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER33R100.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER33R1000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER33R10000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER33R100000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER65R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER65R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER65R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER65R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER65R1.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER65R10.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER65R100.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER65R1000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER65R10000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER65R100000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER129R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER129R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER129R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER129R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER129R1.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER129R10.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER129R100.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER129R1000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER129R10000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER129R100000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER257R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER257R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER257R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER257R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER257R1.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER257R10.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER257R100.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER257R1000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER257R10000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER257R100000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER513R0.00000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER513R0.000001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER513R0.0001.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER513R0.01.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER513R1.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER513R10.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER513R100.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER513R1000.csv\n",
"/home/pranav/Project/Result/Logistic/diabetesITER513R10000.csv\n"
]
}
],
"source": [
"#Logistic\n",
"n_datasets = len(file)\n",
"p1_lg = 10\n",
"p2_lg = 10\n",
"shape2 = (n_datasets, p1_lg, p2_lg)\n",
"accuracies_lg = np.zeros(shape2)\n",
"f1_scores_lg = np.zeros(shape2)\n",
"build_time_lg = np.zeros(shape2)\n",
"for k in range(len(file)):\n",
" for i in range(p1_lg):\n",
" for j in range(p2_lg):\n",
" \n",
" print(s)\n",
" \n",
" p1=str(kval[i])\n",
" p2=str(ridge[j])\n",
" if j == 0:\n",
" p2 ='{0:.8f}'.format(ridge[j])\n",
" if j == 1:\n",
" p2 ='{0:.6f}'.format(ridge[j])\n",
" s='/home/pranav/Project/Result/Logistic/'+file[k]+'ITER'+p1+'R'+p2+'.csv'\n",
" df = pd.read_csv(s)\n",
" df1 = df.as_matrix()\n",
" check1=0\n",
" check2=0\n",
" for q in range(len(df1)):\n",
" df2 = str(df1[q,0])\n",
" if 'Correctly' in df2:\n",
" check1=check1+1\n",
" #print(check1)\n",
" if check1 == 2:\n",
" #print(df2[57:64])\n",
" x=(float(df2[57:64])/100)\n",
" #x=float(x)\n",
" #print(x)\n",
" if 'Weighted' in df2:\n",
" check2=check2+1\n",
" #print(check2)\n",
" if check2 == 2:\n",
" y=float(df2[55:60])\n",
" #print(y)\n",
" \n",
" if 'Time taken to build model' in df2:\n",
" z=float(df2[27:31])\n",
" #print(z)\n",
" \n",
" \n",
" accuracies_lg[k,i,j] = x\n",
" f1_scores_lg[k,i,j] = y\n",
" build_time_lg[k,i,j] = z"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for d in range(len(file)):\n",
" sf1 = pd.DataFrame(accuracies_lg[d], columns=kval, index=ridge)\n",
" sf2 = pd.DataFrame(f1_scores_lg[d], columns=kval, index=ridge)\n",
" sf3 = pd.DataFrame(build_time_lg[d], columns=kval, index=ridge)\n",
" w=str(d)\n",
" path1 = '/home/pranav/Project/results_weka/logistic/d_' + w + '_' +file[d] + '_acc_lg' \n",
" sf1.to_csv(path_or_buf=path1)\n",
" path2 = '/home/pranav/Project/results_weka/logistic/d_' + w + '_' +file[d] + '_fm_lg' \n",
" sf2.to_csv(path_or_buf=path2)\n",
" path3 = '/home/pranav/Project/results_weka/logistic/d_' + w + '_' +file[d] + '_bt_lg' \n",
" sf3.to_csv(path_or_buf=path3)"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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N8vJy1NbWorCwELa2tsjOzn4cuREREZm8JsdIX3rpJXz++eeYPHkyRo8eDXt7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\nRERkhP8H3pLHJO+cuecAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f14305c6a58>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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CtWvXbgRRqfDKK6/g/vvvx/Xr10WEJCIiKyXqZzpKE1Jg+/bti5SUFDg5OTXb/vjjj0Or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\nQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430517ac8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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lrly5gmbNmmHixInG+QyJiIjU3iKV5Rrp+vXrje+jo6Ph6emJ2bNnw8fHB5GRkXKEJCIi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\nWEiJiIis8P8AQ66xLVBnF9gAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430bc6898>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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FWEiJiIgswKFdIiKSldKHdllIiYhIVpxsREREZAGld6SSXCM9ceKE4XlJSQni4uIwffp0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\nMTquoKAAy5cvR1VVFTw8PLB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"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430e903c8>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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uiIi4mAdVXS2kEhERcRJ1uiIi4lKGRZ2uiIiI3GTqdEVExKU86JCuuUXXbreT\nn5+P3W6nUaNGZoYSERE3pVOGbtDXX3/NkiVLyMrK4ttvv6Vdu3YUFhYSFBTE3Llzadq0qRlhRUTE\nDXlQzTXnmO6CBQt4/vnn+eCDD/jb3/5Gp06d+Pjjj3n88ceZNWuWGSFFRERqPVOK7qVLl2jdujUA\nd911F0eOHAGgb9++/Pjjj2aEFBERd2UYNX+4GVOmlzt06MBzzz1H586d2bNnD/fffz8AMTEx3H33\n3WaEFBERN+VJpwyZUnRfeOEF/vGPf3Dy5El++9vf0rdvXwDGjh3LPffcY0ZIERGRWs+UomsYBmFh\nYVdtDwwMNCOciIi4MTecJa4xnacrIiKu5UFVV1ekEhERcRJ1uiIi4lIe1Oiq6IqIiGtp9bKIiIiT\neNJlIHVMV0RExEnU6YqIiGt5TqOrTldERMRZ1OmKiIhLedIxXRVdERFxKRVdERERZ/GgA50quiIi\n4lLqdGuBr/912tUpiJs6c6bI1SmIiFTKg5p6ERER16q1na6IiHgGTS+LiIg4i4k1Ny4ujszMTAzD\nICYmhs6dOzv2paSksHLlSry9vRk8eDBRUVEALF26lH379lFWVsbkyZMJDw+ntLSUOXPmcOrUKW6/\n/XZeeeUVGjRoQFBQEMHBwY4x165di5eXV5X5qOiKiIhLmXXDg71793Lq1CmSkpI4ceIEMTExJCUl\nAXD58mViY2PZunUr/v7+TJw4kbCwME6ePMmxY8dISkoiPz+fYcOGER4ezrvvvkvDhg1Zvnw5SUlJ\nfPbZZzz00EP4+vqyYcOG685JRVdERFzLpOnltLQ0wsLCAGjXrh2FhYUUFRXh6+tLfn4+fn5+2Gw2\nAHr16kVqaiqPPfaYoxv28/PjwoULlJeXs3PnTn7/+98DEBERUeOctJBKRERuSbm5uTRs2NDx2maz\nkZOT43heXFzMyZMnKS0tJT09ndzcXLy8vKhfvz4AycnJ9O3bFy8vL7KysvjnP//JmDFjePbZZyko\nKADg0qVLzJw5k8jISN56661qc1LRFRERj2C32x3PDcMgPj6emJgYpk2bRqtWrSq8NyUlheTkZObP\nn+/4bJs2bdiwYQPt27cnMTERgOjoaF588UXWrFnDBx98wP79+6+Zg4quiIi4lGHU/HEtAQEB5Obm\nOl5nZ2fTpEkTx+uePXuyadMmEhMTsVqttGzZEoA9e/bwxhtvsGrVKqxWKwCNGzemR48eAPz617/m\n+PHjAIyP7JgeAAAT6ElEQVQePZrbb7+d+vXr06tXL44ePXrNnFR0RUTEpQzDqPHjWkJDQ9mxYwcA\nBw8eJCAgAF9fX8f+p556iry8PEpKSti5cychISGcP3+epUuXkpiYiL+/v+O9ffv2Zc+ePY6x2rRp\nw1dffcXMmTOx2+2UlZWRkZFB+/btr5mTFlKJiIhrmbR6OTg4mKCgICIjIzEMgwULFrBlyxasVisD\nBgxg1KhRjB8/HsMwmDRpEjabzbFqecaMGY5xlixZwpgxY5g9ezbJycnUr1+fJUuW0LhxY5o1a8aI\nESOwWCz079+/wilJlTHsV05ym6SsrIyzZ8/StGlT6tS5vjr/Py++aXJWcquqTZeBPJ193tUp1Gpv\nffqxq1Nw6Ne2m6tTqNVW7HzZtLFPvfffNf7snUMfuYmZmM+U6eVFixY5nqempjJgwABmzJhBeHi4\noz0XERHxNKZMLx85csTxPCEhgfXr19O6dWtycnKYNm0affr0MSOsiIi4I8+5CqQ5ne6VB7cbNGhA\n69atAWjSpMl1Ty+LiIjcakypgMeOHWP69OnY7XZOnTrFtm3bGDRoEGvWrHEsvxYREQHd8OCGrVix\nosLrO++8E/ip012+fLkZIUVExE2Zde3l2siUotuzZ89Ktz/66KNmhBMREXemTldERMQ5PGl6WVek\nEhERcRJ1uiIi4lqe0+iq0xUREXEWdboiIuJSWr0sIiLiLB60kEpFV0REXEqrl0VEROSmU6crIiKu\npWO6IiIizqHpZREREbnp1OmKiIhreU6jW3uL7qdffu/qFMRNncz7wdUp1Fp3NbK5OgWRq2h6WURE\nRG66WtvpioiIh9DqZREREefwpOllFV0REXEtDyq6OqYrIiLiJOp0RUTEpTxpelmdroiIiJOo0xUR\nEdfS6mURERHn8KTpZRVdERFxLRXdm++HH37AZtMl6EREpCLDg6aXTVlItWvXLn7zm98wbtw4jh49\nypAhQxgzZgz9+/dn9+7dZoQUERGp9UzpdFeuXMlbb73Fd999x9NPP83rr79OYGAgubm5PP300zzw\nwANmhBUREanVTCm63t7etGjRghYtWhAQEEBgYCAAjRs3pl69emaEFBERd+VBx3RNmV5u1KgRb775\nJgDvvPMOAGfOnCEuLo5mzZqZEVJERNyUYRg1frgbU4pufHw8zZs3r7AtLy+PFi1aEBcXZ0ZIERFx\nV4ZR84ebMWV62cfHh4cffrjCtqCgIIKCgswIJyIibkyrl0VEROSmU9EVERFxEl2RSkREXMsNj83W\nlIquiIi4loquiIiIc7jjqT81paIrIiKupdXLIiIicrOp0xUREZcyDM/p/zznm4qIiLiYOl0REXEt\nLaQSERFxDjNXL8fFxZGZmYlhGMTExNC5c2fHvpSUFFauXIm3tzeDBw8mKioKgKVLl7Jv3z7KysqY\nPHky4eHhzJkzh4MHD+Lv7w/AhAkTePDBB3n//fdZt24dFouFUaNGMXLkyGvmU2uL7qhJPV2dgsgt\n5+t/nXZ1ChV8cqyVq1OQ2sCk1ct79+7l1KlTJCUlceLECWJiYkhKSgLg8uXLxMbGsnXrVvz9/Zk4\ncSJhYWGcPHmSY8eOkZSURH5+PsOGDSM8PByA5557jn79+jnGLykpISEhgeTkZOrWrcuIESMYMGCA\nozBX+lVN+aYiIiIulpaWRlhYGADt2rWjsLCQoqIiAPLz8/Hz88Nms2GxWOjVqxepqan06NGDFStW\nAODn58eFCxcoLy+vdPzMzEw6deqE1WrFx8eH4OBgMjIyrpmTiq6IiLiUWffTzc3NpWHDho7XNpuN\nnJwcx/Pi4mJOnjxJaWkp6enp5Obm4uXlRf369QFITk6mb9++eHl5AfD2228zduxYnn32WX744Qdy\nc3Ox2WyVjl+VWju9LCIiHsJJC6nsdvsVIQ3i4+OJiYnBarXSqlXFQx0pKSkkJyezZs0aAB577DH8\n/f259957+ctf/sJrr71G165dqxy/Kup0RUTklhQQEEBubq7jdXZ2Nk2aNHG87tmzJ5s2bSIxMRGr\n1UrLli0B2LNnD2+88QarVq3CarUCEBISwr333gtA//79OXr0aKXjBwQEXDMnFV0REXEtw1LzxzWE\nhoayY8cOAA4ePEhAQAC+vr6O/U899RR5eXmUlJSwc+dOQkJCOH/+PEuXLiUxMbHCgqhnnnmG06d/\nWoiYnp5O+/bt6dKlC/v37+fcuXMUFxeTkZFB9+7dr5mTppdFRMSlDJNWLwcHBxMUFERkZCSGYbBg\nwQK2bNmC1WplwIABjBo1ivHjx2MYBpMmTcJmszlWLc+YMcMxzpIlS3jyySeZMWMGt912G/Xr12fx\n4sX4+Pgwc+ZMJkyYgGEYTJ061dEZV/ld7dczCe0CWR995OoURG45te2UoSV/+8TVKTi0tTV1dQq1\n2oqdL5s29vmvD9f4s9Y2gTcxE/Op0xUREdfSFalEREScQ/fTFRERcRbdZUhERERuNqd3uufOncPP\nz8/ZYUVEpJYya/VybeT0TnfatGnODikiIlIrmNLpbty4scp9Z8+eNSOkiIi4Ky2kujFr164lJCSk\n0sthlZWVmRFSRETclFYv36CEhAQWLVrEvHnz8Pb2rrAvPT3djJAiIuKuPGj1silFt0OHDiQmJlKn\nztXDz5kzx4yQIiLirjxoIZVpq5dvu+22SrcHBQWZFVJERKRW85yeXkRExMV0RSoREXEpLaQSERFx\nFi2kEhERcQ51uiIiIs7iQZ2u53xTERERF1PRFRERcRJNL4uIiEt50l2GVHRFRMS1tJBKRETEOQwP\nWkiloisiIq7lQZ2uYbfb7a5OQkRExBN4Tk8vIiLiYiq6IiIiTqKiKyIi4iQquiIiIk6ioisiIuIk\nKroiIiJO4pZF9+jRo4SFhfH2229f92e+//57xowZwxNPPMH06dO5dOkSAH/605+IjIwkIiKCVatW\nuTyfw4cP8/jjj/P444+TkJBwXWPFxcURERFBZGQkX375ZYV9qampjBgxgoiIiArjVfaZqnIqLCxk\nwoQJ/P73v7/u73ez8qzJb3szuCrutXKo6u9TG3J4//33GT58OCNHjmTz5s21JofS0lJmzpzJ6NGj\niYqK4vTp07Ui/uHDh4mMjCQyMpIFCxbUqhxWr17NiBEjGDlyJLt37/7Fv5dUw+5miouL7VFRUfZ5\n8+bZN2zYcN2fmzNnjv3DDz+02+12+/Lly+0bN260HzlyxB4REWG32+328vJy+8CBA+3Z2dkuy8du\nt9tHjBhhP3DggL28vNz+7LPP2ktKSq45Tnp6un3SpEl2u91uP378uH3UqFEV9g8aNMj+3Xff2cvL\ny+2jR4+2Hzt2rMrPVJXT9OnT7QkJCfZnnnnmur/fzcizpr/tjXJV3OpyqOrv4+ociouL7eHh4fZz\n587ZL1y4YB88eLA9Pz+/VuSwZcsW+8KFC+12u92+Z88e+/Tp02tF/KioKHtmZqbdbrfbn3vuOfuu\nXbtqRQ7ffPONfdiwYfaLFy/a8/Ly7L/5zW/sZWVlv+g3k2tzu07X29ubVatWERAQ4Nh2/Phxxo4d\ny29/+1umTJnCuXPnrvpceno6Dz30EAD9+vUjLS0Nq9XKxYsXuXTpEhcvXsRisXDbbbe5LJ/c3FxK\nSkoICgrCYrHw8ssvV5tPWloaYWFhALRr147CwkKKiooAOH36NA0aNKB58+ZYLBYeeOAB0tLSqvxM\nZTkBLFq0iG7duv2i3+Vm5FnZb+sMropbXQ5V/X1cnUNmZiadOnXCarXi4+NDcHAwGRkZtSKHtLQ0\nBgwYAEDv3r1/cV5mxL906RJZWVl07ty5whi1IYf09HT69OmDt7c3NpuNli1bcvz48V/0m8m1uV3R\nrVOnDj4+PhW2xcbG8uKLL7Ju3TpCQ0PZuHHjVZ+7cOEC3t7eADRq1IicnByaN2/OwIED6devH/36\n9SMyMhJfX1+X5ZOVlUWDBg2YM2cOkZGRrF27ttr4ubm5NGzY0PHaZrORk5MDQE5ODjab7ap9VX2m\nspyAX/yb3Kw8K/ttncFVcavLoaq/j6tzyM3NrfTvVxtyuHK7xWLBMIxfNC1vRvzc3Fz8/Pwc763u\nb+nMHMz8W8pPbolrL3/55Zf88Y9/BODSpUt06tTpmu+3//9Xvjx9+jQff/wxKSkplJWVERkZycMP\nP0yjRo1cko/dbufbb78lISEBHx8fIiIiCA0NpX379tcd216Dq3pW9pmajHOjMeX61Ybfr6ocnJnb\nL83hZud2M+LfaE5m5lAb/j+71dwSRfe2225j/fr1GFdcNPvzzz/n5ZdfBmDZsmXUr1+fH3/8ER8f\nH86ePUtAQAD79++nS5cujince+65h6NHjxISEuKSfBo1akT79u0dHWG3bt04duzYNYtuQEAAubm5\njtfZ2dk0adKk0n0/x6lbt26ln6ksp5ulJnlKRWb+fW4kh8r+tr/61a9qRQ4BAQHk5OQQGBhIaWkp\ndrvd0SG6Kn6TJk0oKChwvLcmf0uzcggICODrr7++odzk2txuerkygYGB/POf/wTg73//O2lpaXTt\n2pUNGzawYcMGmjZtSu/evdmxYwcAH330EX369OGOO+7gwIEDXL58mdLSUo4ePUrr1q1dlk/r1q0p\nLi6moKCAy5cvc+jQIdq2bXvNWKGhoY5xDh48SEBAgGM6uFWrVhQVFfHtt99SVlbGzp07CQ0NrfIz\nleV0s9QkT6nIzL/PjeTQpUsX9u/fz7lz5yguLiYjI4Pu3bvXihxCQ0PZvn07ADt37uT+++93efy6\ndevStm1bPvvsswpj1IYcevXqxa5du7h06RJnz54lOzubu++++4Z/M/k/bneXoQMHDrBkyRKysrKo\nU6cOTZs2ZcaMGSxfvhyLxUK9evVYvnw5/v7+FT6XnZ3N7NmzuXjxIi1atGDx4sXUrVuXV155hdTU\nVAAGDhzIuHHjXJpPZmYmixYtwjAM+vTpwzPPPFNtDsuWLeOzzz7DMAwWLFjA//t//w+r1cqAAQP4\n9NNPWbZsGQDh4eFMmDCh0s8EBgZWmpPFYmHcuHGcO3eOs2fP0r59e6ZMmVKj2YBfmmdlv+2rr756\n1W95s7kqbnU5LFu2jDlz5lz1/0xtyGH79u28+eabGIZBVFQUQ4YMqRU5lJeXM2/ePE6ePIm3tzfx\n8fE0b97c5fGPHz/O/PnzuXz5Ml26dGHu3Lm1JocNGzbwwQcfYBgGM2bMuOGZP6nI7YquiIiIu7ol\nppdFRETcgYquiIiIk6joioiIOImKroiIiJOo6IqIiDiJiq5IDdxzzz2UlZVd9/v/8pe/sGvXrl8c\n58KFC3z00UcA/POf/2TlypW/eAwRqT10ypBIDdxzzz0cPHiQOnXMvajbvn37+Otf/+o4h1lE3JuK\nrtzS0tPTef3116lXrx79+/fnwIEDnDp1iuLiYh555BHGjx/PxYsXmT17NllZWTRr1gwvLy9CQ0MJ\nCQnhiSeecFxd7NVXX6WsrIxnn33WUXQLCgqIjo6mrKyMoqIixo4dy9ChQ9myZQu7du2isLCQ//iP\n/2D79u1069YNm83muJFFSUkJR48eZf/+/Zw4cYIFCxbg5eVFUVERM2bMoEePHgwdOpRz584xdOhQ\n7r77blJTU1m2bBmZmZnEx8dTp04dDMNg/vz53H333YwZM4aQkBA+//xzTp48yTPPPHPTLlQhIjfu\nlrj2ssi1HDhwgH/84x8kJycTEBDAokWLKC8vZ9SoUfTu3Zv9+/dTVlbG5s2bycnJ4eGHH77uy1Bm\nZ2fz5JNP8tBDD5Gdnc2jjz7K0KFDATh06BB///vf8fb2dlyG76GHHnLckm369OlEREQAP92Fafr0\n6fTo0YPPP/+c2NhYtmzZwqRJk0hNTSU6OpotW7Y44kZHR/PSSy/RuXNndu7cyQsvvMCGDRuAn4r5\nqlWr2Lt3L4sWLVLRFalFVHTlltemTRv8/f1JT0/nzJkzfPrpp8BPd4D65ptvOHToED179gSgSZMm\nv+jewQEBAaxevZrVq1fj5eVV4SLy9913X5UX13/zzTfx9fVl1KhRjrhLly7lT3/6E6WlpRXG+Xfn\nzp0jLy/PcS/Unj178txzzzn2//xdWrRoQWFh4XV/FxExn4qu3PJ+vj6xt7c3U6dOZeDAgRX2p6am\nYrH835rCn59feZcogNLS0qu2/fnPf+bOO+/k5Zdfpri4mODg4Kvi/rt//etf7Nixg7ffftuxLTY2\nlsGDBzNixAiOHj3K008/XeX3+fcc/v0I0ZXHmXX0SKR20epl8RjdunVj27ZtAFy+fJnFixdTUFBA\n27Zt+fzzzwHIy8tj3759APj6+lJYWMiFCxcoLy93dMhXys3Nddx68b//+7+xWCzXvEn6mTNniI2N\n5c9//nOFLvjKcT788EPHGBaL5apV0larlSZNmpCZmQlAWlqaqbfSE5GbR52ueIwnn3ySY8eOERER\nQXl5OQ8++CD+/v48/vjj7Nq1i4iICFq1akX37t3x8vKiQYMGDBs2jOHDh3PHHXdw3333XTVmVFQU\nsbGxbN68meHDhxMSEsLMmTPp169fpTm8/vrrFBUVMXv2bMe2F154gfHjxxMdHU2rVq0YN24cH3/8\nMfHx8YwcOZJly5Yxd+5cevTo4fjMkiVLiI+Px8vLC4vFwsKFC2/67yUiN59WL4vHO3v2LBkZGQwa\nNIjLly8zbNgwFi5cSNeuXV2dmojcYtTpisezWq18+OGHjvuQ9u3bVwVXREyhTldERMRJtJBKRETE\nSVR0RUREnERFV0RExElUdEVERJxERVdERMRJVHRFRESc5P8D4Vma1mvtifQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f143090b240>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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XF1qtFoIgIDIyEomJiXBxccGQIUMwceJEzJw5E4IgYPbs2VCr1VCr1RX2sUSSQvrnP/8Z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\ncwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430677b38>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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igw8+wJUrVwz7LSwssGXLFilCEhGRQqkEweiXOZCkkP7Hf/wH+vXrBz8/P7z99ttITk42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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f143058e828>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"image/png": 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l5eW19tu7dy/CwsLg4uJS7zFZWVkYNmwYACA4OBiZmZkWr4sFloiI1CWowhqN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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1430ec3748>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"for d in range(len(file)):\n",
" ax = sns.heatmap(accuracies_lg[d][::-1], yticklabels=kval[::-1], xticklabels=ridge)\n",
" plt.ylabel('max iter')\n",
" plt.xlabel('regularization')\n",
" plt.title(file[d])\n",
" #plt.savefig()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df1 = df.as_matrix()\n",
"#print(df1)"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(50, 1)"
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df1.shape"
]
},
{
"cell_type": "code",
"execution_count": 165,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.26\n",
"1\n",
"1\n",
"2\n",
"0.908832\n",
"2\n",
"0.907\n"
]
}
],
"source": [
"check1=0\n",
"check2=0\n",
"for i in range(len(df1)):\n",
" df2 = str(df1[i,0])\n",
" if 'Correctly' in df2:\n",
" check1=check1+1\n",
" print(check1)\n",
" if check1 == 2:\n",
" #print(df2[57:64])\n",
" x=(float(df2[57:64])/100)\n",
" #x=float(x)\n",
" print(x)\n",
" if 'Weighted' in df2:\n",
" check2=check2+1\n",
" print(check2)\n",
" if check2 == 2:\n",
" y=float(df2[55:60])\n",
" print(y)\n",
" \n",
" if 'Time taken to build model' in df2:\n",
" z=float(df2[27:31])\n",
" print(z)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"s =['asfsgsd' 'fghsfg']"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'asfsgsdfghsfg'"
]
},
"execution_count": 110,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1 = ''.join(s)\n",
"s1"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'asf' in s1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
},
"widgets": {
"state": {
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| mit |
indranilsinharoy/independentstudy_compphotog | Basic01_Ambiguity_function.ipynb | 1 | 817282 | {
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"metadata": {},
"source": [
"Ambiguity function (AF) and its use in OTF analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**References**\n",
"\n",
"1. Introduction to Fourier Optics, Joseph Goodman\n",
"2. The Ambiguity Function as a polar display of the OTF, K.H. Brenner, A.W. Lohmann and J. Ojeda-Castaneda, Optics Communication, 1982\n",
"3. Misfocus tolerance seen by simple inspection of the ambiguity function, H. Bartelt, J. Ojeda-Castaneda, and Enrique Sicre, Applied Optics, 1984.\n",
"4. Extended depth of field through wave-front coding, Edward R. Dowski, Jr., and W. Thomas Cathey, Applied Optics, 1995."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from __future__ import print_function, division\n",
"import numpy as np\n",
"import scipy as sp\n",
"import matplotlib.cm as cm\n",
"import matplotlib.pyplot as plt\n",
"from IPython.display import Image\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"The 2D AF and its relation to 1D OTF"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The Ambiguity Function (AF) is an useful tool for optical system analysis. As we will see, the AF simultaneously contains all the OTFs associated with an rectangularly separable incoherent optical system with varying degree of defocus [2-4]. Thus by inspecting the AF of an optical system, one can easily predict the performance of the system in the presence of defocus. It has been used in the design of extended-depth-of-field cubic phase mask system.\n",
"\n",
"\n",
"To understand the basic theory, we shall consider a one-dimensional pupil function, which is defined as:\n",
"\n",
"$$\n",
"(1) \\hspace{40pt} \n",
"P(x) = \\left \\{ \\begin{array}{cc} \n",
"1 & \\,\\, \\text{if} \\, & |x| \\leq 1, \\\\\n",
"0 & \\,\\, \\text{if} \\, & |x| > 1, \n",
"\\end{array}\\right .\\ \n",
"$$\n",
"\n",
"The *generalized pupil function* associated with $P(x)$ is the complex function $\\mathcal{P}(x)$ given by the expression [1]:\n",
"\n",
"$$\n",
"(2) \\hspace{40pt}\n",
"\\mathcal{P}(x) = P(x)e^{jkW(x)} \n",
"$$\n",
"\n",
"where $W(x)$ is the aberration function. Then, the amplitude PSF of an aberrated optical system is the Fraunhofer diffraction pattern (Fourier transform with the frequency variable $f_x$ equal to $x/\\lambda z_i$) of the generalized pupil function, and the intensity PSF is the squared magnitude of the amplitude PSF [1]. Note that $z_i$ is the distance between the diffraction pattern/screen and the aperture/pupil. \n",
"\n",
"For the purpose of analyzing optical systems using the ambiguity function, it is convenient to separate the defocus term ($W_{20}$ is the wavefront focus error coefficient [3]) from the rest of the aberration terms in the aberration function $W(x)$:\n",
"\n",
"$$\n",
"(3) \\hspace{40pt}\n",
"\\begin{array}{rl}\n",
"\\mathcal{P}(x) & = & P(x)e^{jkW(x)} \\\\\n",
" & = & P(x)e^{jk(\\tilde{W}(x) + W_{20}x^2)} \\\\\n",
" & = & \\mathcal{P}_o(x)e^{jkW_{20}x^2}\n",
"\\end{array} \n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since the amplitude PSF is the Fourier transform of the pupil function (see above), and the amplitude transfer function (ATF) is the Fourier transform of the amplitude PSF, the ATF is proportional to a scaled pupil function $\\mathcal{P}$. Specifically, the ATF $H(f_x)$, is:\n",
"\n",
"$$\n",
"(4) \\hspace{40pt}\n",
"H(f_x) = \\mathcal{P}(\\lambda z_i f_x) \n",
"$$\n",
"\n",
"The (one dimensional) optical transfer function (OTF) is defined as the normalized autocorrelation of the ATF:\n",
"\n",
"$$\n",
"(5) \\hspace{40pt}\n",
"\\mathcal{H}(f_x) = \\frac{\\int\\limits_{-\\infty}^{\\infty} H \\left(p + \\frac{f_x}{2}\\right) H^* \\left(p - \\frac{f_x}{2} \\right) dp}{\\int\\limits_{-\\infty}^{\\infty} |H(p)|^2 dp}\n",
"$$\n",
"\n",
"$$\n",
"\\hspace{50pt}\n",
"\\mathcal{H}(f_x) = \n",
"\\frac{\\int\\limits_{-\\infty}^{\\infty} \n",
"\\mathcal{P} \\left(q + \\frac{\\lambda z_i f_x}{2}\\right) \\mathcal{P}^* \\left(q - \\frac{\\lambda z_i f_x}{2} \\right) dq}\n",
"{\\int\\limits_{-\\infty}^{\\infty} |\\mathcal{P}(q)|^2 dq}\n",
"$$\n",
"\n",
"writing $u$ in place of $\\lambda z_i f_x$, we have\n",
"\n",
"$$\n",
"\\hspace{50pt}\n",
"\\mathcal{H}(u) = \n",
"\\frac{\\int\\limits_{-\\infty}^{\\infty} \n",
"\\mathcal{P} \\left(q + \\frac{u}{2}\\right) \\mathcal{P}^* \\left(q - \\frac{u}{2} \\right) dq}\n",
"{\\int\\limits_{-\\infty}^{\\infty} |\\mathcal{P}(q)|^2 dq}\n",
"$$\n",
"\n",
"$$\n",
"\\hspace{50pt}\n",
"\\mathcal{H}(u) = \n",
"\\frac{\\int\\limits_{-\\infty}^{\\infty} \n",
"\\mathcal{P}_o\\left(q + \\frac{u}{2}\\right) e^{jkW_{20}(q + u/2)^2}\n",
"\\mathcal{P}_o^* \\left(q - \\frac{u}{2} \\right) e^{-jkW_{20}(q - u/2)^2} dq}\n",
"{\\int\\limits_{-\\infty}^{\\infty} |\\mathcal{P}_o(q)e^{jkW_{20}q^2}|^2 dq}\n",
"$$\n",
"\n",
"$$\n",
"\\hspace{50pt}\n",
"\\mathcal{H}(u) = \n",
"\\frac{\\int\\limits_{-\\infty}^{\\infty} \n",
"\\mathcal{P}_o\\left(q + \\frac{u}{2}\\right)\n",
"\\mathcal{P}_o^* \\left(q - \\frac{u}{2} \\right) \n",
" e^{jkW_{20} [(q + u/2)^2 - (q - u/2)^2 ]} dq}\n",
"{\\int\\limits_{-\\infty}^{\\infty} |P(q)|^2 dq}\n",
"$$\n",
"\n",
"The simplified equation is written as follows:\n",
"\n",
"$$\n",
"(6) \\hspace{40pt}\n",
"\\mathcal{H}(u; W_{20}) = \n",
"0.5 \\int\\limits_{-\\infty}^{\\infty} \n",
"\\mathcal{P}_o\\left(q + \\frac{u}{2}\\right)\n",
"\\mathcal{P}_o^* \\left(q - \\frac{u}{2} \\right) \n",
" e^{j 2\\pi \\left(\\frac{2 W_{20}}{\\lambda} \\right)uq} dq\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ambiguity function, which is used for radar analysis, is defined as the Fourier transform of the product $f(y + u/2) f^*(y - u/2)$:\n",
"\n",
"$$\n",
"(7) \\hspace{40pt}\n",
"A(u, y) = \\int\\limits_{-\\infty}^{\\infty}f\\left(t + \\frac{u}{2} \\right) f^*\\left(t - \\frac{u}{2} \\right)e^{j2\\pi yt} dt\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Comparing the simplified expression of the aberrated OTF, $\\mathcal{H}(u; W_{20})$, and the ambiguity function, $A(u, y)$, we see that:\n",
"\n",
"$$\n",
"(8) \\hspace{40pt}\n",
"\\mathcal{H}(u; W_{20}) = A \\left(u, 2 u \\frac{W_{20}}{\\lambda} \\right)\n",
"$$\n",
"\n",
"This implies that the ambiguity function $A(u, y)$ associated with a base function $\\mathcal{P}_o(u)$ contains the OTF $\\mathcal{H}(u; W_{20})$ along the line $y = \\frac{2 W_{20}}{\\lambda} u$ (which has a slope $\\tan (\\phi) =\\frac{2 W_{20}}{\\lambda}$, and passes through the origin in the $y-u$ plane as shown in the following figure.\n",
"\n",
"Notes \n",
"\n",
"1. The \"base function\" itself does not contain the defocus term, although it may contain other aberrations (see equation (3)). \n",
"\n",
"2. Equation (6) is the \"normalized autocorrelation\" of the generalized pupil function, which implies that the maximum value of the function is 1. In equation (7) there is no explicit normalization and so the maximum value can be greater or less than 1. We must be mindful of this when comparing the AF to the OTF and prefer to chose appropriate amplitude for the base function $f(u)$ (as shown below). "
]
},
{
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"collapsed": false,
"input": [
"Image('images/relationAFandOTF.png', embed=True, width=300) "
],
"language": "python",
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{
"metadata": {
"png": {
"width": 300
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"source": [
"In fact, the whole 2-D AF contains the OTFs $\\mathcal{H}(u; W_{20})$ for various values of defocusing $W_{20}$ arranged in a polar fashion. Of special interest is the in-focus OTF for which $W_{20}=0$ and hence corresponds to the line along the \"$u$\" axis. i.e. $\\mathcal{H}(u; 0) = A(u,0)$"
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Example: Ambiguity function of a diffraction limited 1-D pupil."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If the only aberration is that of defocus, $\\tilde{W}(x)=0$ in the expression for the generalized pupil function. Therefore the base-function which we consider is \n",
"\n",
"$$\n",
"(9) \\hspace{40pt}\n",
"\\mathcal{P}_o(x) = P(x) =\n",
"\\left \\{ \\begin{array}{cc} \n",
"1 & \\,\\, \\text{if} \\, & |x| \\leq 1, \\\\\n",
"0 & \\,\\, \\text{if} \\, & |x| > 1, \n",
"\\end{array}\\right .\\ \n",
"$$\n",
"\n",
"First, we will consider the evaluation of the following integral of the general form:\n",
"\n",
"\n",
"$$\n",
"(10) \\hspace{40pt}\n",
"I(a, y) = \\int\\limits_{-\\infty}^{\\infty}f(t + a) f^*(t - a)e^{j2\\pi yt} dt\n",
"$$\n",
"\n",
"where, the function $f(t)$ is defined as:\n",
"\n",
"$$\n",
"\\hspace{50pt} \n",
"f(t) = \\left \\{ \\begin{array}{cc} \n",
"A & \\,\\, \\text{if} \\, & |t| \\leq L, \\\\\n",
"0 & \\,\\, \\text{if} \\, & |t| > L, \n",
"\\end{array}\\right .\\ \n",
"$$\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following figure, which shows the regions of overlap between $f(t-a)$ and $f(t+a)$ (with $A=1$), is useful to understand the finite limits of the integral."
]
},
{
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"input": [
"Image('images/ambiguityFnKernel.png', embed=True) "
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TRBroKlPKZRI+oJSzHDcolZ0uDQT8MikjZTiFghXWhlIE9z9NqlLK/X6/SiGk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oaG1tfVh4WF1tbVhYeH19TUREZGVldXR0VFVZ7PjY6Orsw7D2HF+dyEpCScywm5ClRlpWadnkX3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\nop+fn4+PD0KNRuPv749thNiGs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"prompt_number": 3,
"text": [
"<IPython.core.display.Image at 0x4681240>"
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}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When $0 < a < L$:\n",
"\n",
"$$\n",
"\\begin{array}{cl}\n",
"I(a, y) & = & \\int\\limits_{a-L}^{-a+L} A^2 e^{j2\\pi yt} dt \\\\\n",
"& = & A^2 \\left| \\frac{e^{j2\\pi yt}}{j2\\pi y} \\right|_{a-L}^{-a+L} \\\\\n",
"& = & A^2 \\frac{1}{\\pi y} \\left[ \\frac{e^{j2\\pi (L-a)y} - e^{-j2\\pi (L-a)y} }{2j} \\right] \\\\\n",
"& = & A^2 \\frac{1}{\\pi y} \\sin{( 2\\pi (L-a)y)} \\\\\n",
"& = & 2(L-a)A^2 \\frac{\\sin{( 2\\pi (L-a)y)}} {2\\pi (L-a) y} \\\\\n",
"& = & 2(L-a)A^2 \\text{sinc}[2(L-a)y]\n",
"\\end{array}\n",
"$$\n",
"\n",
"where, the $\\text{sinc}$ function used above is the *normalized* $\\text{sinc}$ that is defined as $\\text{sinc}(x)=sin(\\pi x)/(\\pi x)$\n",
"\n",
"\n",
"When $-L < a \\leq 0$:\n",
"\n",
"$$\n",
"\\begin{array}{cl}\n",
"I(a, y) & = & \\int\\limits_{-a-L}^{a+L} A^2 e^{j2\\pi yt} dt \\\\\n",
"& = & A^2 \\left| \\frac{e^{j2\\pi yt}}{j2\\pi y} \\right|_{-a-L}^{a+L} \\\\\n",
"& = & A^2 \\frac{1}{\\pi y} \\left[ \\frac{e^{j2\\pi (L+a)y} - e^{-j2\\pi (L+a)y} }{2j} \\right] \\\\\n",
"& = & A^2 \\frac{1}{\\pi y} \\sin{( 2\\pi (L+a)y)} \\\\\n",
"& = & 2(L+a)A^2 \\frac{\\sin{( 2\\pi (L+a)y)}} {2\\pi (L+a) y} \\\\\n",
"& = & 2(L+a)A^2 \\text{sinc}[2(L+a)y]\n",
"\\end{array}\n",
"$$\n",
"\n",
"\n",
"Note that we really didn't have to break the integral into two parts. Instead we could just evaluate the integral between the limits $-(L - |a|)$ and $(L - |a|)$."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can combine the two cases and write the equation more compactly as:\n",
"\n",
"$$\n",
"(11) \\hspace{40pt}\n",
"\\begin{array}{ll}\n",
"I(a, y) & = &\\int\\limits_{-\\infty}^{\\infty}f(t + a) f^*(t - a)e^{j2\\pi yt} dt \\\\\n",
"& = & 2(L - |a|)A^2 \\text{sinc}[2(L - |a|)y], \n",
"\\end{array} \\,\\,\\, \\text{for } |a| \\leq L \n",
"$$\n",
"\n",
"\n",
"In our example, for which the base function is $\\mathcal{P}_o = P(u)$, the parameters $a=u/2$, $A=\\frac{1}{\\sqrt{2}}$ (so that the maximum value is 1) and $L=1$. We can now write:\n",
"\n",
"$$\n",
"(12) \\hspace{40pt}\n",
"\\begin{array}{ll}\n",
"A(u, y) & = &\\int\\limits_{-\\infty}^{\\infty}\\mathcal{P}_o(t + a) \\mathcal{P}_o^*(t - a)e^{j2\\pi yt} dt \\\\\n",
"& = & \\left(1 - \\frac{|u|}{2}\\right)\\text{sinc} \\left[y(2 - |u|)\\right]\n",
"\\end{array}\n",
"$$\n",
"\n",
"\n",
"Now, from equation (8) we have $\\mathcal{H}(u; W_{20}) = A(u, 2 u W_{20}/\\lambda)$. Therefore we can write the expression for the OTF directly from the AF.\n",
" \n",
"\n",
"$$\n",
"(13) \\hspace{40pt}\n",
"\\mathcal{H}(u; W_{20}) = \\left(1 - \\frac{|u|}{2} \\right) \\text{sinc} \\left[ \\frac{2 u W_{20}}{\\lambda} (2 - |u|) \\right]\n",
"$$\n",
"\n",
"Note:\n",
"\n",
"In [2], the authors have used the *un-normalized* $\\text{sinc}$ function that is defined as $\\text{sinc}(x)=\\sin(x)/x$. Hence, there is an \"extra\" $\\pi$ within the $\\text{sinc}$ function in their paper. We use the normalized definition $\\text{sinc}$ as it is used in [1], and in Numpy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The figure below shows the function $\\text{sinc}(x)$ for $x\\in[-8,8]$. It can be seen in figure that roots of the (normalized) $\\text{sinc}$ function occurs at $x=n, n\\neq 0, n \\in \\mathbb{Z}$. Therefore the zero value loci for the AF associated with the one-dimensional rectangular pupil are found by equating $y(2 - |u|)=n$. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.linspace(-8, 8, 150)\n",
"y = np.sinc(x)\n",
"fig, ax = plt.subplots(1,1)\n",
"ax.plot(x, y, label='$sinc(x)$')\n",
"ax.set_ylim(-0.3, 1.02)\n",
"ax.set_xlim(-8, 8)\n",
"ax.set_xlabel('x')\n",
"ax.set_title('sinc(x)', y=1.02)\n",
"rootsx = range(-8, 0) + range(1,9)\n",
"ax.scatter(rootsx, np.zeros(len(rootsx)), \n",
" c='r', zorder=20)\n",
"ax.grid()\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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jojKIr/mRvLwCX7Qn8LhjdDpFZe190x55LI/9/njHjh3k5uYSkJ6eTnp6OvuL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\nnW0/d1s5vLUWrhwW3Lb9kM9NJuczOZsd2hRyIXR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"text": [
"<matplotlib.figure.Figure at 0x8a21898>"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following figures plot the AF of the one dimensional rectangular pupil and variation of OTF with focus error $W_{20}$. In each figure, the plot on the left shows the ambiguity function and several lines corresponding to different defocus error $W_{20}$. The zero value locai, $y = n/(2 - |u|)$ are denoted by the gray dashed lines. The intersection of zero value locai with the line(s) $y = 2uW_{20}/\\lambda$, which in the OTF plot represents the frequencies where the OTF is zero, is shown by the cross ('x') markers. The plots on the right show the corresponding variation of the OTF with different. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def plot_1dRectAF(w20LambdaBy2, N=15, umin=-2, umax=2, ymin=-8, ymax=8):\n",
" \"\"\"rudimentary function to show the AF\n",
" \n",
" Parameters\n",
" ----------\n",
" w20LambdaBy2 : list of real values\n",
" specifies the amount defocus error W_{20} in\n",
" terms of lambda/2. The slope of the OTF \n",
" associated with W_{20} in AF plane is 2*W20/lambda.\n",
" N : integer\n",
" number of zero value loci to plot \n",
" \"\"\"\n",
" u = np.linspace(umin, umax, 200)\n",
" y = np.linspace(ymin, ymax, 400)\n",
" uu, yy = np.meshgrid(u, y) # grid\n",
"\n",
" # Numpy's normalized sinc function = sin(pi*x)/(pi*x)\n",
" af = (1 - np.abs(uu)/2)*np.sinc(yy*(2 - np.abs(uu)))\n",
" # plot\n",
" fig = plt.figure(figsize=(12, 7))\n",
" ax1 = fig.add_axes([0.12, 0, 0.42, 1.0]) # [*left*, *bottom*, *width*,*height*]\n",
" ax2 = fig.add_axes([0.6, 0.12, 0.38, 0.76])\n",
" im = ax1.imshow(af, cmap=cm.bwr, origin='lower', \n",
" extent=[umin, umax, ymin, ymax], \n",
" vmin=-1.0, vmax=1.0, aspect=2./6)\n",
" plt.colorbar(im, ax=ax1, shrink=0.77, aspect=35)\n",
" # zero value loci\n",
" for n in range(1, N+1):\n",
" zvl = n/(2.0 - abs(u[1:-1]))\n",
" ax1.plot(u[1:-1], zvl, color='#888888', \n",
" linestyle='dashed')\n",
" ax1.plot(u[1:-1], -zvl, color='#888888', \n",
" linestyle='dashed')\n",
" # OTF line on AF plane\n",
" for elem in w20LambdaBy2:\n",
" otfY = elem*u # OTF line in AF with slope 2w_{20}/lambda\n",
" ax1.plot(u, otfY)\n",
" # intersections\n",
" def get_intersections(b): \n",
" # b is tan(phi) or 2w_{20}/lambda\n",
" n = np.linspace(1, np.floor(b), np.floor(b))\n",
" u1 = 1 + np.sqrt(1 - n/b)\n",
" u2 = 1 - np.sqrt(1 - n/b)\n",
" y1 = u1*b\n",
" y2 = u2*b\n",
" u = np.hstack((u1, u2))\n",
" y = np.hstack((y1, y2))\n",
" return u, y\n",
" for elem in w20LambdaBy2:\n",
" intersectionsU, intersectionsY = get_intersections(elem)\n",
" ax1.scatter(intersectionsU, intersectionsY,\n",
" marker='x', c='k', zorder=20)\n",
"\n",
" # OTF plots\n",
" for elem in w20LambdaBy2:\n",
" otf = (1 - np.abs(u)/2)*np.sinc(elem*u*(2 - np.abs(u)))\n",
" ax2.plot(u, otf, label='$W_{20}' + '= {}\\lambda/2$'.format(elem))\n",
" # axis settings\n",
" ax1.set_xlim(umin, umax)\n",
" ax1.set_ylim(ymin, ymax)\n",
" ax1.set_title('2-D AF of 1-D rect pupil P(x)', y=1.01)\n",
" ax1.set_xlabel('u', fontsize=14)\n",
" ax1.set_ylabel('y', fontsize=14)\n",
" ax2.axhline(y=0, xmin=-2, xmax=2, color='#888888',\n",
" zorder=0, linestyle='dashed')\n",
" ax2.grid(axis='x')\n",
" ax2.legend(fontsize=12)\n",
" ax2.set_xlim(-2, 2); ax2.set_ylim(-0.2, 1.005)\n",
" ax2.set_title(\"Optical Transfer Function\", y=1.02)\n",
" ax2.set_xlabel(\"u (scaled saptial frequency)\", fontsize=14)\n",
" #fig.tight_layout()\n",
" plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plots of AF and OTF for which $W_{20} = n\\frac{\\lambda}{2}$ for $n=0, 1, 2, 3, \\dots$, i.e. $n \\in \\mathbb{Z}$. The equation for the OTF was obtained directly from the ambiguity function by replacing $y$ with $2uW_{20}/\\lambda$.\n",
"\n",
"The points of intersection between lines $y = 2uW_{20}/\\lambda$ and each zero loci curve $y = n/(2 - |u|)$ (for $n > 0$) can be found by equating $n/(2 - |u|) = 2uW_{20}/\\lambda$, which gives $u = 1 \\pm \\sqrt{1 -\\frac{n \\lambda}{2 W_{20}}}$\n",
"\n",
"For example, the line $y=2u \\frac{W_{20}}{\\lambda}\\Big|_{W_{20}=1\\lambda/2}$ intersects the zero loci curve $y = \\frac{n}{(2 - |u|)}\\Big|_{n=1}$ at $u=1$, the line $y=2u \\frac{W_{20}}{\\lambda}\\Big|_{W_{20}=2\\lambda/2}$ intersects the curve $y = \\frac{n}{(2 - |u|)}\\Big|_{n=1}$ at $u=1 \\pm \\sqrt{1 - 1/2}$, and the curve $y = \\frac{n}{(2 - |u|)}\\Big|_{n=2}$ at $u=1$, and so on. As we can see, there are odd number of intersections of the lines with the zero loci curves when the defocus error $W_{20}$ is an integer multiple of $\\lambda/2$. This corresponds to an odd number of zero-value OTF points in the OTF plots when the defocus error $W_{20}$ is an integer multiple of $\\lambda/2$.\n",
"\n",
"\n",
"Note that we only found the abscissa ($u$ coordinates) for the intersection points above."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_1dRectAF(w20LambdaBy2=[0, 1, 2, 3])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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coEYBg6heDxoBrG27Z/jd9hYaodebDWqtiougEQTZc9Lr9ZxOTSW6Yyc0Go3F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Y2NhOl4Xe4a/ATltmGxWBgxYgSbNm2ira2NixcvcvLkScaN8wz5S0lJ4dixY9TX12O32zl8+DA2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N6DEX422KP7VJh51/rppiEwnZl5PVlGgUFfkYJ7Iir/QbbgbdMCzlRwhITu7B9OnXsmdPJavW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ShwHJ+OCoTNQJW04Nutv6lIoWlQVlrGwYMHg78Ukf9rAOnp6UybNs1/mMlJOIiN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\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xjzEYDPzlL39hw4YNLF+u3dHy4hcfMzFjNtl3/wGDYFA2wHEQFD8JQVGL+hAEqKu7REhI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\nQCBYDf8fBW8yoEIA4qkAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x8bd9e48>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plots of AF and OTF for which $W_{20} = n\\frac{\\lambda}{2}$ for $n=0.5, 0.99, 1.4, 3.6, \\dots$, i.e. $n \\in \\mathbb{R}, n \\notin \\mathbb{Z}$ (the particular values were arbitrarily chosen, and the $W_{20}=0$ line is plotted for comparison). In this case we find that there are even number of intersections for each line with the zero-loci curves in the AF plot. Correspondingly there are even number of zero-value OTF points for each OTF with focus error $W_{20}=n\\lambda/2$ for $n \\in \\mathbb{R}, n \\notin \\mathbb{Z}, n \\neq 0$. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plot_1dRectAF(w20LambdaBy2=[0, 0.5, 0.99, 1.5, 3.6])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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VAJa23TMct76GJuh0JoNai+IiqARBsk06nY5zKSlERXdEpVKZbf6VK1dZu/Zn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/HII4+QlhZZUpkeVYEG3q7dzNL6Uj5u3IldSMyOHc6LGQuY7R5KggjxD6oVA9Afs2IDkoTL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\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fF1auW8+O5uk02Iew+YmRLLt/KD88O4a+gW48sPQIFfXdb0ZnjMCBUwkcfhePrivEXtDy\n9QNDCfKUrrgmCAILBkew4qFh7M+r4sEvj/QaSREEgdu8B5OT9GdeCp7On8q+Je7k7/h7xQ6aND1v\nLimFKnUjC/P/w72Fn/FC0C2sjX4cZ4VDr66xLbuMvoFuxAbIP7hsLD+Hq4H3BCDKT2e46/M0bEWr\nWosoclU9KD0N8SqsaiLSqMqZW1AcLTUXUbfKv+6pSUHsPHup1+5NPW71Tiej30ucaSkj+fQr7Kg3\nUxmyF3CkqYBp5//GqLN/xkXhwNGE1/hHxB142cmrAicH32aV8shXR5mfHs6Se9LxdZP2GioUAo+N\njeXFm/vx0vpsvj9VzoC7/4G9S+95Od/cmsO27DKW3jeEbx4ezvanR3PnsEi+UPyGdTt6J8T1+iAo\nZgwZF1dXIiJsL0fo6OjIU089bZsHRRB4/dtT5NUreHNqJL6uTl2MEEsQRZFDhw4xIDkZF2fnrkf5\nBvNLXeOuPXvw9PQkNvZK8p6xmKHTRW+4NTTUdZ1OiowYH78bWIBaUTBpiGgJggB79/5McHAwM265\nRXdjSsVnGa6lUKBqa2PV6tXU1tYyZ86tCB1d4o2HmxrGhgREt46HmxuTJ0wgI2MfO3ZsQxQ1srmG\nKEJ9QxObt2yhpa3NtNKWsYARu9GHQVkzmruIiiCKgrTVbc7yN9gcc14NY5W7eFMQdIUBpBSVYlMA\nWi2558/z048/oJVhURveM97efmAYGnatWIXca7yBXzSampr417/+xRNPPMELL7zAoUOHzI79/vvv\nWbx4MU8++SRLly6V3delvLycTUcPUK4IYONTkzsNUx9XB/65aBBeLvY8+tVR2jW9xya/c5tPJb48\nbbccL2frBqyypogVDw3jdGk9Dyw9Qqu69wxBJ4U9vwu6hdz+b7HIZxivlK4nIvs5XivdQHFblex5\nLMX8t2nV/PPSD8SffIH9Tbns7Ps7Xg2ZjaKXw3ZEUWSrRPUuazo2lp0ziesPcHfEyV5BcXX38lD0\nxrqLjQTFltwJFwclLT3xoFQ1E2nkQdFX8mosM98PxVjHiYmBtKq17D5X2W1dzCHFJYLjCX9kokci\nU86/y215H5Ino/monH1s06pZWX2QUWffZHDO/9GsbWNP3xfYFvcsKb1Q6toQ350s48kVx3lkTDSv\nzkjETqmwquPCODXjtPv4wvl+6jxsK55iCRszL7JkXwHvzk9hTF9dpUKFQuC12QPp71bFftVovvl2\nW4/XuT4IylWAQqFAoT9SN5eXYYCVRy6w9NAF3p6TyIT0BMCy0W5oc+bnn6e2tobBgwfLVU5XLezi\nRc7k5DB+/ASUSqVsslBVdZnPPvuEgvw804YgFsiCYQ6IteszFhUEuOWWm5k9cyZKQZDF3JpVKlas\nWoVKpWLhwjvw8vI1+1YYe8DWrVvNhZIiBLHrpiQnJXHb3LmcPXuWtWtX097eanICbwhDR0Fbm5qL\nFy+yYqVOJ6uGrBFJuXjxAk1NjWbXMoYutAzTTveWSJEBw9GXTLCFFHXspqkrygoGp6dTW1dHfn6e\npJPJ3Lqg9zHJIAWGb4a1m+8XjhselN7B8uXLsbe355133ulM7C8tLTUZd+rUKbZv385zzz3HX/7y\nFyorK9nUkUNnDTkFRRxQj2BauIoIv66n7q6Odnx8Vzqny+pZmlHYG5fE0aJqPtpbxMuTw3G4sJeC\nH+WdVvYL8mDFQ8M4WVrH0ysz0Wh79zMSYO/BW2HzKU5+l8WBU/mochdR2c8z7uxbLLm8h8r2epvn\nvNBWzV/Kt9Dn5PP8vwureSxgPCcT32Cse79e1V2PY8W1lFSrmD5AfkU6Uauhsews7qFdS6oLgkCk\njyuFl7tHUPShV05XqYqXfu7uhng1tLRT1dRm4kFx9AzC3sWLxtIzsufycXXgphhfNp0w/Wz2Brzs\nXPgi6gG+j1vMudYK+p78PQvy/8XOhjM290xp06r5of4UTxZ/TUT2c9xd+Anh9j78HP8Cu/r+jlHu\nfa1PYiMO5Ffx5IpMHhgVzeLJ8bLCtDRtKrKWPsmDEReIC/bmsa+P9cohSU1TG69uOsVDMVoM1QAA\nIABJREFUo6OZKlHl7ounFtAu2LFs/9Eer3X9ERRjq7gnMA5pMYMDBdW8vOk0iyf3Zc6QGF0sv0xV\nARwdnRg+/Ca8vLxkWyaiKPLTzp3ExsYSFdXHqiFzJYRJw3ffbSE4OJg+UVGGE1qUV2s0qNVqncEs\nk9N0sXEFEScHBxzs7a0zN4UCLfDN2rWo1WoWLlyEu7unLALW2NjIypXLqaqqwtXFRZKARYWHc+fC\nhdTU1LB69TdgpSGnXtTDw4sFCxbR1tbKN6tX09LaKu9eE3UNFX/66UfWr1uDWt3WZW+scQ1Rqwuh\nUqvV8tiGAbSiBtDaRIqKi4v5dssWeW1KO/bXy8ODuLg4jh69Eptq08fPUiyYsYL6f3tKUnrzu8JG\n3CAoPUdrayvHjx9n1qxZODo6EhsbS0pKCgcOHDAZu3//fkaOHElwcDAuLi5Mnz6djIwMWesUNkOw\n4gLvP3aH5N+j/Fz57bhY3v/hPJcaehbq1dSq5plVJ5icGMSicenEz36FvG1/o7bwuEU5fTx930B3\nPr93MLvPVfLiumyuRg8bLzsXXgieTsmAd9ka+wyhDl48UfI1AVlPkXjqJR4t+pIvLu9lX+N58lsv\nUaNuok7TTEBiJJnNxayuOcyLF9cwLOd1IrIX87eK7fzGdyT5yW/zfyFzcFNKh7P1BjYcv0j/UA/i\nOvp6GEMqL6G5shBNWzPuYaYFOSJ8XSiq7l6Ilz706mrmoDg7dD/ES1+hzJigCIKAe1gS9RfM94iT\n0nFOaig/nK6goaX7eUzWMNEjiWMJr7Em+nHK2usYf+5twrKe5a6Cj/l35U/sbsghr+Oe7JPcl7L2\nWrKaS1hVfZCXL65lRu77+J94kknn3+FAUx5PBUyiOPldlkc/wki3vrKIg604U1bPg0uPMH1AML+f\n2q/LGpbe67Mb3qCt4TKpd73DBwsHUXi5iX/t7HnVvb99fw5neyVPT4yT/LuvhzPTwpo5qR7I379c\n0aO1rusqXj2Gte92QaC4RsWjq7KYMSCYx8bIr9hliIiICCIjwq1bIwZW7blz56i4dIlbps+QtYb+\nnj906CBVVVXcd++9ujNrS94hAwv6xx9/pLaujvnzF3RuixwPSudnTSvqLG3jBBnjwR1rKgSBoUOH\nERISgquru8Wt0YvV1VWzZs032Nvbc+fChbi7uZkymo5r9ffx0ZGU2loUgi7dw1A1c9fm5ubB/PkL\nWbVqOd+sWcOC+fNxdHDoMrfUeoJCwZwZM/hy+XI2b97M7NlzJMPVDKHPq2lta2Xz5s0k9OvHpIkT\nryTLSLE1/euCgCiKrF69mqg+fRg8eFjnXllb09HRiVOnTtE3Lo6+cXGySUH6oEEsX7mSS5cu4e8f\nYH4RSzAsAGA1NhJsKBzWCbVaTXNTI55Wyo/fwC8bFRUVKBQKAgKu3GthYWEmjSwBSktLu/zYh4WF\n0dDQQFNTE66uribjDVErejMv2deicfLAqGhWH73AX7bl8Lf53W8A98aW0zS3aXjzVl2BlbCbFlF1\n9meyvnyS4c9vwd7Zeox5aoQ3H92Vxn1fHMbb1YHfT7s63gg7QclUz2SmeibTrG1lf2MeexrPsrvh\nLMurD1CvVUnKOQh2JDgFM8Y9nteCZzPRIxE74eqXsG7XaPk2q5THx9lWgrXh4hkUdg64BkSb/C3K\n14Vjxd0rkqDvT/JLzUEpqmrGXikQ7GlaoMA9NIHafNtO0KckBfHS+pN8d7Kc29J71sDQEpSCgjne\naczxTiO/9RLrao6ysyGHV0s3UKmW7p3lKNiR5BzKAOdw/ho2n+meAwlx8L5qOupRUt3MPUsOMSjS\nm7fmDZBdha/ixDYuZCwn9cHPcPIMJBx48ZYEXt14iomJASSFdO+37XRpPV8fLOLvt6darE74l0fv\nJePlj9l4poWBEd3vj3WDoHQTQkdwjSXUt7Rz/1fHifJ14c9z+iMIQmfzdxlRYUCH/Y/tp8ExMTHM\nmzsPb29fi54Fw5P66uoqDhzIYPy4cVe8NZbQ8YN8JieHzBMnmDt3nklyupSIzsbUV68SZO2lMUQE\n4uP7WT1J1h9+X7pUxtq1a/Dx8WHunDk4WfPUAO6urri7unbqKgpXQtek7GP9dIYk5eSp06SlyjNI\nPDw8mDdnDstXrWLXrp8YP36ixagl/et2do5MmTKNDRvWERERQXxcx8mGFbYhALGxsezavZu4uL54\ne/uY5Rr6/wsC+PkF0L9/Mjt37SImOlrXG0cGwkJDCQwMJCsrk0mTJqOx8LtokL6CQgENDfW0tqgI\n8DfozG3l+jQaNZnHs4jqE4Onp/wfk3PnzvHdd9t49plnusNvegVy+NcNWEZraytOTl1P252cnCR7\n3LS2tuLs7NxlHEBLS4tVguIjVPHMoodQqVS60E4jODs74+zszKszErnviyMsGhJBepSP1fHG2Hqi\nhBWHSvj73H7Q2kh1Rx569KxXOf7BXM588xLJd/+jkygZzn/q1CmSkpI65x8V58/fb0/lt8uP4e1i\nz8NjYmzWx5bxLgpHbrLvQ6pTEE85jUEURS5rG6nUNNJip8XB0YHi3AJGJKYR7uCDnaDsnL++ps7q\n/LbqIzV+T24Ndap2JsX7mIzVjz9y5EjnPupRXXQCt+B4FEp7k/F+TlB4uZHq6mqb9amo0oXECZp2\nQP71nj9/nqFDTXttSI1XihoaVdJfxNb2s6i6iXAfF5QK0/tN4RlJQ+lXVF2uxMXVzeR6MzMziY+P\nN5l/TKwXa4+WSBKUq3F/RjsGsDhoGouDpqFSqbjYeJkKTT21WhX5BQX0i44j1MWXvh4hJiT5an5e\nAEqr6rjz0yP4u9rxp5v70FBXazI+MzOz82BFP39rbRmnV/yOgKELUQanoFKpcHZ2ZtGQCLZll7N4\ndRYbHx+Bpr3VJn2am5t5eV0mKaHu3BTm2HlPS41XKpVMjXbhs7ye5eFctwSlubmZb9auZcb06Xj7\n+MmOCKmrq8HeXomHu7QLWA+NVuTJ1dk0tWn4+r7BnXGkcqLCDBOXuwulnT1RfaJt4jX79+8jMDCQ\n1IEDpct96WHgzaipq+O7HTsYMmQI0dExstc7cuQwZWUXmT1zpu4FmZ4aQ7eLXOIlCNDSoiIiPJyb\np03DXqmU9mYYQv+6Ph9GoeiYz7SXjJQh7+bmyZ133ourq5OOCRiWAJNSusOrERQQwIxbbmHdhg14\neXmRmpouaz9jY+NIS0tn23ffERgYqGs+qrfuzSmpUDBo4EBOnz7Njh3bmT//duS6HEaOHMWnn37M\niaxsBqUMlCUjCAKzZszA3cOj4/9dnT3G0BMiUYQ9e/bQ2NjAwgXya7grFAr2ZWQgKJSkpHhbJET6\n9QDc3d1Rq9U6A9ehdysEycUNgtJzODo6mpARlUplQlqkxup/uKXGnj17tosXJsJV9zN69OhR9u3b\nZzJ+xIgRjBw5kvH9ApmYEMAfNp5i8xMjrY43RFVjKy+syybRoZqcn9aQ81PX8cl3vceRfy7Ct99o\nQofeZqJPQ0NDZ8iafv6bk4P505xkXliXjZeLPaGtxbL1kXO9to7PFFT0cfSXPb639dnRGE6IUknJ\nuZNEBkiP3759u0noX5r2EMF9EiTHZ+/PpKqpDx9+/BkOgtYmfUra3YAozmSfIGjMKNnXGx4eLklQ\npMbnNwVj7xloMtbS/Hr9iy53LTFsON6pvZqE9haWffQ30sfcIvv9UrS7cbAxiou1KkK9nK2ON9TH\nVv2tjW9oaKDZPR/nESOwG2lKmK6mPk2tau76ZD+19c2Mc8/n66UH5c2/92fiqjahEB04WuSG+Mkn\nneMFQeAvc5OZ+v7PfLgzl8HOl2zS/1/fHuD4hWbmuefx6af7rY4flxDDroLDXL4c3iWfLz4+nvh4\neQn71y1BqW9ooKKiAnuDBnBy7OOMjH20trYyb+6tFud/c8d5DhbWsObBIXjYi1y4eJHgkNDuxSga\nu1yklDTpgyHIdkroxaZOmUJLS4tORxmMTa3RsHHzZvz8/Bg5YrRcTkNu7ll2797J5EmTroSRWVLM\n4BqNmzBauy7QeaCio6KIjoy0LYna0G3QMaEgCKhULTg6uVgNh3JyctYtR4enSBCvdIG0sMdx0dFM\nnjgRD0/PTgPeUB0prgEwatRYLl64wKZNm7hj0SKdZ8PKdSoUCqZOnswXy5Zx+vRJkpKSrV4XgJub\nO6mpg9h/YD/Jyf27kj4p11IHAfP29Ox4H62HkxkiPr4fGzas050GyWygKAgCwcHBlJeXAakWI986\n1EMQdNcGuh8oJ19feQrewC8OgYGBaLVaLl261BnmVVJSQqhEefiQkBBKSkpIS0sD4MKFC7i7u0t6\nT4x/YD08dgGQlpZGYmKiyXjD08VXpicy6b09LD9UzDwZ4wFEUeSl9SfxdHHi43ummlR10p9gRk9+\ngjNrX8UrahCugTGy9Fk4JILa5nZeWJfNe7f158EHH7Sqjx5y5rdlvHE8fW/Pb2l8VVMbn/z7GG9M\njyUtVTq8yNz8J95bg3uoqT2QlpaGe3A0335ynEmzb6dvgKtN+v90roptG88xdHCaTfrYsj+NOws5\nXSGdxG9t/uLqZuKD3CXHazXtHP/LBqaPHkhomqn+KSm6k33j+dVakWMfH2fV4RKendQ12fxa3g//\nzfEt7Roe/PII9WolXz0wjFCvsWbHG35m0tLS8KzYTUVVHYkPfcVovyiT8WHeLrx4cwJ/2HiSbx5M\n58EH5enf1KpmTa6WealBvDDpJtnXuzQqmtzcc50l/23FdUtQGhoaEAQBFyvue2OUl5dJ3mSGWHn0\nIksOFPPv2weSFOxB9pkzfP/DDzzxxJMolfLcIlqtFrW6HSfH7p3g2sppBAEc9Enqlo6aDdja6dOn\nqa2t5d57f4NCqZRlbF66dIktW75l8ODBOk+NYSyPGWhFke3ff0/ygAGEhIRZJEKG19V5faAja5a8\nNIbuKgsGdlllJStXrWLWrNlERfXp8mdjEf1UXZLdFQrzBMnAs5E6cGCHgCi7A7tSqWT6jJls2LCO\n+oYGvL29u+ovFSMGBPj5MWTwYH766Seio2NwcnIx69kw2ArS04dSUlJCfX09vt62xeMKNlwXQGRk\nJEqlkoLCQhITEq4QPYPrkEJQUBDnzpkvdykFfePW+oYG/P9LBOWGB6XncHR0JDU1lU2bNnHXXXdR\nXFxMVlYWv//9703GDh8+nM8//5yhQ4fi4eHBli1bGDFihE3rmQuNMESkryuPjI7mne1nuSU5GB8f\n6XAiQ6w7dpEdp8tZ/chwwoLMj4+e/Fuqz+8j68snGfrMOln6ADw6Noba5jaeX3uaJfcOZmScvCal\ncuf/NYxfmZWLl4s9tw6JxcFO+jdaav7W+kraGy7jHmqaIO/s7Ey/SAcEAZpEB6vvtcn89s24Odnj\n4iLdR6M39sfPswrVBekkfmvzl9apdF3gzYx3D+mHWFtsdg5z898+JIIVh4p5cnwsdgb20i/5/umt\n8a1qDY98dZSz5Q2sengYsQGWI3UMobpwgrK9n5F0+9uE9B1kdtzCIeFsO1nGixtz2PTbkWbvd0P8\n/cfztGlEXpoxAC8XeTaps7MzYWHO5Oba9vtriOuvilcHGhoacHV1RalQyg7vam1tpbq6mqCgjqQf\nCcEDhTW8/G0OiyfEMjUpCBQK8vLziYyMxM7OzuI6hkWD8vNz+eij/9DW1ib/ojomsMXwA50B35nn\nYi3h2cCjkZyczD1339OlgpY574lCAa2tKjZtWkd4eDhjx4y5YoVZEBIFgR0//siZnByrSeOG+3eF\nnJg51e8mgvz9SejXj3Xr1lJQkGeyniFM+AACLa2tulwWS9WhjGLXpK7LnIiHhzf33HMfXl5GhEFK\nyOA9HjF8OOPGjsXZ2Un23ePi4sJdd92Nj69ftypdybkuPezt7YmIiCAvr6MKiYzKaIgiwUFBVFVV\nyf4ciSIolXa4uLjQ0CCdMHktYPjRuFHFq/tYtGgRbW1tLF68mCVLlnDHHXcQHBxMVVUVTzzxBDU1\nNQAkJSUxZcoU3n33XV544QX8/f2ZMUNegZHycts6YD86NhY3Rzv+uj3H6tiS6mZe23SKR8bEkBZp\n2cBVKO1Ivut9VNUXOLf57S5/s9Yv4ffT+jE7NYSHlh0hs6R7Sd09hS39O3oTWq3IykMlzEsLt2qs\nGevYcFFXqco9RLrQgKOdEn83R0prpQsCWEJjSztujrafIduyj25OdjS2yuv3YwitVqSstoUQL/MG\nt3toAvUXpSt5WdJxweBwKhta+SnHeq+Sq4lrfT+qNVqeXHGc48W1LLt/qCxyotexramG7K+eISh1\nBiFD5lqU0YV6DeBCjYoPfjpvdY2c8no+21vAizcnyCYnvYXr14PS2Ii7lTwSY1RU6H6IggIDJd0T\nxdXNuopd/QN5bFQUABqNhoKCAsZ1uLjkRhhlZh4jPDwCBwcH3WmxOQGDsK4T2dk4OjkRH2+5Kotx\nfkYXO8/aOl3cAQKeXt5WRfQ4fFhX3rNLI0Yr+HnfPrJPnuTWW+cSFBRitbiAIMD58+ewUyqIjYm2\nnPdhqKRxwo+ZWCpBEJgycSIKQWD9+vXMmjWL6Og4s+FKem+DTlzDl8uWkZiQyMibhlu9dr3eQkdo\nmSgIciLEOosxAAiGrhCpfJQO2NvZMSA5WVfdSwCFDK4qsXDXKlvmvERara6RpQzo9w8gOjqGvXt/\nRqvVorBETgyEggMDEUWRS5cqCA2VXxkmKDBIfvzZDfxi4erqymOPPWbyuq+vLx988EGX1yZNmsSk\nSZOuuk7ODkr+MCORR746yuSkIMbFS1e0U7XpTlOj/Fx5eqK83grO3qEk3f4XTnz+GL59R+CfNF6W\nnCAIvDknmTpVO/d+fojP7x1MasTVr1L0S8Duc5UUVzezcIjtlaPqS7Jx8Y/Czsl81/kQL2cu1tpe\nXrqxVY2709U10dwdu0dQLje10qbREuJlvuSze1gSl7K/R+z43ZSLYE9nxvcLZNmBIiYnmfbZ+F9E\nS7uGZ1Zlsi+3iq8fGEpiiPyO76JWy6nlz6Owsydx/huy9jrUS1e04/+tzSI1wovx/aTzkFraNTy7\n6gRpkd7MHWQaGnu1cV17UHQERf4Hp6ysDE9PT0mXa32LmvuXnyDKx4U/z0zovEkuXLxIW1sb0dEx\nsk87q6urKSoqIjUlpWtdWwvMpq2tjd179lBVVWVywmrOztKFkakBsetgS0IGN78ocx293Tpq1GgW\nLligyyEwJ2RwrH40M5P9Bw5w8823EBkZbZacGJ7EFxbmsXnzRi5cKNFfpHnljJsbSj2MIYoIosik\n8eMZOGAAGzZsID8/V1YzR0FQMmzYCPZl7OP4iRPyGisa6F9XV2fRcWAiYni/WRPqFBaxlrwkdbpv\n1ZQ3uq807e1kZZ2wyUsRHR1LcnIy7e3y6+S7uroydsxY3N1sO4yYN+82m/oJ9DZueFB+PQgKst2I\nmpIUxKIhETy9MpPiKtMcAI1W5Pk1J6iob+Gju9JkhWHoEThwGmE3LeLk8udprdOdQsu5l+2UCv6x\nMJXh0b4s+uQgO6/xCfZ/6/P27915TE4MNOmILgVjHeuKMvGMtKx3qLczF7vhQWloVXfLg2LLPro5\n2dHYYjtBuViju55Qb/MeFK/IFNqbqlFVldis4wOj+vDz+cucvFhncdzVxLW6H+tU7fzm88McyK9i\n2f1DGBjuJVs2JSWFwp0fczlnDwPu+QA7J/m/c7elh3Pn0EieWpHJ2XLT32F97lt5fQvvL0jpXv50\nD3HdEpQxo0YxduxYm4rbOjk5kZCQgGAkpdGKPLkmm6Y2NR/dPqBL59e8vDwC/ANwc5fHiAUBTp7M\nxsvTi6hImSXaBIHjmZlotVrS0uRVfRIEyMrK5KuvvkSUU07YIFEcQejo7q2DJccEXLG/7ZQKPD0s\n7IOBN6O+sZGdu3YxYcIE+vUzn/Nj6AApLi5gw4b1pKSkMGb0aMvJ9x3EoKCoiMrq6s5u5V26snfx\nFhlPITBx3DgGp6fj5OjQhSSZeKUMkJiYxJgx4/j+hx/ILyiQJwRUV1XxyScfk5t7rpM3mRPp9Hyg\nCyuTev/M40p3eatDu8Cg27sMIUEQ2Pvzz5w8mSVXBA8PD8aOHY+Do22N2oYOGYyXt211363TtKuL\nGwTlfx9/mJFIlJ8rd3x2gFOlVwyxplY1D355hJ9yLvHvO9MshtGYQ/zsl3Fw9yX762etf78bwNFO\nyT8XDWJeWhj3Lz3Mf3bncTWaOf5ScLSomkMF1Twy1vYeZaIoyiMoXs7dDPFS43aVPShujna0abS0\nqm3rhVJa24KDUoGfq/mCJW4h/VDYO1JXZHuo1NA+PqRGePHv3T1vLPhLxvmKBmZ/uI/i6mZWPzLc\nZq9ldd5Bcre8Q/ycV/CMkFdN0xB/mJFISoQXt/0ng4y8y52vt6o1vLThJOuPX+CDhand+g7qDVy3\nBMXd3V3X68MGpKZ2GL5G0Ffs+mRhCgHujl2M2sCgINIHWycNejtVFLWcPn2S/v2TrDPWDqF2tZpD\nR46QlpaGg4PuC8OaN6OlRcXevT8TFxvbNVzGgmB9YyM1NbVdKmnJKvOrn7fLSb0F8oDOGL3n7ntI\nTU3vImJurZKSYtavX0f/pCQmjhuHIEPofG4ua9ev59Sp012NPlGgXa0xbzXrQ6+AsaNGER4aamie\nmyUN+vnT0gYzYMBANm7aRGVlpXkh6BTy8fYmNSWFLVu2UFNdbZHTGG51e7uaPT//TH1Do+W8DaNw\nNqGzT408MVGEdrUajc5VZN4D1QGFQkH//v3Jzs5GEEQ5IldgGEomFZ4nJULPynbfwA2Yg605KHo4\n2ilZ+pvBRPu5ceu/Mnj0q6P8fm0WI9/6iVOldXzz8HAGR1lPopeC0sGZAXf/g9r8wxT+9LFN8fRK\nhcDrs/vz+uz+vLvjLPd9cZhL9baHKNmK/0YOyr935TEs2odBMg1DQx1V1Rdoa6zCMzLVokyIp1P3\nCEo3PSi25qAANntRSmtVBHs5WWwcqFDa4xGWTF3RcZt1FASBR8fEsC27jPzKRpt06y1czftRFEW+\nOlDEzH/uI9jTic1PjLQpIR6gtaGSY58+SuDAmwkfcWe39LBXKvjsnsFMSgzijk8PsuiTA7y8IZtp\n7//MtydK+fSedEbEyiuacTVw/f5kyzrltX64vfLoBZYcKOa9uf1JCnY3EU5KSqJ//+TOl60dRrW2\nthIaGnqlGZSMhJXMrCzUajWDBqV3MU4t5bhnZOzF3t6eYUOHIocBiKLIdzt2sGXbVuPUGxMYOh90\nzw38LZaEDARFQYGvn79ZEcP51eo2Nm/eSL/4eKZMnqwjJ1bWyTl/ng2bNpGSksLIkaO7EJSGhkY+\n+fQTzuXm6nIlLIR7GW64IIgmEWLS943A+PETCQ4OoaCoyHTDpCCKjBszBn8/PzZv3ohWq+4iZkYE\nUdQ17trx/Y4rJ6HWGJQocvr0afbt29vF8WLp8tVqDZ999iknT56UVkZirQHJydTV1VFUVChLxATd\nTMy3RrpMBP4LuOFBuT7g5eLA5/cO5tUZSSgUAvmVTTw+LpZtT42mf2j3uj3r4R7Sj76zXyZ367u0\nX7KekG+MO4ZGsu7RERRXNzPpvT0szShErfnfueEOFVTzw5lLPDk+rlvydUWZKOwczCbI6xHi5UxF\nfQvtNu5dY8u1yUEBbM5DuVirIkSig7wxPCMHdsuDAjAxIZC+ge78dftZ64N/RThTVs/CTw7w2qZT\nPDY2hi/vG4KPq23J51qNmuwvn0ZwcCFxwZs9Cr9ysFPwzm0D+Pzewfi5OZJ3qYmJiYFseXKU2dyU\na4Xri6AYxq10JHnbImaMAwXVvLw5h8XjY5iaIJ3oqIfcQlLOzs7MmjX7SqM9vbBU4kUH8vLzSUlJ\ntVjCztAoq6ur5sSJTMaMHq3rA2PN3SIInM7JobCoiPHjJ3SqZE5E/29W1gldwzNjAmTuWgzDxyyI\nGEcsOTo6sGD+fKZNmaIjJ1J7ZkAATufksGnzZtLTBzNu3ASM7wMXF1dd342NGzl1+rR5xmG4hp6k\nGHgejHU1FBEEJXPn3kZ6+hDpMCxDdAgpBYEZt9xCbV0du3fvMtk6KSiVdkyZMpW8vDxyzp2zGrqm\nX0+tVrN//37q6mpliSiVSmJiYjl46BBa432XuBYAb09PIiMiOHEiS3pSI8gpLmENtoatmX5nXBvc\nICi/HnQnB8UQCoXAoqERfLhoEN88MpwHRkXbbLCYQ/iIO/FPnkjTz+/SUlNqs3xymCdbnhzFPTdF\n8ebWM0z82/9n773j4rjO/f/37MIuvfeORBEgEOqoIsmW1SVb7iXucezcOMnNz/nmpucmN8lNrlNu\ncuM0x457iWXZara6rIo6KiABklCh9w4L7M7vj2WX2d2Z2aUIIYnP64UQcM4zzzkzA8/nPO0L3j9y\nla6egYUEuYKRzEExmUR+tqmQO9PCmD2AE2Kpjs1X8vGNmYjGTf1eRQd6YhKhqnlgXqg2Qy/euuub\ng+I9BIKiln9igX98Ni1lhZh6DQPWUaMR+NHKdD47W8XhS/UD0m84MNzP49nyZr727glW/GEfJhNs\n+NpcXrwj2aaUsqso+uRnNF89xcznX1Mt0OAqBEFgQWoYf3h4Mu89l8P3lqcRGyRf3nokcfsQlGE2\nMK7Wt/PC+6dYlRlhrdilBKkd64J97pDj4gDpMbBGw4MPPGCt2e+KAbd3715CQkLM/VxcKNHU0dnJ\nzt27mTZtGhERkYq59FK1SkqK2br1cyrLy50vXmL9in2b4WwdtgazSFhIiDlUTU0xQaChqYlNW7aQ\nk5PDvHm5iKIgU1RAYP78heTkzGLzli0UnDunbKjKJJmXlBTT2tri9JReWjZZtD/aVzDs/f38WLZk\nCYWFhXR2dihOkaoVERHN1KlT2bFzp5kwOnsPRJGJ6ekEBQWxd+9e9bGS602fPoOs7aEOAAAgAElE\nQVSmpiaz58nF3JqJGRlcvHiB7m6DM37m8H+XFRsEent7qampwSjtCTTCJGUMYxgqBEFg4iO/Qe8f\nzsm/P0uvQb7nhRo83LV8a3EKu15aQG5KKD/eUMD0/9rBf6w7zY7C6kFVgbrR+OhEGecqW/jecscO\n8K7ClfwTwNoRfaBhXm2GEchBGUKIlyt5Cf7x2YjGblrLzw1Kv9lJIdyZFs5PNxXedN47k0nkfFUL\nf95zkeX/u4+Vf9zPtYYO/vLYVD74Ss6AKnVJcXXfm1zb/zZZj/8Bn0jXqvvdrLj9ygwPg5HR0tnN\nM2+dICHYtmKXVb7ks7OeJIp26QAMK0EQcHd3Vw3tkl4rPS0NLy9P21Ao+6NYiVI7du9Gr9czZ848\nG0PRfrjlc0tLC1u3fsaUKVMYPy7RpWSVI8eOERwcQuK48ar5LdK9Mue3uMD+JJscFBTEY48+RnhE\nlOI1zF8LzJ5tXu/mzZvRaDSkpaba7pNd3gaASTRy6NAhent7eeSRR9HpPNBobK9jUdMSmmUyIYnl\n7cvhkNurPoVTk5NJiI9H5+FpmWGdYj/V8r05c+ZTUlLCrt27WbF8eX/JYQXFNILAgtxcPlq3junT\npxMeHumwZPtt8PPzJy0tnby8PFKTk13yTyYnJ+Pl44NO5269p1LHoRyqq6vZtWsHD95/P26WDvZq\neyaKHDl2DEEQmDp1ugtaQXNzE//85+t8+dlnCQoMVJZ/neCqx3UMNx6DzUEZKbjpvdDN+iZtW7/P\nmbf+neyn/2IuPz5ARAd48p9rJvLvi1PYcKqCDfkVfHjsGiYRxoV4kxHtz4QIXyL9PYjw9yDS35Mg\nbx1+Hm4uhaDk5+ePiBflWkMHP9tYyLPzxjEudGCnzxYdTb3dtJadJX7+U07n+Hu646XTUtE8CIIy\nyBwUV/fR4qEZKMmsaOokWqXEsAUegdHo/EJpunzShswNRMcfrkxj6e/38cddF/j3xSNnkDvTURRF\n2ruNNLZ3U9/eTV2rgSsNHVyua6e0rp0z5c00d/YQ4efB4vRw/vveTDKj/YcUjlV3fi9F639K6prv\nEZqxaMTemRuFUUdQjhw5wqZNm2hsbMTPz48nn3yS5OTBxYgqoaysjM+2buWJJ57Ezc3dpTlnz54h\nMjKSoMBAvv7BKdq7jbzzxBQ8pOUf7U/ZB/sgSpM8lOKbrLLNIVH2dqYU9gQoJTWln5w4CR9ram6m\npKSEe9fei5ubuypxAHPp4s2bN+Dn58dCSzNGFWKCIHDx8mV279nD0qXL+mTIb4tliiiKfQa9HZNR\n2yuLhwaBiMgoRb4khSjC7NlzAZGOjg4cLGjLIMlnjSBw79138/Z777F+/cfcd98DaDRu1v2R4TRW\n+7rvn/5fYEr3RxTR63Tm9Qvmayo1Vrdsj7u7jsWLl3DmzCmMJhNaC0Gx7JOMYuMSEoiLi+OLL/bw\nwAMPIQiCLNmSipg+fSZvvPEa18rKiIuJkTfsJV/r3d0Zl5CIKAgu2//e3l6UlZVRUVlJXGyso+tI\nund962xpbqa6ptZlguLlZS452tHRYSYoI4wxgjKG4YTWO4TsZ//O0T8+SPGGX5Cy5vuDNpQCvHQ8\nPiuBx2cl0NTRTf61Js6WN3O2vIWPT5RR1dxFe3e/59FdKxDopSPI2/wR6K0juO//0QGeJIR4Ex/s\nxUhUC+s1mvj3D/KJD/HiW0MwdpuvnsbU201AonLHbgsEQSA6wNNamtdVjEQOilYj4K3TDoigdHT3\n0tjR45IHRRAEAhKn0nTpKPG5zsmcHOKDvfnJ6nS++/EZ5iWHMG2QhSMGgs5uI1eaeqguqOJqfQc1\nrV3Ut3VT195NfZuB+rZuGjq66e61/SVtfp69SAj2ZunECKYlBJIa7jskUmJBa2URp//5NaJm3k9c\n7tNDlnczYFQRlMLCQtavX89zzz1HYmIiTU3Xp6Nta1sbLS0tuLm5tnxRFNmxYweLFy/mL3nVHC5t\n5KMvTzdX7JKDIHDixAnaOzqYO8+x6pcanHoE+uRbjWVn8ux4kgAIIo6Gvf2kvs8BgYF85SvP4+Xl\nreoEsXzOyztITU0NTzz+uPl0W87KkhCHxuZmNm7axKRJ2WRmZimm3fSvw8SmTZ+SlpbOhJQUdZIl\n+TAvWbn6mL2Na/kQBIE5c+YjCOYWIeYcE5kYJMn/fby8uP/ee3n73Xf57LPNrFy5GkEQHDwDUuPe\nZIKmpno2bPiUe9euxd++iaickW9lNtgQByVOkJAwjnHjxlnD4mweDhmCIggCC+bPZ8fOXXQbDNby\nvnJTLAgNDWXmzJnm3CaLUvasxn7xogkEDbiYE+bj40tQUBBXrlzpJyhOjJvQ0FDOFhT0GUHOr+Ph\n4YFGo6G9w7FHxRjGIMVQc1BGApZT1swv/Z5Tr38Vdy9/xt314pDlBnjpWJAaxgK7ZpOtXT1UNXdR\n395tPWFukHyU1rVz9HIjZQ0dtPYZx/6e7mQeP0xmjD+TYgLIGRc0rJ2rTSaR76w7w7nKFja8OHdA\nvWUssOxj44U8PEPi8QiMcmneYJo1mvuguHaAKqejq/DxcKN1ACFellC1aBdLzwYl5XDx8z8gbdg4\nUB0fmBbL3pI6vvLWcT74yiySwoaed2GBySRypryZo5cbOFvezOnyZkrr2hFF0GpqiQ7wJMLPg2Af\nHXFBnkyODSDYR0ewt55Ab/d+4u2lw30Q+SSuoL22lOOvPIZ/fDZp9/100Pt4s2FUEZQNGzawcuVK\nEhMTAQZcBthVtLe34+XlheDiqW1LSzM9Pd0cqobXDl7mzw9nkxHpp3rEeb6oiODgYMB5yJUgwIkT\nx6ivq2Pp0iUDWouFa7jibBEs7MSVRUsment5Y3KNB2A09nLHokUEBwbKEy2Jcd/T28v6Tz8lKCiI\nRYvucIlr7Nmzm9LSUmbl5GDtFKmy8F6TCTd3d1AhJ3ZqyXog+qvaavrbrNt7UiQTgwMCuPeee/jg\nX/9i//69zJuXq3oNAF9ff7RaLes/+YRHH34Edzeto4vC/muTyRyuIQgODjtVb410n1Seh8iICB57\n9BFzZTXRbr6dXAvmz1+AIAzsNNQSECkiKO6T9Hvx8QlcuXqVeS7KDw0NxWAw0NbWire389hfQRDw\n9PSio33gMfvDgTEPyhiuB8KzljDx4V9z9r1vo9V5Eb/gmetyHV8Pd3w93HEW+yCKIo0dPZTWtVNU\n1cqZ8mb2ldTy972XMIoiWdH+zE0OYW5SKFPiA9C7aZ1IlEeP0cSPNxSw8XQFbzw1g/EDDO2yR8OF\nPIKSZro8PmqAvVAMvUa6e03XPQcFzL1QBuJBsRAtV3tjBCbl0NP+E9qrSgadMyEIAr+5fxJPvn6E\nx149zNvPzhhwWV4palq72FdcxxfFtey/UEdDezcRfh5kxvhzT3Y0GdF+jAvxITrQ87qRDlfR2VDG\n8T89hldoItnP/BWNduCk9WbFqCEoJpOJq1ev0trayg9+8AN6enrIzs7mvvvuM5/GDiM6OjrwlukG\nL4XUsK+vr6eix4u/77rKS4tTWJoermrU9fT0UFFZSfbkyQ7kQYlAFBYWEhnp5CROotSBQ4fw8/Mj\nY2KWonwprCFeUkajdg3BYjIKVhJkgQzXsE5ZYAnrUgq7kgzetmMH7e3tPP74E2g0brLGrtRZdOpU\nPsePH+Oeu+8hIixM3QMkCFwtK2PTli08/NAj+AcEOHVIWa6tFpUkCuZ/zGRPA8gkTPT9PyYqijWr\nVuHt46MYHSbdS63WjdWr7+Gtt/7Jth3bWb50qW24l70yfTAZjZwtKCAtfSIajVaW09hHjSEI5jsr\nZa8KixYEwcyEZdYgp5L5/4KZpFjkqrld6FdBI6hG0FkRHx9Pfv5JDAZDX7ibOiyHBfV19fj4+Lmk\nkre31w3zoIwRlJsHoz0HBWzj6aNm3Iuxu5NzH/0QjbsnsXMeuWF6CYJgPYHWNl7hkbVmHdsNvRwu\nrWdfSR3bCqr50+6LeOm0zB4fTG5qGAtSQl2uMnStoYNvfZhPYUULf31sKrPGBw9a3/z8fLImptNU\nepyo6Wtdnhcd4MGxyw0uj283mEPkrncOCoCPh/uAkuQrmjoJ9tbZNKRWlR+ejLt3IA0X8qwEZTC5\nEx7uWl59YjrP/PMoq//vAD9bM5F7Jker9mKxoLvXxPErjewtqeWLoloKK1vwdNeSMy6Iry9KYn5K\nKIkh3jbREfn5+SSE3FgPRVdzNcdeeQydXwhTnvsHWp0tKRzLQRkhtLS0YDQaOXHiBP/v//0/NBoN\nf/rTn9i8eTN33333sF6ro6MDLwlBUXMoCAKcu1bNto54VmVF8tXccU49EBWVlRiNRuJi42WdCPZo\nbm6mqqqShQtyXQpZ6u7p4cjRo8yXaRopHd6/PpH8/HwyMtLRW8oKy1lA9mFRgmCjjhzBsplm3+tE\njZFpNEyaNInMrEn4+PjJJkZLh1+5cpkdO7axIDeXlKTxykyj73N1bS3r1q8nOTkZXz9/Wb5kp44V\n9mOllzKZzOPr6+oQEAntM3yVDPykceNsjHQnKSz4+PixcuVqPvroQ6KjosieNEl9giDQ1tbGzp07\naWlpZfacuU49HP1T7Rim0gQAQUSQqS+n5oCxekNcdO20d3Sg0bqh08knX0rvW0xMHABl5eWM7/O4\nqkGv0+Hr60t9Qx0JiYnqeveRl4iICHQukJ8xjOFmQ+zcxzD2dHLuX9/H1NN53Twpg4W33o1FE8Kt\nfRiqmrvYV1LLnuJa/ufz8/zwk7MkhfkwLzmEqfGBpEf6ERvkZT3tbjP0cvxKI1tOV/LxyTLGh/qw\n4cW5Q/acQF/+SU8XgQPwoEQHelLe1Ik0zEkNFsJwvXNQwNwLZUAelEbXKnhZIGg0BI6fSeOFPOLm\nPT4YFa3w0bvxzrMz+c32Yr790Sn+vu8Sj89KYF5yCNEBnmg05p5t9e3dlFS3cfRyA0dKGzh+pZHO\nHiOp4b7kpobyveVpTEsIdJlk3Qh01F3h+F+eQKvzZOpX3sDNY/Aeo5sVI0ZQXn75ZUpKSmR/lpSU\nxL/9278BsHDhQvz8zCEYixcvViQoRUVFFBX1N/Dp7u52WZf2jg68vM1JsM6cCW2GHn55oJEwT4Ff\nrs3sO01WOYYXBK5eu0ZwcDBe3t7OuAwAxcVFeHl5ERMd7dwVApwrKkIURTIyJlrXoKS/IJjl79ix\nnYT4OPRKCb8S/bsMBjw8PW3Copx5Hxy8MyoEy/IRFR1rM1SJPBiNvXz++RYyJ05kxvTp8gRLMqGp\npYV/rVtHTEwMS5YsAwRZtSxTWltbKCw8g79/ABkZGbIn6/bLOXjwAOXl5Tz26KP49z2v6n1rLPXc\nBBv5cpFbcXEJzJ49l127d5OSkoKXh53BbqeYn48PCxcsYPvOnSQlJxMaGi6rsz33qGuop6e7m8iI\nCOUYN1sJCC6GYNn8DXbGBgDRZOKNN99kypQpTJ+eo3D9fnh4ePDss88REODXfw0n+q9etQo//wCb\n4Ur6iyIsXbrMNifMBdTV17Nhwwbr16mpqaSmpro8X6rDmAfl5sDNlIMiRcLCL6Nx9+D8uh/T1VxN\nyqr/GFR1r+GC2klwhL8H90+L5f5psfQaTZy81sSeohoOXqznnbyrdBtNaATw0rlhqa4EkBXjz//c\nN4lVk6LQunDS7oqOl7b/Cc/gODwDo12eF+XvSUe3kebOHpfyaloNPcDgPCgDzkHRDzwHxdX8EwuC\nkmZycdv/WQnaUE793bQavrN0AvdNjeGV3Rf55ZZzfM/Qa0347+ox0d1XkjgxxJsZCUGsnRLNrPHB\nRLrQXNKCG+mZaLl2hhN/fQrPkHimfPkfuHvLpzvcyt4TGEGC8tJLLzkdM5CcE/s//Ht273Z57rIl\nSzCJolO7w2gS+cYHJzGi5Zcr4s1s2xkb6CMocXFxqrKl9lRxcREpKSlopJWVQPFa+fn5pKWlodPp\nVQmE+Roihw4dICMjw1yNyIn+JlHk7XffZeLETGbMmOkaMbF6TxTcLQ6xYBpz+WUV74z0Gjp3Nx58\n4AEC/P3NRr7K4I7OTj786CP8/PxYuXINgqBVJSdtbS188MG7GI1GUlNSmJiRbj3hUqpYBXDXXcv5\n17/e54MPP+TRRx7B29Ozv3SvittCEECDgAnlgmAAM2bkEB8Xh6eXd98PTLb7abegSVlZnC8u5rPP\ntvDYY4+j0Whthkph+frAgQPU19fz5BNPmHvIyCli973Ork70ek/rHik9rpb/dxkMeOjc5dPSJUxG\nEASSk5IoKipixowch6XKISAgwPqMu0JQYmJirE1AXYED0XIBIcHBLFi4cGCTFK49RlDGcL0RN/dL\n6H1DOPPWN+lurSXjoV85bTx4o+Gm1TA9IYjpfdWcDL1GrtZ3cLm+g7Y+wz7Ux4MJkb6E+CgUshkC\nGooPEpTk/BBFCovHobyp0yWCYvGgeA+CoAwU3no36765gvKmTjKi/Ad0jcCkHHo+/k/aKs/jGzX4\n3jNSjA/14TcPTKLXmElhZQsVTZ20dvXiqdMS4edBYog3wdfh/l9v1BftI/+1FwhKnkXW439wCOu6\nnTCqGjXOmTOH3bt309raSnt7Ozt27GDSpEnDfh0vLy98fHxwdjj635+f40hpA68/PYtZk83eCles\nm3vuvpucnFmKP5ca9gZDF/X1deqnrBIrqbKqiqrqarKzJyvawhbZYCY/9fX1zM6Zpc4G+nDq9Bma\nm5tJm5DmkjPkxIljlJVds/VqOPWg9OmMuv7WdQgiwUFB/aVxlVwtgjnvRKPRsHbtfeh0Otnhlh6C\nHR1tfPjhB3jo9Tzz1FPceccdIIpoBNGmeby04aJFlru7jnvvvR+NRsO6devo6e21UxrbCZKPy1dK\nEUWT4nCznhqiomP6vkBetkS+ACy76y6amprIyzvouIeOU5g7N5eGhgbOnDkDchTCTm9DVxd/+9vf\nuHih3xOqpL8omsM2X3nlT1RVVTuSVBkkJydTVVVFW1urqv6DwgC8INbhA5syhtsUN0sOihLCJy1j\nygtvUnt2J0f/7+FBdZwfDqjpqAa9m5bkcF8Wp4dzz+QY7pkcw9zkkOtCTk4cOUjjpaMEpw2sOmeE\nvwcaASpcrORlCbkabA7KQODrMbAQr4rmTqJc6IEihU9kKnr/COoKvxiUjmpw02rIiglg6cRI7p8W\ny8qsKKYlBA2ZnAynjq5ANJko3fkXTvz1KSImr2TSU392Sk5GWseRxqgiKCtWrCAhIYEf/vCH/PjH\nPyY+Pp7ly5ffEF0+PH6Vfx4q5Tf3Z5Me5Y/Dcb8KvLy88O0rE6s2XBDA09ODr33tRXO5VGeDBYHS\nK1eIjIhQDSvoN+pEjhw5TFpaGoGBEu+UgoFv6O5m/8EDTJs2HV8/P9kTcanRWFNTze7du2hqalT2\nHEgmdHR1gSBYT7GdcI2+D9G2Z4tCWJflI3VCGo8//iSenl6qXKmrq5N//esDNBqBB+6/Hw+dTtpK\nHqHP6Fcz8PV6T9auvZ/mllY2bt5itmctE+zbyPfJbWttZf369ezfv1d2vVL51g/sZCqQIH8/PxYu\nWEBZWZkDAZILaQoICGDKlKns278fQ0+3ujKAXq8nMTGRvMN5gOiUQPj6+hEcHMzpM6edsw1RJDY6\nGr1ez4ULF5wNHRjfsHnQbg7WYXnUXfkYwxiGiqDxM8n5/zZg6uni0MsrqS3cc6NVGpXorjwFokhw\nqqv1A81w12oI9/OgvNG1ohtthl68dNphCUtzBnMVL6PzgZijSiqbugYc4iUIAiHpC6gt3DUYFW95\n9LQ3cfIfX+bClt8xYe1PSH/wl2i0oyZF/IZhVBEUrVbLI488wu9//3tefvllHnzwQZd7lQwVUqPn\nyOV6frLxLN+6M5UlGX1EwP6435mF5BqXAcDNzc0cYuMCQZk9axYPPPigS1ypqqqS6uoqcmbMVCdX\nfbIPHT4MwMyZM1XlmlU1sX37VmJj48icONGp16S5pYW//+MfFJdcsHG0gIr3BGxDuuwn2JOBPotW\nq1WuCGYZqtVqCA8P56EHHsDL01PGuySayZEC37DI9fPz55577iUlJdla7leRFYgiPt7eLF2yhMOH\nD3NRYogreSGsJAvBUbYMc5o0cSIPPfggGo3GFU7AzJmzMJlMHD5yRJ1E9O1LzvTpVFZWUlZ2TdWz\nYdnGzMwsCs+dM+eIOVFGq9Ewftw4SkqKlQXbyXfKN+xZtsr4AROf64gxgnLz4GbNQbGHV2gCM76x\njrCsJZz821MUffoLjN0Day44FNwM8fS+HZcJGDcNd0/npcrtERXgSUWz6x6UwXhPYHB9UNq6XAvx\nqm010GsSiQ4ceNhRaPpCmi+foKej+aa41yOlY33xQQ69vJK2ymJmfONfxM59DFcbO94M+zgUjCqC\nMpIQ7UJaLIbJtYYOXvzgOMszo3h+/nhAYijLTXAwuqzZGLJ8Rs5Gc0jEVZ0goNN5qHogLIiKiuLp\np58mNDRExgC3ndDc3Myx48eZN2+eYm6LVP6pU/nU1NRw1+I75bvSSxQRRZFNn32Gn58/CQmJTr0n\nzc0NnDlzCmvehf2H/UIFTV/FMUGxWpeUO2g04OmhY+Xy5fh4ezuW7er7urysjPUff4TJ1CtLVCzD\nIyIiSUubiCiaK5/ZlASTcYukp6YyZfJkNm/ZREtLk6xjRG7Z1TW1jva13QQBzN4fO0tbSbZe78Gc\nOXPJz8+3LTShMCE8LIyE+HiOHDnskvwJE9IxmUwUFZeoD+5DeloawcHBSLtKKw0XRTCJIs0tLdi8\noSryZd9lCaTb1tvbS1l5BT09rsdn36xob2/nlVde4cUXX+S73/0uR44cUR2/adMmvvOd7/CNb3yD\nl19+mYqKGxMWNIbhhVbnQcaDvyTzsd9RfvhDDv5qKfVF+2+0WqMCoihSe243oekLBzXf3KzRNcLX\n1tU7Ij1QYGB9UCz6D6SKlwVBKXNA0FJ/ft+A596K6G5v5Oy73+b4K48SkDCZWS9twj8u60arNapw\nexAUhZNbe2OvtauH5989SkKQN79YkylhsU6Oai3jBMHcyMHFjtiKB8pyVnvfhyi3DhkPhMXoDQ0J\nca6ARoOnlxcLFy4kMzNLlVgJgjl3Y9++L8iZOZPgoCCnyhw7eZLKykpWrVqJm5t6v5Oenm4++WQ9\nZ8+eQZSEXDkcb/dN6DWZQMBMTmSMevn9lhAR+8pblkkmE556PeUVFWzauBFLyJT9Um14jWjWw4ER\nyVjuC+fPJygwkE8//QSjsddhqHR/RNFcGvvtt9/iRH6+g8fIYTPN3+jreq/u1AHIzJzEU089jU6v\nl/VI2WPmzJlcunSJ2toap6p4eHiQnJziGOaloMz4ceO484470GgcG0/K4fz5c7z66qsYLfWfQdlL\nA2zfsZ29+76wfq3m1Glrb+edd96msbERlyYMI0bag/Luu+/i7u7Oyy+/zDPPPMM777yjSDry8/PZ\nu3cv3/72t/nd737H+PHjee2114ZHkZsQN3sOihwip93NnO/uwD8+m+N//hJn3v7Wdc9NGe3x9K1l\nBXS31BKSvmhQ86MH0KyxzdCL7yA9KIPKQXGxildFUyc6Nw3B3gMvpOCm9yYoaSa1hbtG/b2G6/c8\nmoy9lB/+Fwd+uZiG4oNM/vI/yHrij7h7DazwAIz+d2aouD0IigRXr13jT3/+s8OpqNEk8q2PTtLe\n3csrj0xF31cf+8iRPC6VXnIuWDD3J+ntdf6i2x+yu4QBGEYOw1yIXdHp9UyZPBXQOB3e3NxMeHgE\nOTNn9ltScroKAg2Njezdt4+5c+cRFBTikBci3QtBgB07ttHZ2cma1attw97smZJGQ219PX979VVq\nautkPQ5S+dJkdwePj/1i+74XFBTEfWvXUnq5lJ07tyNIkufVPB02OSP2FnzfIDetljWrVqHRaGhv\na7WRKcdpPDy8mDVrNl988QWNjU3yhr79/RZF1cfGMkyj0eLl5dNPrtQgisTHxjJjxgy02v5KYXKw\nfD87Oxt//wBbEuEi7EPr7BEREUlvby+1dbUuvSNGo5GqqipbB5zCFI++8s5dBsOAdB4OjCRBMRgM\nnDx5kjVr1qDX60lKSiI7O5u8vDzZ8ZWVlSQlJRESEoJGo2HmzJlUVlYOXZExjCrofUPIevx/mfzc\n6zRfOcn+ny+k6NOf093e6HzyLYjq05+j9Y3AO3z8oOZHB3hQ3ugaQWkdYQ9Ke7cRo0ndRgCzByU6\noL+K40AROvEOagt2Ihpvfa+0PURRpOb0Ng79ehmFH36fqKl3M/u72wjNGBzhvR1w6xIUBWOlvaOD\nzs5Oh9yWX287x9HLDfz54WmE+npYp588eYLGBhc6wAoCp06f5tV//MMl9YxGI4WFZzFYjB8XSIR0\nmBInsH4N/aFj9i4RGYYk9RGpcAI0GoiJieaRhx/CTevmKFs6QRD4Yt8+wsLCmDZtuuxQqf4FBWcp\nLCxg1cqV+FpCr+QgCLS3t/PRxx8THBRMQECQwzKljgAwVzMTTb0IoskmjMthT6SkyGQiKjycNatW\ncfr0afIOHbB1lkmG29+bqqrqfsNW4Ze5v68vX3rkEWt5bSUvikX+9OkzCQ4OZsvnn9km5CuxJaC9\nrZX6+jo1rtSvv+QZsMJ+TzA/WwtzcwkJDrLxFSrpHh0dy/LlK6yERhH24Vgu/A0MCAjAy8uLcstp\nv5NJgYGBNDY2KnI7KXQ6HYIg0NWpYFSMkDfleqO6uhqNRkNYWJj1ezExMYoelAkTJnDx4kWqq6vp\n7e3l4MGDTJw4caTUHXW4VXJQlBCavoDZ/7GNCff+hKoTG9n/s1yKN/6KrubqYdRwdMfTi6JI1YmN\nxOXcO2jjPCrAk5pWA4Ze5wnpI5qD0ned9m7nh6uD6YEiRXj2cnq72ojWNw9axkhhuJ5Hk7GXqvzN\nHP7t3eS//jx+cVnM/d4uUu/5AW5671Gh42jFrUtQFGAwGPDw8LD5JfPhscWlqVgAACAASURBVKu8\nkVfK/9ybTVpkv5utt7eH1tZWApWaG9qhvKKC8PBw18aWl7F582a6uw22GeMWSIwfk8nE3v37aWlt\nBdQ5gdUoFxTIidw1NBpwEpYm+ztZNNnKtyc+gsDSpUtZuXI1gqBxWKbUSGxqamDHjm3MmjWL+Lg4\nZVKl0WASRT7dtAk3NzdWrV6DVqu1IQf2ep86dYINGz7h6rVr/TKln6ULlFno+MREli1ZwsFDh2hs\nbJA91ZfyHZPJxKbNm9iwcSMmi0wLQ5CuCaxFACzhWFJvkmPYlIalS5dTWVnJ8ZMnbQfIsSWTiZ07\nd7J58yZMfRvj/G+rSoiX/fMEIIg2z50KV3LI+1K8htz7oKStIBAVFUVFeblL44MCA2lpacFoNDrd\nC0EQ8PDwMBPNEW5gN9IeFA+7ZqAeHh50dckn9CYmJjJ79mx+9KMf8bWvfY0TJ05w//33D12RMYxa\naLTuxMx6mLnf38O4JV+n6vgG9v10HmffeYmWsoIbrd51R8vVU3TWXyVyyupBy7DkbVQ3O/fItnX1\n4qN3H/S1BgKLp8aVMK+KpoGXGJZC7xtKUPIsqk5sHLSMmwU97U1c+eJ1DvxiEWfe/AZeYYnM+vYW\nMh/9DZ7BMTdavZsCt10dsy67P8aHS+v58caz/PuiVBan2Z6ENTU1ARBkn2ehgIqKCqZOnSprY9nb\nYhcvXiQsLAw/Hx91EqHRcOXKFQ4dOkRWlm1PGDkbrru7m8OHDzJzxgw87TuQ20OilJJdaG+XCUjK\n/irpLLFYPTy90HvIN1m3PcUWSZuQxtzZsx1LfNlt3p69e6muquKxLz2OXu+hFmHGpUsl7NixnUUL\nF5IYF9cv2zLJPlTKHPNka11rNExMTyc2Jgb/wEBE1JssgoYVK1bx7rtvs2/fPnLnz+8fIJVtuZ7J\nBBqNmTQL/UPk9is4OJQ5c+Zy9OhRsidNwk2rdbxxllAqQWDe3Ln84/XXOXv2NJmZ2TZLlupvmdJP\ncCVMQwVC3z8u8on+i9izSYnOZpEiEgpnc3sssIiIiorm1CnXYnEDg4IQRZGmpiaCgoKdjtfrPehU\nMNSvJ+TIthLq6upUu9e//PLLlJSUyE0lKSmJhx56yIGMdHZ2OpAWC3bt2sW5c+f41a9+hZ+fH3l5\nefz2t7/lJz/5CTrd6G7ydz1ws+SgDMdpq1bnQcLCZ4mb/wTV+Vu4vPvv5L28Et+YDKJzHiRyyhrc\nvQZe4Wo4dbweqDqxCZ/IVC5Ud5AdOTgZlspX5U2dxAV7qY5tM/QS7je4Ph4D3UeLB8WVRPnypi4m\nRg88V0KKyCmrKfjoR6QbOnDTq+/DjcRgnkeTsYf683upOLKOmrM70bjpiJn1IHG5T+EZGD0qdLyZ\ncPsQlD4LR/qH92pDB1977zgrMqN4bp5jXGlDQwMajQZ/f4UXUmJQtbS20traSnR0tFNjTRCgtPSi\nenNGy0CgoKCA2NhY/P391RwtABQWFnDixElmOcsPAapramhsaiIldYKD4Wexn6VTNBrLib9koIre\nIvJ9G+UOzoODg1i2dIlj+JXdpKbmZk6cPMnKlasIDg5RJT61tdVs2rSRqVOmMH3qVFuWZK+I9LN0\n8RLB/r6+YDL1EQmNYiiWIEBoaDjLli1n48YNhIWHk5aa6uhSkOZkiCJg+bpfsJSsWIZNnTqDzIlZ\naN3cbImODIICA5k+bRr79u0jNXUCOp2HVY50mkWNri4Dn366ngW584m0hK7YkxW5zZbAXmdFqOhd\nVlbGqTNnWL58BUYnERHR0TFcvHiBnp4e3C2hmwoEK8DfH0EQaGhocImgxMRE4+nZF9IgJZejCCEh\nISxYsEDx5y+99JLqfIPBgMlkoqamxhrmde3aNaKj5f+gFhQUMGPGDGto4uzZs/nwww+prKwkPj5+\ncIsYw00FjdadyKlriJiympZrZyjP+4CSjb+m+NOfEz5pGVEz7icoKcdcev0mh2gyUpW/idi5X6Jl\nCHL8PNzx1bu5VMmr1TCyOShgzntxhvLGjkFV8JIiLGsJBR9+n9qCnUROWTUkWaMFLWWFVBxdR9Xx\nT+lubyQ4ZQ4TH/k1YZl33dad4IeKm/+3xwBh8aC0dPXw3FtHiQ/25uc2Fbv60dTUiL+/PxrLL1k5\nt0gfKisr0Wg0hIerxyObw5kaaWhoYPy4cU71NXR3U1xSQkZGhrSPoCxEUeTkieNkTsxAr9crezn6\n2Ma+/fs5mZ/vsHYZpwVXrlw2FwCQxp7YL0zyIQoCiIKNTLnts+ZGKG2AXTyVf0Agzz77HMnJqbKO\nFmmX+PXr1xEbG8uihQtVjWtDby/ni4s5X1JCd0+PcryS5f+YT/jlus1Lh6WkpDFjxkw+++wzamrr\nHBduB5PJxPZt2ygruyrbd8WyXk1f1TWc9UbpmzQrJwdBEDh48IDNj+W8EjqdDqOxl0OWBGk5Rewe\nwLbWFlWjXfrIDMTTcvbsWZqbm50mykdHx/Doo4/h7m53ei+zF25ubjz35S8zzoV3D2D58hVkZWa6\nqPTwYSRDvPR6PZMnT2bDhg0YDAZKSko4ffo0OTk5suNjYmI4duwYLS0tmEwmDh06hNFotMlhuZ1w\nq+egqEEQBPzjskh/4Ofk/vQwaff/nM6Gco6/8ij7fjqPkk3/Q3v1xRuq41BRW7ATQ0stkdPuHrKO\nUS5W8mrr6hl0iNdg+qCAcw9Ka1cPLV29xAyRoLh7+ROedRdlB98dkpzrDWf7aGit5fLuVzn462Xk\nvbyC+qJ9xC98lvk/PsDUF94kcuqa605ORus7M1y47QjKHQsXsmTJUr7x3knaDL38WVKxyx6JiePM\nJ5PO4i0Ecxf2+Ph4h+R7OaOstPQSnp6eREYq+IolFllJSQmiKJKSMkF1XRoNXLt2hfqGeqZMmSKr\no1RudXU1Fy9dYlbOLFmHhRSNjfV89NGHXLx4wfYHcsZ+d7e5WlNfPxi1UDfrZ7myvzYDBBv9/fz6\nQwgkEVI29qhO505KSgqrV6wwP+RyzEgwlyn+6OOP2bhpExs2bGDd+k8wWsK87PbM5oImE9illFuG\nS9c8d+58EhISzb067ImPVK7JhCCKdHV1sXnzJrq6uhyWb6+CKCLfwNHu3ujd3cmdP5/8/Hy6ujoV\njX3zcIGcnNmUlJRQU1sru2dStLS28ue//IWy8ms2aTby8kXWffwxxRckz5ECWYuKjESn03H58mVZ\nPWVfR0tomhqbEUUC/P2dJ+zfYIwkQQF45JFH6O7u5qWXXuK1117j0Ucftf5+qq+v58UXX7SWW16+\nfDkRERH89Kc/5Zvf/Ca7du3i+eef7/c0jeG2hJvei+gZ9zLj6x8y9wd7iM55gKqTmzjwyzvJ++0a\nru57g+42FwrOjDJc2/8WYRPvHJYQnagAD5cISrvBOGIeFL2bFp1WQ7sTglLZ12RyqB4UgNi5X6Lx\nQh5tlc6b8o4mGHsMVOVv5sTfnmHvj2dRuuMVAsfPYOa3PmX2d7aSeMfzeASM/gOLmwW3T4hXH3Q6\nHb/afpHDpQ18+NwsQn09FKNVwsLCCA8PA1GdnCAIZGVlkamQI2IvNyIigtzcBeYyuk6Olc8XFTFu\n3Dj0er1q2gfAyZPHSUhIIDg42Kncg3l5REVFERsXbyNTjkzs27eXsLAwUlNSbIXIuFq2bt+B0WRk\nzZp7VCPAHA78FXIopJNEBFlPjL1cjQb0eh13LroDayK/NG5NYvlfvHSJ+vp6nnryGYwmE++99zY7\nd+3irjvvtA33slxYMr/00iWKiotZsmQpGo3gsH/m/2tYvfoec3QQ5hNHmwdDwrAEQeCuO+/ktTfe\nYPv2baxcuUpWrq18sxwBASthkomxykhPJzraHK5k4V9yKSAACQmJREREcCgvjzUrV8rfxL5Jfj4+\nxMbGcuLEcWJj4xz0k265IAiYTEYKCwtJSU5GMXbLZEIjCMTHxXH5cqlN7pU9/7LlIkI/SbHZIDul\nZAjMKI3eGjF4e3vz1a9+VfZnwcHB/PGPf7R+rdfrefzxx0dKtVGP2ykHxVV4hcQzfuk3GLfk6zSV\nHqfy2HoubPktRev/i5D0hURNX0toxkI0bv15FqMxnr695hL1RfuZ+sKbwNB1jA705Ep9h9NxrV09\nQ+qDMhgvSquTbvKW0LQI/8EnyVtwpVWPd0QK1w68Rdp9PxuyvOsByz6KokjLtdOU5/2LqpMbMRo6\nCElfSNaTfyI0fSEatxuXdzca35nhxG3nQXn/6DVeO3iZ3z2YTXqU82QvQa1B4yARFRXNpKxMdWuo\nz9K+6667mDdvvuIQi63V3t7OxYsXmTplijo5EQRq6+spLilh9uzZsqFt0oPyiopySkqKWZCba06O\nV5Kt0XD56lXOnT9nfWHsoqJs5J4/f47NmzeCyagc/2MTA+aop1SudQpIEvmdyBUEUlNTefbZLxMY\nFERISAhr197HtGnTHbw2Dhc2mdC5uVFQUMChgwdsQtSkxbocIF2TzII8dOYO9+fPn+P8+UKHaXJy\nTSYTza0tqnI1QFBAAIJosj7T0iXaqigwa9Zszp8/T31jo3ozElFk6uTJlJSU0NrSIpEhPyUtLZ2L\nFy+ay2s78XYkJiRw5coVawWy4cT1eK+HEyPtQRnDGK4HBEEgcNy0vhCwI2Q+/r8gmjj9xovs+dFM\nCj/8AU2XTyKO0pOBa/vexCs0kaDkOcMiz5Vu8l09Rtq7jQQOohniYBHo5U5DuxOC0thJiI8eD4WI\nk4FAEARi5z5GxdH19HQMJbPn+sFkaOPqvjc49D/LOfzbu2m+ms/4pd8k96d5TH72b4RnLbmh5OR2\nwG1FUPJKG/jBpwW8dFcKSzJGoRtOxlr08/Mze0RQToERBPDx8XaMrZfGwkgMwfxTpwgPDychYZyi\nkWO2HUW++GI3iYmJJEiTX2Xiqnp7e9m+YwcTJkwgLi5R1cvR2dnOzp3b8fK0a/gkI7empoai4hJM\noqCax2DlMdLtU2NIgCgImEQBvd7TKjsyMgZ//0BMYl8ejVRxO4YQExPDXYsXs//AAS6VXlTjB/0G\npSj0l9uVMgTJwuJiY5k+bRo7duygs7NdkR9YZB44cID33n/ftvmoRbbMhjnhfZhMkJiYRHx8vG0X\ndctky6A+JI0fj7e3N2fPnlGMYLPsQXJyMgAlFy7YDpBRJiEhga6uLqqrR/8J9XBjjKDcPLidc1AG\nAq27nojs5Uz+8qvM/89DjF/yDVquneHI79eS95tVhHYVY+we+Yp5SuhqqqIs730SFj5rTfYf6j5a\nusmrEbKG9m6AQXVrh8HpGOytp75NvfxxRVOntRLZUJGdnU3U9LVo3D248oVrveNGCi3XznDmrW/S\n8K9nuLD5NwSOm0bOS5uY9dIm4nOfQufjvLjKSGE0vNfXE7cNQbna0MEL755kVVYkX80dXCdYKxTi\n5kE+GVw6RfEwWsXroZYCIw2VCggI6E/oV8GihQu55561YJeabr+sa9euUl5ezoLcXGUd+yYdPnaM\ntvZ2Fi507Ipqmxohsn37Njw8PJg/b56ylSUIdPf28ummTeSfyrf5hS4TbUVLSwu9Pd3mk3F7JmNv\nNVuZhCC7t/1TZW6aVJbJRFZGBpMnTWLTpo00SYx5FX7gEkOYN3s2WZmZDrkSck6H7OwpdHV1ceTY\nMWW5TvKo7KcIgsD99z/E+KRkxTn9czVkZmZy+sxpRNGk6hhxc9Mzfvx4CgsLHX9op2NgQABPPP44\nERERTpclinDh4kVqpXkzcguzzhNdOrXt6Ojg6tWrTseNYQxjcB163xDic58i5//7lFnf3oxfzETO\nrfsxe38yi+IN/01nfdmNVpHSHX9C7xdO1Mzh6/ETHeBJV4+Jxg5lb4WVoPiM3Ol8sI/Oel0lmJs0\nDj28ywI3vTeJdzzPlT2v0d3e6HzCdYQoitQXH+T4n79E3m9W01F3lfQHfmEu/HDfz/CLybih+t2u\nuLUJSp8h2GLo5Zm3T5AQ7MUv75koG9YkN9WFYTaQy+EYKhya26HCj9TKJUmMYq2bm02iOchPiY2N\n48knniAsNNQxeV2iQEtLC4cOHWLevHn4+PjaJN1b/m+ZUlR0jpKSYpYvX95fElZB7o5du+jq6mLZ\nshWIfR4UqSzLlN5eA+vWfcjuPbsd98RuYWLfjRUR1Dhhv+6WJHTphtsZ0ncsWkRwcDAbN36KIIg2\nNrG0upfl1pw5c4bPt26zvaCdIe3u7s7C3Fw8PfQOfEZ6eVEELy9v5syZS15eHi0tLapyLYuWC3FS\nTj5XIVR9k7IyMkhOSrL14shcHiA1dQI1NTUYndUPhj5y4vzXlCjC/v37KZAjPnbo6enh9//7v1y9\netnpO1peXsZ7779vS2aUQv+GEWMelJsHN0sOymiFb3Q6GQ/9N0H3/Y3EO1+gKn8z+/4rl9NvfoPW\ninM3RKfO+jLKDn3AuCVfR6Ptr6Y11H20JJirJcrX9XkyggbpQRmMjsE+OuqcEpQuovyHx4Ni0TF2\nzmO46b24vPMvwyJ3oBBNJqpPb+Xw7+7h+CuPImjcmP7iB8z45jpqdONHfYng0fxeDwdubYICGE0i\nX//wDO0GI79fm8Zf//KK7B8Ue6OypKSIHTu2u3SNS6WlNLe0yv5M0Y6xz0q3V2AABpAl78LxB/KG\nlGWks8trNALh4eFOr+/r68eqVavJzp7iQNCkctva2ti5czvTp08nJjJSNV6rsKiIM2fOsGLFSnx8\nfBSvLYoin3+2BYPBwJxZsxwJmqRyVkt7O2+8+SY1NbVOK5dZfmY0ihw7fpzWtjbbKlyScC+tIHD3\n6tXceeed5h872S8fHx9OncqnqLhYNW9EiWzK8aVJkybj7+/Prt27caaBKIps3LSJoqLzNpe3j2JT\ndDDIMOSAgADuvOMOPPQ6p+tPSkrm+edfMPdxGYQCSvwgIiLCJWPR3d0drVZLU1OzA9m1vxUeHuY/\nUAaD8+7Pw4kxgjKG2w0avS8Ji55j3g/2MOnJP9FRe5lDv17Oib89TeOloyOmhyiKnFv3I7zDxhE5\ndc2wyg7z1aPVCJQ1KhOU+rZufPVu6N1GrtJgkAshXuVNncNSwUsKrc6D8Uu/wZUvXh/Ril6m3m7K\nD3/Ewf++i1OvfxWv4DhmfXszU77yOoHjZ7h0iD2G649bnqD8Ymsxhy838PfHJuPrZqKrqwt3d/OJ\niJoRVlFRQXV1tVP5oiiyYdMmLl8udTr2ypXLfPDB+5iMRqclg+rq652GoMjmXTjTF8HGG+ESnCR+\nCFoNKSkpaDQaVZkeHjqmT5/OvDkqCYeCQHNzM1u3bWPGjBnExyvns2g0cOzYYS5cvMDda9aYiYxC\n4kt3by/r1q9HBPz8zQ3mnK1fFM25NadOneLj9Z/QYzQqEkdfb2+io6LMZFGjzjvi4hKZMmUqW7dt\no62tzaqj4pYgounzzChBo9Fyxx13UlRURHllhaoCgiii1Wo5cGC/0wR0h7wZF6C2fgCtVutamV+1\nogwyCAsLp6amxqXh/v7+tLQ0q15aFLE2dbXvtD6GMVgwloMyPLDoKGi0hE9aysxvfcLUr76Nqbeb\no394gCP/ez9157647gn1VSc2UnduDxkP/Tcara2Xf6j76KbVEOGnXmq4ob17SOFdg9ExxEmIV6/R\nRFVL17DmoFgQnfMQ/vHZFLz/H4gm5171oaDX0MGVL15n/38tpPDD7xOYNJO5399F1hN/wDc6XVHH\n0YqbQceh4JYmKO8fK+O1Q1f43X2ZZET60dlnZOg9PJwaMc3Nzcod5C3QaGhpa8NgMBAW5tzTcPny\nZbq6OtFq1I09g8HAP994g/Pni1QTwwEuXbpIZWVl/zeUMtNd9MpYUy0QEUTXjmhFlCPLpJfX6XTM\nnjXLShBlBwoCXd3dJCYmMneufPUyC8rKrrFv317uvOMOolU8MiKwafNmOjo6WLv2XtzcdC6fPGu1\n7tx99320tDTz2Wefmf84yu2h5MjblQpR8+bl4unpyWdbt1rDzlzxmCndQlGE2NgE7r/vASKjopzK\nmT1zJo2NjRSdN4dQOLu0SF94nJoCLkAtClEJfQWmnY4LCwuls7OTto52dYaEmaA0NSkTFAv0+htD\nUMY8KGO43SEIAsEpc5j21beZ+a1P0PkGc+KvT3L4t2uoPr0V8To8/J31ZZz/+CfEzX8K//jrY/xF\nO2nWWNduGHR412AR5K2jvq1bkfzVtBowmkSih9mDAiBoNGQ8+Etaywso3fHKsMsH6Glv4uLnf2Df\nT+dyYctviZiyivk/2kf6Az/HKyTeuYAx3BDcsgQlr7SBH2w8x0t3JLE03UweLEaGh97DmQPDNYIC\nVNfUIAgCISEhquMEwZx0Hh8Xpy5QELhYavbGJDrpdi2KIjt37uT8+XNOrb6SkhKn8YrS6CWntrKN\nUe066bGxvBQGh4eHs3r13QiCVjWdpqSkiNTUVLInTVKUhUbDwbw8LpWWcs89a/Hx8ZMdqlE59ff3\n92f16nsoKi7mYF6eapEEwFzWVxAVOaEomonP8uWruHz5svm+KG14317V19ezfv3H9PT0qN6b+IRE\nBEFjd38cERAQQObEiRw8eACQT2y37LvJZOKNN97g3DmVeHCb++oiWcF1z0xHh3I1MwtCQkIBc+U3\nZwhw4kEB85JuFEEZw82DsRyU4YGajv5xk8h++i/M+s7neIWN49TrX+Xgr5dRefxTTEb1BoOuorer\njZOvPotncCzJK749YB1dRVSABxXNKh6Utm6CffSKP3eGQeWgeOvpNpoUu8lbCNVwhXjZ6+gdPp60\n+37GhS2/pfr01mG5BkBXUyVFn/wXe/9zDlf3v0n8gmeY/+MDpKz+D/T+YQPScTTiZtBxKLglCcrV\nhg5eeP8UqzIj+Or8ROv3DQaDNf7cGZqbm/oJigqTqampITg42KGDvBSCAN3dBqqrq4hzRlCAi5cu\nERcXh06n/EtKEMw9SpqaGsnKylKVJwL7DxygorLSKT8oLCzg0KFDzhPuJcnmStsjJTtWw1JpsMSa\nd8VoFQRYtGgRK5YtM49WIGdNTU0cOHiQu+5aQnh4pCwxtTd65Qz16OgY7rprKfv376fkwkXlgZLk\nlvLyMtSM9fDwCJYsWUZcfIJddTFHeOr1XLt2jcOHDzklkaIoSe5XYh4mE7NzcmhuaaGwsEBRR/Nz\noiE0NJSjR48qe5AkEMB6uukS0XXCPC6VlvKnV17BYDCoOpp0Oh05ObPw8vJSv6Yo4u/vT2urfN6Y\nFG5ubsTHxau+39cDYx6UMYzBEb6RqWR96ffM/d5OAhKyOfvutznwyzspO/Q+pl71JG819HS2cPLV\nL9PT0Uz2M39Dqxu+alX2iArwpFwtB6W9m5ARrOAFWK9X3ya/h+VNnXi4awj0Uoh+GAZE5zxAfO4z\nnHn736kt3DMkWW2VxRS89x32/SyX6tNbSV79H8z/0X7GLf433L3kDynHMPpwyxGUlq4ennn7pLli\n15p0m2Snzs5Oa0y5GgwGA11dXbYERcn1WVNDWFg/E1eyv69duwZAbGys8kBBwCSKXLp0ifHjkxT1\nsxhp584VEh4eQUiwel3u8ooKamprmTp1muo4UTRx4MA+OjraVccBHDp8mPxTpxyqdcnqaxZuY7w7\ng9IwqT2rEQSz4agyMCAoiKeeepqJEzOtcu2HCQIYDJ3U19ciCKKiTT9xYiYLFy4iLDy8P/lHIdyp\nubmZ9957j/z8k6pRURkZEwkKCnK6GV6enuTOm8eRI0dobGhQGmazxaJlgQrw8/Nj6pQpdHR0OI0w\nmzp1KlXV1VRWVTllHZu3bGH3nl2O5NQOBoOB06dPm/NglAaKIpEREYiiSEVFheI1Lfd1/vz5REZG\nKivXtzlZWVk8//wLqusAc5jJgw8+SExMjNOxw4kxgnLzYCwHZXgwEB29QhPIeOhXzP3BbkLScjn/\n8U/Y918LuLj1D3Q1Dcyj1V5ziaN/fJDO+qtMfeEtPPyVw7WHYx/NzRqVPbL1bUML8RqMjpbr1bfL\nJ8qXN3USHWDXt2wIUNIxZc13iZp+L/mvPsvVfW8OKIxPNBmpLdjFsVce4+CvltB87TQTH/41c7+/\nm7i5Xxow6bzV3pmbEbcUQTGaRL7+wSnau4389eFsh46n2ZMm8dQTTziVo9Vque++BwgPC3dqAURG\nRpKUZCYTarZ3eXkZ4eER6PV61YEVFRV0dXUxfrx6rxaj0cj58+dIT09TYQZmI/1Efj4xMTGEhoap\nOjCKis7R2trKjOnTlS8sCLR2dHDw0CFrgrUSPxAEka1bt1BZVaGeQ2Dxxgga1fK/UhtWtt+JzEAR\ngeDgEGdRZZQUF/H666+xffs21WirKVOm4+vr1+ehQNGo9vf1Zd7cuezevYvq6mo1+1tSzljdos/K\nzCQsNJQdO7djX87YXmbfytQ9FKLIwgULmDljhkNxMnuEhUUQGRnJyZMnzd9Q8XqEBAVx7tw5pwn4\nnZ2dfP75Z6rEA1HE08ODkJAQysquqQ1zvegDoBWEAaT9j2EMYxiN8AyMJu3e/2TeD/cRNX0tZQfe\nYe9/zuHE35+h8sRGejqUwzi7mqsp2fwyB3+1DK27BzO++TE+EcoHg8OF6EBP6toMdPXIJ4TXt3cT\n7D34EK/BIMBLh0ZQ9qBUXIcKXnIQNFrS7vspSSteomj9zzj6fw9SX3xAkaiIJiMNFw/39dCZzclX\nn0Wr82Tav73LrG9vIXLa3Q6FDsZw8+CWunO/+Ow8h0sb+ejL0wnzdXzBtVotnm7uTkOI3NzcSExM\n7MuZUDeyZuXkmDuSO0n6nTdvPp2dHU4tKRHIzMzEz89fVebly6UYDAbSJ0zot3Jl0N7eTlFREcuX\nr+g/WZeRKYoihw/nkZGRgZ+vH5iMihc/cOAAPj4+ZGVNUl1OQcFZCgoKmD5tmuUismSitq6OU2fP\nkpu7AK3CLxOrAd1HfFQ3W0J4UBkqPeFPS0vDQ6/n040bCQ0NJTt7zd6CgAAAIABJREFUiuL+W79n\nMXItneDtMHP6dK5evcqmjZ/y+ONP4uaus51vL1Mwn9hbDX/pQFFEIwgsvvNO3nz7bUpKiklOTpVd\nl2WbCwsLqK+rJXe+QrEBy3Oj0YLQp4DCMDA3hNy27XMWLliAl6fMH6u+C6dNmMCevXu5fPkyCQmO\neVQW/fz8AggNDaW4pIQYtcR+USQmOpqysnLlMdLlaJxlRQ0U1vjEYZWqBpVXegyjDDdLDspoP20d\nio56v1CSV7zE+KXfpO7cbsoPfcDZd15CNPXiE5GMb3Qaer9wBI2Wno5mWssLab56Cp1vMKl3f5/Y\nOY8iaJyHfg/HPsb2VcIqa+wkKcyxhH5929CqeA1GR61GMCfKK1TyutrQSUygk9DZAUBNR0EQSLzj\neULTF1H0yc84/spjeARE4Z+QjWdQDBo3Hd1tjXTWX6H56ml6O1vwi80kbv5ThE9egVdw7HXXcbTg\nZtBxKLhlCMrZihZeO9vBnx+eTEakcoyhiGtmhquVgyyjnUGj0eDr4+30mDc2NpaYuHhVpwhAVFQU\na9RK6/ahuKQET09PUlLkjVmLzIsXL1BXV8c9d9+tTMoEgfrGRk6fOcPKlSsRBK2ip6O728AXX+xh\n2tSphIaEKHo6TKLIZ9u2odW6odFoVQ2zI0fySE5OIiQoSNmCk3hOAEwKpKzfE2O+1zo3N1JTUlgw\nfz47duwgKDCY2DhzdQ87niCRYX5CBPryMuwGCoLAimXLeP2NN9i9Zxd33bXUQQ+Lbtb2HxadFEhK\nZHg4OTNnmktVI89lLF8LgsDhI0fIzMwkKCBAfiCAaDIn1luuL8gPS02dwJkzp2hubTXneSg8d35+\nfsTFxlJYWEBi4jjVRz4pKYnz54tYtGCB6nMcExPD2YICjEYjguDMkBBw5S23vN/ODiysfNSpxOHD\nGEEZwxgGDo3WjbCJiwmbuJheQzuNFw7TfOUkbVUXzL1URBNavTcBCVNIXPwCIWkLbBoxjgRig7zQ\nCHC5rt2BoHR099LZYxxxDwpYKnnJh3hdrmtnXpJ6IaDhhk9kClNfeIv22lJqTm2l5dppmkpPYOrt\nxt07AM+gWMKzlxOUPHusEtctiluGoOw6X8NLixewNMN5WNbwwnkFK5fCNp1UXZJCowFvby9SU1Kc\nhndlT57M+ORktFplMmFJuJ+QmmrOh1Ay/AWBffv3ExoaSmpqmupSDh8+BMDsWbNUdczPz6empoYn\nn3wKFBLuzQSqhL17vyA8LKyfoMgMrKiqoqe3l7j4BEWPkU3yvtTFIopMnzaN2vp6Pt3wCU899TQ+\nPr4O3EoyHIDWtjb8LM0k7QZ6e3mxdOlSduzcSXe3AZ1OL8vVzHqKbNq0gcSEeCZZCh/IrDN33rw+\nD5GjTlKkpqZx+HAe+w8cYPXKleqDLfsi2Hapl8LNzY2HH3607xEV+z1HMvLS09PZuWsXPT3duLvr\nFL1RiYnjOHToEM0tLfj7+irqFxsTQ0BAAG1tbfj5+astw7IaRtLjMYbbF2M5KMOD4dbRTe9NaMYi\nQjMWDZvM4dBR76YlJtCL0jrHXE9LiNVI90EBcyUvOQ+KoddIWWMHiSHeg9bJHgPR0Ts0kcQ7nx+2\na7uK2/GdGW24ZXJQUsN9+GquelleV2DlCK5UHxIE1XFSzuFSNaPBJqCpWGqCIODr62cdpjR0wYIF\nrFy5UnVQb28vXV1d5M7PBYVcEUEwV846fvwY8+bOQ6/TKbKE1tZWvti3j5ycHAIDgxXJREdHO9u2\nfU52djbjEhMUvTFdBgOfbtzIiZMnVclJd7eB0tJLjmWPRXMTwyV33EHu/Pn4+nirFsISRTh8+Ajv\nvPsuXQaD4sCkxESeffppPPQ6qw5y8kAgMDCQ3Xv2mDvXWwbLXbhPV8tP5YcJzJkzj3PnzlFbW4ci\nrBsl0tPToz4EnNv9okhqSgq+vr40NTWpDo2MjEKv11NaWqr6Dvj5+fHM008TEODv9J0qKDjL2cIC\np++UKIoYus0nhmqvXkNDg22voRGA9LEcS5IfwxhuLSSGeHNJjqC0D52gDBbBPjrZHJRrDR2YREgM\nHT6CMoYxuIJbhqDckRY+LBUmXOAdA/J2DAYuJ/yKrg22GDtyxnr/ekW0Gg2yln3fIDd3dx548EHi\nExJVr9fe3kpCfDxZmRPlB/TJ27F7Nz4+PsyYkaM4DER27NiGTqdjYW6u4lpFYMvnnwOwZMkylbAu\nkc8+28TWrZ/T090ju39uWi3ZWVkIfafwaiQlK8vcg+WzrVtVq2a5abV9YV/K8gBycmbj7e3N9h07\nZGo0O65acNJhfvz4JCIjI9l3YL/8cyu537t372bjhk9V12tO6HcCUcRDr+fZp5+xqXAnB0HQsHDh\nomE9hS4rK6egoMDpe7pz1y4+Wb/eaX+VU6fy2fPFnmHTzxWMEZSbBzdLDspox+2kY2KIN6V1bQ7f\nt4RYBXoNLQdlMAj21slW8bpU245GgNhhzkEZ7RjT8cbjliEobk66swO8+c47HD16VPHnFiNl3bqP\nKC4pcSpv/4EDNCiUfLVAFM0lZ0XRtRK7lr4iSkMt5Ml6+q8Wt4V6fP2AeFbfQAENaqFYgmDOo7nv\n3vvQSAmPHYwmE25ubtx111K0WjcHAmXRqbCwgAsXSlixbDk6d3dFt8jxkye5eOkSq1evQS/TiNOi\n26FDBygtLeWeNWtwd9Mq3w9RNOdmqJAKUTQ38luxYhUlJSWcOnVa3d0iigii7RB7eRqNeU9KSkoo\nLi6WvznSxYm263OE2YvS2dlJr9GoerPj4uK4cPEijY0NTp8L63OlNNDq3RHVip0hijBxYhbhESql\ngaWDXQjbCg0N4f9v78zDmrzyvv+9EyAsYd8hyCIiLigUqqLUfaOire3UqTpO+7xt3/q0Vd8WO2M7\nV0efzjzXzHNprU/b6bSu1dFqtXYUSxVpLa0L7gsgi7IIyA6CQIAQkvv9IyQmkPtOAoEE+X2ui+vS\n5OSc7/3LneT8lnNOfV2dwXbOYrHOWShc12tnZ4fOzr6fsUAQBKGNykHRn0Fxc7SFrXDwp2aeYpHe\nDEpJvRRBHo6ws3lspovEEGFY3XFtbW1gDDgyLMvi3r17mkXIXEilUpw/fx5tbW287RobH2D79i9Q\nX1evPzvRTVlZGU7/+CNY3nItVbmTUrOInWf9iakZHj6vSGs0rqRNz2wM1NsAczQU2tggKWnxo3Nh\n9KDybxSYNnUqJIEBnANX1dTg54wMzJgxA/7+AZzOSXFxIS5cOI+F8xfA38+P//AWDqdCn5MSECDB\ntGkJ+OnnM6irrzfgpKidHu4tfSWSIEyYMFG1hqOrizedoVQqkJeXC+1MT89mI0aE4MUXV0DId9gg\nyyIsJAQeHh64du0adzttDN5n3U6KkbcjpzOtfW8akVn09PRCq1SK9nbuw9AAwNnZGS2trbyfOUDl\noMjUJXyDBGVQhg60BsU8DCeNoV5OqGmWQdrj5PaG1k549uMMFKAfa1DE+nfxKqmXmnX9CTC83uuB\nZCho7A/Dw0Hpnh11dnbyns4OqJwYhUIBZ/WCXQ7qGxoAAJ5eXrxz+8rKStja2vIfpsgwuFNUhOrq\nas4yNfUE7+TJVKSlneLVptoy+BJapVKDSZte2RguzwPqP85L0J2E8g7K8GaKtPuZMGEipk2dyttQ\nLpdj3LjxiI19kvN6m5sf4ocfUhETHY3x48YaV0On5VS0NDejtbWFMxMwadIU+PsH4PKVK9wT9+7+\n5PJOZGdngWW5nYqEhOkYPz7q0YnwHDxsakJqairy8m5zOgEMw3TfV91/HIMyAJ6MjUVOTjZkHdwH\nibEsUFxcjK8PHjQ4udfVYWCOzxhuxHcXqt8uLy9vAEB9Pc+6GwDOLi6Qy+Uq54MHOzs71dqcAS7t\n1IYcFIJ4fFFP+O816GZRGlplFtnBC1CVeD2QdkKp1P1OHwgHhSCMYXg4KICWg8IfnVCXfBh0UOrr\nIXZygr2eciJtqqoq4e/vDwFX5qZ7wlNaWorg4BDeMTs6OlBaWorQ0FD+bEx5OTJ+/RWdMjmvE3Dl\nyiX+2vpubR0yGVjGqAo1w86OQFUiBhj2iR5lY/j1BQWHYOHCROgrPVP3I+voQFBQEGbPmmV4UG26\n2/5w8gccP34MSoVCbzOGEWDJkmdVWwkbOHCxoaEBJ0+eREFBHueQDg6OmDo1Aba2tuBzKtzd3BAT\nHY1ffvmFc4G71mWodv7imWiPGzsWQqEQt7Ju8SaCnJ1dUV5ejvsVHGeT6Ly5xjoxHGkqLWpqqlFY\ndJfTR1DbztHR0bCD0r3zWnNzM287nRKvQcyiEEMDWoNiHoaTxgA3B9jZCHqVeVU0tcPX1bQTz3vS\nV41+rg5QKFlUN+sGp0rqpQgzs4MynN7rgWQoaOwPw8ZBUSgUUCgUBh0UVZScUZ0vwkN9Q4Mqe8IR\nz1XPsaqqKhHIdwgdgFapFPX19QgODuZtV1xcBIFAgJHaDkrPQQUCZGVnQyKRwM3dnVObQtGFq1ev\nwEZd9sPhfVRWVeEf//wnHjY163Uo1NdZU1MFhUL+qC99g3ZP7rSdHf7sDk+jHhNZPm0MA/j6+eC5\nZ5+FDVddlUDw6E8PC+bMQX19PTJ++Zlz4m5v7wCB+rAvnom2n68vYmNj8dNPP3GWIelUNRlwKqZO\nmYLOzk7cuHGNswztka/Ak4NgWdja2GBqfDxEIhGvr+Dp6YmAgEBkZWc/cjw5vJkrly+j9F6J/jFN\n5G5hIX755ReDyYy5c+djxIgRvH2JxWKIRCJ08GSLANUOYkFBQSZli/oLZVAI4vFFKGAQ4umIkjpd\nByW/ugWRfvzB0YFilI8YDAMUVD9al9cq60JtiwyhXvzzIYIYCIaNg6KOgBqTQXFyclIt8AY4J8gN\nDQ3w8uI/uEgu70RdXR0CDDgoZWVlsLGxQWBAoGZZhL6J9p07BQgNDeW9BplMhjt372KC+hwNDgoK\n8tHR0YEnoqN514pcunwZfn5+cHF15eyro6Mdhw9/g1s3b0LvBXRTXlGB2ro6sCyjt4n2PFdzWrwB\nz8OIJqo/Fo8a9GzYc7esnpNtloW7uzsWJSbi2rVruHu3QO+cvJcWHm9h+rRpsLGxQUbGGUPze7As\nz2ycZeHo4IBJTz6JS5cuobNTxhvkZwHI5J28GaMnY2MRM3EidyfdREVFoaCgADIDC8jLysp0MjL6\nbKZQsPjmm0Oq3bd48Pf3R0NDA2Qy/uscPXo0PLjKKrsHtbGxwf9bt86gIxMYKMFvfvMCzLFLoLGQ\ngzJ0oDUo5mG4aQzx1F0o39bZhXsNUozx75+D0leNTiIbBHs4Iq/6UUb5Xre+EC/z7eAFDL/3eqAY\nChr7w7BxUOzt7fH/1q2Dvz//bkGRkWPw/PO/gaHV4NHR0YiMjOTtSyqVwsfHx+CY90pLIZFINIuY\n9QVq5fJO3LtXgoiICN6+8vLzIWAYRIwazTtxv3nzOiIjI+HkxP3FU//gAe7cvYspU6ZodOnTduXK\nJdjY2HAfLsgw6FIo8MPJk7hqYAF26b0S5ORkgTHG8wB01rL05JFzolpHwuvoqPvq6dmoYVlEhIcj\n9okncOrUKbS06C8L0kjuuctVj77sbG0xf+5c5OTkoLTUcHaB5St/YlnExcZCIBAgJyebV1d9fT0+\n//xzNDx4YKBciQXDscWyuq+ICNX9X1BQwKs9IiICxcXF6OqSc/lYYBgGQqEQxSX8tvDvngwaV1bD\nb7PuN4ogCGLQifB1Rm7Vo9+ROzWtYFkg0s/FYpoi/VyQX/Uog5Jb1QwnOyECXB0spokYvgwbB4Vh\nGIhEIgiFQt52Dg4O8PX1BcAx61V1hrFjxyIwUMLbl7u7O1566WU4OTryTrZnTJ+BOXPm8PbV1tYG\niUSC8JEjedtl376N0ZGRsLWz45y419bWoKqqCjExMbx9Xb58Gd7e3ggJCeP01aTSVly/fg1Tp07t\nXi+hn6vXr6OtrQ0JCdM5+5LLO5F2+hTKy8pUD3JcQM7t2zifmQklTyYG6sm1ekcxg+tOGKg3Fyi/\nf58zWzHzqafg5uqK++VlnMeUqIdqlbbjfmUlZ18jw8IwZswYFBUVQWBgHl1cXKyqN+VwKkR2dvjd\nihWIfeIJ3r48PDzh4uKKi5cu6e2nJzyJG9jZiRARMRo1tbXgLBsDMCo8HF1dXbh37x7vWEFBI1BW\nVqbrjGkP2J0tcnN1Nf7gRL46MJPWxwwulEEZOtAaFPMw3DRODvNAfnULHnTvnJVf1QwXexv4W2gN\nCgBE+jsjXyuDcrGoAZNCPbjX0PaR4fZeDxRDQWN/GDYOisXhmSA7iZ3g6clfLubm5oZly34Le5GI\nt6/ExERMmRLP21dtbQ0CAgIQ4OfH2Vdzaytu5+Zi8uQpYBjuc08uXrwAJycnTIyKejSr6tGota0N\nmZmZiI+fCrFYzCn//PmzkMvlmDVzJmfZWYtUivSffkJXl4LX77hw4SzOnv31kSY9ZV0KpbI7e/LI\n0ampqcHxlBOQtrU9WpOiNcG1EQrxu+XLMW7sWIBna1+WBa5evYLvvvvuUV89GyqVeHr+fMydPRs8\n6+DBsqqSwjM//4xWqZSzL3c3NyO29WUwefIU5Obmqk6r568t09eBDgsXJmLu3Hng84oc7O0xIigI\nd+7wZ1pGjBiB1tZWND1s4jWGf0DAoJ/sPtiQg0IQjzdxwR6wFTK4WKzaETS/ugWR/i6DWkrak0g/\nFxTXSSHrUoBlWVwoakD8SJ4dSAliACEHpQfqwiHz9TcAffE4KF6ennDnWByvJipqAlauWKE6KZ2j\nL5ZlEWOgjK21tRlZWbeQkJDw6BR6PZp++fVXODk5ITY2tvc1da+/qK2twfXr1zBn9mw4OuhJJ3eX\nYZ1OT4ezszOmTp0KpZ6ElEAA3L9fjszMTHi4uXNOshUKBQ4dPozr129oJnosy2DevIUQieyQcuIE\nlByzP6H6VPhHl6h3PcrkyVNhb++AU2lpYFV1TL0m3TY2Nt19sZx9AUBMzBNwcnLC+fPndWyrQ/d7\nyXA4TmpGjx4NJycnXLt+/VFfevpRJZ+UUPCcCSTQ3lSAR1NERAQKCws5bQoAPj6+sLOzQ1l5OWcb\nQLXT2MiwUN42psF3nClB8ENrUMzDcNPoYCdEdJAbMovUDkozxphhgXx/NI7xd0aXkkVRrRT3GtpQ\n3dyB+DD+4GlfGG7v9UAxFDT2B3JQ9GCOyQrfBJELjgSE3gkrZx9a/eibvKv7Uk0suZ0nV1dXzJk7\nFwwj4OxLLHbG88//BmPHjOHsp62tDXcLCzFr1hwIBL1PjFddN4szZ35EkESCsWPHcvaVd+cOCouK\nkLgwEQKB/kMHOzo68MMP3yMyMhLj1Oed6CmrO3vhAmpqajCie2tndTNbWxGWLHkWVVVVOHv+fO8F\n9NqNtZwBfdja2iIx8WkUFRXhdl4e98mMamngLqdiGCESEqbjVlYW6hse8PajjT7HiWGEiI2Nw82b\nN3kXuLOsEnv37UNOTrbeDc607zNDbviYyEisWL4cAgHDlQACwwggkUhQbSA7MjIsDBMnTjT4GcvI\nyDCYAlcoFGh++NCAeqCkpETn1PmBhjIo/UcqleLzzz/HmjVr8N577+Hy5cucbS9cuID//u//xtq1\na/HHP/4RR48e5XWmCcIcxI/0QmZxA1iW1WRQLEmQuyMc7YTIr25GZlEDXOxtMDbAspqI4Qs5KANA\nX50TriUSJo5uTGUOx0sZ3Qk5y9+XQMAgNCREdRNxOAKOYjFef/11hPGsnWlra0NXVxfmzpmjuzhe\nq6+29nb8+OOPiI2Ng39AINRD9pT+00/pYFkWC+bNUzmaei6gqLgYly5dwoIFC+Hu7tFrkufh4Y35\n8xfi4sWLKCws5PYatJwUPdVgAICAgEDExsbhzJkzqi2FuZwdpRJgleCb6o8eHQlfXz/88usv+hd/\n6/TF7TgBQFTURISPDFftbsfhBTMMAz8/P9y8eQPqLXa5TGHIrXdwcICPj4+mfIEr2bJo0WLMnz+f\nty9djdyfkYcPm1CmXs/EQWlZGb748kt0dXXxtjt+/BhKDfRlTshB6T9ff/01bG1tsWXLFrzyyis4\ncOAAKisr9baVy+X47W9/i48//hjvvfce8vPzcfr0aaPGoTUo5mE4aowP80RhbSsuFj9AU5vcLFsM\n90ejQMAgwtcZ18sacb6wHpPDPCE08/oTYHi+1wPBUNDYH6zKQWlsbMRnn32Gt99+G+vXr8fBgwfN\nFsW6dv06du3ezfk8wwDNzU3YtXsHHhqIqKb/+CPu3bunM4no2VdFxX2UGyhVkcvlUCgUmjUQfaKn\nU2EMOqFv/YtLWAPFaQKBquRMp+xMnzYAIpEDAIYzE+Ps7ITfr1oFb29vzr6ULItRoyKQkPAUZ6Yp\nPz8PeXm5SFq0CPb29r0bCARobm3F9z/8gIkTozFmzFhOu0dGjsXs2XPg7ePbO/3Ug6KiIpUjg97+\nglIJTJ2aADs7OxSX3OOb4QMAGh88QFdXJ8cWxgymT5+Juro6dMhkvH1lZd3CubO/ciZtRCIRFiUl\nQezswuuAxUyciNraWs6Jnc5L+GrUjEQksgcY076W9GVjlErVhgAND/izTWIn1QFkbe1SXtk6hzUS\nVo9MJsONGzfwzDPPQCQSITw8HNHR0bh48aLe9jNmzEB4eDiEQiHc3NwwadIkzWeaIAaKmBFuENkI\nsHzHRTjb22C0hc5A0WZSqAf2XyxDanYV4sNo/QlhOfTXyViIQ4cOQSwWY/PmzWhra8PHH3+MjIwM\nzJ49u999y2Qy3ucZBmhtbcWDBw94zxlRKpW4lZUFiUTCW9dy7dpVAAxGBOnZ6at7Ipd16xauXL2K\n119fzdlPU1MTLl26gFkzZ8FBZKfXqWBZFnX19fD29tEbyNaZN2rvTsbjFWkH43v2ZaBSSbcfrVPj\n9enS/BvQvzC+e9YodnbGwoUL9WpSExwcgsVJSRgRFKS/LwA//fwzXJxdMGvWHK4mGr0xMXGqRBLL\nsUahe5F9SUkJcvPy8B//8QocHXufuGtra4eXX/4/EInsALCqa+p5IUolFEolDh46hKioKCQkTO8e\nW7evoKAReOWV12BjI+zOuHBz6fJlTIyOgVjsrHOt2reQIb/Wt3ub7JzsW5BIAjWSe+pqampCdnYW\nnkpI6G0r9bUyTLfzYYTzohaltlUfPHgPdw9cbWzkfv8AOHU7KFJpK1xcXPXaHBh8B4XvPh8Izpw5\ng8zMTFRUVGDSpEl4+eWXedunp6cjLS0NnZ2diI2NxcqVKx8d+moF1NTUQCAQwMfHR/OYRCIxuCW2\nmjt37iAwMNCotrQGxTwMR432tkLseflJsACig9zgaNf/z1B/Nb6XGIllcRLcrmzGnDG+/dajj+H4\nXg8EQ0Fjf7CqDEplZSXi4uJgY2MDFxcXjBs3zqjIrTF0dnYaPKRRKm2FQCDoHX3XormlBQqFAu4e\nnrwZhtraWvj6+vC0ACoqKnR+QPVRXKyKzovsuLfwraisxJ6vvkJjY5PWgm/dNnK5DMePHzOYHWpu\nbuF1KnTgSiHxZBuA3kkfHaepZ0M9Q+nLwjAM4OjooFrDwlUrJxBg3rz5eObZpSZNplTnowg4M1Uz\nnnoK9vb2SE9PA7rLvXpmP2xs7Hq/Lz36EQqFmBYfj8uXL6OpqZEzGaHZKls7s6ONUonxY8ZALBbj\n8mXD2wl3L/fnfD5q/HjkFxSgs7NTb1mWUgl0dsqRmZmJKgPlLoZzc9qN+1da4O7hAblcrtqtjAMH\nBwcwDAOpVMrZBlBtqWwJB2WwSrzc3d2xaNEiTJs2zWDb27dvIy0tDcnJyfj73/+Ouro6pKSkmEeI\nmZDJZL2+x+3t7dHR0WHwtefOnUNZWZlJpYYE0VemhnthWrgXnETW4eAzDINwH2c8Ex0IsZVoIoYn\nVuWgjBs3DpcvX0ZnZycaGxuRk5OD8ePHm6Vv4xwUKZycnMC3zV9jYyMA1ba/3GPJ0NjYqHI+uDwG\nAPcrKiDhiNKp58ElJUUIDQ3V3S2pB3l5efD19YUbz+5dBQUFKCoq4na+BAJUVFfjn9u/1FyjPlpb\nW3Hhwnl0yTs5PRiWZVV/4L18/vmnllPBMgKD+ywxDAuGVXIP2D2Yk5OT3vfOgE/FLZplYSsU4umF\nC1FYWIi8PP6T0A05OxOiouDl5YUzZ37irLzSXCLPOEKhEFMmT8atW7cMTr4BqLYJ1ufsQLXA3cvL\ni3eRuJeXN7y8vJGbm2twKFUgoI9rrLoN0PLwIVJTv1et6+HAzU31eWhqauJsIxAI4OjoiNZWQw7K\n413iFRMTg+joaIjFYoNtMzMzkZCQAH9/fzg6OiIpKQkXLlwYBJWP2LJlC15//XW9f5s3b9brjLS3\nt/MGnwDgxo0bOHbsGNatW6fJrhmC1qCYB9JoHkijeSCNlseq3OPFixdj69atWLt2LViWRXx8vNlS\nWMY7KBw/0N2zqabmZjg6OkIkEnFGL+vq6gAAPt7c2ZHm5ma0tLSoSsU4kMs7UVZWhqcTEzk1sQyD\nO3fvIjYujrMfAMjOzsbo0aMhsrPj9BiuXrsGiUQCd3d3ziTEjRtXkZeXi/gpk/XqAcPgVnY2cnNz\n8eJvl3PqqaurhaurS/e5LhyhYAOzV51MDFcDrT/1Wh++0i7tEh91ZZF6HBaAUqGATfc2w9qaJYGB\nmPTkk/jxxx8xYkRwr7Iqg4N1DygAMHf2bBw4eBBFRUUIDeU5mJNlwDJ45FD3KIUaP3Yszp8/jxs3\nrukckKlPU0dHh+q90Olf1djezg6rVq5UOYo81xMZGYlbt25izuzZKk16DJBfUIAfTp7EmjVrIRTa\n6O2PZVk8eNAAJycn2HN8Zm1tbXH79m2MHTsOwcGhetvY29vjxReXqzKZ2qViPQbVrH3iQSKRwMlx\n8E5THuwSr0fjGs5uVVZW6nwvSyQStLS0aAI8g8H69et5n5e8Ei4KAAAYTUlEQVTJZFAqlaitrdVk\nqcvLy3nLtnJycrB//36sWbMGAQEBnO0KCgp0SsUaGxuRkZFh2gUMMvX19byOujVAGs0DaTQPpNE8\n5OXlobn50eGfo0ePxujRo4167aA5KFu2bMHdu3f1PhceHo53330X27ZtQ1xcHN577z3IZDJ89dVX\nOHr0KJ5//vl+j9/Z2QmRgeiZVNpq8Ae2qamJN3sCqMq7nJyc4Cx24pyh3q+ogI2NDXx8fDlLNcrL\ny6BUKhEaqn8CBoZBZWUlWqVSjBoVoe9pMAzQ1PQAFRX38VQCd/lGc0sLCgoKsGTJEs718zKZDDdv\n3sC0adMgVE+uezSSy+U4f/48IiPHdE/Ae2sClDhx4jhGBAVhAUcZBcuyyMjIwMToGLi5uXNM9lko\nlSyEQgFUh6IY8gh6o70+R3f83l2dOnUSSoUCSYsW9e5IqURCfDzq6urR2toKsdiZ19mpqa2Fr7eX\nXsdKEhiIsWPG4Pz5cwgLCwPD9N5gQHX1gKJLAQED1fvR4wJshELExcaiqrpG77Wrl4WUl5fi3/8+\nitX/93U4OnB/RhioLoBrrj9qVATOnTuL6upq+Pv59XbAAIwICoJcLkdZWRlCQ8N6Su7+U21vPG/e\nPERxbDttb28PV1dX1NbWcDoogOrwR84Swm5+u2yZwY0qEhISDPZjTizloBhzSJxMJoOD1llF6qxE\nR0fHoDkohhCJRIiJiUFKSgpWrVqFsrIyZGVlYcOGDXrb5+fnY9euXXjjjTcQEhLC23fPH9iUlBTM\nnDnTjOrND2k0D6TRPJBG8zAUNDY3N2PJkiV9ei3DGhMyGwRaWlqwfv16/O///q/mB+/GjRs4fvw4\nNm3a1Kt9zyhWRUUFxvCcxzHY1NfXw8vL/Acc9RVr0wNYnybSYxhr02RtevLy8nSi9KZEi7QxJSJf\nX1+vkx3uOaYxwSE1x44dQ1NTE+8i+Q8//BCLFi3SHLyq/u7eunWr1TgogCojvnfvXuTl5UEsFmPp\n0qWYNGkSAKChoQGbNm3Chx9+CHd3d3z00UcoLCzUWZs2atQorF271uA4KSkpff4BHixIo3kgjeaB\nNJqHx12j1ZR4icViuLq6IiMjA/Pnz0dHRwcyMzMRFBSkt721R7FIj2GsTRPpMYy1abI2Pf2JFmlj\nzmsyVA6ljTEZlICAAJSXl2sclPv378PZ2dmqnBNAtd7sjTfe0Pucp6cnPv30U83/k5OTB0sWQRAE\nYQRW46AwDIPVq1fj22+/xalTpyAQCBAZGYlly5ZZWhpBEMRjjVKphEKhgEKhgFKphFwuh1Ao1Ls5\nR3x8PPbs2YPJkyfDxcUFqampRu3+9bjSlwzZYEMazQNpNA+k0Tw87hqtxkEBgLCwMPzhD3+wtAyC\nIIhhxffff4/U1FTN/y9duoTFixcjKSmpVznUuHHjsGDBAnz00Ueac1AWL15sQfWW5XGfJAwWpNE8\nkEbzQBrNQ380Ws0alP5SUFBgVW8W6TGMtWkiPYaxNk2khyAIgiAePx4bB4UgCIIgCIIgiKGPVR3U\nSBAEQRAEQRDE8Maq1qAQBEEQhDXS1dWFAwcOID8/H1KpFN7e3li6dCnGjx/P+Zr09HSkpaVp1uqs\nXLlSZyvjgeDMmTPIzMxERUUFJk2axLtl9IULF7B3716dbarXrFmDiIje52pZSiNgGTvybVPdk8G0\noym6LGE3UzQOhfvPUjY0Rael7Gjqd6KptiQHhSAIgiAMoFAo4OHhgfXr18PT0xNZWVnYvn07Nm7c\nCE9Pz17tb9++jbS0NCQnJ8PV1RWff/45UlJS8Nxzzw2oTnd3dyxatAi3b9+GXC432L7nWTiDgSka\nLWXHr7/+Gra2ttiyZQvKy8vx6aefQiKRICAgQG/7wbKjsbosZTdTNALWff9Z0oam6AQsY0dTvhP7\nYssh56BYaxTL2qJW1hahsoZolDVGnqwp0mSNUSVriyANdMSIsF5EIpHObmUTJkyAl5cXysrK9Doo\nmZmZSEhIgL+/PwAgKSkJO3fuHPDJTUxMDACgtLQUjY2NBttbYhmqKRotYUeZTIYbN25g06ZNEIlE\nCA8PR3R0NC5evMg57mDY0RRdlrr/TLWdNd9/lrKhqToBy9jRlO/EvthyyP1SWmsUy9qiVtYWobKG\naJQ1Rp6sKdJkjVEla4sgDXTEiBg6NDc3o6amhvM7rLKyEtHR0Zr/SyQStLS0QCqVDsqhlsZOWMrL\ny/HOO+/AyckJU6ZMQWJiot7zbwYCYzRawo41NTUQCATw8fHRGbegoIDzNYNhR1N0Wer+M9V21nz/\nWfozrMaYz4kl7aiG7zuxL7Yccovk1R6bejKg7bHpQ9trc3R0RFJSEi5cuGB2XTExMYiOjoZYLDaq\n/UB7u6boGWgbqSMqzzzzTK+IChfmto8pGgbrnjHVLtZyzwyWfUzRBAxOBMmU75/BtBMxuHR1dWHn\nzp2YOnUqfH199baRyWRwcHDQ/N/e3h4A0NHRMSgaGYYx2CYiIgKbNm3C1q1bsXr1aly+fBlpaWmD\noE6FMRotYUeZTKYZR3tcrjEHy46m6LLU/WeKRmu//yz9GVZjSKel7QgY/k7siy2HXAalJ9YWxbK2\nqJU1RKisIRpljZEna400WWNUyVojSOaOGBGWY8uWLbh7967e57Szc0qlErt374atrS2WL1/O2Z9I\nJNL58W1vbweAXpO3gdAIGPeZ8fLy0vw7MDAQSUlJOH36NBITE61GoyXs+OKLL/aaOLW3t3OOORB2\n1EdPW/DpGgi7mVvjYNmNC0P3n6Vs2BNDOi1tR2O+E/tiyyHtoPQ3ijUQkwRTolaenp6oqKjA9u3b\nIRAIBuRm6m+Eyhw26ms0ypz2MVfkyZz3TF8iTdZwzwz2Z8oYTYNpHzX9iRiRg2J9rF+/3mAblmWx\nb98+tLa2Ys2aNbwOcEBAAMrLyxEbGwsAuH//Ppydnfv13hujUY0x3/366G8m0twaLWFHmUwGpVKJ\n2tpaTQCpvLwcgYGBRo8xEBldX19fo3UNhN3MrVEfg7mWwtD9Zykb9qQvn+XBsqOx34l9saXVOSjW\nGMWytqiVtUWohkI0yhojT9YaabLGqJK1RZAGKmJEWDcHDhxAdXU13n77bdja2vK2jY+Px549ezB5\n8mS4uLggNTUV06ZNG3CNSqUSCoUCCoUCSqUScrkcQqFQ78QhOzsbwcHBcHFxQVVVFVJTUxEXF2dV\nGi1hR5FIhJiYGKSkpGDVqlUoKytDVlYWNmzYoLf9YNnRFF2Wuv9M0Wjt95+lbGiqTkvZETD+O7Ev\ntrQ6B8Uao1jWFrWytgjVUIhGWWPkyVojTdYYVbKmCNJARowI66WhoQFnz56FjY2NznfeqlWrMGnS\nJDQ0NGDTpk348MMP4e7ujnHjxmHBggX46KOPNLu4ae94M1B8//33SE1N1fz/0qVLWLx4MZKSknpp\nLCgowN69eyGTyeDs7IwpU6bg6aeftiqNlrLjihUrsHfvXqxfvx5isRgrV67U7EBkSTty6bIWu5mi\n0druv/j4eKuxoSk6LWVHvu/EkSNH9t+W7BDkX//6F/u3v/2N7ejoMNg2JyeHTU5OZisrK9nW1lZ2\n8+bN7HfffWd2TQqFgu3s7GSPHj3K7tq1i+3s7GQVCoXetllZWezDhw9ZlmXZyspKduPGjeyJEycs\npmcwbLR9+3Z2x44dbEdHB3vnzh127dq1bGVlpd62A2UfYzUM1j1jiiZrumcG0z7GahoM+6gx9vtn\nMO1EEARBEI8TDMtaYPPkftDQ0ID3338fNjY2OpFLrigWMDhnEaSkpOh4ugA4I0LffvstLl68qOPt\nJiUlmXVBryl6AMuegzJY9uHSYKl7xhRNlrxnekZrgMGzj7GaBsM+AP/3T8+IEUDnoBAEQRBEXxhy\nDgpBEARBEARBEI8vQ+4cFIIgCIIgCIIgHl/IQSEIgiAIgiAIwmogB4UgCIIgCIIgCKuBHBSCIAiC\nIAiCIKwGclAIgiAIgiAIgrAayEEhCIIgCMKi7N+/H5s3bx608RYsWIBz5871q48jR45g1apVZlJk\nXjZv3owPPvjApNcYY5N9+/Zh2bJlWLBgAdLT0/sj8bGhqKgIK1asQGdnp6WlPFaQg0IQBEEQhMVo\nbm7Gd999hxUrVlhaypDj1q1bWLBgAZqbm3Uef/PNN7FhwwazjlVSUoIDBw7g7bffxqFDhzBjxgyz\n9j9UGTlyJMLDw3Hs2DFLS3msIAeFIAiCIAiLkZ6ejpCQEAQGBlpaymODo6MjnJyczNpnZWUlACA+\nPh7u7u6ws7Pr1UYul5t1zKHC3Llz8f3331taxmMFHWlMEARBEES/Wb9+PUJDQ/Hmm29qHtu8eTOa\nm5vxl7/8hfN1GRkZeOqpp3Qey8rKws6dO1FaWgqBQACJRILk5GSEhIQAAPLy8rB7924UFBRAKBRi\n1KhR+OMf/whPT09cuXIFBw8eRGlpKQBg9OjRWL16NUaMGMGpob6+Hl9++SWuX78OABg7dixWr16t\n4zQdPnwYR48eRUdHB6ZNmwY/Pz+DNtm/fz/S0tLw4MEDODs744knnsAf/vAHADCos7q6Gi+99BI2\nbNiAlJQU3L17F76+vnjjjTcQGxuL6upqTV8vvPACAGD+/PlITk7uZfe+2ESbffv24cCBAwBUpWAM\nw+DUqVOaccaPH4/jx49DoVDgm2++6bM909PT8a9//QuA/ntn3759OHfuHLZv3655LC0tDUeOHEF1\ndTV8fHyQlJSEpUuXgmEYjd5169bh2rVruHLlCtzd3fH73/8ec+bM0fTR0NCA7du349q1a5DJZJBI\nJFi9ejV8fX3x0ksv4dNPP0VERISm/Q8//IA9e/bg0KFDEAqFmDRpEv7+978jNzcXY8eONcqmBD/k\noBAEQRAE0W/UE8Kej+l7XE17ezsKCwvx6quvah5TKBTYtGkTEhMT8d5770GhUODu3bsQCFRFH0VF\nRXj33Xcxb948/Od//idsbW2Rk5MDhUIBAJDJZHjuuecQFhYGmUyGr7/+Gn/+85+xc+dO2Nj0nvZ0\ndHTg3Xffxfjx47FlyxbY2triyJEj2LBhA3bu3AmRSIRffvkFe/fuxZtvvomJEyfi119/xTfffAMX\nFxfOazt79iy+/fZbvP/++wgNDUVjYyPy8/M1zxurc8eOHVi9ejXCwsJw/PhxbNq0CV999RV8fHzw\nwQcf4C9/+Qt27NgBZ2dniEQivXY31SY9eeGFF+Dt7Y1t27bh0KFDOs9lZ2dDLBbjb3/7G1iWNZs9\nDd07gMpR2LdvH9566y2MGjUKJSUl2LZtG2xsbLBkyRJNu/379+PVV1/Fq6++ipMnT2Lr1q2IioqC\nj48P2tvbkZycDA8PD/zXf/0XPD09UVJSAoZh4Ofnh9jYWKSlpek4KGlpaZg7dy6EQiEAwN7eHsHB\nwcjKyiIHxUxQiRcxrFm/fj3+8Y9/6DzWl8WFBEEQRG9YlgXLspzPV1dXQ6lUwsfHR/OYVCqFVCrF\n5MmT4e/vD4lEglmzZmmi/YcPH0Z4eDjWrVuHsLAwBAUFITExUdNHQkICEhISEBAQgNDQULzzzjuo\nrq5GQUGBXg0ZGRkAgOTkZISGhkIikWDt2rVob2/HpUuXAAD//ve/MW/ePDz99NMIDAzE8uXLMXr0\naN5rr6mpgYeHB2JjY+Ht7Y2IiAidSbOxOhcvXozp06dDIpHgjTfegLe3N06cOAGBQABnZ2cAgJub\nG9zd3eHo6KjX7qbapCcODg6akjF3d3e4u7trnrOzs0NycjKCg4MREhJiNnsauncA4MCBA3jttdeQ\nkJAAX19fTJkyBcuWLcOJEyd02s2bNw+zZ8+Gv78/XnrpJQgEAuTk5AAAfv75ZzQ1NWHTpk0YN24c\n/Pz8EB8fjwkTJgAAEhMTkZGRoVkEX1ZWhvz8fCxcuFBnDB8fH1RUVBhlT8IwlEEhhjV9ifgRBEEQ\n5qGtrQ2AKgKtxsXFBfPnz8f777+PmJgYREdH46mnntI4IMXFxZg2bRpnn5WVldi7dy8KCgrQ1NSk\nmejW1tZi3LhxvdrfvXsX1dXVeOaZZ3Qel8lkqKqqAgCUl5fj6aef1nl+zJgxmnUZ+pgxYwaOHz+O\nVatWIS4uDnFxcYiPj4etra1JOrUj8gzDIDIyEmVlZZzjmsMmphASEqKThRkoe/akqakJ9fX12LZt\nGz755BPN4+pMmjahoaGafwuFQri5uaGpqQkAUFhYiLCwMM5sWHx8PD777DOcP38es2bNwqlTpxAZ\nGYng4GCddg4ODpBKpUbrJ/ghB4UgemBM1IYgCILQRV9gp6uri/c16oh/R0eHzuPJyclYunQprl69\niosXL+Krr77Cxo0bERcXBwC839EffPABfHx8sG7dOnh5eUEgEOC1117j1MKyLEaOHIk//elPvZ5T\nZyj6gre3N3bt2oWbN2/i+vXr2L59O/bv349PPvkE9vb2JuvU1msqfR3LGNRlZdr6zGFPdUmfNtrO\nh9oO69atM+hk6StjUyqVvfrieu3cuXNx6tQpTJ8+HT/99BNefvnlXu3a2trg5ubGq4MwHirxIgiC\nIAii37i6uqKhoUHnseLiYt6MtJ+fHwQCAWpra3s9FxYWhmXLlmHz5s2YMGGC5tyNkSNH4tatW3r7\na25uxv3797F8+XLExMQgKCgIbW1teqPqakaNGoXKykq4uLjA399f508sFgMAgoKCkJubq/O6vLw8\ng9l2Ozs7TJo0CatXr8ann36K0tJS5ObmmqRTe1yWZVFQUKApd1NPvLUn2+awSX8wlz3d3Nx63U9F\nRUWaNu7u7vD09ERlZWWvcfz9/U3SW1JS0murZm0SExNx69YtpKSkoL29HTNnzuzVpra2lnaiMyPk\noBDDmr5E/AiCIIjeREdH48qVK8jMzER5eTm++OIL1NfX877GwcEB4eHhOmshqqursWvXLuTm5qKm\npgY3b95ESUmJZgevF154AYWFhdi2bRuKi4tRXl6OkydPora2FmKxGC4uLkhNTUVFRQWysrLwySef\naBYz62P27Nlwc3PDxo0bkZWVhaqqKmRlZWH79u2aNQVLly5Feno6Tp48iYqKChw8eNDg+o3Tp0/j\n5MmTKCkpQVVVFdLS0mBjY4PAwECTdKampuLs2bMoLy/HP//5T9TV1WHx4sUAAF9fXzAMg0uXLqGp\nqQnt7e29Xt8Xm/QHc9lz4sSJKCoqQlpaGioqKnD48OFeTs2qVatw5MgRfPfddygvL0dJSQnS09N7\nLeTnY9asWRq9OTk5qKqqQmZmpo4TLJFIMH78eOzcuRPTp0+Hg4ODTh8dHR0oLS1FVFSUqeYiOKAS\nL2JYwxXxMyX6QhAEQQALFy5ESUkJtm7dCgBYsmQJpk2bxhuZBoCZM2fi3LlzWLZsGQBVyVBFRQX+\n+te/4uHDh3B3d8ecOXM0z48cORL/8z//g927d2PdunWwtbVFREQEpkyZAoFAgD/96U/4/PPP8frr\nryMwMBCvvfYa7zbHIpEIH330EXbv3o2//vWvkEql8PT0RHR0tKYkacaMGaiqqsKePXsgk8kQHx+P\n559/nvc0dbFYjMOHD2PHjh3o6upCcHAwNm7cCF9fXwAwWucrr7yCo0ePorCwEL6+vti4cSM8PT0B\nAF5eXli1ahX27NmDjz/+GPPmzUNycrLOWsq+2ISLnkE9fWs2+2PP06dPa/qJi4vD7373O02bOXPm\nYPHixbh48aKmTWJiIuzt7XHkyBHs3r0bIpEIwcHBvda/8GFvb48tW7bgyy+/xJ///GfI5XIEBQVh\n9erVOu0WLFiA7OzsXovjAeDy5cvw9vamHbzMCMNSsT0xjElNTcUXX3yB999/HxKJBKmpqUhLS0NU\nVBQ+/PBDS8sjCIJ47GlubtacNSGRSCwtx2pQn4Py2WefYdSoUZaWM+AcOXIEKSkpmnNQrI1vvvkG\naWlp2L17d6/nPvjgA0RFRWmcaKL/UIkXMaxZuHAhFixYgK1bt+Kdd96Bk5MT7+4wBEEQhHlxcXHB\nb37zG5PKcghisGhvb8e9e/dw7NgxLF26tNfzxcXFKCoqwrPPPmsBdY8vVOJFDGuEQiHeeustvPXW\nW5aWQhAEMWxZuXKlpSVYJcNpy3tr3eL/s88+Q0ZGBqZOnYpFixb1ej4sLAxff/21BZQ93lCJF0EQ\nBEEQBEEQVgOVeBEEQRAEQRAEYTWQg0IQBEEQBEEQhNVADgpBEARBEARBEFYDOSgEQRAEQRAEQVgN\n5KAQBEEQBEEQBGE1kINCEARBEARBEITVQA4KQRAEQRAEQRBWAzkoBEEQBEEQBEFYDf8frqZs5w7w\nym0AAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x8bd6a20>"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Also, note from the above plots that the first occurrence of the OTF intersecting/crossing the zero value happens as $W_{20} \\to \\lambda/2$. This point is also the known as the traditional or Hopkins criterion for misfocus [4]."
]
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Example: Ambiguity function of cubic phase mask "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cubic phase masks (CPM) has been used to make hybrid optical systems largely invariant to defocus, thus extending the depth of field of such systems. For details on CPM systems, refer to [4]. \n",
"\n",
"The expression for the ambiguity function of a cubic phase mask is given below [4]:\n",
"\n",
"$$\n",
"A(u, y) = \\frac{1}{2} \\int\\limits_{-(1 - |u|/2)}^{(1 - |u|/2)} e^{j\\alpha[(t+u/2)^\\gamma - (t-u/2)^\\gamma]}e^{j2\\pi y t} dt, \\hspace{10pt} |u| \\leq 2;\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will numerically evaluation the above expression for the AF of cubic phase mask (CPM), recognizing that the expression is of the form of inverse Fourier Transform of $g_u(t)$ where, \n",
"\n",
"$$\n",
"g_u(t) = \\frac{1}{2} e^{j\\alpha[(t+u/2)^\\gamma - (t-u/2)^\\gamma]}\n",
"$$\n",
"\n",
"\n",
"\n",
"Here are the steps for rendering the 2-D AF for cpm:\n",
"\n",
"1. Create a $u$ vector $-2 \\leq u \\leq 2$\n",
"\n",
"2. Create a $t$ vector $-2 \\leq t \\leq 2$ (i.e. between $-a-L \\leq t \\leq a + L$, which is the maximum region of integration) \n",
"\n",
"3. For every `u` in $u$ create the sequence $g_u(t)$ where, \n",
" \n",
" $g_u(t) = f(t + u/2)f^*(t - u/2) $ using the following rule:\n",
" \n",
" a. if $-1 < u/2 < 0$, then evaluate $g_u(t)$ where $-u/2 -1 < t < u/2 + 1$\n",
" \n",
" b. if $0 < u/2 < 1$, then evaluate $g_u(t)$ where $u/2 -1 < t < -u/2 + 1$\n",
" \n",
"4. Take inverse Fourier Transform of each sequence $g_u(t)$ (with $y$ as the transform variable). i.e. $G_u(y) =$ $A(u,y) =$ IFFT$\\{g_u(t)\\}$\n",
"\n",
"\n",
"The expression for the OTF for the CPM is then given by:\n",
"\n",
"$$\n",
"\\mathcal{H}(u, W_{20}) = A(u, 2uW_{20}/\\lambda) = \\frac{1}{2} \\int\\limits_{-(1 - |u|/2)}^{(1 - |u|/2)} e^{j\\alpha[(t+u/2)^\\gamma - (t-u/2)^\\gamma]}e^{j2\\pi \\left(\\frac{2uW_{20}}{\\lambda} \\right) t} dt, \\hspace{10pt} |u| \\leq 2;\n",
"$$\n",
"\n",
"We will use numerical integration to generate the plots of OTFs for the CPM with various amounts of defocus."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.integrate import quad\n",
"import warnings\n",
"warnings.simplefilter(action='error', category=np.ComplexWarning)\n",
"# Turn on the warning to ensure that the numerical integration is \"reliable\"?\n",
"warnings.simplefilter(action='always', category=sp.integrate.IntegrationWarning)\n",
"\n",
"ifft = np.fft.fft\n",
"fftshift = np.fft.fftshift\n",
"fftfreq = np.fft.fftfreq\n",
"\n",
"# cubic phase mask parameters\n",
"alpha = 90\n",
"gamma = 3\n",
"umin, umax = -2, 2\n",
"ymin, ymax = -60, 60\n",
"w20LambdaBy2 = [0, 5, 15] # amounts of defocus in units of wavelength (by 2)\n",
"\n",
"uVec = np.linspace(umin, umax, 300)\n",
"N = 512 # number of samples along \"t\" ... and for FFT\n",
"L = 1\n",
"\n",
"def gut(t, alpha, gamma, u): \n",
" return 0.5*np.exp(1j*alpha*((t + u/2)**gamma - (t - u/2)**gamma))\n",
"\n",
"guy = np.empty((N, len(uVec)))\n",
"#roi = np.empty((N, len(uVec))) # for debugging & seeing the region of integration\n",
"t = np.linspace(-2*L, 2*L, N)\n",
"dt = (4*L)/(N-1)\n",
"\n",
"for i, u in enumerate(uVec):\n",
" g = np.zeros_like(t, dtype='complex64')\n",
" if -1 <= u/2.0 < 0:\n",
" mask = (t > (-u/2 - 1))*(t < (u/2 + 1))\n",
" #roi[:, i] = mask.astype('float32')\n",
" g[mask] = gut(t[mask], alpha, gamma, u)\n",
" guy[:, i] = np.abs(fftshift(ifft(g)))\n",
" elif 0 <= u/2.0 <= 1:\n",
" mask = (t > (u/2 - 1))*(t < (-u/2 + 1))\n",
" #roi[:, i] = mask.astype('float32')\n",
" g[mask] = gut(t[mask], alpha, gamma, u)\n",
" guy[:, i] = np.abs(fftshift(ifft(g)))\n",
"\n",
"# Normalize to make maximum value = 1\n",
"guyMax = np.max(np.abs(guy.flat))\n",
"guy = guy/guyMax\n",
"\n",
"yindex = fftshift(fftfreq(N, dt))\n",
"ymin, ymax = yindex[0], yindex[-1]\n",
"\n",
"fig = plt.figure(figsize=(12, 7))\n",
"ax1 = fig.add_axes([0.12, 0, 0.5, 1.0]) # [*left*, *bottom*, *width*,*height*]\n",
"ax2 = fig.add_axes([0.66, 0.23, 0.32, 0.54])\n",
" \n",
"im = ax1.imshow(guy**0.8, cmap=cm.YlGnBu_r, origin='lower',\n",
" extent=[umin, umax, ymin, ymax],\n",
" vmin=0.0, vmax=1.0,aspect=1./40) \n",
"plt.colorbar(im, ax=ax1, shrink=0.55, aspect=35)\n",
"\n",
"# OTF line in AF \n",
"for elem in w20LambdaBy2:\n",
" otfY = elem*uVec # OTF line in AF with slope 2w_{20}/lambda\n",
" ax1.plot(uVec, otfY, alpha = 0.6, linestyle='solid')\n",
"\n",
"ax1.set_xlim(umin, umax)\n",
"ax1.set_ylim(ymin, ymax)\n",
"ax1.set_xlabel('u', fontsize=14)\n",
"ax1.set_ylabel('y', fontsize=14)\n",
"ax1.set_title('2-D AF of 1-D cpm', y=1.01)\n",
"\n",
"# Magnitude plots of the OTF of the cpm\n",
"\n",
"def otf_cpm(t, alpha, gamma, u, w20LamBy2): \n",
" return (0.5*np.exp(1j*alpha*((t + u/2)**gamma - (t - u/2)**gamma))\n",
" *np.exp(1j*2*np.pi*u*w20LamBy2*t))\n",
"\n",
"def complex_quad(func, a, b, **kwargs):\n",
" \"\"\"Compute numerical integration of complex function between\n",
" limits a and b\n",
" Adapted from the following SO post:\n",
" stackoverflow.com/questions/5965583/use-scipy-integrate-quad-to-integrate-complex-numbers\n",
" \"\"\"\n",
" def real_func(x, *args):\n",
" if args:\n",
" return sp.real(func(x, *args))\n",
" else:\n",
" return sp.real(func(x))\n",
" def imag_func(x, *args):\n",
" if args:\n",
" return sp.imag(func(x, *args))\n",
" else:\n",
" return sp.imag(func(x))\n",
" real_integral = quad(real_func, a, b, **kwargs)\n",
" imag_integral = quad(imag_func, a, b, **kwargs)\n",
" return (real_integral[0] + 1j*imag_integral[0], real_integral[1:], imag_integral[1:])\n",
"\n",
"for elem in w20LambdaBy2:\n",
" Huw = np.empty_like(uVec, dtype='complex64')\n",
" for i, u in enumerate(uVec):\n",
" if -1 <= u/2.0 < 0:\n",
" Huw[i] = complex_quad(func=otf_cpm, a=-u/2 - 1, b=u/2 + 1, args=(alpha, gamma, u, elem))[0]\n",
" elif 0 <= u/2.0 <= 1:\n",
" Huw[i] = complex_quad(func=otf_cpm, a=u/2 - 1, b= -u/2 + 1, args=(alpha, gamma, u, elem))[0]\n",
" HuwMax = np.max(np.abs(Huw))\n",
" ax2.plot(uVec, np.abs(Huw)/HuwMax, label='$W_{20}' + '= {}\\lambda/2$'.format(elem))\n",
"\n",
"ax2.legend(fontsize=12)\n",
"ax2.set_ylabel(\"Magnitude of OTF\", fontsize=14)\n",
"ax2.set_xlabel(\"Spatial frequency, u\", fontsize=14)\n",
"ax2.set_title('Optical Transfer Function of cpm', y=1.01)\n",
"\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
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HYJIkweeff47169fj6quvxpYtW7DffvthzJgxWL58Obp37+7alpaWomPHjigv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q+mzySeN3wfjGeZMubUyy7TdQVSG0NQJbU9Szoh2Y12phQxbQw0guE/AQFNpUqpSE1An5FzchIz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P9c43uqDD4S1QMq0eEzyCCDHBT55pYteFpIOV599MMnMI8/HJDZ5cAW4Pj7Eu7P27AgI0yn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hpRgk6d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\n",
"text": [
"<matplotlib.figure.Figure at 0xc819b38>"
]
}
],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see from the above plots of the OTF, that the OTFs are insensitive to large amounts of defoucs. More importantly, there are no regions of zero values within the passband (though the bandwidth is not really constant for large amounts of defocus). This property is extremely important from the point of view of the reconstruction filter (such as an inverse filter or an Wiener filter)."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
}
],
"metadata": {}
}
]
} | mit |
tpin3694/tpin3694.github.io | python/geocoding_and_reverse_geocoding.ipynb | 2 | 14470 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Title: Geocoding And Reverse Geocoding \n",
"Slug: geocoding_and_reverse_geocoding \n",
"Summary: Geocoding And Reverse Geocoding \n",
"Date: 2016-05-01 12:00 \n",
"Category: Python \n",
"Tags: Data Wrangling \n",
"Authors: Chris Albon \n",
"\n",
"Geocoding (converting a physical address or location into latitude/longitude) and reverse geocoding (converting a lat/long to a physical address or location) are common tasks when working with geo-data.\n",
"\n",
"Python offers a number of packages to make the task incredibly easy. In the tutorial below, I use pygeocoder, a wrapper for Google's geo-API, to both geocode and reverse geocode."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Preliminaries\n",
"\n",
"First we want to load the packages we will want to use in the script. Specifically, I am loading pygeocoder for its geo-functionality, pandas for its dataframe structures, and numpy for its missing value (np.nan) functionality."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load packages\n",
"from pygeocoder import Geocoder\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Create some simulated geo data\n",
"\n",
"Geo-data comes in a wide variety of forms, in this case we have a Python dictionary of five latitude and longitude strings, with each coordinate in a coordinate pair separated by a comma."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create a dictionary of raw data\n",
"data = {'Site 1': '31.336968, -109.560959',\n",
" 'Site 2': '31.347745, -108.229963',\n",
" 'Site 3': '32.277621, -107.734724',\n",
" 'Site 4': '31.655494, -106.420484',\n",
" 'Site 5': '30.295053, -104.014528'}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"While technically unnecessary, because I originally come from R, I am a big fan of dataframes, so let us turn the dictionary of simulated data into a dataframe."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Convert the dictionary into a pandas dataframe\n",
"df = pd.DataFrame.from_dict(data, orient='index')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Site 4</th>\n",
" <td>31.655494, -106.420484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Site 3</th>\n",
" <td>32.277621, -107.734724</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Site 1</th>\n",
" <td>31.336968, -109.560959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Site 5</th>\n",
" <td>30.295053, -104.014528</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Site 2</th>\n",
" <td>31.347745, -108.229963</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0\n",
"Site 4 31.655494, -106.420484\n",
"Site 3 32.277621, -107.734724\n",
"Site 1 31.336968, -109.560959\n",
"Site 5 30.295053, -104.014528\n",
"Site 2 31.347745, -108.229963"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# View the dataframe\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can see now that we have a a dataframe with five rows, with each now containing a string of latitude and longitude. Before we can work with the data, we'll need to 1) seperate the strings into latitude and longitude and 2) convert them into floats. The function below does just that."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create two lists for the loop results to be placed\n",
"lat = []\n",
"lon = []\n",
"\n",
"# For each row in a varible,\n",
"for row in df[0]:\n",
" # Try to,\n",
" try:\n",
" # Split the row by comma, convert to float, and append\n",
" # everything before the comma to lat\n",
" lat.append(float(row.split(',')[0]))\n",
" # Split the row by comma, convert to float, and append\n",
" # everything after the comma to lon\n",
" lon.append(float(row.split(',')[1]))\n",
" # But if you get an error\n",
" except:\n",
" # append a missing value to lat\n",
" lat.append(np.NaN)\n",
" # append a missing value to lon\n",
" lon.append(np.NaN)\n",
"\n",
"# Create two new columns from lat and lon\n",
"df['latitude'] = lat\n",
"df['longitude'] = lon"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a took a what we have now."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Site 4</th>\n",
" <td>31.655494, -106.420484</td>\n",
" <td>31.655494</td>\n",
" <td>-106.420484</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Site 3</th>\n",
" <td>32.277621, -107.734724</td>\n",
" <td>32.277621</td>\n",
" <td>-107.734724</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Site 1</th>\n",
" <td>31.336968, -109.560959</td>\n",
" <td>31.336968</td>\n",
" <td>-109.560959</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Site 5</th>\n",
" <td>30.295053, -104.014528</td>\n",
" <td>30.295053</td>\n",
" <td>-104.014528</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Site 2</th>\n",
" <td>31.347745, -108.229963</td>\n",
" <td>31.347745</td>\n",
" <td>-108.229963</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 0 latitude longitude\n",
"Site 4 31.655494, -106.420484 31.655494 -106.420484\n",
"Site 3 32.277621, -107.734724 32.277621 -107.734724\n",
"Site 1 31.336968, -109.560959 31.336968 -109.560959\n",
"Site 5 30.295053, -104.014528 30.295053 -104.014528\n",
"Site 2 31.347745, -108.229963 31.347745 -108.229963"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# View the dataframe\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Awesome. This is exactly what we want to see, one column of floats for latitude and one column of floats for longitude."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reverse Geocoding\n",
"\n",
"To reverse geocode, we feed a specific latitude and longitude pair, in this case the first row (indexed as '0') into pygeocoder's reverse_geocoder function. "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Convert longitude and latitude to a location\n",
"results = Geocoder.reverse_geocode(df['latitude'][0], df['longitude'][0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can take can start pulling out the data that we want."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(31.6556534, -106.4204309)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print the lat/long\n",
"results.coordinates"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Ciudad Juárez'"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print the city\n",
"results.city"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Mexico'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print the country\n",
"results.country"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Print the street address (if applicable)\n",
"results.street_address"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Chihuahua'"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print the admin1 level\n",
"results.administrative_area_level_1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Geocoding\n",
"\n",
"For geocoding, we need to submit a string containing an address or location (such as a city) into the geocode function. However, not all strings are formatted in a way that Google's geo-API can make sense of them. We can text if an input is valid by using the .geocode().valid_address function."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Verify that an address is valid (i.e. in Google's system)\n",
"Geocoder.geocode(\"4207 N Washington Ave, Douglas, AZ 85607\").valid_address"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Because the output was True, we now know that this is a valid address and thus can print the latitude and longitude coordinates."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(31.6556534, -106.4204309)"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print the lat/long\n",
"results.coordinates"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"But even more interesting, once the address is processed by the Google geo API, we can parse it and easily separate street numbers, street names, etc. "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Find the lat/long of a certain address\n",
"result = Geocoder.geocode(\"7250 South Tucson Boulevard, Tucson, AZ 85756\")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'7250'"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print the street number\n",
"result.street_number"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'South Tucson Boulevard'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Print the street name\n",
"result.route"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And there you have it. Python makes this entire process easy and inserting it into an analysis only takes a few minutes. Good luck!"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [default]",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
Jorisvansteenbrugge/advbioinf | cluster/clustering.ipynb | 2 | 137513 | {
"cells": [
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy\n",
"import scipy\n",
"import scipy.cluster.hierarchy as sch\n",
"import pandas as pd\n",
"import matplotlib.pylab as plt\n",
"import seaborn as sns\n",
"\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def fileToDict(file_name):\n",
" with open(file_name) as in_file:\n",
" dic = {}\n",
" header = in_file.readline().strip().split(\",\")\n",
" for idx in xrange(len(header)):\n",
" header[idx] = header[idx]\n",
" dic[header[idx]] = []\n",
"\n",
" for line in in_file:\n",
" line = line.strip().split(\",\")\n",
" for idx in xrange(len(line)):\n",
" val = line[idx]\n",
" try:\n",
" val = numpy.double(val)\n",
" except ValueError:\n",
" pass\n",
" dic[header[idx]].append(val)\n",
" \n",
" return dic, header"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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pAMicnFT+j125igcunOux5gkhJH6oJJBYWcjncXh+HiXUMD8/CwCZk5PKv1JZ77HWCSFk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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f3149ff2ad0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fileDic, header = fileToDict(\"/home/steen176/advbioinf/cluster/20161122_MayAppleExpressionData.csv\")\n",
"#Use Gene Name as row names\n",
"df = pd.DataFrame(fileDic, index = fileDic[\"Gene Name\"]\n",
" , columns = header[1:])\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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k/FPgiTHGXxv1spcSQqgDdwKvjDG+P592FnA+2evzRyVkWnTkMr9Oy+uBh5CN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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f314a1b01d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Timepoint as cluster\n",
"df = df.transpose()\n",
"#print(df)\n",
"distances = sch.distance.pdist(df, metric=\"euclidean\")\n",
"clustering = sch.linkage(distances, method='complete')\n",
"tree = sch.dendrogram(clustering)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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AHG2h+5vc73Qvk3Rfd9+f71kv948KZ6UtiHg8P1skn/x8h69ni6S5q/9IQY/5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\nBRVUUEEFFZzjUAUDFVRQQQUVVHCOw/8DGlMEW4YvA74AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f31495e5f50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#Genes as cluster\n",
"distances = sch.distance.pdist(df, metric = \"correlation\")\n",
"clustering = sch.linkage(distances, method=\"complete\")\n",
"tree = sch.dendrogram(clustering, leaf_font_size=2,\n",
" color_threshold = 4, labels = fileDic[\"Gene Name\"])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<seaborn.matrix.ClusterGrid at 0x7f3141e6ca50>"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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tmnn1r6n27VQyj3fsVGQ2l+1UyyWpX8u3eGEU1I7ckzpCNIenr04dIardc5MX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"text/plain": [
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.clustermap(df.transpose(), method=\"complete\", \n",
" metric=\"euclidean\", row_cluster=False)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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| gpl-3.0 |
rgbkrk/commuter | packages/server/examples/table-with-schema.ipynb | 5 | 43694 | {
"cells": [
{
"cell_type": "code",
"source": [
"import pandas as pd\n",
"pd.options.display.html.table_schema = True"
],
"outputs": [],
"execution_count": 1,
"metadata": {
"collapsed": false,
"outputHidden": false,
"inputHidden": false
}
},
{
"cell_type": "code",
"source": [
"baseball_file = \"/Users/kylek/code/src/github.com/pandas-dev/pandas/doc/data/baseball.csv\"\n",
"df = pd.read_csv(baseball_file).set_index(['id', 'player'])"
],
"outputs": [],
"execution_count": 2,
"metadata": {
"collapsed": false,
"outputHidden": false,
"inputHidden": false
}
},
{
"cell_type": "code",
"source": [
"df.head(n=20)"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"\n",
" year stint team lg g ab r h X2b X3b ... \\\n",
"id player ... \n",
"88641 womacto01 2006 2 CHN NL 19 50 6 14 1 0 ... \n",
"88643 schilcu01 2006 1 BOS AL 31 2 0 1 0 0 ... \n",
"88645 myersmi01 2006 1 NYA AL 62 0 0 0 0 0 ... \n",
"88649 helliri01 2006 1 MIL NL 20 3 0 0 0 0 ... \n",
"88650 johnsra05 2006 1 NYA AL 33 6 0 1 0 0 ... \n",
"88652 finlest01 2006 1 SFN NL 139 426 66 105 21 12 ... \n",
"88653 gonzalu01 2006 1 ARI NL 153 586 93 159 52 2 ... \n",
"88662 seleaa01 2006 1 LAN NL 28 26 2 5 1 0 ... \n",
"89177 francju01 2007 2 ATL NL 15 40 1 10 3 0 ... \n",
"89178 francju01 2007 1 NYN NL 40 50 7 10 0 0 ... \n",
"89330 zaungr01 2007 1 TOR AL 110 331 43 80 24 1 ... \n",
"89333 witasja01 2007 1 TBA AL 3 0 0 0 0 0 ... \n",
"89334 williwo02 2007 1 HOU NL 33 59 3 6 0 0 ... \n",
"89335 wickmbo01 2007 2 ARI NL 8 0 0 0 0 0 ... \n",
"89336 wickmbo01 2007 1 ATL NL 47 0 0 0 0 0 ... \n",
"89337 whitero02 2007 1 MIN AL 38 109 8 19 4 0 ... \n",
"89338 whiteri01 2007 1 HOU NL 20 1 0 0 0 0 ... \n",
"89339 wellsda01 2007 2 LAN NL 7 15 2 4 1 0 ... \n",
"89340 wellsda01 2007 1 SDN NL 22 38 1 4 0 0 ... \n",
"89341 weathda01 2007 1 CIN NL 67 0 0 0 0 0 ... \n",
"\n",
" rbi sb cs bb so ibb hbp sh sf gidp \n",
"id player \n",
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"89330 zaungr01 52.0 0.0 0.0 51 55.0 8.0 2.0 1.0 6.0 9.0 \n",
"89333 witasja01 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"89334 williwo02 2.0 0.0 0.0 0 25.0 0.0 0.0 5.0 0.0 1.0 \n",
"89335 wickmbo01 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"89336 wickmbo01 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"89337 whitero02 20.0 0.0 0.0 6 19.0 0.0 3.0 0.0 1.0 2.0 \n",
"89338 whiteri01 0.0 0.0 0.0 0 1.0 0.0 0.0 0.0 0.0 0.0 \n",
"89339 wellsda01 1.0 0.0 0.0 0 6.0 0.0 0.0 0.0 0.0 0.0 \n",
"89340 wellsda01 0.0 0.0 0.0 0 12.0 0.0 0.0 4.0 0.0 0.0 \n",
"89341 weathda01 0.0 0.0 0.0 0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n[20 rows x 21 columns]"
],
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" <th></th>\n",
" <th></th>\n",
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" <td>2006</td>\n",
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" <td>20</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88650</th>\n",
" <th>johnsra05</th>\n",
" <td>2006</td>\n",
" <td>1</td>\n",
" <td>NYA</td>\n",
" <td>AL</td>\n",
" <td>33</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88652</th>\n",
" <th>finlest01</th>\n",
" <td>2006</td>\n",
" <td>1</td>\n",
" <td>SFN</td>\n",
" <td>NL</td>\n",
" <td>139</td>\n",
" <td>426</td>\n",
" <td>66</td>\n",
" <td>105</td>\n",
" <td>21</td>\n",
" <td>12</td>\n",
" <td>...</td>\n",
" <td>40.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>46</td>\n",
" <td>55.0</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>4.0</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88653</th>\n",
" <th>gonzalu01</th>\n",
" <td>2006</td>\n",
" <td>1</td>\n",
" <td>ARI</td>\n",
" <td>NL</td>\n",
" <td>153</td>\n",
" <td>586</td>\n",
" <td>93</td>\n",
" <td>159</td>\n",
" <td>52</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>73.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>69</td>\n",
" <td>58.0</td>\n",
" <td>10.0</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>14.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88662</th>\n",
" <th>seleaa01</th>\n",
" <td>2006</td>\n",
" <td>1</td>\n",
" <td>LAN</td>\n",
" <td>NL</td>\n",
" <td>28</td>\n",
" <td>26</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>7.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89177</th>\n",
" <th>francju01</th>\n",
" <td>2007</td>\n",
" <td>2</td>\n",
" <td>ATL</td>\n",
" <td>NL</td>\n",
" <td>15</td>\n",
" <td>40</td>\n",
" <td>1</td>\n",
" <td>10</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4</td>\n",
" <td>10.0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89178</th>\n",
" <th>francju01</th>\n",
" <td>2007</td>\n",
" <td>1</td>\n",
" <td>NYN</td>\n",
" <td>NL</td>\n",
" <td>40</td>\n",
" <td>50</td>\n",
" <td>7</td>\n",
" <td>10</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>8.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>10</td>\n",
" <td>13.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89330</th>\n",
" <th>zaungr01</th>\n",
" <td>2007</td>\n",
" <td>1</td>\n",
" <td>TOR</td>\n",
" <td>AL</td>\n",
" <td>110</td>\n",
" <td>331</td>\n",
" <td>43</td>\n",
" <td>80</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>52.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>51</td>\n",
" <td>55.0</td>\n",
" <td>8.0</td>\n",
" <td>2.0</td>\n",
" <td>1.0</td>\n",
" <td>6.0</td>\n",
" <td>9.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89333</th>\n",
" <th>witasja01</th>\n",
" <td>2007</td>\n",
" <td>1</td>\n",
" <td>TBA</td>\n",
" <td>AL</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89334</th>\n",
" <th>williwo02</th>\n",
" <td>2007</td>\n",
" <td>1</td>\n",
" <td>HOU</td>\n",
" <td>NL</td>\n",
" <td>33</td>\n",
" <td>59</td>\n",
" <td>3</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>2.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>25.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>5.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89335</th>\n",
" <th>wickmbo01</th>\n",
" <td>2007</td>\n",
" <td>2</td>\n",
" <td>ARI</td>\n",
" <td>NL</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
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" <tr>\n",
" <th>89336</th>\n",
" <th>wickmbo01</th>\n",
" <td>2007</td>\n",
" <td>1</td>\n",
" <td>ATL</td>\n",
" <td>NL</td>\n",
" <td>47</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89337</th>\n",
" <th>whitero02</th>\n",
" <td>2007</td>\n",
" <td>1</td>\n",
" <td>MIN</td>\n",
" <td>AL</td>\n",
" <td>38</td>\n",
" <td>109</td>\n",
" <td>8</td>\n",
" <td>19</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>20.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>6</td>\n",
" <td>19.0</td>\n",
" <td>0.0</td>\n",
" <td>3.0</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89338</th>\n",
" <th>whiteri01</th>\n",
" <td>2007</td>\n",
" <td>1</td>\n",
" <td>HOU</td>\n",
" <td>NL</td>\n",
" <td>20</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89339</th>\n",
" <th>wellsda01</th>\n",
" <td>2007</td>\n",
" <td>2</td>\n",
" <td>LAN</td>\n",
" <td>NL</td>\n",
" <td>7</td>\n",
" <td>15</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>6.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89340</th>\n",
" <th>wellsda01</th>\n",
" <td>2007</td>\n",
" <td>1</td>\n",
" <td>SDN</td>\n",
" <td>NL</td>\n",
" <td>22</td>\n",
" <td>38</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>12.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>4.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89341</th>\n",
" <th>weathda01</th>\n",
" <td>2007</td>\n",
" <td>1</td>\n",
" <td>CIN</td>\n",
" <td>NL</td>\n",
" <td>67</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>20 rows × 21 columns</p>\n",
"</div>"
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"X3b": 0,
"hr": 1,
"rbi": 2,
"sb": 0,
"cs": 0,
"bb": 0,
"so": 25,
"ibb": 0,
"hbp": 0,
"sh": 5,
"sf": 0,
"gidp": 1
},
{
"id": 89335,
"player": "wickmbo01",
"year": 2007,
"stint": 2,
"team": "ARI",
"lg": "NL",
"g": 8,
"ab": 0,
"r": 0,
"h": 0,
"X2b": 0,
"X3b": 0,
"hr": 0,
"rbi": 0,
"sb": 0,
"cs": 0,
"bb": 0,
"so": 0,
"ibb": 0,
"hbp": 0,
"sh": 0,
"sf": 0,
"gidp": 0
},
{
"id": 89336,
"player": "wickmbo01",
"year": 2007,
"stint": 1,
"team": "ATL",
"lg": "NL",
"g": 47,
"ab": 0,
"r": 0,
"h": 0,
"X2b": 0,
"X3b": 0,
"hr": 0,
"rbi": 0,
"sb": 0,
"cs": 0,
"bb": 0,
"so": 0,
"ibb": 0,
"hbp": 0,
"sh": 0,
"sf": 0,
"gidp": 0
},
{
"id": 89337,
"player": "whitero02",
"year": 2007,
"stint": 1,
"team": "MIN",
"lg": "AL",
"g": 38,
"ab": 109,
"r": 8,
"h": 19,
"X2b": 4,
"X3b": 0,
"hr": 4,
"rbi": 20,
"sb": 0,
"cs": 0,
"bb": 6,
"so": 19,
"ibb": 0,
"hbp": 3,
"sh": 0,
"sf": 1,
"gidp": 2
},
{
"id": 89338,
"player": "whiteri01",
"year": 2007,
"stint": 1,
"team": "HOU",
"lg": "NL",
"g": 20,
"ab": 1,
"r": 0,
"h": 0,
"X2b": 0,
"X3b": 0,
"hr": 0,
"rbi": 0,
"sb": 0,
"cs": 0,
"bb": 0,
"so": 1,
"ibb": 0,
"hbp": 0,
"sh": 0,
"sf": 0,
"gidp": 0
},
{
"id": 89339,
"player": "wellsda01",
"year": 2007,
"stint": 2,
"team": "LAN",
"lg": "NL",
"g": 7,
"ab": 15,
"r": 2,
"h": 4,
"X2b": 1,
"X3b": 0,
"hr": 0,
"rbi": 1,
"sb": 0,
"cs": 0,
"bb": 0,
"so": 6,
"ibb": 0,
"hbp": 0,
"sh": 0,
"sf": 0,
"gidp": 0
},
{
"id": 89340,
"player": "wellsda01",
"year": 2007,
"stint": 1,
"team": "SDN",
"lg": "NL",
"g": 22,
"ab": 38,
"r": 1,
"h": 4,
"X2b": 0,
"X3b": 0,
"hr": 0,
"rbi": 0,
"sb": 0,
"cs": 0,
"bb": 0,
"so": 12,
"ibb": 0,
"hbp": 0,
"sh": 4,
"sf": 0,
"gidp": 0
},
{
"id": 89341,
"player": "weathda01",
"year": 2007,
"stint": 1,
"team": "CIN",
"lg": "NL",
"g": 67,
"ab": 0,
"r": 0,
"h": 0,
"X2b": 0,
"X3b": 0,
"hr": 0,
"rbi": 0,
"sb": 0,
"cs": 0,
"bb": 0,
"so": 0,
"ibb": 0,
"hbp": 0,
"sh": 0,
"sf": 0,
"gidp": 0
}
]
}
},
"metadata": {}
}
],
"execution_count": 4,
"metadata": {
"collapsed": false,
"outputHidden": false,
"inputHidden": false
}
},
{
"cell_type": "code",
"source": [],
"outputs": [],
"execution_count": null,
"metadata": {
"collapsed": false,
"outputHidden": false,
"inputHidden": false
}
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"language": "python",
"display_name": "Python 3"
},
"kernel_info": {
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.6.0",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
}
},
"nbformat": 4,
"nbformat_minor": 4
} | bsd-3-clause |
ledeprogram/algorithms | class8/SimpleLinearRegression_Sklearn.ipynb | 1 | 3249 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.linear_model import LinearRegression\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv(\"data/hanford.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"lm = LinearRegression()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = np.asarray(df[['Mortality','Exposure']])\n",
"x = data[:,1:]\n",
"y = data[:,0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lm.fit(x,y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lm.coef_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lm.score(x,y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"slope = lm.coef_[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"intercept = lm.intercept_"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df.plot(kind='scatter',x='Exposure',y='Mortality')\n",
"plt.plot(df['Exposure'],slope*df['Exposure']+intercept,'-')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"lm.predict(10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
deepchem/deepchem | examples/tutorials/Transfer_Learning_With_ChemBERTa_Transformers.ipynb | 1 | 4764299 | null | mit |
SHDShim/pytheos | examples/6_p_scale_test_Jamieson_Au.ipynb | 1 | 9527 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Source and citation\n",
"\n",
"- This notebook is a part of the `pytheos` package.\n",
"- Website: http://github.com/SHDShim/pytheos.\n",
"- How to cite: S.-H. Shim (2017) Pytheos - a python tool set for equations of state. DOI: 10.5281/zenodo.802392\n"
]
}
],
"source": [
"%cat 0Source_Citation.txt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"# %matplotlib notebook # for interactive"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For high dpi displays."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%config InlineBackend.figure_format = 'retina'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 0. General note"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This example compares pressure calculated from `pytheos` and original publication for the gold scale by Jamieson 1983."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1. Global setup"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from uncertainties import unumpy as unp\n",
"import pytheos as eos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3. Compare"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0. 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 0.055\n",
" 0.06 0.065 0.07 0.075 0.08 0.085 0.09 0.095 0.1 0.105 0.11 0.115\n",
" 0.12 0.125 0.13 0.135 0.14 0.145 0.15 0.155 0.16 0.165 0.17 0.175\n",
" 0.18 0.185 0.19 0.195 0.2 0.205 0.21 0.215 0.22 0.225]\n"
]
}
],
"source": [
"eta = np.linspace(0., 0.225, 46)\n",
"print(eta)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"jamieson_aul = eos.gold.Jamieson1982L()\n",
"jamieson_auh = eos.gold.Jamieson1982H()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P_static: Jamieson\n",
"P_thermal: Constq\n",
"P_anharmonic: None\n",
"P_electronic: None\n"
]
}
],
"source": [
"jamieson_aul.print_equations()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P_static: Jamieson\n",
"P_thermal: Constq\n",
"P_anharmonic: None\n",
"P_electronic: None\n"
]
}
],
"source": [
"jamieson_auh.print_equations()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Static: OrderedDict([('rho0', 19.2827+/-0), ('a', 2.975+/-0), ('b', 1.896+/-0), ('c', -0.309+/-0)])\n",
"Thermal: OrderedDict([('v0', 67.84747902176544+/-0.001), ('gamma0', 3.215+/-0), ('q', 1.0+/-0), ('theta0', 170.0+/-0)])\n",
"Anharmonic: None\n",
"Electronic: None\n"
]
}
],
"source": [
"jamieson_aul.print_parameters()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Static: OrderedDict([('rho0', 19.2827+/-0), ('c0', 3.071+/-0), ('s', 1.536+/-0)])\n",
"Thermal: OrderedDict([('v0', 67.84747902176544+/-0.001), ('gamma0', 3.215+/-0), ('q', 1.0+/-0), ('theta0', 170.0+/-0)])\n",
"Anharmonic: None\n",
"Electronic: None\n"
]
}
],
"source": [
"jamieson_auh.print_parameters()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"v0 = 67.84747902176544"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"24.62081875"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jamieson_aul.three_r"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"24.62081875"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"jamieson_auh.three_r"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"v = v0 * (1.-eta) \n",
"temp = 1500."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p = jamieson_auh.cal_p(v, temp * np.ones_like(v))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src='./tables/Jamieson_Au_1.png'>\n",
"<img src='./tables/Jamieson_Au_2.png'>"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"for T = 1500.0\n",
" 0.000 9.27+/-0.00\n",
" 0.005 10.15+/-0.00\n",
" 0.010 11.07+/-0.00\n",
" 0.015 12.01+/-0.00\n",
" 0.020 12.98+/-0.00\n",
" 0.025 13.98+/-0.00\n",
" 0.030 15.02+/-0.00\n",
" 0.035 16.08+/-0.00\n",
" 0.040 17.18+/-0.00\n",
" 0.045 18.31+/-0.00\n",
" 0.050 19.48+/-0.00\n",
" 0.055 20.69+/-0.00\n",
" 0.060 21.93+/-0.00\n",
" 0.065 23.20+/-0.00\n",
" 0.070 24.52+/-0.00\n",
" 0.075 25.88+/-0.00\n",
" 0.080 27.27+/-0.00\n",
" 0.085 28.71+/-0.00\n",
" 0.090 30.20+/-0.00\n",
" 0.095 31.72+/-0.00\n",
" 0.100 33.30+/-0.00\n",
" 0.105 34.92+/-0.00\n",
" 0.110 36.58+/-0.00\n",
" 0.115 38.30+/-0.00\n",
" 0.120 40.07+/-0.00\n",
" 0.125 41.89+/-0.00\n",
" 0.130 43.77+/-0.00\n",
" 0.135 45.70+/-0.00\n",
" 0.140 47.69+/-0.00\n",
" 0.145 49.74+/-0.00\n",
" 0.150 51.85+/-0.00\n",
" 0.155 54.02+/-0.00\n",
" 0.160 56.25+/-0.00\n",
" 0.165 58.55+/-0.00\n",
" 0.170 60.92+/-0.00\n",
" 0.175 63.36+/-0.00\n",
" 0.180 65.87+/-0.00\n",
" 0.185 68.46+/-0.00\n",
" 0.190 71.12+/-0.00\n",
" 0.195 73.86+/-0.00\n",
" 0.200 76.68+/-0.00\n",
" 0.205 79.59+/-0.00\n",
" 0.210 82.58+/-0.00\n",
" 0.215 85.66+/-0.00\n",
" 0.220 88.83+/-0.00\n",
" 0.225 92.09+/-0.00\n"
]
}
],
"source": [
"print('for T = ', temp)\n",
"for eta_i, p_i in zip(eta, p):\n",
" print(\"{0: .3f} {1: .2f}\".format(eta_i, p_i))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0. 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04 0.045 0.05 0.055\n",
" 0.06 0.065 0.07 0.075 0.08 0.085 0.09 0.095 0.1 0.105 0.11 0.115\n",
" 0.12 0.125 0.13 0.135 0.14 0.145 0.15 0.155 0.16 0.165 0.17 0.175\n",
" 0.18 0.185 0.19 0.195 0.2 0.205 0.21 0.215 0.22 0.225]\n"
]
}
],
"source": [
"v = jamieson_auh.cal_v(p, temp * np.ones_like(p), min_strain=0.6)\n",
"print(1.-(v/v0))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 0,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
rsignell-usgs/notebook | OOI/OOI_equipment_mapping.ipynb | 1 | 24808 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# OOI Equipment mapping\n",
"- by Landung Setiawan\n",
"- 6/14/2016\n",
"- This notebook is for retrieving information from google sheets and then mapping to a JSON file, each instrument has its own JSON file configuration\n",
"- The required libraries for this manipulation is *gspread*, *oauth2client*, and *pycrypto*"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"oauth2client version : 1.5.2\n",
"gspread version : 0.3.0\n"
]
}
],
"source": [
"# Google Authentication Libraries\n",
"import oauth2client, gspread\n",
"import json\n",
"\n",
"# oauth2client version check and gspread\n",
"oauth_ver = oauth2client.__version__\n",
"gspread_ver = gspread.__version__\n",
"\n",
"print \"oauth2client version : {}\".format(oauth_ver) \n",
"print \"gspread version : {}\".format(gspread_ver)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"if oauth_ver < \"2.0.2\":\n",
" from oauth2client.client import SignedJwtAssertionCredentials\n",
"\n",
" json_key = json.load(open('XXXX.json'))\n",
" # Get scope for google sheets\n",
" # Gather all spreadsheets shared with the client_email: [email protected]\n",
" scope = ['https://spreadsheets.google.com/feeds']\n",
" \n",
" # Retrieve credentials from JSON key of service account\n",
" credentials = SignedJwtAssertionCredentials(json_key['client_email'], json_key['private_key'], scope)\n",
" \n",
" # Authorize gspread to connect to google sheets\n",
" gc = gspread.authorize(credentials)\n",
"else:\n",
" from oauth2client.service_account import ServiceAccountCredentials\n",
" # Get scope for google sheets\n",
" # Gather all spreadsheets shared with the client_email: [email protected]\n",
" scope = ['https://spreadsheets.google.com/feeds']\n",
"\n",
" # Retrieve credentials from JSON key of service account\n",
" credentials = ServiceAccountCredentials.from_json_keyfile_name('XXXX.json', scope)\n",
"\n",
" # Authorize gspread to connect to google sheets\n",
" gc = gspread.authorize(credentials)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 sensor_configurations_mappings\n",
"1 GOA-ON_structering\n",
"2 2016_Spring_NEMO\n",
"3 ooi_equipment\n",
"4 AGGI_Table\n",
"5 WINTER 2014 SCHEDULE\n",
"6 ocean_extents\n",
"7 nanoos_asset_list_20160427T092836\n",
"8 nanoos_asset_list_20160429T083128\n"
]
}
],
"source": [
"# Get all spreadsheets available for NANOOS\n",
"gsheets = gc.openall()\n",
"# Get title of the spreadsheets\n",
"for i in range(0,len(gsheets)):\n",
" print \"{0} {1}\".format(i,gsheets[i].title)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Open sensor_configurations_mappings only\n",
"sc = gc.open(\"sensor_configurations_mappings\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<Worksheet 'instruments' id:o5yzc1h>, <Worksheet 'measurements' id:odfoenj>]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get all worksheets in a sheet\n",
"wks = sc.worksheets()\n",
"wks"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<Worksheet 'instruments' id:o5yzc1h> <Worksheet 'measurements' id:odfoenj>\n"
]
}
],
"source": [
"s1 = sc.get_worksheet(0)\n",
"s2 = sc.get_worksheet(1)\n",
"print s1, s2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Parsing data to a pandas dataframe\n",
"- Now that connection has been established, data is parsed to be viewed"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/lsetiawan/anaconda2/envs/uwapl_em_mc_1aui/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
" warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n",
"/home/lsetiawan/anaconda2/envs/uwapl_em_mc_1aui/lib/python2.7/site-packages/pytz/__init__.py:29: UserWarning: Module argparse was already imported from /home/lsetiawan/anaconda2/envs/uwapl_em_mc_1aui/lib/python2.7/argparse.pyc, but /home/lsetiawan/anaconda2/envs/uwapl_em_mc_1aui/lib/python2.7/site-packages/argparse-1.4.0-py2.7.egg is being added to sys.path\n",
" from pkg_resources import resource_stream\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"pandas version: 0.18.1\n",
"numpy version: 1.10.4\n"
]
}
],
"source": [
"# Import pandas and numpy to make data easier to view\n",
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"print \"pandas version: {}\".format(pd.__version__)\n",
"print \"numpy version: {}\".format(np.__version__)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Getting all the values of sheet1\n",
"array1 = s1.get_all_values()\n",
"array2 = s2.get_all_values()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/lsetiawan/anaconda2/envs/uwapl_em_mc_1aui/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>platform_label</th>\n",
" <th>depth_m</th>\n",
" <th>mfn</th>\n",
" <th>base_url</th>\n",
" <th>platform</th>\n",
" <th>deployment</th>\n",
" <th>data_logger</th>\n",
" <th>instrument</th>\n",
" <th>subtype</th>\n",
" <th>raw_url</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>OOI_CE07SHSM</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>https://rawdata.oceanobservatories.org/files</td>\n",
" <td>CE07SHSM</td>\n",
" <td>D00003</td>\n",
" <td>cg_data/dcl11</td>\n",
" <td>metbk</td>\n",
" <td></td>\n",
" <td>https://rawdata.oceanobservatories.org/files/C...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>OOI_CE02SHSM</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>https://rawdata.oceanobservatories.org/files</td>\n",
" <td>CE02SHSM</td>\n",
" <td>D00003</td>\n",
" <td>cg_data/dcl11</td>\n",
" <td>metbk</td>\n",
" <td></td>\n",
" <td>https://rawdata.oceanobservatories.org/files/C...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>OOI_CE04OSSM</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>https://rawdata.oceanobservatories.org/files</td>\n",
" <td>CE04OSSM</td>\n",
" <td>D00002</td>\n",
" <td>cg_data/dcl11</td>\n",
" <td>metbk</td>\n",
" <td></td>\n",
" <td>https://rawdata.oceanobservatories.org/files/C...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>OOI_CE09OSSM</td>\n",
" <td>0</td>\n",
" <td></td>\n",
" <td>https://rawdata.oceanobservatories.org/files</td>\n",
" <td>CE09OSSM</td>\n",
" <td>D00003</td>\n",
" <td>cg_data/dcl11</td>\n",
" <td>metbk</td>\n",
" <td></td>\n",
" <td>https://rawdata.oceanobservatories.org/files/C...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>OOI_CE01ISSM</td>\n",
" <td>-7</td>\n",
" <td></td>\n",
" <td>https://rawdata.oceanobservatories.org/files</td>\n",
" <td>CE01ISSM</td>\n",
" <td>D00005</td>\n",
" <td>dcl16</td>\n",
" <td>ctdbp1</td>\n",
" <td>ctd_type::2</td>\n",
" <td>https://rawdata.oceanobservatories.org/files/C...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" platform_label depth_m mfn base_url \\\n",
"1 OOI_CE07SHSM 0 https://rawdata.oceanobservatories.org/files \n",
"2 OOI_CE02SHSM 0 https://rawdata.oceanobservatories.org/files \n",
"3 OOI_CE04OSSM 0 https://rawdata.oceanobservatories.org/files \n",
"4 OOI_CE09OSSM 0 https://rawdata.oceanobservatories.org/files \n",
"5 OOI_CE01ISSM -7 https://rawdata.oceanobservatories.org/files \n",
"\n",
" platform deployment data_logger instrument subtype \\\n",
"1 CE07SHSM D00003 cg_data/dcl11 metbk \n",
"2 CE02SHSM D00003 cg_data/dcl11 metbk \n",
"3 CE04OSSM D00002 cg_data/dcl11 metbk \n",
"4 CE09OSSM D00003 cg_data/dcl11 metbk \n",
"5 CE01ISSM D00005 dcl16 ctdbp1 ctd_type::2 \n",
"\n",
" raw_url \n",
"1 https://rawdata.oceanobservatories.org/files/C... \n",
"2 https://rawdata.oceanobservatories.org/files/C... \n",
"3 https://rawdata.oceanobservatories.org/files/C... \n",
"4 https://rawdata.oceanobservatories.org/files/C... \n",
"5 https://rawdata.oceanobservatories.org/files/C... "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert data into pandas dataframe\n",
"df = pd.DataFrame(array1)\n",
"df.columns = array1[0]\n",
"df.drop(df.index[0], inplace=True)\n",
"df = df.convert_objects(convert_numeric=True)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/lsetiawan/anaconda2/envs/uwapl_em_mc_1aui/lib/python2.7/site-packages/ipykernel/__main__.py:5: FutureWarning: convert_objects is deprecated. Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>instrument</th>\n",
" <th>data_products</th>\n",
" <th>relative_depth_m</th>\n",
" <th>OOI_units</th>\n",
" <th>measurement_label</th>\n",
" <th>notes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>metbk</td>\n",
" <td>air_temperature</td>\n",
" <td>4.1</td>\n",
" <td>degC</td>\n",
" <td>A1_AirTemp</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>metbk</td>\n",
" <td>barometric_pressure</td>\n",
" <td>4.3</td>\n",
" <td>mbar</td>\n",
" <td>A1_BarPress</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>metbk</td>\n",
" <td>relative_humidity</td>\n",
" <td>4.1</td>\n",
" <td>%</td>\n",
" <td>A1_RelHumidity</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>metbk</td>\n",
" <td>eastward_wind_velocity</td>\n",
" <td>4.7</td>\n",
" <td>m/s</td>\n",
" <td>A1_WindSpeed</td>\n",
" <td></td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>metbk</td>\n",
" <td>northward_wind_velocity</td>\n",
" <td>4.7</td>\n",
" <td>m/s</td>\n",
" <td>A1_WindSpeed</td>\n",
" <td></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" instrument data_products relative_depth_m OOI_units \\\n",
"1 metbk air_temperature 4.1 degC \n",
"2 metbk barometric_pressure 4.3 mbar \n",
"3 metbk relative_humidity 4.1 % \n",
"4 metbk eastward_wind_velocity 4.7 m/s \n",
"5 metbk northward_wind_velocity 4.7 m/s \n",
"\n",
" measurement_label notes \n",
"1 A1_AirTemp \n",
"2 A1_BarPress \n",
"3 A1_RelHumidity \n",
"4 A1_WindSpeed \n",
"5 A1_WindSpeed "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Convert data into pandas dataframe\n",
"df1 = pd.DataFrame(array2)\n",
"df1.columns = array2[0]\n",
"df1.drop(df1.index[0], inplace=True)\n",
"df1 = df1.convert_objects(convert_numeric=True)\n",
"df1.head()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"CE07SHSM\": {\n",
" \"ctdbp1\": {\n",
" \"data_logger\": \"cg_data/dcl27\",\n",
" \"deployment\": \"D00003\",\n",
" \"depth_m\": -7.0,\n",
" \"mfn\": false,\n",
" \"subtype\": \"1\"\n",
" },\n",
" \"ctdbp2\": {\n",
" \"data_logger\": \"cg_data/dcl37\",\n",
" \"deployment\": \"D00003\",\n",
" \"depth_m\": -87.0,\n",
" \"mfn\": true,\n",
" \"subtype\": \"2\"\n",
" },\n",
" \"metbk\": {\n",
" \"data_logger\": \"cg_data/dcl11\",\n",
" \"deployment\": \"D00003\",\n",
" \"depth_m\": 0.0,\n",
" \"mfn\": false,\n",
" \"subtype\": null\n",
" },\n",
" \"wavss\": {\n",
" \"data_logger\": \"cg_data/dcl12\",\n",
" \"deployment\": \"D00003\",\n",
" \"depth_m\": 0.0,\n",
" \"mfn\": false,\n",
" \"subtype\": null\n",
" }\n",
" }\n",
"}\n",
"{\n",
" \"CE02SHSM\": {\n",
" \"ctdbp\": {\n",
" \"data_logger\": \"cg_data/dcl27\",\n",
" \"deployment\": \"D00003\",\n",
" \"depth_m\": -7.0,\n",
" \"mfn\": false,\n",
" \"subtype\": \"1\"\n",
" },\n",
" \"metbk\": {\n",
" \"data_logger\": \"cg_data/dcl11\",\n",
" \"deployment\": \"D00003\",\n",
" \"depth_m\": 0.0,\n",
" \"mfn\": false,\n",
" \"subtype\": null\n",
" },\n",
" \"wavss\": {\n",
" \"data_logger\": \"cg_data/dcl12\",\n",
" \"deployment\": \"D00003\",\n",
" \"depth_m\": 0.0,\n",
" \"mfn\": false,\n",
" \"subtype\": null\n",
" }\n",
" }\n",
"}\n",
"{\n",
" \"CE04OSSM\": {\n",
" \"ctdbp\": {\n",
" \"data_logger\": \"cg_data/dcl27\",\n",
" \"deployment\": \"D00002\",\n",
" \"depth_m\": -7.0,\n",
" \"mfn\": false,\n",
" \"subtype\": \"1\"\n",
" },\n",
" \"metbk\": {\n",
" \"data_logger\": \"cg_data/dcl11\",\n",
" \"deployment\": \"D00002\",\n",
" \"depth_m\": 0.0,\n",
" \"mfn\": false,\n",
" \"subtype\": null\n",
" },\n",
" \"wavss\": {\n",
" \"data_logger\": \"cg_data/dcl12\",\n",
" \"deployment\": \"D00002\",\n",
" \"depth_m\": 0.0,\n",
" \"mfn\": false,\n",
" \"subtype\": null\n",
" }\n",
" }\n",
"}\n",
"{\n",
" \"CE09OSSM\": {\n",
" \"ctdbp1\": {\n",
" \"data_logger\": \"cg_data/dcl27\",\n",
" \"deployment\": \"D00003\",\n",
" \"depth_m\": -7.0,\n",
" \"mfn\": false,\n",
" \"subtype\": \"1\"\n",
" },\n",
" \"ctdbp2\": {\n",
" \"data_logger\": \"cg_data/dcl37\",\n",
" \"deployment\": \"D00001\",\n",
" \"depth_m\": -540.0,\n",
" \"mfn\": true,\n",
" \"subtype\": \"2\"\n",
" },\n",
" \"metbk\": {\n",
" \"data_logger\": \"cg_data/dcl11\",\n",
" \"deployment\": \"D00003\",\n",
" \"depth_m\": 0.0,\n",
" \"mfn\": false,\n",
" \"subtype\": null\n",
" },\n",
" \"wavss\": {\n",
" \"data_logger\": \"cg_data/dcl12\",\n",
" \"deployment\": \"D00003\",\n",
" \"depth_m\": 0.0,\n",
" \"mfn\": false,\n",
" \"subtype\": null\n",
" }\n",
" }\n",
"}\n",
"{\n",
" \"CE01ISSM\": {\n",
" \"ctdbp1\": {\n",
" \"data_logger\": \"dcl16\",\n",
" \"deployment\": \"D00005\",\n",
" \"depth_m\": -7.0,\n",
" \"mfn\": false,\n",
" \"subtype\": \"2\"\n",
" },\n",
" \"ctdbp2\": {\n",
" \"data_logger\": \"dcl37\",\n",
" \"deployment\": \"D00001\",\n",
" \"depth_m\": -25.0,\n",
" \"mfn\": true,\n",
" \"subtype\": \"2\"\n",
" }\n",
" }\n",
"}\n",
"{\n",
" \"CE02SHBP\": {\n",
" \"\": {\n",
" \"data_logger\": \"LJ01D\",\n",
" \"deployment\": \"\",\n",
" \"depth_m\": -80.0,\n",
" \"mfn\": false,\n",
" \"subtype\": null\n",
" }\n",
" }\n",
"}\n",
"{\n",
" \"CE04OSBP\": {\n",
" \"\": {\n",
" \"data_logger\": \"LJ01C\",\n",
" \"deployment\": \"\",\n",
" \"depth_m\": -580.0,\n",
" \"mfn\": false,\n",
" \"subtype\": null\n",
" }\n",
" }\n",
"}\n",
"{\n",
" \"CE06ISSM\": {\n",
" \"ctdbp1\": {\n",
" \"data_logger\": \"dcl16\",\n",
" \"deployment\": \"D00004\",\n",
" \"depth_m\": -7.0,\n",
" \"mfn\": false,\n",
" \"subtype\": \"2\"\n",
" },\n",
" \"ctdbp2\": {\n",
" \"data_logger\": \"dcl37\",\n",
" \"deployment\": \"D00004\",\n",
" \"depth_m\": -29.0,\n",
" \"mfn\": true,\n",
" \"subtype\": \"2\"\n",
" }\n",
" }\n",
"}\n"
]
}
],
"source": [
"def createJSON(df):\n",
" # Get Platforms\n",
" json_data = df[['platform','instrument','depth_m','mfn','deployment','data_logger','subtype']].reset_index(drop=True)\n",
" platforms = json_data['platform'].unique()\n",
" mainkey = dict()\n",
" prop = dict()\n",
" \n",
" # Gather Platform info together\n",
" plat = [json_data.loc[json_data['platform'] == p] for p in platforms]\n",
" \n",
" # Create JSON\n",
" for i in range(0, len(plat)):\n",
" instrum = dict()\n",
" mainkey = dict()\n",
" for j in range(0, len(plat[i]['platform'].values)):\n",
" platform_name = plat[i]['platform'].values[j]\n",
" instrument_name = plat[i]['instrument'].values[j]\n",
" depth_m = plat[i]['depth_m'].values[j]\n",
" mfn = plat[i]['mfn'].values[j]\n",
" deployment = plat[i]['deployment'].values[j]\n",
" data_logger = plat[i]['data_logger'].values[j]\n",
" subtype = plat[i]['subtype'].values[j]\n",
"\n",
" # Check for mfn\n",
" if mfn != '':\n",
" mfn = True\n",
" else:\n",
" mfn = False\n",
" # Getting subtype\n",
" if subtype != '':\n",
" subtype = subtype.split('::')[1]\n",
" else:\n",
" subtype = None\n",
"\n",
" prop['depth_m'] = float(depth_m)\n",
" prop['mfn'] = mfn\n",
" prop['deployment'] = deployment\n",
" prop['data_logger'] = data_logger\n",
" prop['subtype'] = subtype\n",
" instrum['{}'.format(instrument_name)] = prop\n",
" mainkey['{}'.format(platform_name)] = instrum\n",
" prop = dict()\n",
" \n",
" # prints the JSON structured dictionary\n",
" print json.dumps(mainkey, sort_keys=True, indent=4, separators=(',', ': '))\n",
" # Output to JSON file \n",
" fj = open(\"{}.json\".format(platform_name), 'w')\n",
" fj.write(json.dumps(mainkey, sort_keys=False, indent=4, separators=(',', ': ')))\n",
" fj.close()\n",
"createJSON(df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
bj0rni32dbm/VB-MK-LMF | VB-MK-LMF-BBVI.ipynb | 1 | 23645 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Logistic MF with multiple kernels using TensorFlow and Edward\n",
"\n",
"This is a somewhat more accessible demonstration of VB-MK-LMF with a different variational approximation strategy and slightly modified priors. In particular, this version utilizes BBVI by Ranganath <i>et al.</i> (2013), as implemented in the Edward package by the Blei lab (Tran <i>et al.</i> (2016)). Since Gamma distributions lead to very noisy graidents with BBVI, they have been replaced by LogNormals. We also impose priors on the $\\alpha$ params ($L_2$ regularization)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" from ._conv import register_converters as _register_converters\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/contrib/learn/python/learn/datasets/base.py:198: retry (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
"Instructions for updating:\n",
"Use the retry module or similar alternatives.\n"
]
}
],
"source": [
"%pylab inline\n",
"\n",
"import edward as ed\n",
"from edward.models import Normal, MultivariateNormalTriL, TransformedDistribution, NormalWithSoftplusScale\n",
"from edward.models.random_variable import RandomVariable\n",
"\n",
"import tensorflow as tf\n",
"from tensorflow.contrib.distributions import Distribution\n",
"\n",
"import numpy as np\n",
"from sklearn.metrics import precision_recall_curve, roc_curve, roc_auc_score, auc"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load interaction matrix\n",
"admat = \"data/nr/mat/nr_admat_dgc.txt\"\n",
"with open(admat) as f:\n",
" ncols = len(f.readline().split('\\t'))\n",
"R_ = np.loadtxt(admat,skiprows=1,usecols=range(1,ncols),delimiter='\\t',dtype=np.float32)\n",
"I,J = R_.shape\n",
"\n",
"# Load similarity matrices\n",
"simmat_u = [\"data/nr/mat/nr_simmat_dg.txt\"]\n",
"Ku = np.array([np.loadtxt(mat,skiprows=1,usecols=range(1,I+1),delimiter='\\t',dtype=np.float32) for mat in simmat_u])\n",
"\n",
"simmat_v = [\"data/nr/mat/nr_simmat_dc.txt\",\n",
" \"data/nr/mat/nr_simmat_dc_maccs_rbf.txt\",\n",
" \"data/nr/mat/nr_simmat_dc_maccs_tanimoto.txt\",\n",
" \"data/nr/mat/nr_simmat_dc_morgan_rbf.txt\",\n",
" \"data/nr/mat/nr_simmat_dc_morgan_tanimoto.txt\"]\n",
"Kv = np.array([np.loadtxt(mat,skiprows=1,usecols=range(1,J+1),delimiter='\\t',dtype=np.float32) for mat in simmat_v])"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Nearest neighbors truncation + regularization\n",
"def truncate_kernel(K):\n",
" idx = np.argsort(-K,axis=1)\n",
" for i in range(K.shape[0]):\n",
" K[i,idx[i,5:]] = 0\n",
" K += K.T\n",
" K -= (np.real_if_close(np.min(np.linalg.eigvals(K))-0.1))*np.eye(K.shape[0])\n",
"\n",
"for i in range(len(Ku)):\n",
" truncate_kernel(Ku[i])\n",
"\n",
"for i in range(len(Kv)):\n",
" truncate_kernel(Kv[i])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Load CV folds\n",
"folds = []\n",
"with open(\"data/nr/cv/nr_all_folds_cvs1.txt\") as f:\n",
" for i in f.readlines():\n",
" rec = i.strip().split(\",\")\n",
" ln = len(rec)//2\n",
" folds += [[(int(rec[j*2])-1,int(rec[j*2+1])-1) for j in range(ln)]]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Latent dims and augmented Bernoulli parameter\n",
"L = 12\n",
"c = 3.0\n",
"\n",
"# Insert your favorite neural network here\n",
"def nn(Uw1,Vw1):\n",
" return tf.matmul(Uw1,Vw1,transpose_a = True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Augmented Bernoulli distribution\n",
"# sampling is not used and therefore omitted\n",
"\n",
"class dAugmentedBernoulli(Distribution):\n",
" def __init__(self,logits,c,obs,\n",
" validate_args=False,\n",
" allow_nan_stats=True,\n",
" name=\"AugmentedBernoulli\"):\n",
" parameters = locals()\n",
" with tf.name_scope(name):\n",
" with tf.control_dependencies([]):\n",
" self._logits = tf.identity(logits)\n",
" self._c = tf.identity(c)\n",
" self._obs = tf.identity(obs)\n",
" super(dAugmentedBernoulli,self).__init__(dtype=tf.int32,validate_args=validate_args,allow_nan_stats=allow_nan_stats,\n",
" reparameterization_type=tf.contrib.distributions.NOT_REPARAMETERIZED,\n",
" parameters=parameters,graph_parents=[self._logits,self._c,self._obs],name=name)\n",
"\n",
" def _log_prob(self,event):\n",
" event = tf.cast(event,tf.float32)\n",
" cond = self._logits >= 0\n",
" neg_abs = tf.where(cond,-self._logits,self._logits)\n",
" sig = ((self._c-1.0)*tf.cast(event,tf.float32)+1.0)*tf.log1p(tf.exp(neg_abs))\n",
" return self._obs * tf.where(cond,(event-1)*self._logits-sig,self._c*event*self._logits-sig)\n",
"\n",
"def __init__(self, *args, **kwargs):\n",
" RandomVariable.__init__(self, *args, **kwargs)\n",
"AugmentedBernoulli = type(\"AugmentedBernoulli\", (RandomVariable, dAugmentedBernoulli), {'__init__': __init__})"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Construct VB-MK-LMF model\n",
"# Gamma distributions can lead to very noisy gradients so LogNormals are used instead\n",
"\n",
"def construct_model():\n",
" nku = len(Ku)\n",
" nkv = len(Kv)\n",
"\n",
" obs = tf.placeholder(tf.float32,R_.shape)\n",
"\n",
" Ug = TransformedDistribution(distribution=Normal(tf.zeros([nku]),tf.ones([nku])),\n",
" bijector=tf.contrib.distributions.bijectors.Exp())\n",
" Vg = TransformedDistribution(distribution=Normal(tf.zeros([nkv]),tf.ones([nkv])),\n",
" bijector=tf.contrib.distributions.bijectors.Exp())\n",
"\n",
" Ua = TransformedDistribution(distribution=Normal(tf.zeros([1]),tf.ones([1])),\n",
" bijector=tf.contrib.distributions.bijectors.Exp())\n",
" Va = TransformedDistribution(distribution=Normal(tf.zeros([1]),tf.ones([1])),\n",
" bijector=tf.contrib.distributions.bijectors.Exp())\n",
"\n",
" cKu = tf.cholesky(Ku+tf.eye(I)/Ua) #TODO: rank 1 chol update\n",
" cKv = tf.cholesky(Kv+tf.eye(J)/Va)\n",
"\n",
" Uw1 = MultivariateNormalTriL(tf.zeros([L,I]),tf.reduce_sum(cKu/tf.reshape(tf.sqrt(Ug),[nku,1,1]),axis=0))\n",
" Vw1 = MultivariateNormalTriL(tf.zeros([L,J]),tf.reduce_sum(cKv/tf.reshape(tf.sqrt(Vg),[nkv,1,1]),axis=0))\n",
"\n",
" logits = nn(Uw1,Vw1)\n",
" R = AugmentedBernoulli(logits=logits,c=c,obs=obs,value=tf.cast(logits>0,tf.int32))\n",
"\n",
" qUg = TransformedDistribution(distribution=NormalWithSoftplusScale(tf.Variable(tf.zeros([nku])),\n",
" tf.Variable(tf.ones([nku]))),\n",
" bijector=tf.contrib.distributions.bijectors.Exp())\n",
" qVg = TransformedDistribution(distribution=NormalWithSoftplusScale(tf.Variable(tf.zeros([nkv])),\n",
" tf.Variable(tf.ones([nkv]))),\n",
" bijector=tf.contrib.distributions.bijectors.Exp())\n",
" qUa = TransformedDistribution(distribution=NormalWithSoftplusScale(tf.Variable(tf.zeros([1])),\n",
" tf.Variable(tf.ones([1]))),\n",
" bijector=tf.contrib.distributions.bijectors.Exp())\n",
" qVa = TransformedDistribution(distribution=NormalWithSoftplusScale(tf.Variable(tf.zeros([1])),\n",
" tf.Variable(tf.ones([1]))),\n",
" bijector=tf.contrib.distributions.bijectors.Exp())\n",
" qUw1 = MultivariateNormalTriL(tf.Variable(tf.zeros([L,I])),tf.Variable(tf.eye(I)))\n",
" qVw1 = MultivariateNormalTriL(tf.Variable(tf.zeros([L,J])),tf.Variable(tf.eye(J)))\n",
" \n",
" return obs,Ug,Vg,Ua,Va,cKu,cKv,Uw1,Vw1,R,qUg,qVg,qUa,qVa,qUw1,qVw1"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/edward/util/random_variables.py:52: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
" not np.issubdtype(value.dtype, np.float) and \\\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"3000/3000 [100%] ██████████████████████████████ Elapsed: 165s | Loss: 686.201\n",
"AUPR: 0.7907644593258405\tAUROC: 0.9738461538461538\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 163s | Loss: 698.946\n",
"AUPR: 0.8467848124098125\tAUROC: 0.9829545454545454\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 163s | Loss: 667.156\n",
"AUPR: 0.808971088435374\tAUROC: 0.9838882921589688\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 646.558\n",
"AUPR: 0.9190323884289402\tAUROC: 0.9856770833333333\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 666.854\n",
"AUPR: 0.8046793292913982\tAUROC: 0.97\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 163s | Loss: 688.001\n",
"AUPR: 0.8932687748477223\tAUROC: 0.9753846153846154\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 163s | Loss: 670.754\n",
"AUPR: 0.6808335369827305\tAUROC: 0.9577114427860698\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 161s | Loss: 686.461\n",
"AUPR: 0.6745142323414259\tAUROC: 0.9270568278201865\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 695.615\n",
"AUPR: 0.6809694927913402\tAUROC: 0.9485553206483439\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 711.265\n",
"AUPR: 0.8807234432234432\tAUROC: 0.9906152241918665\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 669.910\n",
"AUPR: 0.725818862207751\tAUROC: 0.9609838846480068\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 695.032\n",
"AUPR: 0.9395833333333333\tAUROC: 0.9933712121212122\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 165s | Loss: 697.820\n",
"AUPR: 0.7795690915769746\tAUROC: 0.9704016913319239\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 161s | Loss: 667.250\n",
"AUPR: 0.7577885261935327\tAUROC: 0.9526515151515151\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 619.678\n",
"AUPR: 0.8316798352337568\tAUROC: 0.9576822916666666\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 166s | Loss: 701.688\n",
"AUPR: 0.8128822790113112\tAUROC: 0.9677765843179377\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 161s | Loss: 701.307\n",
"AUPR: 0.7765460729746444\tAUROC: 0.9828141783029002\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 664.228\n",
"AUPR: 0.7579489930832641\tAUROC: 0.9464411557434813\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 163s | Loss: 687.055\n",
"AUPR: 1.0\tAUROC: 1.0\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 168s | Loss: 711.864\n",
"AUPR: 0.8903061224489797\tAUROC: 0.9937434827945776\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 161s | Loss: 670.811\n",
"AUPR: 0.7846884018759018\tAUROC: 0.9820075757575757\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 687.799\n",
"AUPR: 0.9082483660130719\tAUROC: 0.9676923076923076\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 633.585\n",
"AUPR: 0.7273976049750044\tAUROC: 0.9753846153846154\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 682.021\n",
"AUPR: 0.7707399626517274\tAUROC: 0.9602272727272727\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 171s | Loss: 700.737\n",
"AUPR: 0.8490629880564442\tAUROC: 0.9830866807610994\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 684.510\n",
"AUPR: 0.9380952380952381\tAUROC: 0.997037037037037\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 163s | Loss: 673.411\n",
"AUPR: 0.7380120798319328\tAUROC: 0.9744318181818181\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 660.716\n",
"AUPR: 0.8704351092455932\tAUROC: 0.9774489076814659\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 685.691\n",
"AUPR: 0.836515185132825\tAUROC: 0.9518229166666667\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 165s | Loss: 705.102\n",
"AUPR: 0.7695120367896375\tAUROC: 0.9478623566214807\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 174s | Loss: 697.132\n",
"AUPR: 0.5592592592592593\tAUROC: 0.9781021897810219\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 163s | Loss: 644.457\n",
"AUPR: 0.7866287094547963\tAUROC: 0.9784615384615384\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 164s | Loss: 668.181\n",
"AUPR: 0.5801604278074866\tAUROC: 0.9496296296296297\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 166s | Loss: 708.151\n",
"AUPR: 0.8387084053962647\tAUROC: 0.9795918367346939\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 664.040\n",
"AUPR: 0.7950231481481481\tAUROC: 0.9753787878787878\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 163s | Loss: 725.362\n",
"AUPR: 0.9587912087912088\tAUROC: 0.9957591178965225\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 164s | Loss: 703.045\n",
"AUPR: 0.7931517345080324\tAUROC: 0.9583333333333334\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 177s | Loss: 671.974\n",
"AUPR: 0.7451439763939763\tAUROC: 0.9706439393939394\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 165s | Loss: 644.585\n",
"AUPR: 0.8062266057809634\tAUROC: 0.9402199904351984\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 164s | Loss: 682.581\n",
"AUPR: 0.786700036075036\tAUROC: 0.953125\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 164s | Loss: 689.783\n",
"AUPR: 0.7802578863022942\tAUROC: 0.9734848484848485\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 164s | Loss: 715.697\n",
"AUPR: 0.7903615991851286\tAUROC: 0.9602577873254565\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 163s | Loss: 620.093\n",
"AUPR: 0.6135608510309467\tAUROC: 0.9591261451726568\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 163s | Loss: 671.031\n",
"AUPR: 0.6227494800521116\tAUROC: 0.9584615384615384\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 665.176\n",
"AUPR: 0.8617065854119426\tAUROC: 0.9674479166666667\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 646.275\n",
"AUPR: 0.8106610709551886\tAUROC: 0.9821882951653944\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 176s | Loss: 700.085\n",
"AUPR: 0.8524305555555556\tAUROC: 0.9908088235294118\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 164s | Loss: 681.846\n",
"AUPR: 0.8930596067628154\tAUROC: 0.9901338971106413\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 679.086\n",
"AUPR: 0.783280522121248\tAUROC: 0.9546153846153846\n",
"3000/3000 [100%] ██████████████████████████████ Elapsed: 162s | Loss: 689.961\n",
"AUPR: 0.818819973130318\tAUROC: 0.9733455882352942\n",
"Overall\n",
"AUPR: 0.7984410657786333 +- 0.18423997840612222, AUROC: 0.9705534515705121 +- 0.032372401574204374\n"
]
}
],
"source": [
"auroc_all = []\n",
"aupr_all = []\n",
"for f in folds:\n",
" # Edward does not delete nodes so we have to reset the graph manually\n",
" ed.get_session().close()\n",
" tf.reset_default_graph()\n",
" obs,Ug,Vg,Ua,Va,cKu,cKv,Uw1,Vw1,R,qUg,qVg,qUa,qVa,qUw1,qVw1 = construct_model()\n",
"\n",
" # Hide test examples\n",
" cv = np.zeros((I,J),dtype=np.bool)\n",
" for i in f:\n",
" cv[i[1],i[0]] = True\n",
" data = np.copy(R_)\n",
" data[cv] = 0\n",
"\n",
" # Construct observation matrix for the augmented Bernoulli distribution\n",
" obs_ = (np.logical_and.outer(np.any(data>0,axis=1),np.any(data>0,axis=0))*1).astype(np.float32)\n",
"\n",
" # Variational approximation using BBVI\n",
" inference = ed.KLqp({Uw1: qUw1, Vw1: qVw1, Ug: qUg, Vg: qVg, Ua: qUa, Va: qVa},data={R: data, obs: obs_})\n",
" inference.initialize(n_samples=10,n_iter=3000)\n",
" tf.global_variables_initializer().run()\n",
" for _ in range(inference.n_iter):\n",
" info_dict = inference.update()\n",
" inference.print_progress(info_dict)\n",
" inference.finalize()\n",
"\n",
" # Evaluation\n",
" res = tf.nn.sigmoid(nn(qUw1.mean(),qVw1.mean())**c).eval()\n",
"\n",
" prc,rec,_ = precision_recall_curve(R_[cv],res[cv])\n",
" fpr,tpr,_ = roc_curve(R_[cv],res[cv])\n",
"\n",
" auroc = auc(fpr,tpr,reorder=True)\n",
" aupr = auc(rec,prc,reorder=True)\n",
" auroc_all += [auroc]\n",
" aupr_all += [aupr]\n",
" print(\"AUPR: {}\\tAUROC: {}\".format(aupr,auroc))\n",
"print(\"Overall\\nAUPR: {} +- {}, AUROC: {} +- {}\".format(np.mean(aupr_all),np.std(aupr_all)*2,np.mean(auroc_all),np.std(auroc_all)*2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
xrafael/readmission | notebook/test/03-data-classification-single-data.ipynb | 1 | 70285 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#TO RE-RUN\n",
"%reset -f"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn import preprocessing\n",
"from time import time\n",
"import numpy as np\n",
"import csv\n",
"from sklearn import metrics\n",
"from sklearn.preprocessing import scale\n",
"from sklearn.feature_selection import VarianceThreshold\n",
"from sklearn.cross_validation import StratifiedShuffleSplit, cross_val_score\n",
"\n",
"from sklearn.svm import SVC\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.naive_bayes import BernoulliNB, MultinomialNB, GaussianNB\n",
"\n",
"from sklearn.grid_search import GridSearchCV, ParameterGrid\n",
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"from imblearn.over_sampling import SMOTE,ADASYN, RandomOverSampler\n",
"from imblearn.pipeline import Pipeline\n",
"from imblearn.pipeline import make_pipeline\n",
"\n",
"from operator import truediv\n",
"from sklearn import metrics\n",
"import pandas as pd\n",
"import time\n",
"import os\n",
"\n",
"from pylab import *\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"\n",
"\n",
"np.set_printoptions(suppress=True)\n",
"pd.options.display.float_format = '{:,.2f}'.format\n",
"plt.style.use('classic')\n",
"\n",
"%matplotlib inline\n",
"\n",
"import sys\n",
"sys.path.insert(1, \"../../src/\")\n",
"from TypeFeatImputer import TypeFeatImputer\n",
"from UnivCombineFilter import UnivCombineFilter"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(71518, 20)\n",
"Index([u'diss_1', u'race_AfricanAmerican', u'race_Caucasian', u'race_Other',\n",
" u'medSpec_cardio', u'medSpec_Family/GeneralPractice',\n",
" u'medSpec_InternalMedicine', u'medSpec_surgery', u'age_cat',\n",
" u'Diabetis', u'Circulatory', u'Digestive', u'Genitourinary',\n",
" u'Poisoning', u'Muscoskeletal', u'Neoplasms', u'Respiratory', u'HbA1c',\n",
" u'Change', u'readmitted'],\n",
" dtype='object')\n",
"0 42985\n",
"2 22240\n",
"1 6293\n",
"Name: readmitted, dtype: int64\n",
"0 0.60\n",
"2 0.31\n",
"1 0.09\n",
"Name: readmitted, dtype: float64\n"
]
}
],
"source": [
"#df_all=pd.read_csv(os.path.join('resources','diabetic_data_processed_withweight.csv'),';')\n",
"df_all=pd.read_pickle(os.path.join('resources','clean_data.pkl'))\n",
"print df_all.shape\n",
"print df_all.columns\n",
"print df_all.readmitted.value_counts()\n",
"print df_all.readmitted.value_counts()/float(df_all.shape[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Compute class label"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 2] 42985 6293 22240\n",
"[0 1] 42985 28533\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ilmira/.conda/envs/readmision/lib/python2.7/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
" This is separate from the ipykernel package so we can avoid doing imports until\n"
]
}
],
"source": [
"# Readmitted\n",
"print df_all.loc[:,\"readmitted\"].sort_values().unique(), np.sum(df_all[\"readmitted\"] == 0), np.sum(df_all[\"readmitted\"] == 1), np.sum(df_all[\"readmitted\"] == 2)\n",
"df_all[\"readmitted\"][df_all[\"readmitted\"].values > 0] = 1\n",
"print df_all.iloc[:,-1].sort_values().unique(), np.sum(df_all[\"readmitted\"] == 0), np.sum(df_all[\"readmitted\"] == 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Compute type fields"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cat cols: 66 \n",
"Index([u'gender', u'age', u'metformin', u'repaglinide', u'nateglinide',\n",
" u'chlorpropamide', u'glimepiride', u'acetohexamide', u'glipizide',\n",
" u'glyburide', u'tolbutamide', u'pioglitazone', u'rosiglitazone',\n",
" u'acarbose', u'miglitol', u'troglitazone', u'tolazamide', u'examide',\n",
" u'citoglipton', u'insulin', u'glyburide-metformin',\n",
" u'glipizide-metformin', u'glimepiride-pioglitazone',\n",
" u'metformin-rosiglitazone', u'metformin-pioglitazone', u'Change',\n",
" u'diabetesMed', u'Diabetis', u'Infectious and parasitic diseases',\n",
" u'Neoplasms', u'Endocrine', u'Blood', u'Mental', u'Nervous', u'Organs',\n",
" u'Circulatory', u'Respiratory', u'Digestive', u'Genitourinary',\n",
" u'Pregnancy', u'Skin', u'Muscoskeletal', u'Congenital', u'Perinatal',\n",
" u'Ill-defined', u'race_AfricanAmerican', u'race_Asian',\n",
" u'race_Caucasian', u'race_Hispanic', u'race_Other', u'adm_1', u'adm_2',\n",
" u'adm_3', u'adm_4', u'adm_7', u'adm_src_1', u'adm_src_2', u'adm_src_3',\n",
" u'adm_src_4', u'adm_src_5', u'adm_src_6', u'adm_src_7', u'adm_src_8',\n",
" u'adm_src_10', u'Poisoning', u'External_causes'],\n",
" dtype='object')\n",
"Num cols: 8 \n",
"Index([u'time_in_hospital', u'num_lab_procedures', u'num_procedures',\n",
" u'num_medications', u'number_outpatient', u'number_emergency',\n",
" u'number_inpatient', u'number_diagnoses'],\n",
" dtype='object')\n",
"74\n"
]
}
],
"source": [
"numCols = [\n",
" \"time_in_hospital\",\"num_lab_procedures\",\"num_procedures\",\"num_medications\",\n",
" \"number_outpatient\",\"number_emergency\",\"number_inpatient\",\"number_diagnoses\"\n",
"]\n",
"\n",
"catCols = []\n",
"cols = df_all.columns\n",
"reducedCols = cols[:-1]\n",
"\n",
"for i in range(len(cols)-1):\n",
" if cols[i] not in numCols:\n",
" catCols.append(1)\n",
" else:\n",
" catCols.append(0)\n",
"catCols = np.array(catCols)\n",
"\n",
"print \"Cat cols:\", np.sum(catCols==1), \"\\n\", reducedCols[catCols==1]\n",
"print \"Num cols:\", np.sum(catCols==0), \"\\n\", reducedCols[catCols==0]\n",
"print len(reducedCols)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Compute partition (train, test)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1]\n",
"0 17030\n",
"1 10775\n",
"Name: readmitted, dtype: int64\n",
"\n",
"(19463, 74) (19463,)\n",
"11921 0.61 7542 0.39\n",
"(8342, 74) (8342,)\n",
"5109 0.61 3233 0.39\n"
]
}
],
"source": [
"y = df_all.readmitted\n",
"print y.unique()\n",
"print y.value_counts()\n",
"y = y.values\n",
"\n",
"X = df_all.iloc[:,:-1].values\n",
"sss = StratifiedShuffleSplit(y, 1, test_size=0.30, random_state=32) #random_state=42\n",
"for train_index, test_index in sss:\n",
" X_train, X_test = X[train_index], X[test_index]\n",
" y_train, y_test = y[train_index], y[test_index]\n",
"\n",
"print\n",
"print X_train.shape, y_train.shape\n",
"print np.sum(y_train == 0), round(np.sum(y_train == 0)/float(y_train.shape[0]),2), \\\n",
" np.sum(y_train > 0), round(np.sum(y_train > 0)/float(y_train.shape[0]),2)\n",
"print X_test.shape, y_test.shape\n",
"print np.sum(y_test == 0), round(np.sum(y_test == 0)/float(y_test.shape[0]),2), \\\n",
" np.sum(y_test > 0), round(np.sum(y_test > 0)/float(y_test.shape[0]),2)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"#### Simple pipeline"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"basePipeline = Pipeline([\n",
" (\"Imputer\", TypeFeatImputer(catCols, reducedCols)),\n",
" (\"Variance\", VarianceThreshold(threshold=(.995 * (1 - .995)))),\n",
" (\"Scaler\", StandardScaler())\n",
" ])\n",
"\n",
"params = {}\n",
"pipeline = []\n",
"pipe = Pipeline(list(basePipeline.steps))"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"fs_method = \"combine_fs\"\n",
"pipe.steps.insert(1,(fs_method, UnivCombineFilter(catCols,np.array(reducedCols))))\n",
"params.update({fs_method + '__percentile':[30,50,70,80]})"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cls_method = \"logReg\"\n",
"pipe.steps.append((cls_method, LogisticRegression(random_state=42)))\n",
"params.update({cls_method + '__C': [1e-5,0.001,0.01,0.1,1,5,10]})\n",
"params.update({cls_method + '__class_weight': [None, 'balanced']})\n",
"params.update({cls_method + '__penalty': [\"l1\",\"l2\"]})"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cls_method = \"rf\"\n",
"pipe.steps.append((cls_method, RandomForestClassifier(n_jobs=-1,random_state=42,class_weight=\"balanced\")))\n",
"params.update({cls_method + '__n_estimators': [150,300,500,700], \n",
" cls_method + '__criterion': ['gini'],\n",
" cls_method + '__max_depth' : [None,4,6,8,10]})"
]
},
{
"cell_type": "code",
"execution_count": 302,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cls_method = \"knn\"\n",
"pipe.steps.append((cls_method, KNeighborsClassifier(n_jobs=-1)))\n",
"\n",
"params.update({cls_method + '__n_neighbors': [3,5,7,9], \n",
" cls_method + '__weights': ['uniform', 'distance']})"
]
},
{
"cell_type": "code",
"execution_count": 419,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cls_method = \"svm\"\n",
"pipe.steps.append((cls_method, SVC(kernel = \"rbf\", random_state=42,probability=True)))\n",
"params.update({cls_method + '__C': [0.01,0.1,0.5,1,5,10,15,30,50], \n",
" cls_method + '__gamma' : [0.0001,0.001,0.01, 0.1,1,5],\n",
" cls_method + '__class_weight': [None, 'balanced']})"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cls_method = \"nb\"\n",
"pipe.steps.append((cls_method, GaussianNB()))\n",
"#params.update({cls_method + '__alpha': [1e-3,0.001,0.01,0.1,0.5,1,5]})"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Post process pipeline\n",
"pipe_imb = make_pipeline(*[p[1] for p in pipe.steps])\n",
"stps = len(pipe_imb.steps) \n",
"for s in range(stps):\n",
" pipe_imb.steps.remove(pipe_imb.steps[0])\n",
"for s in range(stps):\n",
" pipe_imb.steps.append(pipe.steps[s])"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Add sampling\n",
"sm_method = \"smote\" \n",
"pipe_imb.steps.insert(stps - 1, \n",
" (sm_method, SMOTE(ratio='auto', kind='regular', random_state=32)))\n",
"params.update({sm_method + \"__k_neighbors\":[3,4,5]})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Pipeline setup"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[('Imputer', TypeFeatImputer(allNameCols=Index([u'gender', u'age', u'time_in_hospital', u'num_lab_procedures',\n",
" u'num_procedures', u'num_medications', u'number_outpatient',\n",
" u'number_emergency', u'number_inpatient', u'number_diagnoses',\n",
" u'metformin', u'repaglinide', u'nateglinide', u'chlorpropamide',\n",
" u...src_7', u'adm_src_8', u'adm_src_10',\n",
" u'Poisoning', u'External_causes'],\n",
" dtype='object'),\n",
" dataCatCols=array([1, 1, ..., 1, 1]))), ('Variance', VarianceThreshold(threshold=0.004975)), ('Scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('logReg', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
" penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n",
" verbose=0, warm_start=False))]\n"
]
}
],
"source": [
"verbose = False\n",
"mtrs = [\"f1_weighted\"] #\"f1\",\"recall\",\"precision\"\n",
"cv_thr = 0.3\n",
"cv_folds = 5\n",
"\n",
"print pipe.steps"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Run pipeline"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ALL TRAIN: (19463, 74)\n",
"TRAIN: [0's: 11921 1's: 7542 ]\n",
"ALL TEST: (8342, 74)\n",
"TEST: [0's: 5109 1's: 3233 ]\n",
"TEST: [0's: 0.612443059218 1's: 0.387556940782 ]\n",
"\n",
"CV TRAIN: 13624\n",
"CV_TEST: 5839\n",
"Fitting 5 folds for each of 84 candidates, totalling 420 fits\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"[Parallel(n_jobs=-1)]: Done 18 tasks | elapsed: 18.4s\n",
"[Parallel(n_jobs=-1)]: Done 168 tasks | elapsed: 1.8min\n",
"[Parallel(n_jobs=-1)]: Done 420 out of 420 | elapsed: 4.5min finished\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total time: 279.275966167\n"
]
}
],
"source": [
"print \"ALL TRAIN:\", X_train.shape\n",
"print \"TRAIN:\", \"[0's:\", np.sum(y_train==0), \"1's:\", np.sum(y_train==1), \"]\"\n",
"print \"ALL TEST:\", X_test.shape\n",
"print \"TEST:\", \"[0's:\", np.sum(y_test==0), \"1's:\", np.sum(y_test==1), \"]\"\n",
"print \"TEST:\", \"[0's:\", np.sum(y_test==0)/float(y_test.shape[0]), \"1's:\", np.sum(y_test==1)/float(y_test.shape[0]), \"]\"\n",
"\n",
"# Run experiment\n",
"start = time.time()\n",
"\n",
"#Prepare pipe_cls \n",
"pipeline_cls = pipe_imb\n",
"pipeline_params = params\n",
"\n",
"if verbose:\n",
" print \"\\n\",pipeline_cls.steps\n",
"\n",
"\n",
"#Prepare cv\n",
"cv_inner = StratifiedShuffleSplit(y_train, n_iter=cv_folds, test_size=cv_thr,random_state=24)\n",
"\n",
"print \"\\nCV TRAIN:\", cv_inner.n_train\n",
"print \"CV_TEST:\", cv_inner.n_test\n",
"\n",
"#Fit pipeline with CV \n",
"grid_pipelines = []\n",
"\n",
"for m in mtrs:\n",
" grid_pipeline = GridSearchCV(pipeline_cls, param_grid=pipeline_params, verbose=1, \n",
" n_jobs=-1, cv=cv_inner, scoring= m, error_score = 0,\n",
" refit=True) \n",
" grid_pipeline.fit(X_train, y_train)\n",
" grid_pipelines.append([m,grid_pipeline])\n",
"\n",
"end = time.time()\n",
"print \"Total time:\", end - start"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"f1_weighted\n",
"*****\n",
"\n",
"('logReg', LogisticRegression(C=0.1, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
" penalty='l1', random_state=42, solver='liblinear', tol=0.0001,\n",
" verbose=0, warm_start=False))\n",
"\n",
"Discarded feats: 20 Index([u'acetohexamide', u'tolbutamide', u'miglitol', u'troglitazone',\n",
" u'tolazamide', u'examide', u'citoglipton', u'glipizide-metformin',\n",
" u'glimepiride-pioglitazone', u'metformin-rosiglitazone',\n",
" u'metformin-pioglitazone', u'Diabetis', u'Congenital', u'Perinatal',\n",
" u'adm_4', u'adm_7', u'adm_src_3', u'adm_src_5', u'adm_src_8',\n",
" u'adm_src_10'],\n",
" dtype='object')\n"
]
}
],
"source": [
"for gp in grid_pipelines:\n",
" print\n",
" print gp[0]\n",
" print \"*****\\n\"\n",
" print gp[1].best_estimator_.steps[-1]\n",
" print\n",
" \n",
" if gp[1].best_estimator_.steps[1][0] == \"combine_fs\":\n",
" varFilter = gp[1].best_estimator_.steps[1][1]\n",
"\n",
" print \"Selected thr:\", varFilter.percentile\n",
" print \"Selected columns:\"\n",
" feats = reducedCols[varFilter.ixCols].tolist()\n",
" print feats\n",
" print \"Num useful features:\", len(feats), feats\n",
" \n",
" if gp[1].best_estimator_.steps[1][0] == \"Variance\":\n",
" varFilter = gp[1].best_estimator_.steps[1][1]\n",
" feats = reducedCols[varFilter.get_support() == False]\n",
" print \"Discarded feats:\", len(feats), feats"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"TRAIN: \n",
"**********\n",
"\n",
"Metric: f1_weighted\n",
"TR Prec score: 0.631902812588\n",
"TR Rec score: 0.620767610338\n",
"TR F1 score: 0.624446683871\n"
]
}
],
"source": [
"# Computel Train score (with best CV params)\n",
"for gp in grid_pipelines:\n",
" \n",
" cls = gp[1]\n",
" y_pred = cls.predict(X_train) \n",
" train_prec_scores = metrics.precision_score(y_train, y_pred, average='weighted', pos_label=None)\n",
" train_rec_scores = metrics.recall_score(y_train, y_pred, average='weighted', pos_label=None)\n",
" train_f1_scores = metrics.f1_score(y_train, y_pred, average='weighted', pos_label=None)\n",
"\n",
" print \"\\nTRAIN: \"\n",
" print \"**********\\n\"\n",
"\n",
" print \"Metric:\",gp[0]\n",
" print \"TR Prec score:\", train_prec_scores\n",
" print \"TR Rec score:\", train_rec_scores\n",
" print \"TR F1 score:\", train_f1_scores"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"CV: \n",
"******\n",
"\n",
"Metric: f1_weighted\n",
"CV selected params ['balanced', 0.1, 'l1']\n",
"CV f1_weighted score: 0.619332387764\n",
"CV f1 score: 0.619 (+/-0.001)\n"
]
}
],
"source": [
"# Compute pipeline evaluation with CV\n",
"for gp in grid_pipelines:\n",
" \n",
" cls = gp[1]\n",
" print \"\\nCV: \"\n",
" print \"******\\n\"\n",
" \n",
" print \"Metric:\", gp[0]\n",
" print \"CV selected params {}\".format(cls.best_params_.values())\n",
" cv_inner_f1 = cross_val_score(cls.best_estimator_, X_train, y_train, \n",
" cv=cv_inner, scoring='f1_weighted', n_jobs=-1)\n",
" print \"CV {} score: {}\".format(gp[0], cls.best_score_)\n",
" print \"CV f1 score: %0.3f (+/-%0.03f)\" % (np.mean(cv_inner_f1), np.std(cv_inner_f1))"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"f1_weighted\n",
" params score_mean score_std \\\n",
"0 ['gini', 6, 100] 0.62 0.00 \n",
"1 ['gini', 6, 150] 0.62 0.00 \n",
"2 ['gini', 6, 300] 0.62 0.00 \n",
"3 ['gini', 6, 500] 0.62 0.00 \n",
"4 ['gini', 8, 100] 0.62 0.00 \n",
"5 ['gini', 8, 150] 0.62 0.00 \n",
"6 ['gini', 8, 300] 0.62 0.00 \n",
"7 ['gini', 8, 500] 0.62 0.00 \n",
"8 ['gini', 10, 100] 0.63 0.00 \n",
"9 ['gini', 10, 150] 0.63 0.00 \n",
"10 ['gini', 10, 300] 0.63 0.00 \n",
"11 ['gini', 10, 500] 0.63 0.00 \n",
"12 ['gini', 12, 100] 0.63 0.00 \n",
"13 ['gini', 12, 150] 0.63 0.00 \n",
"14 ['gini', 12, 300] 0.63 0.00 \n",
"15 ['gini', 12, 500] 0.63 0.00 \n",
"16 ['entropy', 6, 100] 0.62 0.00 \n",
"17 ['entropy', 6, 150] 0.62 0.01 \n",
"18 ['entropy', 6, 300] 0.62 0.00 \n",
"19 ['entropy', 6, 500] 0.62 0.00 \n",
"20 ['entropy', 8, 100] 0.62 0.00 \n",
"21 ['entropy', 8, 150] 0.62 0.00 \n",
"22 ['entropy', 8, 300] 0.62 0.00 \n",
"23 ['entropy', 8, 500] 0.62 0.00 \n",
"24 ['entropy', 10, 100] 0.63 0.00 \n",
"25 ['entropy', 10, 150] 0.63 0.00 \n",
"26 ['entropy', 10, 300] 0.63 0.00 \n",
"27 ['entropy', 10, 500] 0.62 0.00 \n",
"28 ['entropy', 12, 100] 0.63 0.01 \n",
"29 ['entropy', 12, 150] 0.63 0.00 \n",
"30 ['entropy', 12, 300] 0.63 0.00 \n",
"31 ['entropy', 12, 500] 0.63 0.00 \n",
"\n",
" scores \n",
"0 [0.623972673343, 0.618297324615, 0.62511557122... \n",
"1 [0.622435143864, 0.616422035211, 0.62358733624... \n",
"2 [0.622250824515, 0.614819490672, 0.62502650513... \n",
"3 [0.622616221555, 0.614640558473, 0.62336336672... \n",
"4 [0.62803809614, 0.622052403578, 0.627159125471... \n",
"5 [0.62565658651, 0.623014561766, 0.626968849742... \n",
"6 [0.626117478365, 0.624417239522, 0.62643044871... \n",
"7 [0.626053932455, 0.621116097403, 0.62706477250... \n",
"8 [0.626627870628, 0.626979819568, 0.63237180153... \n",
"9 [0.627978765846, 0.626001397648, 0.63239146506... \n",
"10 [0.626524189268, 0.625859709989, 0.63158101851... \n",
"11 [0.627102979385, 0.622887218451, 0.63089654293... \n",
"12 [0.624905298436, 0.625224065357, 0.63247143925... \n",
"13 [0.629705083018, 0.627375712613, 0.63095510602... \n",
"14 [0.632635886588, 0.625351735038, 0.63054424893... \n",
"15 [0.632884100557, 0.62709127276, 0.631250148937... \n",
"16 [0.622596005494, 0.615874184757, 0.62716149574... \n",
"17 [0.623594361983, 0.614435384974, 0.62970133448... \n",
"18 [0.623789580308, 0.616302478544, 0.62845446854... \n",
"19 [0.623456202853, 0.616017362411, 0.62560803679... \n",
"20 [0.624525109172, 0.61898347693, 0.62561528024,... \n",
"21 [0.62717302492, 0.621898539555, 0.623959536111... \n",
"22 [0.628017133103, 0.620216536904, 0.62441242213... \n",
"23 [0.62667746732, 0.61889614222, 0.626843490646,... \n",
"24 [0.626202649752, 0.621247829191, 0.63118065640... \n",
"25 [0.627373201226, 0.623841415259, 0.63111089642... \n",
"26 [0.626796024166, 0.622957553882, 0.62864511450... \n",
"27 [0.628046036932, 0.622479755224, 0.62658846925... \n",
"28 [0.624942195477, 0.618709623022, 0.63324829271... \n",
"29 [0.626948217226, 0.623151177592, 0.63577496074... \n",
"30 [0.630792465718, 0.623142641741, 0.63240617227... \n",
"31 [0.630212067355, 0.624639006813, 0.63319050698... \n",
"recall\n",
" params score_mean score_std \\\n",
"0 ['gini', 6, 100] 0.56 0.01 \n",
"1 ['gini', 6, 150] 0.56 0.00 \n",
"2 ['gini', 6, 300] 0.56 0.00 \n",
"3 ['gini', 6, 500] 0.56 0.00 \n",
"4 ['gini', 8, 100] 0.56 0.01 \n",
"5 ['gini', 8, 150] 0.56 0.01 \n",
"6 ['gini', 8, 300] 0.56 0.01 \n",
"7 ['gini', 8, 500] 0.55 0.01 \n",
"8 ['gini', 10, 100] 0.54 0.00 \n",
"9 ['gini', 10, 150] 0.54 0.00 \n",
"10 ['gini', 10, 300] 0.54 0.01 \n",
"11 ['gini', 10, 500] 0.54 0.01 \n",
"12 ['gini', 12, 100] 0.52 0.00 \n",
"13 ['gini', 12, 150] 0.52 0.01 \n",
"14 ['gini', 12, 300] 0.52 0.01 \n",
"15 ['gini', 12, 500] 0.52 0.01 \n",
"16 ['entropy', 6, 100] 0.56 0.01 \n",
"17 ['entropy', 6, 150] 0.56 0.01 \n",
"18 ['entropy', 6, 300] 0.56 0.00 \n",
"19 ['entropy', 6, 500] 0.56 0.00 \n",
"20 ['entropy', 8, 100] 0.56 0.01 \n",
"21 ['entropy', 8, 150] 0.56 0.01 \n",
"22 ['entropy', 8, 300] 0.56 0.01 \n",
"23 ['entropy', 8, 500] 0.56 0.00 \n",
"24 ['entropy', 10, 100] 0.55 0.01 \n",
"25 ['entropy', 10, 150] 0.55 0.01 \n",
"26 ['entropy', 10, 300] 0.55 0.01 \n",
"27 ['entropy', 10, 500] 0.54 0.01 \n",
"28 ['entropy', 12, 100] 0.53 0.01 \n",
"29 ['entropy', 12, 150] 0.53 0.01 \n",
"30 ['entropy', 12, 300] 0.53 0.01 \n",
"31 ['entropy', 12, 500] 0.52 0.00 \n",
"\n",
" scores \n",
"0 [0.568272205038, 0.567388422448, 0.55103844454... \n",
"1 [0.566062748564, 0.56473707468, 0.552364118427... \n",
"2 [0.561643835616, 0.556783031374, 0.54927087936... \n",
"3 [0.558992487848, 0.55634114008, 0.551038444543... \n",
"4 [0.562085726911, 0.560760053027, 0.54750331418... \n",
"5 [0.562527618206, 0.560760053027, 0.54396818382... \n",
"6 [0.562085726911, 0.559876270437, 0.54573574900... \n",
"7 [0.557224922669, 0.554131683606, 0.54573574900... \n",
"8 [0.543526292532, 0.550154661953, 0.53645603181... \n",
"9 [0.542200618648, 0.546619531595, 0.53424657534... \n",
"10 [0.547503314185, 0.543968183827, 0.52894387980... \n",
"11 [0.543526292532, 0.541758727353, 0.52806009721... \n",
"12 [0.515687140963, 0.522757401679, 0.51480335837... \n",
"13 [0.519664162616, 0.524524966858, 0.50905877154... \n",
"14 [0.519222271321, 0.522315510384, 0.50375607600... \n",
"15 [0.518338488732, 0.524966858153, 0.50684931506... \n",
"16 [0.566062748564, 0.567388422448, 0.55059655324... \n",
"17 [0.570923552806, 0.563411400795, 0.55678303137... \n",
"18 [0.56385329209, 0.561643835616, 0.554131683606... \n",
"19 [0.55634114008, 0.558550596553, 0.554573574901... \n",
"20 [0.566062748564, 0.560318161732, 0.55148033583... \n",
"21 [0.571807335395, 0.56473707468, 0.54706142289,... \n",
"22 [0.565178965974, 0.55545735749, 0.549712770658... \n",
"23 [0.560318161732, 0.554573574901, 0.55059655324... \n",
"24 [0.553689792311, 0.550154661953, 0.53247901016... \n",
"25 [0.558108705259, 0.551038444543, 0.52894387980... \n",
"26 [0.552806009722, 0.549270879364, 0.52717631462... \n",
"27 [0.549270879364, 0.549270879364, 0.5293857711,... \n",
"28 [0.533362792753, 0.521431727795, 0.51480335837... \n",
"29 [0.527618205921, 0.527176314627, 0.51745470614... \n",
"30 [0.533804684048, 0.524524966858, 0.51391957578... \n",
"31 [0.527176314627, 0.525850640742, 0.51612903225... \n"
]
}
],
"source": [
"# Compute pipeline evaluation with CV\n",
"dd = []\n",
"for gp in grid_pipelines:\n",
" \n",
" cls = gp[1] \n",
" params = np.array([str(d.values()) for d in np.array(cls.grid_scores_[:])[:,0]])\n",
" mean = np.array(cls.grid_scores_)[:,1]\n",
" values = np.array(cls.grid_scores_)[:,2]\n",
" std = np.array([np.std(v) for v in values])\n",
" \n",
" dd = np.hstack((params.reshape(-1,1), mean.reshape(-1,1), std.reshape(-1,1), values.reshape(-1,1)))\n",
" \n",
"\n",
" res = pd.DataFrame(dd,columns=[\"params\",\"score_mean\",\"score_std\",\"scores\"])\n",
" print gp[0]\n",
" print res"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"f1_weighted :\n",
"**********\n",
"\n",
"Test f1: 0.628\n",
"with following performance in test:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.70 0.68 0.69 5109\n",
" 1 0.52 0.54 0.53 3233\n",
"\n",
"avg / total 0.63 0.63 0.63 8342\n",
"\n",
"\n",
"Confusion matrix:\n",
"[[3483 1626]\n",
" [1492 1741]]\n",
"\n",
"Accuracy: 0.626228722129\n",
"Sensitivity: 0.538509124652\n",
"Specificity: 0.681738109219\n",
"AUC: 0.661631975062\n",
"\n",
"recall :\n",
"**********\n",
"\n",
"Test f1: 0.619\n",
"with following performance in test:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.70 0.64 0.67 5109\n",
" 1 0.50 0.58 0.54 3233\n",
"\n",
"avg / total 0.63 0.62 0.62 8342\n",
"\n",
"\n",
"Confusion matrix:\n",
"[[3269 1840]\n",
" [1370 1863]]\n",
"\n",
"Accuracy: 0.615200191801\n",
"Sensitivity: 0.576244973709\n",
"Specificity: 0.639851242905\n",
"AUC: 0.653975320688\n"
]
}
],
"source": [
"#Compute test score\n",
"for gp in grid_pipelines:\n",
" \n",
" cls = gp[1]\n",
" y_pred =cls.predict(X_test)\n",
" test_f1 = metrics.f1_score(y_test, y_pred, average='weighted', pos_label=None)\n",
"\n",
" print \"\\n\",gp[0],\":\"\n",
" print \"**********\\n\"\n",
" print \"Test f1: %0.3f\" % (test_f1)\n",
" print \"with following performance in test:\"\n",
" print metrics.classification_report(y_test, y_pred)\n",
" cm = metrics.confusion_matrix(y_test, y_pred)\n",
" print \"\\nConfusion matrix:\"\n",
" print cm\n",
"\n",
" print \"\\nAccuracy:\", (cm[0,0] + cm[1,1])/ float(cm[0,0] + cm[1,1]+cm[0,1] + cm[1,0])\n",
" print \"Sensitivity:\", cm[1,1] / float(cm[1,1] + cm[1,0]) #Reduce FN (recall)\n",
" print \"Specificity:\", cm[0,0] / float(cm[0,0] + cm[0,1]) #Reduce FP\n",
"\n",
" y_probs = cls.best_estimator_.predict_proba(X_test)\n",
" fpr, tpr, thresholds = metrics.roc_curve(y_test, y_probs[:,1], pos_label=1)\n",
" print \"AUC:\", metrics.auc(fpr, tpr)"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"#### Learning curve"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GridSearchCV(cv=StratifiedShuffleSplit(labels=[0 1 ..., 0 0], n_iter=10, test_size=0.2, random_state=24),\n",
" error_score=0,\n",
" estimator=Pipeline(steps=[('Imputer', TypeFeatImputer(allNameCols=Index([u'gender', u'age', u'time_in_hospital', u'num_lab_procedures',\n",
" u'num_procedures', u'num_medications', u'number_outpatient',\n",
" u'number_emergency', u'number_inpatient', u'number_diagnoses',\n",
" u'metformin', u'repaglinide', u'glimep...alty='l2', random_state=42, solver='liblinear', tol=0.0001,\n",
" verbose=0, warm_start=False))]),\n",
" fit_params={}, iid=True, n_jobs=-1,\n",
" param_grid={'logReg__class_weight': [None, 'balanced'], 'logReg__C': [0.001, 1, 10], 'logReg__penalty': ['l1', 'l2']},\n",
" pre_dispatch='2*n_jobs', refit=True, scoring='recall', verbose=1)\n"
]
}
],
"source": [
"cls = grid_pipelines[1][1]\n",
"print cls"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.model_selection import learning_curve\n",
"from sklearn.model_selection import ShuffleSplit\n",
"\n",
"\n",
"def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
" n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
"\n",
" plt.figure(figsize=(8,6))\n",
" plt.title(title)\n",
" if ylim is not None:\n",
" plt.ylim(*ylim)\n",
" plt.xlabel(\"% Training set\")\n",
" plt.ylabel(\"F1-score\")\n",
" train_sizes_lc, train_scores, test_scores = learning_curve(\n",
" estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes,scoring=\"f1_weighted\")\n",
" train_scores_mean = np.mean(train_scores, axis=1)\n",
" train_scores_std = np.std(train_scores, axis=1)\n",
" test_scores_mean = np.mean(test_scores, axis=1)\n",
" test_scores_std = np.std(test_scores, axis=1)\n",
" plt.grid(True)\n",
"\n",
" plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
" train_scores_mean + train_scores_std, alpha=0.2,\n",
" color=\"r\")\n",
" plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
" test_scores_mean + test_scores_std, alpha=0.2, color=\"g\")\n",
" plt.plot(train_sizes, train_scores_mean, 'o-', color=\"b\",\n",
" label=\"Training score\")\n",
" plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
" label=\"Cross-validation score\")\n",
" plt.axhline(0.5,color='r',ls='--', label=\"random\")\n",
" \n",
" plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
" return plt"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n",
"/home/aegle/miniconda2/envs/readmision/lib/python2.7/site-packages/sklearn/metrics/classification.py:1113: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
" 'precision', 'predicted', average, warn_for)\n"
]
},
{
"data": {
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MmjWLWbNmcccdd3SbR2/BggVa/4Trd9xxR161Z19d7/g6X9qzL67r34fg1/Xv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\ngg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f167d978290>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Cross validation with 100 iterations to get smoother mean test and train\n",
"# score curves, each time with 20% data randomly selected as a validation set.\n",
"title = \"\"\n",
"plot_learning_curve(cls.best_estimator_, title, X_train, y_train, ylim=(0.4, 1.01), \n",
" cv=cv_inner,\n",
" train_sizes=[0.05,0.10,0.15,0.25,0.50,0.75,0.80,1.0], \n",
" n_jobs=-1)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
danmerl/PFBDE.jl | gaussian_known_variance_example.ipynb | 1 | 213608 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example 2: Predictivist Bayes Take On A Gaussian Likelihood With Known Variance."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"using Gadfly\n",
"using Distributions;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we have $X_i \\sim N(\\theta,1)$ with $\\theta \\sim N(\\mu_0, \\phi_0^{-1})$, which admits predictions like\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"PosteriorPredictiveRule2 = function(pastX::Array{Float64})\n",
" μ_0 = 0.\n",
" ϕ_0 = 1.\n",
" \n",
" n = size(pastX,1)\n",
" if n > 0\n",
" ϕ_t = ϕ_0 + n # the precision of the posterior of θ\n",
" μ_t = (μ_0 * ϕ_0 + sum(pastX)) / (ϕ_t) # the mean of the posterior of θ\n",
" else\n",
" ϕ_t = ϕ_0\n",
" μ_t = μ_0\n",
" end\n",
" \n",
" x_new = rand(Normal(μ_t, sqrt((1 + ϕ_t^-1)^-1)))\n",
" \n",
" return x_new\n",
"end; "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Repeated simulation from this predictive rule should produce sample paths whose averages constitute draws from the standard posterior distribution on $\\theta$ [cf https://en.wikipedia.org/wiki/Conjugate_prior]. If we're not actually conditioning on any observed data, this distribution is $N(\\theta | \\mu_0, \\phi_0+1)$ with $\\mu_0$ and $\\phi_0$ as above. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# define the terms of the forward simulations\n",
"horizon = 1000\n",
"nits = 5000;\n",
"sims = zeros(Float64,horizon, nits);"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# simulate out to the forecast horizon for a number of separate Monte Carlo iterations\n",
"for jj = 1:nits\n",
" for ii = 1:horizon\n",
" if ii > 1\n",
" sims[ii,jj] = PosteriorPredictiveRule2(sims[1:(ii-1), jj])\n",
" else\n",
" sims[ii,jj] = PosteriorPredictiveRule2(Float64[])\n",
" end\n",
" end\n",
"end;\n",
"\n",
"# calculate the sample means of each sample path\n",
"samplemeans = mean(sims,1)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<?xml version=\"1.0\" encoding=\"UTF-8\"?>\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\"\n",
" xmlns:xlink=\"http://www.w3.org/1999/xlink\"\n",
" xmlns:gadfly=\"http://www.gadflyjl.org/ns\"\n",
" version=\"1.2\"\n",
" width=\"141.42mm\" height=\"100mm\" viewBox=\"0 0 141.42 100\"\n",
" stroke=\"none\"\n",
" fill=\"#000000\"\n",
" stroke-width=\"0.3\"\n",
" font-size=\"3.88\"\n",
">\n",
"<g class=\"plotroot xscalable yscalable\" id=\"img-1d9407c4-1\">\n",
" <g font-size=\"3.88\" font-family=\"'PT Sans','Helvetica Neue','Helvetica',sans-serif\" fill=\"#564A55\" stroke=\"#000000\" stroke-opacity=\"0.000\" id=\"img-1d9407c4-2\">\n",
" <text x=\"77.53\" y=\"88.39\" text-anchor=\"middle\" dy=\"0.6em\">θ</text>\n",
" </g>\n",
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"\n",
"(function (glob, factory) {\n",
" // AMD support\n",
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" define(\"Gadfly\", [\"Snap.svg\"], function (Snap) {\n",
" return factory(Snap);\n",
" });\n",
" } else {\n",
" // Browser globals (glob is window)\n",
" // Snap adds itself to window\n",
" glob.Gadfly = factory(glob.Snap);\n",
" }\n",
"}(this, function (Snap) {\n",
"\n",
"var Gadfly = {};\n",
"\n",
"// Get an x/y coordinate value in pixels\n",
"var xPX = function(fig, x) {\n",
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"\n",
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"};\n",
"\n",
"\n",
"Snap.plugin(function (Snap, Element, Paper, global) {\n",
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" };\n",
"\n",
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" return {\n",
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" x1: bbox.x + bbox.width,\n",
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" y1: bbox.y + bbox.height\n",
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" };\n",
"\n",
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" var root = this.plotroot()\n",
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" return {\n",
" x: bbox.x + bbox.width / 2,\n",
" y: bbox.y + bbox.height / 2\n",
" };\n",
" };\n",
"\n",
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"\n",
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" return;\n",
" }\n",
"\n",
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" event.type == \"mouseout\" ? event.toElement : event.fromElement;\n",
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"\n",
" if (reltg != this.node) {\n",
" return fn.apply(this, event);\n",
" }\n",
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"\n",
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"\n",
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" fn2);\n",
"\n",
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" };\n",
"\n",
"\n",
" // Snap's attr function can be too slow for things like panning/zooming.\n",
" // This is a function to directly update element attributes without going\n",
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" } else {\n",
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" }\n",
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"\n",
" Element.prototype.init_gadfly = function() {\n",
" this.mouseenter(Gadfly.plot_mouseover)\n",
" .mouseleave(Gadfly.plot_mouseout)\n",
" .dblclick(Gadfly.plot_dblclick)\n",
" .mousewheel(Gadfly.guide_background_scroll)\n",
" .drag(Gadfly.guide_background_drag_onmove,\n",
" Gadfly.guide_background_drag_onstart,\n",
" Gadfly.guide_background_drag_onend);\n",
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" });\n",
" return this;\n",
" };\n",
"});\n",
"\n",
"\n",
"// When the plot is moused over, emphasize the grid lines.\n",
"Gadfly.plot_mouseover = function(event) {\n",
" var root = this.plotroot();\n",
"\n",
" var keyboard_zoom = function(event) {\n",
" if (event.which == 187) { // plus\n",
" increase_zoom_by_position(root, 0.1, true);\n",
" } else if (event.which == 189) { // minus\n",
" increase_zoom_by_position(root, -0.1, true);\n",
" }\n",
" };\n",
" root.data(\"keyboard_zoom\", keyboard_zoom);\n",
" window.addEventListener(\"keyup\", keyboard_zoom);\n",
"\n",
" var xgridlines = root.select(\".xgridlines\"),\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"};\n",
"\n",
"// Reset pan and zoom on double click\n",
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"};\n",
"\n",
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"\n",
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"\n",
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"\n",
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" .selectAll(\"path\")\n",
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"\n",
" destcolor = root.data(\"unfocused_ygrid_color\");\n",
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" .selectAll(\"path\")\n",
" .animate({stroke: destcolor}, 250);\n",
"\n",
" // hide zoom slider\n",
" root.select(\".zoomslider\")\n",
" .animate({opacity: 0.0}, 250);\n",
"};\n",
"\n",
"\n",
"var set_geometry_transform = function(root, tx, ty, scale) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
"\n",
" var old_scale = root.data(\"scale\");\n",
"\n",
" var xscale = xscalable ? scale : 1.0,\n",
" yscale = yscalable ? scale : 1.0;\n",
"\n",
" tx = xscalable ? tx : 0.0;\n",
" ty = yscalable ? ty : 0.0;\n",
"\n",
" var t = new Snap.Matrix().translate(tx, ty).scale(xscale, yscale);\n",
"\n",
" root.selectAll(\".geometry, image\")\n",
" .forEach(function (element, i) {\n",
" element.transform(t);\n",
" });\n",
"\n",
" bounds = root.plotbounds();\n",
"\n",
" if (yscalable) {\n",
" var xfixed_t = new Snap.Matrix().translate(0, ty).scale(1.0, yscale);\n",
" root.selectAll(\".xfixed\")\n",
" .forEach(function (element, i) {\n",
" element.transform(xfixed_t);\n",
" });\n",
"\n",
" root.select(\".ylabels\")\n",
" .transform(xfixed_t)\n",
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" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
" unscale_t = new Snap.Matrix();\n",
" unscale_t.scale(1, 1/scale, cx, cy).add(st);\n",
" element.transform(unscale_t);\n",
"\n",
" var y = cy * scale + ty;\n",
" element.attr(\"visibility\",\n",
" bounds.y0 <= y && y <= bounds.y1 ? \"visible\" : \"hidden\");\n",
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" }\n",
"\n",
" if (xscalable) {\n",
" var yfixed_t = new Snap.Matrix().translate(tx, 0).scale(xscale, 1.0);\n",
" var xtrans = new Snap.Matrix().translate(tx, 0);\n",
" root.selectAll(\".yfixed\")\n",
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" .selectAll(\"text\")\n",
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" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var cx = element.asPX(\"x\"),\n",
" cy = element.asPX(\"y\");\n",
" var st = element.data(\"static_transform\");\n",
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"\n",
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"\n",
" var x = cx * scale + tx;\n",
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" }\n",
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" cy = element.attribute(\"cy\");\n",
" unscale_t = new Snap.Matrix().scale(1/xscale, 1/yscale,\n",
" cx, cy);\n",
" element.transform(unscale_t);\n",
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"\n",
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"\n",
" var tickscales = root.data(axis + \"tickscales\");\n",
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" };\n",
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"};\n",
"\n",
"\n",
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" var old_scale = root.data(\"scale\");\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
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" x_max = width * scale,\n",
" y_min = -height * scale - (scale * height - height),\n",
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"\n",
" var x0 = bounds.x0 - scale * bounds.x0,\n",
" y0 = bounds.y0 - scale * bounds.y0;\n",
"\n",
" var tx = Math.max(Math.min(tx - x0, x_max), x_min),\n",
" ty = Math.max(Math.min(ty - y0, y_max), y_min);\n",
"\n",
" tx += x0;\n",
" ty += y0;\n",
"\n",
" // when the scale change, we may need to alter which set of\n",
" // ticks is being displayed\n",
" if (scale != old_scale) {\n",
" update_tickscale(root, scale, \"x\");\n",
" update_tickscale(root, scale, \"y\");\n",
" }\n",
"\n",
" set_geometry_transform(root, tx, ty, scale);\n",
"\n",
" root.data(\"scale\", scale);\n",
" root.data(\"tx\", tx);\n",
" root.data(\"ty\", ty);\n",
"};\n",
"\n",
"\n",
"var scale_centered_translation = function(root, scale) {\n",
" var bounds = root.plotbounds();\n",
"\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var scale0 = root.data(\"scale\");\n",
"\n",
" // how off from center the current view is\n",
" var xoff = tx0 - (bounds.x0 * (1 - scale0) + (width * (1 - scale0)) / 2),\n",
" yoff = ty0 - (bounds.y0 * (1 - scale0) + (height * (1 - scale0)) / 2);\n",
"\n",
" // rescale offsets\n",
" xoff = xoff * scale / scale0;\n",
" yoff = yoff * scale / scale0;\n",
"\n",
" // adjust for the panel position being scaled\n",
" var x_edge_adjust = bounds.x0 * (1 - scale),\n",
" y_edge_adjust = bounds.y0 * (1 - scale);\n",
"\n",
" return {\n",
" x: xoff + x_edge_adjust + (width - width * scale) / 2,\n",
" y: yoff + y_edge_adjust + (height - height * scale) / 2\n",
" };\n",
"};\n",
"\n",
"\n",
"// Initialize data for panning zooming if it isn't already.\n",
"var init_pan_zoom = function(root) {\n",
" if (root.data(\"zoompan-ready\")) {\n",
" return;\n",
" }\n",
"\n",
" // The non-scaling-stroke trick. Rather than try to correct for the\n",
" // stroke-width when zooming, we force it to a fixed value.\n",
" var px_per_mm = root.node.getCTM().a;\n",
"\n",
" // Drag events report deltas in pixels, which we'd like to convert to\n",
" // millimeters.\n",
" root.data(\"px_per_mm\", px_per_mm);\n",
"\n",
" root.selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" sw = element.asPX(\"stroke-width\") * px_per_mm;\n",
" if (sw > 0) {\n",
" element.attribute(\"stroke-width\", sw);\n",
" element.attribute(\"vector-effect\", \"non-scaling-stroke\");\n",
" }\n",
" });\n",
"\n",
" // Store ticks labels original tranformation\n",
" root.selectAll(\".xlabels > text, .ylabels > text\")\n",
" .forEach(function (element, i) {\n",
" var lm = element.transform().localMatrix;\n",
" element.data(\"static_transform\",\n",
" new Snap.Matrix(lm.a, lm.b, lm.c, lm.d, lm.e, lm.f));\n",
" });\n",
"\n",
" var xgridlines = root.select(\".xgridlines\");\n",
" var ygridlines = root.select(\".ygridlines\");\n",
" var xlabels = root.select(\".xlabels\");\n",
" var ylabels = root.select(\".ylabels\");\n",
"\n",
" if (root.data(\"tx\") === undefined) root.data(\"tx\", 0);\n",
" if (root.data(\"ty\") === undefined) root.data(\"ty\", 0);\n",
" if (root.data(\"scale\") === undefined) root.data(\"scale\", 1.0);\n",
" if (root.data(\"xtickscales\") === undefined) {\n",
"\n",
" // index all the tick scales that are listed\n",
" var xtickscales = {};\n",
" var ytickscales = {};\n",
" var add_x_tick_scales = function (element, i) {\n",
" xtickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
" var add_y_tick_scales = function (element, i) {\n",
" ytickscales[element.attribute(\"gadfly:scale\")] = true;\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(add_x_tick_scales);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(add_y_tick_scales);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(add_x_tick_scales);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(add_y_tick_scales);\n",
"\n",
" root.data(\"xtickscales\", xtickscales);\n",
" root.data(\"ytickscales\", ytickscales);\n",
" root.data(\"xtickscale\", 1.0);\n",
" }\n",
"\n",
" var min_scale = 1.0, max_scale = 1.0;\n",
" for (scale in xtickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" for (scale in ytickscales) {\n",
" min_scale = Math.min(min_scale, scale);\n",
" max_scale = Math.max(max_scale, scale);\n",
" }\n",
" root.data(\"min_scale\", min_scale);\n",
" root.data(\"max_scale\", max_scale);\n",
"\n",
" // store the original positions of labels\n",
" if (xlabels) {\n",
" xlabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"x\", element.asPX(\"x\"));\n",
" });\n",
" }\n",
"\n",
" if (ylabels) {\n",
" ylabels.selectAll(\"text\")\n",
" .forEach(function (element, i) {\n",
" element.data(\"y\", element.asPX(\"y\"));\n",
" });\n",
" }\n",
"\n",
" // mark grid lines and ticks as in or out of scale.\n",
" var mark_inscale = function (element, i) {\n",
" element.attribute(\"gadfly:inscale\", element.attribute(\"gadfly:scale\") == 1.0);\n",
" };\n",
"\n",
" if (xgridlines) xgridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (ygridlines) ygridlines.selectAll(\"path\").forEach(mark_inscale);\n",
" if (xlabels) xlabels.selectAll(\"text\").forEach(mark_inscale);\n",
" if (ylabels) ylabels.selectAll(\"text\").forEach(mark_inscale);\n",
"\n",
" // figure out the upper ond lower bounds on panning using the maximum\n",
" // and minum grid lines\n",
" var bounds = root.plotbounds();\n",
" var pan_bounds = {\n",
" x0: 0.0,\n",
" y0: 0.0,\n",
" x1: 0.0,\n",
" y1: 0.0\n",
" };\n",
"\n",
" if (xgridlines) {\n",
" xgridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.x1 - bbox.x < pan_bounds.x0) {\n",
" pan_bounds.x0 = bounds.x1 - bbox.x;\n",
" }\n",
" if (bounds.x0 - bbox.x > pan_bounds.x1) {\n",
" pan_bounds.x1 = bounds.x0 - bbox.x;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" if (ygridlines) {\n",
" ygridlines\n",
" .selectAll(\"path\")\n",
" .forEach(function (element, i) {\n",
" if (element.attribute(\"gadfly:inscale\") == \"true\") {\n",
" var bbox = element.node.getBBox();\n",
" if (bounds.y1 - bbox.y < pan_bounds.y0) {\n",
" pan_bounds.y0 = bounds.y1 - bbox.y;\n",
" }\n",
" if (bounds.y0 - bbox.y > pan_bounds.y1) {\n",
" pan_bounds.y1 = bounds.y0 - bbox.y;\n",
" }\n",
" element.attr(\"visibility\", \"visible\");\n",
" }\n",
" });\n",
" }\n",
"\n",
" // nudge these values a little\n",
" pan_bounds.x0 -= 5;\n",
" pan_bounds.x1 += 5;\n",
" pan_bounds.y0 -= 5;\n",
" pan_bounds.y1 += 5;\n",
" root.data(\"pan_bounds\", pan_bounds);\n",
"\n",
" root.data(\"zoompan-ready\", true)\n",
"};\n",
"\n",
"\n",
"// drag actions, i.e. zooming and panning\n",
"var pan_action = {\n",
" start: function(root, x, y, event) {\n",
" root.data(\"dx\", 0);\n",
" root.data(\"dy\", 0);\n",
" root.data(\"tx0\", root.data(\"tx\"));\n",
" root.data(\"ty0\", root.data(\"ty\"));\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var tx0 = root.data(\"tx\"),\n",
" ty0 = root.data(\"ty\");\n",
"\n",
" var dx0 = root.data(\"dx\"),\n",
" dy0 = root.data(\"dy\");\n",
"\n",
" root.data(\"dx\", dx);\n",
" root.data(\"dy\", dy);\n",
"\n",
" dx = dx - dx0;\n",
" dy = dy - dy0;\n",
"\n",
" var tx = tx0 + dx,\n",
" ty = ty0 + dy;\n",
"\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\"));\n",
" },\n",
" end: function(root, event) {\n",
"\n",
" },\n",
" cancel: function(root) {\n",
" set_plot_pan_zoom(root, root.data(\"tx0\"), root.data(\"ty0\"), root.data(\"scale\"));\n",
" }\n",
"};\n",
"\n",
"var zoom_box;\n",
"var zoom_action = {\n",
" start: function(root, x, y, event) {\n",
" var bounds = root.plotbounds();\n",
" var width = bounds.x1 - bounds.x0,\n",
" height = bounds.y1 - bounds.y0;\n",
" var ratio = width / height;\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" x = xscalable ? x / px_per_mm : bounds.x0;\n",
" y = yscalable ? y / px_per_mm : bounds.y0;\n",
" var w = xscalable ? 0 : width;\n",
" var h = yscalable ? 0 : height;\n",
" zoom_box = root.rect(x, y, w, h).attr({\n",
" \"fill\": \"#000\",\n",
" \"opacity\": 0.25\n",
" });\n",
" zoom_box.data(\"ratio\", ratio);\n",
" },\n",
" update: function(root, dx, dy, x, y, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" var bounds = root.plotbounds();\n",
" if (yscalable) {\n",
" y /= px_per_mm;\n",
" y = Math.max(bounds.y0, y);\n",
" y = Math.min(bounds.y1, y);\n",
" } else {\n",
" y = bounds.y1;\n",
" }\n",
" if (xscalable) {\n",
" x /= px_per_mm;\n",
" x = Math.max(bounds.x0, x);\n",
" x = Math.min(bounds.x1, x);\n",
" } else {\n",
" x = bounds.x1;\n",
" }\n",
"\n",
" dx = x - zoom_box.attr(\"x\");\n",
" dy = y - zoom_box.attr(\"y\");\n",
" if (xscalable && yscalable) {\n",
" var ratio = zoom_box.data(\"ratio\");\n",
" var width = Math.min(Math.abs(dx), ratio * Math.abs(dy));\n",
" var height = Math.min(Math.abs(dy), Math.abs(dx) / ratio);\n",
" dx = width * dx / Math.abs(dx);\n",
" dy = height * dy / Math.abs(dy);\n",
" }\n",
" var xoffset = 0,\n",
" yoffset = 0;\n",
" if (dx < 0) {\n",
" xoffset = dx;\n",
" dx = -1 * dx;\n",
" }\n",
" if (dy < 0) {\n",
" yoffset = dy;\n",
" dy = -1 * dy;\n",
" }\n",
" if (isNaN(dy)) {\n",
" dy = 0.0;\n",
" }\n",
" if (isNaN(dx)) {\n",
" dx = 0.0;\n",
" }\n",
" zoom_box.transform(\"T\" + xoffset + \",\" + yoffset);\n",
" zoom_box.attr(\"width\", dx);\n",
" zoom_box.attr(\"height\", dy);\n",
" },\n",
" end: function(root, event) {\n",
" var xscalable = root.hasClass(\"xscalable\"),\n",
" yscalable = root.hasClass(\"yscalable\");\n",
" var zoom_bounds = zoom_box.getBBox();\n",
" if (zoom_bounds.width * zoom_bounds.height <= 0) {\n",
" return;\n",
" }\n",
" var plot_bounds = root.plotbounds();\n",
" var zoom_factor = 1.0;\n",
" if (yscalable) {\n",
" zoom_factor = (plot_bounds.y1 - plot_bounds.y0) / zoom_bounds.height;\n",
" } else {\n",
" zoom_factor = (plot_bounds.x1 - plot_bounds.x0) / zoom_bounds.width;\n",
" }\n",
" var tx = (root.data(\"tx\") - zoom_bounds.x) * zoom_factor + plot_bounds.x0,\n",
" ty = (root.data(\"ty\") - zoom_bounds.y) * zoom_factor + plot_bounds.y0;\n",
" set_plot_pan_zoom(root, tx, ty, root.data(\"scale\") * zoom_factor);\n",
" zoom_box.remove();\n",
" },\n",
" cancel: function(root) {\n",
" zoom_box.remove();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onstart = function(x, y, event) {\n",
" var root = this.plotroot();\n",
" var scalable = root.hasClass(\"xscalable\") || root.hasClass(\"yscalable\");\n",
" var zoomable = !event.altKey && !event.ctrlKey && event.shiftKey && scalable;\n",
" var panable = !event.altKey && !event.ctrlKey && !event.shiftKey && scalable;\n",
" var drag_action = zoomable ? zoom_action :\n",
" panable ? pan_action :\n",
" undefined;\n",
" root.data(\"drag_action\", drag_action);\n",
" if (drag_action) {\n",
" var cancel_drag_action = function(event) {\n",
" if (event.which == 27) { // esc key\n",
" drag_action.cancel(root);\n",
" root.data(\"drag_action\", undefined);\n",
" }\n",
" };\n",
" window.addEventListener(\"keyup\", cancel_drag_action);\n",
" root.data(\"cancel_drag_action\", cancel_drag_action);\n",
" drag_action.start(root, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.update(root, dx, dy, x, y, event);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_drag_onend = function(event) {\n",
" var root = this.plotroot();\n",
" window.removeEventListener(\"keyup\", root.data(\"cancel_drag_action\"));\n",
" root.data(\"cancel_drag_action\", undefined);\n",
" var drag_action = root.data(\"drag_action\");\n",
" if (drag_action) {\n",
" drag_action.end(root, event);\n",
" }\n",
" root.data(\"drag_action\", undefined);\n",
"};\n",
"\n",
"\n",
"Gadfly.guide_background_scroll = function(event) {\n",
" if (event.shiftKey) {\n",
" increase_zoom_by_position(this.plotroot(), 0.001 * event.wheelDelta);\n",
" event.preventDefault();\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseover = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseover_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_button_mouseout = function(event) {\n",
" this.select(\".button_logo\")\n",
" .animate({fill: this.data(\"mouseout_color\")}, 100);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomout_click = function(event) {\n",
" increase_zoom_by_position(this.plotroot(), -0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_zoomin_click = function(event) {\n",
" increase_zoom_by_position(this.plotroot(), 0.1, true);\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_track_click = function(event) {\n",
" // TODO\n",
"};\n",
"\n",
"\n",
"// Map slider position x to scale y using the function y = a*exp(b*x)+c.\n",
"// The constants a, b, and c are solved using the constraint that the function\n",
"// should go through the points (0; min_scale), (0.5; 1), and (1; max_scale).\n",
"var scale_from_slider_position = function(position, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return a * Math.exp(b * position) + c;\n",
"}\n",
"\n",
"// inverse of scale_from_slider_position\n",
"var slider_position_from_scale = function(scale, min_scale, max_scale) {\n",
" var a = (1 - 2 * min_scale + min_scale * min_scale) / (min_scale + max_scale - 2),\n",
" b = 2 * Math.log((max_scale - 1) / (1 - min_scale)),\n",
" c = (min_scale * max_scale - 1) / (min_scale + max_scale - 2);\n",
" return 1 / b * Math.log((scale - c) / a);\n",
"}\n",
"\n",
"var increase_zoom_by_position = function(root, delta_position, animate) {\n",
" var scale = root.data(\"scale\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var position = slider_position_from_scale(scale, min_scale, max_scale);\n",
" position += delta_position;\n",
" scale = scale_from_slider_position(position, min_scale, max_scale);\n",
" set_zoom(root, scale, animate);\n",
"}\n",
"\n",
"var set_zoom = function(root, scale, animate) {\n",
" var min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"scale\");\n",
" var new_scale = Math.max(min_scale, Math.min(scale, max_scale));\n",
" if (animate) {\n",
" Snap.animate(\n",
" old_scale,\n",
" new_scale,\n",
" function (new_scale) {\n",
" update_plot_scale(root, new_scale);\n",
" },\n",
" 200);\n",
" } else {\n",
" update_plot_scale(root, new_scale);\n",
" }\n",
"}\n",
"\n",
"\n",
"var update_plot_scale = function(root, new_scale) {\n",
" var trans = scale_centered_translation(root, new_scale);\n",
" set_plot_pan_zoom(root, trans.x, trans.y, new_scale);\n",
"\n",
" root.selectAll(\".zoomslider_thumb\")\n",
" .forEach(function (element, i) {\n",
" var min_pos = element.data(\"min_pos\"),\n",
" max_pos = element.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\");\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(new_scale, min_scale, max_scale);\n",
" element.transform(new Snap.Matrix().translate(\n",
" Math.max(min_pos, Math.min(\n",
" max_pos, min_pos + (max_pos - min_pos) * xpos)) - xmid, 0));\n",
" });\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragmove = function(dx, dy, x, y, event) {\n",
" var root = this.plotroot();\n",
" var min_pos = this.data(\"min_pos\"),\n",
" max_pos = this.data(\"max_pos\"),\n",
" min_scale = root.data(\"min_scale\"),\n",
" max_scale = root.data(\"max_scale\"),\n",
" old_scale = root.data(\"old_scale\");\n",
"\n",
" var px_per_mm = root.data(\"px_per_mm\");\n",
" dx /= px_per_mm;\n",
" dy /= px_per_mm;\n",
"\n",
" var xmid = (min_pos + max_pos) / 2;\n",
" var xpos = slider_position_from_scale(old_scale, min_scale, max_scale) +\n",
" dx / (max_pos - min_pos);\n",
"\n",
" // compute the new scale\n",
" var new_scale = scale_from_slider_position(xpos, min_scale, max_scale);\n",
" new_scale = Math.min(max_scale, Math.max(min_scale, new_scale));\n",
"\n",
" update_plot_scale(root, new_scale);\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragstart = function(x, y, event) {\n",
" this.animate({fill: this.data(\"mouseover_color\")}, 100);\n",
" var root = this.plotroot();\n",
"\n",
" // keep track of what the scale was when we started dragging\n",
" root.data(\"old_scale\", root.data(\"scale\"));\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"Gadfly.zoomslider_thumb_dragend = function(event) {\n",
" this.animate({fill: this.data(\"mouseout_color\")}, 100);\n",
" event.stopPropagation();\n",
"};\n",
"\n",
"\n",
"var toggle_color_class = function(root, color_class, ison) {\n",
" var guides = root.selectAll(\".guide.\" + color_class + \",.guide .\" + color_class);\n",
" var geoms = root.selectAll(\".geometry.\" + color_class + \",.geometry .\" + color_class);\n",
" if (ison) {\n",
" guides.animate({opacity: 0.5}, 250);\n",
" geoms.animate({opacity: 0.0}, 250);\n",
" } else {\n",
" guides.animate({opacity: 1.0}, 250);\n",
" geoms.animate({opacity: 1.0}, 250);\n",
" }\n",
"};\n",
"\n",
"\n",
"Gadfly.colorkey_swatch_click = function(event) {\n",
" var root = this.plotroot();\n",
" var color_class = this.data(\"color_class\");\n",
"\n",
" if (event.shiftKey) {\n",
" root.selectAll(\".colorkey text\")\n",
" .forEach(function (element) {\n",
" var other_color_class = element.data(\"color_class\");\n",
" if (other_color_class != color_class) {\n",
" toggle_color_class(root, other_color_class,\n",
" element.attr(\"opacity\") == 1.0);\n",
" }\n",
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" require([\"Snap.svg\", \"Gadfly\"], function (Snap, Gadfly) {\n",
" factory(Snap, Gadfly);\n",
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"text/plain": [
"Plot(...)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot(layer(x=samplemeans, Geom.histogram(density=true, bincount=50), order=1),\n",
"layer(x=-3:.1:3, y=pdf(Normal(0, 2^-.5), -3:.1:3), Geom.line, Theme(default_color=colorant\"grey\"), order=2),\n",
"Guide.xlabel(\"θ\"), Guide.ylabel(\"density\"))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The samples from the posterior provide a good approximation to the analytical posterior distribution shown in grey. This result is alluded to but there's no corresponding figure in the paper. End of example the second."
]
}
],
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| mit |
ioggstream/python-course | ansible-101/notebooks/02_delivery_layout.ipynb | 1 | 7939 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Delivery Layout - ansible.cfg\n",
"\n",
"When you deliver something you'll probably have a layout:\n",
"\n",
" - a static or dynamic inventory of all the nodes to manage\n",
" - ssh keys to use\n",
" - users and secrets to connect to the hosts\n",
" - whether to do privilege escalation (eg. sudo, ...) before running tasks\n",
" - if nodes should be accessed via a bastion host, docker, ...\n",
" \n",
"Put those informations, together with a brief description of the playbook usage (eg. 2/3 lines) into ansible.cfg\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"cd /notebooks/exercise-00"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ansible.cfg\n",
"\n",
"When running ansible, the first file read is ansible.cfg, resolved in the following order:\n",
"\n",
" - `ANSIBLE_CONFIG` (env var)\n",
" - `./ansible.cfg` (in the current directory)\n",
" - `~/ansible.cfg` (in the home directory)\n",
" - `/etc/ansible/ansible.cfg`\n",
" \n",
" \n",
"`ansible.cfg` is divided in stanzas\n",
"\n",
"```\n",
"# defaults, ends with \"s\". Without \"s\" it won't work :D\n",
"[defaults]\n",
"...\n",
"\n",
"[ssh_connection]\n",
"...\n",
"\n",
"```\n",
"\n",
"Always check [ansible source code](https://raw.github.com/ansible/ansible/devel/examples/ansible.cfg) to get in touch with new parameters.\n",
"\n",
"We'll create a new ansible.cfg for every project!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Exercise\n",
"\n",
"We mentioned a couple of ansible.cfg sections: defaults and ssh_connection.\n",
" \n",
"Name a couple more."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Write here some more ansible.cfg sections."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# When running ansible, the first file to be read is\n",
"!cat ansible.cfg"
]
},
{
"cell_type": "markdown",
"metadata": {
"solution": "shown",
"solution_first": true
},
"source": [
"#### Exercise \n",
"\n",
" - ping all hosts without specifying an inventory file\n",
" - comment the \"inventory\" line out of [ansible.cfg](/edit/notebooks/exercise-00/ansible.cfg)\n",
" - try to ping then again"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": [],
"solution": "shown"
},
"outputs": [],
"source": [
"# Solution\n",
"!sed -i 's/^inventory/#inventory/' ansible.cfg\n",
"!ansible -m ping all\n",
"!sed -i 's/#inventory/inventory/' ansible.cfg"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"solution": "shown"
},
"outputs": [],
"source": [
"# Use this cell for the exercise\n",
"!ansible -m ping all"
]
},
{
"cell_type": "markdown",
"metadata": {
"solution": "hidden",
"solution_first": true
},
"source": [
"#### Exercise\n",
"\n",
"You can subscript host groups, eg: `all[0]` is the first host in inventory.\n",
"\n",
" - ping only the first host\n",
" - then the second"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": [
0
],
"solution": "hidden"
},
"outputs": [],
"source": [
"# Solution\n",
"!ansible -m ping all[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Use this cell for the exercise\n",
"!ansible -m ping all[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Exercise\n",
"\n",
"Can you find a default ansible.cfg on this machine? \n",
"If not, have a look at it on your local distro.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Authentication\n",
"\n",
"You can manage machines via `ssh` or `docker`, but what happens via ssh if `PermitRootLogin=no`?\n",
"\n",
"Just use \n",
"```\n",
"[privilege_escalation]\n",
"become = yes\n",
"become_user = root\n",
"become_method = sudo # defaults to sudo\n",
"```\n",
"\n",
"#### Exercise\n",
"\n",
"You can specify which ssh key to use: \n",
"\n",
" - which parameter allows to set the default ssh identity?\n",
" - find the answer on the official documentation ;)\n",
" \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": []
},
"outputs": [],
"source": [
"# Write here the answer!\n",
"[defaults] # ansible.cfg\n",
"private_key_file = "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inventory\n",
"\n",
"The inventory contains the infrastructure hosts. Maintaining an inventory helps to:\n",
"\n",
" - clearly state each host and its functionalities\n",
" - communicate to others all the involved machines\n",
" - describe the infrastructure\n",
"\n",
"Via `ansible.cfg` you can set a default inventory. You could eg. default to staging and require `-i production` to run on actual machines.\n",
"\n",
"Ansible supports dynamic inventories (ldap, script, ..) [see inventory chapter](/notebooks/notebooks/05_inventories.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Encrypt secrets\n",
"\n",
"You can use and deliver secrets in your infrastructure using an encrypted file (aka vault).\n",
"\n",
"Decryption password can be typed each time or can be stored in a pin file configured in `ansible.cfg`.\n",
"\n",
"```\n",
"# either\n",
"ask_vault_pass = True\n",
"# or\n",
"vault_password_file = /path/to/pin_file\n",
"\n",
"```\n",
"\n",
"\n",
"REMEMBER: clear your pin file at logout ;) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Bastion\n",
"\n",
"A bastion host is the unique management entrypoint for an infrastructure.\n",
"\n",
"Ansible *leverages ssh functionalities* to manage resources from your local machine thru a bastion.\n",
"With a proper configuration you can run your commands/playbooks without continusly moving files to and fro your bastion.\n",
"\n",
"Those includes:\n",
" \n",
" - socks \n",
" - local and reverse tunnels (ssh -L | -R )\n",
"\n",
"\n",
" \n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recap Exercise\n",
"\n",
"Check the latest ansible.cfg source code and find 2 parameters you consider useful.\n",
"\n",
"Write down their name and functionality\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Write the solution here"
]
}
],
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"kernelspec": {
"display_name": "Python 2",
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"name": "python2"
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"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| agpl-3.0 |
tensorflow/workshops | extras/amld/notebooks/solutions/3_eager.ipynb | 1 | 12180498 | null | apache-2.0 |
JohnPHogan/FantasyFootball | Analysis2.ipynb | 1 | 49553 | {
"cells": [
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false,
"scrolled": true
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],
"text/plain": [
" Name Year Career Year Date Team Opp Result Pass Att \\\n",
"0 feeley, aj 2002 2 11/25/02 Phi @ SF W, 38-17 3 \n",
"1 feeley, aj 2002 2 12/01/02 Phi Stl W, 10-3 30 \n",
"2 feeley, aj 2002 2 12/08/02 Phi @ Sea W, 27-20 35 \n",
"3 feeley, aj 2002 2 12/15/02 Phi Was W, 34-21 28 \n",
"4 feeley, aj 2002 2 12/21/02 Phi @ Dal W, 27-3 33 \n",
"5 feeley, aj 2002 2 12/28/02 Phi @ NYG L, 10-7 25 \n",
"6 feeley, aj 2001 1 01/06/02 Phi @ TB W, 17-13 14 \n",
"7 feeley, aj 2004 4 09/11/04 Mia Ten L, 17-7 31 \n",
"8 feeley, aj 2004 4 09/19/04 Mia @ Cin L, 16-13 39 \n",
"9 feeley, aj 2004 4 09/26/04 Mia Pit L, 13-3 27 \n",
"\n",
" Pass Cmp Pass Pct ... Pass Int Pass Lg Pass Sack Pass Rate \\\n",
"0 3 100.0 ... 0 8 0/0 129.9 \n",
"1 14 46.7 ... 0 41 3/24 66.1 \n",
"2 21 60.0 ... 1 22 0/0 81.8 \n",
"3 16 57.1 ... 1 53 2/10 91.4 \n",
"4 19 57.6 ... 2 41 1/8 66.9 \n",
"5 13 52.0 ... 1 35 1/6 53.8 \n",
"6 10 71.4 ... 1 27 0/0 114.0 \n",
"7 21 67.7 ... 1 19 2/11 78.4 \n",
"8 21 53.8 ... 2 37 2/17 57.4 \n",
"9 13 48.1 ... 2 36 3/20 32.5 \n",
"\n",
" Rush Att Rush Yds Rush Avg Rush Lg Rush TD Rush FD \n",
"0 2 -3 -1.5 -1 0 0 \n",
"1 2 3 1.5 4 0 0 \n",
"2 3 0 0.0 1 0 0 \n",
"3 2 5 2.5 6 0 0 \n",
"4 2 2 1.0 4 0 0 \n",
"5 1 -1 -1.0 -1 0 0 \n",
"6 0 0 0.0 0 0 0 \n",
"7 0 0 0.0 0 0 0 \n",
"8 0 0 0.0 0 0 0 \n",
"9 0 0 0.0 0 0 0 \n",
"\n",
"[10 rows x 23 columns]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import pandas as pd\n",
"import matplotlib as mp\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.patches as mpatches\n",
"\n",
"qb_games = pd.read_csv('qb_games.csv')\n",
"#qb_games = qb_games.groupby(['Year'])\n",
"qb_games.head(10)\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"done!\n",
"<class 'pandas.core.frame.DataFrame'>\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Career Year</th>\n",
" <th>Year</th>\n",
" <th>Date</th>\n",
" <th>Pass Yds</th>\n",
" <th>Pass TD</th>\n",
" <th>Pass Int</th>\n",
" <th>Rush Yds</th>\n",
" <th>Rush TD</th>\n",
" <th>Fantasy Points</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>4192</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>09/11/16</td>\n",
" <td>334</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>25.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4193</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>09/18/16</td>\n",
" <td>396</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31.84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4194</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>09/26/16</td>\n",
" <td>240</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>21.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4195</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>10/02/16</td>\n",
" <td>503</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>42.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4196</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>10/09/16</td>\n",
" <td>267</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>16.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4197</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>10/16/16</td>\n",
" <td>335</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>29.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4198</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>10/23/16</td>\n",
" <td>273</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>14.92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4199</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>10/30/16</td>\n",
" <td>288</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>29.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4200</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>11/03/16</td>\n",
" <td>344</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>37.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4201</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>11/13/16</td>\n",
" <td>267</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>14.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4202</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>11/27/16</td>\n",
" <td>269</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>20.76</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4203</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>12/04/16</td>\n",
" <td>297</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4204</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>12/11/16</td>\n",
" <td>237</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>27.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4205</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>12/18/16</td>\n",
" <td>286</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4206</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>12/24/16</td>\n",
" <td>277</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>23.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4207</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>01/01/17</td>\n",
" <td>331</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>37.24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4208</th>\n",
" <td>ryan, matt</td>\n",
" <td>9</td>\n",
" <td>2016</td>\n",
" <td>01/14/17</td>\n",
" <td>338</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>31.52</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Career Year Year Date Pass Yds Pass TD Pass Int \\\n",
"4192 ryan, matt 9 2016 09/11/16 334 2 0 \n",
"4193 ryan, matt 9 2016 09/18/16 396 3 1 \n",
"4194 ryan, matt 9 2016 09/26/16 240 2 0 \n",
"4195 ryan, matt 9 2016 10/02/16 503 4 1 \n",
"4196 ryan, matt 9 2016 10/09/16 267 1 0 \n",
"4197 ryan, matt 9 2016 10/16/16 335 3 1 \n",
"4198 ryan, matt 9 2016 10/23/16 273 1 1 \n",
"4199 ryan, matt 9 2016 10/30/16 288 3 0 \n",
"4200 ryan, matt 9 2016 11/03/16 344 4 0 \n",
"4201 ryan, matt 9 2016 11/13/16 267 1 1 \n",
"4202 ryan, matt 9 2016 11/27/16 269 2 1 \n",
"4203 ryan, matt 9 2016 12/04/16 297 1 1 \n",
"4204 ryan, matt 9 2016 12/11/16 237 3 0 \n",
"4205 ryan, matt 9 2016 12/18/16 286 2 0 \n",
"4206 ryan, matt 9 2016 12/24/16 277 2 0 \n",
"4207 ryan, matt 9 2016 01/01/17 331 4 0 \n",
"4208 ryan, matt 9 2016 01/14/17 338 3 0 \n",
"\n",
" Rush Yds Rush TD Fantasy Points \n",
"4192 0 0 25.36 \n",
"4193 0 0 31.84 \n",
"4194 0 0 21.60 \n",
"4195 0 0 42.12 \n",
"4196 0 0 16.68 \n",
"4197 0 0 29.40 \n",
"4198 0 0 14.92 \n",
"4199 0 0 29.52 \n",
"4200 0 0 37.76 \n",
"4201 0 0 14.68 \n",
"4202 0 0 20.76 \n",
"4203 0 0 15.88 \n",
"4204 0 0 27.48 \n",
"4205 0 0 23.44 \n",
"4206 0 0 23.08 \n",
"4207 0 0 37.24 \n",
"4208 0 0 31.52 "
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"qb_games['Fantasy Points'] = (qb_games['Pass Yds']/25) + (6 * qb_games['Pass TD']) - (2 * qb_games['Pass Int']) + (qb_games['Rush Yds'] /10) + (6 * qb_games['Rush TD'])\n",
"qb_fantasy = qb_games[['Name','Career Year', 'Year', 'Date', 'Pass Yds', 'Pass TD', 'Pass Int', 'Rush Yds', 'Rush TD', 'Fantasy Points']]\n",
"qb_fantasy_2016 = qb_fantasy.loc[qb_fantasy['Year'] == 2016]\n",
"ryan_2016 = qb_fantasy_2016.loc[qb_fantasy_2016['Name'] == 'ryan, matt']\n",
"print(\"done!\")\n",
"print(type(ryan_2016))\n",
"ryan_2016\n",
"\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Pass Yds</th>\n",
" <th>Pass TD</th>\n",
" <th>Pass Int</th>\n",
" <th>Rush Yds</th>\n",
" <th>Rush TD</th>\n",
" <th>Fantasy Points</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Year</th>\n",
" <th>Career Year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <td>41</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pass Yds Pass TD Pass Int Rush Yds Rush TD \\\n",
"Year Career Year \n",
"2016 9 5282 41 7 0 0 \n",
"\n",
" Fantasy Points \n",
"Year Career Year \n",
"2016 9 443.28 "
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ryan_2016 = ryan_2016.groupby(['Year', 'Career Year']).sum()\n",
"ryan_2016"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"done!\n"
]
},
{
"data": {
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" <td>12/24/16</td>\n",
" <td>347</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>37.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>181</th>\n",
" <td>rodgers, aaron</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>01/01/17</td>\n",
" <td>300</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>36.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>182</th>\n",
" <td>rodgers, aaron</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>01/08/17</td>\n",
" <td>362</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>38.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>183</th>\n",
" <td>rodgers, aaron</td>\n",
" <td>12</td>\n",
" <td>2016</td>\n",
" <td>01/15/17</td>\n",
" <td>356</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>24.24</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Career Year Year Date Pass Yds Pass TD Pass Int \\\n",
"166 rodgers, aaron 12 2016 09/11/16 199 2 0 \n",
"167 rodgers, aaron 12 2016 09/18/16 213 1 1 \n",
"168 rodgers, aaron 12 2016 09/25/16 205 4 0 \n",
"169 rodgers, aaron 12 2016 10/09/16 259 2 2 \n",
"170 rodgers, aaron 12 2016 10/16/16 294 1 1 \n",
"171 rodgers, aaron 12 2016 10/20/16 326 3 0 \n",
"172 rodgers, aaron 12 2016 10/30/16 246 4 0 \n",
"173 rodgers, aaron 12 2016 11/06/16 297 3 1 \n",
"174 rodgers, aaron 12 2016 11/13/16 371 2 2 \n",
"175 rodgers, aaron 12 2016 11/20/16 351 3 0 \n",
"176 rodgers, aaron 12 2016 11/28/16 313 2 0 \n",
"177 rodgers, aaron 12 2016 12/04/16 209 2 0 \n",
"178 rodgers, aaron 12 2016 12/11/16 246 3 0 \n",
"179 rodgers, aaron 12 2016 12/18/16 252 0 0 \n",
"180 rodgers, aaron 12 2016 12/24/16 347 4 0 \n",
"181 rodgers, aaron 12 2016 01/01/17 300 4 0 \n",
"182 rodgers, aaron 12 2016 01/08/17 362 4 0 \n",
"183 rodgers, aaron 12 2016 01/15/17 356 2 1 \n",
"\n",
" Rush Yds Rush TD Fantasy Points \n",
"166 0 0 19.96 \n",
"167 0 0 12.52 \n",
"168 0 0 32.20 \n",
"169 0 0 18.36 \n",
"170 0 0 15.76 \n",
"171 0 0 31.04 \n",
"172 0 0 33.84 \n",
"173 0 0 27.88 \n",
"174 0 0 22.84 \n",
"175 0 0 32.04 \n",
"176 0 0 24.52 \n",
"177 0 0 20.36 \n",
"178 0 0 27.84 \n",
"179 0 0 10.08 \n",
"180 0 0 37.88 \n",
"181 0 0 36.00 \n",
"182 0 0 38.48 \n",
"183 0 0 24.24 "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rodgers_2016 = qb_fantasy_2016.loc[qb_fantasy_2016['Name'] == 'rodgers, aaron']\n",
"print(\"done!\")\n",
"rodgers_2016"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Pass Yds</th>\n",
" <th>Pass TD</th>\n",
" <th>Pass Int</th>\n",
" <th>Rush Yds</th>\n",
" <th>Rush TD</th>\n",
" <th>Fantasy Points</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Year</th>\n",
" <th>Career Year</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <td>46</td>\n",
" <td>8</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Pass Yds Pass TD Pass Int Rush Yds Rush TD \\\n",
"Year Career Year \n",
"2016 12 5146 46 8 0 0 \n",
"\n",
" Fantasy Points \n",
"Year Career Year \n",
"2016 12 465.84 "
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rodgers_2016 = rodgers_2016.groupby(['Year', 'Career Year']).sum()\n",
"rodgers_2016\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"done!\n"
]
},
{
"data": {
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" <th>Pass TD</th>\n",
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" <th>Fantasy Points</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>892</th>\n",
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" <tr>\n",
" <th>896</th>\n",
" <td>newton, cam</td>\n",
" <td>6</td>\n",
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" <td>6</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>898</th>\n",
" <td>newton, cam</td>\n",
" <td>6</td>\n",
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" <td>225</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
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" <td>16.60</td>\n",
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" <tr>\n",
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" <td>newton, cam</td>\n",
" <td>6</td>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
" <td>54</td>\n",
" <td>1</td>\n",
" <td>25.84</td>\n",
" </tr>\n",
" <tr>\n",
" <th>900</th>\n",
" <td>newton, cam</td>\n",
" <td>6</td>\n",
" <td>2016</td>\n",
" <td>11/17/16</td>\n",
" <td>192</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>14.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>901</th>\n",
" <td>newton, cam</td>\n",
" <td>6</td>\n",
" <td>2016</td>\n",
" <td>11/27/16</td>\n",
" <td>246</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>26.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>902</th>\n",
" <td>newton, cam</td>\n",
" <td>6</td>\n",
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" <th>903</th>\n",
" <td>newton, cam</td>\n",
" <td>6</td>\n",
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" <td>160</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>31</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>904</th>\n",
" <td>newton, cam</td>\n",
" <td>6</td>\n",
" <td>2016</td>\n",
" <td>12/19/16</td>\n",
" <td>300</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>24.00</td>\n",
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" <tr>\n",
" <th>905</th>\n",
" <td>newton, cam</td>\n",
" <td>6</td>\n",
" <td>2016</td>\n",
" <td>12/24/16</td>\n",
" <td>198</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>36</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>906</th>\n",
" <td>newton, cam</td>\n",
" <td>6</td>\n",
" <td>2016</td>\n",
" <td>01/01/17</td>\n",
" <td>237</td>\n",
" <td>1</td>\n",
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" <td>6</td>\n",
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" </tr>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" Name Career Year Year Date Pass Yds Pass TD Pass Int \\\n",
"892 newton, cam 6 2016 09/08/16 194 1 1 \n",
"893 newton, cam 6 2016 09/18/16 353 4 1 \n",
"894 newton, cam 6 2016 09/25/16 262 0 3 \n",
"895 newton, cam 6 2016 10/02/16 165 1 0 \n",
"896 newton, cam 6 2016 10/16/16 322 2 1 \n",
"897 newton, cam 6 2016 10/30/16 212 0 0 \n",
"898 newton, cam 6 2016 11/06/16 225 1 0 \n",
"899 newton, cam 6 2016 11/13/16 261 1 1 \n",
"900 newton, cam 6 2016 11/17/16 192 1 0 \n",
"901 newton, cam 6 2016 11/27/16 246 2 1 \n",
"902 newton, cam 6 2016 12/04/16 182 1 0 \n",
"903 newton, cam 6 2016 12/11/16 160 1 1 \n",
"904 newton, cam 6 2016 12/19/16 300 2 0 \n",
"905 newton, cam 6 2016 12/24/16 198 1 2 \n",
"906 newton, cam 6 2016 01/01/17 237 1 3 \n",
"\n",
" Rush Yds Rush TD Fantasy Points \n",
"892 54 1 23.16 \n",
"893 37 0 39.82 \n",
"894 26 1 13.08 \n",
"895 30 0 15.60 \n",
"896 1 1 28.98 \n",
"897 43 0 12.78 \n",
"898 16 0 16.60 \n",
"899 54 1 25.84 \n",
"900 7 0 14.38 \n",
"901 6 1 26.44 \n",
"902 12 0 14.48 \n",
"903 31 0 13.50 \n",
"904 0 0 24.00 \n",
"905 36 0 13.52 \n",
"906 6 0 10.08 "
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"newton_2016 = qb_fantasy_2016.loc[qb_fantasy_2016['Name'] == 'newton, cam']\n",
"print(\"done!\")\n",
"newton_2016"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
" <th>Pass Yds</th>\n",
" <th>Pass TD</th>\n",
" <th>Pass Int</th>\n",
" <th>Rush Yds</th>\n",
" <th>Rush TD</th>\n",
" <th>Fantasy Points</th>\n",
" </tr>\n",
" <tr>\n",
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" <th>Career Year</th>\n",
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],
"text/plain": [
" Pass Yds Pass TD Pass Int Rush Yds Rush TD \\\n",
"Year Career Year \n",
"2016 6 3509 19 14 359 5 \n",
"\n",
" Fantasy Points \n",
"Year Career Year \n",
"2016 6 292.26 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"newton_2016 = newton_2016.groupby(['Year', 'Career Year']).sum()\n",
"newton_2016"
]
}
],
"metadata": {
"anaconda-cloud": {},
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"display_name": "Python [Root]",
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| bsd-2-clause |
astroumd/GradMap | notebooks/Lectures2019/Lecture4/Lecture4-2BodyProblem2019-Student.ipynb | 1 | 18399 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to numerical simulations: The 2 Body Problem\n",
"\n",
"\n",
"Many problems in statistical physics and astrophysics require solving problems consisting of many particles at once (sometimes on the order of thousands or more!) This can't be done by the traditional pen and paper techniques you would encounter in a physics class. Instead, we must implement numerical solutions to these problems. \n",
"\n",
"Today, you will create your own numerical simulation for a simple problem is that solvable by pen and paper already, the 2 body problem in 2D. In this problem, we will describe the motion between two particles that share a force between them (such as Gravity). We'll design the simulation from an astronomer's mindset with astronomical units in mind. This simulation will be used to confirm the general motion of the earth around the Sun, and later will be used to predict the motion between two stars within relatively close range.\n",
"<br>\n",
"<br>\n",
"<br>\n",
"We will guide you through the physics and math required to create this simulation. \n",
"\n",
"First, a brief review of the kinematic equations (remembering Order of Operations or PEMDAS, and that values can be positive or negative depending on the reference frame):\n",
"\n",
"* new time = old time + time change ($t = t_0 + \\Delta t$)\n",
"\n",
"* new position = old position + velocity x time change ($x = x_0 + v \\times \\Delta t$) \n",
"\n",
"* new velocity = old velocity + acceleration x time change ($v = v_0 + a \\times \\Delta t$)\n",
"\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The problem here is designed to use the knowledge of scientific python you have been developing this week.\n",
"\n",
"\n",
"Like any code in python, The first thing we need to do is import the libraries we need. Go ahead and import Numpy and Pyplot below as np and plt respectively. Don't forget to put matplotlib inline to get everything within the notebook."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will define the physical constants of our system, which will also establish the unit system we have chosen. We'll use SI units here. Below, I've already created the constants. Make sure you understand what they are before moving on."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Physical Constants (SI units)\n",
"G=6.67e-11 #Universal Gravitational constant in m^3 per kg per s^2\n",
"AU=1.5e11 #Astronomical Unit in meters = Distance between sun and earth\n",
"daysec=24.0*60*60 #seconds in a day"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Next, we will need parameters for the simulation. These are known as initial condititons. For a 2 body gravitation problem, we'll need to know the masses of the two objects, the starting posistions of the two objects, and the starting velocities of the two objects. \n",
"\n",
"Below, I've included the initial conditions for the earth (a) and the Sun (b) at the average distance from the sun and the average velocity around the sun. We also need a starting time, and ending time for the simulation, and a \"time-step\" for the system. Feel free to adjust all of these as you see fit once you have built the system!\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"a note on `dt`:\n",
"As already stated, numeric simulations are approximations. In our case, we are approximating how time flows. We know it flows continiously, but the computer cannot work with this. So instead, we break up our time into equal chunks called \"dt\". The smaller the chunks, the more accurate you will become, but at the cost of computer time."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#####run specific constants. Change as needed#####\n",
"#Masses in kg\n",
"Ma=6.0e24 #always set as smaller mass\n",
"Mb=2.0e30 #always set as larger mass\n",
"\n",
"#Time settings\n",
"t=0.0 #Starting time\n",
"dt=.01*daysec #Time set for simulation\n",
"tend=300*daysec #Time where simulation ends\n",
"\n",
"#Initial conditions (position [m] and velocities [m/s] in x,y,z coordinates)\n",
"#For Ma\n",
"xa=1.0*AU\n",
"ya=0.0\n",
"\n",
"vxa=0.0\n",
"vya=30000.0\n",
"\n",
"#For Mb\n",
"xb=0.0\n",
"yb=0.0\n",
"\n",
"vxb=0.0\n",
"vyb=0.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It will be nice to create a function for the force between Ma and Mb. Below is the physics for the force of Ma on Mb. How the physics works here is not important for the moment. Right now, I want to make sure you can translate the math shown into a python function. (I'll show a picture of the physics behind this math for those interested.)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\\vec{F_g}=\\frac{-GM_aM_b}{r^3}\\vec{r}$$\n",
"and\n",
"$$\\vec{r}=(x_b-x_a)\\hat{x}+ (y_b-y_a)\\hat{y}$$\n",
"$$r^3=((x_b-x_a)^2+(y_b-y_a)^2)^{3/2}$$\n",
"\n",
"If we break `Fg` into the x and y componets we get:\n",
"$$F_x=\\frac{-GM_aM_b}{r^3}r_x$$\n",
"$$F_y=\\frac{-GM_aM_b}{r^3}r_y$$\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<br><br>So, $Fg$ will only need to be a function of `xa`, `xb`, `ya`, and `yb`. The velocities of the bodies will not be needed. Create a function that calculates the force between the bodies given the positions of the bodies. My recommendation here will be to feed the inputs as separate components and also return the force in terms of components (say, `fx` and `fy`). This will make your code easier to write and easier to read."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Function to compute the force between the two objects\n",
"def Fg(Ma,Mb,G,xa,xb,ya,yb):\n",
" #Compute rx and ry between Ma and Mb\n",
" rx=xb-xa\n",
" ry=#Write it in\n",
" \n",
" #compute r^3\n",
" r3=#Write in r^3 using the equation above. Make use of np.sqrt()\n",
" \n",
" #Compute the force in Newtons. Use the equations above as a Guide!\n",
" fx=-#Write it in\n",
" fy=-#Write it in\n",
" \n",
" return #What do we return?\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that we have our force function, we will make a new function which does the whole simulation for a set of initial conditions. We call this function 'simulate' and it will take all the initial conditions as inputs. It will loop over each time step and call the force function to find the new positions for the asteroids at each time step.\n",
"\n",
"The first part of our simulate function will be to initialize the loop and choose a loop type, for or while. Below is the general outline for how each type of loop can go.\n",
"<br>\n",
"<br>\n",
"<br>\n",
"For loop:\n",
"\n",
"- initialize position and velocity arrays with `np.zeros` or `np.linspace` for the amount of steps needed to go through the simulation (which is `numSteps=(tend-t)/dt` the way we have set up the problem). The for loop condition is based off time and should read rough like: `for i in range(numSteps)`\n",
"<br>\n",
"<br>\n",
"<br>\n",
"While loop:\n",
"- initialize position and velocity arrays with `np.array([])` and use `np.append()` to tact on new values at each step like so, `xaArray=np.append(xaArray,NEWVALUE)`. The while condition should read, `while t<tend`\n",
"\n",
"My preference here is `while` since it keeps my calculations and appending separate. But, feel free to use which ever feels best for you!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now for the actual simulation. This is the hardest part to code in. The general idea behind our loop is that as we step through time, we calculate the force, then calculate the new velocity, then the new position for each particle. At the end, we must update our arrays to reflect the new changes and update the time of the system. The time is super important! If we don't change the time (say in a while loop), the simulation would never end and we would never get our result. :(\n",
"\n",
"# Outline for the loop (order matters here)\n",
"- Calculate the force with the last known positions (use your function!)\n",
"\n",
"- Calculate the new velocities using the approximation: `vb = vb + dt*fg/Mb` and `va= va - dt*fg/Ma` *Note the minus sign here, and the need to do this for the x and y directions!*\n",
"\n",
"- Calculate the new positions using the approximation: `xb = xb + dt*Vb` (same for a and for y's. No minus problem here)\n",
"\n",
"- Update the arrays to reflect our new values\n",
"\n",
"- Update the time using `t=t+dt`\n",
"<br>\n",
"<br>\n",
"<br>\n",
"<br>\n",
"Now when the loop closes back in, the cycle repeats in a logical way. Go one step at a time when creating this loop and use comments to help guide yourself. Ask for help if it gets tricky!\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def simulate(Ma,Mb,G,xa,ya,vxa,vya,xb,yb,vxb,vyb):\n",
" t=0\n",
" #Run a loop for the simulation. Keep track of Ma and Mb posistions and velocites\n",
" #Initialize vectors (otherwise there is nothing to append to!)\n",
" xaAr=np.array([])\n",
" yaAr=np.array([])\n",
"\n",
" vxaAr=np.array([])\n",
" vyaAr=np.array([])\n",
"\n",
" xbAr=#Write it in for Particle B\n",
" ybAr=#Write it in for Particle B\n",
"\n",
" vxbAr=np.array([])\n",
" vybAr=np.array([])\n",
"\n",
" #using while loop method with appending. Can also be done with for loops\n",
" while #Write the end condition here.\n",
" #Compute current force on Ma and Mb. Ma recieves the opposite force of Mb\n",
" fx,fy=Fg(Ma,Mb,G,xa,xb,ya,yb)\n",
" \n",
" #Update the velocities and positions of the particles\n",
" vxa=vxa-fx*dt/Ma\n",
" vya=#Write it in for y\n",
" \n",
" vxb=#Write it in for x\n",
" vyb=vyb+fy*dt/Mb\n",
" \n",
" xa=xa+vxa*dt\n",
" ya=#Write it in for y\n",
" \n",
" xb=#Write it in for x\n",
" yb=yb+vyb*dt\n",
" \n",
" #Save data to lists\n",
" xaAr=np.append(xaAr,xa)\n",
" yaAr=np.append(yaAr,ya)\n",
" \n",
" xbAr=#How will we append it here?\n",
" ybAr=np.append(ybAr,yb)\n",
" \n",
" #update the time by one time step, dt\n",
" t=t+dt\n",
" return(xaAr,yaAr,xbAr,ybAr)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we will call our simulate function with the initial conditions we defined earlier! We will take the output of `simulate` and store the x and y positions of the two particles. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Do simulation with these parameters\n",
"xaAr,yaAr,xbAr,ybAr = simulate(Ma,Mb,G,xa,ya,vxa,vya,xb,yb,vxb,#Insert the variable for y position of B particle)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now for the fun part (or not so fun part if your simulation has an issue), plot your results! This is something well covered in previous lectures. Show me a plot of (xa,ya) and (xb,yb). Does it look sort of familiar? Hopefully you get something like the below image (in units of AU)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Image\n",
"Image(\"Earth-Sun-averageResult.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"plt.plot(xaAr/AU,yaAr/AU)\n",
"plt.plot(#Add positions for B particle)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Challenge \\#1: Random Sampling of Initial Simulation Conditions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's try to plot a few different asteroids with different initial conditions at once! Let's first produce the orbits of three asteroids with different masses. Suppose the masses of all asteroids in the main asteroid belt follow a Gaussian distribution. The parameters of the distribution of asteroid masses are defined below."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Mass distribution parameters \n",
"Mave=7.0e24 #The average asteroid mass\n",
"Msigma=1.0e24 #The standard deviation of asteroid masses\n",
"Size=3 #The number of asteroids we wish to simulate"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We now wish to draw a random sample of asteroid masses from this distribution (Hint: Look back at [Lecture \\#3](../Lecture3/Lecture3_Instructor.ipynb)). "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Draw 3 masses from normally distributed asteroid mass distribution\n",
"MassAr = # Add your normal a.k.a. Gaussian distribution function, \n",
" # noting that the input to your numpy random number generator\n",
" # function will be: (Size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's loop over our random asteroid sample, run simulate and plot the results, for each one!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"\n",
"for mass in #What array should we loop over?:\n",
" xaAr,yaAr,xbAr,ybAr=simulate(mass,Mb,G,xa,ya,vxa,vya,xb,yb,vyb,vyb)\n",
" plt.plot(xaAr/AU,yaAr/AU,label='Mass = %.2e'%mass) #Provide labels for each asteroid mass so we can generate a legend. \n",
" #Pro tip: The percent sign replaces '%.2e' in the string with the variable formatted the way we want!\n",
"\n",
"plt.legend()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Going further:\n",
"Can you make a plot with 5 asteroid masses instead of 3?\n",
"<b>\n",
"If you've got some extra time, now is a great chance to experiment with plotting various initial conditions and how the orbits change! What happens if we draw some random initial velocities instead of random masses, for example? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Challenge \\#2: Fancy Plotting Fun!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When showing off your results to people unfamiliar with your research, it helps to make them more easy to understand through different visualization techniques (like legends, labels, patterns, different shapes, and sizes). You may have found that textbooks or news articles are more fun and easy when concepts are illustrated colorfully yet clearly, such as the example figure below, which shows different annotations in the form of text:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import Image\n",
"Image(filename=\"fig_example.jpg\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Additionally, publications won't always be printed in color, and not all readers have the ability to distinguish colors or text size in the same way, so differences in style improve accessibility as well.\n",
"\n",
"\n",
"Luckily, Matplotlib can do all of this and more! Let's experiment with some variations in how we can make our plots. We can use the 'marker =' argument in plt.plot to choose a marker for every datapoint. We can use the 'linestyle = ' argument to have a dotted line instead of a solid line. Try experimenting with the extra arguments in the below plotting code to make it look good to you!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"plt.plot(xaAr/AU,yaAr/AU,marker='x',linestyle='--',linewidth=1)\n",
"plt.plot()#Add positions for B particle\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, add some plotting arguments to your loop like those you experimented with above. Can you make your plot more interesting and clear by changing the plotting parameters, or adding new plotting commands? \n",
"\n",
"See the jupyter notebook called [Plotting Demos](PlottingDemos.ipynb) in this same folder for some more examples of ways to make your plots pop! "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.6"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
jastarex/DeepLearningCourseCodes | 04_CNN_advances/use_vgg_finetune.ipynb | 1 | 17179 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 使用预训练的VGG模型Fine-tune CNN"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Package loaded\n",
"Current folder is /home/xrong/Documents/Code/course/DeepLearningCourseCodes/04_CNN_advances\n"
]
}
],
"source": [
"# Import packs\n",
"import numpy as np\n",
"import os\n",
"import scipy.io\n",
"from scipy.misc import imread, imresize\n",
"import matplotlib.pyplot as plt\n",
"import skimage.io\n",
"import skimage.transform\n",
"import tensorflow as tf\n",
"%matplotlib inline\n",
"cwd = os.getcwd()\n",
"print (\"Package loaded\")\n",
"print (\"Current folder is %s\" % (cwd) )"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# 下载预先训练好的vgg-19模型，为Matlab的.mat格式，之后会用scipy读取\n",
"# (注意此版本模型与此处http://www.vlfeat.org/matconvnet/pretrained/最新版本不同)\n",
"import os.path\n",
"if not os.path.isfile('./data/imagenet-vgg-verydeep-19.mat'):\n",
" !wget -O data/imagenet-vgg-verydeep-19.mat http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 载入图像，调节尺寸，生成数据集"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of total images is 87 (train: 69, test: 18)\n",
"Shape of an image is (64, 64, 3)\n"
]
}
],
"source": [
"# Configure the locations of the images and reshaping sizes\n",
"# ------------------------------------------------------------------- #\n",
"paths = {\"images/cats\", \"images/dogs\"}\n",
"imgsize = [64, 64] # The reshape size\n",
"use_gray = 0 # Grayscale\n",
"data_name = \"data4vgg\" # Save name\n",
"valid_exts = [\".jpg\",\".gif\",\".png\",\".tga\", \".jpeg\"]\n",
"# ------------------------------------------------------------------- #\n",
"\n",
"imgcnt = 0\n",
"nclass = len(paths)\n",
"for relpath in paths:\n",
" fullpath = cwd + \"/\" + relpath\n",
" flist = os.listdir(fullpath)\n",
" for f in flist:\n",
" if os.path.splitext(f)[1].lower() not in valid_exts:\n",
" continue\n",
" fullpath = os.path.join(fullpath, f)\n",
" imgcnt = imgcnt + 1\n",
"# Grayscale\n",
"def rgb2gray(rgb):\n",
" if len(rgb.shape) is 3:\n",
" return np.dot(rgb[...,:3], [0.299, 0.587, 0.114])\n",
" else:\n",
" print (\"Current Image is GRAY!\")\n",
" return rgb\n",
"if use_gray:\n",
" totalimg = np.ndarray((imgcnt, imgsize[0]*imgsize[1]))\n",
"else:\n",
" totalimg = np.ndarray((imgcnt, imgsize[0]*imgsize[1]*3))\n",
"totallabel = np.ndarray((imgcnt, nclass))\n",
"imgcnt = 0\n",
"for i, relpath in zip(range(nclass), paths):\n",
" path = cwd + \"/\" + relpath\n",
" flist = os.listdir(path)\n",
" for f in flist:\n",
" if os.path.splitext(f)[1].lower() not in valid_exts:\n",
" continue\n",
" fullpath = os.path.join(path, f)\n",
" currimg = imread(fullpath)\n",
" # Convert to grayscale \n",
" if use_gray:\n",
" grayimg = rgb2gray(currimg)\n",
" else:\n",
" grayimg = currimg\n",
" # Reshape\n",
" graysmall = imresize(grayimg, [imgsize[0], imgsize[1]])/255.\n",
" grayvec = np.reshape(graysmall, (1, -1))\n",
" # Save \n",
" totalimg[imgcnt, :] = grayvec\n",
" totallabel[imgcnt, :] = np.eye(nclass, nclass)[i]\n",
" imgcnt = imgcnt + 1\n",
" \n",
"# Divide total data into training and test set\n",
"randidx = np.random.randint(imgcnt, size=imgcnt)\n",
"trainidx = randidx[0:int(4*imgcnt/5)]\n",
"testidx = randidx[int(4*imgcnt/5):imgcnt]\n",
"trainimg = totalimg[trainidx, :]\n",
"trainlabel = totallabel[trainidx, :]\n",
"testimg = totalimg[testidx, :]\n",
"testlabel = totallabel[testidx, :]\n",
"ntrain = trainimg.shape[0]\n",
"nclass = trainlabel.shape[1]\n",
"dim = trainimg.shape[1]\n",
"ntest = testimg.shape[0]\n",
"\n",
"print (\"Number of total images is %d (train: %d, test: %d)\" \n",
" % (imgcnt, ntrain, ntest)) \n",
"print (\"Shape of an image is (%d, %d, %d)\" % (imgsize[0], imgsize[1], 3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 定义VGG网络结构"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"VGG net ready\n"
]
}
],
"source": [
"def net(data_path, input_image):\n",
" layers = (\n",
" 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',\n",
" 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',\n",
" 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',\n",
" 'relu3_3', 'conv3_4', 'relu3_4', 'pool3',\n",
" 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',\n",
" 'relu4_3', 'conv4_4', 'relu4_4', 'pool4',\n",
" 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',\n",
" 'relu5_3', 'conv5_4', 'relu5_4'\n",
" )\n",
" data = scipy.io.loadmat(data_path)\n",
" mean = data['normalization'][0][0][0]\n",
" mean_pixel = np.mean(mean, axis=(0, 1))\n",
" weights = data['layers'][0]\n",
" net = {}\n",
" current = input_image\n",
" for i, name in enumerate(layers):\n",
" kind = name[:4]\n",
" if kind == 'conv':\n",
" kernels, bias = weights[i][0][0][0][0]\n",
" # matconvnet: weights are [width, height, in_channels, out_channels]\n",
" # tensorflow: weights are [height, width, in_channels, out_channels]\n",
" kernels = np.transpose(kernels, (1, 0, 2, 3))\n",
" bias = bias.reshape(-1)\n",
" current = _conv_layer(current, kernels, bias)\n",
" elif kind == 'relu':\n",
" current = tf.nn.relu(current)\n",
" elif kind == 'pool':\n",
" current = _pool_layer(current)\n",
" net[name] = current\n",
" assert len(net) == len(layers)\n",
" return net, mean_pixel\n",
"def _conv_layer(input, weights, bias):\n",
" conv = tf.nn.conv2d(input, tf.constant(weights), strides=(1, 1, 1, 1),\n",
" padding='SAME')\n",
" return tf.nn.bias_add(conv, bias)\n",
"def _pool_layer(input):\n",
" return tf.nn.max_pool(input, ksize=(1, 2, 2, 1), strides=(1, 2, 2, 1),\n",
" padding='SAME')\n",
"def preprocess(image, mean_pixel):\n",
" return image - mean_pixel\n",
"def unprocess(image, mean_pixel):\n",
" return image + mean_pixel\n",
"print (\"VGG net ready\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 使用VGG计算卷积特征图"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of trainimg_tensor is (69, 64, 64, 3)\n",
"Shape of trainimg_tensor is (18, 64, 64, 3)\n",
"Convolutional map extraction done\n"
]
}
],
"source": [
"# Preprocess\n",
"trainimg_tensor = np.ndarray((ntrain, imgsize[0], imgsize[1], 3))\n",
"testimg_tensor = np.ndarray((ntest, imgsize[0], imgsize[1], 3))\n",
"for i in range(ntrain):\n",
" currimg = trainimg[i, :]\n",
" currimg = np.reshape(currimg, [imgsize[0], imgsize[1], 3])\n",
" trainimg_tensor[i, :, :, :] = currimg \n",
"print (\"Shape of trainimg_tensor is %s\" % (trainimg_tensor.shape,)) \n",
"\n",
"for i in range(ntest):\n",
" currimg = testimg[i, :]\n",
" currimg = np.reshape(currimg, [imgsize[0], imgsize[1], 3])\n",
" testimg_tensor[i, :, :, :] = currimg \n",
"print (\"Shape of trainimg_tensor is %s\" % (testimg_tensor.shape,))\n",
" \n",
"# Get conv features\n",
"VGG_PATH = cwd + \"/data/imagenet-vgg-verydeep-19.mat\"\n",
"with tf.Graph().as_default(), tf.Session() as sess:\n",
" with tf.device(\"/cpu:0\"):\n",
" img_placeholder = tf.placeholder(tf.float32\n",
" , shape=(None, imgsize[0], imgsize[1], 3))\n",
" nets, mean_pixel = net(VGG_PATH, img_placeholder)\n",
" train_features = nets['relu5_4'].eval(feed_dict={img_placeholder: trainimg_tensor})\n",
" test_features = nets['relu5_4'].eval(feed_dict={img_placeholder: testimg_tensor})\n",
"print(\"Convolutional map extraction done\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 卷积特征图的形状"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of 'train_features' is (69, 4, 4, 512)\n",
"Shape of 'test_features' is (18, 4, 4, 512)\n"
]
}
],
"source": [
"print (\"Shape of 'train_features' is %s\" % (train_features.shape,))\n",
"print (\"Shape of 'test_features' is %s\" % (test_features.shape,))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 向量化 "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape of 'train_vectorized' is (69, 4, 4, 512)\n",
"Shape of 'test_vectorized' is (18, 4, 4, 512)\n"
]
}
],
"source": [
"# Vectorize\n",
"train_vectorized = np.ndarray((ntrain, 4*4*512))\n",
"test_vectorized = np.ndarray((ntest, 4*4*512))\n",
"for i in range(ntrain):\n",
" curr_feat = train_features[i, :, :, :]\n",
" curr_feat_vec = np.reshape(curr_feat, (1, -1))\n",
" train_vectorized[i, :] = curr_feat_vec\n",
"for i in range(ntest):\n",
" curr_feat = test_features[i, :, :, :]\n",
" curr_feat_vec = np.reshape(curr_feat, (1, -1))\n",
" test_vectorized[i, :] = curr_feat_vec\n",
" \n",
"print (\"Shape of 'train_vectorized' is %s\" % (train_features.shape,))\n",
"print (\"Shape of 'test_vectorized' is %s\" % (test_features.shape,))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 定义finetuning的结构"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING:tensorflow:From /home/xrong/.pyenv/versions/anaconda2-4.4.0/lib/python2.7/site-packages/tensorflow/python/util/tf_should_use.py:175: initialize_all_variables (from tensorflow.python.ops.variables) is deprecated and will be removed after 2017-03-02.\n",
"Instructions for updating:\n",
"Use `tf.global_variables_initializer` instead.\n",
"Network Ready to Go!\n"
]
}
],
"source": [
"# Parameters\n",
"learning_rate = 0.0001\n",
"training_epochs = 100\n",
"batch_size = 100\n",
"display_step = 10\n",
"# tf Graph input\n",
"x = tf.placeholder(tf.float32, [None, 4*4*512])\n",
"y = tf.placeholder(tf.float32, [None, nclass])\n",
"keepratio = tf.placeholder(tf.float32)\n",
"# Network\n",
"with tf.device(\"/cpu:0\"):\n",
" n_input = dim\n",
" n_output = nclass\n",
" weights = {\n",
" 'wd1': tf.Variable(tf.random_normal([4*4*512, 1024], stddev=0.1)),\n",
" 'wd2': tf.Variable(tf.random_normal([1024, n_output], stddev=0.1))\n",
" }\n",
" biases = {\n",
" 'bd1': tf.Variable(tf.random_normal([1024], stddev=0.1)),\n",
" 'bd2': tf.Variable(tf.random_normal([n_output], stddev=0.1))\n",
" }\n",
" def conv_basic(_input, _w, _b, _keepratio):\n",
" # Input\n",
" _input_r = _input\n",
" # Vectorize\n",
" _dense1 = tf.reshape(_input_r, [-1, _w['wd1'].get_shape().as_list()[0]])\n",
" # Fc1\n",
" _fc1 = tf.nn.relu(tf.add(tf.matmul(_dense1, _w['wd1']), _b['bd1']))\n",
" _fc_dr1 = tf.nn.dropout(_fc1, _keepratio)\n",
" # Fc2\n",
" _out = tf.add(tf.matmul(_fc_dr1, _w['wd2']), _b['bd2'])\n",
" # Return everything\n",
" out = {'input_r': _input_r, 'dense1': _dense1,\n",
" 'fc1': _fc1, 'fc_dr1': _fc_dr1, 'out': _out }\n",
" return out\n",
" # Functions! \n",
" _pred = conv_basic(x, weights, biases, keepratio)['out']\n",
" cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=_pred, labels=y))\n",
" optm = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n",
" _corr = tf.equal(tf.argmax(_pred,1), tf.argmax(y,1)) \n",
" accr = tf.reduce_mean(tf.cast(_corr, tf.float32)) \n",
" init = tf.initialize_all_variables()\n",
"\n",
"print (\"Network Ready to Go!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 优化"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 000/100 cost: 5.317642212\n",
" Training accuracy: 0.430\n",
" Test accuracy: 0.722\n",
"Epoch: 010/100 cost: 0.489501685\n",
" Training accuracy: 0.800\n",
" Test accuracy: 0.833\n",
"Epoch: 020/100 cost: 0.225540578\n",
" Training accuracy: 0.940\n",
" Test accuracy: 0.944\n",
"Epoch: 030/100 cost: 0.107955739\n",
" Training accuracy: 0.970\n",
" Test accuracy: 0.889\n",
"Epoch: 040/100 cost: 0.014527633\n",
" Training accuracy: 1.000\n",
" Test accuracy: 0.944\n",
"Epoch: 050/100 cost: 0.004626322\n",
" Training accuracy: 1.000\n",
" Test accuracy: 0.889\n",
"Epoch: 060/100 cost: 0.001182456\n",
" Training accuracy: 1.000\n",
" Test accuracy: 0.944\n",
"Epoch: 070/100 cost: 0.000013701\n",
" Training accuracy: 1.000\n",
" Test accuracy: 0.944\n",
"Epoch: 080/100 cost: 0.000004261\n",
" Training accuracy: 1.000\n",
" Test accuracy: 0.944\n",
"Epoch: 090/100 cost: 0.000000751\n",
" Training accuracy: 1.000\n",
" Test accuracy: 0.944\n",
"Optimization Finished!\n"
]
}
],
"source": [
"# Launch the graph\n",
"sess = tf.Session()\n",
"sess.run(init)\n",
"\n",
"# Training cycle\n",
"for epoch in range(training_epochs):\n",
" avg_cost = 0.\n",
" num_batch = int(ntrain/batch_size)+1\n",
" # Loop over all batches\n",
" for i in range(num_batch): \n",
" randidx = np.random.randint(ntrain, size=batch_size)\n",
" batch_xs = train_vectorized[randidx, :]\n",
" batch_ys = trainlabel[randidx, :] \n",
" # Fit training using batch data\n",
" sess.run(optm, feed_dict={x: batch_xs, y: batch_ys, keepratio:0.7})\n",
" # Compute average loss\n",
" avg_cost += sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keepratio:1.})/num_batch\n",
"\n",
" # Display logs per epoch step\n",
" if epoch % display_step == 0:\n",
" print (\"Epoch: %03d/%03d cost: %.9f\" % (epoch, training_epochs, avg_cost))\n",
" train_acc = sess.run(accr, feed_dict={x: batch_xs, y: batch_ys, keepratio:1.})\n",
" print (\" Training accuracy: %.3f\" % (train_acc))\n",
" test_acc = sess.run(accr, feed_dict={x: test_vectorized, y: testlabel, keepratio:1.})\n",
" print (\" Test accuracy: %.3f\" % (test_acc))\n",
"\n",
"print (\"Optimization Finished!\")\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 1
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| apache-2.0 |
AlphaSmartDog/DeepLearningNotes | Note-6 A3CNet/Note 6 simple ACNet/.ipynb_checkpoints/test-checkpoint.ipynb | 1 | 117140 | {
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{
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"execution_count": 1,
"metadata": {
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},
"outputs": [
{
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"20373 4.6328 34.9458 -106.388\n",
"20389 1.5591 63.5234 -105.948\n",
"20403 1.1224 36.4369 -87.0085\n",
"20412 -0.521637 15.8168 -58.3827\n",
"20450 -2.82128 206.698 -206.314\n",
"20468 -0.81383 48.7425 -116.758\n",
"20486 -1.26036 68.5159 -120.919\n",
"20503 -6.15667 253.746 -86.656\n",
"20536 -2.10948 59.3057 -213.879\n",
"20586 2.30409 309.862 -306.349\n",
"20617 -4.12891 98.8122 -185.814\n",
"20665 -10.2516 146.648 -310.746\n",
"20695 -0.190022 390.75 -187.617\n",
"20731 -5.55889 135.216 -229.457\n",
"20781 -6.3813 99.1346 -324.402\n",
"20831 -2.35267 189.897 -313.664\n",
"20845 -4.24913 74.707 -86.0857\n",
"20865 -2.93401 156.451 -135.328\n",
"20874 -0.149903 91.4871 -31.548\n",
"20886 2.68298 64.0133 -39.9196\n",
"20897 0.00263608 214.66 -68.4694\n",
"20909 -3.3576 51.7227 -69.1747\n",
"20917 -0.325379 13.5464 -29.6059\n",
"20927 0.697846 16.8502 -50.9222\n",
"20937 1.60169 36.0249 -57.4466\n",
"20947 0.98105 81.1982 -60.0565\n",
"20959 3.52434 74.0238 -69.3423\n",
"20970 -3.91971 78.6373 -67.9643\n",
"20986 -0.925015 79.6269 -101.52\n",
"21009 -1.44677 118.062 -148.072\n",
"21029 -6.55417 305.229 -113.041\n",
"21078 -1.62105 303.647 -283.78\n",
"21105 -4.83436 199.102 -165.708\n",
"21135 -9.69658 195.14 -166.603\n",
"21158 -14.3983 176.93 -123.092\n",
"21184 -7.40633 254.502 -152.355\n",
"21234 -8.7814 221.935 -299.181\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"21276 10.5505 2453.3 -213.829\n",
"21326 -3.98498 17.662 -289.249\n",
"21376 -12.7556 691.026 -222.593\n",
"21402 -13.8927 621.785 -129.378\n",
"21427 6.11626 204.901 -161.408\n",
"21451 2.40871 511.601 -149.532\n",
"21461 -1.07507 183.858 -64.8585\n",
"21481 4.33143 172.73 -132.412\n",
"21495 0.429591 113.216 -93.0364\n",
"21505 -2.65763 65.1502 -64.1812\n",
"21536 -1.0662 340.738 -179.65\n",
"21568 -0.232568 745.585 -192.127\n",
"21582 -0.238442 180.462 -73.1307\n",
"21594 0.857876 160.744 -59.3825\n",
"21604 -5.80096 127.323 -50.7321\n",
"done\n",
"1001 223.3944905849538\n",
"done\n"
]
},
{
"data": {
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kkvZCl+8ZDcqBqHBOwNlLm6NUt6oYC+ZsOoZiqbppHOH1QuWchaCCtVqh6zqKpSoSsTAS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\nrkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1ec182362e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"from Agent_adjust import Agent\n",
"sns.set_style('whitegrid')\n",
"%matplotlib inline\n",
"\n",
"\n",
"\n",
"GAME = 'CartPole-v0'\n",
"MAX_EPISODES = 1000\n",
"\n",
"class NewAgent(Agent):\n",
" def __init__(self, env_name):\n",
" super().__init__(env_name)\n",
" \n",
" def run_plot(self, MAX_EPISODES):\n",
" while self.episode < MAX_EPISODES:\n",
" self.run_adjust(MAX_EPISODES, 50)\n",
" self.episode += 1\n",
" self.accumulate_reward_list.append(self.accumulate_reward)\n",
" print (self.episode, self.accumulate_reward)\n",
" print('done') \n",
"\n",
"\n",
"def main():\n",
" A = NewAgent(GAME)\n",
" A.run_plot(MAX_EPISODES)\n",
" pd.Series(A.accumulate_reward_list).plot(figsize=(16,6))\n",
"\n",
"if __name__ == '__main__':\n",
" main()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
Leguark/pygeomod | notebooks_GeoPyMC/PyMC for Geology Tutorial/PyMC geomod_1.ipynb | 1 | 85513 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## PyMC Geomod 1: Basic concepts \n",
"\n",
"The goal of this notebook is to show how to use pygeomod to change the position of points in a section in combination with PyMC to use Metropolis to define the mentionated positions\n",
"\n",
"**Importing**"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from IPython.core.display import Image"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import sys, os\n",
"import shutil\n",
"#import geobayes_simple as gs\n",
"\n",
"import pymc as pm # PyMC 2\n",
"from pymc.Matplot import plot\n",
"from pymc import graph as gr\n",
"import numpy as np\n",
"#import daft\n",
"from IPython.core.pylabtools import figsize\n",
"figsize(12.5, 10)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "invalid syntax (geogrid.py, line 156)",
"output_type": "error",
"traceback": [
"\u001b[1;36m File \u001b[1;32m\"C:\\Users\\Miguel\\workspace\\pygeomod\\pygeomod\\geogrid.py\"\u001b[1;36m, line \u001b[1;32m156\u001b[0m\n\u001b[1;33m else:\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
]
}
],
"source": [
"# as we have our model and pygeomod in different paths, let's change the pygeomod path to the default path.\n",
"sys.path.append(\"C:\\Users\\Miguel\\workspace\\pygeomod\\pygeomod\")\n",
"#sys.path.append(r'/home/jni/git/tmp/pygeomod_tmp')\n",
"import geogrid\n",
"import geomodeller_xml_obj as gxml\n",
"reload(gxml)\n",
"reload(geogrid)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simplest case: three horizontal layers, with depth unknow\n",
"#### Loading pre-made Geomodeller model "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"..\\Geomodeller\\Basic_case\\3_horizontal_layers\\horizontal_layers.xml\n"
]
}
],
"source": [
"hor_lay = r'..\\Geomodeller\\Basic_case\\3_horizontal_layers\\horizontal_layers.xml'\n",
"print hor_lay"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'geogrid' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-444c962e2b4e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mreload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgeogrid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mG1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgeogrid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGeoGrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;31m# Using G1, we can read the dimensions of our Murci geomodel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mG1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_dimensions_from_geomodeller_xml_project\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhor_lay\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'geogrid' is not defined"
]
}
],
"source": [
"reload(geogrid)\n",
"G1 = geogrid.GeoGrid()\n",
"\n",
"# Using G1, we can read the dimensions of our Murci geomodel\n",
"G1.get_dimensions_from_geomodeller_xml_project(hor_lay)\n",
"\n",
"#G1.set_dimensions(dim=(0,23000,0,16000,-8000,1000))\n",
"nx = 400\n",
"ny = 2\n",
"nz = 400\n",
"G1.define_regular_grid(nx,ny,nz)\n",
"\n",
"G1.update_from_geomodeller_project(hor_lay)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tha axis here represent the number of cells not the real values of geomodeller"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Miguel\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_base.py:1057: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal\n",
" if aspect == 'normal':\n",
"C:\\Users\\Miguel\\Anaconda\\lib\\site-packages\\matplotlib\\axes\\_base.py:1062: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal\n",
" elif aspect in ('equal', 'auto'):\n"
]
},
{
"data": {
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XA28Cvgc83botIt494aKkScD2iPhVekbdlRExU9JbgdMjYn7a793A70TEhZIej4hDWzIe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\nM+uw/w+JmfdAmU7I3gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1a0ce630>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"G1.plot_section('y',cell_pos=1,colorbar = True, cmap='RdBu', figsize=(6,6),interpolation= 'nearest' ,ve = 1, geomod_coord= True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"####Setting Bayes Model"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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"text/plain": [
"<IPython.core.display.Image object>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Image(\"Nice Notebooks\\THL_no_thickness.png\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"alpha = pm.Normal(\"alpha\", -350, 0.005, value = -200)#, value= 250)\n",
"beta = pm.Normal(\"beta\", -500, 0.0001, value = -300)#, value=0)\n",
"gamma = pm.Normal(\"gamma\", -650, 0.0001, value = -650)#, value = 0)\n",
"\n",
"#MODEL!!\n",
"model = pm.Model([alpha, beta, gamma])"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\r",
" [-----------------100%-----------------] 1500 of 1500 complete in 0.1 sec"
]
}
],
"source": [
"M = pm.MCMC(model)\n",
"M.sample(iter=1500, burn = 800)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Plotting Posteriors**"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot(M)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Extracting Posterior Traces to Arrays **"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"n_samples = 10\n",
"\n",
"alpha_samples, alpha_samples_all = M.trace('alpha')[-n_samples:], M.trace(\"alpha\")[:]\n",
"beta_samples, beta_samples_all = M.trace('beta')[-n_samples:], M.trace(\"beta\")[:]\n",
"gamma_samples, gamma_samples_all = M.trace('gamma')[-n_samples:], M.trace('gamma')[:]\n",
"\n",
"samples = zip (alpha_samples,beta_samples, gamma_samples,alpha_samples,beta_samples, gamma_samples)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generating new model in Geomodeller"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Setting the new folder where we want to work"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The folder is already created\n"
]
}
],
"source": [
"try:\n",
" shutil.copytree('C:/Users/Miguel/workspace/Thesis/Geomodeller/Basic_case/3_horizontal_layers', 'Temp/')\n",
"except:\n",
" print \"The folder is already created\"\n",
"#r'..\\Geomodeller\\Basic_case\\3_horizontal_layers\\"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Loading GeoModeller project where we want to apply Bayes Inferences (As in first part of the notebook):"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"reload(gxml)\n",
"gmod_obj = gxml.GeomodellerClass()\n",
"gmod_obj.load_geomodeller_file(hor_lay)\n",
"gmod_obj.write_xml(\"backup\\orihor_lay.xml\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Showing pygeomod functions to extract data from the GeoModeller Project"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"section names ['Section1', 'SurfaceTopography'] \n",
"\n",
"Chosen section by position <Element '{http://www.geomodeller.com/geo}Section' at 0x2e31f390> \n",
"\n",
"Chosen section by entry <Element '{http://www.geomodeller.com/geo}Section' at 0x2e31f390> \n",
"\n",
"formation names ['Form1', 'Form2', 'Form3'] \n",
"\n",
"Contact points on the chosen section [<Element '{http://www.geomodeller.com/geo}Interface' at 0x2e31f940>, <Element '{http://www.geomodeller.com/geo}Interface' at 0x2e31fa90>, <Element '{http://www.geomodeller.com/geo}Interface' at 0x2e31fbe0>, <Element '{http://www.geomodeller.com/geo}Interface' at 0x2e31fd30>, <Element '{http://www.geomodeller.com/geo}Interface' at 0x2e31fe80>, <Element '{http://www.geomodeller.com/geo}Interface' at 0x2e31ffd0>] \n",
"<type 'list'>\n",
"Contact points on the chosen section [<Element '{http://www.geomodeller.com/geo}Interface' at 0x2e31f940>, <Element '{http://www.geomodeller.com/geo}Interface' at 0x2e31fa90>, <Element '{http://www.geomodeller.com/geo}Interface' at 0x2e31fbe0>, <Element '{http://www.geomodeller.com/geo}Interface' at 0x2e31fd30>, <Element '{http://www.geomodeller.com/geo}Interface' at 0x2e31fe80>, <Element '{http://www.geomodeller.com/geo}Interface' at 0x2e31ffd0>] \n",
"<type 'list'>\n",
"Points coordinates ['Form3 50,-350 150,-350 ', 'Form2 50,-500 150,-500 ', 'Form1 50,-650 150,-650 ', 'Form3 850,-350 950,-350 ', 'Form2 850,-500 950,-500 ', 'Form1 850,-650 950,-650 ']\n"
]
}
],
"source": [
"# Section names:\n",
"section_names = gmod_obj.get_section_names()\n",
"print \"section names\",section_names, \"\\n\" \n",
"\n",
"# Choose the section we want to use with Positon\n",
"sections = gmod_obj.get_sections()[0]\n",
"print \"Chosen section by position\", sections, \"\\n\"\n",
"\n",
"# Create a dictionary so we can acces the section through the name\n",
"section_dict = gmod_obj.create_sections_dict()\n",
"print \"Chosen section by entry\", section_dict['Section1'], \"\\n\"\n",
"\n",
"# Formation names\n",
"formation_names = gmod_obj.get_formation_names()\n",
"print \"formation names\", formation_names, \"\\n\"\n",
"\n",
"# Get the points of all formation for a given section: Position\n",
"contact_points = gmod_obj.get_formation_point_data(sections) #to extract points you have to choose one of the sections\n",
"print \"Contact points on the chosen section\", contact_points, \"\\n\", type(contact_points)\n",
"\n",
"## Get the points of all formation for a given section: Dictionary\n",
"contact_points = gmod_obj.get_formation_point_data(section_dict['Section1']) #to extract points you have to choose one of the sections\n",
"print \"Contact points on the chosen section\", contact_points, \"\\n\", type(contact_points)\n",
"\n",
"# Showing contact points\n",
"points = gmod_obj.get_point_coordinates(contact_points)\n",
"print \"Points coordinates\", points"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"** Changing point position values and creating new xml projects **\n",
"\n",
"We want to change all points of the three formations. To do so we use the Section 1 that is what we are plotting"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(-336.76603718274771,\n",
" -332.88033393639932,\n",
" -749.09585711070451,\n",
" -336.76603718274771,\n",
" -332.88033393639932,\n",
" -749.09585711070451)"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"for j in range(n_samples):\n",
" for i, point in enumerate(contact_points):\n",
" gmod_obj.change_formation_point_pos(point, y_coord = [samples[j][i],samples[j][i]])\n",
" gmod_obj.write_xml(\"Temp/test\"+ str(j)+\".xml\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can see the position of the points has changed (in this case just the last iteration, i.e. last value of our Metropolis chain)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Points coordinates ['Form3 50.000000,-356.191320 150.000000,-356.191320 ', 'Form2 50.000000,-616.210664 150.000000,-616.210664 ', 'Form1 50.000000,-572.489090 150.000000,-572.489090 ', 'Form3 850.000000,-356.191320 950.000000,-356.191320 ', 'Form2 850.000000,-616.210664 950.000000,-616.210664 ', 'Form1 850.000000,-572.489090 950.000000,-572.489090 ']\n"
]
}
],
"source": [
"# Showing contact points\n",
"points_changed = gmod_obj.get_point_coordinates(contact_points)\n",
"print \"Points coordinates\", points_changed\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Plotting the results ** "
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"c_char_p('Temp/test0.xml')\n",
"c_char_p('Temp/test1.xml')\n",
"c_char_p('Temp/test2.xml')\n",
"c_char_p('Temp/test3.xml')\n",
"c_char_p('Temp/test4.xml')\n",
"c_char_p('Temp/test5.xml')\n",
"c_char_p('Temp/test6.xml')\n",
"c_char_p('Temp/test7.xml')\n",
"c_char_p('Temp/test8.xml')\n",
"c_char_p('Temp/test9.xml')\n"
]
},
{
"data": {
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ZZSV1ARsBG5vZK5npjwA7AG1m9oKZfW1ZZHXOOeecS1HSBblUn5OTV5VnTiyL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\nERERkYxRQ05ERERERCRj/n8Sq2NJULEePwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x33141cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1, 2, figsize=(15, 5))\n",
"ax[0].hist(alpha_samples_all, histtype='stepfilled', bins=30, alpha=1,\n",
" label=\"Upper most layer\", normed=True)\n",
"ax[0].hist(beta_samples_all, histtype='stepfilled', bins=30, alpha=1,\n",
" label=\"Middle layer\", normed=True, color = \"g\")\n",
"ax[0].hist(gamma_samples_all, histtype='stepfilled', bins=30, alpha=1,\n",
" label=\"Bottom most layer\", normed=True, color = \"r\")\n",
"\n",
"\n",
"ax[0].invert_xaxis()\n",
"ax[0].legend()\n",
"ax[0].set_title(r\"\"\"Posterior distributions of the layers\"\"\")\n",
"ax[0].set_xlabel(\"Depth(m)\")\n",
"\n",
"\n",
"ax[1].set_title(\"Representation\")\n",
"\n",
"\n",
"for j in range(n_samples):\n",
" hor_lay_new = 'Temp/test'+str(j)+'.xml'\n",
" \n",
" # Read the new xml\n",
" #hor_lay_new = 'Temp_test/new.xml'\n",
" G1 = geogrid.GeoGrid()\n",
" \n",
" # Getting dimensions and definning grid\n",
" \n",
" G1.get_dimensions_from_geomodeller_xml_project(hor_lay_new)\n",
" nx = 400\n",
" ny = 2\n",
" nz = 400\n",
" G1.define_regular_grid(nx,ny,nz)\n",
" \n",
" # Updating project\n",
" G1.update_from_geomodeller_project(hor_lay_new)\n",
" \n",
" # Printing new model\n",
" G1.plot_section('y',cell_pos=1,colorbar = True, ax = ax[1], alpha = 0.3, cmap='RdBu', figsize=(6,6),interpolation= 'nearest' ,ve = 1, geomod_coord= True, contour = True)\n",
" "
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
russellclarke82/CV | Pi/Tuples.ipynb | 1 | 4539 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tuples in Python are immutable, they cannot be changed. Once an element is inside a tuple (1, 2, 3), it cannot be changed."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"t = (1,2,3)\n",
"l = [1,2,3]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 2, 3)"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(t)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#can use slicing and indexing\n",
"t[2] # indexing\n",
"t[-1] # indexing"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 2, 3)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"t[0:] # slicing"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l[2]"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[2, 3]"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l[1:]"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"l[0] = 'NEW'"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['NEW', 2, 3]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"l"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "'tuple' object does not support item assignment",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-15-995dc306a50f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'NEW'\u001b[0m \u001b[0;31m#Point and case: cannot change data in a tuple.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
]
}
],
"source": [
"t[0] = 'NEW' #Point and case: cannot change data in a tuple."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
gnestor/jupyter-renderers | notebooks/nteract/geojson.ipynb | 4 | 13114 | {
"cells": [
{
"cell_type": "code",
"source": [
"# With the release of IPython 6 (coming April 2017), there's a GeoJSON display class that allows you to visualize GeoJSON data quickly.\n",
"# from IPython.display import GeoJSON\n",
"# In the meantime...\n",
"from IPython.display import display\n",
"def GeoJSON(data, **kwargs):\n",
" bundle = {\n",
" 'application/geo+json': data,\n",
" 'text/plain': '<IPython.display.GeoJSON object>'\n",
" }\n",
" metadata = {\n",
" 'application/geo+json': kwargs\n",
" }\n",
" display(bundle, metadata=metadata, raw=True)"
],
"outputs": [],
"execution_count": 9,
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"source": [
"data = {\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"properties\":{\"place\":\"The coffee bar\",\"login\":\"espresso\",\"lat\":\"38.91427\",\"lon\":\"-77.02827\"},\"geometry\":{\"type\":\"Point\",\"coordinates\":[-77.02827,38.91427]}},{\"type\":\"Feature\",\"properties\":{\"place\":\"Bistro Bohem\",\"login\":\"2027355895\",\"lat\":\"38.91538\",\"lon\":\"-77.02013\"},\"geometry\":{\"type\":\"Point\",\"coordinates\":[-77.02013,38.91538]}},{\"type\":\"Feature\",\"properties\":{\"place\":\"Black Cat\",\"login\":\"luckycat\",\"lat\":\"38.91458\",\"lon\":\"-77.03155\"},\"geometry\":{\"type\":\"Point\",\"coordinates\":[-77.03155,38.91458]}},{\"type\":\"Feature\",\"properties\":{\"place\":\"Snap\",\"login\":\"nutella1\",\"lat\":\"38.92239\",\"lon\":\"-77.04227\"},\"geometry\":{\"type\":\"Point\",\"coordinates\":[-77.04227,38.92239]}},{\"type\":\"Feature\",\"properties\":{\"place\":\"Columbia Heights Coffee\",\"login\":\"FAIRTRADE1\",\"lat\":\"38.93222\",\"lon\":\"-77.02854\"},\"geometry\":{\"type\":\"Point\",\"coordinates\":[-77.02854,38.93222]}},{\"type\":\"Feature\",\"properties\":{\"place\":\"Azi's Cafe\",\"login\":\"sunny\",\"lat\":\"38.90842\",\"lon\":\"-77.02419\"},\"geometry\":{\"type\":\"Point\",\"coordinates\":[-77.02419,38.90842]}},{\"type\":\"Feature\",\"properties\":{\"place\":\"Blind Dog Cafe\",\"login\":\"baxtercantsee\",\"lat\":\"38.91931\",\"lon\":\"-77.02518\"},\"geometry\":{\"type\":\"Point\",\"coordinates\":[-77.02518,38.91931]}},{\"type\":\"Feature\",\"properties\":{\"place\":\"Le Caprice\",\"login\":\"baguette\",\"lat\":\"38.93260\",\"lon\":\"-77.03304\"},\"geometry\":{\"type\":\"Point\",\"coordinates\":[-77.03304,38.9326]}},{\"type\":\"Feature\",\"properties\":{\"place\":\"Filter\",\"login\":\"\",\"lat\":\"38.91368\",\"lon\":\"-77.04509\"},\"geometry\":{\"type\":\"Point\",\"coordinates\":[-77.04509,38.91368]}},{\"type\":\"Feature\",\"properties\":{\"place\":\"Peregrine\",\"login\":\"espresso\",\"lat\":\"38.88516\",\"lon\":\"-76.99656\"},\"geometry\":{\"type\":\"Point\",\"coordinates\":[-76.99656,38.88516]}},{\"type\":\"Feature\",\"properties\":{\"place\":\"Tryst\",\"login\":\"coupetnt\",\"lat\":\"38.921894\",\"lon\":\"-77.042438\"},\"geometry\":{\"type\":\"Point\",\"coordinates\":[-77.042438,38.921894]}},{\"type\":\"Feature\",\"properties\":{\"place\":\"The Coupe\",\"login\":\"voteforus\",\"lat\":\"38.93206\",\"lon\":\"-77.02821\"},\"geometry\":{\"type\":\"Point\",\"coordinates\":[-77.02821,38.93206]}},{\"type\":\"Feature\",\"properties\":{\"place\":\"Big Bear Cafe\",\"login\":\"\",\"lat\":\"38.91275\",\"lon\":\"-77.01239\"},\"geometry\":{\"type\":\"Point\",\"coordinates\":[-77.01239,38.91275]}}]}"
],
"outputs": [],
"execution_count": 10,
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"source": [
"GeoJSON(data)"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"application/geo+json": {
"features": [
{
"geometry": {
"type": "Point",
"coordinates": [
-77.02827,
38.91427
]
},
"properties": {
"login": "espresso",
"place": "The coffee bar",
"lon": "-77.02827",
"lat": "38.91427"
},
"type": "Feature"
},
{
"geometry": {
"type": "Point",
"coordinates": [
-77.02013,
38.91538
]
},
"properties": {
"login": "2027355895",
"place": "Bistro Bohem",
"lon": "-77.02013",
"lat": "38.91538"
},
"type": "Feature"
},
{
"geometry": {
"type": "Point",
"coordinates": [
-77.03155,
38.91458
]
},
"properties": {
"login": "luckycat",
"place": "Black Cat",
"lon": "-77.03155",
"lat": "38.91458"
},
"type": "Feature"
},
{
"geometry": {
"type": "Point",
"coordinates": [
-77.04227,
38.92239
]
},
"properties": {
"login": "nutella1",
"place": "Snap",
"lon": "-77.04227",
"lat": "38.92239"
},
"type": "Feature"
},
{
"geometry": {
"type": "Point",
"coordinates": [
-77.02854,
38.93222
]
},
"properties": {
"login": "FAIRTRADE1",
"place": "Columbia Heights Coffee",
"lon": "-77.02854",
"lat": "38.93222"
},
"type": "Feature"
},
{
"geometry": {
"type": "Point",
"coordinates": [
-77.02419,
38.90842
]
},
"properties": {
"login": "sunny",
"place": "Azi's Cafe",
"lon": "-77.02419",
"lat": "38.90842"
},
"type": "Feature"
},
{
"geometry": {
"type": "Point",
"coordinates": [
-77.02518,
38.91931
]
},
"properties": {
"login": "baxtercantsee",
"place": "Blind Dog Cafe",
"lon": "-77.02518",
"lat": "38.91931"
},
"type": "Feature"
},
{
"geometry": {
"type": "Point",
"coordinates": [
-77.03304,
38.9326
]
},
"properties": {
"login": "baguette",
"place": "Le Caprice",
"lon": "-77.03304",
"lat": "38.93260"
},
"type": "Feature"
},
{
"geometry": {
"type": "Point",
"coordinates": [
-77.04509,
38.91368
]
},
"properties": {
"login": "",
"place": "Filter",
"lon": "-77.04509",
"lat": "38.91368"
},
"type": "Feature"
},
{
"geometry": {
"type": "Point",
"coordinates": [
-76.99656,
38.88516
]
},
"properties": {
"login": "espresso",
"place": "Peregrine",
"lon": "-76.99656",
"lat": "38.88516"
},
"type": "Feature"
},
{
"geometry": {
"type": "Point",
"coordinates": [
-77.042438,
38.921894
]
},
"properties": {
"login": "coupetnt",
"place": "Tryst",
"lon": "-77.042438",
"lat": "38.921894"
},
"type": "Feature"
},
{
"geometry": {
"type": "Point",
"coordinates": [
-77.02821,
38.93206
]
},
"properties": {
"login": "voteforus",
"place": "The Coupe",
"lon": "-77.02821",
"lat": "38.93206"
},
"type": "Feature"
},
{
"geometry": {
"type": "Point",
"coordinates": [
-77.01239,
38.91275
]
},
"properties": {
"login": "",
"place": "Big Bear Cafe",
"lon": "-77.01239",
"lat": "38.91275"
},
"type": "Feature"
}
],
"type": "FeatureCollection"
},
"text/plain": [
"<IPython.display.GeoJSON object>"
]
},
"metadata": {
"application/geo+json": {}
}
}
],
"execution_count": 11,
"metadata": {
"collapsed": false
}
},
{
"cell_type": "code",
"source": [
"GeoJSON(data={\n",
" \"type\": \"Feature\",\n",
" \"geometry\": {\n",
" \"type\": \"Point\",\n",
" \"coordinates\": [11.8, -45.04]\n",
" }\n",
"}, url_template=\"http://s3-eu-west-1.amazonaws.com/whereonmars.cartodb.net/{basemap_id}/{z}/{x}/{y}.png\", \n",
"layer_options={\n",
" \"basemap_id\": \"mola-color\",\n",
" \"attribution\" : \"NASA/MOLA\",\n",
" \"tms\": True,\n",
" \"minZoom\" : 0,\n",
" \"maxZoom\" : 6\n",
"})"
],
"outputs": [
{
"output_type": "display_data",
"data": {
"application/geo+json": {
"geometry": {
"type": "Point",
"coordinates": [
11.8,
-45.04
]
},
"type": "Feature"
},
"text/plain": [
"<IPython.display.GeoJSON object>"
]
},
"metadata": {
"application/geo+json": {
"url_template": "http://s3-eu-west-1.amazonaws.com/whereonmars.cartodb.net/{basemap_id}/{z}/{x}/{y}.png",
"layer_options": {
"basemap_id": "mola-color",
"attribution": "NASA/MOLA",
"tms": true,
"maxZoom": 6,
"minZoom": 0
}
}
}
}
],
"execution_count": 8,
"metadata": {}
},
{
"cell_type": "code",
"source": [],
"outputs": [],
"execution_count": null,
"metadata": {}
}
],
"metadata": {
"kernel_info": {
"name": "python3"
},
"kernelspec": {
"name": "python3",
"language": "python",
"display_name": "Python 3"
},
"language_info": {
"mimetype": "text/x-python",
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"codemirror_mode": {
"version": 3,
"name": "ipython"
},
"version": "3.5.2",
"name": "python",
"file_extension": ".py"
}
},
"nbformat": 4,
"nbformat_minor": 1
} | bsd-3-clause |
eduardojvieira/Curso-Python-MEC-UCV | 5-sympy.ipynb | 1 | 142788 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<table width=\"100%\" border=\"0\">\n",
" <tr> \n",
" <td><img src=\"./images/ing.png\" alt=\"\" align=\"left\" /></td>\n",
" <td><img src=\"./images/ucv.png\" alt=\"\" align=\"center\" height=\"100\" width=\"100\" /></td>\n",
" <td><img src=\"./images/mec.png\" alt=\"\" align=\"right\"/></td>\n",
" </tr>\n",
"</table>\n",
"\n",
"<br>\n",
"\n",
"<h1 style=\"text-align: center;\"> Curso de Python para Ingenieros Mecánicos </h1> \n",
"<h3 style=\"text-align: center;\"> Por: Eduardo Vieira</h3>\n",
"<br>\n",
"<br>\n",
"<h1 style=\"text-align: center;\"> Sympy - Sistema de algebra simbólica </h1> \n",
"<br>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"__SymPy es una biblioteca de Python para matemática simbólica__. Apunta a convertirse en un sistema de algebra computacional (__CAS__) con todas sus prestaciones manteniendo el código tan simple como sea posible para manterlo comprensible y fácilmente extensible. SymPy está __escrito totalmente en Python y no requiere bibliotecas adicionales__. _Este proyecto comenzó en 2005, fue lanzado al público en 2007 y a él han contribuido durante estos años cientos de personas._\n",
"\n",
"_ Otros CAS conocidos son Mathematica y Maple, sin embargo ambos son software privativo y de pago. [Aquí](https://github.com/sympy/sympy/wiki/SymPy-vs.-Maple) puedes encontrar una comparativa de SymPy con Maple. _\n",
"\n",
"Puede hacer cosas como:\n",
"\n",
"* Manipular expresiones (simplificación, expansión...)\n",
"* Calcular derivadas e integrales.\n",
"* Límites y desarrollos en serie.\n",
"* Resolución de ecuaciones.\n",
"* Resolción de EDOs.\n",
"* Matrices\n",
"\n",
"Sin embargo, SymPy no acaba aquí ni mucho menos..."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sympy as sym\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Para que los resultados sean formateados en $\\LaTeX$ podemos usar:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sym.init_printing(use_latex=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Variables simbólicas\n",
"\n",
"En SymPy podemos crear símbolos para las variables con las que deseamos trabajar. Podemos crear un nuevo símbolo usando la clase `Symbol`:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = sym.Symbol('x')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAEgAAAAbBAMAAAAt2dQtAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nRK5CYII=\n",
"text/latex": [
"$$\\left(x + \\pi\\right)^{2}$$"
],
"text/plain": [
" 2\n",
"(x + π) "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(sym.pi + x)**2"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# forma alternativa de definir (varios) símbolos\n",
"a, b, c = sym.symbols(\"a, b, c\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"sympy.core.symbol.Symbol"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podemos agregar algunas propiedades a los símbolos cuando son creados:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = sym.Symbol('x', real=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x.is_imaginary"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x = sym.Symbol('x', positive=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAACoAAAAPBAMAAABgjEDtAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA782r3SJ2ZjIQmUS7\nVIlAnjihAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVQYGWNg/GQs72z0hQEV8Acw5BcwNKIK\nMsxiAIkyo4mGg0XZJqAKR4BFOUCi0Q2c3QFwWaAJ3Iq5j0LXH+A9n8DAuvwxWAooysC4dn4B0wEG\n/gSGLRO4JUEaQKJMDgwMPGBROQYGMaAgRNQAKsrxq7zcHC66ACrK/hckBARgExbA1H4DiyFEmQ8w\nxCcwODEwTIOpZQGqZRdguHiSob+AYSUDA/caeZkV3Of/XGBgeJc2RWQCp1XeBKghaBQAM0c287zN\nvm0AAAAASUVORK5CYII=\n",
"text/latex": [
"$$\\mathrm{True}$$"
],
"text/plain": [
"True"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x > 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Números complejos\n",
"\n",
"La unidad imaginaria es denotada por `I` en Sympy. "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAACoAAAAQBAMAAACSDPCjAAAALVBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAOrOgAAAADnRSTlMAVO8Qq5l2zWaJ3SK7RPx7\nN2kAAAAJcEhZcwAADsQAAA7EAZUrDhsAAABySURBVBgZY2DABCwHGBgYldHFWRoYTEIeQ0UZE5Ck\n2SgRNQsDmoRhwoRLWES5BcLhopzl5VXq5eUODAzMDM/gokAGzGWMIEdhmMvuYIBF1E5gAhbRe9wN\nDKxOzz2A5gABzFyWDggfQsJEkcWATtoA5wMA/Fcc5MixWvAAAAAASUVORK5CYII=\n",
"text/latex": [
"$$1 + i$$"
],
"text/plain": [
"1 + ⅈ"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1+1*sym.I"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAABgAAAAPBAMAAAAMihLoAAAAJFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAADHJj5lAAAAC3RSTlMAEM3dMlTvq5l2ZtVdCTcAAAAJcEhZcwAA\nDsQAAA7EAZUrDhsAAAAqSURBVAgdY2DAClgTEcLi7RsRHAZOMjlCxiCgwkC2ATA3cJRtqoKxwTQA\nC0AL2ft3JesAAAAASUVORK5CYII=\n",
"text/latex": [
"$$-1$$"
],
"text/plain": [
"-1"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.I**2"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAEwAAAAbBAMAAAAkMnRXAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIma7zZnddlTvRImr\nEDIioekeAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABW0lEQVQ4EY2Sv0vDUBDHv6kYNfHVoCCODxF3\nBUehYgaRIO46ODjoliU4FXHxxyB2dtFJB8GpxUmkFXE0gpt/hHGyCFrv5ULo08TmII+7731yj7t3\nQG9rzIW9IdjOcKUAZjrl9yLYufFZAAMGvwphpl8IaxBlSDryrORQprRKxxh993kYmpS5wjWwQE4r\nEzuiqb4A5aXDea6oUQcciYcnwmwfQ51OBFPdrlmCAbuEiRrnxjVEBRqGZc4H9EfgQ+w0L29Y0TE1\nDLJZut+q4RXT/iYrOjbF4hYw0V/BLe7CvSxsn8VTIBzxEWIxjuuuu+26XuyrFvCWYjhR8QfHv1tI\nMLoUnqAhfmdjFyxTC6Jt43ggQpWVzBaoYSsatdp9kSGzsDUWVdGWFI/VYJKFdLzrZ54EVlj957EU\nYGwwFi8Tu8n53B3R07PNdKt/fFokNrWW+ZYWMWQ+hHjJKf8D7HlSJpFOQHwAAAAASUVORK5CYII=\n",
"text/latex": [
"$$\\left(i x + 1\\right)^{2}$$"
],
"text/plain": [
" 2\n",
"(ⅈ⋅x + 1) "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(x * sym.I + 1)**2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Números racionales\n",
"\n",
"Existen tres tipos distintos de números en SymPy: `Real`, `Rational`, `Integer`: "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r1 = sym.Rational(4,5)\n",
"r2 = sym.Rational(5,4)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAAsAAAAqBAMAAACXcryGAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMpndu3bvImbNiRBU\nq0Qb3U6NAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAkklEQVQYGWNgYGAQAmIgMAGTrClgqmIKmFoA\npjgKwNRWBjC1AEzxCICpXQxg6uzdu9+ugnVAtDN8AXOW/L8Bpski/oPAB6K0Tt0gdACoMP//L5Dy\nWVcLQNQFEAGnbrQqgnjeDPUPQDQDiwGYYvrOwMA7gYHrHwMD2wQGpt8MDEwMDMwTGBjYFRjaQMYU\nrdVmYAAALnIpDsFeUO4AAAAASUVORK5CYII=\n",
"text/latex": [
"$$\\frac{4}{5}$$"
],
"text/plain": [
"4/5"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r1"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAABUAAAAqBAMAAACuFQ3dAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMpndu3bvImbNiRBU\nq0Qb3U6NAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVQYGWNgYGAQAmIYMAEyjm0A81hTGLgv\n3YewK6YAheIh7AUINkcBgr2VAcFegGDzCCDYuxgQ7LN37367CjeTIQVhPsMXBHvJ/xsMuvlzG4Ai\nNAb/4eADZTYVqTgyMGxasRBoCvcFhvUPGOYwbC5gYODdwMCWwJHAwDWBgYHNgIH3I8sFBp7fDAyc\nv4Fs/gsMfH/A1jL/3q/AwPcVzH6/oH4BA+MvMNuWoV4BymZSYICrCWJgAOrlAenlUWAoZ3Fg4ACa\nCYrRcKBdTEC72K1W6Row2DBsEwDaBfSjAcOu0IsMDACGnlMXi4yUmQAAAABJRU5ErkJggg==\n",
"text/latex": [
"$$\\frac{41}{20}$$"
],
"text/plain": [
"41\n",
"──\n",
"20"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r1+r2"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAABUAAAAqBAMAAACuFQ3dAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAVO8Qq5l2zWaJMkS7\nIt2ZnNffAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVQYGWNggICZmQtADCEDBgbzDawFDAzM\nrv5A9lEGngMg8XwDBu6/IAYQANk8CmAWmM1fNS0azAOK21cwMAmAOCD2ZwZWZSib/wEDx28om6+A\ngeMHlM2igBDnAqpXgIozeDIYgcwJ0S9pYOAM9QIJ0xj8h4MPlNl0NeYUA0OZwVKg+5kdGOZvYND/\n/xNoIosBA5MCQ6XbBSCb6QEDyy8GB7A97J8RbKAA32cGj7YosAzD/gkMhxnubwBznoBIngcgkjUA\nTIKjIxVocAEDJyhsGQMY7jIVMLB+BrKXMjDksgINK2Bg4H05M+QBbwBDE9BidqAfHzBcnRLMwAAA\nq1hKQiigoh8AAAAASUVORK5CYII=\n",
"text/latex": [
"$$\\frac{16}{25}$$"
],
"text/plain": [
"16\n",
"──\n",
"25"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r1/r2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Evaluación numérica\n",
"\n",
"SymPy usa una librería para trabajar con números con precisión arbitraria, y tiene expresiones SymPy predefinidas para varias constantes matemáticas, tales como: `pi`, `e` y `oo` para el infinito.\n",
"\n",
"Para evaluar numéricamente una expresión podemos usar la función `evalf` (o bien `N`). Ésta usa un argumento `n` que especifíca el número de cifras significativas."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAgsAAAAPBAMAAACYf5HCAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nuErRtoiwcMr5//oXWhd7due/PDYAAAAASUVORK5CYII=\n",
"text/latex": [
"$$3.1415926535897932384626433832795028841971693993751$$"
],
"text/plain": [
"3.1415926535897932384626433832795028841971693993751"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.pi.evalf(n=50)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y = (x + sym.pi)**2"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAHUAAAAbBAMAAACw1N2lAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nZwkTauY/hwp/AUIch6GgpxacAAAAAElFTkSuQmCC\n",
"text/latex": [
"$$\\left(x + 3.1416\\right)^{2}$$"
],
"text/plain": [
" 2\n",
"(x + 3.1416) "
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.N(y, 5) # equivalente a evalf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cuando evaluamos numéricamente expresiones a menudo deseamos substituir un símbolo por un valor numérico. En SymPy hacemos esto usando la función `subs`:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAFYAAAAbBAMAAAAUvmV2AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$\\left(1.5 + \\pi\\right)^{2}$$"
],
"text/plain": [
" 2\n",
"(1.5 + π) "
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.subs(x, 1.5)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAKAAAAAPBAMAAACRq9klAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nQmCC\n",
"text/latex": [
"$$21.5443823618587$$"
],
"text/plain": [
"21.5443823618587"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.N(y.subs(x, 1.5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La función `subs` también puede ser usada para substituir símbolos y expresiones:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAFIAAAAbBAMAAAAdVcUMAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nAABJRU5ErkJggg==\n",
"text/latex": [
"$$\\left(a + 2 \\pi\\right)^{2}$$"
],
"text/plain": [
" 2\n",
"(a + 2⋅π) "
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y.subs(x, a+sym.pi)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"También podemos combinar la evaluación numérica de expresiones con arreglos NumPy:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x_vec = np.arange(0, 10, 0.1)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_vec = np.array([sym.N(((x + sym.pi)**2).subs(x, xx)) for xx in x_vec])"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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f47HAgnLtY3yzbwYB+d8O8YhIYCi7KVk6d0xfSdP6kSx4YLBCPsSdtUdvZrOBIUAzM8sA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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7a29cd9908>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.plot(x_vec, y_vec);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sin embargo, este tipo de evaluación numérica puede ser muy lenta, y existe una forma mucho más eficiente de realizar la misma tarea: Usar la función `lambdify` para \"mapear\" una expresión de Sympy a una función que es mucho más eficiente para la evaluación numérica:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f = sym.lambdify([x], (x + sym.pi)**2, 'numpy') # el primer argumento es una lista de variables de las que la función f dependerá: en este caso sólo x -> f(x)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"function"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(f)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y_vec = f(x_vec) # ahora podemos pasar directamente un arreglo Numpy. Así f(x) es evaluado más eficientemente"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La mayor eficiencia de usar funciones \"lambdificadas\" en lugar de usar evalación numérica directa puede ser significativa, a menudo de varios órdenes de magnitud. Aún en este sencillo ejemplo obtenemos un aumento de velocidad importante:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"32.5 ms ± 1.5 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"\n",
"y_vec = np.array([sym.N(((x + sym.pi)**2).subs(x, xx)) for xx in x_vec])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"7.26 µs ± 30.9 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
]
}
],
"source": [
"%%timeit\n",
"\n",
"y_vec = f(x_vec)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Manipulaciones algebráicas\n",
"\n",
"Uno de los usos principales de un sistema de cálculo simbólico es que realiza manipulaciones algebráicas de expresiones. Por ejemplo, si queremos expandir un producto, factorizar una expresión, o simplificar un resultado. En esta sección presentamos las funciones para realizar estas operaciones básicas en SymPy."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Expand and factor\n",
"\n",
"Primeros pasos en la manipulación algebráica"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAMEAAAAUBAMAAADGn0QzAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nrkJggg==\n",
"text/latex": [
"$$\\left(x + 1\\right) \\left(x + 2\\right) \\left(x + 3\\right)$$"
],
"text/plain": [
"(x + 1)⋅(x + 2)⋅(x + 3)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(x+1)*(x+2)*(x+3)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAKsAAAAWBAMAAABNknGBAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nESpbZVPGoMA/vluPpCTgHFcAAAAASUVORK5CYII=\n",
"text/latex": [
"$$x^{3} + 6 x^{2} + 11 x + 6$$"
],
"text/plain": [
" 3 2 \n",
"x + 6⋅x + 11⋅x + 6"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.expand((x+1)*(x+2)*(x+3))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La función `expand` acepta varias argumentos clave con los que se puede indicar qué tipo de expansión deseamos realizar. Por ejemplo, para expandir expresiones trigonométricas, usamos el argumento clave `trig=True`:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAFgAAAAUBAMAAAD7IecQAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMnZmzRC73UTvIomZ\nVKu7zOipAAAACXBIWXMAAA7EAAAOxAGVKw4bAAABl0lEQVQoFX2TPyxDURTGv6aeNq86EJOhfYkJ\njTSNxGBBnhAR3iBNJMJAJESkMdgk3UQsIoKIoYtaDAZjhw5iMRADYVFtLCY0pEjVd1/7/vQPN7nv\nfOecX+87575ToGLJwYqA4TpjReXYMiJA3JKWCu9SPxd9x7cVH7akTY1Sj9v8onQm7SGvUvLytB7D\nKcUAV8yUFAbsfqcjbeopudEkjkwlhAF7IsIbgqM11Bv/QfNBU6fCQEBEjxNtmrAmXJcOXAEJhIEI\nWP+KIn8xPcPt3seYQC14ekBisgvrGhQsAfNAlulV7okgzgVqwQ8aeGG3cH20Q8CzwBvTe9yXGpZp\nPKo6uK2qKcozSIT9kE4LSQHPWfAOpFcSXEaDI3Dz8vw4hDNng1kGD/G+lMNZNPDy1nANZGwwG5Ty\nqO+9Lzs5J/pgg3caFkQNpTIShPrRnTopgzNYpH+BjVBP1Fd48hUebz5T4lXAVEdLOkpr1TwZ4MHo\n02O2R+3PrQNyxMbpsnyQpJgtXz1IbOCvVT2itYdf/32NY/77W/0Ck3Zd48SNIlIAAAAASUVORK5C\nYII=\n",
"text/latex": [
"$$\\sin{\\left (a + b \\right )}$$"
],
"text/plain": [
"sin(a + b)"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.sin(a+b)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAP8AAAAUBAMAAACwpfa4AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAMnZmzRC73UTvIomZ\nVKu7zOipAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAEM0lEQVRIDXWWb2gcRRjGn+vt3l02uV4sgkaw\nOVI/qAlyVEHQgqec4D/aE6RQW3pHLEKL2MMPghH1vkkRSbVEpVRZKaYUip6xYGmDXKP4wVA8/KDo\nl5wpgohQY02Jtk183pmd2cmxHdjdZ97fOzvPzrx7e0BvO64D6bAX6L7GN6J2TFBS8gEbWCciCoNT\n78Y4qEf6jzjmKOKd77OfRIeKceI0/P0NYGMYhxw1HWmDU1dj6IWRfiaOOUrwdvaT6HArTnwC8LqA\nX45DjiJVLQlvM3n9RaPcq+DrPCz9waVWp9tAocPujA05QqhuCXjCMP+wUe6VOLfMgKXJBrIhMFll\n3i53sNFCdYtwcJMJIGhY+ZhVsRDcr1IMdQx8PGgTT1B9KT2vyVNvE6qb10Rqy9by9CpuPrbpniKD\nG9o8nZq9qwrMUmHPuQvwZ0ab2HTvHewK9hbHOhFlJDJAHBzsph4/P7LIIMZ4bB+5QLdtKn9+toPd\nI2fUZBEN5uemFN4JNKSsXi4G/5FlSlzjo9hBeR+P4EMMhbeEGPfryEa49qjPTKHStAGFWXXZcXwW\nMvocZ/2nWguRLrN3spM6mppApqgmUxSHQu8vCD5URREvAvuBy8zN8uF2lfAt5U88vDIWBl8Fhm+d\nwoYIL1TBt0aoNG0gJzjTReYwCnwEvAbkl1FoId9gr4L8UraF1JKaTFH/CAbqTGoge+VuiIF9wN/M\n7WsC31fxEuUwj1obSLHqC603xkP2BX8DnwaE4nSl8kqlIq+lYDFQRqHI7gfAQBk1TsrH9K8xMswn\nu6om03QZWSYS+6fX2mLg+djAe/CXZAiPSSblV2igvnuH3EYMPIUcLSkDjOgVgGAxULcGvDYmO8pA\nTp5sgSOvqMmUgb46aoPKwCdIrzgGuAV8vjx9400edgW+q+Ih3kJ26DIG+CIKlaYNBIIdA9wCbgWX\nMtdwVuCamkxtARdngaOJeYOLjgEWoX+dK/mLLjPWADrHuBJvF7GxBFWjK1IkPUWYF+wYYBEWmtxn\npHkD1gDyfW3kVtRkqghrHezLqxr9uYoDsv7RFshr8wju77aAeQ4NjqA/HBrEk/kpZEL9Vl3ECyRC\npekVUJjrb7aArzANtVkJvBFOlnA29Q68lppMvcJeMf16v8JvbX2wuXntt81rv/74b1etCfaO3rbY\nBB7mUOydO4tg/qtm7ostZ9iVFd0zVqJSlFdtQHDq0urTl1ZvP/gRo9wgf+5rCm+QJ//P0RCnzv0O\nmSyii59/emeEGXHahNHOb6IJ8WqwpdqAk6Gk/bE90UukbykS8DYzwn5uTEBdDbb02XXYdOznZsZE\n3KulSd8q2WnVkj64rMIIJ9NoKC8HtPTLcchREU38Wts/JCbJGUdpcDKNc6e1NH5joFRE7eOsw8d1\nLx2ui9qOxjeiNi2QQoX9z2XjWkRU8P9JXilJ0TyAqwAAAABJRU5ErkJggg==\n",
"text/latex": [
"$$\\sin{\\left (a \\right )} \\cos{\\left (b \\right )} + \\sin{\\left (b \\right )} \\cos{\\left (a \\right )}$$"
],
"text/plain": [
"sin(a)⋅cos(b) + sin(b)⋅cos(a)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.expand(sym.sin(a+b), trig=True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ver `help(expand)` para una descripción detallada de los distintos tipos de expansiones que la función `expand` puede realizar."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"También podemos factorizar expresiones, usando la función `factor` de SymPy: "
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAMEAAAAUBAMAAADGn0QzAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nrkJggg==\n",
"text/latex": [
"$$\\left(x + 1\\right) \\left(x + 2\\right) \\left(x + 3\\right)$$"
],
"text/plain": [
"(x + 1)⋅(x + 2)⋅(x + 3)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.factor(x**3 + 6 * x**2 + 11*x + 6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Simplify\n",
"\n",
"La función `simplify` intenta simplificar una expresión usando distinta técnicas. Existen también alternativas más específicas a la función `simplify`: `trigsimp`, `powsimp`, `logcombine`, etc. \n",
"\n",
"El uso básico de estas funciones en el siguiente:"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAMEAAAAUBAMAAADGn0QzAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nrkJggg==\n",
"text/latex": [
"$$\\left(x + 1\\right) \\left(x + 2\\right) \\left(x + 3\\right)$$"
],
"text/plain": [
"(x + 1)⋅(x + 2)⋅(x + 3)"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# simplify expande un producto\n",
"sym.simplify((x+1)*(x+2)*(x+3))"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAAgAAAAPBAMAAAArJJMAAAAAHlBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAACGjDitAAAACXRSTlMAVO8Qq5l2zWYZcMvdAAAACXBIWXMAAA7EAAAOxAGV\nKw4bAAAAHUlEQVQIHWNgAANGZQYGk5DJQDYbqQSr03QPsBkAJYgIYEZbtZEAAAAASUVORK5CYII=\n",
"text/latex": [
"$$1$$"
],
"text/plain": [
"1"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# simplify usa identidades trigonometricas\n",
"sym.simplify(sym.sin(a)**2 + sym.cos(a)**2)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAD4AAAAvBAMAAABJZWRJAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nAAAAAElFTkSuQmCC\n",
"text/latex": [
"$$\\frac{1}{\\tan{\\left (x \\right )}}$$"
],
"text/plain": [
" 1 \n",
"──────\n",
"tan(x)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.simplify(sym.cos(x)/sym.sin(x))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## apart and together\n",
"\n",
"Podemos también manipular expresiones simbólicas que involucran fracciones usando las funciones `apart` y `together`. La primera de estas funciones separa una fracción en sus correspondientes fracciones parciales; la segunda hace todo lo contrario."
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f1 = 1/((a+1)*(a+2))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAIIAAAAvBAMAAADdrw/+AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$\\frac{1}{\\left(a + 1\\right) \\left(a + 2\\right)}$$"
],
"text/plain": [
" 1 \n",
"───────────────\n",
"(a + 1)⋅(a + 2)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f1"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAI4AAAAsBAMAAABBB53eAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nUyuKBLIyAOpdmg617KB4AAAAAElFTkSuQmCC\n",
"text/latex": [
"$$- \\frac{1}{a + 2} + \\frac{1}{a + 1}$$"
],
"text/plain": [
" 1 1 \n",
"- ───── + ─────\n",
" a + 2 a + 1"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.apart(f1)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f2 = 1/(a+2) + 1/(a+3)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAHwAAAAsBAMAAABVvsF6AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAVO8Qq5l2zWYiiUS7\n3TIuwQ1sAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAB30lEQVRIDe2Wv0sCYRzGHzs0zx8ZQUGTUENF\nRRIuTSdF0RIKaUMESWMNNUQ3hf4DwQ21BIEEEQRFBO01NdQQEQ0tubYpDpFR9nrv+173+l4m1NDg\nDfe+3+d5Pu99+eqhwJ9dHZFGjnL1OqaUyXgjeDRZcsSB1UZweJq4PL/m6OSZiMp3X5vkyHpWTDpV\n7vGXaSf9H2qu9K+aauJ1xldxvgoE8en6Zp+ux8g25Jyq1Dm4ajUn/8OAauzdo0uu1BmdmrrJ85h9\nVRMIZ5mgGHZH2EfhexOELlp5igjdCoZQ8IYmgB7BYLh/ENqaYAgFx4eBeN7uMJxIVvN2m+05nomY\nuDo/x36xLVx5lLGdg6WIqXKcFE9EOTNaCzTNcfV4VsL9A8hQ8Qv3lwGlH+4LEQf2T2v50SwWavFg\nDHCXEEwT41DX73R9nUZapD8gixE8EM/2EgIpIrRcQMtTiDWvGggUqfJ1H4LyQSureU+CCFoOYZZi\neKgk40oZLjYhC+8EeZDWjiuXYR7AcNJP8JWdyBeliMBat1lxvC0Bj4HWtPfeR1MM92YRTnOOrxvY\niuXMguN7RwczZPJTJ9srNMRwnKeuOWWt0eXnKfpxcDxTqbxbdnXDcUGUCo5LxpikOAnCS/gJnQmX\nnOfh0q8AAAAASUVORK5CYII=\n",
"text/latex": [
"$$\\frac{1}{a + 3} + \\frac{1}{a + 2}$$"
],
"text/plain": [
" 1 1 \n",
"───── + ─────\n",
"a + 3 a + 2"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f2"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAIIAAAAvBAMAAADdrw/+AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nrkJggg==\n",
"text/latex": [
"$$\\frac{2 a + 5}{\\left(a + 2\\right) \\left(a + 3\\right)}$$"
],
"text/plain": [
" 2⋅a + 5 \n",
"───────────────\n",
"(a + 2)⋅(a + 3)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.together(f2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podemos usar también `Simplify`:"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAIIAAAAvBAMAAADdrw/+AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAIpm7MhCriUTv3c12\nVGZoascqAAAACXBIWXMAAA7EAAAOxAGVKw4bAAACtklEQVRIDe2WzWsTQRjGn02y+dpuSCMo9LQk\nYC9KK3pS0OhfsKdCvSQIXrRgiGIugrnWi5YiWAsSPNSDivVqhKQFUU9G/wHr1UPpwdDi1/rOzs42\n231NFkoPggPZd/eZ3zz77uzMvgHCrVA6FRYB403iOKeHNb2K282wjIzj1BiZkZI2MvcGdLMrL4yb\nawPqsNNMC8lvA4BySA5ow0+N/n4dyD/bhzZ57L68k5/D5npZKhGOd2uYzxtbklQOcVvbiTBWIivQ\nHyE2HXSgq9NRHWIWYn1ku8Sn2u1Xy+12VQ6dyss48rgOZKZRKUtQPcVV8OuEsTMtjFdq+Ox1KYdl\nYMpmcEa6AKxVejhjypyVgwU8ZWhG0p40jraMbm4lJTuVQwG5BQZnJMNxnJY+d+Pwu6CDNnkp4kPs\nNVU57NWjX+tRX2F0y//kAc0ALZ/9tQPK61+01Tb+lvUX2TESOMQZaMWTZSTlnuKBxvWeAl5yDjNI\n7UBfdbtYoADztwfkrEEH9TG4DCwBt0QXD3zo4bsHZAPbXzk8BK6VMSsceGC+qf+ABCYE5Tfl0LGF\ng9GkDh6AeAoJUHFgiiSJL2ykLIoCQKH+3hYR6hbAxRoVJIuks/QLF0kgvU1TIKZSAOkFdChQ8x2O\nPCZLF3gOvkhmq4S3aAwBmN1AiQI13wGpBx5AkS2SRcITW3QgQBSrZxSCVXTJ9gGmSGLMohG+wyJ0\n72+NymEcONeTACXJFEl8gpZHukU2BOjbMEU61JSDYwsHF6CJYopkwsJY3p9J/Rfiqx8DDov0sjyg\nTm81XCQnGvUrNEE1GkUAXuNrVZzv5rAJ86cH3KEkw0Wy4zi0aI0yjSEAM2/PzzUp7jqkiyUSXIBf\ntC4tl+NIILhxgkWS2VkMgBPyfszR290jAfYD4vrF824YCYz8iA0H/gDROfuPYC+5AgAAAABJRU5E\nrkJggg==\n",
"text/latex": [
"$$\\frac{2 a + 5}{\\left(a + 2\\right) \\left(a + 3\\right)}$$"
],
"text/plain": [
" 2⋅a + 5 \n",
"───────────────\n",
"(a + 2)⋅(a + 3)"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.simplify(f2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Cálculo\n",
"\n",
"Además de realizar manipulaciones algebráicas, SimPy puede realizar operaciones de cálculo, tales como derivar y derivar expresiones."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Derivación\n",
"\n",
"Derviar es usualmente algo simple. Usamos la función `diff`. El primer argumento es una expresión que será derivada, y el segundo argumento es el símbolo respecto al cual se realizará la derivada:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAEgAAAAbBAMAAAAt2dQtAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nRK5CYII=\n",
"text/latex": [
"$$\\left(x + \\pi\\right)^{2}$$"
],
"text/plain": [
" 2\n",
"(x + π) "
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAFgAAAAbBAMAAAAKd1XFAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYII=\n",
"text/latex": [
"$$4 \\left(x + \\pi\\right)^{3}$$"
],
"text/plain": [
" 3\n",
"4⋅(x + π) "
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.diff(y**2, x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Para calcular derivadas de orden superior podemos usar:"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAGEAAAAbBAMAAACekfw3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAVO8Qq5l2zWYiuzKJ\nRN0MreaOAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAByklEQVQ4EZWTO0gDQRCG/7y95C5RREEbQQs7\nPcRSMCj4aDSgNlaBoGCj6QIWJo2gjaS0ipZiFStLA6IgCqayTqVYiBFRCx/nzN5tkktM5P5id+af\n+W5ujz3AidTlBSft1HuFaYfEI86yzpAnHBSdEUCf7pS4dwqosXpiv96o5rscbstcWTrOc6wcSqdx\n92YBraBRn2sAGIX6zT0BcpvJkwROO3p1jC6+ApNAP3dWhgrMFRebXC6BYcOgzE/EEDBP4/Aoq2Kv\nI0ZkkYm0LgilJM2/iEDRqjJBetZpXAzwrM7e0XyWnLFhGMYbUPmyJqF9UUswA4xhL/7A/RVCve7e\nX8sCbUnTFucAwlFKw+3AOW71HrNkzTjBlT9Pjqtk2haxxJm7COjYFAU1ldoaTKWinBx6dVpDZY5J\n4q34BCYBvHPIkucIRcOc2okuKFnzrRD65DJLEr5MhGdoJTZJPCMUg58IPnmnv4yEKFSIcT1SJMd2\n8tzNxRx59P20l2BZIZAlZ6zDXaDUxwvJN/Exg7Rh/FBMcz1TiZUdUagSR/AWyArkLb92a3lLcrWd\nMrbfRA8drUbWTahxKAzae2xFvu2NavVHNXnaf38tDfkFyOBpNfkwCbcAAAAASUVORK5CYII=\n",
"text/latex": [
"$$12 \\left(x + \\pi\\right)^{2}$$"
],
"text/plain": [
" 2\n",
"12⋅(x + π) "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.diff(y**2, x, x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"o bien"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAGEAAAAbBAMAAACekfw3AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$12 \\left(x + \\pi\\right)^{2}$$"
],
"text/plain": [
" 2\n",
"12⋅(x + π) "
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.diff(y**2, x, 2) # hace lo mismo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calculando la derivada de una función de varias variables:"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"x, y, z = sym.symbols(\"x,y,z\")"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"f = sym.sin(x*y) + sym.cos(y*z)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$\\frac{d^3f}{dxdy^2}$"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAAUBAMAAABSee3BAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$- x \\left(x y \\cos{\\left (x y \\right )} + 2 \\sin{\\left (x y \\right )}\\right)$$"
],
"text/plain": [
"-x⋅(x⋅y⋅cos(x⋅y) + 2⋅sin(x⋅y))"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.diff(f, x, 1, y, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Integracion\n",
"\n",
"La integración se realiza de la misma forma:"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAKAAAAAUBAMAAAD4uit9AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ntB/cFjDxD7vBsgEiRPNUAAAAAElFTkSuQmCC\n",
"text/latex": [
"$$\\sin{\\left (x y \\right )} + \\cos{\\left (y z \\right )}$$"
],
"text/plain": [
"sin(x⋅y) + cos(y⋅z)"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVAAAAA/BAMAAABEE43RAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nZSa5JzbMUJZE68HWcfsffYLrdl/zhJcAAAAASUVORK5CYII=\n",
"text/latex": [
"$$x \\cos{\\left (y z \\right )} + \\begin{cases} 0 & \\text{for}\\: y = 0 \\\\- \\frac{1}{y} \\cos{\\left (x y \\right )} & \\text{otherwise} \\end{cases}$$"
],
"text/plain": [
" ⎛⎧ 0 for y = 0⎞\n",
" ⎜⎪ ⎟\n",
"x⋅cos(y⋅z) + ⎜⎨-cos(x⋅y) ⎟\n",
" ⎜⎪────────── otherwise⎟\n",
" ⎝⎩ y ⎠"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.integrate(f, x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Si le damos los límites de integración calculamos la integral definida"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAFAAAAAUBAMAAADo9qfkAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n4i7UkmdVs74e+P9XOAbg2mQ9baVdmAAAAABJRU5ErkJggg==\n",
"text/latex": [
"$$2 \\cos{\\left (y z \\right )}$$"
],
"text/plain": [
"2⋅cos(y⋅z)"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.integrate(f, (x, -1, 1))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tambien podemos calcular integrales impropias"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sympy import oo\n",
"# oo es el infinito"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAB0AAAAVBAMAAABI7vhRAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAInarRM2ZVBDdiWbv\nuzJCz3LGAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVQYGWNggADG/2DwAcplYHaAsSC0KCqX\nIR2VzzEBlc9WgMrvROUyrEblg7TrAx3wDSrMtIGBa12RtKIAkO8CxJwMDM8ZFjI9ADKZjgIJJSBm\nuMDcACQl9B0YGEC28xkAVQFB/wUG7gVAmm0DfwOQYmD7yMAJYvQ38DsAKQbGbwy7QLQmA88CEM1g\n3zADRN1mYF4AohneL08A0zCC9WgDjAmm2WGuhIkGwhhAGgDwdic2xV4k0wAAAABJRU5ErkJggg==\n",
"text/latex": [
"$$\\sqrt{\\pi}$$"
],
"text/plain": [
"√π"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.integrate(sym.exp(-x**2), (x, -oo, oo))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Sumatoria y productos\n",
"\n",
"Podemos evaluar sumatorias usando: 'Sum'"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"n = sym.Symbol(\"n\")"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAADsAAAA9BAMAAADhUgydAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nb2oXAAAAAElFTkSuQmCC\n",
"text/latex": [
"$$\\sum_{n=1}^{10} \\frac{1}{n^{2}}$$"
],
"text/plain": [
" 10 \n",
" ____ \n",
" ╲ \n",
" ╲ 1 \n",
" ╲ ──\n",
" ╱ 2\n",
" ╱ n \n",
" ╱ \n",
" ‾‾‾‾ \n",
"n = 1 "
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.Sum(1/n**2, (n, 1, 10))"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAJ8AAAAPBAMAAAAIUwCQAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nt0ciqgAAAABJRU5ErkJggg==\n",
"text/latex": [
"$$1.54976773116654$$"
],
"text/plain": [
"1.54976773116654"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.Sum(1/n**2, (n,1, 10)).evalf()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAJ8AAAAPBAMAAAAIUwCQAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$1.64493406684823$$"
],
"text/plain": [
"1.64493406684823"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.Sum(1/n**2, (n, 1, oo)).evalf()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lo mismo con productos"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAC8AAAA9BAMAAADPFy0PAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$\\prod_{n=1}^{10} n$$"
],
"text/plain": [
" 10 \n",
"┬───┬ \n",
"│ │ n\n",
"│ │ \n",
"n = 1 "
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.Product(n, (n, 1, 10)) # 10!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Límites\n",
"\n",
"Los límites se evalúan con la función `limit`"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAAgAAAAPBAMAAAArJJMAAAAAHlBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAACGjDitAAAACXRSTlMAVO8Qq5l2zWYZcMvdAAAACXBIWXMAAA7EAAAOxAGV\nKw4bAAAAHUlEQVQIHWNgAANGZQYGk5DJQDYbqQSr03QPsBkAJYgIYEZbtZEAAAAASUVORK5CYII=\n",
"text/latex": [
"$$1$$"
],
"text/plain": [
"1"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.limit(sym.sin(x)/x, x, 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Se puede usar el límite para verificar una derivada"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAKAAAAAUBAMAAAD4uit9AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\ntB/cFjDxD7vBsgEiRPNUAAAAAElFTkSuQmCC\n",
"text/latex": [
"$$\\sin{\\left (x y \\right )} + \\cos{\\left (y z \\right )}$$"
],
"text/plain": [
"sin(x⋅y) + cos(y⋅z)"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"f"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAFMAAAAUBAMAAAADwRznAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$y \\cos{\\left (x y \\right )}$$"
],
"text/plain": [
"y⋅cos(x⋅y)"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.diff(f, x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La derivada por definicion es \n",
"$$\\frac{d}{{dx}}f\\left( x \\right) = \\mathop {\\lim }\\limits_{h \\to 0} \\frac{{f\\left( {x + h } \\right) - f\\left( x \\right)}}{h }$$"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"h = sym.Symbol(\"h\")"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAFMAAAAUBAMAAAADwRznAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$y \\cos{\\left (x y \\right )}$$"
],
"text/plain": [
"y⋅cos(x⋅y)"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.limit((f.subs(x, x+h) - f)/h, h, 0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"OK!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podemos calcular los limites laterales usando el argumento `dir`:"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAABMAAAALBAMAAABv+6sJAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEKvvZom7mXYyzVQi\n3UQ6SGZXAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAaklEQVQIHWNgYBBgAAIQwaj82YGBIayogYGB\nbQLHLwapDQxTGRg8GRj2J6xkYGA5wACUYP0LJBgcQEyGfBDRAGYm/wNqd2BwZGDgiDE+wMBxgIGd\ngSGcYb4dgytQolxtAwNjvXEAUDncNgBJUBUwaYAbUgAAAABJRU5ErkJggg==\n",
"text/latex": [
"$$\\infty$$"
],
"text/plain": [
"∞"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.limit(1/x, x, 0, dir=\"+\")"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAACMAAAALBAMAAAAHCCkxAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEM3dMqvvZom7mXZU\nIkRJD0iWAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAfklEQVQIHWNggAMBEAtMQIUYw74VMDB0Lt0A\nV8LA6cD9iUHoAIMHQqiEgeH8BBUGBvYLDELGIKDCANTA8RmkC6gdCkC8+SAChCEAxJr2j4GBsQAm\nwlDIwMDdm3aBgfsCXIiLgaGLwT+PoQIuwsC4KvIAA+P6tAaEENThAgwMAMSLGqu/gFQwAAAAAElF\nTkSuQmCC\n",
"text/latex": [
"$$-\\infty$$"
],
"text/plain": [
"-∞"
]
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.limit(1/x, x, 0, dir=\"-\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Series\n",
"\n",
"Las series tambien son una de las funciones mas útiles de un CAS. En SymPy se puede calcular la serie de una funcion usando `series`"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVoAAAAwBAMAAACiZ6/NAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$1 + x + \\frac{x^{2}}{2} + \\frac{x^{3}}{6} + \\frac{x^{4}}{24} + \\frac{x^{5}}{120} + \\mathcal{O}\\left(x^{6}\\right)$$"
],
"text/plain": [
" 2 3 4 5 \n",
" x x x x ⎛ 6⎞\n",
"1 + x + ── + ── + ── + ─── + O⎝x ⎠\n",
" 2 6 24 120 "
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.series(sym.exp(x), x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Por defecto, se calcula la expansión alrededor de $x=0$ (serie de Taylor), pero podemos expandir la serie al rededor de otro punto"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAvwAAAAoBAMAAABwRjOsAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$e + e \\left(x - 1\\right) + \\frac{e}{2} \\left(x - 1\\right)^{2} + \\frac{e}{6} \\left(x - 1\\right)^{3} + \\frac{e}{24} \\left(x - 1\\right)^{4} + \\frac{e}{120} \\left(x - 1\\right)^{5} + \\mathcal{O}\\left(\\left(x - 1\\right)^{6}; x\\rightarrow 1\\right)$$"
],
"text/plain": [
" 2 3 4 5 \n",
" ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) ⎛ 6\n",
"ℯ + ℯ⋅(x - 1) + ────────── + ────────── + ────────── + ────────── + O⎝(x - 1) \n",
" 2 6 24 120 \n",
"\n",
" \n",
" ⎞\n",
"; x → 1⎠\n",
" "
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.series(sym.exp(x), x, 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Tambien podemos especificar el orden"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAABUUAAAAoBAMAAAArnobcAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nAAAASUVORK5CYII=\n",
"text/latex": [
"$$e + e \\left(x - 1\\right) + \\frac{e}{2} \\left(x - 1\\right)^{2} + \\frac{e}{6} \\left(x - 1\\right)^{3} + \\frac{e}{24} \\left(x - 1\\right)^{4} + \\frac{e}{120} \\left(x - 1\\right)^{5} + \\frac{e}{720} \\left(x - 1\\right)^{6} + \\frac{e}{5040} \\left(x - 1\\right)^{7} + \\frac{e}{40320} \\left(x - 1\\right)^{8} + \\frac{e}{362880} \\left(x - 1\\right)^{9} + \\mathcal{O}\\left(\\left(x - 1\\right)^{10}; x\\rightarrow 1\\right)$$"
],
"text/plain": [
" 2 3 4 5 6\n",
" ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) \n",
"ℯ + ℯ⋅(x - 1) + ────────── + ────────── + ────────── + ────────── + ──────────\n",
" 2 6 24 120 720 \n",
"\n",
" 7 8 9 \n",
" ℯ⋅(x - 1) ℯ⋅(x - 1) ℯ⋅(x - 1) ⎛ 10 ⎞\n",
" + ────────── + ────────── + ────────── + O⎝(x - 1) ; x → 1⎠\n",
" 5040 40320 362880 "
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.series(sym.exp(x), x, 1, 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La expansion en serie incluye la aproximación de orden. Lo cual es útil para trabajar con series de diferente orden"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAMQAAAAwBAMAAAC8i8hXAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nRK5CYII=\n",
"text/latex": [
"$$1 - \\frac{x^{2}}{2} + \\frac{x^{4}}{24} + \\mathcal{O}\\left(x^{5}\\right)$$"
],
"text/plain": [
" 2 4 \n",
" x x ⎛ 5⎞\n",
"1 - ── + ── + O⎝x ⎠\n",
" 2 24 "
]
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s1 = sym.cos(x).series(x, 0, 5)\n",
"s1"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAF8AAAAcBAMAAAD1rn4EAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMAEHarIkSJZt3NVLsy\nme8Q6PJIAAAACXBIWXMAAA7EAAAOxAGVKw4bAAACCUlEQVQ4EY1Sv2tTURT+Xsx7SV5+9FKh0MnX\nCm61obhYB99SHBv8A0wUwbFO6qJDUcShtCiCwcEIuuhglg7i0Afi4CBJ7SDSwS4OTiYiCCLEc867\n971cSfFdOOd833fOx7l5N0DG4+3vZpzUY5dxOpvhnR67jV6Yy2CpdPXQHaw23Pr/HSWVzLxSuJKQ\nf4C7O7P5ULSPaWcdKKb2VGe04G5/GDUI5IdJw+sDOYpJ50hwQWFxi1o8pc9NqvmBYVL9QNM95ysw\nxc1aR0twW24E3DVUqjG4w2oXKPFtespMvH/xkvC+oZah2PeJl3nDrWTg+2hE+CTF9PGVE4HoZkMz\nYlo+oPSZwpk/df4SVT5NBadVWC9uCTOGL3KPWp/ENxQzuBHckwG6YgRPeUM/FG4MO8J6DSoPKC5i\nTT0TCfTacFDqMvPa7Uff2u06wSeKhdecNigUHjOUs9qhMhUIBsyGsxEJlZ+s8gbgh2ROtEHuFQvG\nsMZrZrkVGyp/4j7lXghfLSInd0g2HPtFb7otQ2coP80NMKct9JWanR2ci6nZUBgtr+yFotF3d3+X\nB/mr2kDvcnR++pOmxoC3mxtRPMHffWlu4bqeP+SlTZdqTX5JKtxPISEntCiTasuSnPTfbukpqRyk\nmFChbtFJZNkSi5FFJ5FZS7xmsYnE74/J+e4YOQw+H2tU1RjJCv8Cl4Bl3Hr5MZEAAAAASUVORK5C\nYII=\n",
"text/latex": [
"$$x + \\mathcal{O}\\left(x^{2}\\right)$$"
],
"text/plain": [
" ⎛ 2⎞\n",
"x + O⎝x ⎠"
]
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"s2 = sym.sin(x).series(x, 0, 2)\n",
"s2"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAF8AAAAcBAMAAAD1rn4EAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nYII=\n",
"text/latex": [
"$$x + \\mathcal{O}\\left(x^{2}\\right)$$"
],
"text/plain": [
" ⎛ 2⎞\n",
"x + O⎝x ⎠"
]
},
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.expand(s1 * s2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Si queremos eliminar la aproximación de orden usamos el método `removeO`"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAG8AAAAwBAMAAADtMzlxAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nRK5CYII=\n",
"text/latex": [
"$$\\frac{x^{5}}{24} - \\frac{x^{3}}{2} + x$$"
],
"text/plain": [
" 5 3 \n",
"x x \n",
"── - ── + x\n",
"24 2 "
]
},
"execution_count": 79,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.expand(s1.removeO() * s2.removeO())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pero hay que tener en cuenta que esta no es la expansión correcta de $\\cos(x)\\sin(x)$ al $5$to orden:"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAANsAAAAwBAMAAABqLhIyAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n",
"text/latex": [
"$$x - \\frac{2 x^{3}}{3} + \\frac{2 x^{5}}{15} + \\mathcal{O}\\left(x^{6}\\right)$$"
],
"text/plain": [
" 3 5 \n",
" 2⋅x 2⋅x ⎛ 6⎞\n",
"x - ──── + ──── + O⎝x ⎠\n",
" 3 15 "
]
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(sym.cos(x)*sym.sin(x)).series(x, 0, 6)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Álgebra Lineal"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Matrices\n",
"\n",
"Las matrices se definen usando la clase `Matrix`:"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"m11, m12, m21, m22 = sym.symbols(\"m11, m12, m21, m22\")\n",
"b1, b2 = sym.symbols(\"b1, b2\")"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAGgAAAAyBAMAAABCJ4MDAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nt/sAAAAASUVORK5CYII=\n",
"text/latex": [
"$$\\left[\\begin{matrix}m_{11} & m_{12}\\\\m_{21} & m_{22}\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡m₁₁ m₁₂⎤\n",
"⎢ ⎥\n",
"⎣m₂₁ m₂₂⎦"
]
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A = sym.Matrix([[m11, m12],[m21, m22]])\n",
"A"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAB4AAAAyBAMAAAC5cHbcAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAv3aB7AAAAD3RSTlMA74lUMhC73c2rRHaZ\nImaqCQggAAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVQoFWOQ//+JAQb0/39kEHZxhXEZQlwM\nGUTgPBDDEcRnTSyACYL5DBwTUPn8Bqj89QGofHUYF2IeQ+fuw1ARiPk/AuIvMHiAhMB85q8M/Ao+\n0+F8ngaGeAUGczif4wDDegMkPv8DhvMByPwFrHIMSHzOB2wHkPmsp88BzUKYB+Rg4RuCBCH+BbHu\n1M9G4YPEkOXpyKdLfDAdOwrzHzg+dBlmwPjg+Khl8L8A9T84PqoY7i+A8iHxwWAPTAXg8OMHxwdD\nNUw/JD6YDsD4kPh4A+QihTeXApcDMt/D2AxmHkghg/z//0ASET9gQSL46OkdLT8AAPfcTiuXVltr\nAAAAAElFTkSuQmCC\n",
"text/latex": [
"$$\\left[\\begin{matrix}b_{1}\\\\b_{2}\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡b₁⎤\n",
"⎢ ⎥\n",
"⎣b₂⎦"
]
},
"execution_count": 83,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b = sym.Matrix([[b1], [b2]])\n",
"b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Con la clase `Matrix` podemos realizar las operaciones matriciales típicas"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXIAAAAzBAMAAAB4eZ5HAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nN3P+AQTjoqnEhDFNAAAAAElFTkSuQmCC\n",
"text/latex": [
"$$\\left[\\begin{matrix}m_{11}^{2} + m_{12} m_{21} & m_{11} m_{12} + m_{12} m_{22}\\\\m_{11} m_{21} + m_{21} m_{22} & m_{12} m_{21} + m_{22}^{2}\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡ 2 ⎤\n",
"⎢ m₁₁ + m₁₂⋅m₂₁ m₁₁⋅m₁₂ + m₁₂⋅m₂₂⎥\n",
"⎢ ⎥\n",
"⎢ 2 ⎥\n",
"⎣m₁₁⋅m₂₁ + m₂₁⋅m₂₂ m₁₂⋅m₂₁ + m₂₂ ⎦"
]
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A**2"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAJEAAAAyBAMAAACufiRQAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nDbOYk3PHxJxH96vsyH4p/gHeHTH4ApAfnQAAAABJRU5ErkJggg==\n",
"text/latex": [
"$$\\left[\\begin{matrix}b_{1} m_{11} + b_{2} m_{12}\\\\b_{1} m_{21} + b_{2} m_{22}\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡b₁⋅m₁₁ + b₂⋅m₁₂⎤\n",
"⎢ ⎥\n",
"⎣b₁⋅m₂₁ + b₂⋅m₂₂⎦"
]
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A * b"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y tambien determinantes e inversas:"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAKYAAAAMBAMAAAAaIdvMAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n9AyNtKqM6qgiUQAAAABJRU5ErkJggg==\n",
"text/latex": [
"$$m_{11} m_{22} - m_{12} m_{21}$$"
],
"text/plain": [
"m₁₁⋅m₂₂ - m₁₂⋅m₂₁"
]
},
"execution_count": 86,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A.det()"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAVQAAAA1BAMAAADsYw7NAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nAEBbmtYB7fg0AAAAAElFTkSuQmCC\n",
"text/latex": [
"$$\\left[\\begin{matrix}\\frac{m_{22}}{m_{11} m_{22} - m_{12} m_{21}} & - \\frac{m_{12}}{m_{11} m_{22} - m_{12} m_{21}}\\\\- \\frac{m_{21}}{m_{11} m_{22} - m_{12} m_{21}} & \\frac{m_{11}}{m_{11} m_{22} - m_{12} m_{21}}\\end{matrix}\\right]$$"
],
"text/plain": [
"⎡ m₂₂ -m₁₂ ⎤\n",
"⎢───────────────── ─────────────────⎥\n",
"⎢m₁₁⋅m₂₂ - m₁₂⋅m₂₁ m₁₁⋅m₂₂ - m₁₂⋅m₂₁⎥\n",
"⎢ ⎥\n",
"⎢ -m₂₁ m₁₁ ⎥\n",
"⎢───────────────── ─────────────────⎥\n",
"⎣m₁₁⋅m₂₂ - m₁₂⋅m₂₁ m₁₁⋅m₂₂ - m₁₂⋅m₂₁⎦"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A.inv()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## resolviendo ecuaciones\n",
"\n",
"Para resolver ecuaciones podemos utilizar la función `solve`"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAEoAAAAUBAMAAADYerbFAAAALVBMVEX///8AAAAAAAAAAAAAAAAA\nAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADAOrOgAAAADnRSTlMAdt3NMolEEFTvq5lmIsfa\npuIAAAAJcEhZcwAADsQAAA7EAZUrDhsAAAB3SURBVCgVYxAyYSAEDqsxhBFSA5QPQ1bFMR2Ljs4C\nVFVVu55jqGJfvQ5NFQMjpioGBrlBrqpYCQSAgTuYXV9ngBb+6KHKvfLxGgYmdVRVu+ZZHUCNbbB8\nL6oqMA8lTYBFAohRxS5AjCoeLIqA7hJSwSaOIiakBgD0ZimSClhTrgAAAABJRU5ErkJggg==\n",
"text/latex": [
"$$\\left [ -1, \\quad 1\\right ]$$"
],
"text/plain": [
"[-1, 1]"
]
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.solve(x**2 - 1, x)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
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"text/latex": [
"$$\\left [ - i \\sqrt{- \\frac{1}{2} + \\frac{\\sqrt{5}}{2}}, \\quad i \\sqrt{- \\frac{1}{2} + \\frac{\\sqrt{5}}{2}}, \\quad - \\sqrt{\\frac{1}{2} + \\frac{\\sqrt{5}}{2}}, \\quad \\sqrt{\\frac{1}{2} + \\frac{\\sqrt{5}}{2}}\\right ]$$"
],
"text/plain": [
"⎡ __________ __________ ________ ________⎤\n",
"⎢ ╱ 1 √5 ╱ 1 √5 ╱ 1 √5 ╱ 1 √5 ⎥\n",
"⎢-ⅈ⋅ ╱ - ─ + ── , ⅈ⋅ ╱ - ─ + ── , - ╱ ─ + ── , ╱ ─ + ── ⎥\n",
"⎣ ╲╱ 2 2 ╲╱ 2 2 ╲╱ 2 2 ╲╱ 2 2 ⎦"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.solve(x**4 - x**2 - 1, x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sistemas de ecuaciones"
]
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAH4AAAAVBAMAAAByPkciAAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\n/9868APnYWXSLV3ceQAAAABJRU5ErkJggg==\n",
"text/latex": [
"$$\\left \\{ x : 1, \\quad y : 0\\right \\}$$"
],
"text/plain": [
"{x: 1, y: 0}"
]
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.solve([x + y - 1, x - y - 1], [x,y])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La ecuacion puede estar en términos de otras variables simbólicas"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAOAAAAAmBAMAAAAvsop7AAAAMFBMVEX///8AAAAAAAAAAAAAAAAA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\nLBsEAAAAAElFTkSuQmCC\n",
"text/latex": [
"$$\\left \\{ x : \\frac{a}{2} + \\frac{c}{2}, \\quad y : \\frac{a}{2} - \\frac{c}{2}\\right \\}$$"
],
"text/plain": [
"⎧ a c a c⎫\n",
"⎨x: ─ + ─, y: ─ - ─⎬\n",
"⎩ 2 2 2 2⎭"
]
},
"execution_count": 91,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sym.solve([x + y - a, x - y - c], [x,y])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lecturas complementarias\n",
"\n",
"* http://sympy.org/en/index.html - The SymPy projects web page.\n",
"* https://github.com/sympy/sympy - The source code of SymPy.\n",
"* http://live.sympy.org - Online version of SymPy for testing and demonstrations.\n"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"/* This template is inspired in the one used by Lorena Barba\n",
"in the numerical-mooc repository: https://github.com/numerical-mooc/numerical-mooc\n",
"We thank her work and hope you also enjoy the look of the notobooks with this style */\n",
"\n",
"<link href='http://fonts.googleapis.com/css?family=Source+Sans+Pro|Josefin+Sans:400,700,400italic|Ubuntu+Condensed' rel='stylesheet' type='text/css'>\n",
"\n",
"El estilo se ha aplicado =)\n",
"\n",
"<style>\n",
"\n",
"\n",
"\n",
"#notebook_panel { /* main background */\n",
" background: #f7f7f7;\n",
"}\n",
"\n",
"div.cell { /* set cell width */\n",
" width: 900px;\n",
"}\n",
"\n",
"div #notebook { /* centre the content */\n",
" background: #fff; /* white background for content */\n",
" width: 950px;\n",
" margin: auto;\n",
" padding-left: 0em;\n",
"}\n",
"\n",
"#notebook li { /* More space between bullet points */\n",
" margin-top:0.7em;\n",
"}\n",
"\n",
"/* draw border around running cells */\n",
"div.cell.border-box-sizing.code_cell.running { \n",
" border: 1px solid #111;\n",
"}\n",
"\n",
"/* Put a solid color box around each cell and its output, visually linking them*/\n",
"div.cell.code_cell {\n",
" font-family: 'Source Sans Pro', sans-serif;\n",
" background-color: rgb(256,256,256);\n",
" font-size: 110%;\n",
" border-radius: 0px; \n",
" padding: 0.5em;\n",
" margin-left:1em;\n",
" margin-top: 1em;\n",
"}\n",
"\n",
"div.text_cell_render{\n",
" font-family: 'Josefin Sans', serif;\n",
" line-height: 145%;\n",
" font-size: 125%;\n",
" font-weight: 500;\n",
" width:750px;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
"}\n",
"\n",
"\n",
"/* Formatting for header cells */\n",
".text_cell_render h1, .text_cell_render h2, .text_cell_render h3,\n",
".text_cell_render h4, .text_cell_render h5 {\n",
" font-family: 'Ubuntu Condensed', sans-serif;\n",
"}\n",
"/*\n",
".text_cell_render h1 {\n",
" font-family: Flux, 'Ubuntu Condensed', serif;\n",
" font-style:regular;\n",
" font-weight: 400; \n",
" font-size: 30pt;\n",
" text-align: center;\n",
" line-height: 100%;\n",
" color: #335082;\n",
" margin-bottom: 0.5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
"}\n",
"*/\n",
".text_cell_render h1 {\n",
" font-weight: 600;\n",
" font-size: 35pt;\n",
" line-height: 100%;\n",
" color: #000000;\n",
" margin-bottom: 0.1em;\n",
" margin-top: 0.3em;\n",
" display: block;\n",
"}\n",
"\n",
".text_cell_render h2 {\n",
" margin-top:16px;\n",
" font-size: 27pt;\n",
" font-weight: 550;\n",
" margin-bottom: 0.1em;\n",
" margin-top: 0.3em;\n",
" font-style: regular;\n",
" color: #2c6391;\n",
"}\t\n",
"\n",
".text_cell_render h3 {\n",
" font-size: 20pt;\n",
" font-weight: 550\n",
" text-align: left;\n",
" margin-bottom: 0.1em;\n",
" margin-top: 0.3em;\n",
" font-style: regular;\n",
" color: #387eb8;\n",
"}\n",
"\n",
".text_cell_render h4 { /*Use this for captions*/\n",
" font-size: 18pt;\n",
" font-weight: 450\n",
" text-align: left;\n",
" margin-bottom: 0.1em;\n",
" margin-top: 0.3em;\n",
" font-style: regular;\n",
" color: #5797cc;\n",
"}\n",
"\n",
".text_cell_render h5 { /*Use this for small titles*/\n",
" font-size: 18pt;\n",
" font-weight: 550;\n",
" color: rgb(163,0,0);\n",
" font-style: italic;\n",
" margin-bottom: .1em;\n",
" margin-top: 0.8em;\n",
" display: block;\n",
" color: #b21c0d;\n",
"}\n",
"\n",
".text_cell_render h6 { /*use this for copyright note*/\n",
" font-family: 'Ubuntu Condensed', sans-serif;\n",
" font-weight: 300;\n",
" font-size: 14pt;\n",
" line-height: 100%;\n",
" color: #252525;\n",
" text-align: right;\n",
" margin-bottom: 1px;\n",
" margin-top: 1px;\n",
"}\n",
"\n",
".CodeMirror{\n",
" font-family: 'Duru Sans', sans-serif;\n",
" font-size: 100%;\n",
"}\n",
"\n",
"</style>\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {\n",
" extensions: [\"AMSmath.js\"],\n",
" equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
" },\n",
" tex2jax: {\n",
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {\n",
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
" }\n",
" });\n",
"</script>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Esta celda da el estilo al notebook\n",
"from IPython.core.display import HTML\n",
"css_file = './css/aeropython.css'\n",
"HTML(open(css_file, \"r\").read())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
ScottWales/threddsclient | examples/noaa_example.ipynb | 2 | 11513 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"THREDDS PSD Test Catalog http://www.esrl.noaa.gov/psd/thredds/catalog.xml\n",
"num refs = 2\n",
"num datasets = 0\n"
]
}
],
"source": [
"import threddsclient\n",
"cat = threddsclient.read_url('http://www.esrl.noaa.gov/psd/thredds/catalog.xml')\n",
"print cat.name, cat.url\n",
"print 'num refs =', len(cat.references)\n",
"print 'num datasets =', len(cat.datasets)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Datasets\n",
"Aggregations\n"
]
}
],
"source": [
"for ref in cat.references:\n",
" print ref.name"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Datasets http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/catalog.xml\n",
"num refs = 0\n",
"num datasets = 1\n"
]
}
],
"source": [
"cat2 = cat.references[0].follow()\n",
"print cat2.name, cat2.url\n",
"print 'num refs =', len(cat2.references)\n",
"print 'num datasets =', len(cat2.datasets)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Datasets http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/catalog.xml?dataset=Datasets True 0 49\n"
]
}
],
"source": [
"ds = cat2.datasets[0]\n",
"print ds.name, ds.url, ds.is_collection(), len(ds.datasets), len(ds.references)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 20thC_ReanV2 http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/20thC_ReanV2/catalog.xml\n",
"1 20thC_ReanV2c http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/20thC_ReanV2c/catalog.xml\n",
"2 COBE http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/COBE/catalog.xml\n",
"3 COBE2 http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/COBE2/catalog.xml\n",
"4 CarbonTracker http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/CarbonTracker/catalog.xml\n",
"5 NARR http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/NARR/catalog.xml\n",
"6 Timeseries http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/Timeseries/catalog.xml\n",
"7 cmap http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/cmap/catalog.xml\n",
"8 coads http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/coads/catalog.xml\n",
"9 cpc_us_hour_precip http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/cpc_us_hour_precip/catalog.xml\n",
"10 cpc_us_precip http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/cpc_us_precip/catalog.xml\n",
"11 cpcsoil http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/cpcsoil/catalog.xml\n",
"12 cru http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/cru/catalog.xml\n",
"13 dai_pdsi http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/dai_pdsi/catalog.xml\n",
"14 ghcncams http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ghcncams/catalog.xml\n",
"15 ghcngridded http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ghcngridded/catalog.xml\n",
"16 gistemp http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/gistemp/catalog.xml\n",
"17 godas http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/godas/catalog.xml\n",
"18 gpcc http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/gpcc/catalog.xml\n",
"19 gpcp http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/gpcp/catalog.xml\n",
"20 icoads http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/icoads/catalog.xml\n",
"21 interp_OLR http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/interp_OLR/catalog.xml\n",
"22 kaplan_sst http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/kaplan_sst/catalog.xml\n",
"23 mlost http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/mlost/catalog.xml\n",
"24 mlostv3b http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/mlostv3b/catalog.xml\n",
"25 msu http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/msu/catalog.xml\n",
"26 ncep http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep/catalog.xml\n",
"27 ncep.marine http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.marine/catalog.xml\n",
"28 ncep.pac.ocean http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.pac.ocean/catalog.xml\n",
"29 ncep.reanalysis http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis/catalog.xml\n",
"30 ncep.reanalysis.dailyavgs http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis.dailyavgs/catalog.xml\n",
"31 ncep.reanalysis.derived http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis.derived/catalog.xml\n",
"32 ncep.reanalysis2 http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis2/catalog.xml\n",
"33 ncep.reanalysis2.dailyavgs http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis2.dailyavgs/catalog.xml\n",
"34 ncep.reanalysis2.derived http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis2.derived/catalog.xml\n",
"35 noaa.ersst http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/noaa.ersst/catalog.xml\n",
"36 noaa.oisst.v2 http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/noaa.oisst.v2/catalog.xml\n",
"37 noaa.oisst.v2.derived http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/noaa.oisst.v2.derived/catalog.xml\n",
"38 noaa.oisst.v2.highres http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/noaa.oisst.v2.highres/catalog.xml\n",
"39 noaa_hrc http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/noaa_hrc/catalog.xml\n",
"40 noaamergedtemp http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/noaamergedtemp/catalog.xml\n",
"41 nodc.woa94 http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/nodc.woa94/catalog.xml\n",
"42 nodc.woa98 http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/nodc.woa98/catalog.xml\n",
"43 olrcdr http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/olrcdr/catalog.xml\n",
"44 prec http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/prec/catalog.xml\n",
"45 precl http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/precl/catalog.xml\n",
"46 snowcover http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/snowcover/catalog.xml\n",
"47 udel.airt.precip http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/udel.airt.precip/catalog.xml\n",
"48 uninterp_OLR http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/uninterp_OLR/catalog.xml\n"
]
}
],
"source": [
"for i in range(0, len(ds.references)):\n",
" print i, ds.references[i].name, ds.references[i].url"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ncep.reanalysis2.dailyavgs http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis2.dailyavgs/catalog.xml 0 1\n"
]
}
],
"source": [
"cat3 = ds.references[33].follow()\n",
"print cat3.name, cat3.url, len(cat3.references), len(cat3.datasets)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"gaussian_grid http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis2.dailyavgs/gaussian_grid/catalog.xml\n",
"pressure http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis2.dailyavgs/pressure/catalog.xml\n",
"surface http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis2.dailyavgs/surface/catalog.xml\n"
]
}
],
"source": [
"for ref in cat3.flat_references():\n",
" print ref.name, ref.url"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"surface http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis2.dailyavgs/surface/catalog.xml 109\n"
]
}
],
"source": [
"cat4 = cat3.flat_references()[2].follow()\n",
"print cat4.name, cat4.url, len(cat4.flat_datasets())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hgt.sfc.nc, http://www.esrl.noaa.gov/psd/thredds/catalog/Datasets/ncep.reanalysis2.dailyavgs/surface/catalog.xml?dataset=Datasets/ncep.reanalysis2.dailyavgs/surface/hgt.sfc.nc, 2010-08-19T21:22:40Z, 24030\n"
]
}
],
"source": [
"print '{0.name}, {0.url}, {0.modified}, {0.bytes}'.format(cat4.flat_datasets()[0])"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis2.dailyavgs/surface/hgt.sfc.nc\n"
]
}
],
"source": [
"print cat4.flat_datasets()[0].download_url()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis2.dailyavgs/surface/hgt.sfc.nc\n"
]
}
],
"source": [
"print cat4.flat_datasets()[0].opendap_url()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.esrl.noaa.gov/psd/thredds/fileServer/Datasets/ncep.reanalysis2.dailyavgs/surface/hgt.sfc.nc\n"
]
}
],
"source": [
"print cat4.download_urls()[0]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://www.esrl.noaa.gov/psd/thredds/dodsC/Datasets/ncep.reanalysis2.dailyavgs/surface/hgt.sfc.nc\n"
]
}
],
"source": [
"print cat4.opendap_urls()[0]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
deepmind/graph_nets | graph_nets/demos/physics.ipynb | 1 | 40230 | {
"cells": [
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "k2QMetc6kava"
},
"outputs": [],
"source": [
"#@title ##### License\n",
"# Copyright 2018 The GraphNets Authors. All Rights Reserved.\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# http://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License.\n",
"# ============================================================================"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "c5CPvyHM2CnU"
},
"source": [
"# Physical dynamics of a mass-spring system\n",
"This notebook and the accompanying code demonstrates how to use the Graph Nets library to learn to predict the motion of a set of masses connected by springs.\n",
"\n",
"The network is trained to predict the behaviour of a chain of five masses, connected by identical springs. The first and last masses are fixed; the others are subject to gravity.\n",
"\n",
"After training, the network's prediction ability is illustrated by comparing its output to the true behaviour of the structure. Then the network's ability to generalise is tested, by using it to predict the behaviour of a similar but more complicated mass/spring structure."
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "Ss54UNGvkz5M"
},
"outputs": [],
"source": [
"#@title ### Install the Graph Nets library on this Colaboratory runtime { form-width: \"60%\", run: \"auto\"}\n",
"#@markdown \u003cbr\u003e1. Connect to a local or hosted Colaboratory runtime by clicking the **Connect** button at the top-right.\u003cbr\u003e2. Choose \"Yes\" below to install the Graph Nets library on the runtime machine with the correct dependencies. Note, this works both with local and hosted Colaboratory runtimes.\n",
"\n",
"install_graph_nets_library = \"No\" #@param [\"Yes\", \"No\"]\n",
"\n",
"if install_graph_nets_library.lower() == \"yes\":\n",
" print(\"Installing Graph Nets library and dependencies:\")\n",
" print(\"Output message from command:\\n\")\n",
" !pip install graph_nets \"dm-sonnet\u003c2\" \"tensorflow_probability\u003c0.9\"\n",
"else:\n",
" print(\"Skipping installation of Graph Nets library\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "7E4elJkXFR4a"
},
"source": [
"### Install dependencies locally\n",
"\n",
"If you are running this notebook locally (i.e., not through Colaboratory), you will also need to install a few more dependencies. Run the following on the command line to install the graph networks library, as well as a few other dependencies:\n",
"\n",
"```\n",
"pip install graph_nets matplotlib scipy \"tensorflow\u003e=1.15,\u003c2\" \"dm-sonnet\u003c2\" \"tensorflow_probability\u003c0.9\"\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "hyVaNA-bGug3"
},
"source": [
"# Code"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "RzKvtoAgRA8e"
},
"outputs": [],
"source": [
"#@title Imports { form-width: \"30%\" }\n",
"\n",
"# The demo dependencies are not installed with the library, but you can install\n",
"# them with:\n",
"#\n",
"# $ pip install jupyter matplotlib scipy\n",
"#\n",
"# Run the demo with:\n",
"#\n",
"# $ jupyter notebook \u003cpath\u003e/\u003cto\u003e/\u003cdemos\u003e/shortest_path.ipynb\n",
"%tensorflow_version 1.x # For Google Colab only.\n",
"\n",
"from __future__ import absolute_import\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"import time\n",
"\n",
"from graph_nets import blocks\n",
"from graph_nets import utils_tf\n",
"from graph_nets.demos import models\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"import sonnet as snt\n",
"import tensorflow as tf\n",
"\n",
"\n",
"try:\n",
" import seaborn as sns\n",
"except ImportError:\n",
" pass\n",
"else:\n",
" sns.reset_orig()\n",
"\n",
"SEED = 1\n",
"np.random.seed(SEED)\n",
"tf.set_random_seed(SEED)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "toCQhJIM93en"
},
"outputs": [],
"source": [
"#@title Helper functions { form-width: \"30%\" }\n",
"\n",
"# pylint: disable=redefined-outer-name\n",
"\n",
"def base_graph(n, d):\n",
" \"\"\"Define a basic mass-spring system graph structure.\n",
"\n",
" These are n masses (1kg) connected by springs in a chain-like structure. The\n",
" first and last masses are fixed. The masses are vertically aligned at the\n",
" start and are d meters apart; this is also the rest length for the springs\n",
" connecting them. Springs have spring constant 50 N/m and gravity is 10 N in\n",
" the negative y-direction.\n",
"\n",
" Args:\n",
" n: number of masses\n",
" d: distance between masses (as well as springs' rest length)\n",
"\n",
" Returns:\n",
" data_dict: dictionary with globals, nodes, edges, receivers and senders\n",
" to represent a structure like the one above.\n",
" \"\"\"\n",
" # Nodes\n",
" # Generate initial position and velocity for all masses.\n",
" # The left-most mass has is at position (0, 0); other masses (ordered left to\n",
" # right) have x-coordinate d meters apart from their left neighbor, and\n",
" # y-coordinate 0. All masses have initial velocity 0m/s.\n",
" nodes = np.zeros((n, 5), dtype=np.float32)\n",
" half_width = d * n / 2.0\n",
" nodes[:, 0] = np.linspace(\n",
" -half_width, half_width, num=n, endpoint=False, dtype=np.float32)\n",
" # indicate that the first and last masses are fixed\n",
" nodes[(0, -1), -1] = 1.\n",
"\n",
" # Edges.\n",
" edges, senders, receivers = [], [], []\n",
" for i in range(n - 1):\n",
" left_node = i\n",
" right_node = i + 1\n",
" # The 'if' statements prevent incoming edges to fixed ends of the string.\n",
" if right_node \u003c n - 1:\n",
" # Left incoming edge.\n",
" edges.append([50., d])\n",
" senders.append(left_node)\n",
" receivers.append(right_node)\n",
" if left_node \u003e 0:\n",
" # Right incoming edge.\n",
" edges.append([50., d])\n",
" senders.append(right_node)\n",
" receivers.append(left_node)\n",
"\n",
" return {\n",
" \"globals\": [0., -10.],\n",
" \"nodes\": nodes,\n",
" \"edges\": edges,\n",
" \"receivers\": receivers,\n",
" \"senders\": senders\n",
" }\n",
"\n",
"\n",
"def hookes_law(receiver_nodes, sender_nodes, k, x_rest):\n",
" \"\"\"Applies Hooke's law to springs connecting some nodes.\n",
"\n",
" Args:\n",
" receiver_nodes: Ex5 tf.Tensor of [x, y, v_x, v_y, is_fixed] features for the\n",
" receiver node of each edge.\n",
" sender_nodes: Ex5 tf.Tensor of [x, y, v_x, v_y, is_fixed] features for the\n",
" sender node of each edge.\n",
" k: Spring constant for each edge.\n",
" x_rest: Rest length of each edge.\n",
"\n",
" Returns:\n",
" Nx2 Tensor of the force [f_x, f_y] acting on each edge.\n",
" \"\"\"\n",
" diff = receiver_nodes[..., 0:2] - sender_nodes[..., 0:2]\n",
" x = tf.norm(diff, axis=-1, keepdims=True)\n",
" force_magnitude = -1 * tf.multiply(k, (x - x_rest) / x)\n",
" force = force_magnitude * diff\n",
" return force\n",
"\n",
"\n",
"def euler_integration(nodes, force_per_node, step_size):\n",
" \"\"\"Applies one step of Euler integration.\n",
"\n",
" Args:\n",
" nodes: Ex5 tf.Tensor of [x, y, v_x, v_y, is_fixed] features for each node.\n",
" force_per_node: Ex2 tf.Tensor of the force [f_x, f_y] acting on each edge.\n",
" step_size: Scalar.\n",
"\n",
" Returns:\n",
" A tf.Tensor of the same shape as `nodes` but with positions and velocities\n",
" updated.\n",
" \"\"\"\n",
" is_fixed = nodes[..., 4:5]\n",
" # set forces to zero for fixed nodes\n",
" force_per_node *= 1 - is_fixed\n",
" new_vel = nodes[..., 2:4] + force_per_node * step_size\n",
" return new_vel\n",
"\n",
"\n",
"class SpringMassSimulator(snt.AbstractModule):\n",
" \"\"\"Implements a basic Physics Simulator using the blocks library.\"\"\"\n",
"\n",
" def __init__(self, step_size, name=\"SpringMassSimulator\"):\n",
" super(SpringMassSimulator, self).__init__(name=name)\n",
" self._step_size = step_size\n",
"\n",
" with self._enter_variable_scope():\n",
" self._aggregator = blocks.ReceivedEdgesToNodesAggregator(\n",
" reducer=tf.unsorted_segment_sum)\n",
"\n",
" def _build(self, graph):\n",
" \"\"\"Builds a SpringMassSimulator.\n",
"\n",
" Args:\n",
" graph: A graphs.GraphsTuple having, for some integers N, E, G:\n",
" - edges: Nx2 tf.Tensor of [spring_constant, rest_length] for each\n",
" edge.\n",
" - nodes: Ex5 tf.Tensor of [x, y, v_x, v_y, is_fixed] features for each\n",
" node.\n",
" - globals: Gx2 tf.Tensor containing the gravitational constant.\n",
"\n",
" Returns:\n",
" A graphs.GraphsTuple of the same shape as `graph`, but where:\n",
" - edges: Holds the force [f_x, f_y] acting on each edge.\n",
" - nodes: Holds positions and velocities after applying one step of\n",
" Euler integration.\n",
" \"\"\"\n",
" receiver_nodes = blocks.broadcast_receiver_nodes_to_edges(graph)\n",
" sender_nodes = blocks.broadcast_sender_nodes_to_edges(graph)\n",
"\n",
" spring_force_per_edge = hookes_law(receiver_nodes, sender_nodes,\n",
" graph.edges[..., 0:1],\n",
" graph.edges[..., 1:2])\n",
" graph = graph.replace(edges=spring_force_per_edge)\n",
"\n",
" spring_force_per_node = self._aggregator(graph)\n",
" gravity = blocks.broadcast_globals_to_nodes(graph)\n",
" updated_velocities = euler_integration(\n",
" graph.nodes, spring_force_per_node + gravity, self._step_size)\n",
" graph = graph.replace(nodes=updated_velocities)\n",
" return graph\n",
"\n",
"\n",
"def prediction_to_next_state(input_graph, predicted_graph, step_size):\n",
" # manually integrate velocities to compute new positions\n",
" new_pos = input_graph.nodes[..., :2] + predicted_graph.nodes * step_size\n",
" new_nodes = tf.concat(\n",
" [new_pos, predicted_graph.nodes, input_graph.nodes[..., 4:5]], axis=-1)\n",
" return input_graph.replace(nodes=new_nodes)\n",
"\n",
"\n",
"def roll_out_physics(simulator, graph, steps, step_size):\n",
" \"\"\"Apply some number of steps of physical laws to an interaction network.\n",
"\n",
" Args:\n",
" simulator: A SpringMassSimulator, or some module or callable with the same\n",
" signature.\n",
" graph: A graphs.GraphsTuple having, for some integers N, E, G:\n",
" - edges: Nx2 tf.Tensor of [spring_constant, rest_length] for each edge.\n",
" - nodes: Ex5 tf.Tensor of [x, y, v_x, v_y, is_fixed] features for each\n",
" node.\n",
" - globals: Gx2 tf.Tensor containing the gravitational constant.\n",
" steps: An integer.\n",
" step_size: Scalar.\n",
"\n",
" Returns:\n",
" A pair of:\n",
" - The graph, updated after `steps` steps of simulation;\n",
" - A `steps+1`xNx5 tf.Tensor of the node features at each step.\n",
" \"\"\"\n",
"\n",
" def body(t, graph, nodes_per_step):\n",
" predicted_graph = simulator(graph)\n",
" if isinstance(predicted_graph, list):\n",
" predicted_graph = predicted_graph[-1]\n",
" graph = prediction_to_next_state(graph, predicted_graph, step_size)\n",
" return t + 1, graph, nodes_per_step.write(t, graph.nodes)\n",
"\n",
" nodes_per_step = tf.TensorArray(\n",
" dtype=graph.nodes.dtype, size=steps + 1, element_shape=graph.nodes.shape)\n",
" nodes_per_step = nodes_per_step.write(0, graph.nodes)\n",
"\n",
" _, g, nodes_per_step = tf.while_loop(\n",
" lambda t, *unused_args: t \u003c= steps,\n",
" body,\n",
" loop_vars=[1, graph, nodes_per_step])\n",
" return g, nodes_per_step.stack()\n",
"\n",
"\n",
"def apply_noise(graph, node_noise_level, edge_noise_level, global_noise_level):\n",
" \"\"\"Applies uniformly-distributed noise to a graph of a physical system.\n",
"\n",
" Noise is applied to:\n",
" - the x and y coordinates (independently) of the nodes;\n",
" - the spring constants of the edges;\n",
" - the y coordinate of the global gravitational constant.\n",
"\n",
" Args:\n",
" graph: a graphs.GraphsTuple having, for some integers N, E, G:\n",
" - nodes: Nx5 Tensor of [x, y, _, _, _] for each node.\n",
" - edges: Ex2 Tensor of [spring_constant, _] for each edge.\n",
" - globals: Gx2 tf.Tensor containing the gravitational constant.\n",
" node_noise_level: Maximum distance to perturb nodes' x and y coordinates.\n",
" edge_noise_level: Maximum amount to perturb edge spring constants.\n",
" global_noise_level: Maximum amount to perturb the Y component of gravity.\n",
"\n",
" Returns:\n",
" The input graph, but with noise applied.\n",
" \"\"\"\n",
" node_position_noise = tf.random_uniform(\n",
" [graph.nodes.shape[0].value, 2],\n",
" minval=-node_noise_level,\n",
" maxval=node_noise_level)\n",
" edge_spring_constant_noise = tf.random_uniform(\n",
" [graph.edges.shape[0].value, 1],\n",
" minval=-edge_noise_level,\n",
" maxval=edge_noise_level)\n",
" global_gravity_y_noise = tf.random_uniform(\n",
" [graph.globals.shape[0].value, 1],\n",
" minval=-global_noise_level,\n",
" maxval=global_noise_level)\n",
"\n",
" return graph.replace(\n",
" nodes=tf.concat(\n",
" [graph.nodes[..., :2] + node_position_noise, graph.nodes[..., 2:]],\n",
" axis=-1),\n",
" edges=tf.concat(\n",
" [\n",
" graph.edges[..., :1] + edge_spring_constant_noise,\n",
" graph.edges[..., 1:]\n",
" ],\n",
" axis=-1),\n",
" globals=tf.concat(\n",
" [\n",
" graph.globals[..., :1],\n",
" graph.globals[..., 1:] + global_gravity_y_noise\n",
" ],\n",
" axis=-1))\n",
"\n",
"\n",
"def set_rest_lengths(graph):\n",
" \"\"\"Computes and sets rest lengths for the springs in a physical system.\n",
"\n",
" The rest length is taken to be the distance between each edge's nodes.\n",
"\n",
" Args:\n",
" graph: a graphs.GraphsTuple having, for some integers N, E:\n",
" - nodes: Nx5 Tensor of [x, y, _, _, _] for each node.\n",
" - edges: Ex2 Tensor of [spring_constant, _] for each edge.\n",
"\n",
" Returns:\n",
" The input graph, but with [spring_constant, rest_length] for each edge.\n",
" \"\"\"\n",
" receiver_nodes = blocks.broadcast_receiver_nodes_to_edges(graph)\n",
" sender_nodes = blocks.broadcast_sender_nodes_to_edges(graph)\n",
" rest_length = tf.norm(\n",
" receiver_nodes[..., :2] - sender_nodes[..., :2], axis=-1, keepdims=True)\n",
" return graph.replace(\n",
" edges=tf.concat([graph.edges[..., :1], rest_length], axis=-1))\n",
"\n",
"\n",
"def generate_trajectory(simulator, graph, steps, step_size, node_noise_level,\n",
" edge_noise_level, global_noise_level):\n",
" \"\"\"Applies noise and then simulates a physical system for a number of steps.\n",
"\n",
" Args:\n",
" simulator: A SpringMassSimulator, or some module or callable with the same\n",
" signature.\n",
" graph: a graphs.GraphsTuple having, for some integers N, E, G:\n",
" - nodes: Nx5 Tensor of [x, y, v_x, v_y, is_fixed] for each node.\n",
" - edges: Ex2 Tensor of [spring_constant, _] for each edge.\n",
" - globals: Gx2 tf.Tensor containing the gravitational constant.\n",
" steps: Integer; the length of trajectory to generate.\n",
" step_size: Scalar.\n",
" node_noise_level: Maximum distance to perturb nodes' x and y coordinates.\n",
" edge_noise_level: Maximum amount to perturb edge spring constants.\n",
" global_noise_level: Maximum amount to perturb the Y component of gravity.\n",
"\n",
" Returns:\n",
" A pair of:\n",
" - The input graph, but with rest lengths computed and noise applied.\n",
" - A `steps+1`xNx5 tf.Tensor of the node features at each step.\n",
" \"\"\"\n",
" graph = apply_noise(graph, node_noise_level, edge_noise_level,\n",
" global_noise_level)\n",
" graph = set_rest_lengths(graph)\n",
" _, n = roll_out_physics(simulator, graph, steps, step_size)\n",
" return graph, n\n",
"\n",
"\n",
"def create_loss_ops(target_op, output_ops):\n",
" \"\"\"Create supervised loss operations from targets and outputs.\n",
"\n",
" Args:\n",
" target_op: The target velocity tf.Tensor.\n",
" output_ops: The list of output graphs from the model.\n",
"\n",
" Returns:\n",
" A list of loss values (tf.Tensor), one per output op.\n",
" \"\"\"\n",
" loss_ops = [\n",
" tf.reduce_mean(\n",
" tf.reduce_sum((output_op.nodes - target_op[..., 2:4])**2, axis=-1))\n",
" for output_op in output_ops\n",
" ]\n",
" return loss_ops\n",
"\n",
"\n",
"def make_all_runnable_in_session(*args):\n",
" \"\"\"Apply make_runnable_in_session to an iterable of graphs.\"\"\"\n",
" return [utils_tf.make_runnable_in_session(a) for a in args]\n",
"\n",
"\n",
"# pylint: enable=redefined-outer-name"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "pf7u0zuN_ktd"
},
"outputs": [],
"source": [
"#@title Set up model training and evaluation { form-width: \"30%\" }\n",
"\n",
"# The model we explore includes three components:\n",
"# - An \"Encoder\" graph net, which independently encodes the edge, node, and\n",
"# global attributes (does not compute relations etc.).\n",
"# - A \"Core\" graph net, which performs N rounds of processing (message-passing)\n",
"# steps. The input to the Core is the concatenation of the Encoder's output\n",
"# and the previous output of the Core (labeled \"Hidden(t)\" below, where \"t\" is\n",
"# the processing step).\n",
"# - A \"Decoder\" graph net, which independently decodes the edge, node, and\n",
"# global attributes (does not compute relations etc.), on each\n",
"# message-passing step.\n",
"#\n",
"# Hidden(t) Hidden(t+1)\n",
"# | ^\n",
"# *---------* | *------* | *---------*\n",
"# | | | | | | | |\n",
"# Input ---\u003e| Encoder | *-\u003e| Core |--*-\u003e| Decoder |---\u003e Output(t)\n",
"# | |----\u003e| | | |\n",
"# *---------* *------* *---------*\n",
"#\n",
"# The model is trained by supervised learning. Input mass-spring systems are\n",
"# procedurally generated, where the nodes represent the positions, velocities,\n",
"# and indicators of whether the mass is fixed in space or free to move, the\n",
"# edges represent the spring constant and spring rest length, and the global\n",
"# attribute represents the variable coefficient of gravitational acceleration.\n",
"# The outputs/targets have the same structure, with the nodes representing the\n",
"# masses' next-step states.\n",
"#\n",
"# The training loss is computed on the output of each processing step. The\n",
"# reason for this is to encourage the model to try to solve the problem in as\n",
"# few steps as possible. It also helps make the output of intermediate steps\n",
"# more interpretable.\n",
"#\n",
"# There's no need for a separate evaluate dataset because the inputs are\n",
"# never repeated, so the training loss is the measure of performance on graphs\n",
"# from the input distribution.\n",
"#\n",
"# We also evaluate how well the models generalize to systems which are one mass\n",
"# larger, and smaller, than those from the training distribution. The loss is\n",
"# computed as the mean over a 50-step rollout, where each step's input is the\n",
"# the previous step's output.\n",
"#\n",
"# Variables with the suffix _tr are training parameters, and variables with the\n",
"# suffix _ge are test/generalization parameters.\n",
"#\n",
"# After around 10000-20000 training iterations the model reaches good\n",
"# performance on mass-spring systems with 5-8 masses.\n",
"\n",
"tf.reset_default_graph()\n",
"\n",
"rand = np.random.RandomState(SEED)\n",
"\n",
"# Model parameters.\n",
"num_processing_steps_tr = 1\n",
"num_processing_steps_ge = 1\n",
"\n",
"# Data / training parameters.\n",
"num_training_iterations = 100000\n",
"batch_size_tr = 256\n",
"batch_size_ge = 100\n",
"num_time_steps = 50\n",
"step_size = 0.1\n",
"num_masses_min_max_tr = (5, 9)\n",
"dist_between_masses_min_max_tr = (0.2, 1.0)\n",
"\n",
"# Create the model.\n",
"model = models.EncodeProcessDecode(node_output_size=2)\n",
"\n",
"# Data.\n",
"# Base graphs for training.\n",
"num_masses_tr = rand.randint(*num_masses_min_max_tr, size=batch_size_tr)\n",
"dist_between_masses_tr = rand.uniform(\n",
" *dist_between_masses_min_max_tr, size=batch_size_tr)\n",
"static_graph_tr = [\n",
" base_graph(n, d) for n, d in zip(num_masses_tr, dist_between_masses_tr)\n",
"]\n",
"base_graph_tr = utils_tf.data_dicts_to_graphs_tuple(static_graph_tr)\n",
"# Base graphs for testing.\n",
"# 4 masses 1m apart in a chain like structure.\n",
"base_graph_4_ge = utils_tf.data_dicts_to_graphs_tuple(\n",
" [base_graph(4, 0.5)] * batch_size_ge)\n",
"# 9 masses 0.5m apart in a chain like structure.\n",
"base_graph_9_ge = utils_tf.data_dicts_to_graphs_tuple(\n",
" [base_graph(9, 0.5)] * batch_size_ge)\n",
"# True physics simulator for data generation.\n",
"simulator = SpringMassSimulator(step_size=step_size)\n",
"# Training.\n",
"# Generate a training trajectory by adding noise to initial\n",
"# position, spring constants and gravity\n",
"initial_conditions_tr, true_trajectory_tr = generate_trajectory(\n",
" simulator,\n",
" base_graph_tr,\n",
" num_time_steps,\n",
" step_size,\n",
" node_noise_level=0.04,\n",
" edge_noise_level=5.0,\n",
" global_noise_level=1.0)\n",
"# Random start step.\n",
"t = tf.random_uniform([], minval=0, maxval=num_time_steps - 1, dtype=tf.int32)\n",
"input_graph_tr = initial_conditions_tr.replace(nodes=true_trajectory_tr[t])\n",
"target_nodes_tr = true_trajectory_tr[t + 1]\n",
"output_ops_tr = model(input_graph_tr, num_processing_steps_tr)\n",
"# Test data: 4-mass string.\n",
"initial_conditions_4_ge, true_trajectory_4_ge = generate_trajectory(\n",
" lambda x: model(x, num_processing_steps_ge),\n",
" base_graph_4_ge,\n",
" num_time_steps,\n",
" step_size,\n",
" node_noise_level=0.04,\n",
" edge_noise_level=5.0,\n",
" global_noise_level=1.0)\n",
"_, true_nodes_rollout_4_ge = roll_out_physics(\n",
" simulator, initial_conditions_4_ge, num_time_steps, step_size)\n",
"_, predicted_nodes_rollout_4_ge = roll_out_physics(\n",
" lambda x: model(x, num_processing_steps_ge), initial_conditions_4_ge,\n",
" num_time_steps, step_size)\n",
"# Test data: 9-mass string.\n",
"initial_conditions_9_ge, true_trajectory_9_ge = generate_trajectory(\n",
" lambda x: model(x, num_processing_steps_ge),\n",
" base_graph_9_ge,\n",
" num_time_steps,\n",
" step_size,\n",
" node_noise_level=0.04,\n",
" edge_noise_level=5.0,\n",
" global_noise_level=1.0)\n",
"_, true_nodes_rollout_9_ge = roll_out_physics(\n",
" simulator, initial_conditions_9_ge, num_time_steps, step_size)\n",
"_, predicted_nodes_rollout_9_ge = roll_out_physics(\n",
" lambda x: model(x, num_processing_steps_ge), initial_conditions_9_ge,\n",
" num_time_steps, step_size)\n",
"\n",
"# Training loss.\n",
"loss_ops_tr = create_loss_ops(target_nodes_tr, output_ops_tr)\n",
"# Training loss across processing steps.\n",
"loss_op_tr = sum(loss_ops_tr) / num_processing_steps_tr\n",
"# Test/generalization loss: 4-mass.\n",
"loss_op_4_ge = tf.reduce_mean(\n",
" tf.reduce_sum(\n",
" (predicted_nodes_rollout_4_ge[..., 2:4] -\n",
" true_nodes_rollout_4_ge[..., 2:4])**2,\n",
" axis=-1))\n",
"# Test/generalization loss: 9-mass string.\n",
"loss_op_9_ge = tf.reduce_mean(\n",
" tf.reduce_sum(\n",
" (predicted_nodes_rollout_9_ge[..., 2:4] -\n",
" true_nodes_rollout_9_ge[..., 2:4])**2,\n",
" axis=-1))\n",
"\n",
"# Optimizer.\n",
"learning_rate = 1e-3\n",
"optimizer = tf.train.AdamOptimizer(learning_rate)\n",
"step_op = optimizer.minimize(loss_op_tr)\n",
"\n",
"input_graph_tr = make_all_runnable_in_session(input_graph_tr)\n",
"initial_conditions_4_ge = make_all_runnable_in_session(initial_conditions_4_ge)\n",
"initial_conditions_9_ge = make_all_runnable_in_session(initial_conditions_9_ge)"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "TpABTYk0Ap-V"
},
"outputs": [],
"source": [
"#@title Reset session { form-width: \"30%\" }\n",
"\n",
"# This cell resets the Tensorflow session, but keeps the same computational\n",
"# graph.\n",
"\n",
"try:\n",
" sess.close()\n",
"except NameError:\n",
" pass\n",
"sess = tf.Session()\n",
"sess.run(tf.global_variables_initializer())\n",
"\n",
"last_iteration = 0\n",
"logged_iterations = []\n",
"losses_tr = []\n",
"losses_4_ge = []\n",
"losses_9_ge = []"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "4PCPvXiHA7q7"
},
"outputs": [],
"source": [
"#@title Run training { form-width: \"30%\" }\n",
"\n",
"# You can interrupt this cell's training loop at any time, and visualize the\n",
"# intermediate results by running the next cell (below). You can then resume\n",
"# training by simply executing this cell again.\n",
"\n",
"# How much time between logging and printing the current results.\n",
"log_every_seconds = 20\n",
"\n",
"print(\"# (iteration number), T (elapsed seconds), \"\n",
" \"Ltr (training 1-step loss), \"\n",
" \"Lge4 (test/generalization rollout loss for 4-mass strings), \"\n",
" \"Lge9 (test/generalization rollout loss for 9-mass strings)\")\n",
"\n",
"start_time = time.time()\n",
"last_log_time = start_time\n",
"for iteration in range(last_iteration, num_training_iterations):\n",
" last_iteration = iteration\n",
" train_values = sess.run({\n",
" \"step\": step_op,\n",
" \"loss\": loss_op_tr,\n",
" \"input_graph\": input_graph_tr,\n",
" \"target_nodes\": target_nodes_tr,\n",
" \"outputs\": output_ops_tr\n",
" })\n",
" the_time = time.time()\n",
" elapsed_since_last_log = the_time - last_log_time\n",
" if elapsed_since_last_log \u003e log_every_seconds:\n",
" last_log_time = the_time\n",
" test_values = sess.run({\n",
" \"loss_4\": loss_op_4_ge,\n",
" \"true_rollout_4\": true_nodes_rollout_4_ge,\n",
" \"predicted_rollout_4\": predicted_nodes_rollout_4_ge,\n",
" \"loss_9\": loss_op_9_ge,\n",
" \"true_rollout_9\": true_nodes_rollout_9_ge,\n",
" \"predicted_rollout_9\": predicted_nodes_rollout_9_ge\n",
" })\n",
" elapsed = time.time() - start_time\n",
" losses_tr.append(train_values[\"loss\"])\n",
" losses_4_ge.append(test_values[\"loss_4\"])\n",
" losses_9_ge.append(test_values[\"loss_9\"])\n",
" logged_iterations.append(iteration)\n",
" print(\"# {:05d}, T {:.1f}, Ltr {:.4f}, Lge4 {:.4f}, Lge9 {:.4f}\".format(\n",
" iteration, elapsed, train_values[\"loss\"], test_values[\"loss_4\"],\n",
" test_values[\"loss_9\"]))"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"cellView": "form",
"colab": {},
"colab_type": "code",
"id": "j1FgiIm-pmRq"
},
"outputs": [],
"source": [
"#@title Visualize loss curves { form-width: \"30%\" }\n",
"\n",
"# This cell visualizes the results of training. You can visualize the\n",
"# intermediate results by interrupting execution of the cell above, and running\n",
"# this cell. You can then resume training by simply executing the above cell\n",
"# again.\n",
"\n",
"def get_node_trajectories(rollout_array, batch_size): # pylint: disable=redefined-outer-name\n",
" return np.split(rollout_array[..., :2], batch_size, axis=1)\n",
"\n",
"\n",
"fig = plt.figure(1, figsize=(18, 3))\n",
"fig.clf()\n",
"x = np.array(logged_iterations)\n",
"# Next-step Loss.\n",
"y = losses_tr\n",
"ax = fig.add_subplot(1, 3, 1)\n",
"ax.plot(x, y, \"k\")\n",
"ax.set_title(\"Next step loss\")\n",
"# Rollout 5 loss.\n",
"y = losses_4_ge\n",
"ax = fig.add_subplot(1, 3, 2)\n",
"ax.plot(x, y, \"k\")\n",
"ax.set_title(\"Rollout loss: 5-mass string\")\n",
"# Rollout 9 loss.\n",
"y = losses_9_ge\n",
"ax = fig.add_subplot(1, 3, 3)\n",
"ax.plot(x, y, \"k\")\n",
"ax.set_title(\"Rollout loss: 9-mass string\")\n",
"\n",
"# Visualize trajectories.\n",
"true_rollouts_4 = get_node_trajectories(test_values[\"true_rollout_4\"],\n",
" batch_size_ge)\n",
"predicted_rollouts_4 = get_node_trajectories(test_values[\"predicted_rollout_4\"],\n",
" batch_size_ge)\n",
"true_rollouts_9 = get_node_trajectories(test_values[\"true_rollout_9\"],\n",
" batch_size_ge)\n",
"predicted_rollouts_9 = get_node_trajectories(test_values[\"predicted_rollout_9\"],\n",
" batch_size_ge)\n",
"\n",
"true_rollouts = true_rollouts_4\n",
"predicted_rollouts = predicted_rollouts_4\n",
"true_rollouts = true_rollouts_9\n",
"predicted_rollouts = predicted_rollouts_9\n",
"\n",
"num_graphs = len(true_rollouts)\n",
"num_time_steps = true_rollouts[0].shape[0]\n",
"\n",
"# Plot state sequences.\n",
"max_graphs_to_plot = 1\n",
"num_graphs_to_plot = min(num_graphs, max_graphs_to_plot)\n",
"num_steps_to_plot = 24\n",
"max_time_step = num_time_steps - 1\n",
"step_indices = np.floor(np.linspace(0, max_time_step,\n",
" num_steps_to_plot)).astype(int).tolist()\n",
"w = 6\n",
"h = int(np.ceil(num_steps_to_plot / w))\n",
"fig = plt.figure(101, figsize=(18, 8))\n",
"fig.clf()\n",
"for i, (true_rollout, predicted_rollout) in enumerate(\n",
" zip(true_rollouts, predicted_rollouts)):\n",
" xys = np.hstack([predicted_rollout, true_rollout]).reshape([-1, 2])\n",
" xs = xys[:, 0]\n",
" ys = xys[:, 1]\n",
" b = 0.05\n",
" xmin = xs.min() - b * xs.ptp()\n",
" xmax = xs.max() + b * xs.ptp()\n",
" ymin = ys.min() - b * ys.ptp()\n",
" ymax = ys.max() + b * ys.ptp()\n",
" if i \u003e= num_graphs_to_plot:\n",
" break\n",
" for j, step_index in enumerate(step_indices):\n",
" iax = i * w + j + 1\n",
" ax = fig.add_subplot(h, w, iax)\n",
" ax.plot(\n",
" true_rollout[step_index, :, 0],\n",
" true_rollout[step_index, :, 1],\n",
" \"k\",\n",
" label=\"True\")\n",
" ax.plot(\n",
" predicted_rollout[step_index, :, 0],\n",
" predicted_rollout[step_index, :, 1],\n",
" \"r\",\n",
" label=\"Predicted\")\n",
" ax.set_title(\"Example {:02d}: frame {:03d}\".format(i, step_index))\n",
" ax.set_xlim(xmin, xmax)\n",
" ax.set_ylim(ymin, ymax)\n",
" ax.set_xticks([])\n",
" ax.set_yticks([])\n",
" if j == 0:\n",
" ax.legend(loc=3)\n",
"\n",
"# Plot x and y trajectories over time.\n",
"max_graphs_to_plot = 3\n",
"num_graphs_to_plot = min(len(true_rollouts), max_graphs_to_plot)\n",
"w = 2\n",
"h = num_graphs_to_plot\n",
"fig = plt.figure(102, figsize=(18, 12))\n",
"fig.clf()\n",
"for i, (true_rollout, predicted_rollout) in enumerate(\n",
" zip(true_rollouts, predicted_rollouts)):\n",
" if i \u003e= num_graphs_to_plot:\n",
" break\n",
" t = np.arange(num_time_steps)\n",
" for j in range(2):\n",
" coord_string = \"x\" if j == 0 else \"y\"\n",
" iax = i * 2 + j + 1\n",
" ax = fig.add_subplot(h, w, iax)\n",
" ax.plot(t, true_rollout[..., j], \"k\", label=\"True\")\n",
" ax.plot(t, predicted_rollout[..., j], \"r\", label=\"Predicted\")\n",
" ax.set_xlabel(\"Time\")\n",
" ax.set_ylabel(\"{} coordinate\".format(coord_string))\n",
" ax.set_title(\"Example {:02d}: Predicted vs actual coords over time\".format(\n",
" i))\n",
" ax.set_frame_on(False)\n",
" if i == 0 and j == 1:\n",
" handles, labels = ax.get_legend_handles_labels()\n",
" unique_labels = []\n",
" unique_handles = []\n",
" for i, (handle, label) in enumerate(zip(handles, labels)): # pylint: disable=redefined-outer-name\n",
" if label not in unique_labels:\n",
" unique_labels.append(label)\n",
" unique_handles.append(handle)\n",
" ax.legend(unique_handles, unique_labels, loc=3)"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"last_runtime": {
"build_target": "//learning/deepmind/dm_python:dm_notebook3",
"kind": "private"
},
"name": "physics.ipynb",
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
j3ny/Analysis | thermodynamic_addressability/.ipynb_checkpoints/Thermodynamic Addressability-checkpoint.ipynb | 1 | 168571 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import pandas\n",
"\n",
"sns.set_style(\"whitegrid\", {\"font.family\": \"DejaVu Sans\"})\n",
"sns.set(palette=\"pastel\", color_codes=True)\n",
"sns.set_context(\"poster\")\n",
"\n",
"%matplotlib inline\n",
"\n",
"from matplotlib import rc\n",
"rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})\n",
"rc('text', usetex=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" id sequence probability size origin\n",
"5 stp_42_0 TTGCATTC 0.855 small DBS\n",
"6 stp_42_2 GCGGTTTT 0.896 small DBS\n",
"10 stp_43_0 CGTACACA 0.901 small DBS\n",
"11 stp_43_2 CAATCGTA 0.801 small DBS\n",
"21 stp_64_0 AATTTTTC 0.782 small DBS\n",
"25 stp_66_0 AAAGGACG 0.942 small DBS\n",
"27 stp_67_0 GCAGAGAC 0.929 small DBS\n",
"30 stp_68_2 GTTTCCTG 0.843 small DBS\n",
"33 stp_69_2 ACCATCTT 0.876 small DBS\n",
"39 stp_24_0 CTTCTGTA 0.878 small DBS\n",
"40 stp_24_2 AACTTAGC 0.903 small DBS\n",
"42 stp_25_0 AAGTCTTC 0.790 small DBS\n",
"43 stp_25_2 TGAGTCCC 0.963 small DBS\n",
"45 stp_26_0 AATACGCT 0.685 small DBS\n",
"46 stp_26_2 CTCCCTTG 0.899 small DBS\n",
"48 stp_27_0 GCCACTTT 0.882 small DBS\n",
"49 stp_27_2 AAGAAGAA 0.685 small DBS\n",
"51 stp_20_0 GTCTCTCG 0.949 small DBS\n",
"52 stp_20_2 GTGGGTCA 0.889 small DBS\n",
"54 stp_21_0 GACTATAC 0.676 small DBS\n",
"55 stp_21_2 GGCTTCTA 0.804 small DBS\n",
"57 stp_22_0 CCTTAAAG 0.653 small DBS\n",
"58 stp_22_2 GATGCAAG 0.802 small DBS\n",
"60 stp_23_0 TCCATACA 0.889 small DBS\n",
"61 stp_23_2 TCCCTGGC 0.968 small DBS\n",
"63 stp_46_0 ATGACACG 0.928 small DBS\n",
"64 stp_46_2 CAAGAAAC 0.770 small DBS\n",
"66 stp_47_0 CTTACTTT 0.612 small DBS\n",
"67 stp_47_2 TAGTCCTA 0.514 small DBS\n",
"69 stp_44_0 GTAGGCTT 0.755 small DBS\n",
"... ... ... ... ... ...\n",
"4391 stp_150_0_2 AGAAAAAT 0.038 large viral\n",
"4394 stp_150_2_0 TTTTTTCG 0.046 large viral\n",
"4396 stp_150_2_2 TGCTCCGC 0.234 large viral\n",
"4397 stp_150_3_0 GGTTGTTC 0.123 large viral\n",
"4399 stp_150_3_2 AATACTCA 0.085 large viral\n",
"4400 stp_150_4_0 GCTGACGT 0.108 large viral\n",
"4402 stp_150_4_2 CCGGTGGT 0.242 large viral\n",
"4403 stp_150_5_0 ACGTCCCA 0.191 large viral\n",
"4405 stp_150_5_2 CCAGACAG 0.121 large viral\n",
"4406 stp_150_6_0 TCATCAAC 0.076 large viral\n",
"4408 stp_150_6_2 AACAGTTG 0.143 large viral\n",
"4409 stp_150_7_0 ACCGACAC 0.169 large viral\n",
"4411 stp_150_7_2 GAAAACAT 0.051 large viral\n",
"4414 stp_150_9_0 GACGCTGC 0.095 large viral\n",
"4416 stp_150_9_2 CTGAAATC 0.042 large viral\n",
"4417 stp_151_0_0 TAATTTAT 0.027 large viral\n",
"4419 stp_151_0_2 CGCAGCTG 0.115 large viral\n",
"4420 stp_151_1_0 GCGGGAGA 0.361 large viral\n",
"4422 stp_151_1_2 CTTTTGCG 0.137 large viral\n",
"4423 stp_151_2_0 CTGGCAAA 0.068 large viral\n",
"4425 stp_151_2_2 ATTAATCT 0.056 large viral\n",
"4428 stp_151_4_0 CACCAGGC 0.123 large viral\n",
"4430 stp_151_4_2 CGGCATAG 0.030 large viral\n",
"4431 stp_151_5_0 TGTGGCTC 0.157 large viral\n",
"4433 stp_151_6_0 TGTCATTT 0.098 large viral\n",
"4435 stp_151_6_2 AAGACTTA 0.061 large viral\n",
"4438 stp_151_8_0 TGGCTTTC 0.107 large viral\n",
"4440 stp_151_8_2 GGCACCTG 0.113 large viral\n",
"4441 stp_151_9_0 ATAAATGC 0.059 large viral\n",
"4443 stp_151_9_2 GCACATCG 0.144 large viral\n",
"\n",
"[6730 rows x 5 columns]\n"
]
}
],
"source": [
"path = 'data/'\n",
"filename_DB = 'DeBruijn_alpha.json'\n",
"filename_pUC19 = 'pUC19_alpha.json'\n",
"filename_M13 = 'M13_square.json'\n",
"filename_DB7k = 'DB_7k_square.json'\n",
"\n",
"#filename_DB_small = 'DB_small.csv'\n",
"#filename_pUC19 =\n",
"#filename_M13 =\n",
"#filename_lambda =\n",
"\n",
"DB_small = pandas.read_csv(path + 'DB_small.csv')\n",
"DB_medium = pandas.read_csv(path + 'DB_medium.csv')\n",
"DB_large = pandas.read_csv(path + 'DB_large.csv')\n",
"pUC19_small = pandas.read_csv(path + 'pUC19_small.csv')\n",
"M13_medium = pandas.read_csv(path + 'M13_medium.csv')\n",
"lambda_large = pandas.read_csv(path + 'lambda_large.csv')\n",
"\n",
"DB_small['size'] = 'small'\n",
"DB_medium['size'] = 'medium'\n",
"DB_large['size'] = 'large'\n",
"pUC19_small['size'] = 'small'\n",
"M13_medium['size'] = 'medium'\n",
"lambda_large['size'] = 'large'\n",
"\n",
"DB_small['origin']= 'DBS'\n",
"DB_medium['origin']= 'DBS'\n",
"DB_large['origin']= 'DBS'\n",
"pUC19_small['origin']= 'viral'\n",
"M13_medium['origin']= 'viral'\n",
"lambda_large['origin']= 'viral'\n",
"\n",
"data = pandas.concat([DB_small, DB_medium, DB_large, pUC19_small, M13_medium, lambda_large])\n",
"\n",
"#filtering sequences\n",
"#data = data[data.sequence == 8]\n",
"data = data[data['sequence'].map(len) == 8]\n",
"\n",
"print data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Approximation data (based on Levenshtein distance)\n",
"path = '/home/j3ny/repo/thermodynamic addressability/Analysis/thermodynamic_addressability/data/'\n",
"\n",
"DB_medium = pandas.read_csv(path + 'DBS_medium_raw2.csv')\n",
"M13_medium = pandas.read_csv(path + 'M13_medium_raw2.csv')"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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EiH2GMUGWidsdlCCrIv+PgGKbXqXCNwvy8f75JnzdHbhFfvDgF9BqdVi6tEzm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qdMslgsuOuue/X94UuHOaq5xnaWlujbHR1tAiOhXOHzxVs2WhyFKa/lCLJxMUGmvLKvsiKhLjGC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\nfRFms2VZseUCP7oTEelcrhPkhCzj5OgY/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V1bMKvFd1O43zkRUQFyOJwIBPwAgOJiJsik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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x54eeb10>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(10, 10))\n",
"\n",
"sns.violinplot(x=\"size\", y=\"probability\", hue=\"origin\", data=data, split=True,\n",
" palette={\"DBS\": \"b\", \"viral\": \"r\"}, scale=\"width\", inner=\"quartiles\", bw=.1)\n",
"\n",
"sns.despine(left=True)\n",
"plt.tight_layout()\n",
"plt.ylim(0,1)\n",
"#plt.xlim(0,1)\n",
"plt.ylabel('Probability')\n",
"plt.xlabel('Size')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"path = 'data/'\n",
"filename_DB = 'DeBruijn_alpha.json'\n",
"filename_pUC19 = 'pUC19_alpha.json'\n",
"filename_M13 = 'M13_square.json'\n",
"filename_DB7k = 'DB_7k_square.json'\n",
"\n",
"DB_small = seaborn.ree\n",
"\n",
"#ids, sequences, energies\n",
"#_, _, energies_DB = read_data(path + filename_DB)\n",
"#_, _, energies_pUC19 = read_data(path + filename_pUC19)\n",
"#_, _, energies_M13 = read_data(path + filename_M13)\n",
"\n",
"_, _, energies_DB_short = read_data(path + filename_DB, short=True)\n",
"_, _, energies_pUC19_short = read_data(path + filename_pUC19, short=True)\n",
"_, _, energies_M13_short = read_data(path + filename_M13, short=True)\n",
"_, _, energies_DB7k_short = read_data(path + filename_DB7k, short=True)\n",
"\n",
"#DB_dist_2 = get_boltzmann_distribution(d[:2] for d in energies_DB_short)\n",
"#pUC19_dist_2 = get_boltzmann_distribution(d[:2] for d in energies_pUC19_short)\n",
"#M13_dist_2 = get_boltzmann_distribution(d[:2] for d in energies_M13_short)\n",
"\n",
"#DB_dist_10 = get_boltzmann_distribution(d[:10] for d in energies_DB_short)\n",
"#pUC19_dist_10 = get_boltzmann_distribution(d[:10] for d in energies_pUC19_short)\n",
"#M13_dist_10 = get_boltzmann_distribution(d[:10] for d in energies_M13_short)\n",
"\n",
"#DB_dist_100 = get_boltzmann_distribution(d[:100] for d in energies_DB_short)\n",
"#pUC19_dist_100 = get_boltzmann_distribution(d[:100] for d in energies_pUC19_short)\n",
"#M13_dist_100 = get_boltzmann_distribution(d[:100] for d in energies_M13_short)\n",
"\n",
"DB_dist_all = get_boltzmann_distribution(d for d in energies_DB_short)\n",
"pUC19_dist_all = get_boltzmann_distribution(d for d in energies_pUC19_short)\n",
"M13_dist_all = get_boltzmann_distribution(d for d in energies_M13_short)\n",
"DB7k_dist_all = get_boltzmann_distribution(d for d in energies_DB7k_short)\n",
"\n",
"#DB_dist = get_boltzmann_distribution(d[:100] for d in energies_DB_short)\n",
"#pUC19_dist = get_boltzmann_distribution(d[:100] for d in energies_pUC19_short)\n",
"#M13_dist = get_boltzmann_distribution(d[:100] for d in energies_M13_short)\n",
"\n",
"#DB_dist = get_boltzmann_distribution(energies_DB_short)\n",
"#pUC19_dist = get_boltzmann_distribution(energies_pUC19_short)\n",
"\n",
"#dist = [d[0] for d in DB_dist]"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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AADBouesgr0i6Y2Z/V1hIflzSkvR2At+KQu/xajcbCQAA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\ne4nFoqSdDmV2Jd2oVx0AAABguHIbyM8lTXUoMyP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"text/plain": [
"<matplotlib.figure.Figure at 0x7f813c5a5f90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.close('all')\n",
"\n",
"fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(nrows=2, ncols=2)\n",
"\n",
"data_set = OrderedDict()\n",
"data_set['pUC19 (all)'] = pUC19_dist_all\n",
"data_set['DB (all)'] = DB_dist_all\n",
"data_set['M13 (all)'] = M13_dist_all\n",
"data_set['DB7k (all)'] = DB7k_dist_all\n",
"\n",
"xlabel = r'Specific binding probability'\n",
"ylabel = r'Fraction of staples'\n",
"\n",
"distribution_plot(ax1, r'pUC19', pUC19_dist_all, '', ylabel)\n",
"distribution_plot(ax2, r'DB (2.4 knt)', DB_dist_all, '', '')\n",
"distribution_plot(ax3, r'M13', M13_dist_all, xlabel, ylabel)\n",
"distribution_plot(ax4, r'DBS (6.9 knt)', DB7k_dist_all, xlabel, '')\n",
"\n",
"#%matplotlib inline\n",
"\n",
"fig.set_size_inches(10, 10)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig(\"/home/j3ny/repos/analysis/Analysis/thermodynamic_addressability/output/addressability_comparison.pdf\",format='pdf',dpi=600)\n",
"#plt.savefig(\"/home/j3ny/repos/analysis/Analysis/thermodynamic_addressability/output/addressability_comparison_long.pdf\",format='pdf',dpi=600)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Code to convert the data from energies to probabilities"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## CONVERT DATA\n",
"\n",
"path = 'data/'\n",
"filename_DB = 'DeBruijn_alpha.json'\n",
"filename_pUC19 = 'pUC19_alpha.json'\n",
"filename_M13 = 'M13_square.json'\n",
"filename_DB7k = 'DB_7k_square.json'\n",
"\n",
"ids, sequences, energies = read_data(path + filename_DB7k, short=True)\n",
"dist_all = get_boltzmann_distribution(d for d in energies)\n",
"\n",
"with open('data/DB_medium.csv', 'w') as out:\n",
" for i in range(len(ids)):\n",
" out.write(ids[i] + ',' + sequences[i] + ',')\n",
" out.write('%.3f' % dist_all[i][0])\n",
" out.write('\\n')\n",
" #print idsi], sequences[i], energies_DB_short[i], DB_dist_all[i]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## --------------------------------------\n",
"## OBSOLETE CODE BELOW\n",
"## --------------------------------------"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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lNbOSz5jZnZjWCdxBQ8lFRKSOVjMreT9hYATAdXfXVZOIiNRV0XtOxMZosUGK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\nAH6oAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f81375ac1d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(nrows=2, ncols=2)\n",
"\n",
"bar_width = 1.0\n",
"\n",
"data_set = OrderedDict()\n",
"data_set['pUC19'] = pUC19_dist_all\n",
"data_set['DBS (2.4 knt)'] = DB_dist_all\n",
"data_set['M13'] = M13_dist_all\n",
"data_set['DBS (6.9 knt)'] = DB7k_dist_all\n",
"\n",
"plt.close('all')\n",
"fig = plt.figure()\n",
"\n",
"from mpl_toolkits.axes_grid1 import Grid\n",
"grid = Grid(fig, rect=111, nrows_ncols=(2,2),\n",
" axes_pad=0.4, label_mode='O',\n",
" add_all = True,\n",
" )\n",
"\n",
"for ax, (data_label, data) in zip(grid, data_set.items()):\n",
" xlabel = 'Specific binding probability' if ('6.9' in data_label or 'M13' in data_label) else ''\n",
" ylabel = 'Fraction of staples'if ('pUC' in data_label or 'M13' in data_label) else ''\n",
" distribution_plot(ax, data_label, data, xlabel, ylabel)\n",
"\n",
"#axes[0,0].set_title('pUC19')\n",
" \n",
"#grid[0].set_title('pUC19')\n",
"#grid[0].set_ylabel('Fraction of staples', fontsize=15)\n",
"#grid[1].set_title('DBS (2.4 knt)')\n",
"\n",
"#grid[2].set_title('M13')\n",
"#grid[2].set_xlabel('Specific binding probability', fontsize=15)\n",
"#grid[2].set_ylabel('Fraction of staples', fontsize=15)\n",
"\n",
"#grid[3].set_title('DBS (6.9 knt)')\n",
"\n",
"#axes[1].set_title('M13')\n",
"#axes[2].set_title(r'$\\lambda$-phage')\n",
"\n",
"#fig.text(0.16, 0.92, 'pUC19', fontsize=15)\n",
"#fig.text(0.6, 0.92, 'DBS (2.4 knt)', fontsize=15)\n",
"\n",
"#fig.text(0.16, 0.46, 'M13mp18', fontsize=15)\n",
"#fig.text(0.6, 0.46, 'DBS (6.9 knt)', fontsize=15)\n",
"\n",
"fig.set_size_inches(6, 6)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig(\"/home/j3ny/repos/analysis/Analysis/thermodynamic_addressability/output/addressability_comparison.pdf\",format='pdf',dpi=600)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "TypeError",
"evalue": "distribution_plot() takes at least 5 arguments (1 given)",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-44-a465f27404ef>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 17\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0max0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdata_label\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_set\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m \u001b[0mdistribution_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 19\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;31m#fig.text(0.19, 0.96, 'De Bruijn', ha='center')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mTypeError\u001b[0m: distribution_plot() takes at least 5 arguments (1 given)"
]
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f81374819d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#%matplotlib inline\n",
"\n",
"fig, axes = plt.subplots(nrows=2, ncols=2)\n",
"\n",
"bar_width = 1.0\n",
"\n",
"data_set = OrderedDict()\n",
"data_set['pUC19 (all)'] = pUC19_dist_all\n",
"data_set['DB (all)'] = DB_dist_all\n",
"data_set['M13 (all)'] = M13_dist_all\n",
"data_set['DB7k (all)'] = DB7k_dist_all\n",
"\n",
"for ax0, (data_label, data) in zip(axes.flat, data_set.items()):\n",
" distribution_plot(ax0)\n",
"\n",
"#fig.text(0.19, 0.96, 'De Bruijn', ha='center')\n",
"fig.text(0.3, 1, 'pUC19 (2.6 knt)', ha='center')\n",
"fig.text(0.7, 1, 'DBS (2.4 knt)', ha='center')\n",
"\n",
"fig.text(0.5, 0.008, 'Specific binding probability', ha='center')\n",
"fig.text(0.001, 0.5, 'Fraction of staples', va='center', rotation='vertical')\n",
"\n",
"fig.set_size_inches(7, 7)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig(\"/home/j3ny/repos/analysis/Analysis/thermodynamic_addressability/output/addressability_comparison.pdf\",format='pdf',dpi=600)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.5+"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
DrClick/ARCRacing | camera_calibration/Convert Calibration to protocol 2.ipynb | 1 | 1130 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pickle"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"with open(\"fisheye_f1p8_camera_calibration.pkl\", 'rb') as f:\n",
" calibration = pickle.load(f)\n",
"with open(\"fisheye_f1p8_camera_calibration_protocol_2.pkl\", 'wb') as f:\n",
" pickle.dump(calibration, f, protocol=2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
AlvaroGVentas/EFP | Services - Close, Mode.ipynb | 1 | 1833 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import sys\n",
"import rospy\n",
"from std_msgs.msg import Float32\n",
"from std_msgs.msg import String\n",
"from std_srvs.srv import Trigger, SetBool\n",
"import std_srvs.srv"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"trigger_start_stop = rospy.ServiceProxy('start_stop', SetBool)\n",
"trigger_mode = rospy.ServiceProxy('change_mode', SetBool)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"True\n",
"Communication established\n"
]
}
],
"source": [
"trigger_start_stop = rospy.ServiceProxy('start_stop', SetBool)\n",
"trigger_mode = rospy.ServiceProxy('change_mode', SetBool)\n",
"response_start=trigger_start_stop(1)\n",
"print(response_start.success)\n",
"print(response_start.message)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
sot/image_reconstruction | sim_asol_centroids.ipynb | 1 | 30978 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Estimate aspect solution uncertainty from star centroid residuals\n",
"\n",
"When the ground aspect pipeline gets run there is no accurate estimate of the\n",
"aspect solution uncertainty. One proxy that has been used informally is to\n",
"use the aspect solution to de-dither star centroids and thus determine star\n",
"centroid residuals (from the mean aspect solution). The star centroid residuals\n",
"have a component from both the true residuals and the aspect solution uncertainty.\n",
"Thus one can state that the minimum star centroid standard deviation (out of the\n",
"5 to 8 guide stars) represents an upper limit on aspect solution error.\n",
"However, this is somewhat circular because the aspect solution is influenced by\n",
"the star centroids.\n",
"\n",
"In order to understand the impact of this circularity, this notebook does\n",
"a simple simulation:\n",
"\n",
"- Simulate an observation with 5 stars each having a centroid residual sigma of between 0.05 and 0.20 arcsec. \n",
"- Use a \"snapshot\" method of determining the \"aspect solution\", which in this case simply means the weighted mean of the observed centroids. \n",
"- This is done for 1-axis only. \n",
"- The actual mean values of the star centroids do not matter and only the standard deviation matters.\n",
"- Plot the minimum star standard deviation vs. the aspect solution RMS\n",
"\n",
"Under these assumptions it is seen that the **aspect solution RMS is always less than the minimum star standard deviation**.\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"N_READOUT = 1000"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"def sim_obs():\n",
" \"\"\"\n",
" Simulate an observation with 5 stars each having a centroid residual sigma\n",
" of between 0.05 and 0.20 arcsec. Use a \"snapshot\" method of determining the\n",
" \"aspect solution\", which in this case simply means the weighted mean of the\n",
" observed centroids. This is done for 1-axis only. The actual mean values\n",
" of the star centroids does not matter and is just set to 0.0.\n",
" \n",
" :returns: RMS of aspect solution, stddev of centroid residuals\n",
" \"\"\"\n",
" # Star centroid residual sigmas\n",
" n_stars = 5\n",
" sigmas = np.random.uniform(0.05, 0.20, size=n_stars)\n",
" \n",
" # Simulated star centroids (all with mean value 0.0)\n",
" y = np.random.normal(size=(N_READOUT, n_stars)) * sigmas\n",
" \n",
" # Aspect solution as weighted average of star centroids\n",
" y_asol = np.average(y, weights=1/sigmas, axis=1)\n",
" \n",
" # Measured residuals from \"aspect solution\" and std dev\n",
" y_resid = y.T - y_asol\n",
" y_resid_std = np.std(y_resid, axis=1)\n",
" \n",
" return np.sqrt(np.mean(y_asol ** 2)), np.min(y_resid_std)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"n_sim = 1000\n",
"yc_std = np.zeros(n_sim)\n",
"yr_std_min = np.zeros(n_sim)\n",
"\n",
"for i in range(n_sim):\n",
" yc_std[i], yr_std_min[i] = sim_obs()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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w46G0bdA6ZvYU8A18ud8jwCIzu6aDbR3xhM0jp4N9MYE8FEhRRVO2HxQd8MaR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\noGVCeARBEAQt8/8Bxs4/8PGUEGYAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f64273f4590>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(yc_std, yr_std_min, '.')\n",
"plt.plot([0.0, 0.14], [0.0, 0.14], '--r')\n",
"plt.grid()\n",
"plt.xlabel('Aspect solution RMS error (arcsec)')\n",
"plt.ylabel('Min star centroid std dev (arcsec)')\n",
"plt.xlim(0, 0.1);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Figure 1** - plot showing the minimum star centroid standard deviation (out of 5 stars) versus simulated aspect solution RMS error in one axis. This highlights that the minimum star centroid stddev seen in the set of 5 stars is always greater than the true aspect solution error. This assumes gaussian statistics and an instantaneous \"snapshot\" method of deriving the aspect solution (weighted mean). In reality the Kalman filter uses gyro data and optimal filtering, so the actual error for Chandra aspect solutions should be even smaller.\n",
"\n",
"Therefore the **minimum star centroid standard deviation** for an observation is a reasonable upper limit on the image reconstruction RMS error."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.9"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
jaybo/Python-Notebooks | OpenCV/Basic capture.ipynb | 1 | 3038 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ImportError",
"evalue": "DLL load failed: The specified procedure could not be found.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-b7ce820de564>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mcv2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mclicked\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0monMouse\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mevent\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparam\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mglobal\u001b[0m \u001b[0mclicked\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mImportError\u001b[0m: DLL load failed: The specified procedure could not be found."
]
}
],
"source": [
"import cv2 \n",
"\n",
"clicked = False \n",
"def onMouse( event, x, y, flags, param): \n",
" global clicked \n",
" if event == cv2.EVENT_LBUTTONUP: \n",
" clicked = True \n",
"\n",
"cameraCapture = cv2.VideoCapture(0) \n",
"cv2.namedWindow('MyWindow') \n",
"cv2.setMouseCallback('MyWindow', onMouse) \n",
"print 'Showing camera feed. Click window or press any key to stop.' \n",
"success, frame = cameraCapture.read() \n",
"print success\n",
"while success and cv2. waitKey( 1) == -1 and not clicked: \n",
" cv2. imshow('MyWindow', frame) \n",
" success, frame = cameraCapture.read()\n",
"\n",
"cameraCapture.release()\n",
"cv2. destroyWindow('MyWindow')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
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}
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"file_extension": ".py",
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
queirozfcom/python-sandbox | python3/notebooks/IMDB/IMDB-characterizing.ipynb | 2 | 54038 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import scipy.sparse\n",
"import pandas as pd\n",
"from pandas import DataFrame,Series\n",
"from scipy.sparse import lil_matrix,vstack\n",
"from skmultilearn.dataset import Dataset"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"path_to_imdb_dataset = \"/home/felipe/auto-tagger/data/imdb/IMDB-F.arff\""
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"(<120919x1001 sparse matrix of type '<type 'numpy.float64'>'\n",
" \twith 2343710 stored elements in LInked List format>,\n",
" <120919x28 sparse matrix of type '<type 'numpy.int64'>'\n",
" \twith 241798 stored elements in LInked List format>)"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X,Y = Dataset.load_arff_to_numpy(path_to_imdb_dataset,labelcount=28,load_sparse=True)\n",
"X,Y"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"4503"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = DataFrame.from_records(Y.toarray())\n",
"num_unique_labelsets = len(df.drop_duplicates())\n",
"num_unique_labelsets"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120889</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120890</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120891</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120891</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120892</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120892</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120893</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120894</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120894</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120895</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120895</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120896</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120896</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120897</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120897</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120898</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120898</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120899</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120899</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120900</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120901</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120901</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120902</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120902</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120903</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120903</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120904</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120905</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>120905</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120906</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>120919 rows × 29 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5 6 7 8 9 ... 19 20 21 22 23 24 25 26 \\\n",
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"29 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 \n",
"... .. .. .. .. .. .. .. .. .. .. ... .. .. .. .. .. .. .. .. \n",
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"\n",
" 27 id \n",
"0 0 0 \n",
"1 0 1 \n",
"2 0 2 \n",
"3 0 3 \n",
"4 0 4 \n",
"5 0 5 \n",
"6 0 6 \n",
"7 0 7 \n",
"8 0 8 \n",
"9 0 9 \n",
"10 0 10 \n",
"11 0 11 \n",
"12 0 12 \n",
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"14 0 14 \n",
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"17 0 17 \n",
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"24 0 24 \n",
"25 0 25 \n",
"26 0 26 \n",
"27 0 27 \n",
"28 0 28 \n",
"29 0 29 \n",
"... .. ... \n",
"120889 0 120889 \n",
"120890 0 120890 \n",
"120891 0 120891 \n",
"120892 0 120892 \n",
"120893 0 120893 \n",
"120894 0 120894 \n",
"120895 0 120895 \n",
"120896 0 120896 \n",
"120897 0 120897 \n",
"120898 0 120898 \n",
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"120901 0 120901 \n",
"120902 0 120902 \n",
"120903 0 120903 \n",
"120904 0 120904 \n",
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"120909 0 120909 \n",
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"120916 0 120916 \n",
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"\n",
"[120919 rows x 29 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"indices = Series(np.array(range(0,120919)), index = df.index)\n",
"df['id'] = indices\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"grouped = df.groupby(list(range(0,28))).count()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2263"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"num_labelsets_appearing_only_once = len(grouped[grouped['id']==1])\n",
"num_labelsets_appearing_only_once"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
maxis42/ML-DA-Coursera-Yandex-MIPT | 2 Supervised learning/Lectures notebooks/1 sklearn datasets/sklearn.datasets.ipynb | 1 | 10023 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sklearn"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## sklearn.datasets"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"документация: http://scikit-learn.org/stable/datasets/"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn import datasets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%pylab inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Генерация выборок"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Способы генерации данных:** \n",
"* make_classification\n",
"* make_regression\n",
"* make_circles\n",
"* make_checkerboard\n",
"* etc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### datasets.make_circles"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"circles = datasets.make_circles()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print \"features: {}\".format(circles[0][:10])\n",
"print \"target: {}\".format(circles[1][:10])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from matplotlib.colors import ListedColormap"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"colors = ListedColormap(['red', 'yellow'])\n",
"\n",
"pyplot.figure(figsize(8, 8))\n",
"pyplot.scatter(map(lambda x: x[0], circles[0]), map(lambda x: x[1], circles[0]), c = circles[1], cmap = colors)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def plot_2d_dataset(data, colors):\n",
" pyplot.figure(figsize(8, 8))\n",
" pyplot.scatter(map(lambda x: x[0], data[0]), map(lambda x: x[1], data[0]), c = data[1], cmap = colors)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"noisy_circles = datasets.make_circles(noise = 0.15)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_2d_dataset(noisy_circles, colors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### datasets.make_classification"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"simple_classification_problem = datasets.make_classification(n_features = 2, n_informative = 1, \n",
" n_redundant = 1, n_clusters_per_class = 1,\n",
" random_state = 1 )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_2d_dataset(simple_classification_problem, colors)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"classification_problem = datasets.make_classification(n_features = 2, n_informative = 2, n_classes = 4, \n",
" n_redundant = 0, n_clusters_per_class = 1, random_state = 1)\n",
"\n",
"colors = ListedColormap(['red', 'blue', 'green', 'yellow'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plot_2d_dataset(classification_problem, colors)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### \"Игрушечные\" наборы данных"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Наборы данных:** \n",
"* load_iris \n",
"* load_boston\n",
"* load_diabetes\n",
"* load_digits\n",
"* load_linnerud\n",
"* etc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### datasets.load_iris"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iris = datasets.load_iris()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iris"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iris.keys()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"print iris.DESCR"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print \"feature names: {}\".format(iris.feature_names)\n",
"print \"target names: {names}\".format(names = iris.target_names)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iris.data[:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iris.target"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Визуализация выбокри"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pandas import DataFrame"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iris_frame = DataFrame(iris.data)\n",
"iris_frame.columns = iris.feature_names\n",
"iris_frame['target'] = iris.target"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iris_frame.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iris_frame.target = iris_frame.target.apply(lambda x : iris.target_names[x])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iris_frame.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"iris_frame[iris_frame.target == 'setosa'].hist('sepal length (cm)')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"pyplot.figure(figsize(20, 24))\n",
"\n",
"plot_number = 0\n",
"for feature_name in iris['feature_names']:\n",
" for target_name in iris['target_names']:\n",
" plot_number += 1\n",
" pyplot.subplot(4, 3, plot_number)\n",
" pyplot.hist(iris_frame[iris_frame.target == target_name][feature_name])\n",
" pyplot.title(target_name)\n",
" pyplot.xlabel('cm')\n",
" pyplot.ylabel(feature_name[:-4])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Бонус: библиотека seaborn"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.pairplot(iris_frame, hue = 'target')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"?sns.set()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"sns.set(font_scale = 1.3)\n",
"data = sns.load_dataset(\"iris\")\n",
"sns.pairplot(data, hue = \"species\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### **Если Вас заинтересовала библиотека seaborn:**\n",
"* установка: https://stanford.edu/~mwaskom/software/seaborn/installing.html\n",
"* установка c помощью анаконды: https://anaconda.org/anaconda/seaborn\n",
"* руководство: https://stanford.edu/~mwaskom/software/seaborn/tutorial.html\n",
"* примеры: https://stanford.edu/~mwaskom/software/seaborn/examples/"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
ndanielsen/liberia-media | Untitled0.ipynb | 1 | 1452 | {
"metadata": {
"name": "",
"signature": "sha256:80c85493f06815b8a767c6bb8905a39f45e7e1547e4f150f0b1ffa777a94bc18"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import arrow"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"thing = '11/18/2013 - 14:31'\n",
"'Mon, 11/18/2013 - 14:38'\n",
"'DDD, MM/DD/YYYY - HH:mm'\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"'DDD, MM/DD/YYYY - HH:mm'"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arrow.get(thing, 'MM/DD/YYYY - HH:mm')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"<Arrow [2013-11-18T14:31:00+00:00]>"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | mit |
zonemercy/Kaggle | quora/solution/FEATURE_GENERATE.ipynb | 2 | 548311 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['GoogleNews-vectors-negative300.bin.gz', 'sample_submission.csv', 'sample_submission.csv .zip', 'test.csv', 'test.csv.zip', 'test_interaction.pkl', 'test_jaccard.pkl', 'test_len.pkl', 'test_porter.csv', 'test_porter_interaction.pkl', 'test_porter_jaccard.pkl', 'test_question1_porter_tfidf.pkl', 'test_question1_tfidf.pkl', 'test_question2_porter_tfidf.pkl', 'test_question2_tfidf.pkl', 'train.csv', 'train.csv.zip', 'train_interaction.pkl', 'train_jaccard.pkl', 'train_len.pkl', 'train_porter.csv', 'train_porter_interaction.pkl', 'train_porter_jaccard.pkl', 'train_question1_porter_tfidf.pkl', 'train_question1_tfidf.pkl', 'train_question2_porter_tfidf.pkl', 'train_question2_tfidf.pkl', 'X_t_tfidf.svm', 'X_test_tfidf.svm', 'X_tfidf.svm', 'X_train_tfidf.svm']\n"
]
}
],
"source": [
"from __future__ import division\n",
"import time, os, gc\n",
"import numpy as np\n",
"import pandas as pd\n",
"import scipy\n",
"from sklearn.preprocessing import LabelEncoder, OneHotEncoder, StandardScaler\n",
"from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
"from sklearn.model_selection import StratifiedKFold\n",
"from sklearn.feature_selection import SelectPercentile, f_classif\n",
"from nltk.stem.porter import PorterStemmer\n",
"from nltk.stem.snowball import SnowballStemmer\n",
"from sklearn.preprocessing import OneHotEncoder,LabelEncoder,StandardScaler\n",
"from sklearn.decomposition import TruncatedSVD,PCA\n",
"from sklearn.feature_extraction import text\n",
"from sklearn.metrics import log_loss\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"%matplotlib inline\n",
"sns.set_style('whitegrid')\n",
"\n",
"from IPython.core.interactiveshell import InteractiveShell\n",
"InteractiveShell.ast_node_interactivity = \"all\"\n",
"\n",
"PATH = os.path.expanduser(\"~\") + \"/data/quora/\"\n",
"print os.listdir(PATH)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
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"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m/usr/local/lib/python2.7/site-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m 2235\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mboxer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2237\u001b[0;31m \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2238\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2239\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframe\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/src/inference.pyx\u001b[0m in \u001b[0;36mpandas.lib.map_infer (pandas/lib.c:63043)\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m<ipython-input-2-f33d5a92a184>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0msnowball\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSnowballStemmer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'english'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mtrain_orig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'question1'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_orig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'question1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstem_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msnowball\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0mtrain_orig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'question1'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_orig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'question1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstem_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msnowball\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0mtrain_orig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'question2'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_orig\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'question2'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mstem_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msnowball\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m<ipython-input-2-f33d5a92a184>\u001b[0m in \u001b[0;36mstem_str\u001b[0;34m(x, stemmer)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mstem_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mstemmer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mSnowballStemmer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'english'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mre\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"[^a-zA-Z0-9]\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstemmer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mz\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\" \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/site-packages/nltk/stem/snowball.pyc\u001b[0m in \u001b[0;36mstem\u001b[0;34m(self, word)\u001b[0m\n\u001b[1;32m 721\u001b[0m \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 722\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 723\u001b[0;31m \u001b[0mr1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_r1r2_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__vowels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 724\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 725\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/site-packages/nltk/stem/snowball.pyc\u001b[0m in \u001b[0;36m_r1r2_standard\u001b[0;34m(self, word, vowels)\u001b[0m\n\u001b[1;32m 230\u001b[0m \u001b[0mr1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0mr2\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 232\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 233\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvowels\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mvowels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 234\u001b[0m \u001b[0mr1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mword\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"train_orig = pd.read_csv(PATH+'train.csv', header=0)\n",
"test_orig = pd.read_csv(PATH+'test.csv', header=0)\n",
"\n",
"def stem_str(x,stemmer=SnowballStemmer('english')):\n",
" x = text.re.sub(\"[^a-zA-Z0-9]\",\" \", x)\n",
" x = (\" \").join([stemmer.stem(z) for z in x.split(\" \")])\n",
" x = \" \".join(x.split())\n",
" return x\n",
"porter = PorterStemmer()\n",
"snowball = SnowballStemmer('english')\n",
"\n",
"train_orig['question1'] = train_orig['question1'].astype(str).apply(lambda x:stem_str(x.lower(),snowball))\n",
"train_orig['question1'] = train_orig['question1'].astype(str).apply(lambda x:stem_str(x.lower(),snowball))\n",
"train_orig['question2'] = train_orig['question2'].astype(str).apply(lambda x:stem_str(x.lower(),snowball))\n",
"train_orig['question2'] = train_orig['question2'].astype(str).apply(lambda x:stem_str(x.lower(),snowball))\n",
"\n",
"df1 = train_orig[['question1']].copy()\n",
"df2 = train_orig[['question2']].copy()\n",
"df1_test = test_orig[['question1']].copy()\n",
"df2_test = test_orig[['question2']].copy()\n",
"\n",
"df2.rename(columns = {'question2':'question1'},inplace=True)\n",
"df2_test.rename(columns = {'question2':'question1'},inplace=True)\n",
"\n",
"train_questions = df1.append(df2)\n",
"train_questions = train_questions.append(df1_test)\n",
"# train_questions = df1.append(df1_test)\n",
"# train_questions = train_questions.append(df2)\n",
"train_questions = train_questions.append(df2_test)\n",
"#train_questions.drop_duplicates(subset = ['qid1'],inplace=True)\n",
"train_questions.drop_duplicates(subset = ['question1'],inplace=True)\n",
"\n",
"train_questions.reset_index(inplace=True,drop=True)\n",
"questions_dict = pd.Series(train_questions.index.values,index=train_questions.question1.values).to_dict()\n",
"train_cp = train_orig.copy()\n",
"test_cp = test_orig.copy()\n",
"train_cp.drop(['qid1','qid2'],axis=1,inplace=True)\n",
"\n",
"test_cp['is_duplicate'] = -1\n",
"test_cp.rename(columns={'test_id':'id'},inplace=True)\n",
"comb = pd.concat([train_cp,test_cp])\n",
"\n",
"comb['q1_hash'] = comb['question1'].map(questions_dict)\n",
"comb['q2_hash'] = comb['question2'].map(questions_dict)\n",
"\n",
"q1_vc = comb.q1_hash.value_counts().to_dict()\n",
"q2_vc = comb.q2_hash.value_counts().to_dict()\n",
"\n",
"def try_apply_dict(x,dict_to_apply):\n",
" try:\n",
" return dict_to_apply[x]\n",
" except KeyError:\n",
" return 0\n",
"#map to frequency space\n",
"comb['q1_freq'] = comb['q1_hash'].map(lambda x: try_apply_dict(x,q1_vc) + try_apply_dict(x,q2_vc))\n",
"comb['q2_freq'] = comb['q2_hash'].map(lambda x: try_apply_dict(x,q1_vc) + try_apply_dict(x,q2_vc))\n",
"comb['q1_hash_freq'] = comb['q1_hash'].map(lambda x: try_apply_dict(x,q1_vc))\n",
"comb['q2_hash_freq'] = comb['q2_hash'].map(lambda x: try_apply_dict(x,q2_vc))\n",
"\n",
"comb['freq_diff'] = (abs(comb['q1_freq'] - comb['q2_freq'])+0.1) / \\\n",
" (comb['q1_freq'] * comb['q2_freq'])\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>q1_hash</th>\n",
" <th>q2_hash</th>\n",
" <th>q1_freq</th>\n",
" <th>q2_freq</th>\n",
" <th>is_duplicate</th>\n",
" <th>freq_diff</th>\n",
" <th>q1_hash_freq</th>\n",
" <th>q2_hash_freq</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>id</th>\n",
" <td>1.000000</td>\n",
" <td>0.690308</td>\n",
" <td>0.041179</td>\n",
" <td>-0.002600</td>\n",
" <td>-0.000871</td>\n",
" <td>-0.008784</td>\n",
" <td>0.001727</td>\n",
" <td>-0.002885</td>\n",
" <td>-0.001022</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_hash</th>\n",
" <td>0.690308</td>\n",
" <td>1.000000</td>\n",
" <td>0.282445</td>\n",
" <td>-0.359849</td>\n",
" <td>-0.243217</td>\n",
" <td>-0.207682</td>\n",
" <td>0.035621</td>\n",
" <td>-0.386223</td>\n",
" <td>-0.207151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_hash</th>\n",
" <td>0.041179</td>\n",
" <td>0.282445</td>\n",
" <td>1.000000</td>\n",
" <td>-0.397311</td>\n",
" <td>-0.471671</td>\n",
" <td>-0.346925</td>\n",
" <td>0.069128</td>\n",
" <td>-0.406026</td>\n",
" <td>-0.365688</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_freq</th>\n",
" <td>-0.002600</td>\n",
" <td>-0.359849</td>\n",
" <td>-0.397311</td>\n",
" <td>1.000000</td>\n",
" <td>0.599397</td>\n",
" <td>0.343747</td>\n",
" <td>-0.166016</td>\n",
" <td>0.898113</td>\n",
" <td>0.528905</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_freq</th>\n",
" <td>-0.000871</td>\n",
" <td>-0.243217</td>\n",
" <td>-0.471671</td>\n",
" <td>0.599397</td>\n",
" <td>1.000000</td>\n",
" <td>0.265540</td>\n",
" <td>-0.099017</td>\n",
" <td>0.583196</td>\n",
" <td>0.948601</td>\n",
" </tr>\n",
" <tr>\n",
" <th>is_duplicate</th>\n",
" <td>-0.008784</td>\n",
" <td>-0.207682</td>\n",
" <td>-0.346925</td>\n",
" <td>0.343747</td>\n",
" <td>0.265540</td>\n",
" <td>1.000000</td>\n",
" <td>-0.332427</td>\n",
" <td>0.334511</td>\n",
" <td>0.228869</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq_diff</th>\n",
" <td>0.001727</td>\n",
" <td>0.035621</td>\n",
" <td>0.069128</td>\n",
" <td>-0.166016</td>\n",
" <td>-0.099017</td>\n",
" <td>-0.332427</td>\n",
" <td>1.000000</td>\n",
" <td>-0.158787</td>\n",
" <td>-0.078082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_hash_freq</th>\n",
" <td>-0.002885</td>\n",
" <td>-0.386223</td>\n",
" <td>-0.406026</td>\n",
" <td>0.898113</td>\n",
" <td>0.583196</td>\n",
" <td>0.334511</td>\n",
" <td>-0.158787</td>\n",
" <td>1.000000</td>\n",
" <td>0.476849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_hash_freq</th>\n",
" <td>-0.001022</td>\n",
" <td>-0.207151</td>\n",
" <td>-0.365688</td>\n",
" <td>0.528905</td>\n",
" <td>0.948601</td>\n",
" <td>0.228869</td>\n",
" <td>-0.078082</td>\n",
" <td>0.476849</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id q1_hash q2_hash q1_freq q2_freq is_duplicate \\\n",
"id 1.000000 0.690308 0.041179 -0.002600 -0.000871 -0.008784 \n",
"q1_hash 0.690308 1.000000 0.282445 -0.359849 -0.243217 -0.207682 \n",
"q2_hash 0.041179 0.282445 1.000000 -0.397311 -0.471671 -0.346925 \n",
"q1_freq -0.002600 -0.359849 -0.397311 1.000000 0.599397 0.343747 \n",
"q2_freq -0.000871 -0.243217 -0.471671 0.599397 1.000000 0.265540 \n",
"is_duplicate -0.008784 -0.207682 -0.346925 0.343747 0.265540 1.000000 \n",
"freq_diff 0.001727 0.035621 0.069128 -0.166016 -0.099017 -0.332427 \n",
"q1_hash_freq -0.002885 -0.386223 -0.406026 0.898113 0.583196 0.334511 \n",
"q2_hash_freq -0.001022 -0.207151 -0.365688 0.528905 0.948601 0.228869 \n",
"\n",
" freq_diff q1_hash_freq q2_hash_freq \n",
"id 0.001727 -0.002885 -0.001022 \n",
"q1_hash 0.035621 -0.386223 -0.207151 \n",
"q2_hash 0.069128 -0.406026 -0.365688 \n",
"q1_freq -0.166016 0.898113 0.528905 \n",
"q2_freq -0.099017 0.583196 0.948601 \n",
"is_duplicate -0.332427 0.334511 0.228869 \n",
"freq_diff 1.000000 -0.158787 -0.078082 \n",
"q1_hash_freq -0.158787 1.000000 0.476849 \n",
"q2_hash_freq -0.078082 0.476849 1.000000 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corr_list = ['id','q1_hash','q2_hash','q1_freq','q2_freq','is_duplicate','freq_diff','q1_hash_freq','q2_hash_freq']\n",
"train_comb = comb[comb['is_duplicate'] >= 0][corr_list]\n",
"test_comb = comb[comb['is_duplicate'] < 0][corr_list]\n",
"train_comb.corr()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>q1_hash</th>\n",
" <th>q2_hash</th>\n",
" <th>q1_freq</th>\n",
" <th>q2_freq</th>\n",
" <th>is_duplicate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>id</th>\n",
" <td>1.000000</td>\n",
" <td>0.692730</td>\n",
" <td>0.286969</td>\n",
" <td>-0.001608</td>\n",
" <td>-0.000777</td>\n",
" <td>-0.008784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_hash</th>\n",
" <td>0.692730</td>\n",
" <td>1.000000</td>\n",
" <td>0.492993</td>\n",
" <td>-0.341777</td>\n",
" <td>-0.202545</td>\n",
" <td>-0.206498</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_hash</th>\n",
" <td>0.286969</td>\n",
" <td>0.492993</td>\n",
" <td>1.000000</td>\n",
" <td>-0.392605</td>\n",
" <td>-0.466434</td>\n",
" <td>-0.349626</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_freq</th>\n",
" <td>-0.001608</td>\n",
" <td>-0.341777</td>\n",
" <td>-0.392605</td>\n",
" <td>1.000000</td>\n",
" <td>0.494315</td>\n",
" <td>0.296621</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_freq</th>\n",
" <td>-0.000777</td>\n",
" <td>-0.202545</td>\n",
" <td>-0.466434</td>\n",
" <td>0.494315</td>\n",
" <td>1.000000</td>\n",
" <td>0.198609</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id q1_hash q2_hash q1_freq q2_freq is_duplicate\n",
"id 1.000000 0.692730 0.286969 -0.001608 -0.000777 -0.008784\n",
"q1_hash 0.692730 1.000000 0.492993 -0.341777 -0.202545 -0.206498\n",
"q2_hash 0.286969 0.492993 1.000000 -0.392605 -0.466434 -0.349626\n",
"q1_freq -0.001608 -0.341777 -0.392605 1.000000 0.494315 0.296621\n",
"q2_freq -0.000777 -0.202545 -0.466434 0.494315 1.000000 0.198609"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"corr_mat = train_comb.corr()\n",
"corr_mat.head()\n",
"#more frequenct questions are more likely to be duplicates"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>q1_hash</th>\n",
" <th>q2_hash</th>\n",
" <th>q1_freq</th>\n",
" <th>q2_freq</th>\n",
" <th>is_duplicate</th>\n",
" <th>freq_diff</th>\n",
" <th>q1_hash_freq</th>\n",
" <th>q2_hash_freq</th>\n",
" <th>q_hash_pos</th>\n",
" <th>q_hash_pos_1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>id</th>\n",
" <td>1.000000</td>\n",
" <td>0.690308</td>\n",
" <td>0.041179</td>\n",
" <td>-0.002600</td>\n",
" <td>-0.000871</td>\n",
" <td>-0.008784</td>\n",
" <td>0.001727</td>\n",
" <td>-0.002885</td>\n",
" <td>-0.001022</td>\n",
" <td>0.115121</td>\n",
" <td>0.071093</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_hash</th>\n",
" <td>0.690308</td>\n",
" <td>1.000000</td>\n",
" <td>0.282445</td>\n",
" <td>-0.359849</td>\n",
" <td>-0.243217</td>\n",
" <td>-0.207682</td>\n",
" <td>0.035621</td>\n",
" <td>-0.386223</td>\n",
" <td>-0.207151</td>\n",
" <td>0.060618</td>\n",
" <td>-0.046562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_hash</th>\n",
" <td>0.041179</td>\n",
" <td>0.282445</td>\n",
" <td>1.000000</td>\n",
" <td>-0.397311</td>\n",
" <td>-0.471671</td>\n",
" <td>-0.346925</td>\n",
" <td>0.069128</td>\n",
" <td>-0.406026</td>\n",
" <td>-0.365688</td>\n",
" <td>-0.583149</td>\n",
" <td>-0.472782</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_freq</th>\n",
" <td>-0.002600</td>\n",
" <td>-0.359849</td>\n",
" <td>-0.397311</td>\n",
" <td>1.000000</td>\n",
" <td>0.599397</td>\n",
" <td>0.343747</td>\n",
" <td>-0.166016</td>\n",
" <td>0.898113</td>\n",
" <td>0.528905</td>\n",
" <td>0.128596</td>\n",
" <td>0.210646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_freq</th>\n",
" <td>-0.000871</td>\n",
" <td>-0.243217</td>\n",
" <td>-0.471671</td>\n",
" <td>0.599397</td>\n",
" <td>1.000000</td>\n",
" <td>0.265540</td>\n",
" <td>-0.099017</td>\n",
" <td>0.583196</td>\n",
" <td>0.948601</td>\n",
" <td>0.292966</td>\n",
" <td>0.292321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>is_duplicate</th>\n",
" <td>-0.008784</td>\n",
" <td>-0.207682</td>\n",
" <td>-0.346925</td>\n",
" <td>0.343747</td>\n",
" <td>0.265540</td>\n",
" <td>1.000000</td>\n",
" <td>-0.332427</td>\n",
" <td>0.334511</td>\n",
" <td>0.228869</td>\n",
" <td>0.123509</td>\n",
" <td>0.207493</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq_diff</th>\n",
" <td>0.001727</td>\n",
" <td>0.035621</td>\n",
" <td>0.069128</td>\n",
" <td>-0.166016</td>\n",
" <td>-0.099017</td>\n",
" <td>-0.332427</td>\n",
" <td>1.000000</td>\n",
" <td>-0.158787</td>\n",
" <td>-0.078082</td>\n",
" <td>0.072430</td>\n",
" <td>-0.164259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_hash_freq</th>\n",
" <td>-0.002885</td>\n",
" <td>-0.386223</td>\n",
" <td>-0.406026</td>\n",
" <td>0.898113</td>\n",
" <td>0.583196</td>\n",
" <td>0.334511</td>\n",
" <td>-0.158787</td>\n",
" <td>1.000000</td>\n",
" <td>0.476849</td>\n",
" <td>0.129154</td>\n",
" <td>0.210356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_hash_freq</th>\n",
" <td>-0.001022</td>\n",
" <td>-0.207151</td>\n",
" <td>-0.365688</td>\n",
" <td>0.528905</td>\n",
" <td>0.948601</td>\n",
" <td>0.228869</td>\n",
" <td>-0.078082</td>\n",
" <td>0.476849</td>\n",
" <td>1.000000</td>\n",
" <td>0.206482</td>\n",
" <td>0.205859</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q_hash_pos</th>\n",
" <td>0.115121</td>\n",
" <td>0.060618</td>\n",
" <td>-0.583149</td>\n",
" <td>0.128596</td>\n",
" <td>0.292966</td>\n",
" <td>0.123509</td>\n",
" <td>0.072430</td>\n",
" <td>0.129154</td>\n",
" <td>0.206482</td>\n",
" <td>1.000000</td>\n",
" <td>0.805124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q_hash_pos_1</th>\n",
" <td>0.071093</td>\n",
" <td>-0.046562</td>\n",
" <td>-0.472782</td>\n",
" <td>0.210646</td>\n",
" <td>0.292321</td>\n",
" <td>0.207493</td>\n",
" <td>-0.164259</td>\n",
" <td>0.210356</td>\n",
" <td>0.205859</td>\n",
" <td>0.805124</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id q1_hash q2_hash q1_freq q2_freq is_duplicate \\\n",
"id 1.000000 0.690308 0.041179 -0.002600 -0.000871 -0.008784 \n",
"q1_hash 0.690308 1.000000 0.282445 -0.359849 -0.243217 -0.207682 \n",
"q2_hash 0.041179 0.282445 1.000000 -0.397311 -0.471671 -0.346925 \n",
"q1_freq -0.002600 -0.359849 -0.397311 1.000000 0.599397 0.343747 \n",
"q2_freq -0.000871 -0.243217 -0.471671 0.599397 1.000000 0.265540 \n",
"is_duplicate -0.008784 -0.207682 -0.346925 0.343747 0.265540 1.000000 \n",
"freq_diff 0.001727 0.035621 0.069128 -0.166016 -0.099017 -0.332427 \n",
"q1_hash_freq -0.002885 -0.386223 -0.406026 0.898113 0.583196 0.334511 \n",
"q2_hash_freq -0.001022 -0.207151 -0.365688 0.528905 0.948601 0.228869 \n",
"q_hash_pos 0.115121 0.060618 -0.583149 0.128596 0.292966 0.123509 \n",
"q_hash_pos_1 0.071093 -0.046562 -0.472782 0.210646 0.292321 0.207493 \n",
"\n",
" freq_diff q1_hash_freq q2_hash_freq q_hash_pos q_hash_pos_1 \n",
"id 0.001727 -0.002885 -0.001022 0.115121 0.071093 \n",
"q1_hash 0.035621 -0.386223 -0.207151 0.060618 -0.046562 \n",
"q2_hash 0.069128 -0.406026 -0.365688 -0.583149 -0.472782 \n",
"q1_freq -0.166016 0.898113 0.528905 0.128596 0.210646 \n",
"q2_freq -0.099017 0.583196 0.948601 0.292966 0.292321 \n",
"is_duplicate -0.332427 0.334511 0.228869 0.123509 0.207493 \n",
"freq_diff 1.000000 -0.158787 -0.078082 0.072430 -0.164259 \n",
"q1_hash_freq -0.158787 1.000000 0.476849 0.129154 0.210356 \n",
"q2_hash_freq -0.078082 0.476849 1.000000 0.206482 0.205859 \n",
"q_hash_pos 0.072430 0.129154 0.206482 1.000000 0.805124 \n",
"q_hash_pos_1 -0.164259 0.210356 0.205859 0.805124 1.000000 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# train_comb['q_hash_diff'] = train_comb['q1_hash'] - train_comb['q2_hash']\n",
"train_comb['q_hash_pos'] = train_comb['q1_hash']-train_comb['q2_hash']>0\n",
"train_comb['q_hash_pos'] = train_comb['q_hash_pos'].astype(int)\n",
"train_comb['q_hash_pos_1'] = train_comb[['q1_freq','q_hash_pos']].apply(lambda x: 1 if x[0]>1 and x[1]>0 else 0, axis=1)\n",
"train_comb.corr()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.36919785302629299"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(47463, 8)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"0.53262541347997383"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(32092, 8)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"0.71020815156425277"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(354682, 8)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"0.34458472660016576"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_comb['is_duplicate'].mean()\n",
"pos = train_comb[train_comb['q1_hash']-train_comb['q2_hash']>0]\n",
"pos.shape\n",
"pos['is_duplicate'].mean()\n",
"pos = pos[pos['q1_freq']>1]\n",
"pos.shape\n",
"pos['is_duplicate'].mean()\n",
"pos = train_comb[train_comb['q1_hash']-train_comb['q2_hash']<0]\n",
"pos.shape\n",
"pos['is_duplicate'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"list1 = []\n",
"list1.append(0)\n",
"gpf = train_comb['q2_hash'].values\n",
"tag = gpf[0]\n",
"for i in range(train_comb.shape[0])[1:]:\n",
" if gpf[i]-tag<0:\n",
" list1.append(gpf[i]-tag)\n",
" if gpf[i]-tag>=0:\n",
" list1.append(gpf[i]-tag)\n",
" tag=gpf[i]\n",
"\n",
"train_comb['q2_change'] = list1\n",
"\n",
"list1 = []\n",
"list1.append(0)\n",
"gpf = train_comb['q1_hash'].values\n",
"tag = gpf[0]\n",
"for i in range(train_comb.shape[0])[1:]:\n",
" if gpf[i]-tag<0:\n",
" list1.append(gpf[i]-tag)\n",
" if gpf[i]-tag>=0:\n",
" list1.append(gpf[i]-tag)\n",
" tag=gpf[i]\n",
" \n",
"train_comb['q1_change'] = list1"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(79710,)"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"0.78024087316522395"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(209993,)"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"0.23252679851233135"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(34915,)"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"0.15153945295718171"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(79672,)"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"0.41357063962245205"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v1 = 0\n",
"train_comb[(train_comb['q1_change']<=v1) & (train_comb['q2_change']<=v1)]['is_duplicate'].shape\n",
"train_comb[(train_comb['q1_change']<=v1) & (train_comb['q2_change']<=v1)]['is_duplicate'].mean()\n",
"train_comb[(train_comb['q1_change']==1)&(train_comb['q2_change']==1)]['is_duplicate'].shape\n",
"train_comb[(train_comb['q1_change']==1)&(train_comb['q2_change']==1)]['is_duplicate'].mean()\n",
"train_comb[(train_comb['q1_change']!=1)&(train_comb['q2_change']==1)]['is_duplicate'].shape\n",
"train_comb[(train_comb['q1_change']!=1)&(train_comb['q2_change']==1)]['is_duplicate'].mean()\n",
"train_comb[(train_comb['q1_change']==1)&(train_comb['q2_change']!=1)]['is_duplicate'].shape\n",
"train_comb[(train_comb['q1_change']==1)&(train_comb['q2_change']!=1)]['is_duplicate'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>q1_hash</th>\n",
" <th>q2_hash</th>\n",
" <th>q1_freq</th>\n",
" <th>q2_freq</th>\n",
" <th>is_duplicate</th>\n",
" <th>freq_diff</th>\n",
" <th>q1_hash_freq</th>\n",
" <th>q2_hash_freq</th>\n",
" <th>q_hash_pos</th>\n",
" <th>q_hash_pos_1</th>\n",
" <th>q2_change</th>\n",
" <th>q1_change</th>\n",
" <th>q1_q2_change_mean</th>\n",
" <th>q1_q2_change_min</th>\n",
" <th>q1_q2_change_max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>id</th>\n",
" <td>1.000000</td>\n",
" <td>0.690308</td>\n",
" <td>0.041179</td>\n",
" <td>-0.002600</td>\n",
" <td>-0.000871</td>\n",
" <td>-0.008784</td>\n",
" <td>0.001727</td>\n",
" <td>-0.002885</td>\n",
" <td>-0.001022</td>\n",
" <td>0.115121</td>\n",
" <td>0.071093</td>\n",
" <td>-0.020399</td>\n",
" <td>-0.356764</td>\n",
" <td>-0.040099</td>\n",
" <td>-0.027226</td>\n",
" <td>-0.281257</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_hash</th>\n",
" <td>0.690308</td>\n",
" <td>1.000000</td>\n",
" <td>0.282445</td>\n",
" <td>-0.359849</td>\n",
" <td>-0.243217</td>\n",
" <td>-0.207682</td>\n",
" <td>0.035621</td>\n",
" <td>-0.386223</td>\n",
" <td>-0.207151</td>\n",
" <td>0.060618</td>\n",
" <td>-0.046562</td>\n",
" <td>0.239944</td>\n",
" <td>0.427433</td>\n",
" <td>0.259142</td>\n",
" <td>0.247919</td>\n",
" <td>0.364492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_hash</th>\n",
" <td>0.041179</td>\n",
" <td>0.282445</td>\n",
" <td>1.000000</td>\n",
" <td>-0.397311</td>\n",
" <td>-0.471671</td>\n",
" <td>-0.346925</td>\n",
" <td>0.069128</td>\n",
" <td>-0.406026</td>\n",
" <td>-0.365688</td>\n",
" <td>-0.583149</td>\n",
" <td>-0.472782</td>\n",
" <td>0.998102</td>\n",
" <td>0.315344</td>\n",
" <td>0.995484</td>\n",
" <td>0.997163</td>\n",
" <td>0.495795</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_freq</th>\n",
" <td>-0.002600</td>\n",
" <td>-0.359849</td>\n",
" <td>-0.397311</td>\n",
" <td>1.000000</td>\n",
" <td>0.599397</td>\n",
" <td>0.343747</td>\n",
" <td>-0.166016</td>\n",
" <td>0.898113</td>\n",
" <td>0.528905</td>\n",
" <td>0.128596</td>\n",
" <td>0.210646</td>\n",
" <td>-0.397409</td>\n",
" <td>-0.464393</td>\n",
" <td>-0.415473</td>\n",
" <td>-0.400677</td>\n",
" <td>-0.519822</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_freq</th>\n",
" <td>-0.000871</td>\n",
" <td>-0.243217</td>\n",
" <td>-0.471671</td>\n",
" <td>0.599397</td>\n",
" <td>1.000000</td>\n",
" <td>0.265540</td>\n",
" <td>-0.099017</td>\n",
" <td>0.583196</td>\n",
" <td>0.948601</td>\n",
" <td>0.292966</td>\n",
" <td>0.292321</td>\n",
" <td>-0.471925</td>\n",
" <td>-0.315102</td>\n",
" <td>-0.480048</td>\n",
" <td>-0.471667</td>\n",
" <td>-0.424641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>is_duplicate</th>\n",
" <td>-0.008784</td>\n",
" <td>-0.207682</td>\n",
" <td>-0.346925</td>\n",
" <td>0.343747</td>\n",
" <td>0.265540</td>\n",
" <td>1.000000</td>\n",
" <td>-0.332427</td>\n",
" <td>0.334511</td>\n",
" <td>0.228869</td>\n",
" <td>0.123509</td>\n",
" <td>0.207493</td>\n",
" <td>-0.346618</td>\n",
" <td>-0.259241</td>\n",
" <td>-0.354151</td>\n",
" <td>-0.345165</td>\n",
" <td>-0.369878</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq_diff</th>\n",
" <td>0.001727</td>\n",
" <td>0.035621</td>\n",
" <td>0.069128</td>\n",
" <td>-0.166016</td>\n",
" <td>-0.099017</td>\n",
" <td>-0.332427</td>\n",
" <td>1.000000</td>\n",
" <td>-0.158787</td>\n",
" <td>-0.078082</td>\n",
" <td>0.072430</td>\n",
" <td>-0.164259</td>\n",
" <td>0.069072</td>\n",
" <td>0.044340</td>\n",
" <td>0.070160</td>\n",
" <td>0.058338</td>\n",
" <td>0.276045</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_hash_freq</th>\n",
" <td>-0.002885</td>\n",
" <td>-0.386223</td>\n",
" <td>-0.406026</td>\n",
" <td>0.898113</td>\n",
" <td>0.583196</td>\n",
" <td>0.334511</td>\n",
" <td>-0.158787</td>\n",
" <td>1.000000</td>\n",
" <td>0.476849</td>\n",
" <td>0.129154</td>\n",
" <td>0.210356</td>\n",
" <td>-0.406110</td>\n",
" <td>-0.498197</td>\n",
" <td>-0.425902</td>\n",
" <td>-0.410139</td>\n",
" <td>-0.544893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_hash_freq</th>\n",
" <td>-0.001022</td>\n",
" <td>-0.207151</td>\n",
" <td>-0.365688</td>\n",
" <td>0.528905</td>\n",
" <td>0.948601</td>\n",
" <td>0.228869</td>\n",
" <td>-0.078082</td>\n",
" <td>0.476849</td>\n",
" <td>1.000000</td>\n",
" <td>0.206482</td>\n",
" <td>0.205859</td>\n",
" <td>-0.365866</td>\n",
" <td>-0.268117</td>\n",
" <td>-0.373507</td>\n",
" <td>-0.365617</td>\n",
" <td>-0.358035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q_hash_pos</th>\n",
" <td>0.115121</td>\n",
" <td>0.060618</td>\n",
" <td>-0.583149</td>\n",
" <td>0.128596</td>\n",
" <td>0.292966</td>\n",
" <td>0.123509</td>\n",
" <td>0.072430</td>\n",
" <td>0.129154</td>\n",
" <td>0.206482</td>\n",
" <td>1.000000</td>\n",
" <td>0.805124</td>\n",
" <td>-0.590683</td>\n",
" <td>-0.068443</td>\n",
" <td>-0.582470</td>\n",
" <td>-0.590505</td>\n",
" <td>-0.147689</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q_hash_pos_1</th>\n",
" <td>0.071093</td>\n",
" <td>-0.046562</td>\n",
" <td>-0.472782</td>\n",
" <td>0.210646</td>\n",
" <td>0.292321</td>\n",
" <td>0.207493</td>\n",
" <td>-0.164259</td>\n",
" <td>0.210356</td>\n",
" <td>0.205859</td>\n",
" <td>0.805124</td>\n",
" <td>1.000000</td>\n",
" <td>-0.477532</td>\n",
" <td>-0.152411</td>\n",
" <td>-0.476366</td>\n",
" <td>-0.477399</td>\n",
" <td>-0.232615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_change</th>\n",
" <td>-0.020399</td>\n",
" <td>0.239944</td>\n",
" <td>0.998102</td>\n",
" <td>-0.397409</td>\n",
" <td>-0.471925</td>\n",
" <td>-0.346618</td>\n",
" <td>0.069072</td>\n",
" <td>-0.406110</td>\n",
" <td>-0.365866</td>\n",
" <td>-0.590683</td>\n",
" <td>-0.477532</td>\n",
" <td>1.000000</td>\n",
" <td>0.337464</td>\n",
" <td>0.998590</td>\n",
" <td>0.999479</td>\n",
" <td>0.513399</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_change</th>\n",
" <td>-0.356764</td>\n",
" <td>0.427433</td>\n",
" <td>0.315344</td>\n",
" <td>-0.464393</td>\n",
" <td>-0.315102</td>\n",
" <td>-0.259241</td>\n",
" <td>0.044340</td>\n",
" <td>-0.498197</td>\n",
" <td>-0.268117</td>\n",
" <td>-0.068443</td>\n",
" <td>-0.152411</td>\n",
" <td>0.337464</td>\n",
" <td>1.000000</td>\n",
" <td>0.386955</td>\n",
" <td>0.356322</td>\n",
" <td>0.824397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_q2_change_mean</th>\n",
" <td>-0.040099</td>\n",
" <td>0.259142</td>\n",
" <td>0.995484</td>\n",
" <td>-0.415473</td>\n",
" <td>-0.480048</td>\n",
" <td>-0.354151</td>\n",
" <td>0.070160</td>\n",
" <td>-0.425902</td>\n",
" <td>-0.373507</td>\n",
" <td>-0.582470</td>\n",
" <td>-0.476366</td>\n",
" <td>0.998590</td>\n",
" <td>0.386955</td>\n",
" <td>1.000000</td>\n",
" <td>0.999143</td>\n",
" <td>0.549391</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_q2_change_min</th>\n",
" <td>-0.027226</td>\n",
" <td>0.247919</td>\n",
" <td>0.997163</td>\n",
" <td>-0.400677</td>\n",
" <td>-0.471667</td>\n",
" <td>-0.345165</td>\n",
" <td>0.058338</td>\n",
" <td>-0.410139</td>\n",
" <td>-0.365617</td>\n",
" <td>-0.590505</td>\n",
" <td>-0.477399</td>\n",
" <td>0.999479</td>\n",
" <td>0.356322</td>\n",
" <td>0.999143</td>\n",
" <td>1.000000</td>\n",
" <td>0.514343</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_q2_change_max</th>\n",
" <td>-0.281257</td>\n",
" <td>0.364492</td>\n",
" <td>0.495795</td>\n",
" <td>-0.519822</td>\n",
" <td>-0.424641</td>\n",
" <td>-0.369878</td>\n",
" <td>0.276045</td>\n",
" <td>-0.544893</td>\n",
" <td>-0.358035</td>\n",
" <td>-0.147689</td>\n",
" <td>-0.232615</td>\n",
" <td>0.513399</td>\n",
" <td>0.824397</td>\n",
" <td>0.549391</td>\n",
" <td>0.514343</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id q1_hash q2_hash q1_freq q2_freq \\\n",
"id 1.000000 0.690308 0.041179 -0.002600 -0.000871 \n",
"q1_hash 0.690308 1.000000 0.282445 -0.359849 -0.243217 \n",
"q2_hash 0.041179 0.282445 1.000000 -0.397311 -0.471671 \n",
"q1_freq -0.002600 -0.359849 -0.397311 1.000000 0.599397 \n",
"q2_freq -0.000871 -0.243217 -0.471671 0.599397 1.000000 \n",
"is_duplicate -0.008784 -0.207682 -0.346925 0.343747 0.265540 \n",
"freq_diff 0.001727 0.035621 0.069128 -0.166016 -0.099017 \n",
"q1_hash_freq -0.002885 -0.386223 -0.406026 0.898113 0.583196 \n",
"q2_hash_freq -0.001022 -0.207151 -0.365688 0.528905 0.948601 \n",
"q_hash_pos 0.115121 0.060618 -0.583149 0.128596 0.292966 \n",
"q_hash_pos_1 0.071093 -0.046562 -0.472782 0.210646 0.292321 \n",
"q2_change -0.020399 0.239944 0.998102 -0.397409 -0.471925 \n",
"q1_change -0.356764 0.427433 0.315344 -0.464393 -0.315102 \n",
"q1_q2_change_mean -0.040099 0.259142 0.995484 -0.415473 -0.480048 \n",
"q1_q2_change_min -0.027226 0.247919 0.997163 -0.400677 -0.471667 \n",
"q1_q2_change_max -0.281257 0.364492 0.495795 -0.519822 -0.424641 \n",
"\n",
" is_duplicate freq_diff q1_hash_freq q2_hash_freq \\\n",
"id -0.008784 0.001727 -0.002885 -0.001022 \n",
"q1_hash -0.207682 0.035621 -0.386223 -0.207151 \n",
"q2_hash -0.346925 0.069128 -0.406026 -0.365688 \n",
"q1_freq 0.343747 -0.166016 0.898113 0.528905 \n",
"q2_freq 0.265540 -0.099017 0.583196 0.948601 \n",
"is_duplicate 1.000000 -0.332427 0.334511 0.228869 \n",
"freq_diff -0.332427 1.000000 -0.158787 -0.078082 \n",
"q1_hash_freq 0.334511 -0.158787 1.000000 0.476849 \n",
"q2_hash_freq 0.228869 -0.078082 0.476849 1.000000 \n",
"q_hash_pos 0.123509 0.072430 0.129154 0.206482 \n",
"q_hash_pos_1 0.207493 -0.164259 0.210356 0.205859 \n",
"q2_change -0.346618 0.069072 -0.406110 -0.365866 \n",
"q1_change -0.259241 0.044340 -0.498197 -0.268117 \n",
"q1_q2_change_mean -0.354151 0.070160 -0.425902 -0.373507 \n",
"q1_q2_change_min -0.345165 0.058338 -0.410139 -0.365617 \n",
"q1_q2_change_max -0.369878 0.276045 -0.544893 -0.358035 \n",
"\n",
" q_hash_pos q_hash_pos_1 q2_change q1_change \\\n",
"id 0.115121 0.071093 -0.020399 -0.356764 \n",
"q1_hash 0.060618 -0.046562 0.239944 0.427433 \n",
"q2_hash -0.583149 -0.472782 0.998102 0.315344 \n",
"q1_freq 0.128596 0.210646 -0.397409 -0.464393 \n",
"q2_freq 0.292966 0.292321 -0.471925 -0.315102 \n",
"is_duplicate 0.123509 0.207493 -0.346618 -0.259241 \n",
"freq_diff 0.072430 -0.164259 0.069072 0.044340 \n",
"q1_hash_freq 0.129154 0.210356 -0.406110 -0.498197 \n",
"q2_hash_freq 0.206482 0.205859 -0.365866 -0.268117 \n",
"q_hash_pos 1.000000 0.805124 -0.590683 -0.068443 \n",
"q_hash_pos_1 0.805124 1.000000 -0.477532 -0.152411 \n",
"q2_change -0.590683 -0.477532 1.000000 0.337464 \n",
"q1_change -0.068443 -0.152411 0.337464 1.000000 \n",
"q1_q2_change_mean -0.582470 -0.476366 0.998590 0.386955 \n",
"q1_q2_change_min -0.590505 -0.477399 0.999479 0.356322 \n",
"q1_q2_change_max -0.147689 -0.232615 0.513399 0.824397 \n",
"\n",
" q1_q2_change_mean q1_q2_change_min q1_q2_change_max \n",
"id -0.040099 -0.027226 -0.281257 \n",
"q1_hash 0.259142 0.247919 0.364492 \n",
"q2_hash 0.995484 0.997163 0.495795 \n",
"q1_freq -0.415473 -0.400677 -0.519822 \n",
"q2_freq -0.480048 -0.471667 -0.424641 \n",
"is_duplicate -0.354151 -0.345165 -0.369878 \n",
"freq_diff 0.070160 0.058338 0.276045 \n",
"q1_hash_freq -0.425902 -0.410139 -0.544893 \n",
"q2_hash_freq -0.373507 -0.365617 -0.358035 \n",
"q_hash_pos -0.582470 -0.590505 -0.147689 \n",
"q_hash_pos_1 -0.476366 -0.477399 -0.232615 \n",
"q2_change 0.998590 0.999479 0.513399 \n",
"q1_change 0.386955 0.356322 0.824397 \n",
"q1_q2_change_mean 1.000000 0.999143 0.549391 \n",
"q1_q2_change_min 0.999143 1.000000 0.514343 \n",
"q1_q2_change_max 0.549391 0.514343 1.000000 "
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_comb['q1_q2_change_mean'] = (train_comb['q1_change'] + train_comb['q2_change'])/2.0\n",
"train_comb['q1_q2_change_min'] = train_comb[['q1_change','q2_change']].apply(lambda x: min(x[0],x[1]),axis=1)\n",
"train_comb['q1_q2_change_max'] = train_comb[['q1_change','q2_change']].apply(lambda x: max(x[0],x[1]),axis=1)\n",
"train_comb.corr()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>q1_hash</th>\n",
" <th>q2_hash</th>\n",
" <th>q1_freq</th>\n",
" <th>q2_freq</th>\n",
" <th>is_duplicate</th>\n",
" <th>freq_diff</th>\n",
" <th>q1_hash_freq</th>\n",
" <th>q2_hash_freq</th>\n",
" <th>q_hash_pos</th>\n",
" <th>q_hash_pos_1</th>\n",
" <th>q2_change</th>\n",
" <th>q1_change</th>\n",
" <th>q1_q2_change_mean</th>\n",
" <th>q1_q2_change_min</th>\n",
" <th>q1_q2_change_max</th>\n",
" <th>q_change_pair</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>id</th>\n",
" <td>1.000000</td>\n",
" <td>0.690308</td>\n",
" <td>0.041179</td>\n",
" <td>-0.002600</td>\n",
" <td>-0.000871</td>\n",
" <td>-0.008784</td>\n",
" <td>0.001727</td>\n",
" <td>-0.002885</td>\n",
" <td>-0.001022</td>\n",
" <td>0.115121</td>\n",
" <td>0.071093</td>\n",
" <td>-0.020399</td>\n",
" <td>-0.356764</td>\n",
" <td>-0.040099</td>\n",
" <td>-0.027226</td>\n",
" <td>-0.281257</td>\n",
" <td>0.169849</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_hash</th>\n",
" <td>0.690308</td>\n",
" <td>1.000000</td>\n",
" <td>0.282445</td>\n",
" <td>-0.359849</td>\n",
" <td>-0.243217</td>\n",
" <td>-0.207682</td>\n",
" <td>0.035621</td>\n",
" <td>-0.386223</td>\n",
" <td>-0.207151</td>\n",
" <td>0.060618</td>\n",
" <td>-0.046562</td>\n",
" <td>0.239944</td>\n",
" <td>0.427433</td>\n",
" <td>0.259142</td>\n",
" <td>0.247919</td>\n",
" <td>0.364492</td>\n",
" <td>-0.355028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_hash</th>\n",
" <td>0.041179</td>\n",
" <td>0.282445</td>\n",
" <td>1.000000</td>\n",
" <td>-0.397311</td>\n",
" <td>-0.471671</td>\n",
" <td>-0.346925</td>\n",
" <td>0.069128</td>\n",
" <td>-0.406026</td>\n",
" <td>-0.365688</td>\n",
" <td>-0.583149</td>\n",
" <td>-0.472782</td>\n",
" <td>0.998102</td>\n",
" <td>0.315344</td>\n",
" <td>0.995484</td>\n",
" <td>0.997163</td>\n",
" <td>0.495795</td>\n",
" <td>-0.545675</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_freq</th>\n",
" <td>-0.002600</td>\n",
" <td>-0.359849</td>\n",
" <td>-0.397311</td>\n",
" <td>1.000000</td>\n",
" <td>0.599397</td>\n",
" <td>0.343747</td>\n",
" <td>-0.166016</td>\n",
" <td>0.898113</td>\n",
" <td>0.528905</td>\n",
" <td>0.128596</td>\n",
" <td>0.210646</td>\n",
" <td>-0.397409</td>\n",
" <td>-0.464393</td>\n",
" <td>-0.415473</td>\n",
" <td>-0.400677</td>\n",
" <td>-0.519822</td>\n",
" <td>0.526781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_freq</th>\n",
" <td>-0.000871</td>\n",
" <td>-0.243217</td>\n",
" <td>-0.471671</td>\n",
" <td>0.599397</td>\n",
" <td>1.000000</td>\n",
" <td>0.265540</td>\n",
" <td>-0.099017</td>\n",
" <td>0.583196</td>\n",
" <td>0.948601</td>\n",
" <td>0.292966</td>\n",
" <td>0.292321</td>\n",
" <td>-0.471925</td>\n",
" <td>-0.315102</td>\n",
" <td>-0.480048</td>\n",
" <td>-0.471667</td>\n",
" <td>-0.424641</td>\n",
" <td>0.435390</td>\n",
" </tr>\n",
" <tr>\n",
" <th>is_duplicate</th>\n",
" <td>-0.008784</td>\n",
" <td>-0.207682</td>\n",
" <td>-0.346925</td>\n",
" <td>0.343747</td>\n",
" <td>0.265540</td>\n",
" <td>1.000000</td>\n",
" <td>-0.332427</td>\n",
" <td>0.334511</td>\n",
" <td>0.228869</td>\n",
" <td>0.123509</td>\n",
" <td>0.207493</td>\n",
" <td>-0.346618</td>\n",
" <td>-0.259241</td>\n",
" <td>-0.354151</td>\n",
" <td>-0.345165</td>\n",
" <td>-0.369878</td>\n",
" <td>0.422098</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq_diff</th>\n",
" <td>0.001727</td>\n",
" <td>0.035621</td>\n",
" <td>0.069128</td>\n",
" <td>-0.166016</td>\n",
" <td>-0.099017</td>\n",
" <td>-0.332427</td>\n",
" <td>1.000000</td>\n",
" <td>-0.158787</td>\n",
" <td>-0.078082</td>\n",
" <td>0.072430</td>\n",
" <td>-0.164259</td>\n",
" <td>0.069072</td>\n",
" <td>0.044340</td>\n",
" <td>0.070160</td>\n",
" <td>0.058338</td>\n",
" <td>0.276045</td>\n",
" <td>-0.323348</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_hash_freq</th>\n",
" <td>-0.002885</td>\n",
" <td>-0.386223</td>\n",
" <td>-0.406026</td>\n",
" <td>0.898113</td>\n",
" <td>0.583196</td>\n",
" <td>0.334511</td>\n",
" <td>-0.158787</td>\n",
" <td>1.000000</td>\n",
" <td>0.476849</td>\n",
" <td>0.129154</td>\n",
" <td>0.210356</td>\n",
" <td>-0.406110</td>\n",
" <td>-0.498197</td>\n",
" <td>-0.425902</td>\n",
" <td>-0.410139</td>\n",
" <td>-0.544893</td>\n",
" <td>0.545906</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_hash_freq</th>\n",
" <td>-0.001022</td>\n",
" <td>-0.207151</td>\n",
" <td>-0.365688</td>\n",
" <td>0.528905</td>\n",
" <td>0.948601</td>\n",
" <td>0.228869</td>\n",
" <td>-0.078082</td>\n",
" <td>0.476849</td>\n",
" <td>1.000000</td>\n",
" <td>0.206482</td>\n",
" <td>0.205859</td>\n",
" <td>-0.365866</td>\n",
" <td>-0.268117</td>\n",
" <td>-0.373507</td>\n",
" <td>-0.365617</td>\n",
" <td>-0.358035</td>\n",
" <td>0.373949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q_hash_pos</th>\n",
" <td>0.115121</td>\n",
" <td>0.060618</td>\n",
" <td>-0.583149</td>\n",
" <td>0.128596</td>\n",
" <td>0.292966</td>\n",
" <td>0.123509</td>\n",
" <td>0.072430</td>\n",
" <td>0.129154</td>\n",
" <td>0.206482</td>\n",
" <td>1.000000</td>\n",
" <td>0.805124</td>\n",
" <td>-0.590683</td>\n",
" <td>-0.068443</td>\n",
" <td>-0.582470</td>\n",
" <td>-0.590505</td>\n",
" <td>-0.147689</td>\n",
" <td>0.213000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q_hash_pos_1</th>\n",
" <td>0.071093</td>\n",
" <td>-0.046562</td>\n",
" <td>-0.472782</td>\n",
" <td>0.210646</td>\n",
" <td>0.292321</td>\n",
" <td>0.207493</td>\n",
" <td>-0.164259</td>\n",
" <td>0.210356</td>\n",
" <td>0.205859</td>\n",
" <td>0.805124</td>\n",
" <td>1.000000</td>\n",
" <td>-0.477532</td>\n",
" <td>-0.152411</td>\n",
" <td>-0.476366</td>\n",
" <td>-0.477399</td>\n",
" <td>-0.232615</td>\n",
" <td>0.323326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_change</th>\n",
" <td>-0.020399</td>\n",
" <td>0.239944</td>\n",
" <td>0.998102</td>\n",
" <td>-0.397409</td>\n",
" <td>-0.471925</td>\n",
" <td>-0.346618</td>\n",
" <td>0.069072</td>\n",
" <td>-0.406110</td>\n",
" <td>-0.365866</td>\n",
" <td>-0.590683</td>\n",
" <td>-0.477532</td>\n",
" <td>1.000000</td>\n",
" <td>0.337464</td>\n",
" <td>0.998590</td>\n",
" <td>0.999479</td>\n",
" <td>0.513399</td>\n",
" <td>-0.556578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_change</th>\n",
" <td>-0.356764</td>\n",
" <td>0.427433</td>\n",
" <td>0.315344</td>\n",
" <td>-0.464393</td>\n",
" <td>-0.315102</td>\n",
" <td>-0.259241</td>\n",
" <td>0.044340</td>\n",
" <td>-0.498197</td>\n",
" <td>-0.268117</td>\n",
" <td>-0.068443</td>\n",
" <td>-0.152411</td>\n",
" <td>0.337464</td>\n",
" <td>1.000000</td>\n",
" <td>0.386955</td>\n",
" <td>0.356322</td>\n",
" <td>0.824397</td>\n",
" <td>-0.677780</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_q2_change_mean</th>\n",
" <td>-0.040099</td>\n",
" <td>0.259142</td>\n",
" <td>0.995484</td>\n",
" <td>-0.415473</td>\n",
" <td>-0.480048</td>\n",
" <td>-0.354151</td>\n",
" <td>0.070160</td>\n",
" <td>-0.425902</td>\n",
" <td>-0.373507</td>\n",
" <td>-0.582470</td>\n",
" <td>-0.476366</td>\n",
" <td>0.998590</td>\n",
" <td>0.386955</td>\n",
" <td>1.000000</td>\n",
" <td>0.999143</td>\n",
" <td>0.549391</td>\n",
" <td>-0.583421</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_q2_change_min</th>\n",
" <td>-0.027226</td>\n",
" <td>0.247919</td>\n",
" <td>0.997163</td>\n",
" <td>-0.400677</td>\n",
" <td>-0.471667</td>\n",
" <td>-0.345165</td>\n",
" <td>0.058338</td>\n",
" <td>-0.410139</td>\n",
" <td>-0.365617</td>\n",
" <td>-0.590505</td>\n",
" <td>-0.477399</td>\n",
" <td>0.999479</td>\n",
" <td>0.356322</td>\n",
" <td>0.999143</td>\n",
" <td>1.000000</td>\n",
" <td>0.514343</td>\n",
" <td>-0.558467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_q2_change_max</th>\n",
" <td>-0.281257</td>\n",
" <td>0.364492</td>\n",
" <td>0.495795</td>\n",
" <td>-0.519822</td>\n",
" <td>-0.424641</td>\n",
" <td>-0.369878</td>\n",
" <td>0.276045</td>\n",
" <td>-0.544893</td>\n",
" <td>-0.358035</td>\n",
" <td>-0.147689</td>\n",
" <td>-0.232615</td>\n",
" <td>0.513399</td>\n",
" <td>0.824397</td>\n",
" <td>0.549391</td>\n",
" <td>0.514343</td>\n",
" <td>1.000000</td>\n",
" <td>-0.814297</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q_change_pair</th>\n",
" <td>0.169849</td>\n",
" <td>-0.355028</td>\n",
" <td>-0.545675</td>\n",
" <td>0.526781</td>\n",
" <td>0.435390</td>\n",
" <td>0.422098</td>\n",
" <td>-0.323348</td>\n",
" <td>0.545906</td>\n",
" <td>0.373949</td>\n",
" <td>0.213000</td>\n",
" <td>0.323326</td>\n",
" <td>-0.556578</td>\n",
" <td>-0.677780</td>\n",
" <td>-0.583421</td>\n",
" <td>-0.558467</td>\n",
" <td>-0.814297</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id q1_hash q2_hash q1_freq q2_freq \\\n",
"id 1.000000 0.690308 0.041179 -0.002600 -0.000871 \n",
"q1_hash 0.690308 1.000000 0.282445 -0.359849 -0.243217 \n",
"q2_hash 0.041179 0.282445 1.000000 -0.397311 -0.471671 \n",
"q1_freq -0.002600 -0.359849 -0.397311 1.000000 0.599397 \n",
"q2_freq -0.000871 -0.243217 -0.471671 0.599397 1.000000 \n",
"is_duplicate -0.008784 -0.207682 -0.346925 0.343747 0.265540 \n",
"freq_diff 0.001727 0.035621 0.069128 -0.166016 -0.099017 \n",
"q1_hash_freq -0.002885 -0.386223 -0.406026 0.898113 0.583196 \n",
"q2_hash_freq -0.001022 -0.207151 -0.365688 0.528905 0.948601 \n",
"q_hash_pos 0.115121 0.060618 -0.583149 0.128596 0.292966 \n",
"q_hash_pos_1 0.071093 -0.046562 -0.472782 0.210646 0.292321 \n",
"q2_change -0.020399 0.239944 0.998102 -0.397409 -0.471925 \n",
"q1_change -0.356764 0.427433 0.315344 -0.464393 -0.315102 \n",
"q1_q2_change_mean -0.040099 0.259142 0.995484 -0.415473 -0.480048 \n",
"q1_q2_change_min -0.027226 0.247919 0.997163 -0.400677 -0.471667 \n",
"q1_q2_change_max -0.281257 0.364492 0.495795 -0.519822 -0.424641 \n",
"q_change_pair 0.169849 -0.355028 -0.545675 0.526781 0.435390 \n",
"\n",
" is_duplicate freq_diff q1_hash_freq q2_hash_freq \\\n",
"id -0.008784 0.001727 -0.002885 -0.001022 \n",
"q1_hash -0.207682 0.035621 -0.386223 -0.207151 \n",
"q2_hash -0.346925 0.069128 -0.406026 -0.365688 \n",
"q1_freq 0.343747 -0.166016 0.898113 0.528905 \n",
"q2_freq 0.265540 -0.099017 0.583196 0.948601 \n",
"is_duplicate 1.000000 -0.332427 0.334511 0.228869 \n",
"freq_diff -0.332427 1.000000 -0.158787 -0.078082 \n",
"q1_hash_freq 0.334511 -0.158787 1.000000 0.476849 \n",
"q2_hash_freq 0.228869 -0.078082 0.476849 1.000000 \n",
"q_hash_pos 0.123509 0.072430 0.129154 0.206482 \n",
"q_hash_pos_1 0.207493 -0.164259 0.210356 0.205859 \n",
"q2_change -0.346618 0.069072 -0.406110 -0.365866 \n",
"q1_change -0.259241 0.044340 -0.498197 -0.268117 \n",
"q1_q2_change_mean -0.354151 0.070160 -0.425902 -0.373507 \n",
"q1_q2_change_min -0.345165 0.058338 -0.410139 -0.365617 \n",
"q1_q2_change_max -0.369878 0.276045 -0.544893 -0.358035 \n",
"q_change_pair 0.422098 -0.323348 0.545906 0.373949 \n",
"\n",
" q_hash_pos q_hash_pos_1 q2_change q1_change \\\n",
"id 0.115121 0.071093 -0.020399 -0.356764 \n",
"q1_hash 0.060618 -0.046562 0.239944 0.427433 \n",
"q2_hash -0.583149 -0.472782 0.998102 0.315344 \n",
"q1_freq 0.128596 0.210646 -0.397409 -0.464393 \n",
"q2_freq 0.292966 0.292321 -0.471925 -0.315102 \n",
"is_duplicate 0.123509 0.207493 -0.346618 -0.259241 \n",
"freq_diff 0.072430 -0.164259 0.069072 0.044340 \n",
"q1_hash_freq 0.129154 0.210356 -0.406110 -0.498197 \n",
"q2_hash_freq 0.206482 0.205859 -0.365866 -0.268117 \n",
"q_hash_pos 1.000000 0.805124 -0.590683 -0.068443 \n",
"q_hash_pos_1 0.805124 1.000000 -0.477532 -0.152411 \n",
"q2_change -0.590683 -0.477532 1.000000 0.337464 \n",
"q1_change -0.068443 -0.152411 0.337464 1.000000 \n",
"q1_q2_change_mean -0.582470 -0.476366 0.998590 0.386955 \n",
"q1_q2_change_min -0.590505 -0.477399 0.999479 0.356322 \n",
"q1_q2_change_max -0.147689 -0.232615 0.513399 0.824397 \n",
"q_change_pair 0.213000 0.323326 -0.556578 -0.677780 \n",
"\n",
" q1_q2_change_mean q1_q2_change_min q1_q2_change_max \\\n",
"id -0.040099 -0.027226 -0.281257 \n",
"q1_hash 0.259142 0.247919 0.364492 \n",
"q2_hash 0.995484 0.997163 0.495795 \n",
"q1_freq -0.415473 -0.400677 -0.519822 \n",
"q2_freq -0.480048 -0.471667 -0.424641 \n",
"is_duplicate -0.354151 -0.345165 -0.369878 \n",
"freq_diff 0.070160 0.058338 0.276045 \n",
"q1_hash_freq -0.425902 -0.410139 -0.544893 \n",
"q2_hash_freq -0.373507 -0.365617 -0.358035 \n",
"q_hash_pos -0.582470 -0.590505 -0.147689 \n",
"q_hash_pos_1 -0.476366 -0.477399 -0.232615 \n",
"q2_change 0.998590 0.999479 0.513399 \n",
"q1_change 0.386955 0.356322 0.824397 \n",
"q1_q2_change_mean 1.000000 0.999143 0.549391 \n",
"q1_q2_change_min 0.999143 1.000000 0.514343 \n",
"q1_q2_change_max 0.549391 0.514343 1.000000 \n",
"q_change_pair -0.583421 -0.558467 -0.814297 \n",
"\n",
" q_change_pair \n",
"id 0.169849 \n",
"q1_hash -0.355028 \n",
"q2_hash -0.545675 \n",
"q1_freq 0.526781 \n",
"q2_freq 0.435390 \n",
"is_duplicate 0.422098 \n",
"freq_diff -0.323348 \n",
"q1_hash_freq 0.545906 \n",
"q2_hash_freq 0.373949 \n",
"q_hash_pos 0.213000 \n",
"q_hash_pos_1 0.323326 \n",
"q2_change -0.556578 \n",
"q1_change -0.677780 \n",
"q1_q2_change_mean -0.583421 \n",
"q1_q2_change_min -0.558467 \n",
"q1_q2_change_max -0.814297 \n",
"q_change_pair 1.000000 "
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"v1=0\n",
"train_comb['q_change_pair'] = (train_comb['q1_change']<v1) & (train_comb['q2_change']<v1)\n",
"train_comb['q_change_pair'] = train_comb['q_change_pair'].astype(int)\n",
"train_comb.corr()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(79709, 17)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(32092, 17)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(11706, 17)"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"0.5579190158892875"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_comb[train_comb['q_change_pair']==1].shape\n",
"train_comb[train_comb['q_hash_pos_1']==1].shape\n",
"train_comb[(train_comb['q_change_pair']==0) & (train_comb['q_hash_pos_1']==1)].shape\n",
"train_comb[(train_comb['q_change_pair']==0) & (train_comb['q_hash_pos_1']==1)]['is_duplicate'].mean()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
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"3621 3621 7174 7175 \n",
"4816 4816 9508 6261 \n",
"5124 5124 10097 10098 \n",
"5248 5248 10333 10334 \n",
"5855 5855 11498 11499 \n",
"5869 5869 11524 11525 \n",
"6211 6211 12177 12178 \n",
"7689 7689 15008 15009 \n",
"7725 7725 15080 15081 \n",
"9016 9016 15008 17546 \n",
"9444 9444 18346 18347 \n",
"9491 9491 18434 18435 \n",
"9703 9703 18844 18845 \n",
"10032 10032 18435 19474 \n",
"10409 10409 10097 20167 \n",
"11046 11046 21361 21362 \n",
"11050 11050 21368 21369 \n",
"11834 11834 22837 22838 \n",
"11839 11839 22847 22848 \n",
"11894 11894 22950 22951 \n",
"12050 12050 23246 23247 \n",
"13383 13383 10333 25707 \n",
"14437 14437 27653 27654 \n",
"14758 14758 28244 28245 \n",
"... ... ... ... \n",
"388551 388551 15081 120999 \n",
"388566 388566 85620 228882 \n",
"389350 389350 521838 93535 \n",
"389608 389608 52188 69314 \n",
"390410 390410 61020 10097 \n",
"390732 390732 49343 206784 \n",
"391610 391610 15080 524207 \n",
"391745 391745 193638 405559 \n",
"391994 391994 76559 231771 \n",
"392468 392468 40744 170140 \n",
"393066 393066 525816 49344 \n",
"393349 393349 56907 279523 \n",
"394030 394030 167955 526888 \n",
"394877 394877 28245 61020 \n",
"394958 394958 306499 85131 \n",
"395069 395069 528013 528014 \n",
"395937 395937 85132 322215 \n",
"395996 395996 166093 18434 \n",
"396726 396726 529808 347375 \n",
"396924 396924 61020 10098 \n",
"397703 397703 480398 62994 \n",
"398993 398993 532234 532235 \n",
"398996 398996 532240 532241 \n",
"399365 399365 532624 532625 \n",
"400213 400213 533554 49344 \n",
"401474 401474 12178 31473 \n",
"401533 401533 534932 534933 \n",
"401620 401620 79563 6261 \n",
"402384 402384 335452 535860 \n",
"402487 402487 121599 238385 \n",
"\n",
" question1 \\\n",
"1056 I search for someone who is definitely on Snap... \n",
"1869 How do u get olds messages from snapchat? \n",
"2115 Why is Snapchat currently more or less success... \n",
"2333 Why can I not add my friend back on Snapchat? \n",
"3158 I forgot my password and the email address I u... \n",
"3499 Is Snapchat purposefully leaking false acquisi... \n",
"3621 How can I increase my Snapchat score instantly? \n",
"4816 How do I address formally two persons in an em... \n",
"5124 How do you delete saved Snapchat messages that... \n",
"5248 What happens when I block and unblock someone ... \n",
"5855 If it shows up as pending on Snapchat did they... \n",
"5869 When I take pictures and send them in SnapChat... \n",
"6211 Why can I not see a friends Snapchat score any... \n",
"7689 How do you delete a private message which fail... \n",
"7725 On Snapchat, how do I know if someone deleted ... \n",
"9016 How do you delete a private message which fail... \n",
"9444 How can you tell if someone blocked you from v... \n",
"9491 Is there a way to block someone's Snapchat sto... \n",
"9703 Why won't Snapchat let me sign in? \n",
"10032 Is there any way to view the person's profile ... \n",
"10409 How do you delete saved Snapchat messages that... \n",
"11046 Someone deleted me from Snapchat but I can sti... \n",
"11050 Can Instagram stories kill Snapchat from the s... \n",
"11834 Is there a way to unsend Snapchats that haven'... \n",
"11839 My dear Quorans, Do we still need sales or mar... \n",
"11894 How can I make my Snapchat score increase faster? \n",
"12050 What does a red heart emoji next to somebody's... \n",
"13383 What happens when I block and unblock someone ... \n",
"14437 How is Snapchat using so much storage on my iP... \n",
"14758 On Snapchat, why does someone I deleted from f... \n",
"... ... \n",
"388551 On Snapchat, how do I know someone is still fo... \n",
"388566 On Snapchat, if I block someone and they saved... \n",
"389350 How long will it take me to learn programming ... \n",
"389608 If somebody adds you on Snapchat, why can't yo... \n",
"390410 How do I retrieve deleted Snapchat messages? \n",
"390732 How can I find out my child's Snapchat password? \n",
"391610 On Snapchat, how do I know if someone deleted ... \n",
"391745 How do I delete save message from everyone on ... \n",
"391994 How do I delete my Snapchat conversations in b... \n",
"392468 Does Snapchat send screenshot notifications fo... \n",
"393066 What's the site to go on if you forget both em... \n",
"393349 On Snapchat, what happens when you block someone? \n",
"394030 Which Typeface / Font does Snapchat use? \n",
"394877 When you delete someone off Snapchat do they a... \n",
"394958 If you logout of Snapchat, will your messages ... \n",
"395069 Someone deleted me on snapchat, so I deleted h... \n",
"395937 Can I see my deleted Snapchat history? \n",
"395996 Why can I not see someone's story on snapchat? \n",
"396726 What is Snapchat's strategy? \n",
"396924 How do I retrieve deleted Snapchat messages? \n",
"397703 If I unfriend someone on Snapchat can I still ... \n",
"398993 How would you evaluate Snapchat's RSU offer ag... \n",
"398996 Historical gun confiscations? \n",
"399365 Which database is used by snapchat? \n",
"400213 How do I get into someone's Snapchat account? \n",
"401474 Why can't I delete my messages on Snapchat? \n",
"401533 What is the salary for new grads starting at S... \n",
"401620 How can I figure out my Snapchat password and ... \n",
"402384 What are the Snapchat usernames of celebrities? \n",
"402487 How do I see old snapchat conversations? \n",
"\n",
" question2 is_duplicate \n",
"1056 On Snapchat, someone blocked me, but still sho... 0 \n",
"1869 What should I do to hack Snapchat messages rem... 0 \n",
"2115 How is Snapchat valued more than Twitter? 0 \n",
"2333 If you delete someone off snapchat how do you ... 0 \n",
"3158 How do I delete an account on instagram if I c... 0 \n",
"3499 How can I create and host an Our Story / Live ... 0 \n",
"3621 Is Snapchat dead? 0 \n",
"4816 I forgot my password and the email address I u... 0 \n",
"5124 How do you delete messages on Snapchat? 0 \n",
"5248 On Snapchat, when you block somebody, then unb... 0 \n",
"5855 Today “Mumbai” is Live on Snapchat. What shoul... 0 \n",
"5869 I deleted my Snapchat memories after I backed ... 0 \n",
"6211 Why can't I delete my messages on Snapchat? 0 \n",
"7689 Is there any way to delete Snapchat saved mess... 0 \n",
"7725 On Snapchat, how do I know someone is still fo... 0 \n",
"9016 How can I delete \"pending\" messages in snapchat? 0 \n",
"9444 Can someone view my story on Snapchat if they'... 0 \n",
"9491 Is there any way to view the person's profile ... 0 \n",
"9703 Which is the best broadband service in Vishal ... 0 \n",
"10032 I added someone on snapchat and as far as I'm ... 0 \n",
"10409 Why can't I unsave messages on Snapchat? 0 \n",
"11046 If I deleted someone on snapchat, and then mad... 0 \n",
"11050 Will Instagram Stories outdo Snapchat? 1 \n",
"11834 Why can’t I send a chat on Snapchat? 0 \n",
"11839 In the future will we still see startups like ... 0 \n",
"11894 How does score increase on Snapchat? 1 \n",
"12050 What do the different colors of hearts in emoj... 0 \n",
"13383 If you block someone on WhatsApp what happen t... 0 \n",
"14437 What technology stack does Snapchat use? 0 \n",
"14758 When you delete someone off Snapchat do they a... 0 \n",
"... ... ... \n",
"388551 Someone deleted me on Snapchat so I deleted th... 0 \n",
"388566 How can I see if someone has saved your messag... 0 \n",
"389350 How long would it take to learn programming we... 0 \n",
"389608 On Snapchat, I deleted someone. Can they re-ad... 0 \n",
"390410 How do you delete saved Snapchat messages that... 0 \n",
"390732 How can I hack my snapchat password? 1 \n",
"391610 How do you know if someone delete you on snapc... 1 \n",
"391745 How do I delete a conversation from snapchat? 1 \n",
"391994 If you delete your snapchat does it delete the... 0 \n",
"392468 Does Snapchat tell the user who screenshotted ... 0 \n",
"393066 How do I get someone's Snapchat password? 0 \n",
"393349 On Snapchat, when someone blocks you, can they... 0 \n",
"394030 What font is on Snapchat for iPhone 6? 0 \n",
"394877 How do I retrieve deleted Snapchat messages? 0 \n",
"394958 How do I delete sent pictures on chat for Snap... 0 \n",
"395069 My ex blocked me on Snapchat, then appeared on... 0 \n",
"395937 How do I recover deleted snapchats? 0 \n",
"395996 Is there a way to block someone's Snapchat sto... 0 \n",
"396726 What is Snapchat? 0 \n",
"396924 How do you delete messages on Snapchat? 0 \n",
"397703 If I unfriend someone on Snapchat can they sti... 1 \n",
"398993 I am trying to estimate the value of 1500 RSU ... 0 \n",
"398996 How effective are the game mechanics in Snapch... 0 \n",
"399365 Why would someone use Instagram Stories over S... 0 \n",
"400213 How do I get someone's Snapchat password? 0 \n",
"401474 On Snapchat, if I remove someone as a friend, ... 0 \n",
"401533 What is the salary for new grads starting at A... 0 \n",
"401620 I forgot my password and the email address I u... 0 \n",
"402384 How can I change my Snapchat username? 0 \n",
"402487 Why does Snapchat automatically delete the his... 0 \n",
"\n",
"[782 rows x 6 columns]"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df= pd.read_csv(PATH+'train.csv')\n",
"pos = df\n",
"v1 = 'Snapchat'\n",
"record = pos[(pos['question1'].str.contains(v1)) | (pos['question2'].str.contains(v1))]\n",
"record"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>qid1</th>\n",
" <th>qid2</th>\n",
" <th>question1</th>\n",
" <th>question2</th>\n",
" <th>is_duplicate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>93883</th>\n",
" <td>93883</td>\n",
" <td>51852</td>\n",
" <td>156879</td>\n",
" <td>What's the best way to learn faster?</td>\n",
" <td>How can I learn to speak faster?</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>188448</th>\n",
" <td>188448</td>\n",
" <td>286982</td>\n",
" <td>43764</td>\n",
" <td>How do I learn anything fast?</td>\n",
" <td>How can you learn fast?</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367493</th>\n",
" <td>367493</td>\n",
" <td>43764</td>\n",
" <td>230277</td>\n",
" <td>How can you learn fast?</td>\n",
" <td>What is the fastest, and the most efficient wa...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id qid1 qid2 question1 \\\n",
"93883 93883 51852 156879 What's the best way to learn faster? \n",
"188448 188448 286982 43764 How do I learn anything fast? \n",
"367493 367493 43764 230277 How can you learn fast? \n",
"\n",
" question2 is_duplicate \n",
"93883 How can I learn to speak faster? 0 \n",
"188448 How can you learn fast? 0 \n",
"367493 What is the fastest, and the most efficient wa... 0 "
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list1 = [\"What's the best way to learn faster?\",\n",
" 'How do I learn quickly?',\n",
" 'How can l learn faster?',\n",
" 'How can I learn faster?',\n",
" 'How should I learn faster?',\n",
" 'How do I learn with a minimal amount of time?',\n",
" 'How do you learn the most in the shortest time?',\n",
" 'How can you learn fast?']\n",
"# df= pd.read_csv(PATH+'train.csv')\n",
"record = df[(df['question1'].isin(list1)) | (df['question2'].isin(list1))]\n",
"record[record['is_duplicate']==0]"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df1= pd.read_csv(PATH+'train.csv',nrows=50000)\n",
"pos = df1[df1.is_duplicate==0]\n",
"g = nx.Graph()\n",
"g.add_nodes_from(pos.question1)\n",
"g.add_nodes_from(pos.question2)\n",
"edges = list(pos[['question1','question2']].to_records(index=False))\n",
"g.add_edges_from(edges)\n",
"cc = filter(lambda x : (len(x) > 2), \n",
" nx.connected_component_subgraphs(g))\n",
"dict0 = {}\n",
"for i in range(len(cc)):\n",
" dict0[i] = cc[i].nodes()\n",
" \n",
"pos = df1[df1.is_duplicate==1]\n",
"g = nx.Graph()\n",
"g.add_nodes_from(pos.question1)\n",
"g.add_nodes_from(pos.question2)\n",
"edges = list(pos[['question1','question2']].to_records(index=False))\n",
"g.add_edges_from(edges)\n",
"cc = filter(lambda x : (len(x) > 2), \n",
" nx.connected_component_subgraphs(g))\n",
"dict1 = {}\n",
"for i in range(len(cc)):\n",
" dict1[i] = cc[i].nodes()\n"
]
},
{
"cell_type": "code",
"execution_count": 178,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"What is the best way to deal with social anxiety disorder?\n",
"[\"What's it like to have social anxiety disorder?\", 'How common is social anxiety disorder?', 'What is the best way to deal with social anxiety disorder?']\n"
]
},
{
"data": {
"text/plain": [
"[['What are different ways to deal with social anxiety?',\n",
" 'How do I deal with social anxiety disorder?',\n",
" \"What's the best advice you could give to a person suffering from social anxiety disorder?\",\n",
" 'How should you deal with social anxiety?',\n",
" 'How do I deal with my social anxiety?',\n",
" 'What is the best way to deal with social anxiety disorder?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Why does temperature decrease with increase in altitude?\n",
"['What is the maximum altitude for a drone?', 'Why is it generally colder at higher elevations?', 'Why does temperature decrease with increase in altitude?']\n"
]
},
{
"data": {
"text/plain": [
"[['Why does temperature decrease when altitude increases?',\n",
" 'Why does the air temperature decrease with an increase in height?',\n",
" 'Why does temperature decrease with increase in altitude?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"How do I open a private Instagram account?\n",
"['How do I open a private Instagram account?', 'How many Oriya girls wear mini skirts?', 'How do I recover a deleted Instagram name?', \"Is it possible to view someone's private Instagram account?\", 'Is there a way to view a private Instagram?', 'Does viewing your own Instagram video count as a view?', 'Why are Instagram filters free?', \"How can you look at someone's private Instagram account without following them?\", \"How do I see followers on someone's private Instagram?\", \"Why did my crush followed my cousin's private account on instagram, what does this mean?\", 'Why did Symbian fail?', 'If I link my Instagram account to my Facebook account, will my friends from Facebook be able to see the photos I post even if my Instagram account is private?', 'Can you view pictures on Instagram without an account?']\n"
]
},
{
"data": {
"text/plain": [
"[['How do I open a private Instagram account?',\n",
" 'How do I look at the followers of a private instagram account?',\n",
" \"How do I look at someone's Instagram when it's private?\",\n",
" 'How can I view a private Instagram?',\n",
" \"How do I look at photos on an Instagram account if it's private?\",\n",
" 'How do I see a private Instagram account?',\n",
" \"How can I see someone's private instagram account?\",\n",
" 'Can I view a private Instagram?',\n",
" 'Can I see a private Instagram?',\n",
" \"How do I view someones's private instagram pictures?\"]]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Which is the best place to visit in Goa?\n",
"['Which is the best place to visit in Goa?', 'Any hippie places to visit in Goa?', 'What are the 10 best things that can be done in Goa?']\n"
]
},
{
"data": {
"text/plain": [
"[['We are Planning to visit Goa for three days,which are the best places to visit?',\n",
" 'What are the best places to visit in Goa?',\n",
" 'What are some of the best local places to visit in Goa?',\n",
" 'Which are best places to visit in GOA during vacations?',\n",
" 'Which are the best places in Goa to visit alone?',\n",
" 'What places should one visit in Goa?',\n",
" 'What are all the best places to visit in goa?',\n",
" 'What are the places in Goa to visit?',\n",
" 'Which is the best place to visit in Goa with Friends?',\n",
" 'What are the best places to visit in Goa in 2 days?',\n",
" 'Which is the best place to visit in Goa?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"How do I get started with Android application development?\n",
"['How do I get started with Android application development?', 'How do I make an Android application using Python?', 'What should I do to make an Android app?']\n"
]
},
{
"data": {
"text/plain": [
"[['How do I begin with android application development?',\n",
" 'How do I get started with Android application development?',\n",
" 'What is the best way to get started with learning Android development?',\n",
" 'How do I start with Android development?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"How do you get a girl to like you?\n",
"['How do you get a girl to like you?', 'How do I get over a girl I like?', 'How do I get over a girl that I like?', 'What you can do to Get a Boy to like You?']\n"
]
},
{
"data": {
"text/plain": [
"[[\"How do I get a girl's attention?\",\n",
" 'How do you get a girl to like you?',\n",
" 'There is a girl that I like. How do I get her to like me?',\n",
" 'What are some ways to get a girlfriend?',\n",
" 'What is the best way to get a girl to like you?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"What is the best way to start learning hacking?\n",
"['What is the best way to start learning hacking?', 'How does one become a hacker?', 'How do I find a hacker?']\n"
]
},
{
"data": {
"text/plain": [
"[['What is the best way to learn how to hack (whitehat)?',\n",
" 'What are the best way to learn hacking?',\n",
" 'What is the best way to learn hacking in short time?',\n",
" 'How do I start hack with no knowledge?',\n",
" 'What is the best way to start learning hacking?',\n",
" 'How should I learn hacking by myself?',\n",
" 'How can I learn to hack seriously?',\n",
" 'How does a person learn how to hack?',\n",
" 'What is the best possible way for learning hacking?',\n",
" 'Which is the best way to learn hacking?',\n",
" 'Which is the best way to learn hacking just as a hobby?',\n",
" 'How does one learn how to hack?',\n",
" 'How can I learn hacking for security purposes?',\n",
" 'What is the best way to learn white hat hacking?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"What is best way to make money online?\n",
"['What is best way to make money online?', 'What is the hardest way to make money online?', 'What is best way to ask for money online?']\n"
]
},
{
"data": {
"text/plain": [
"[['What are ways I can make money online?',\n",
" 'What are ways of earning money online?',\n",
" 'What should I do to earn money online?',\n",
" 'How can I earn money on internet?',\n",
" 'What is make money online?',\n",
" 'What should I do to make money online in India?',\n",
" 'What is the easiest way to earn money from online?',\n",
" 'What are some easy ways to make done extra money online?',\n",
" 'How can I earn money easily online?',\n",
" 'How do I earn money from the Internet?',\n",
" 'How can one make money online?',\n",
" 'How can I realistically make money online?',\n",
" 'How do I really make money online?',\n",
" 'How do I make money from home?',\n",
" 'How can we earn money online without investment?',\n",
" 'How do you earn money from internet?',\n",
" 'How can I earn money part time online?',\n",
" 'How can I earn money online easily?',\n",
" \"I'm 18. How can I make money online?\",\n",
" 'What is the best way for making money online?',\n",
" \"What's the easiest way to make money online?\",\n",
" 'What are the easiest ways to make good money using the Internet?',\n",
" 'What are the best ways to earn money from home?',\n",
" 'How can I make money online quickly and easily?',\n",
" 'How should I earn money online working from home?',\n",
" 'Am not starting big? How can I make $1000 per month online?',\n",
" 'How can I make money online consistently?',\n",
" 'How can I earn from online?',\n",
" 'What are the easiest ways to earn money online?',\n",
" 'How can I earn money online, seriously?',\n",
" 'What is the easiest way to earn money using internet?',\n",
" 'What is an easy way make money online?',\n",
" 'What are ways to make money online at home?',\n",
" 'Can I earn money online?',\n",
" 'What are the various ways through which one can earn money online?',\n",
" 'How can I earn money online from home only?',\n",
" 'How do we make money online?',\n",
" 'How could I make money online?',\n",
" 'How can I start to make money online?',\n",
" 'What is the easiest way to make a little money online?',\n",
" 'How do I earn money online?',\n",
" 'What is best way to make money online?',\n",
" 'How can I make money online for job?',\n",
" 'What is the easy way to make money online?',\n",
" 'How can we earn money online in india?',\n",
" 'What is a way to make money online?',\n",
" 'How can I earn money online?',\n",
" 'What are the best ways to make money online?',\n",
" 'What are some of the best ways of earning money by working at home?',\n",
" 'How do you make money online?',\n",
" 'Is there any easy way to make money online?',\n",
" 'What are the easy ways to earn money online?',\n",
" 'How do you make easy money online?',\n",
" 'How can i make money online easily?',\n",
" 'How do I earn more money through internet/online?',\n",
" 'Can I make money online?',\n",
" 'How does one earn money online without an investment from home?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"How can I increase the traffic on my website?\n",
"['How can I increase the traffic on my website?', 'How can I increase traffic on buymarijuanaonline.store?', 'I am a blogger. I want to get huge traffic on my blog. What should I do?']\n"
]
},
{
"data": {
"text/plain": [
"[['How can I increase traffic to a story blog?',\n",
" 'How can I increase the traffic to my website?',\n",
" 'What is the best way to drive traffic to a website?',\n",
" 'How Do I get traffic on website?',\n",
" 'How do I increase traffic on my site?',\n",
" 'How can I get traffic in my website?',\n",
" 'How do I get more traffic on my website?',\n",
" 'How can I increase traffic to my website using social media?',\n",
" 'How can I increase traffic on my blog?',\n",
" 'How can I Increase the traffic of my blog?',\n",
" 'What is the best way to get free traffic to my website?',\n",
" 'How to increase my website Traffic?',\n",
" 'How can I increase the traffic on a site?',\n",
" 'How do I increase organic traffic to website?',\n",
" 'What is the best way to increase traffic for a new blog?',\n",
" 'How can I build traffic for my website?',\n",
" 'How can I increase traffic to my site and what are some suggestions on how to get more of it?',\n",
" 'How do I increase traffic on my site?',\n",
" 'How can I get traffic on website?',\n",
" 'How can I get traffic for my website?',\n",
" 'What is the best way to get traffic on your website?',\n",
" 'How can I increase the traffic on my blog (www.midnightexpressions.wordpress.com)?',\n",
" 'How can I increase the traffic on my website? Jeenkart.com',\n",
" 'What are the best way to increase website traffic organically?',\n",
" 'How do i get traffic for website?',\n",
" 'How can I increase the traffic to a website?',\n",
" 'How do I get more traffic to my site?',\n",
" 'How can I increase traffic to my websites by Facebook?',\n",
" 'How can I increase website traffic?',\n",
" 'How do I flow traffic to my website?',\n",
" 'How can I increase the traffic on my website without investing?',\n",
" 'What is the increase organic traffic of websites?',\n",
" 'How traffic increased for websites through backlinks?',\n",
" 'How can I increase the traffic on my website?',\n",
" 'How can I increase traffic very soon on my blog?',\n",
" 'How can I increase a website traffic?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
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{
"name": "stdout",
"output_type": "stream",
"text": [
"What's your new year resolution for 2017?\n",
"[\"What's your new year resolution for 2017?\", \"Can you suggest some good new year resolution's that anyone can implement for 2017 ?\", \"What's your new year resolution do or not to do?\"]\n"
]
},
{
"data": {
"text/plain": [
"[[\"What will be your New Year's resolution for 2017?\",\n",
" 'What are your new year resolutions for 2017?',\n",
" \"What are some of your New Year's resolutions for 2017?\",\n",
" 'What is your new year resolution, short term and long term goal for 2017?',\n",
" \"What is your New Year's resolution for 2017?\",\n",
" 'What are your resolutions for 2017? And why?',\n",
" \"What's your New Year resolutions for 2017 and what will you do to accomplish your goal?\",\n",
" \"What's are your resolutions for 2017?\",\n",
" \"What's your New Year's resolution for 2017?\",\n",
" 'What is your resolution for 2017?',\n",
" \"What are your New Year's resolutions for 2017?\",\n",
" 'Do you have any New Years resolutions for 2017?',\n",
" 'What is your new year resolution?',\n",
" \"What's your resolutions for 2017?\",\n",
" 'What is your resolution for this year 2017?',\n",
" 'What would be your New Year resolutions for 2017?',\n",
" 'What will be your new year resolution for 2017 and your plan of execution?',\n",
" 'What can be my new year resolution for 2017?',\n",
" 'What is your new year resolution for 2017 or goal for 2017?',\n",
" 'What are some of the best New Years resolutions for 2017?',\n",
" 'What are your 2017 resolutions?',\n",
" 'What are your New Year\\xe2\\x80\\x99s resolutions?',\n",
" \"What is your creative New Year's resolution for 2017?\",\n",
" \"What's your 2017 new year resolution?\",\n",
" 'What should be my resolution for 2017?',\n",
" \"What's your new year resolution for 2017?\",\n",
" 'What Is your New year resolutions in 2017?',\n",
" 'What is your New Year resolution?',\n",
" 'What are your New Years resolutions for 2017?',\n",
" 'What are your New Year resolutions for the upcoming year 2017?',\n",
" \"What is your New Year's resolutions for 2017?\",\n",
" 'What is/are your New Year resolutions for 2017?',\n",
" 'What are some new year resolutions for 2017?',\n",
" 'What is your 2017 New Year\\xe2\\x80\\x99s resolution?',\n",
" 'What is your New Year Resolution for 2017?',\n",
" 'What are some meaningful new year resolutions for 2017?',\n",
" 'What is your New Year\\xe2\\x80\\x99s Resolution(s) for 2017?',\n",
" \"What's your New Year 2017 resolution?\",\n",
" 'What are your new year resolutions\\xe2\\x80\\x992017?',\n",
" 'What are the resolutions you are going to take for the upcoming New year 2017?',\n",
" \"What's your new year 2017 resolution to improve your daily life routine?\",\n",
" \"What are your New Year's resolutions?\",\n",
" 'What will be your 2017 resolution?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"What app allows you to listen to music without WiFi or Internet?\n",
"['What app allows you to listen to music without WiFi or Internet?', 'Which is the best no WiFi music app for the iPhone?', 'How do you see a saved Wi-Fi password on Android without root privileges?', 'What is a downloadable app or device which allows you to listen to audio academic lessons without wifi?']\n"
]
},
{
"data": {
"text/plain": [
"[['What app allows you to listen to music without WiFi or Internet?',\n",
" 'What music app is free without wifi connection?',\n",
" 'Are there any music apps that I can listen to without needing an Internet connection?',\n",
" 'What app for music without wifi for iPod?',\n",
" 'What are some free music apps to download whereby you can download music in the app itself and listen to the music when offline?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Where can I find delicious cupcakes at Gold Coast?\n",
"['Where can I find delicious cupcakes at Gold Coast?', 'Where can I found different types of gluten free vegan cupcakes in Gold Coast?', 'Where can I get best and freshest ingredients on cupcakes in Gold Coast?']\n"
]
},
{
"data": {
"text/plain": [
"[['Where can I get an unique taste for cupcakes in Gold Coast?',\n",
" 'Where can I get best flavors, designs and decorations for cupcakes at Gold Coast?',\n",
" 'Where can I buy best quality customized cupcakes in Gold Coast?',\n",
" 'Where can I find delicious cupcakes at Gold Coast?',\n",
" 'Where can I found different cupcake flavors in Gold Coast?',\n",
" 'Where can I buy special flavor cupcake at Gold Coast?',\n",
" 'Where can I buy very incredible and most amazing cupcakes in Gold Coast?',\n",
" 'Where can I get good quality cupcakes and a lot of different flavor in Gold Coast?',\n",
" 'Where can I found different flavours for cupcakes at Gold Coast?',\n",
" 'Where can I get highest quality, tastiest cupcakes across the Gold Coast?',\n",
" 'Where can I buy best quality gourmet cupcakes in Gold Coast?',\n",
" 'Where can I get wonderful flavors on cupcakes in Gold Coast?',\n",
" 'Where can I find the best quality cupcakes in Gold Coast?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"What are the most annoying types of questions on Quora?\n",
"['What are the most annoying types of questions on Quora?', 'What are the most annoying fitness questions that need to die on Quora?', 'What is the silliest question on Quora?']\n"
]
},
{
"data": {
"text/plain": [
"[['What are some dumb questions ever asked on Quora?',\n",
" 'What are the most annoying questions that you come across in Quora?',\n",
" 'What are the most annoying questions you see on Quora?',\n",
" 'What are the dumbest questions ever asked on Quora?',\n",
" 'What are the most annoying types of questions on Quora?',\n",
" 'What is the most stupid question asked on Quora?',\n",
" 'What are the most annoying questions that you feel ridiculous in Quora?',\n",
" 'What is the stupidest question asked on Quora?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"How do I reset my Gmail password when I don't remember my recovery information?\n",
"[\"How do I reset my Gmail password when I don't remember my recovery information?\", 'How do I reset my password for old Gmail account?', 'How do I recover password for Gmail password without security questions?', \"How do I recover my Gmail password when I don't remember my recovery mail ID?\", 'How do I recover my forgotten Gmail password?', 'How can I reset/change my password for a different Gmail account from my new account?', 'Can I reset my Gmail password without having to change my username?', 'How do I recover my Gmail password with a recovery mobile number and a Gmail address?', 'How do I recover my Gmail account without the recovery email?', \"I have a Gmail address and can't remember the password. The only pictures of my son I own are on it. How do I go about getting into my account?\", 'How can you recover an gmail account without any information?']\n"
]
},
{
"data": {
"text/plain": [
"[[\"How do you rest your rescue password if you don't remember your answers to the sequrity questions?\",\n",
" \"How do I reset my Gmail password when I don't remember my recovery information?\",\n",
" 'How do I reset my Gmail password when I forgotten it?',\n",
" 'How do I reset my gmail password when they are not highlighting my recovery email option?',\n",
" \"How do I reset my Gmail password when I don't have access to my recovery information?\",\n",
" \"I was suddenly logged off Gmail. I can't remember my Gmail password and just realized the recovery email is no longer alive. What can I do?\",\n",
" 'How can I add a recovery phone number to my Gmail account without password to my account?',\n",
" \"I can't remember my Gmail password or my recovery email. How can I recover my e-mail?\",\n",
" 'How can I recover my Gmail forgot my password and recovery no?',\n",
" \"I forgot my Gmail password and I can't answer the Gmail recovery questions. What can I do?\",\n",
" \"How can I reset my Gmail password if I don't remember my recovery Email and current password?\",\n",
" 'I lost my password with my Gmail account. How do I reset it without the account recovery info?',\n",
" \"With a forgotten Gmail password, how do you find an old Gmail password when you don't remember the recovery information?\",\n",
" \"How do I recover my lost Gmail password if I don't have the same number and don't remember the recovery email?\",\n",
" 'How do I reset my password to Gmail without my recovery information?',\n",
" \"How do I gain access to my gmail when I don't have access to the phone number or recovery email?\",\n",
" \"How can I reset my Gmail password when I don't remember my recovery information?\"]]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Which is the easiest programming language to learn?\n",
"['Which is the easiest programming language to learn?', 'Which is the best programming language for beginners?', 'Which language should I start with to learn coding?']\n"
]
},
{
"data": {
"text/plain": [
"[['Which is the best easiest programming language?',\n",
" 'Which is the easiest programming language to learn?',\n",
" 'Which is the easiest programming language to master?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"What are the habits of highly successful people?\n",
"['What are the habits of highly successful people?', 'What are good eating habits of successful people?', 'How could I develop the habits of highly successful people?']\n"
]
},
{
"data": {
"text/plain": [
"[['What are the habits of highly successful people?',\n",
" 'What are habits of successful people?',\n",
" 'What are some positive habits successful people practice on a daily basis?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"What are the best movies of all time?\n",
"['What are the best movies of all time?', 'What are the must watch movies to see before you die?', 'What movie can you watch all the time and never get tired of watching?', 'Which Austin Powers movie is the best?']\n"
]
},
{
"data": {
"text/plain": [
"[['What are the best movies of all time?',\n",
" 'Which is best movie in history?',\n",
" 'Which according to you is the best movie of all time? Select only one choice.']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"How could I get rich?\n",
"['How could I get rich?', 'What are the best ways to become rich quickly?', 'How do you get rich overnight?']\n"
]
},
{
"data": {
"text/plain": [
"[['How could I get rich?',\n",
" 'How can I get rich soon?',\n",
" 'What are some ways to get rich?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Which is better: an arranged marriage or a love marriage?\n",
"['Which is better: an arranged marriage or a love marriage?', 'What kind of marriages last longer? Love or arrange?', 'What are the differences between a love marriage and an arranged marriage?']\n"
]
},
{
"data": {
"text/plain": [
"[['Which is better: an arranged marriage or a love marriage?',\n",
" 'Which is better - love or an arranged marriage?',\n",
" 'Which is better - an arranged marriage or a love marriage?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"In your opinion, who won the first Trump–Clinton U.S. Presidential debate?\n",
"['In your opinion, who won the first Trump\\xe2\\x80\\x93Clinton U.S. Presidential debate?', 'Is Hillary Clinton right that Donald Trump has refused to pay workers?', 'Should first generation Americans be afraid of Trump?', 'Why are you voting for Donald Trump over Hillary Clinton?']\n"
]
},
{
"data": {
"text/plain": [
"[['Republicans: What are your opinions on the first Trump-Clinton Presidential debate?',\n",
" 'Who won the first presidential debate September 2016?',\n",
" 'Who won the first 2016 debate?',\n",
" 'Who won the first Clinton-Trump debate? And why?',\n",
" 'In your opinion, who won the first Trump\\xe2\\x80\\x93Clinton U.S. Presidential debate?',\n",
" 'Was Donald Trump trumped on the first Presidential debate?',\n",
" 'Who do you think won the first presidential debate between Hillary Clinton and Donald Trump and why?',\n",
" 'Who won the First Presidential Debate of 2016?',\n",
" 'Who won the debate between Hillary Clinton and Donald Trump on 9/26/2016?',\n",
" 'Who won the first debate in your opinion Hillary Clinton or Donald Trump?',\n",
" 'Who won the September 26, 2016 presidential debate?',\n",
" 'Who won the debate Hillary or Trump?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"How do people earn money from YouTube?\n",
"['How do people earn money from YouTube?', 'Is it worth to earn money from youtube?', 'Is there any need of money to upload videos on YouTube?']\n"
]
},
{
"data": {
"text/plain": [
"[['How can I make money fast from Youtube?',\n",
" 'How can I earn money using YouTube?',\n",
" 'How winning money from YouTube?',\n",
" 'How do people earn money through YouTube in India?',\n",
" 'How do I make money with YouTube?',\n",
" 'How can I earn money from YouTube?',\n",
" 'How do people earn money from YouTube?',\n",
" 'What are some ways to make money from YouTube?',\n",
" 'How can I make money from YouTube?',\n",
" 'Can I make money by uploading videos on YouTube (if I have subscribers)?',\n",
" 'How can I earn money in YouTube?',\n",
" 'How do I make money through YouTube?',\n",
" 'How can I make money on YouTube?',\n",
" 'How can i earn through youtube?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"How can guys last longer during sex?\n",
"['How can guys last longer during sex?', 'How do I control my premature ejaculation?', 'How do I last long during banging your girl?']\n"
]
},
{
"data": {
"text/plain": [
"[['How can guys last longer during sex?',\n",
" 'How do men last longer in bed?',\n",
" 'How can I last for a longer time during sex?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"How will demonetization affect India?\n",
"['How will demonetization affect India?', 'How long will it take for the economy to become normal again since demonetization?', 'Does the demonetization affect the normal people?']\n"
]
},
{
"data": {
"text/plain": [
"[['How will demonetization affect India?',\n",
" 'How is demonetization affecting people of India?',\n",
" 'What are the effects of demonetization in India?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"What is the best photo editing software or app?\n",
"['What is the best photo editing software or app?', 'What is the best photo editing app for Android mobile?', 'Which is the best photo editing app for Android and iPhone?']\n"
]
},
{
"data": {
"text/plain": [
"[['Which is the best photo editing software?',\n",
" 'What is the best software for photo editing?',\n",
" 'What is the best photo editing software or app?',\n",
" 'What is the best photo editing application and software available online or offline?',\n",
" 'Which are best apps for photo edit?',\n",
" 'What is the best tool for photo editing?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"What are some ways to lose weight fast?\n",
"['What are some ways to lose weight fast?', 'I have to reduce my weight by 15 kgs. What is the healthy rate at which I should aim to reduce per month? 21yr old-female-66kg-5.2ft?', 'What are the best ways to lose weight fast?', 'How can I lose weight fast and never gain it again?']\n"
]
},
{
"data": {
"text/plain": [
"[['How do I lose fats and excessive weight from body?',\n",
" 'How should I lose weight?',\n",
" 'Could you please give some weight loss advice for me and my husband?',\n",
" 'What are the best way of loose the weight?',\n",
" 'How do I lose 38 pounds in a year?',\n",
" 'What are the best simple ways to loose weight?',\n",
" 'How do I lose weight without stopping?',\n",
" 'How can I lose 25 kg?',\n",
" 'How can I efficiently lose weight?',\n",
" 'How do I lose weight from 70 to 50?',\n",
" 'How can I lose weight loss?',\n",
" 'How do I actually go about losing weight?',\n",
" 'What is the most effective everlasting method of losing weight?',\n",
" 'What is the best method of losing weight?',\n",
" 'What should you do if you want to lose a lot of weight?',\n",
" 'The best way for weight loss?',\n",
" 'How do lose weight with healthy way?',\n",
" \"What's the best, most effective tips for losing weight?\",\n",
" 'What are the best was to lose weight?',\n",
" 'What is the best way to reduce body weight?',\n",
" \"I'm fat. How do I lose weight?\",\n",
" 'How do I lose my weight from 58 to 50 kgs?',\n",
" 'How can I lose weight slowly and naturally?',\n",
" \"What's the best plan to lose weight?\",\n",
" 'How do I lose weight ayurvedically?',\n",
" 'What can I do to loose 20-30kg?',\n",
" 'What are the ways of losing weight?',\n",
" 'How do I actually lose weight?',\n",
" 'How do I lose 15 kilos?',\n",
" 'What is the best way to lose weight and not gain it back?',\n",
" 'How can I make a plan to lose 12-15 pounds in 2-3 months?',\n",
" 'How can I lose an extreme amount of weight?',\n",
" 'What are the best ways to lose weight? What is the best diet plan?',\n",
" 'How should I loose weight?',\n",
" 'What can I do to lose 20 pounds?',\n",
" 'How do I suck it up and lose weight?',\n",
" 'What are the best things to do when working on losing weight?',\n",
" 'How do I lose 20-30 kg?',\n",
" 'I am ugly and fat, how to lose weight?',\n",
" 'How do I lose 45 pounds the easiest way if I have cravings?',\n",
" 'How do I get rid of excessive weight?',\n",
" 'How can I lose weight safely?',\n",
" 'Can you offer me any advice on how to lose weight?',\n",
" 'How can I lose weight effectively?',\n",
" 'How do I lose weight?',\n",
" 'How can I lose post marriage weight?',\n",
" 'How can I lose weight at age 55?',\n",
" 'What is the fastest possible way to lose weight?',\n",
" 'How do I lose weight without quitting?',\n",
" 'Which are the best ways to lose weight?',\n",
" \"I love food and have a big appetite. I'm also quite busy. What tips can you give me to lose weight?\",\n",
" 'What are some good ways to lose weight?',\n",
" \"I'm overweight. How can I begin to lose weight?\",\n",
" 'How can you lose weight fast in a healthy way?',\n",
" 'What is the best way to be in a calorie deficit and lose weight successfully?',\n",
" 'What should I do to reduce weight?',\n",
" 'How can I lose 10 Kilos?',\n",
" 'How should one change their diet to lose weight?',\n",
" 'How can I slowly lose weight?',\n",
" 'How can I really start losing weight?',\n",
" 'What would be a realistic plan to lose weight?',\n",
" 'What are some ways to lose weight fast?',\n",
" 'What is the best guide to lose unwanted pounds?',\n",
" 'How can I lose 4kg weight?',\n",
" 'Where do I find a simple to understand solution on how to lose weight?',\n",
" 'How do I lose 30 pounds?',\n",
" 'How do i lose weight?',\n",
" 'What are the best ways to lose weight, especially around your core?',\n",
" 'What are the best ways to lose weight?',\n",
" \"I'm 12 and at 60 kg and about 144 cm how do I lose weight?\",\n",
" 'What is the easiest way to loose weight?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"How do I know the AO code and AO type for a PAN card application?\n",
"['How do I know the AO code and AO type for a PAN card application?', 'What is a pan card?', 'What is PAN?']\n"
]
},
{
"data": {
"text/plain": [
"[['I have to apply for a new pan card, How should I find my AO number?',\n",
" 'How do I know the AO code and AO type for a PAN card application?',\n",
" 'How do I select my AO code for new Pan Card application?',\n",
" 'What AO code should NRIs or OCIs use when applying for a PAN card?',\n",
" 'What is AO code for a student who is applying for PAN card but does not have any source of income?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"How do I get tickets for The Kapil Sharma Show ?\n",
"['How do I get tickets for The Kapil Sharma Show ?', 'How do I participate in The Kapil Sharma Show as audience member?', 'Does Kapil Sharma pay celebrities to come to his show?', \"When is Kapil Sharma's next show?\"]\n"
]
},
{
"data": {
"text/plain": [
"[['How can I watch The Kapil Sharma Show live in Mumbai?',\n",
" 'How do I get tickets for The Kapil Sharma Show ?',\n",
" 'How can I get an entry in The Kapil Sharma Show?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"What do you people think of Mr. Arvind Kejriwal and his AAP?\n",
"['What do you people think of Mr. Arvind Kejriwal and his AAP?', 'What Anna Hazare think about PM Modi?', 'Why did Kumar Vishwas lie to people about what was written in the suicide letter of Gajendra in the AAP rally?']\n"
]
},
{
"data": {
"text/plain": [
"[['What do you people think of Mr. Arvind Kejriwal and his AAP?',\n",
" 'May 2016: What do Delhi people think about Kejriwal, are YOU really satisfied with Governance and his attitude to PM Modi?',\n",
" 'What Delhi people think about kejriwal?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"How can you tell if you're a narcissist?\n",
"[\"How can you tell if you're a narcissist?\", 'How can I tell if I\\xe2\\x80\\x99m a narcissist?', 'How can I identify a narcissist?', 'How do you tell a narcissist they are narcissist?']\n"
]
},
{
"data": {
"text/plain": [
"[[\"How can you tell if you're a narcissist?\",\n",
" 'How can you tell if you are a narcissist?',\n",
" 'How can I identify a narcissist?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"What is the best phone to buy below 15k?\n",
"['What is the best phone to buy below 15k?', 'Why is cellphone called as cellphone?', 'Which is the best mobile phone to buy below 2k?']\n"
]
},
{
"data": {
"text/plain": [
"[['Can you suggest a best budget phone below 15k?',\n",
" 'Which is best mobile under 15000?',\n",
" 'Which is the best phone under \\xe2\\x82\\xb915000?',\n",
" 'Which mobile phone should I buy under Rs.15000?',\n",
" 'Which phone would be the best for \\xe2\\x82\\xb915,000?',\n",
" 'Which smartphone would be best under 15000? (2016)',\n",
" 'Which mobile is better under 15k?',\n",
" 'Which phone is best under 15k?',\n",
" 'What is the best phone I can get for below 15k?',\n",
" 'Which are best mobile phones to buy under 15000?',\n",
" 'What are the good options for mobile phones under 15000?',\n",
" 'Which is the best phone below 15000?',\n",
" 'Which phone is best to buy under 15k?',\n",
" 'Which phone should I buy under 15k?',\n",
" 'Which is the best phone under 15000 Rs.?',\n",
" 'What is the best phone to buy below 15k?',\n",
" 'Which phone should I buy under INR 15K?',\n",
" 'What is the best phone I can buy under the price of 15000?',\n",
" 'What are some good smartphones under 15k?',\n",
" 'What phone should I buy under Rs 15000?',\n",
" 'Which is the best mobile below 15000?']]"
]
},
"execution_count": 178,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cnt=0\n",
"for key,value in dict0.iteritems():\n",
" for v1 in value:\n",
"# record = test[(test['question1'].isin(v1)) | (test['question2'].isin(v1))]\n",
" result = [value1 for key,value1 in dict1.iteritems() if v1 in value1]\n",
" if len(result)>0:\n",
" # nx.draw_circular(cc[key], with_labels=True, alpha=0.5, font_size=12)\n",
" # plt.show()\n",
" print v1\n",
" print value\n",
" result\n",
" cnt+=1\n",
" if cnt>1:\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_or = pd.read_csv(PATH+'train.csv', header=0)\n",
"test_or = pd.read_csv(PATH+'test.csv', header=0)"
]
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train = pd.read_csv(PATH+'train.csv', header=0)\n",
"test = pd.read_csv(PATH+'test.csv', header=0)\n",
"\n",
"def stem_str(x,stemmer=SnowballStemmer('english')):\n",
" x = text.re.sub(\"[^a-zA-Z0-9]\",\" \", x)\n",
" x = (\" \").join([stemmer.stem(z) for z in x.split(\" \")])\n",
" x = \" \".join(x.split())\n",
" return x\n",
"porter = PorterStemmer()\n",
"snowball = SnowballStemmer('english')\n",
"\n",
"train['question1'] = train['question1'].astype(str).apply(lambda x:stem_str(x.lower(),snowball))\n",
"test['question1'] = test['question1'].astype(str).apply(lambda x:stem_str(x.lower(),snowball))\n",
"train['question2'] = train['question2'].astype(str).apply(lambda x:stem_str(x.lower(),snowball))\n",
"test['question2'] = test['question2'].astype(str).apply(lambda x:stem_str(x.lower(),snowball))"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(147698, 147698)"
]
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# train= pd.read_csv(PATH+'train.csv')\n",
"pos = train[train.is_duplicate==1]\n",
"\n",
"import networkx as nx\n",
"\n",
"g = nx.Graph()\n",
"g.add_nodes_from(pos.question1)\n",
"g.add_nodes_from(pos.question2)\n",
"edges = list(pos[['question1','question2']].to_records(index=False))\n",
"g.add_edges_from(edges)\n",
"len(set(pos.question1) | set(pos.question2)), g.number_of_nodes()"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"506\n"
]
},
{
"data": {
"text/plain": [
"[u'how can i earn from onlin',\n",
" u'what are the easiest way to make good money use the internet',\n",
" u'how can i make money onlin easili',\n",
" u'what is an easi way make money onlin',\n",
" u'how should i earn money onlin work from home',\n",
" u'how can i earn money onlin easili',\n",
" u'what is a way to make money onlin',\n",
" u'what are the various way through which one can earn money onlin',\n",
" u'how do i earn money from the internet',\n",
" u'how do you make money onlin',\n",
" u'what should i do to earn money onlin',\n",
" u'how can i earn money onlin from home onli',\n",
" u'what are way of earn money onlin',\n",
" u'what are way to make money onlin at home',\n",
" u'how can i earn money easili onlin',\n",
" u'how do i realli make money onlin',\n",
" u'what are the best way to earn money from home',\n",
" u'how do you make easi money onlin',\n",
" u'what is the easi way to make money onlin',\n",
" u'is there ani easi way to make money onlin',\n",
" u'am not start big how can i make 1000 per month onlin',\n",
" u'i m 18 how can i make money onlin',\n",
" u'what are some of the best way of earn money by work at home',\n",
" u'how doe one earn money onlin without an invest from home',\n",
" u'what is the easiest way to earn money from onlin',\n",
" u'can i make money onlin',\n",
" u'how do i earn more money through internet onlin',\n",
" u'how can we earn money onlin without invest',\n",
" u'can i earn money onlin',\n",
" u'how can i start to make money onlin',\n",
" u'what are some easi way to make done extra money onlin',\n",
" u'how can we earn money onlin in india',\n",
" u'how can i start make money use internet',\n",
" u'what should i do to make money onlin in india',\n",
" u'what is make money onlin',\n",
" u'how do we make money onlin',\n",
" u'how can i earn money on internet',\n",
" u'how can i make money onlin quick and easili',\n",
" u'how could i make money onlin',\n",
" u'how can i earn money onlin',\n",
" u'what are the easiest way to earn money onlin',\n",
" u'what are the easi way to earn money onlin',\n",
" u'what is the easiest way to make a littl money onlin',\n",
" u'how can i make money onlin consist',\n",
" u'how can i earn money part time onlin',\n",
" u'how can one make money onlin',\n",
" u'how can i realist make money onlin',\n",
" u'what s the easiest way to make money onlin',\n",
" u'how do i earn money onlin',\n",
" u'how do you earn money from internet',\n",
" u'what are the best way to make money onlin',\n",
" u'how can i make money onlin for job',\n",
" u'how can i earn money onlin serious',\n",
" u'what is the easiest way to earn money use internet',\n",
" u'what are way i can make money onlin',\n",
" u'how do i make money from home',\n",
" u'what is best way to make money onlin',\n",
" u'what is the best way for make money onlin']"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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pQEQcNBm7B3DQZIwxdkdVVFRgzZo1UCgUmDJlCsrLy7FmzRqcP38eZWVlUsce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rpEO5OJa1VVHCgsZFpiImENGtDsxAlWvvcedrsdHx8fBg8eDEBgYCC33347p06d4o033uDLL79U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\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jRoxg5cqVmJub99uuqKiIiIgIioqKsLGxITQ0dIAj0P3794mJiUFJSQlVVVWam5sJCgoa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\n+gAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb27766be90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import warnings\n",
"from random import randint\n",
"cc = filter(lambda x : (len(x) >9), \n",
" nx.connected_component_subgraphs(g))\n",
"print len(cc)\n",
"with warnings.catch_warnings():\n",
" warnings.simplefilter('ignore')\n",
" for i in range(len(cc)):\n",
" i = randint(0,len(cc))\n",
" if len(cc[i])>9:\n",
" cc[i].nodes()\n",
" text = cc[i].nodes()\n",
" nx.draw(cc[i], with_labels=True, alpha=0.5, font_size=12)\n",
" plt.show()\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(u'money', 56),\n",
" (u'onlin', 49),\n",
" (u'how', 32),\n",
" (u'make', 32),\n",
" (u'i', 30),\n",
" (u'earn', 26),\n",
" (u'can', 24),\n",
" (u'what', 23),\n",
" (u'way', 21),\n",
" (u'to', 18)]"
]
},
"execution_count": 132,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from collections import Counter\n",
"\n",
"text1 = \" \".join(text) \n",
"c = Counter(text1.split())\n",
"c.most_common()[:10]\n",
"topic = [i[0] for i in c.most_common()[:3]]"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"how can i earn from onlin what are the easiest way to make good money use the internet how can i make money onlin easili what is an easi way make money onlin how should i earn money onlin work from home how can i earn money onlin easili what is a way to make money onlin what are the various way through which one can earn money onlin how do i earn money from the internet how do you make money onlin what should i do to earn money onlin how can i earn money onlin from home onli what are way of earn money onlin what are way to make money onlin at home how can i earn money easili onlin how do i realli make money onlin what are the best way to earn money from home how do you make easi money onlin what is the easi way to make money onlin is there ani easi way to make money onlin am not start big how can i make 1000 per month onlin i m 18 how can i make money onlin what are some of the best way of earn money by work at home how doe one earn money onlin without an invest from home what is the easiest way to earn money from onlin can i make money onlin how do i earn more money through internet onlin how can we earn money onlin without invest can i earn money onlin how can i start to make money onlin what are some easi way to make done extra money onlin how can we earn money onlin in india how can i start make money use internet what should i do to make money onlin in india what is make money onlin how do we make money onlin how can i earn money on internet how can i make money onlin quick and easili how could i make money onlin how can i earn money onlin what are the easiest way to earn money onlin what are the easi way to earn money onlin what is the easiest way to make a littl money onlin how can i make money onlin consist how can i earn money part time onlin how can one make money onlin how can i realist make money onlin what s the easiest way to make money onlin how do i earn money onlin how do you earn money from internet what are the best way to make money onlin how can i make money onlin for job how can i earn money onlin serious what is the easiest way to earn money use internet what are way i can make money onlin how do i make money from home what is best way to make money onlin what is the best way for make money onlin\n",
"[u'onlin', u'money', u'way']\n"
]
}
],
"source": [
"from gensim.summarization import keywords\n",
"from gensim.summarization import summarize\n",
"\n",
"text1 = \" \".join(text) \n",
"print text1\n",
"# text1 = \"have been captured by a race of. heat and electrochemical energy and who imprison their minds within an artificial reality known as the Matrix. As a rebel against the machines, Neo must return to the Matrix and confront the agents: super-powerful \"\n",
"# print text1\n",
"# summarize(text1, split=True)\n",
"topic = keywords(text1, split=True,words=3)\n",
"print topic"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['money', 'onlin', 'how']\n"
]
},
{
"data": {
"text/plain": [
"(693, 6)"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"0.82106782106782106"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(651, 6)"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"0.86021505376344087"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(3431, 3)"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/plain": [
"(3459, 3)"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>id</th>\n",
" <th>qid1</th>\n",
" <th>qid2</th>\n",
" <th>question1</th>\n",
" <th>question2</th>\n",
" <th>is_duplicate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>284</th>\n",
" <td>284</td>\n",
" <td>568</td>\n",
" <td>569</td>\n",
" <td>How can I make money online with free of cost?</td>\n",
" <td>How do I to make money online?</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1284</th>\n",
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" <th>2462</th>\n",
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" <th>3076</th>\n",
" <td>3076</td>\n",
" <td>6099</td>\n",
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" <td>I'm 18. How can I make money online?</td>\n",
" <td>What is the easiest way to earn money from onl...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3112</th>\n",
" <td>3112</td>\n",
" <td>6171</td>\n",
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" <td>What is the easiest way to earn money from onl...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id qid1 qid2 question1 \\\n",
"284 284 568 569 How can I make money online with free of cost? \n",
"1284 1284 2560 2561 How can I make money online in India? \n",
"2462 2462 4893 4894 How can i make money online easily? \n",
"3076 3076 6099 6100 I'm 18. How can I make money online? \n",
"3112 3112 6171 6100 How can we earn money online without investment? \n",
"\n",
" question2 is_duplicate \n",
"284 How do I to make money online? 1 \n",
"1284 What's the easiest way to make money online? 0 \n",
"2462 How do I quickly and easily make money online ... 0 \n",
"3076 What is the easiest way to earn money from onl... 1 \n",
"3112 What is the easiest way to earn money from onl... 1 "
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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" <th>test_id</th>\n",
" <th>question1</th>\n",
" <th>question2</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1339</th>\n",
" <td>1339</td>\n",
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" <tr>\n",
" <th>3739</th>\n",
" <td>3739</td>\n",
" <td>How played do I make money online?</td>\n",
" <td>What should I do to british money online?</td>\n",
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" <tr>\n",
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" <td>4200</td>\n",
" <td>How can I earn money online without investment...</td>\n",
" <td>I am working in one of the software product-ba...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" test_id question1 \\\n",
"1339 1339 How can a 16 year boy in india earn donald onl... \n",
"1760 1760 How do learn you make money online? \n",
"1875 1875 How can I earn money him online? \n",
"3739 3739 How played do I make money online? \n",
"4200 4200 How can I earn money online without investment... \n",
"\n",
" question2 \n",
"1339 I'm a 15 number year old teen. What ways are t... \n",
"1760 What is the easy way people earn money online? \n",
"1875 How do you\" you earn money through internet? \n",
"3739 What should I do to british money online? \n",
"4200 I am working in one of the software product-ba... "
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def multi_words(words,list_):\n",
" if all(word in str(words) for word in list_):\n",
" return 1\n",
" else: return 0\n",
"topic = [_i.encode('utf-8') for _i in topic]\n",
"print topic\n",
"# topic=['Ancient','test']\n",
"for qst in ['question1','question2']:\n",
" q1 = train[train[qst].apply(lambda x: multi_words(x,topic))==1]\n",
" q1.shape\n",
" q1['is_duplicate'].mean()\n",
"\n",
"test[test['question1'].apply(lambda x: multi_words(x,topic))==1].shape\n",
"test[test['question2'].apply(lambda x: multi_words(x,topic))==1].shape\n",
"train_or[train['question1'].apply(lambda x: multi_words(x,topic))==1].head()\n",
"test_or[test['question1'].apply(lambda x: multi_words(x,topic))==1].head()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 261,
"metadata": {},
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{
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"execution_count": 261,
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"source": [
"def multi_words(words,list_):\n",
" if all(word in str(words[0]) for word in list_):\n",
" return 1\n",
" elif all(word in str(words[1]) for word in list_):\n",
" return 1\n",
" else: return 0\n",
"\n",
"topic=['Microsoft','acquisition']\n",
"train[(train[['question1','question2']].apply(lambda x: multi_words(x,topic), \n",
" axis=1)==1)]['is_duplicate'].mean()\n",
"test[(test[['question1','question2']].apply(lambda x: multi_words(x,topic), \n",
" axis=1)==1)].shape"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {},
"outputs": [
{
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" <th>id</th>\n",
" <th>qid1</th>\n",
" <th>qid2</th>\n",
" <th>question1</th>\n",
" <th>question2</th>\n",
" <th>is_duplicate</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>980</th>\n",
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" <td>How do I get job in Google or Microsoft?</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1194</th>\n",
" <td>1194</td>\n",
" <td>2380</td>\n",
" <td>2381</td>\n",
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" <th>14907</th>\n",
" <td>14907</td>\n",
" <td>28519</td>\n",
" <td>28520</td>\n",
" <td>How can we get Microsoft Customer service?</td>\n",
" <td>How do we get Microsoft Customer Service?</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>14923</th>\n",
" <td>14923</td>\n",
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" <th>16334</th>\n",
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" <td>Can Microsoft surface pro be used for transcri...</td>\n",
" <td>Where can I buy a Microsoft Surface Pro 3 in S...</td>\n",
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" <tr>\n",
" <th>18365</th>\n",
" <td>18365</td>\n",
" <td>34802</td>\n",
" <td>34803</td>\n",
" <td>How do I change direction from ltr to rtl (not...</td>\n",
" <td>How can I put Microsoft Office on a Mac?</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>19996</th>\n",
" <td>19996</td>\n",
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" <td>37761</td>\n",
" <td>How can I remove IRM protection from Microsoft...</td>\n",
" <td>How can I remove IRM protection from Microsoft...</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>22197</th>\n",
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" <td>41696</td>\n",
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" <td>1</td>\n",
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" <th>23105</th>\n",
" <td>23105</td>\n",
" <td>43318</td>\n",
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" <td>What are the consequences for the stakeholders...</td>\n",
" <td>How is the future of the Windows Phone after t...</td>\n",
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" <th>24165</th>\n",
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" <td>28786</td>\n",
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" <td>53339</td>\n",
" <td>What is your review of Microsoft Surface Pro 3?</td>\n",
" <td>What is your review of Microsoft Surface Pro 4?</td>\n",
" <td>0</td>\n",
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" <td>In hiring an executive assistant, can I assume...</td>\n",
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" <td>0</td>\n",
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" <td>What can I do to get a job at Microsoft?</td>\n",
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" <th>35256</th>\n",
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" <th>362553</th>\n",
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" <tr>\n",
" <th>363026</th>\n",
" <td>363026</td>\n",
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" <td>How do I updates my Microsoft account?</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>363504</th>\n",
" <td>363504</td>\n",
" <td>493491</td>\n",
" <td>493492</td>\n",
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" <td>How common are 40 hours/week data scientist jo...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>364033</th>\n",
" <td>364033</td>\n",
" <td>152238</td>\n",
" <td>339541</td>\n",
" <td>Will Microsoft ever make Windows open source?</td>\n",
" <td>Will Microsoft open source Windows?</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>364078</th>\n",
" <td>364078</td>\n",
" <td>172865</td>\n",
" <td>494117</td>\n",
" <td>How can I learn Microsoft Excel by myself?</td>\n",
" <td>What is the best way to learn Microsoft Excel?</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>367965</th>\n",
" <td>367965</td>\n",
" <td>498306</td>\n",
" <td>498307</td>\n",
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" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>368113</th>\n",
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" <td>Can I get Microsoft Office to work on Windows 7?</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>369216</th>\n",
" <td>369216</td>\n",
" <td>499628</td>\n",
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" <tr>\n",
" <th>369811</th>\n",
" <td>369811</td>\n",
" <td>500287</td>\n",
" <td>500288</td>\n",
" <td>How does Microsoft benefit from LinkedIn acqui...</td>\n",
" <td>Why did Microsoft buy LinkedIn for $26.2 billi...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>370308</th>\n",
" <td>370308</td>\n",
" <td>223958</td>\n",
" <td>64346</td>\n",
" <td>How can I land a job at Microsoft?</td>\n",
" <td>How can I work in Microsoft?</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>371588</th>\n",
" <td>371588</td>\n",
" <td>502223</td>\n",
" <td>21614</td>\n",
" <td>What has been Microsoft's most successful acqu...</td>\n",
" <td>What has been Microsoft's worst acquisition?</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>372638</th>\n",
" <td>372638</td>\n",
" <td>79022</td>\n",
" <td>1955</td>\n",
" <td>How can I get a job in Microsoft?</td>\n",
" <td>What can I do to get a job at Microsoft?</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>375792</th>\n",
" <td>375792</td>\n",
" <td>506858</td>\n",
" <td>506859</td>\n",
" <td>Is it true that Google chrome uses most amount...</td>\n",
" <td>Is it true that your laptop's battery will run...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>377024</th>\n",
" <td>377024</td>\n",
" <td>508249</td>\n",
" <td>508250</td>\n",
" <td>What is \"program management\" at Microsoft?</td>\n",
" <td>What does a program manager do at Microsoft?</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>377481</th>\n",
" <td>377481</td>\n",
" <td>508781</td>\n",
" <td>508782</td>\n",
" <td>How can an electronics and communications engi...</td>\n",
" <td>Do big companies like google, Facebook, Micros...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>379270</th>\n",
" <td>379270</td>\n",
" <td>510776</td>\n",
" <td>510777</td>\n",
" <td>Is there a free download for Microsoft Windows 7?</td>\n",
" <td>How can I download Microsoft Windows 7 for free?</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>386239</th>\n",
" <td>386239</td>\n",
" <td>518426</td>\n",
" <td>518427</td>\n",
" <td>Can I install Android apps in Microsoft lumia ...</td>\n",
" <td>In terms of audio quality, display & camera, s...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>386887</th>\n",
" <td>386887</td>\n",
" <td>519158</td>\n",
" <td>519159</td>\n",
" <td>How do I remove virus from Microsoft Edge brow...</td>\n",
" <td>How do I remove virus named \"evotracker\" from ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>388278</th>\n",
" <td>388278</td>\n",
" <td>520655</td>\n",
" <td>520656</td>\n",
" <td>What are the packages and benefits offered by ...</td>\n",
" <td>What is the general CGPA cut-off for appearing...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>389068</th>\n",
" <td>389068</td>\n",
" <td>83729</td>\n",
" <td>521521</td>\n",
" <td>How do you put a squared symbol in Microsoft W...</td>\n",
" <td>How do you type the symbol x-bar in Microsoft ...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>390763</th>\n",
" <td>390763</td>\n",
" <td>523305</td>\n",
" <td>523306</td>\n",
" <td>What kind of projects get big tech companies' ...</td>\n",
" <td>Are open source contributions less impressive ...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>391030</th>\n",
" <td>391030</td>\n",
" <td>523594</td>\n",
" <td>523595</td>\n",
" <td>How many users does Microsoft Excel have?</td>\n",
" <td>What do you need to know to call yourself a po...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>392934</th>\n",
" <td>392934</td>\n",
" <td>525677</td>\n",
" <td>525678</td>\n",
" <td>Which search engine do Microsoft employees use...</td>\n",
" <td>Why is Microsoft investing more time on Bing w...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>397727</th>\n",
" <td>397727</td>\n",
" <td>83729</td>\n",
" <td>460405</td>\n",
" <td>How do you put a squared symbol in Microsoft W...</td>\n",
" <td>How do I get word count in Microsoft Word 2003?</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>401630</th>\n",
" <td>401630</td>\n",
" <td>535044</td>\n",
" <td>535045</td>\n",
" <td>What is it like to work at Microsoft Research?</td>\n",
" <td>Does Microsoft Research take interns? What is ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>402705</th>\n",
" <td>402705</td>\n",
" <td>536217</td>\n",
" <td>536218</td>\n",
" <td>If I install Microsoft PowerPoint 2012 to my P...</td>\n",
" <td>If I install Microsoft PowerPoint 2012 to my P...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>403853</th>\n",
" <td>403853</td>\n",
" <td>537469</td>\n",
" <td>537470</td>\n",
" <td>What is Microsoft Surface Studio?</td>\n",
" <td>What do you think about the Microsoft Surface ...</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>261 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" id qid1 qid2 \\\n",
"980 980 1955 1956 \n",
"1194 1194 2380 2381 \n",
"2739 2739 5439 5440 \n",
"3255 3255 6452 6453 \n",
"4069 4069 8054 8055 \n",
"6184 6184 12123 12124 \n",
"10538 10538 20403 20404 \n",
"10948 10948 21178 21179 \n",
"11181 11181 21613 21614 \n",
"14538 14538 27833 27834 \n",
"14907 14907 28519 28520 \n",
"14923 14923 28548 12124 \n",
"16334 16334 31144 31145 \n",
"18365 18365 34802 34803 \n",
"19996 19996 37760 37761 \n",
"22197 22197 41696 41697 \n",
"23105 23105 43318 43319 \n",
"24165 24165 45185 45186 \n",
"24291 24291 20403 45405 \n",
"25563 25563 47636 47637 \n",
"26933 26933 50066 50067 \n",
"27132 27132 50426 50427 \n",
"28411 28411 52687 52688 \n",
"28786 28786 53338 53339 \n",
"33078 33078 60819 60820 \n",
"33887 33887 20403 62186 \n",
"34115 34115 45186 62567 \n",
"35192 35192 64346 1955 \n",
"35256 35256 64457 64458 \n",
"36350 36350 31650 66291 \n",
"... ... ... ... \n",
"355849 355849 211036 485094 \n",
"359524 359524 489198 97500 \n",
"361783 361783 491649 491650 \n",
"362553 362553 60819 492468 \n",
"363026 363026 492968 492969 \n",
"363504 363504 493491 493492 \n",
"364033 364033 152238 339541 \n",
"364078 364078 172865 494117 \n",
"367965 367965 498306 498307 \n",
"368113 368113 498458 498459 \n",
"369216 369216 499628 499629 \n",
"369811 369811 500287 500288 \n",
"370308 370308 223958 64346 \n",
"371588 371588 502223 21614 \n",
"372638 372638 79022 1955 \n",
"375792 375792 506858 506859 \n",
"377024 377024 508249 508250 \n",
"377481 377481 508781 508782 \n",
"379270 379270 510776 510777 \n",
"386239 386239 518426 518427 \n",
"386887 386887 519158 519159 \n",
"388278 388278 520655 520656 \n",
"389068 389068 83729 521521 \n",
"390763 390763 523305 523306 \n",
"391030 391030 523594 523595 \n",
"392934 392934 525677 525678 \n",
"397727 397727 83729 460405 \n",
"401630 401630 535044 535045 \n",
"402705 402705 536217 536218 \n",
"403853 403853 537469 537470 \n",
"\n",
" question1 \\\n",
"980 What can I do to get a job at Microsoft? \n",
"1194 Is it worth it to work as an application suppo... \n",
"2739 What is better, Microsoft or Apple? \n",
"3255 How many stocks does Microsoft India give for ... \n",
"4069 Is LinkedIn a good acquisition for Microsoft? \n",
"6184 Do you have to know what's taught in a compute... \n",
"10538 Which company will fall first: Google, Apple, ... \n",
"10948 What are the best programs for drawing on a Mi... \n",
"11181 What are Microsoft's best and worst acquisitions? \n",
"14538 What is the difference between SQL and Microso... \n",
"14907 How can we get Microsoft Customer service? \n",
"14923 What do IT companies like Accenture, Cognizant... \n",
"16334 Can Microsoft surface pro be used for transcri... \n",
"18365 How do I change direction from ltr to rtl (not... \n",
"19996 How can I remove IRM protection from Microsoft... \n",
"22197 What's the best way to learn Microsoft Office ... \n",
"23105 What are the consequences for the stakeholders... \n",
"24165 Is it true that, once you are in a big company... \n",
"24291 Which company will fall first: Google, Apple, ... \n",
"25563 How do you circle a number in Microsoft Word? \n",
"26933 How can you fix Microsoft Word if it won't open? \n",
"27132 Do software companies Amazon, Google, Microsof... \n",
"28411 How was Microsoft word coded? \n",
"28786 What is your review of Microsoft Surface Pro 3? \n",
"33078 What are the differences between Microsoft Exc... \n",
"33887 Which company will fall first: Google, Apple, ... \n",
"34115 What is the way to get a job at big companies ... \n",
"35192 How can I work in Microsoft? \n",
"35256 Does Microsoft own Google? \n",
"36350 I am being offered a job as a Solutions Archit... \n",
"... ... \n",
"355849 How can I enable fullscreen mode in Microsoft ... \n",
"359524 What are some amazing facts about giants like ... \n",
"361783 How do I get a dynamic table in Microsoft Excel? \n",
"362553 What are the differences between Microsoft Exc... \n",
"363026 How do I update my Microsoft account payment i... \n",
"363504 What are the levels of data scientists at Micr... \n",
"364033 Will Microsoft ever make Windows open source? \n",
"364078 How can I learn Microsoft Excel by myself? \n",
"367965 Why is Microsoft incapable of creating a prope... \n",
"368113 What's the most recent version of Microsoft Of... \n",
"369216 Is there a replacement for Microsoft Office Ac... \n",
"369811 How does Microsoft benefit from LinkedIn acqui... \n",
"370308 How can I land a job at Microsoft? \n",
"371588 What has been Microsoft's most successful acqu... \n",
"372638 How can I get a job in Microsoft? \n",
"375792 Is it true that Google chrome uses most amount... \n",
"377024 What is \"program management\" at Microsoft? \n",
"377481 How can an electronics and communications engi... \n",
"379270 Is there a free download for Microsoft Windows 7? \n",
"386239 Can I install Android apps in Microsoft lumia ... \n",
"386887 How do I remove virus from Microsoft Edge brow... \n",
"388278 What are the packages and benefits offered by ... \n",
"389068 How do you put a squared symbol in Microsoft W... \n",
"390763 What kind of projects get big tech companies' ... \n",
"391030 How many users does Microsoft Excel have? \n",
"392934 Which search engine do Microsoft employees use... \n",
"397727 How do you put a squared symbol in Microsoft W... \n",
"401630 What is it like to work at Microsoft Research? \n",
"402705 If I install Microsoft PowerPoint 2012 to my P... \n",
"403853 What is Microsoft Surface Studio? \n",
"\n",
" question2 is_duplicate \n",
"980 How do I get job in Google or Microsoft? 1 \n",
"1194 What is the salary for a Software Engineering ... 0 \n",
"2739 Which is better: Microsoft or Apple? 1 \n",
"3255 Is Director and Principal same level in Micros... 0 \n",
"4069 How does the LinkedIn acquisition help Microso... 1 \n",
"6184 Should I do an MTech from IIT in order to impr... 0 \n",
"10538 Which is the better company to work for as a p... 0 \n",
"10948 Is Microsoft surface pro 4 worth buying? 0 \n",
"11181 What has been Microsoft's worst acquisition? 1 \n",
"14538 Difference between Microsoft SQL Server vs Ora... 0 \n",
"14907 How do we get Microsoft Customer Service? 1 \n",
"14923 Should I do an MTech from IIT in order to impr... 0 \n",
"16334 Where can I buy a Microsoft Surface Pro 3 in S... 0 \n",
"18365 How can I put Microsoft Office on a Mac? 0 \n",
"19996 How can I remove IRM protection from Microsoft... 0 \n",
"22197 How can I learn Microsoft office? 1 \n",
"23105 How is the future of the Windows Phone after t... 0 \n",
"24165 What is the way to get a job at big companies ... 0 \n",
"24291 How will our lives be affected if Google, Micr... 0 \n",
"25563 How can you circle a word in Microsoft word? 1 \n",
"26933 Why did my footnote go to the next page in Mic... 0 \n",
"27132 How many problems one should be able to solve ... 0 \n",
"28411 How can Microsoft Word be improved? 0 \n",
"28786 What is your review of Microsoft Surface Pro 4? 0 \n",
"33078 In hiring an executive assistant, can I assume... 0 \n",
"33887 Does Google have plans to compete with Microso... 0 \n",
"34115 Can I get a job or internship at Google, Micro... 0 \n",
"35192 What can I do to get a job at Microsoft? 1 \n",
"35256 Why doesn't Microsoft own Google? 0 \n",
"36350 I have a job offer from Microsoft that I have ... 0 \n",
"... ... ... \n",
"355849 Why is Microsoft not fixing the fullscreen mod... 0 \n",
"359524 What are some amazing facts about Google/Micro... 0 \n",
"361783 What are pivot tables in Microsoft Excel? 0 \n",
"362553 Is there any website or blog to teach Microsof... 0 \n",
"363026 How do I updates my Microsoft account? 0 \n",
"363504 How common are 40 hours/week data scientist jo... 0 \n",
"364033 Will Microsoft open source Windows? 1 \n",
"364078 What is the best way to learn Microsoft Excel? 0 \n",
"367965 Why can't Microsoft make a decent browser? 1 \n",
"368113 Can I get Microsoft Office to work on Windows 7? 0 \n",
"369216 Do Microsoft employees use Google at the office? 0 \n",
"369811 Why did Microsoft buy LinkedIn for $26.2 billi... 1 \n",
"370308 How can I work in Microsoft? 1 \n",
"371588 What has been Microsoft's worst acquisition? 1 \n",
"372638 What can I do to get a job at Microsoft? 1 \n",
"375792 Is it true that your laptop's battery will run... 1 \n",
"377024 What does a program manager do at Microsoft? 1 \n",
"377481 Do big companies like google, Facebook, Micros... 0 \n",
"379270 How can I download Microsoft Windows 7 for free? 1 \n",
"386239 In terms of audio quality, display & camera, s... 0 \n",
"386887 How do I remove virus named \"evotracker\" from ... 1 \n",
"388278 What is the general CGPA cut-off for appearing... 0 \n",
"389068 How do you type the symbol x-bar in Microsoft ... 0 \n",
"390763 Are open source contributions less impressive ... 0 \n",
"391030 What do you need to know to call yourself a po... 0 \n",
"392934 Why is Microsoft investing more time on Bing w... 0 \n",
"397727 How do I get word count in Microsoft Word 2003? 0 \n",
"401630 Does Microsoft Research take interns? What is ... 1 \n",
"402705 If I install Microsoft PowerPoint 2012 to my P... 1 \n",
"403853 What do you think about the Microsoft Surface ... 1 \n",
"\n",
"[261 rows x 6 columns]"
]
},
"execution_count": 231,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"topic= \"Microsoft\"\n",
"train[train['question1'].str.contains(topic, na=False)]['is_duplicate'].mean()\n",
"train[train['question2'].str.contains(topic, na=False)]['is_duplicate'].mean()\n",
"train[(train['question1'].str.contains(topic, na=False)) |\n",
" (train['question2'].str.contains(topic, na=False))]['is_duplicate'].mean()\n",
"train[(train['question1'].str.contains(topic, na=False)) &\n",
" (train['question2'].str.contains(topic, na=False))]['is_duplicate'].mean()\n",
"train[(train['question1'].str.contains(topic, na=False)) |\n",
" (train['question2'].str.contains(topic, na=False))].shape#['is_duplicate'].mean()\n",
"test[(test['question1'].str.contains(topic, na=False)) | \n",
" (test['question2'].str.contains(topic, na=False))].shape\n",
"test[(test['question1'].str.contains(topic, na=False)) & \n",
" (test['question2'].str.contains(topic, na=False))].shape\n",
"train[(train['question1'].str.contains(topic, na=False)) &\n",
" (train['question2'].str.contains(topic, na=False))]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dict1 = {}\n",
"for i in range(len(cc)):\n",
" dict1[i] = cc[i].nodes()\n",
"\n",
"from multiprocessing import Pool\n",
"num_partitions = 24 #number of partitions to split dataframe\n",
"num_cores = 24 #number of cores on your machine\n",
"\n",
"def parallelize_dataframe(df, func):\n",
" df_split = np.array_split(df, num_partitions)\n",
" pool = Pool(num_cores)\n",
" df = pd.concat(pool.map(func, df_split))\n",
" pool.close()\n",
" pool.join()\n",
" return df\n",
"\n",
"def network_size(search_value1):\n",
" result = [len(value) for key,value in dict1.iteritems() if search_value1 in value]\n",
" if len(result)>0:\n",
" return result[0]\n",
" return int(2)\n",
"\n",
"def multiply_columns(df):\n",
" df['netsize1'] = df['question1'].apply(lambda x: network_size(x)) \n",
" return df\n",
" \n",
"iris = parallelize_dataframe(df1.sample(n=100000), multiply_columns)"
]
},
{
"cell_type": "code",
"execution_count": 174,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
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" <th>is_duplicate</th>\n",
" <th>netsize1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>is_duplicate</th>\n",
" <td>1.000000</td>\n",
" <td>0.083557</td>\n",
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" <tr>\n",
" <th>netsize1</th>\n",
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"</table>\n",
"</div>"
],
"text/plain": [
" is_duplicate netsize1\n",
"is_duplicate 1.000000 0.083557\n",
"netsize1 0.083557 1.000000"
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},
"execution_count": 174,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"iris[['is_duplicate','netsize1']].corr()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train_comb['netsize'] = iris['netsize1']"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
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" <th>q_hash_pos_1</th>\n",
" <th>q2_change</th>\n",
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" <th>q1_q2_change_mean</th>\n",
" <th>q1_q2_change_min</th>\n",
" <th>q1_q2_change_max</th>\n",
" <th>q_change_pair</th>\n",
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" <td>-0.126377</td>\n",
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" <tr>\n",
" <th>q2_hash</th>\n",
" <td>0.041179</td>\n",
" <td>0.282445</td>\n",
" <td>1.000000</td>\n",
" <td>-0.397311</td>\n",
" <td>-0.471671</td>\n",
" <td>-0.346925</td>\n",
" <td>0.069128</td>\n",
" <td>-0.406026</td>\n",
" <td>-0.365688</td>\n",
" <td>-0.583149</td>\n",
" <td>-0.472782</td>\n",
" <td>0.998102</td>\n",
" <td>0.315344</td>\n",
" <td>0.995484</td>\n",
" <td>0.997163</td>\n",
" <td>0.495795</td>\n",
" <td>-0.545675</td>\n",
" <td>-0.205244</td>\n",
" <td>-0.078206</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_freq</th>\n",
" <td>-0.002600</td>\n",
" <td>-0.359849</td>\n",
" <td>-0.397311</td>\n",
" <td>1.000000</td>\n",
" <td>0.599397</td>\n",
" <td>0.343747</td>\n",
" <td>-0.166016</td>\n",
" <td>0.898113</td>\n",
" <td>0.528905</td>\n",
" <td>0.128596</td>\n",
" <td>0.210646</td>\n",
" <td>-0.397409</td>\n",
" <td>-0.464393</td>\n",
" <td>-0.415473</td>\n",
" <td>-0.400677</td>\n",
" <td>-0.519822</td>\n",
" <td>0.526781</td>\n",
" <td>0.217691</td>\n",
" <td>0.028268</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_freq</th>\n",
" <td>-0.000871</td>\n",
" <td>-0.243217</td>\n",
" <td>-0.471671</td>\n",
" <td>0.599397</td>\n",
" <td>1.000000</td>\n",
" <td>0.265540</td>\n",
" <td>-0.099017</td>\n",
" <td>0.583196</td>\n",
" <td>0.948601</td>\n",
" <td>0.292966</td>\n",
" <td>0.292321</td>\n",
" <td>-0.471925</td>\n",
" <td>-0.315102</td>\n",
" <td>-0.480048</td>\n",
" <td>-0.471667</td>\n",
" <td>-0.424641</td>\n",
" <td>0.435390</td>\n",
" <td>0.250374</td>\n",
" <td>0.021752</td>\n",
" </tr>\n",
" <tr>\n",
" <th>is_duplicate</th>\n",
" <td>-0.008784</td>\n",
" <td>-0.207682</td>\n",
" <td>-0.346925</td>\n",
" <td>0.343747</td>\n",
" <td>0.265540</td>\n",
" <td>1.000000</td>\n",
" <td>-0.332427</td>\n",
" <td>0.334511</td>\n",
" <td>0.228869</td>\n",
" <td>0.123509</td>\n",
" <td>0.207493</td>\n",
" <td>-0.346618</td>\n",
" <td>-0.259241</td>\n",
" <td>-0.354151</td>\n",
" <td>-0.345165</td>\n",
" <td>-0.369878</td>\n",
" <td>0.422098</td>\n",
" <td>0.085704</td>\n",
" <td>0.004848</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq_diff</th>\n",
" <td>0.001727</td>\n",
" <td>0.035621</td>\n",
" <td>0.069128</td>\n",
" <td>-0.166016</td>\n",
" <td>-0.099017</td>\n",
" <td>-0.332427</td>\n",
" <td>1.000000</td>\n",
" <td>-0.158787</td>\n",
" <td>-0.078082</td>\n",
" <td>0.072430</td>\n",
" <td>-0.164259</td>\n",
" <td>0.069072</td>\n",
" <td>0.044340</td>\n",
" <td>0.070160</td>\n",
" <td>0.058338</td>\n",
" <td>0.276045</td>\n",
" <td>-0.323348</td>\n",
" <td>0.024984</td>\n",
" <td>0.085937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_hash_freq</th>\n",
" <td>-0.002885</td>\n",
" <td>-0.386223</td>\n",
" <td>-0.406026</td>\n",
" <td>0.898113</td>\n",
" <td>0.583196</td>\n",
" <td>0.334511</td>\n",
" <td>-0.158787</td>\n",
" <td>1.000000</td>\n",
" <td>0.476849</td>\n",
" <td>0.129154</td>\n",
" <td>0.210356</td>\n",
" <td>-0.406110</td>\n",
" <td>-0.498197</td>\n",
" <td>-0.425902</td>\n",
" <td>-0.410139</td>\n",
" <td>-0.544893</td>\n",
" <td>0.545906</td>\n",
" <td>0.199510</td>\n",
" <td>0.028500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_hash_freq</th>\n",
" <td>-0.001022</td>\n",
" <td>-0.207151</td>\n",
" <td>-0.365688</td>\n",
" <td>0.528905</td>\n",
" <td>0.948601</td>\n",
" <td>0.228869</td>\n",
" <td>-0.078082</td>\n",
" <td>0.476849</td>\n",
" <td>1.000000</td>\n",
" <td>0.206482</td>\n",
" <td>0.205859</td>\n",
" <td>-0.365866</td>\n",
" <td>-0.268117</td>\n",
" <td>-0.373507</td>\n",
" <td>-0.365617</td>\n",
" <td>-0.358035</td>\n",
" <td>0.373949</td>\n",
" <td>0.236189</td>\n",
" <td>0.017627</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q_hash_pos</th>\n",
" <td>0.115121</td>\n",
" <td>0.060618</td>\n",
" <td>-0.583149</td>\n",
" <td>0.128596</td>\n",
" <td>0.292966</td>\n",
" <td>0.123509</td>\n",
" <td>0.072430</td>\n",
" <td>0.129154</td>\n",
" <td>0.206482</td>\n",
" <td>1.000000</td>\n",
" <td>0.805124</td>\n",
" <td>-0.590683</td>\n",
" <td>-0.068443</td>\n",
" <td>-0.582470</td>\n",
" <td>-0.590505</td>\n",
" <td>-0.147689</td>\n",
" <td>0.213000</td>\n",
" <td>0.124839</td>\n",
" <td>0.053049</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q_hash_pos_1</th>\n",
" <td>0.071093</td>\n",
" <td>-0.046562</td>\n",
" <td>-0.472782</td>\n",
" <td>0.210646</td>\n",
" <td>0.292321</td>\n",
" <td>0.207493</td>\n",
" <td>-0.164259</td>\n",
" <td>0.210356</td>\n",
" <td>0.205859</td>\n",
" <td>0.805124</td>\n",
" <td>1.000000</td>\n",
" <td>-0.477532</td>\n",
" <td>-0.152411</td>\n",
" <td>-0.476366</td>\n",
" <td>-0.477399</td>\n",
" <td>-0.232615</td>\n",
" <td>0.323326</td>\n",
" <td>0.125530</td>\n",
" <td>0.040480</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q2_change</th>\n",
" <td>-0.020399</td>\n",
" <td>0.239944</td>\n",
" <td>0.998102</td>\n",
" <td>-0.397409</td>\n",
" <td>-0.471925</td>\n",
" <td>-0.346618</td>\n",
" <td>0.069072</td>\n",
" <td>-0.406110</td>\n",
" <td>-0.365866</td>\n",
" <td>-0.590683</td>\n",
" <td>-0.477532</td>\n",
" <td>1.000000</td>\n",
" <td>0.337464</td>\n",
" <td>0.998590</td>\n",
" <td>0.999479</td>\n",
" <td>0.513399</td>\n",
" <td>-0.556578</td>\n",
" <td>-0.205419</td>\n",
" <td>-0.078339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_change</th>\n",
" <td>-0.356764</td>\n",
" <td>0.427433</td>\n",
" <td>0.315344</td>\n",
" <td>-0.464393</td>\n",
" <td>-0.315102</td>\n",
" <td>-0.259241</td>\n",
" <td>0.044340</td>\n",
" <td>-0.498197</td>\n",
" <td>-0.268117</td>\n",
" <td>-0.068443</td>\n",
" <td>-0.152411</td>\n",
" <td>0.337464</td>\n",
" <td>1.000000</td>\n",
" <td>0.386955</td>\n",
" <td>0.356322</td>\n",
" <td>0.824397</td>\n",
" <td>-0.677780</td>\n",
" <td>-0.165039</td>\n",
" <td>-0.058009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_q2_change_mean</th>\n",
" <td>-0.040099</td>\n",
" <td>0.259142</td>\n",
" <td>0.995484</td>\n",
" <td>-0.415473</td>\n",
" <td>-0.480048</td>\n",
" <td>-0.354151</td>\n",
" <td>0.070160</td>\n",
" <td>-0.425902</td>\n",
" <td>-0.373507</td>\n",
" <td>-0.582470</td>\n",
" <td>-0.476366</td>\n",
" <td>0.998590</td>\n",
" <td>0.386955</td>\n",
" <td>1.000000</td>\n",
" <td>0.999143</td>\n",
" <td>0.549391</td>\n",
" <td>-0.583421</td>\n",
" <td>-0.210527</td>\n",
" <td>-0.080009</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_q2_change_min</th>\n",
" <td>-0.027226</td>\n",
" <td>0.247919</td>\n",
" <td>0.997163</td>\n",
" <td>-0.400677</td>\n",
" <td>-0.471667</td>\n",
" <td>-0.345165</td>\n",
" <td>0.058338</td>\n",
" <td>-0.410139</td>\n",
" <td>-0.365617</td>\n",
" <td>-0.590505</td>\n",
" <td>-0.477399</td>\n",
" <td>0.999479</td>\n",
" <td>0.356322</td>\n",
" <td>0.999143</td>\n",
" <td>1.000000</td>\n",
" <td>0.514343</td>\n",
" <td>-0.558467</td>\n",
" <td>-0.207436</td>\n",
" <td>-0.080218</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q1_q2_change_max</th>\n",
" <td>-0.281257</td>\n",
" <td>0.364492</td>\n",
" <td>0.495795</td>\n",
" <td>-0.519822</td>\n",
" <td>-0.424641</td>\n",
" <td>-0.369878</td>\n",
" <td>0.276045</td>\n",
" <td>-0.544893</td>\n",
" <td>-0.358035</td>\n",
" <td>-0.147689</td>\n",
" <td>-0.232615</td>\n",
" <td>0.513399</td>\n",
" <td>0.824397</td>\n",
" <td>0.549391</td>\n",
" <td>0.514343</td>\n",
" <td>1.000000</td>\n",
" <td>-0.814297</td>\n",
" <td>-0.174425</td>\n",
" <td>-0.038350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>q_change_pair</th>\n",
" <td>0.169849</td>\n",
" <td>-0.355028</td>\n",
" <td>-0.545675</td>\n",
" <td>0.526781</td>\n",
" <td>0.435390</td>\n",
" <td>0.422098</td>\n",
" <td>-0.323348</td>\n",
" <td>0.545906</td>\n",
" <td>0.373949</td>\n",
" <td>0.213000</td>\n",
" <td>0.323326</td>\n",
" <td>-0.556578</td>\n",
" <td>-0.677780</td>\n",
" <td>-0.583421</td>\n",
" <td>-0.558467</td>\n",
" <td>-0.814297</td>\n",
" <td>1.000000</td>\n",
" <td>0.193357</td>\n",
" <td>0.053250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>netsize</th>\n",
" <td>0.000735</td>\n",
" <td>-0.126377</td>\n",
" <td>-0.205244</td>\n",
" <td>0.217691</td>\n",
" <td>0.250374</td>\n",
" <td>0.085704</td>\n",
" <td>0.024984</td>\n",
" <td>0.199510</td>\n",
" <td>0.236189</td>\n",
" <td>0.124839</td>\n",
" <td>0.125530</td>\n",
" <td>-0.205419</td>\n",
" <td>-0.165039</td>\n",
" <td>-0.210527</td>\n",
" <td>-0.207436</td>\n",
" <td>-0.174425</td>\n",
" <td>0.193357</td>\n",
" <td>1.000000</td>\n",
" <td>0.751904</td>\n",
" </tr>\n",
" <tr>\n",
" <th>net2freq</th>\n",
" <td>0.001416</td>\n",
" <td>-0.043419</td>\n",
" <td>-0.078206</td>\n",
" <td>0.028268</td>\n",
" <td>0.021752</td>\n",
" <td>0.004848</td>\n",
" <td>0.085937</td>\n",
" <td>0.028500</td>\n",
" <td>0.017627</td>\n",
" <td>0.053049</td>\n",
" <td>0.040480</td>\n",
" <td>-0.078339</td>\n",
" <td>-0.058009</td>\n",
" <td>-0.080009</td>\n",
" <td>-0.080218</td>\n",
" <td>-0.038350</td>\n",
" <td>0.053250</td>\n",
" <td>0.751904</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id q1_hash q2_hash q1_freq q2_freq \\\n",
"id 1.000000 0.690308 0.041179 -0.002600 -0.000871 \n",
"q1_hash 0.690308 1.000000 0.282445 -0.359849 -0.243217 \n",
"q2_hash 0.041179 0.282445 1.000000 -0.397311 -0.471671 \n",
"q1_freq -0.002600 -0.359849 -0.397311 1.000000 0.599397 \n",
"q2_freq -0.000871 -0.243217 -0.471671 0.599397 1.000000 \n",
"is_duplicate -0.008784 -0.207682 -0.346925 0.343747 0.265540 \n",
"freq_diff 0.001727 0.035621 0.069128 -0.166016 -0.099017 \n",
"q1_hash_freq -0.002885 -0.386223 -0.406026 0.898113 0.583196 \n",
"q2_hash_freq -0.001022 -0.207151 -0.365688 0.528905 0.948601 \n",
"q_hash_pos 0.115121 0.060618 -0.583149 0.128596 0.292966 \n",
"q_hash_pos_1 0.071093 -0.046562 -0.472782 0.210646 0.292321 \n",
"q2_change -0.020399 0.239944 0.998102 -0.397409 -0.471925 \n",
"q1_change -0.356764 0.427433 0.315344 -0.464393 -0.315102 \n",
"q1_q2_change_mean -0.040099 0.259142 0.995484 -0.415473 -0.480048 \n",
"q1_q2_change_min -0.027226 0.247919 0.997163 -0.400677 -0.471667 \n",
"q1_q2_change_max -0.281257 0.364492 0.495795 -0.519822 -0.424641 \n",
"q_change_pair 0.169849 -0.355028 -0.545675 0.526781 0.435390 \n",
"netsize 0.000735 -0.126377 -0.205244 0.217691 0.250374 \n",
"net2freq 0.001416 -0.043419 -0.078206 0.028268 0.021752 \n",
"\n",
" is_duplicate freq_diff q1_hash_freq q2_hash_freq \\\n",
"id -0.008784 0.001727 -0.002885 -0.001022 \n",
"q1_hash -0.207682 0.035621 -0.386223 -0.207151 \n",
"q2_hash -0.346925 0.069128 -0.406026 -0.365688 \n",
"q1_freq 0.343747 -0.166016 0.898113 0.528905 \n",
"q2_freq 0.265540 -0.099017 0.583196 0.948601 \n",
"is_duplicate 1.000000 -0.332427 0.334511 0.228869 \n",
"freq_diff -0.332427 1.000000 -0.158787 -0.078082 \n",
"q1_hash_freq 0.334511 -0.158787 1.000000 0.476849 \n",
"q2_hash_freq 0.228869 -0.078082 0.476849 1.000000 \n",
"q_hash_pos 0.123509 0.072430 0.129154 0.206482 \n",
"q_hash_pos_1 0.207493 -0.164259 0.210356 0.205859 \n",
"q2_change -0.346618 0.069072 -0.406110 -0.365866 \n",
"q1_change -0.259241 0.044340 -0.498197 -0.268117 \n",
"q1_q2_change_mean -0.354151 0.070160 -0.425902 -0.373507 \n",
"q1_q2_change_min -0.345165 0.058338 -0.410139 -0.365617 \n",
"q1_q2_change_max -0.369878 0.276045 -0.544893 -0.358035 \n",
"q_change_pair 0.422098 -0.323348 0.545906 0.373949 \n",
"netsize 0.085704 0.024984 0.199510 0.236189 \n",
"net2freq 0.004848 0.085937 0.028500 0.017627 \n",
"\n",
" q_hash_pos q_hash_pos_1 q2_change q1_change \\\n",
"id 0.115121 0.071093 -0.020399 -0.356764 \n",
"q1_hash 0.060618 -0.046562 0.239944 0.427433 \n",
"q2_hash -0.583149 -0.472782 0.998102 0.315344 \n",
"q1_freq 0.128596 0.210646 -0.397409 -0.464393 \n",
"q2_freq 0.292966 0.292321 -0.471925 -0.315102 \n",
"is_duplicate 0.123509 0.207493 -0.346618 -0.259241 \n",
"freq_diff 0.072430 -0.164259 0.069072 0.044340 \n",
"q1_hash_freq 0.129154 0.210356 -0.406110 -0.498197 \n",
"q2_hash_freq 0.206482 0.205859 -0.365866 -0.268117 \n",
"q_hash_pos 1.000000 0.805124 -0.590683 -0.068443 \n",
"q_hash_pos_1 0.805124 1.000000 -0.477532 -0.152411 \n",
"q2_change -0.590683 -0.477532 1.000000 0.337464 \n",
"q1_change -0.068443 -0.152411 0.337464 1.000000 \n",
"q1_q2_change_mean -0.582470 -0.476366 0.998590 0.386955 \n",
"q1_q2_change_min -0.590505 -0.477399 0.999479 0.356322 \n",
"q1_q2_change_max -0.147689 -0.232615 0.513399 0.824397 \n",
"q_change_pair 0.213000 0.323326 -0.556578 -0.677780 \n",
"netsize 0.124839 0.125530 -0.205419 -0.165039 \n",
"net2freq 0.053049 0.040480 -0.078339 -0.058009 \n",
"\n",
" q1_q2_change_mean q1_q2_change_min q1_q2_change_max \\\n",
"id -0.040099 -0.027226 -0.281257 \n",
"q1_hash 0.259142 0.247919 0.364492 \n",
"q2_hash 0.995484 0.997163 0.495795 \n",
"q1_freq -0.415473 -0.400677 -0.519822 \n",
"q2_freq -0.480048 -0.471667 -0.424641 \n",
"is_duplicate -0.354151 -0.345165 -0.369878 \n",
"freq_diff 0.070160 0.058338 0.276045 \n",
"q1_hash_freq -0.425902 -0.410139 -0.544893 \n",
"q2_hash_freq -0.373507 -0.365617 -0.358035 \n",
"q_hash_pos -0.582470 -0.590505 -0.147689 \n",
"q_hash_pos_1 -0.476366 -0.477399 -0.232615 \n",
"q2_change 0.998590 0.999479 0.513399 \n",
"q1_change 0.386955 0.356322 0.824397 \n",
"q1_q2_change_mean 1.000000 0.999143 0.549391 \n",
"q1_q2_change_min 0.999143 1.000000 0.514343 \n",
"q1_q2_change_max 0.549391 0.514343 1.000000 \n",
"q_change_pair -0.583421 -0.558467 -0.814297 \n",
"netsize -0.210527 -0.207436 -0.174425 \n",
"net2freq -0.080009 -0.080218 -0.038350 \n",
"\n",
" q_change_pair netsize net2freq \n",
"id 0.169849 0.000735 0.001416 \n",
"q1_hash -0.355028 -0.126377 -0.043419 \n",
"q2_hash -0.545675 -0.205244 -0.078206 \n",
"q1_freq 0.526781 0.217691 0.028268 \n",
"q2_freq 0.435390 0.250374 0.021752 \n",
"is_duplicate 0.422098 0.085704 0.004848 \n",
"freq_diff -0.323348 0.024984 0.085937 \n",
"q1_hash_freq 0.545906 0.199510 0.028500 \n",
"q2_hash_freq 0.373949 0.236189 0.017627 \n",
"q_hash_pos 0.213000 0.124839 0.053049 \n",
"q_hash_pos_1 0.323326 0.125530 0.040480 \n",
"q2_change -0.556578 -0.205419 -0.078339 \n",
"q1_change -0.677780 -0.165039 -0.058009 \n",
"q1_q2_change_mean -0.583421 -0.210527 -0.080009 \n",
"q1_q2_change_min -0.558467 -0.207436 -0.080218 \n",
"q1_q2_change_max -0.814297 -0.174425 -0.038350 \n",
"q_change_pair 1.000000 0.193357 0.053250 \n",
"netsize 0.193357 1.000000 0.751904 \n",
"net2freq 0.053250 0.751904 1.000000 "
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_comb['net2freq'] = train_comb['netsize'] / (train_comb['q1_freq']+train_comb['q2_freq'])\n",
"train_comb.corr()"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {
"scrolled": false
},
"outputs": [
{
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],
"text/plain": [
" id q1_hash q2_hash q1_freq q2_freq is_duplicate freq_diff \\\n",
"28327 28327 26907 2520383 14 2 0 0.432143 \n",
"95021 95021 82881 26907 3 14 1 0.264286 \n",
"124452 124452 105625 26907 1 14 0 0.935714 \n",
"136699 136699 20653 26907 6 14 1 0.096429 \n",
"158792 158792 131039 26907 2 14 0 0.432143 \n",
"170187 170187 10259 26907 5 14 1 0.130000 \n",
"201534 201534 26907 2625604 14 3 1 0.264286 \n",
"236164 236164 3895 26907 4 14 0 0.180357 \n",
"240858 240858 188003 26907 3 14 1 0.264286 \n",
"291500 291500 220905 26907 1 14 0 0.935714 \n",
"324156 324156 45596 26907 5 14 1 0.130000 \n",
"338058 338058 156205 26907 6 14 1 0.096429 \n",
"348057 348057 26907 2714267 14 1 0 0.935714 \n",
"395057 395057 26907 74557 14 20 0 0.021786 \n",
"\n",
" q1_hash_freq q2_hash_freq q_hash_pos q_hash_pos_1 q2_change \\\n",
"28327 4 2 0 0 1 \n",
"95021 3 10 1 1 -2537436 \n",
"124452 1 10 1 0 -2556000 \n",
"136699 4 10 0 0 -2563610 \n",
"158792 2 10 1 1 -2577158 \n",
"170187 4 10 0 0 -2584149 \n",
"201534 4 3 0 0 -4262 \n",
"236164 4 10 0 0 -2623603 \n",
"240858 1 10 1 1 -2626348 \n",
"291500 1 10 1 0 -2655646 \n",
"324156 4 10 1 1 -2674083 \n",
"338058 2 10 1 1 -2681823 \n",
"348057 4 1 0 0 1 \n",
"395057 4 11 0 0 -2665910 \n",
"\n",
" q1_change q1_q2_change_mean q1_q2_change_min q1_q2_change_max \\\n",
"28327 1 1.0 1 1 \n",
"95021 1 -1268717.5 -2537436 1 \n",
"124452 1 -1277999.5 -2556000 1 \n",
"136699 -94170 -1328890.0 -2563610 -94170 \n",
"158792 1 -1288578.5 -2577158 1 \n",
"170187 -128978 -1356563.5 -2584149 -128978 \n",
"201534 -134304 -69283.0 -134304 -4262 \n",
"236164 -180988 -1402295.5 -2623603 -180988 \n",
"240858 1 -1313173.5 -2626348 1 \n",
"291500 1 -1327822.5 -2655646 1 \n",
"324156 -195735 -1434909.0 -2674083 -195735 \n",
"338058 -93608 -1387715.5 -2681823 -93608 \n",
"348057 -228960 -114479.5 -228960 1 \n",
"395057 -257307 -1461608.5 -2665910 -257307 \n",
"\n",
" q_change_pair netsize net2freq \n",
"28327 0 10201 637.562500 \n",
"95021 0 10201 600.058824 \n",
"124452 0 10201 680.066667 \n",
"136699 1 10201 510.050000 \n",
"158792 0 10201 637.562500 \n",
"170187 1 10201 536.894737 \n",
"201534 1 10201 600.058824 \n",
"236164 1 10201 566.722222 \n",
"240858 0 10201 600.058824 \n",
"291500 0 10201 680.066667 \n",
"324156 1 10201 536.894737 \n",
"338058 1 10201 510.050000 \n",
"348057 0 10201 680.066667 \n",
"395057 1 10201 300.029412 "
]
},
"execution_count": 209,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"for i in range(100):\n",
" a=comb.sample()\n",
" if a['q1_freq'].values>3:\n",
" a = int(a['q2_hash'].values)\n",
" if train_comb[(train_comb['q1_hash']==a)|(train_comb['q2_hash']==a)].shape[0]>1:\n",
"# test_comb[(test_comb['q1_hash']==a)|(test_comb['q2_hash']==a)].shape\n",
" train_comb[(train_comb['q1_hash']==a)|(train_comb['q2_hash']==a)]\n",
" break"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {},
"outputs": [
{
"data": {
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" <th>35104</th>\n",
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" <tr>\n",
" <th>74153</th>\n",
" <td>74153</td>\n",
" <td>how is the word potent use in a sentenc</td>\n",
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" <tr>\n",
" <th>82459</th>\n",
" <td>82459</td>\n",
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" <tr>\n",
" <th>82752</th>\n",
" <td>82752</td>\n",
" <td>how is the word aver use in a sentenc</td>\n",
" <td>how is the word wist use in a sentenc</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>88220</th>\n",
" <td>88220</td>\n",
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" <tr>\n",
" <th>90920</th>\n",
" <td>90920</td>\n",
" <td>how is the word zealot use in a sentenc</td>\n",
" <td>how is the word wist use in a sentenc</td>\n",
" <td>0</td>\n",
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" <tr>\n",
" <th>173957</th>\n",
" <td>173957</td>\n",
" <td>how is the word viscous use in a sentenc</td>\n",
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" <td>0</td>\n",
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" <tr>\n",
" <th>179175</th>\n",
" <td>179175</td>\n",
" <td>how is the word prejud use in a sentenc</td>\n",
" <td>how is the word wist use in a sentenc</td>\n",
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" <td>0.940000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>224713</th>\n",
" <td>224713</td>\n",
" <td>how is the word merci use in a sentenc</td>\n",
" <td>how is the word wist use in a sentenc</td>\n",
" <td>0</td>\n",
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" <td>15</td>\n",
" <td>0.268889</td>\n",
" </tr>\n",
" <tr>\n",
" <th>261096</th>\n",
" <td>261096</td>\n",
" <td>how is the word motif use in a sentenc</td>\n",
" <td>how is the word wist use in a sentenc</td>\n",
" <td>0</td>\n",
" <td>50848</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>343621</th>\n",
" <td>343621</td>\n",
" <td>how is the word patho use in a sentenc</td>\n",
" <td>how is the word wist use in a sentenc</td>\n",
" <td>0</td>\n",
" <td>122259</td>\n",
" <td>2513495</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>0.185000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>372920</th>\n",
" <td>372920</td>\n",
" <td>how is the word omin use in a sentenc</td>\n",
" <td>how is the word wist use in a sentenc</td>\n",
" <td>0</td>\n",
" <td>133678</td>\n",
" <td>2513495</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>15</td>\n",
" <td>0.185000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>378272</th>\n",
" <td>378272</td>\n",
" <td>how is the word rescind use in a sentenc</td>\n",
" <td>how is the word wist use in a sentenc</td>\n",
" <td>0</td>\n",
" <td>78968</td>\n",
" <td>2513495</td>\n",
" <td>8</td>\n",
" <td>15</td>\n",
" <td>8</td>\n",
" <td>15</td>\n",
" <td>0.059167</td>\n",
" </tr>\n",
" <tr>\n",
" <th>386989</th>\n",
" <td>386989</td>\n",
" <td>how is the word coloni use in a sentenc</td>\n",
" <td>how is the word wist use in a sentenc</td>\n",
" <td>0</td>\n",
" <td>63281</td>\n",
" <td>2513495</td>\n",
" <td>4</td>\n",
" <td>15</td>\n",
" <td>3</td>\n",
" <td>15</td>\n",
" <td>0.185000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id question1 \\\n",
"18353 18353 how is the word calumni use in a sentenc \n",
"35104 35104 how is the word subcontin use in a sentenc \n",
"74153 74153 how is the word potent use in a sentenc \n",
"82459 82459 how is the word impedi use in a sentenc \n",
"82752 82752 how is the word aver use in a sentenc \n",
"88220 88220 how is the word anticip use in a sentenc \n",
"90920 90920 how is the word zealot use in a sentenc \n",
"173957 173957 how is the word viscous use in a sentenc \n",
"179175 179175 how is the word prejud use in a sentenc \n",
"224713 224713 how is the word merci use in a sentenc \n",
"261096 261096 how is the word motif use in a sentenc \n",
"343621 343621 how is the word patho use in a sentenc \n",
"372920 372920 how is the word omin use in a sentenc \n",
"378272 378272 how is the word rescind use in a sentenc \n",
"386989 386989 how is the word coloni use in a sentenc \n",
"\n",
" question2 is_duplicate q1_hash q2_hash \\\n",
"18353 how is the word wist use in a sentenc 0 17715 2513495 \n",
"35104 how is the word wist use in a sentenc 0 32934 2513495 \n",
"74153 how is the word wist use in a sentenc 0 35287 2513495 \n",
"82459 how is the word wist use in a sentenc 0 72867 2513495 \n",
"82752 how is the word wist use in a sentenc 0 73102 2513495 \n",
"88220 how is the word wist use in a sentenc 0 21015 2513495 \n",
"90920 how is the word wist use in a sentenc 0 33978 2513495 \n",
"173957 how is the word wist use in a sentenc 0 141954 2513495 \n",
"179175 how is the word wist use in a sentenc 0 145655 2513495 \n",
"224713 how is the word wist use in a sentenc 0 98914 2513495 \n",
"261096 how is the word wist use in a sentenc 0 50848 2513495 \n",
"343621 how is the word wist use in a sentenc 0 122259 2513495 \n",
"372920 how is the word wist use in a sentenc 0 133678 2513495 \n",
"378272 how is the word wist use in a sentenc 0 78968 2513495 \n",
"386989 how is the word wist use in a sentenc 0 63281 2513495 \n",
"\n",
" q1_freq q2_freq q1_hash_freq q2_hash_freq freq_diff \n",
"18353 7 15 4 15 0.077143 \n",
"35104 4 15 4 15 0.185000 \n",
"74153 3 15 3 15 0.268889 \n",
"82459 5 15 4 15 0.134667 \n",
"82752 3 15 3 15 0.268889 \n",
"88220 5 15 5 15 0.134667 \n",
"90920 6 15 6 15 0.101111 \n",
"173957 2 15 2 15 0.436667 \n",
"179175 1 15 1 15 0.940000 \n",
"224713 3 15 3 15 0.268889 \n",
"261096 5 15 5 15 0.134667 \n",
"343621 4 15 4 15 0.185000 \n",
"372920 4 15 3 15 0.185000 \n",
"378272 8 15 8 15 0.059167 \n",
"386989 4 15 3 15 0.185000 "
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comb[(comb['q1_hash']==2513495)|(comb['q2_hash']==2513495)]"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
" id q1_hash q2_hash q1_freq q2_freq is_duplicate freq_diff \\\n",
"18353 18353 17715 2513495 7 15 0 0.077143 \n",
"35104 35104 32934 2513495 4 15 0 0.185000 \n",
"74153 74153 35287 2513495 3 15 0 0.268889 \n",
"82459 82459 72867 2513495 5 15 0 0.134667 \n",
"82752 82752 73102 2513495 3 15 0 0.268889 \n",
"88220 88220 21015 2513495 5 15 0 0.134667 \n",
"90920 90920 33978 2513495 6 15 0 0.101111 \n",
"173957 173957 141954 2513495 2 15 0 0.436667 \n",
"179175 179175 145655 2513495 1 15 0 0.940000 \n",
"224713 224713 98914 2513495 3 15 0 0.268889 \n",
"261096 261096 50848 2513495 5 15 0 0.134667 \n",
"343621 343621 122259 2513495 4 15 0 0.185000 \n",
"372920 372920 133678 2513495 4 15 0 0.185000 \n",
"378272 378272 78968 2513495 8 15 0 0.059167 \n",
"386989 386989 63281 2513495 4 15 0 0.185000 \n",
"\n",
" q1_hash_freq q2_hash_freq q_hash_pos q_hash_pos_1 q2_change \\\n",
"18353 4 15 0 0 1 \n",
"35104 4 15 0 0 -11447 \n",
"74153 3 15 0 0 -37360 \n",
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"\n",
" q1_change q1_q2_change_mean q1_q2_change_min q1_q2_change_max \\\n",
"18353 1 1.0 1 1 \n",
"35104 1 -5723.0 -11447 1 \n",
"74153 -30800 -34080.0 -37360 -30800 \n",
"82459 1 -21377.0 -42755 1 \n",
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"372920 -137158 -175826.5 -214495 -137158 \n",
"378272 -195153 -206342.5 -217532 -195153 \n",
"386989 -216161 -219298.5 -222436 -216161 \n",
"\n",
" q_change_pair netsize net2freq \n",
"18353 0 255 11.590909 \n",
"35104 0 255 13.421053 \n",
"74153 1 255 14.166667 \n",
"82459 0 255 12.750000 \n",
"82752 0 255 14.166667 \n",
"88220 1 255 12.750000 \n",
"90920 1 255 12.142857 \n",
"173957 0 255 15.000000 \n",
"179175 0 255 15.937500 \n",
"224713 1 255 14.166667 \n",
"261096 1 255 12.750000 \n",
"343621 1 255 13.421053 \n",
"372920 1 255 13.421053 \n",
"378272 1 255 11.086957 \n",
"386989 1 255 13.421053 "
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_comb[train_comb['q2_hash']==2513495]"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'UBTgram'"
]
},
"execution_count": 213,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_ngram_str_map = {\n",
" 1: \"Unigram\",\n",
" 2: \"Bigram\",\n",
" 3: \"Trigram\",\n",
" 4: \"Fourgram\",\n",
" 5: \"Fivegram\",\n",
" 12: \"UBgram\",\n",
" 123: \"UBTgram\",\n",
"}\n",
"_ngram_str_map[123]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
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"outputs": [],
"source": [
"def _unigrams(words):\n",
" \"\"\"\n",
" Input: a list of words, e.g., [\"I\", \"am\", \"Denny\"]\n",
" Output: a list of unigram\n",
" \"\"\"\n",
" assert type(words) == list\n",
" return words\n",
"\n",
"\n",
"def _bigrams(words, join_string, skip=0):\n",
" \"\"\"\n",
" Input: a list of words, e.g., [\"I\", \"am\", \"Denny\"]\n",
" Output: a list of bigram, e.g., [\"I_am\", \"am_Denny\"]\n",
" I use _ as join_string for this example.\n",
" \"\"\"\n",
" assert type(words) == list\n",
" L = len(words)\n",
" if L > 1:\n",
" lst = []\n",
" for i in range(L-1):\n",
" for k in range(1,skip+2):\n",
" if i+k < L:\n",
" lst.append( join_string.join([words[i], words[i+k]]) )\n",
" else:\n",
" # set it as unigram\n",
" lst = _unigrams(words)\n",
" return lst\n",
"\n",
"\n",
"def _trigrams(words, join_string, skip=0):\n",
" \"\"\"\n",
" Input: a list of words, e.g., [\"I\", \"am\", \"Denny\"]\n",
" Output: a list of trigram, e.g., [\"I_am_Denny\"]\n",
" I use _ as join_string for this example.\n",
" \"\"\"\n",
" assert type(words) == list\n",
" L = len(words)\n",
" if L > 2:\n",
" lst = []\n",
" for i in range(L-2):\n",
" for k1 in range(1,skip+2):\n",
" for k2 in range(1,skip+2):\n",
" if i+k1 < L and i+k1+k2 < L:\n",
" lst.append( join_string.join([words[i], words[i+k1], words[i+k1+k2]]) )\n",
" else:\n",
" # set it as bigram\n",
" lst = _bigrams(words, join_string, skip)\n",
" return lst\n",
"\n",
"def _ngrams(words, ngram, join_string=\" \"):\n",
" \"\"\"wrapper for ngram\"\"\"\n",
" if ngram == 1:\n",
" return _unigrams(words)\n",
" elif ngram == 2:\n",
" return _bigrams(words, join_string)\n",
" elif ngram == 3:\n",
" return _trigrams(words, join_string)\n",
" elif ngram == 12:\n",
" unigram = _unigrams(words)\n",
" bigram = [x for x in _bigrams(words, join_string) if len(x.split(join_string)) == 2]\n",
" return unigram + bigram\n",
" elif ngram == 123:\n",
" unigram = _unigrams(words)\n",
" bigram = [x for x in _bigrams(words, join_string) if len(x.split(join_string)) == 2]\n",
" trigram = [x for x in _trigrams(words, join_string) if len(x.split(join_string)) == 3]\n",
" return unigram + bigram + trigram\n",
" \n",
"def _tokenize(text, token_pattern=\" \"):\n",
" # token_pattern = r\"(?u)\\b\\w\\w+\\b\"\n",
" # token_pattern = r\"\\w{1,}\"\n",
" # token_pattern = r\"\\w+\"\n",
" # token_pattern = r\"[\\w']+\"\n",
" if token_pattern == \" \":\n",
" # just split the text into tokens\n",
" return text.split(\" \")\n",
" else:\n",
" token_pattern = re.compile(token_pattern, flags = re.UNICODE | re.LOCALE)\n",
" group = token_pattern.findall(text)\n",
" return group\n",
" \n",
"def _try_divide(x, y, val=0.0):\n",
" \"\"\"try to divide two numbers\"\"\"\n",
" if y != 0.0:\n",
" val = float(x) / y\n",
" return val\n",
" \n",
" \n",
"def UniqueRatio_Ngram:\n",
" obs_tokens = _tokenize(obs, token_pattern)\n",
" obs_ngrams = _ngrams(obs_tokens, 12)\n",
" return _try_divide(len(set(obs_ngrams)), len(obs_ngrams))"
]
},
{
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"mimetype": "text/x-python",
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"bibliofile": "biblio.bib",
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| mit |
vzg100/Post-Translational-Modification-Prediction | old/Methylation Sequence Tests -Bagging -dbptm+ELM-VectorAvr..ipynb | 1 | 16362 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Template for test"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/mark/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
" \"This module will be removed in 0.20.\", DeprecationWarning)\n"
]
}
],
"source": [
"from pred import Predictor\n",
"from pred import sequence_vector\n",
"from pred import chemical_vector"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Controlling for Random Negatve vs Sans Random in Imbalanced Techniques using S, T, and Y Phosphorylation.\n",
"\n",
"Included is N Phosphorylation however no benchmarks are available, yet. \n",
"\n",
"\n",
"Training data is from phospho.elm and benchmarks are from dbptm. \n",
"\n",
"Note: SMOTEEN seems to preform best"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"y pass\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [5, 18, 10, 12, 10, 9, 7, 11, 12, 20, 17, 9, 19, -1.1615384615384614, 41.34615384615385, 0.07692307692307693]\n",
"Finished working with Data\n",
"Training Data Points: 21290\n",
"Test Data Points: 5323\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.01818181818181818\n",
"Specificity : 1.0\n",
"Accuracy: 0.9797106894608304\n",
"ROC 0.509090909091\n",
"TP 2 FP 0 TN 5213 FN 108\n",
"\n",
"\n",
"\n",
"None\n",
"Cross Validation: [ 0.98065001 0.98046215 0.98083787 0.98083787 0.98026687]\n",
"Number of data points in benchmark 2612\n",
"Benchmark Results \n",
"Sensitivity: 0.12878787878787878\n",
"Specificity : 0.9987903225806452\n",
"Accuracy: 0.9548238897396631\n",
"ROC 0.563789100684\n",
"TP 17 FP 3 TN 2477 FN 115\n",
"\n",
"\n",
"\n",
"None\n",
"x pass\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [16, 17, 12, 12, 13, 1, 7, 19, 20, 17, 3, 10, 1, -0.4, -8.861538461538462, 0.0]\n",
"Finished working with Data\n",
"Training Data Points: 21290\n",
"Test Data Points: 5323\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.009433962264150943\n",
"Specificity : 0.9996166379145103\n",
"Accuracy: 0.9798985534473041\n",
"ROC 0.504525300089\n",
"TP 1 FP 2 TN 5215 FN 105\n",
"\n",
"\n",
"\n",
"None\n",
"Cross Validation: [ 0.98140147 0.98083787 0.98102574 0.98102574 0.98101861]\n",
"Number of data points in benchmark 2612\n",
"Benchmark Results \n",
"Sensitivity: 0.10606060606060606\n",
"Specificity : 0.9979838709677419\n",
"Accuracy: 0.9529096477794793\n",
"ROC 0.552022238514\n",
"TP 14 FP 5 TN 2475 FN 118\n",
"\n",
"\n",
"\n",
"None\n",
"y ADASYN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [12, 10, 13, 5, 10, 3, 7, 9, 13, 8, 3, 8, 7, 0.28461538461538444, 42.5923076923077, 0.07692307692307693]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 41577\n",
"Test Data Points: 10395\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.9778640776699029\n",
"Specificity : 0.998093422306959\n",
"Accuracy: 0.9880711880711881\n",
"ROC 0.987978749988\n",
"TP 5036 FP 10 TN 5235 FN 114\n",
"\n",
"\n",
"\n",
"None\n",
"Cross Validation: [ 0.92198172 0.98691679 0.98220298 0.97719838 0.98056384]\n",
"Number of data points in benchmark 2612\n",
"Benchmark Results \n",
"Sensitivity: 0.14393939393939395\n",
"Specificity : 0.9975806451612903\n",
"Accuracy: 0.9544410413476263\n",
"ROC 0.57076001955\n",
"TP 19 FP 6 TN 2474 FN 113\n",
"\n",
"\n",
"\n",
"None\n",
"x ADASYN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [9, 1, 17, 19, 17, 7, 7, 19, 7, 8, 16, 3, 7, -2.9538461538461536, 8.553846153846154, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 41585\n",
"Test Data Points: 10397\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.9784466019417476\n",
"Specificity : 0.9984753192300362\n",
"Accuracy: 0.988554390689622\n",
"ROC 0.988460960586\n",
"TP 5039 FP 8 TN 5239 FN 111\n",
"\n",
"\n",
"\n",
"None\n",
"Cross Validation: [ 0.92411272 0.98759257 0.97951332 0.97960754 0.97835498]\n",
"Number of data points in benchmark 2612\n",
"Benchmark Results \n",
"Sensitivity: 0.15151515151515152\n",
"Specificity : 0.9975806451612903\n",
"Accuracy: 0.9548238897396631\n",
"ROC 0.574547898338\n",
"TP 20 FP 6 TN 2474 FN 112\n",
"\n",
"\n",
"\n",
"None\n",
"y SMOTEENN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [10, 10, 1, 13, 9, 3, 7, 13, 5, 18, 1, 8, 5, 0.12307692307692301, 20.89230769230769, 0.15384615384615385]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 41756\n",
"Test Data Points: 10439\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.9755585258735917\n",
"Specificity : 0.9978854286812764\n",
"Accuracy: 0.9866845483283839\n",
"ROC 0.986721977277\n",
"TP 5109 FP 11 TN 5191 FN 128\n",
"\n",
"\n",
"\n",
"None\n",
"Cross Validation: [ 0.95507663 0.99597663 0.99712616 0.99664719 0.99626365]\n",
"Number of data points in benchmark 2612\n",
"Benchmark Results \n",
"Sensitivity: 0.14393939393939395\n",
"Specificity : 0.9963709677419355\n",
"Accuracy: 0.9532924961715161\n",
"ROC 0.570155180841\n",
"TP 19 FP 9 TN 2471 FN 113\n",
"\n",
"\n",
"\n",
"None\n",
"x SMOTEENN\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [2, 1, 1, 1, 11, 17, 7, 17, 7, 3, 10, 10, 10, -1.2615384615384617, 70.5923076923077, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 41755\n",
"Test Data Points: 10439\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.9733207190160833\n",
"Specificity : 0.996895615056267\n",
"Accuracy: 0.9849602452342179\n",
"ROC 0.985108167036\n",
"TP 5144 FP 16 TN 5138 FN 141\n",
"\n",
"\n",
"\n",
"None\n",
"Cross Validation: [ 0.95535971 0.99588083 0.99482709 0.99578504 0.99540142]\n",
"Number of data points in benchmark 2612\n",
"Benchmark Results \n",
"Sensitivity: 0.15151515151515152\n",
"Specificity : 0.9971774193548387\n",
"Accuracy: 0.9544410413476263\n",
"ROC 0.574346285435\n",
"TP 20 FP 7 TN 2473 FN 112\n",
"\n",
"\n",
"\n",
"None\n",
"y random_under_sample\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [13, 11, 3, 7, 18, 13, 7, 16, 3, 2, 1, 12, 11, 0.34615384615384615, 42.569230769230764, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 814\n",
"Test Data Points: 204\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.38\n",
"Specificity : 0.6153846153846154\n",
"Accuracy: 0.5\n",
"ROC 0.497692307692\n",
"TP 38 FP 40 TN 64 FN 62\n",
"\n",
"\n",
"\n",
"None\n",
"Cross Validation: [ 0.5 0.51960784 0.53431373 0.57352941 0.53465347]\n",
"Number of data points in benchmark 2612\n",
"Benchmark Results \n",
"Sensitivity: 0.4772727272727273\n",
"Specificity : 0.6040322580645161\n",
"Accuracy: 0.5976263399693721\n",
"ROC 0.540652492669\n",
"TP 63 FP 982 TN 1498 FN 69\n",
"\n",
"\n",
"\n",
"None\n",
"x random_under_sample\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [7, 4, 12, 10, 18, 20, 7, 10, 12, 19, 20, 7, 2, -0.7384615384615383, 4.653846153846153, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 814\n",
"Test Data Points: 204\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.4\n",
"Specificity : 0.5769230769230769\n",
"Accuracy: 0.49019607843137253\n",
"ROC 0.488461538462\n",
"TP 40 FP 44 TN 60 FN 60\n",
"\n",
"\n",
"\n",
"None\n",
"Cross Validation: [ 0.47058824 0.51960784 0.4754902 0.50980392 0.56435644]\n",
"Number of data points in benchmark 2612\n",
"Benchmark Results \n",
"Sensitivity: 0.5\n",
"Specificity : 0.6241935483870967\n",
"Accuracy: 0.6179173047473201\n",
"ROC 0.562096774194\n",
"TP 66 FP 932 TN 1548 FN 66\n",
"\n",
"\n",
"\n",
"None\n",
"y ncl\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [9, 17, 17, 18, 6, 12, 7, 13, 16, 5, 18, 17, 10, -1.6384615384615382, 74.84615384615385, 0.15384615384615385]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 20571\n",
"Test Data Points: 5143\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.010869565217391304\n",
"Specificity : 0.9998020194020986\n",
"Accuracy: 0.9821116080108886\n",
"ROC 0.50533579231\n",
"TP 1 FP 1 TN 5050 FN 91\n",
"\n",
"\n",
"\n",
"None\n",
"Cross Validation: [ 0.9805561 0.98016722 0.98036166 0.97997278 0.98055231]\n",
"Number of data points in benchmark 2612\n",
"Benchmark Results \n",
"Sensitivity: 0.12121212121212122\n",
"Specificity : 0.9987903225806452\n",
"Accuracy: 0.9544410413476263\n",
"ROC 0.560001221896\n",
"TP 16 FP 3 TN 2477 FN 116\n",
"\n",
"\n",
"\n",
"None\n",
"x ncl\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [11, 10, 1, 2, 1, 7, 7, 20, 17, 13, 4, 14, 13, -0.07692307692307693, 10.984615384615386, 0.0]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 20570\n",
"Test Data Points: 5143\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.010869565217391304\n",
"Specificity : 1.0\n",
"Accuracy: 0.9823060470542485\n",
"ROC 0.505434782609\n",
"TP 1 FP 0 TN 5051 FN 91\n",
"\n",
"\n",
"\n",
"None\n",
"Cross Validation: [ 0.98075053 0.98016722 0.98016722 0.98036166 0.9801595 ]\n",
"Number of data points in benchmark 2612\n",
"Benchmark Results \n",
"Sensitivity: 0.11363636363636363\n",
"Specificity : 0.9987903225806452\n",
"Accuracy: 0.9540581929555896\n",
"ROC 0.556213343109\n",
"TP 15 FP 3 TN 2477 FN 117\n",
"\n",
"\n",
"\n",
"None\n",
"y near_miss\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [8, 8, 2, 8, 8, 17, 7, 18, 19, 15, 13, 13, 20, -1.5846153846153845, 38.861538461538466, 0.07692307692307693]\n",
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 814\n",
"Test Data Points: 204\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.65\n",
"Specificity : 0.6923076923076923\n",
"Accuracy: 0.6715686274509803\n",
"ROC 0.671153846154\n",
"TP 65 FP 32 TN 72 FN 35\n",
"\n",
"\n",
"\n",
"None\n",
"Cross Validation: [ 0.70098039 0.68627451 0.72058824 0.62745098 0.61881188]\n",
"Number of data points in benchmark 2612\n",
"Benchmark Results \n",
"Sensitivity: 0.7727272727272727\n",
"Specificity : 0.3157258064516129\n",
"Accuracy: 0.3388208269525268\n",
"ROC 0.544226539589\n",
"TP 102 FP 1697 TN 783 FN 30\n",
"\n",
"\n",
"\n",
"None\n",
"x near_miss\n",
"Loading Data\n",
"Loaded Data\n",
"Working on Data\n",
"Sample Vector [2, 15, 9, 5, 10, 10, 7, 9, 10, 11, 5, 11, 17, -1.146153846153846, 90.69230769230771, 0.23076923076923078]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Balancing Data\n",
"Balanced Data\n",
"Finished working with Data\n",
"Training Data Points: 814\n",
"Test Data Points: 204\n",
"Starting Training\n",
"Done training\n",
"Test Results\n",
"Sensitivity: 0.65\n",
"Specificity : 0.6923076923076923\n",
"Accuracy: 0.6715686274509803\n",
"ROC 0.671153846154\n",
"TP 65 FP 32 TN 72 FN 35\n",
"\n",
"\n",
"\n",
"None\n",
"Cross Validation: [ 0.73529412 0.70098039 0.67647059 0.63235294 0.67821782]\n",
"Number of data points in benchmark 2612\n",
"Benchmark Results \n",
"Sensitivity: 0.7803030303030303\n",
"Specificity : 0.3088709677419355\n",
"Accuracy: 0.33269525267993877\n",
"ROC 0.544586999022\n",
"TP 103 FP 1714 TN 766 FN 29\n",
"\n",
"\n",
"\n",
"None\n"
]
}
],
"source": [
"par = [\"pass\", \"ADASYN\", \"SMOTEENN\", \"random_under_sample\", \"ncl\", \"near_miss\"]\n",
"for i in par:\n",
" print(\"y\", i)\n",
" y = Predictor()\n",
" y.load_data(file=\"Data/Training/k_methylation.csv\")\n",
" y.process_data(vector_function=\"sequence\", amino_acid=\"K\", imbalance_function=i, random_data=0)\n",
" y.supervised_training(\"bagging\")\n",
" y.benchmark(\"Data/Benchmarks/methylation.csv\", \"K\")\n",
" del y\n",
" print(\"x\", i)\n",
" x = Predictor()\n",
" x.load_data(file=\"Data/Training/k_methylation.csv\")\n",
" x.process_data(vector_function=\"sequence\", amino_acid=\"K\", imbalance_function=i, random_data=1)\n",
" x.supervised_training(\"bagging\")\n",
" x.benchmark(\"Data/Benchmarks/methylation.csv\", \"K\")\n",
" del x\n"
]
},
{
"cell_type": "code",
"execution_count": null,
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"collapsed": true
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"outputs": [],
"source": []
}
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}
| mit |
opengeostat/pygslib | sandbox/Model From DXF/Generate_Geology.ipynb | 1 | 97654 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Create geological model and drillhole from sections\n",
"\n",
"This is to extract points from geological model defined in sections (dxf files) We also generate drillhole data. The section must be difined as dxf section with layers properly defined:\n",
"\n",
"<img src='fig1.JPG' height = '50%' width = '50%'>\n",
"\n",
"Note that we could do geological interpretation in 2d with snapping if get point in line with same coordinates of drillhole intersects. "
]
},
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\" />'\n",
"</script>\n",
"\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# import modules\n",
"import pygslib\n",
"import ezdxf\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we extract data from the dxf sections. We basically extract points from the lines and we asign it to its corresponding object, defined by the dxf 'layer'. "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['bbox' 'topo' 'fw' 'hw' 'dhole']\n"
]
}
],
"source": [
"# get sections in dxf format, the sufix is the N coordinate (for now only for EW sections)\n",
"N = [-10, 0,50,100,150,200, 210]\n",
"s = {'x':[], # noth coordinates of the sections\n",
" 'y':[], \n",
" 'z':[], \n",
" 'layer':[],\n",
" 'id':[]}\n",
"pl_id = -1\n",
"for y in N:\n",
" dwg = ezdxf.readfile('S_{}.dxf'.format(y))\n",
" msp= dwg.modelspace()\n",
" for e in msp.query('LWPOLYLINE'):\n",
" p = e.get_rstrip_points()\n",
" if e.dxfattribs()['layer']=='dhole':\n",
" pl_id=pl_id+1\n",
" aid = int(pl_id)\n",
" else:\n",
" aid = None \n",
" for j in p:\n",
" s['x'].append(j[0])\n",
" s['y'].append(y)\n",
" s['z'].append(j[1])\n",
" s['layer'].append(e.dxfattribs()['layer'])\n",
" s['id'].append(aid)\n",
"\n",
"S=pd.DataFrame(s)\n",
"S['id']=S['id'].values.astype(int)\n",
"print (S['layer'].unique())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate working region\n",
"For this to work we need to define a bounding box. We will generate surfaces that have the same size or extend outside the box, otherwise we may get non expected results when we work with open surfaces"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# define working region \n",
"xorg = -10.\n",
"yorg = -10.\n",
"zorg = -10.\n",
"dx = 5.\n",
"dy = 5.\n",
"dz = 5.\n",
"nx = 40\n",
"ny = 44\n",
"nz = 36"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate solids \n",
"To generate solids, asuming we have stratigraphic units with contact surfaces that may be modeled indivicually, we defined surfaces interpolating in a 2D grid and optionally snapping points with known coordinates in 3D. \n",
"\n",
"The surfaces are then used to cut the region to obtain individual closed solids. "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"# get points defining each surface \n",
"hw_p = S.loc[S['layer']=='hw',['x','y','z']]\n",
"fw_p = S.loc[S['layer']=='fw',['x','y','z']]\n",
"topo_p = S.loc[S['layer']=='topo',['x','y','z']]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# generate vtk open surfaces\n",
"topo,x_topo,y_topo,z_topo = pygslib.vtktools.rbfinterpolate(x=topo_p['x'].values.astype('float'),\n",
" y=topo_p['y'].values.astype('float'),\n",
" z=topo_p['z'].values.astype('float'),\n",
" xorg=xorg, yorg=yorg,dx=dx,dy=dy,nx=nx,ny=ny,\n",
" snap = False)\n",
"\n",
"hw,x_hw,y_hw,z_hw = pygslib.vtktools.rbfinterpolate( x=hw_p['x'].values.astype('float'),\n",
" y=hw_p['y'].values.astype('float'),\n",
" z=hw_p['z'].values.astype('float'),\n",
" xorg=xorg, yorg=yorg,dx=dx,dy=dy,nx=nx,ny=ny,\n",
" snap = False)\n",
"\n",
"fw,x_fw,y_fw,z_fw = pygslib.vtktools.rbfinterpolate( x=fw_p['x'].values.astype('float'),\n",
" y=fw_p['y'].values.astype('float'),\n",
" z=fw_p['z'].values.astype('float'),\n",
" xorg=xorg, yorg=yorg,dx=dx,dy=dy,nx=nx,ny=ny,\n",
" snap = False)\n",
"\n",
"# save the open surfaces\n",
"pygslib.vtktools.SavePolydata(topo, 'topo')\n",
"pygslib.vtktools.SavePolydata(hw, 'hw')\n",
"pygslib.vtktools.SavePolydata(fw, 'fw')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By now we have surfaces that can be used for modeling, but you will get the best performance and number of octions with closed surfaces\n",
"\n",
"<img src = 'fig2.JPG' height=\"50%\" width=\"50%\">\n",
"\n",
"To generate solids we define implicit surfaces from surfaces and we cut the region using implisit surfaces. Note that implisit surfaces cluould be used to select points (drillholes and block centroids) but will not allow you to easily calculate proportion of blocks inside a given domain.\n",
"\n",
"Implisit surfaces require consistent normals to determine `inside` or `outside` and sign of distances. The function `pygslib.vtktools.implicit_surface()` will update/calculate the normals. You can use the function `pygslib.vtktools.calculate_normals()` to invert the `inside` or `outside` direction of solids. \n",
" \n",
"The behaviour of implisit surfaces can be changed by manipulating the normals manually and setting update_normals==False in the function `pygslib.vtktools.implicit_surface()`\n",
" \n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# create implicit surfaces\n",
"impl_topo = pygslib.vtktools.implicit_surface(topo)\n",
"impl_hw = pygslib.vtktools.implicit_surface(hw)\n",
"impl_fw = pygslib.vtktools.implicit_surface(fw)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# this is a grid (a box, we cut to generate geology). We can generate a grid ot tetras with surface point included to emulate snapping \n",
"region = pygslib.vtktools.define_region_grid(xorg, yorg, zorg, dx/2, dy/2, dz/4, nx*2, ny*2, nz*4) #, snapping_points = [topo,hw,fw])\n",
"pygslib.vtktools.SaveUnstructuredGrid(region, \"region\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\AMartinez\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\vtk\\util\\numpy_support.py:137: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" assert not numpy.issubdtype(z.dtype, complex), \\\n"
]
}
],
"source": [
"# evaluate surfaces\n",
"#below topo\n",
"region,topo_d = pygslib.vtktools.evaluate_region(region, implicit_func = impl_topo, func_name='topo_d', invert=False, capt = -10000)\n",
"#above hanging wall\n",
"region, hw_u = pygslib.vtktools.evaluate_region(region, implicit_func = impl_hw, func_name='hw_u', invert=True, capt = -10000)\n",
"#below hanging wall\n",
"region, hw_d = pygslib.vtktools.evaluate_region(region, implicit_func = impl_hw, func_name='hw_d', invert=False, capt = -10000)\n",
"#above footwall\n",
"region, fw_u = pygslib.vtktools.evaluate_region(region, implicit_func = impl_fw, func_name='fw_u', invert=True, capt = -10000)\n",
"#below footwall\n",
"region, fw_d = pygslib.vtktools.evaluate_region(region, implicit_func = impl_fw, func_name='fw_d', invert=False, capt = -10000)\n",
"\n",
"\n",
"# create intersection between hanging wall and foot wall\n",
"dom1= np.minimum(hw_d, fw_u)\n",
"region = pygslib.vtktools.set_region_field(region, dom1, 'dom1')\n",
"# extract surface\n",
"dom1_poly = pygslib.vtktools.extract_surface(region,'dom1')\n",
"# Save surface\n",
"pygslib.vtktools.SavePolydata(dom1_poly, 'dom1')\n",
"\n",
"# create intersection between topo and hanging wall\n",
"dom_topo= np.minimum(topo_d, hw_u)\n",
"region = pygslib.vtktools.set_region_field(region, dom_topo, 'dom_topo')\n",
"# extract surface\n",
"dom_topo_poly = pygslib.vtktools.extract_surface(region,'dom_topo')\n",
"# Save surface\n",
"pygslib.vtktools.SavePolydata(dom_topo_poly, 'dom_topo')\n",
"\n",
"# not boolean required below fw\n",
"# extract surface\n",
"dom_fw_poly = pygslib.vtktools.extract_surface(region,'fw_d')\n",
"# Save surface\n",
"pygslib.vtktools.SavePolydata(dom_fw_poly, 'dom_fw')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"by now we have a geological model defined with solids\n",
"<img src='fig3.JPG' height=\"50%\" width=\"50%\" >"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Generate drillhole data\n",
"Now we extract drillhole traces from dxf files to:\n",
"- generate drillhole tables\n",
"- from tables generate drillhole object\n",
"- then we split drillhole data ('composite') and label drillhole intervals with Domain 1 (between hanging and footwall surfaces)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"# generate table collar from dxf traces\n",
"tcollar = {}\n",
"tcollar['BHID'] = S.loc[S['layer']=='dhole','id'].unique()\n",
"tcollar['XCOLLAR'] = S.loc[S['layer']=='dhole',['x','id']].groupby('id').first().values.ravel().astype(float)\n",
"tcollar['YCOLLAR'] = S.loc[S['layer']=='dhole',['y','id']].groupby('id').first().values.ravel().astype(float)\n",
"tcollar['ZCOLLAR'] = S.loc[S['layer']=='dhole',['z','id']].groupby('id').first().values.ravel().astype(float)\n",
"collar = pd.DataFrame(tcollar)\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# generate table survey from dxf traces\n",
"tsurvey = {'BHID':[], 'AT':[], 'DIP':[], 'AZ':[]}\n",
"for i in collar['BHID']:\n",
" h = S.loc[(S['layer']=='dhole') & (S['id']==i),['x','y','z']].values\n",
" x= h[1][0]-h[0][0]\n",
" y= h[1][1]-h[0][1]\n",
" z= h[1][2]-h[0][2]\n",
" d0=np.sqrt(x**2+y**2+z**2)\n",
" az,dip = pygslib.drillhole.cart2ang(x/d0,y/d0,z/d0)\n",
" # add first interval\n",
" tsurvey['BHID'].append(i)\n",
" tsurvey['AT'].append(0)\n",
" tsurvey['AZ'].append(az)\n",
" tsurvey['DIP'].append(dip)\n",
" for j in range(1,h.shape[0]): \n",
" x= h[j][0]-h[j-1][0]\n",
" y= h[j][1]-h[j-1][1]\n",
" z= h[j][2]-h[j-1][2]\n",
" d=np.sqrt(x**2+y**2+z**2)\n",
" az,dip = pygslib.drillhole.cart2ang(x/d,y/d,z/d)\n",
" tsurvey['BHID'].append(i)\n",
" tsurvey['AT'].append(d+d0)\n",
" tsurvey['AZ'].append(az)\n",
" tsurvey['DIP'].append(dip)\n",
" d0 = d+d0\n",
"survey = pd.DataFrame(tsurvey)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# generate 'LENGTH' field of collar from table of surveys\n",
"collar['LENGTH'] = 0\n",
"for i in collar['BHID']:\n",
" collar.loc[collar['BHID']==i, 'LENGTH'] = survey.groupby('BHID')['AT'].max()[i]\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# generate a dum assay table\n",
"assay = pd.DataFrame({'BHID':collar['BHID'],'TO':collar['LENGTH']})\n",
"assay['FROM'] = 0"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"# generate drillhole object\n",
"collar['BHID'] = collar['BHID'].values.astype('str')\n",
"survey['BHID'] = survey['BHID'].values.astype('str')\n",
"assay['BHID'] = assay['BHID'].values.astype('str')\n",
"assay['DUM'] = 0.\n",
"\n",
"dhole = pygslib.drillhole.Drillhole(collar,survey)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"# add assay table\n",
"dhole.addtable(assay, 'assay')"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"# validate results\n",
"dhole.validate()\n",
"dhole.validate_table('assay')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"# composite. This is normaly completed after tagging but we need small intervals here to emulate real assay table\n",
"dhole.downh_composite(table_name='assay',variable_name='DUM', new_table_name='cmp',cint = 1)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\AMartinez\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\vtk\\util\\numpy_support.py:137: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" assert not numpy.issubdtype(z.dtype, complex), \\\n"
]
}
],
"source": [
"# desurvey and export\n",
"dhole.desurvey(table_name='cmp', endpoints=True, warns=True)\n",
"dhole.intervals2vtk('cmp','cmp')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src='fig4.JPG' height=\"50%\" width=\"50%\" >"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Tag drillholes with domain code"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are two main ways here: \n",
"- tagging samples using implicit functions with surfaces \n",
"- tagging samples using implicit functions with solids\n",
"\n",
"The easiest is using solids. \n",
"\n",
"In both cases we evaluate the distance between a point and a surface using the function `pygslib.vtktools.evaluate_implicit_points()`. The output of this function is a signed distance, where sign indicates:\n",
"- negative: the point is inside or above\n",
"- positive: the point is outside or below\n",
"- zero: the point is in the surface\n",
"\n",
"The samples between two surfaces can be selected by evaluating the points with the two implicit surfaces and doing boolean operation with signed values"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\AMartinez\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\vtk\\util\\numpy_support.py:137: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" assert not numpy.issubdtype(z.dtype, complex), \\\n"
]
}
],
"source": [
"# tag using surfaces \n",
"dhole.table['cmp']['dist_hw'] = pygslib.vtktools.evaluate_implicit_points(implicit_mesh=impl_hw, \n",
" x=dhole.table['cmp']['xm'].values, \n",
" y=dhole.table['cmp']['ym'].values, \n",
" z=dhole.table['cmp']['zm'].values, \n",
" cap_dist=1, \n",
" normalize=False)\n",
"\n",
"\n",
"dhole.table['cmp']['dist_fw'] = pygslib.vtktools.evaluate_implicit_points(implicit_mesh=impl_fw, \n",
" x=dhole.table['cmp']['xm'].values, \n",
" y=dhole.table['cmp']['ym'].values, \n",
" z=dhole.table['cmp']['zm'].values, \n",
" cap_dist=1, \n",
" normalize=False)\n",
"dhole.table['cmp']['D1_surf'] = np.round((dhole.table['cmp']['dist_fw']+dhole.table['cmp']['dist_hw'])/2) \n",
"dhole.intervals2vtk('cmp','cmp')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\AMartinez\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\vtk\\util\\numpy_support.py:137: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" assert not numpy.issubdtype(z.dtype, complex), \\\n"
]
}
],
"source": [
"# tag using solid dom1\n",
"inside1 = pygslib.vtktools.pointinsolid(dom1_poly, \n",
" x=dhole.table['cmp']['xm'].values, \n",
" y=dhole.table['cmp']['ym'].values, \n",
" z=dhole.table['cmp']['zm'].values)\n",
"\n",
"dhole.table['cmp']['D1_solid'] = inside1.astype(int)\n",
"dhole.intervals2vtk('cmp','cmp')\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Modeling block model\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\AMartinez\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\vtk\\util\\numpy_support.py:137: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" assert not numpy.issubdtype(z.dtype, complex), \\\n"
]
},
{
"data": {
"text/plain": [
"(vtkCommonDataModelPython.vtkImageData)000001C8D4CC7DC8"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mod = pygslib.blockmodel.Blockmodel(xorg=xorg, yorg=yorg, zorg=zorg, dx=dx*2,dy=dy*2, dz=dz*2, nx=nx/2, ny=ny/2, nz=nz/2)\n",
"mod.fillwireframe(surface=dom1_poly)\n",
"mod.blocks2vtkImageData(path='d1_mod')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"when blocks are too large cmpared to solid resolution this algorithm fails, and require refinement\n",
"\n",
"<img src='fig6.JPG' height=\"50%\" width=\"50%\">"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\AMartinez\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\vtk\\util\\numpy_support.py:137: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" assert not numpy.issubdtype(z.dtype, complex), \\\n"
]
},
{
"data": {
"text/plain": [
"(vtkCommonDataModelPython.vtkImageData)000001C8D4CC7C48"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mod = pygslib.blockmodel.Blockmodel(xorg=xorg, yorg=yorg, zorg=zorg, dx=dx,dy=dy, dz=dz/2, nx=nx, ny=ny, nz=nz*2)\n",
"mod.fillwireframe(surface=dom1_poly)\n",
"mod.blocks2vtkImageData(path='d1_mod')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"working with smaller blocks fix this problem\n",
"\n",
"<img src='fig7.JPG' height=\"50%\" width=\"50%\">\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Simulated Au grades in fine grid"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(vtkCommonDataModelPython.vtkImageData)000001C8D4CC7B88"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# block model definition of xorg is for the corner of the block. To aline this with GSLIB grids use xorg-dx/2\n",
"sim_grid = pygslib.blockmodel.Blockmodel(xorg=xorg, yorg=yorg, zorg=zorg, \n",
" dx=dx/5,dy=dy/5, dz=dz/5, nx=nx*5, ny=ny*5, nz=nz*5)\n",
"sim_grid.fillwireframe(surface=dom1_poly)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-10.0 -10.0 -10.0\n",
"200 220 180\n",
"1.0 1.0 1.0\n"
]
}
],
"source": [
"print (sim_grid.xorg, sim_grid.yorg, sim_grid.zorg)\n",
"print (sim_grid.nx, sim_grid.ny, sim_grid.nz)\n",
"print (sim_grid.dx, sim_grid.dy, sim_grid.dz)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import subprocess\n",
"subprocess.call('echo sgsim.par | c:\\gslib\\sgsim.exe',shell=True)\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\AMartinez\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\vtk\\util\\numpy_support.py:137: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" assert not numpy.issubdtype(z.dtype, complex), \\\n"
]
},
{
"data": {
"text/plain": [
"(vtkCommonDataModelPython.vtkImageData)000001C8D4CC7AC8"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sim1=pygslib.gslib.read_gslib_file('sgsim.out')\n",
"sim_grid.bmtable['sim1'] = np.exp(sim1['value'].values)*sim_grid.bmtable['__in'].values # to emulate Au grade with lognormal distribution\n",
"sim_grid.blocks2vtkImageData(path='sim1')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Assigning grade to drillholes"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"# we migrate block data to points within blocks\n",
"dhole.table['cmp']['Au']=sim_grid.block2point(dhole.table['cmp']['xm'].values, \n",
" dhole.table['cmp']['ym'].values, \n",
" dhole.table['cmp']['zm'].values, \n",
" 'sim1')"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\AMartinez\\AppData\\Local\\Continuum\\miniconda3\\lib\\site-packages\\vtk\\util\\numpy_support.py:137: FutureWarning: Conversion of the second argument of issubdtype from `complex` to `np.complexfloating` is deprecated. In future, it will be treated as `np.complex128 == np.dtype(complex).type`.\n",
" assert not numpy.issubdtype(z.dtype, complex), \\\n"
]
}
],
"source": [
"dhole.intervals2vtk('cmp','cmp')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<img src = 'fig8.JPG'>"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"dhole.collar.to_csv('collar.csv', index = False)\n",
"dhole.survey.to_csv('survey.csv', index = False)\n",
"dhole.table['cmp'].to_csv('assay.csv', index = False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
cb4ds/mr4ds | Student-Resources/Labs/Lab1-dplyr.ipynb | 1 | 4670 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lab 1 - `dplyr` examples\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"autoscroll": false,
"collapsed": true
},
"outputs": [],
"source": [
"options(jupyter.rich_display=FALSE)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"autoscroll": false,
"collapsed": true
},
"outputs": [],
"source": [
"library(dplyr)\n",
"library(stringr)\n",
"taxi_url <- \"http://alizaidi.blob.core.windows.net/training/taxi_df.rds\"\n",
"taxi_df <- readRDS(gzcon(url(taxi_url)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"autoscroll": false,
"collapsed": true
},
"outputs": [],
"source": [
"ls()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"autoscroll": false,
"collapsed": true
},
"outputs": [],
"source": [
"class(taxi_df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"autoscroll": false,
"collapsed": true
},
"outputs": [],
"source": [
"taxi_df <- taxi_df %>% mutate(tip_pct = tip_amount/fare_amount)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"autoscroll": false,
"collapsed": true
},
"outputs": [],
"source": [
"taxi_df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Exploratory Data Analysis - Data Validation\n",
"Let's see if we can find out anything about the different numeric fields, `tip_amount` and `fare_amount` and see if we can spot any outliers.\n",
"How should we deal with them?\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"autoscroll": false,
"collapsed": true
},
"outputs": [],
"source": [
"## Some useful functions\n",
"\n",
"# summary\n",
"# quantile\n",
"# ggplot() + geom_histogram\n",
"# ggplot() + geom_density"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Summarize data by payment type\n",
"\n",
"There is a payment type column that is an label for the type of payment used for the taxi ride.\n",
"Let's see if we can find out anything strange about the various payment types.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"autoscroll": false,
"collapsed": true
},
"outputs": [],
"source": [
"## some useful functions\n",
"# group_by(payment_type) %>% summarise(tip_amount)\n",
"# ggplot() + facet_wrap(~payment_type)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"## Two-table joins\n",
"\n",
"Let's see examples of the two-table functions in `dplyr`.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"autoscroll": false,
"collapsed": true
},
"outputs": [],
"source": [
"library(broom)\n",
"taxi_coefs <- taxi_df %>% sample_n(10^5) %>%\n",
" group_by(dropoff_dow) %>%\n",
" do(tidy(lm(tip_pct ~ pickup_nhood + passenger_count + pickup_hour,\n",
" data = .), conf.int = TRUE)) %>% select(dropoff_dow, conf.low, conf.high)\n",
"\n",
"taxi_metrics <- taxi_df %>% sample_n(10^5) %>%\n",
" group_by(dropoff_dow) %>%\n",
" do(glance(lm(tip_pct ~ pickup_nhood + passenger_count + pickup_hour,\n",
" data = .)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"Use the `left_join` function in `dplyr` to append the model metrics to the coefficients.\n",
"\n",
"# `tidyr`\n",
"\n",
"The `tidyr` package is a very handy package for transforming data that is _wide_ into data that is **tall**.\n",
"\n",
"Take a look at the `tidyr` [cheatsheet](https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf) and try to convert the coeffs data from _tall_ to **wide**\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"autoscroll": false,
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "R",
"language": "R",
"name": "ir"
},
"language_info": {
"codemirror_mode": "r",
"file_extension": ".r",
"mimetype": "text/x-r-source",
"name": "R",
"pygments_lexer": "r"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
dpshelio/2015-EuroScipy-pandas-tutorial | 05 - Time series data.ipynb | 1 | 23513 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Working with time series data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"Some imports:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"try:\n",
" import seaborn\n",
"except:\n",
" pass\n",
"\n",
"pd.options.display.max_rows = 8"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Case study: air quality data of European monitoring stations (AirBase)\n",
"\n",
"AirBase (The European Air quality dataBase): hourly measurements of all air quality monitoring stations from Europe. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from IPython.display import HTML\n",
"HTML('<iframe src=http://www.eea.europa.eu/data-and-maps/data/airbase-the-european-air-quality-database-8#tab-data-by-country width=900 height=350></iframe>')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I downloaded and preprocessed some of the data ([python-airbase](https://github.com/jorisvandenbossche/python-airbase)): `data/airbase_data.csv`. This file includes the hourly concentrations of NO2 for 4 different measurement stations:\n",
"\n",
"- FR04037 (PARIS 13eme): urban background site at Square de Choisy\n",
"- FR04012 (Paris, Place Victor Basch): urban traffic site at Rue d'Alesia\n",
"- BETR802: urban traffic site in Antwerp, Belgium\n",
"- BETN029: rural background site in Houtem, Belgium\n",
"\n",
"See http://www.eea.europa.eu/themes/air/interactive/no2"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Importing the data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Import the csv file:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!head -5 data/airbase_data.csv"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, the missing values are indicated by `-9999`. This can be recognized by `read_csv` by passing the `na_values` keyword:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = pd.read_csv('data/airbase_data.csv', index_col=0, parse_dates=True, na_values=[-9999])"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Exploring the data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Some useful methods:\n",
"\n",
"`head` and `tail`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"data.head(3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.tail()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"`info()`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.info()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Getting some basic summary statistics about the data with `describe`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Quickly visualizing the data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "-"
}
},
"outputs": [],
"source": [
"data.plot(kind='box', ylim=[0,250])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"data['BETR801'].plot(kind='hist', bins=50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": [
"data.plot(figsize=(12,6))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This does not say too much .."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"We can select part of the data (eg the latest 500 data points):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data[-500:].plot(figsize=(12,6))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Or we can use some more advanced time series features -> next section!"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"## Working with time series data"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "fragment"
}
},
"source": [
"When we ensure the DataFrame has a `DatetimeIndex`, time-series related functionality becomes available:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.index"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Indexing a time series works with strings:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data[\"2010-01-01 09:00\": \"2010-01-01 12:00\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"A nice feature is **\"partial string\" indexing**, where we can do implicit slicing by providing a partial datetime string.\n",
"\n",
"E.g. all data of 2012:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"data['2012']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Normally you would expect this to access a column named '2012', but as for a DatetimeIndex, pandas also tries to interprete it as a datetime slice."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "-"
}
},
"source": [
"Or all data of January up to March 2012:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data['2012-01':'2012-03']"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Time and date components can be accessed from the index:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.index.hour"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.index.year"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>EXERCISE</b>: select all data starting from 1999\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = data['1999':]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>EXERCISE</b>: select all data in January for all different years\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data[data.index.month == 1]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>EXERCISE</b>: select all data in January, February and March for all different years\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data['months'] = data.index.month\n",
"data[data['months'].isin([1, 2, 3])]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>EXERCISE</b>: select all 'daytime' data (between 8h and 20h) for all days\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data[(data.index.hour >= 8) & (data.index.hour < 20)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.between_time('08:00', '20:00')"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## The power of pandas: `resample`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A very powerfull method is **`resample`: converting the frequency of the time series** (e.g. from hourly to daily data).\n",
"\n",
"The time series has a frequency of 1 hour. I want to change this to daily:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.resample('D').head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"By default, `resample` takes the mean as aggregation function, but other methods can also be specified:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.resample('D', how='max').head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "skip"
}
},
"source": [
"The string to specify the new time frequency: http://pandas.pydata.org/pandas-docs/dev/timeseries.html#offset-aliases \n",
"These strings can also be combined with numbers, eg `'10D'`."
]
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Further exploring the data:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.resample('M').plot() # 'A'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# data['2012'].resample('D').plot()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>QUESTION</b>: plot the monthly mean and median concentration of the 'FR04037' station for the years 2009-2012\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>QUESTION</b>: plot the monthly mininum and maximum daily concentration of the 'BETR801' station\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>QUESTION</b>: make a bar plot of the mean of the stations in year of 2012\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>QUESTION</b>: The evolution of the yearly averages with, and the overall mean of all stations?\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"## Combination with groupby"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`resample` can actually be seen as a specific kind of `groupby`. E.g. taking annual means with `data.resample('A', 'mean')` is equivalent to `data.groupby(data.index.year).mean()` (only the result of `resample` still has a DatetimeIndex).\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data.groupby(data.index.year).mean().plot()"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"But, `groupby` is more flexible and can also do resamples that do not result in a new continuous time series, e.g. by grouping by the hour of the day to get the diurnal cycle."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>QUESTION</b>: how does the *typical monthly profile* look like for the different stations?\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1\\. add a column to the dataframe that indicates the month (integer value of 1 to 12):"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"2\\. Now, we can calculate the mean of each month over the different years:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3\\. plot the typical monthly profile of the different stations:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>QUESTION</b>: plot the weekly 95% percentiles of the concentration in 'BETR801' and 'BETN029' for 2011\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>QUESTION</b>: The typical diurnal profile for the different stations?\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false,
"slideshow": {
"slide_type": "fragment"
}
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>QUESTION</b>: What is the difference in the typical diurnal profile between week and weekend days?\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"Add a column indicating week/weekend"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
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"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false,
"slideshow": {
"slide_type": "subslide"
}
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"source": []
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"cell_type": "code",
"execution_count": null,
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"clear_cell": true,
"collapsed": true
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"outputs": [],
"source": []
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{
"cell_type": "markdown",
"metadata": {
"clear_cell": false,
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>QUESTION</b>: What are the number of exceedances of hourly values above the European limit 200 µg/m3 ?\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false
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"outputs": [],
"source": []
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{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false
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"outputs": [],
"source": []
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{
"cell_type": "code",
"execution_count": null,
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"clear_cell": true,
"collapsed": false
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"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>QUESTION</b>: And are there exceedances of the yearly limit value of 40 µg/m3 since 200 ?\n",
"</div>"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false
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"outputs": [],
"source": []
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"metadata": {
"slideshow": {
"slide_type": "subslide"
}
},
"source": [
"<div class=\"alert alert-success\">\n",
" <b>QUESTION</b>: Visualize the typical week profile for the different stations as boxplots.\n",
"</div>\n",
"\n",
"Tip: the boxplot method of a DataFrame expects the data for the different boxes in different columns)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false,
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"metadata": {
"slideshow": {
"slide_type": "subslide"
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"source": [
"<div class=\"alert alert-success\">\n",
" <b>QUESTION</b>: Calculate the correlation between the different stations\n",
"</div>\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"clear_cell": true,
"collapsed": false
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| bsd-2-clause |
DOV-Vlaanderen/pydov | docs/notebooks/caching.ipynb | 1 | 22610 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example of XML caching for pydov"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[](https://mybinder.org/v2/gh/DOV-Vlaanderen/pydov/master?filepath=docs%2Fnotebooks%2Fcaching.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Introduction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To speed up subsequent queries involving similar data, pydov uses a caching mechanism where raw DOV XML data is cached locally for later reuse. For regular usage of the package and data requests, the cache will be a *convenient* feature speeding up the time for subsequent queries. However, in case you want to alter the configuration or cache handling, this notebook illustrates some use cases on the cache handling."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Use cases:\n",
"* Check cached files\n",
"* Speed up subsequent queries\n",
"* Disabling the cache\n",
"* Changing the location of cached data\n",
"* Changing the maximum age of cached data\n",
"* Cleaning the cache"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"# check pydov path\n",
"import warnings; warnings.simplefilter('ignore')\n",
"import pydov"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Use cases"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check cached files"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"from pydov.search.boring import BoringSearch\n",
"boring = BoringSearch()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `pydov.cache.cachedir` defines the directory on the file system used to cache DOV files:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"C:\\Users\\haestp\\AppData\\Local\\Temp\\pydov\n",
"directories: []\n"
]
}
],
"source": [
"# check the cache dir\n",
"import os\n",
"import pydov.util.caching\n",
"cachedir = pydov.cache.cachedir\n",
"print(cachedir)\n",
"print('directories: ', os.listdir(cachedir))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Speed up subsequent queries"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To illustrate the convenience of the caching during subsequent data requests, consider the following request, while measuring the time:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[000/144] ..................................................\n",
"[050/144] ..................................................\n",
"[100/144] ............................................\n",
"Wall time: 5.51 s\n"
]
}
],
"source": [
"from pydov.util.location import Within, Box\n",
"\n",
"# Get all borehole data in a bounding box (llx, llxy, ulx, uly) and timeit\n",
"%time df = boring.search(location=Within(Box(150145, 205030, 155150, 206935)))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of files: 144\n",
"files present: ['1879-119364.xml.gz', '1879-121292.xml.gz', '1879-121293.xml.gz', '1879-121387.xml.gz', '1879-121401.xml.gz', '1879-121412.xml.gz', '1879-121424.xml.gz', '1879-122256.xml.gz', '1894-121258.xml.gz', '1894-122153.xml.gz', '1894-122154.xml.gz', '1894-122155.xml.gz', '1895-121232.xml.gz', '1895-121241.xml.gz', '1895-121242.xml.gz', '1895-121244.xml.gz', '1895-121247.xml.gz', '1895-121248.xml.gz', '1923-121199.xml.gz', '1923-121200.xml.gz', '1932-121315.xml.gz', '1936-122224.xml.gz', '1938-121359.xml.gz', '1938-121360.xml.gz', '1953-121327.xml.gz', '1953-121361.xml.gz', '1953-121362.xml.gz', '1969-033206.xml.gz', '1969-033207.xml.gz', '1969-033208.xml.gz', '1969-033209.xml.gz', '1969-033211.xml.gz', '1969-033212.xml.gz', '1969-033213.xml.gz', '1969-033214.xml.gz', '1969-033215.xml.gz', '1969-033216.xml.gz', '1969-033217.xml.gz', '1969-033218.xml.gz', '1969-033219.xml.gz', '1969-033220.xml.gz', '1969-092685.xml.gz', '1969-092686.xml.gz', '1969-092687.xml.gz', '1969-092688.xml.gz', '1969-092689.xml.gz', '1970-018757.xml.gz', '1970-018762.xml.gz', '1970-018763.xml.gz', '1970-061362.xml.gz', '1970-061363.xml.gz', '1970-061364.xml.gz', '1970-061365.xml.gz', '1970-061366.xml.gz', '1970-061442.xml.gz', '1970-061443.xml.gz', '1970-061444.xml.gz', '1970-061445.xml.gz', '1970-061446.xml.gz', '1970-061447.xml.gz', '1970-061450.xml.gz', '1970-061454.xml.gz', '1970-104897.xml.gz', '1970-104898.xml.gz', '1970-104899.xml.gz', '1970-104900.xml.gz', '1973-018152.xml.gz', '1973-060207.xml.gz', '1973-060208.xml.gz', '1973-081811.xml.gz', '1973-104723.xml.gz', '1973-104727.xml.gz', '1973-104728.xml.gz', '1974-010351.xml.gz', '1975-010345.xml.gz', '1976-014856.xml.gz', '1976-015297.xml.gz', '1976-015298.xml.gz', '1976-015779.xml.gz', '1976-015780.xml.gz', '1976-015781.xml.gz', '1976-015782.xml.gz', '1978-012352.xml.gz', '1978-121458.xml.gz', '1984-081833.xml.gz', '1984-081834.xml.gz', '1985-084552.xml.gz', '1986-005594.xml.gz', '1986-005596.xml.gz', '1986-005597.xml.gz', '1986-005598.xml.gz', '1986-059814.xml.gz', '1986-059815.xml.gz', '1986-059816.xml.gz', '1987-119382.xml.gz', '1996-021717.xml.gz', '1996-081802.xml.gz', '2017-148854.xml.gz', '2017-152011.xml.gz', '2017-153161.xml.gz', '2018-153957.xml.gz', '2018-154057.xml.gz', '2018-155266.xml.gz', '2018-155580.xml.gz', '2018-156632.xml.gz', '2018-156633.xml.gz', '2018-156634.xml.gz', '2018-157193.xml.gz', '2018-157294.xml.gz', '2018-157386.xml.gz', '2018-170089.xml.gz', '2018-173275.xml.gz', '2019-160294.xml.gz', '2019-160705.xml.gz', '2019-160757.xml.gz', '2019-161338.xml.gz', '2019-161547.xml.gz', '2019-162035.xml.gz', '2019-166024.xml.gz', '2019-166049.xml.gz', '2019-166213.xml.gz', '2019-166704.xml.gz', '2019-166707.xml.gz', '2019-167509.xml.gz', '2020-169408.xml.gz', '2020-169780.xml.gz', '2020-171440.xml.gz', '2020-172608.xml.gz', '2020-174025.xml.gz', '2020-174046.xml.gz', '2020-174745.xml.gz', '2020-175374.xml.gz', '2020-175375.xml.gz', '2020-175376.xml.gz', '2020-175377.xml.gz', '2020-175378.xml.gz', '2020-175379.xml.gz', '2020-175467.xml.gz', '2020-175766.xml.gz', '2020-176796.xml.gz', '2020-176854.xml.gz', '2020-177041.xml.gz', '2020-177353.xml.gz', '2021-178632.xml.gz']\n"
]
}
],
"source": [
"# The structure of cachedir implies a separate directory for each query type, since permalinks are not unique across types\n",
"# In this example 'boring' will be queried, therefore list xmls in the cache of the 'boring' type\n",
"# list files present\n",
"print('number of files: ', len(os.listdir(os.path.join(pydov.cache.cachedir, 'boring'))))\n",
"print('files present: ', os.listdir(os.path.join(pydov.cache.cachedir, 'boring')))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Rerun the previous request and timeit again:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[000/144] cccccccccccccccccccccccccccccccccccccccccccccccccc\n",
"[050/144] cccccccccccccccccccccccccccccccccccccccccccccccccc\n",
"[100/144] cccccccccccccccccccccccccccccccccccccccccccc\n",
"Wall time: 433 ms\n"
]
}
],
"source": [
"%time df = boring.search(location=Within(Box(150145, 205030, 155150, 206935)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The use of the cache decreased the runtime by a factor 100 in the current example. This will increase drastically if more permalinks are queried since the download takes much longer than the IO at runtime."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Disabling the cache"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can (temporarily!) disable the caching mechanism. This disables both the saving of newly downloaded data in the cache, \n",
"as well as reusing existing data in the cache. It remains valid for the time being of the instantiated pydov.cache object.\n",
"It does not delete existing data in the cache."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of files: 144\n"
]
}
],
"source": [
"# list number of files\n",
"print('number of files: ', len(os.listdir(os.path.join(cachedir, 'boring'))))"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[000/002] ..\n",
" pkey_boring boornummer x \\\n",
"0 https://www.dov.vlaanderen.be/data/boring/1895... kb15d43w-B47 151600.0 \n",
"1 https://www.dov.vlaanderen.be/data/boring/1984... kb15d43w-B403 151041.0 \n",
"\n",
" y mv_mtaw start_boring_mtaw gemeente diepte_boring_van \\\n",
"0 205998.0 15.00 15.00 Antwerpen 0.0 \n",
"1 205933.0 21.07 21.07 Antwerpen 0.0 \n",
"\n",
" diepte_boring_tot datum_aanvang uitvoerder \\\n",
"0 3.3 1895-01-04 onbekend \n",
"1 7.0 1984-09-26 Universiteit Gent - Geologisch Instituut \n",
"\n",
" boorgatmeting diepte_methode_van diepte_methode_tot boormethode \n",
"0 False 0.0 3.3 onbekend \n",
"1 False 0.0 7.0 droge boring \n"
]
}
],
"source": [
"# disable caching\n",
"cache_orig = pydov.cache\n",
"pydov.cache = None\n",
"# new query\n",
"df = boring.search(location=Within(Box(151000, 205930, 153000, 206000)))\n",
"print(df.head())"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of files: 144\n"
]
}
],
"source": [
"# list number of files\n",
"print('number of files: ', len(os.listdir(os.path.join(cachedir, 'boring'))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hence, no new files were added to the cache when disabling it."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The caching is disabled by removing the pydov.cache object from the namespace. If you want to enable caching again you must instantiate it anew."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"pydov.cache = cache_orig"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Changing the location of cached data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, pydov stores the cache in a temporary directory provided by the user's operating system. On Windows, the cache is usually located in: `C:\\Users\\username\\AppData\\Local\\Temp\\pydov\\`\n",
"If you want the cached xml files to be saved in another location you can define your own cache for the current runtime. Mind that this does not change the location of previously saved data. No lookup in the old datafolder will be performed after changing the directory's location.\n",
"Besides controlling the cache's location, this also allows using different scripts or projects."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"import pydov.util.caching\n",
"\n",
"pydov.cache = pydov.util.caching.GzipTextFileCache(\n",
" cachedir=r'C:\\temp\\pydov'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"C:\\temp\\pydov\n"
]
}
],
"source": [
"cachedir = pydov.cache.cachedir\n",
"print(cachedir)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"# for the sake of the example, change dir location back \n",
"pydov.cache = cache_orig\n",
"cachedir = pydov.cache.cachedir"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Changing the maximum age of cached data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you work with rapidly changing data or want to control when cached data is renewed, you can do so by changing the maximum age of cached data to be considered valid for the currenct runtime. You can use 'weeks', 'days' or any other common datetime format.\n",
"If a cached version exists and is younger than the maximum age, it is used in favor of renewing the data from DOV services. If no cached version exists or is older than the maximum age, the data is renewed and saved in the cache.\n",
"Note that data older than the maximum age is not automatically deleted from the cache."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0:00:01\n"
]
}
],
"source": [
"import pydov.util.caching\n",
"import datetime\n",
"pydov.cache = pydov.util.caching.GzipTextFileCache(\n",
" max_age=datetime.timedelta(seconds=1)\n",
" )\n",
"print(pydov.cache.max_age)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1879-119364.xml.gz\n"
]
},
{
"data": {
"text/plain": [
"'Tue Mar 2 22:40:34 2021'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from time import ctime\n",
"print(os.listdir(os.path.join(cachedir, 'boring'))[0])\n",
"ctime(os.path.getmtime(os.path.join(os.path.join(cachedir, 'boring'),\n",
" os.listdir(os.path.join(cachedir, 'boring'))[0]\n",
" )\n",
" )\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[000/144] ..................................................\n",
"[050/144] ..................................................\n",
"[100/144] ............................................\n",
"Wall time: 3.37 s\n"
]
}
],
"source": [
"# rerun previous query \n",
"%time df = boring.search(location=Within(Box(150145, 205030, 155150, 206935)))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1879-119364.xml.gz\n"
]
},
{
"data": {
"text/plain": [
"'Tue Mar 2 22:40:38 2021'"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from time import ctime\n",
"print(os.listdir(os.path.join(cachedir, 'boring'))[0])\n",
"ctime(os.path.getmtime(os.path.join(os.path.join(cachedir, 'boring'),\n",
" os.listdir(os.path.join(cachedir, 'boring'))[0]\n",
" )\n",
" )\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Cleaning the cache"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we use a temporary directory provided by the operating system, we rely on the operating system to clean the folder when it deems necessary."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To clean the cache, removing all records older than the maximum age"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"jupyter": {
"outputs_hidden": true
}
},
"outputs": [],
"source": [
"from time import sleep"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of files before clean: 144\n",
"number of files after clean: 0\n"
]
}
],
"source": [
"print('number of files before clean: ', len(os.listdir(os.path.join(cachedir, 'boring'))))\n",
"sleep(2) # remember we've put the caching age on 1 second\n",
"pydov.cache.clean()\n",
"print('number of files after clean: ', len(os.listdir(os.path.join(cachedir, 'boring'))))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Should you want to remove the pydov cache from code yourself, you can do so as illustrated below. Note that this will erase the entire cache, not only the records older than the maximum age:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"pydov.cache.remove()\n",
"# check existence of the cache directory:\n",
"print(os.path.exists(os.path.join(cachedir, 'boring')))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Disabling stale responses on error"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, pydov will return stale data (i.e. XML documents still present in\n",
"the cache, but no longer considered valid) in case it fails to download a fresh\n",
"copy from the DOV webservices. We believe this behaviour to benefit most users, as we think stale data is still better than no data at all. \n",
"\n",
"If your application cannot afford stale data, you can switch the default\n",
"behaviour by issuing:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"pydov.cache.stale_on_error = False"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This will cause pydov not to return stale data and instead set the XML fields\n",
"to NaN, as if the stale data wasn't available."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Custom caching"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By default, pydov caches files on disk as gzipped XML documents. Should you\n",
"for any reason want to use plain text XML documents instead, you can do so by\n",
"using the PlainTextFileCache instead of the GzipTextFileCache. \n",
"Mind that this can increase the disk usage of the cache by 10x.:"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"import pydov.util.caching\n",
"pydov.cache = pydov.util.caching.PlainTextFileCache()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Implementing custom caching\n",
"\n",
"Should you want to implement your own caching mechanism, you can do so by\n",
"subclassing :class:`pydov.util.caching.AbstractCache` and implementing its\n",
"abstract methods ``get``, ``clean`` and ``remove``. Hereby you can use the\n",
"available methods ``_get_remote`` to request data from the DOV webservices\n",
"and ``_emit_cache_hit`` to notify hooks a file has been retrieved from the\n",
"cache.\n",
"\n",
"Note that the ``get`` method will be called from multiple threads\n",
"simultaneously, so implementations must be threadsafe or use locking.\n",
"\n",
"A (naive) implementation for an in-memory cache would be something like:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"from pydov.util.caching import AbstractCache\n",
"\n",
"class MemoryCache(AbstractCache):\n",
" def __init__(self):\n",
" self.cache = {}\n",
"\n",
" def get(self, url):\n",
" if url not in self.cache:\n",
" self.cache[url] = self._get_remote(url)\n",
" else:\n",
" self._emit_cache_hit(url)\n",
" return self.cache[url]\n",
"\n",
" def clean(self):\n",
" self.cache = {}\n",
"\n",
" def remove(self):\n",
" self.cache = {}\n",
"\n",
"pydov.cache = MemoryCache()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
},
"nbsphinx": {
"execute": "never"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
kadnoise/codes | Untitled2.ipynb | 1 | 1221 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import graphlab\n",
"import pandas as pd\n",
"from graphlab import SFrame as sf"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = sf.read_csv('Bases/Prontas/superbow/sentic.patter.en-superbow2013.txt', delimiter='\\t', header=None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-3.0 |
midnighteuler/projecteuler | src/Prob18and67.ipynb | 1 | 18579 | {
"cells": [
{
"cell_type": "code",
"execution_count": 73,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d1 = '''3\n",
"7 4\n",
"2 4 6\n",
"8 5 9 3'''\n",
"\n",
"d2 = '''3\n",
"7 4\n",
"2 4 4\n",
"8 5 9 3'''\n",
"\n",
"d = '''75\n",
"95 64\n",
"17 47 82\n",
"18 35 87 10\n",
"20 04 82 47 65\n",
"19 01 23 75 03 34\n",
"88 02 77 73 07 63 67\n",
"99 65 04 28 06 16 70 92\n",
"41 41 26 56 83 40 80 70 33\n",
"41 48 72 33 47 32 37 16 94 29\n",
"53 71 44 65 25 43 91 52 97 51 14\n",
"70 11 33 28 77 73 17 78 39 68 17 57\n",
"91 71 52 38 17 14 91 43 58 50 27 29 48\n",
"63 66 04 68 89 53 67 30 73 16 69 87 40 31\n",
"04 62 98 27 23 09 70 98 73 93 38 53 60 04 23'''\n",
"\n",
"d3 = '''59\n",
"73 41\n",
"52 40 09\n",
"26 53 06 34\n",
"10 51 87 86 81\n",
"61 95 66 57 25 68\n",
"90 81 80 38 92 67 73\n",
"30 28 51 76 81 18 75 44\n",
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"64 66 84 24 18 16 27 48 20 14 47 69 30 86 48 40 23 16 61 21 51 50 26 47 35 33 91 28 78 64 43 68 04 79 51 08 19 60 52 95 06 68 46 86 35 97 27 58 04 65 30 58 99 12 12 75 91 39 50 31 42 64 70 04 46 07 98 73 98 93 37 89 77 91 64 71 64 65 66 21 78 62 81 74 42 20 83 70 73 95 78 45 92 27 34 53 71 15\n",
"30 11 85 31 34 71 13 48 05 14 44 03 19 67 23 73 19 57 06 90 94 72 57 69 81 62 59 68 88 57 55 69 49 13 07 87 97 80 89 05 71 05 05 26 38 40 16 62 45 99 18 38 98 24 21 26 62 74 69 04 85 57 77 35 58 67 91 79 79 57 86 28 66 34 72 51 76 78 36 95 63 90 08 78 47 63 45 31 22 70 52 48 79 94 15 77 61 67 68\n",
"23 33 44 81 80 92 93 75 94 88 23 61 39 76 22 03 28 94 32 06 49 65 41 34 18 23 08 47 62 60 03 63 33 13 80 52 31 54 73 43 70 26 16 69 57 87 83 31 03 93 70 81 47 95 77 44 29 68 39 51 56 59 63 07 25 70 07 77 43 53 64 03 94 42 95 39 18 01 66 21 16 97 20 50 90 16 70 10 95 69 29 06 25 61 41 26 15 59 63 35'''"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[3], [7, 4], [2, 4, 6], [8, 5, 9, 3]]\n"
]
},
{
"data": {
"text/plain": [
"[7273]"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from pprint import pprint\n",
"a = triangle_str_to_array(d1)\n",
"pprint(a)\n",
"\n",
"def triangle_str_to_array(d):\n",
" a = d.split(\"\\n\")\n",
" for i,n in enumerate(a):\n",
" a[i] = [int(j) for j in n.split(\" \")]\n",
" return a\n",
"\n",
"def max_path_sum(d):\n",
" if len(d) > 1:\n",
" for i,v in enumerate(d[-2]):\n",
" d[-2][i] = max([v + d[-1][i], v + d[-1][i+1]])\n",
" del d[-1]\n",
" \n",
" return max_path_sum(d)\n",
" else:\n",
" return max(d)\n",
" \n",
"max_path_sum(d=triangle_str_to_array(d3))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.12"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-2.0 |
mmathioudakis/moderndb | 2016/assignment 3.ipynb | 1 | 22752 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Student Information\n",
"\n",
"**First Name:**\n",
"\n",
"**Last Name:**\n",
"\n",
"**Student ID:**\n",
"\n",
"**Aalto E-mail:**\n",
"\n",
"\n",
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Assignment 3: Programming Part\n",
"\n",
"For this part, you'll be using PySpark to process tweets, i.e., messages generated on [twitter](http://twitter.com). \n",
"\n",
"This Jupyter notebook contains: (i) instructions to setup the programming environment, and (ii) the programming problems. You will use this notebook to write your code and submit it for grading.\n",
"\n",
"## Setup Instructions\n",
"\n",
"To run your code, you will need to connect to a Jupyter server maintained by [CSC](https://www.csc.fi/). Please read the following instructions very carefully.\n",
"\n",
"### Main Steps\n",
"\n",
"* Open your browser and go to server address https://86.50.170.119.\n",
"* If you are asked for credentials, provide **username:** *student* and **password:** *moderndb*.\n",
"* Once you're in the Jupyter \"Home\" page, **upload** your copy of the notebook by clicking *Upload* on the top-right corner.\n",
"* You will now see the notebook listed in \"Home\". Click to open it on a new tab and work on the programming problems.\n",
"* Once you have completed all or part of your work, make sure you **download** the notebook to your computer. You do that by selecting *File > Dowload as > IPython Notebook*. At the end of your session (after you download your notebook), select *File > Close and Halt*.\n",
"* Submit the notebook that contains your final solutions (code and output) to mycourses.\n",
"\n",
"### Saving Your Work\n",
"\n",
"The server will restart after **15 minutes of inaction**.\n",
"\n",
"Therefore, if you have made changes to the notebook but want to pause your work, make sure you **download** the notebook to your computer. You can **upload** it again when you're ready to resume your work.\n",
"\n",
"**What to do if the server restarts while inactive** \n",
"To save the code you wrote, copy and paste it manually to a text file on your computer. You can then follow again the *main steps* described above.\n",
"\n",
"\n",
"### Small and big dataset\n",
"\n",
"You will work with two datasets:\n",
"* A **small** dataset. This dataset is already available. Its purpose is for you to work with it to develop solutions for the assignment.\n",
"* A **big** dataset. This dataset will be available on *Friday April 1st, 2016*, together with instructions on how to access it. You will use it to produce your *final solutions* for the assignment.\n",
"\n",
"### Server Availability\n",
"\n",
"When working with the big dataset, you will be using a bigger cluster than the one you'll be using when working with the small dataset. To see how many other students are using it and if there are available resources, visit this webpage: https://86.50.170.119/resources.\n",
"\n",
"If you receive a message that says that the server is full, allow 15-20 minutes before you try to access it again.\n",
"\n",
"**Attention!** You **must** develop and run your code before the day of the deadline. We cannot guarantee support for any failures that happen on that day.\n",
"\n",
"### PySpark Setup\n",
"\n",
"Run the following cells once before you start working on your solutions.\n",
"\n",
"*Note:* If you attempt to create a `SparkContext` twice, you will get an error.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## Imports and SparkContext\n",
"\n",
"import pyspark\n",
"import numpy as np # use numpy for advanced numeric operations\n",
"import ast # we'll use this module to read data"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## SMALL dataset\n",
"\n",
"## Run this cell *ONLY* if you want to be\n",
"## using the SMALL dataset\n",
"\n",
"SMALL_FILE = \"file:///data/small.input\"\n",
"\n",
"DATA_FILE = SMALL_FILE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## BIG dataset\n",
"\n",
"## Run this cell *ONLY* if you want to be\n",
"## using the BIG dataset\n",
"\n",
"BIG_FILE = \"hdfs:///moderndb/input_2.5m.input\"\n",
"\n",
"## Environment parameters for the big cluster\n",
"## Use them ONLY if you're working with the big dataset\n",
"import os\n",
"os.environ[\"PYSPARK_PYTHON\"]=\"/opt/conda/bin/python3\"\n",
"os.environ[\"SPARK_HOME\"]=\"/usr/hdp/current/spark-client\"\n",
"os.environ[\"HDP_VERSION\"]=\"current\"\n",
"\n",
"DATA_FILE = BIG_FILE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sc = pyspark.SparkContext()\n",
"load_func = sc.textFile"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Tweets\n",
"\n",
"Run the following cell to assign the data to an RDD."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = load_func(DATA_FILE)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most lines in the file contain a string representation of a python dictionary that represents a `tweet`. A tweet is a message that has been generated on [twitter](twitter.com). Below you see the example of one tweet (one line in the file)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"```\n",
"{u'contributors': None, u'coordinates': None, u'created_at': u'Fri Sep 26 08:01:38 +0000 2014', u'entities': {u'hashtags': [], u'symbols': [], u'trends': [], u'urls': [{u'display_url': u'bbc.in/1qBcZ4G', u'expanded_url': u'http://bbc.in/1qBcZ4G', u'indices': [126, 140], u'url': u'http://t.co/8kPs90syqo'}], u'user_mentions': [{u'id': 2190056023, u'id_str': u'2190056023', u'indices': [3, 9], u'name': u'BBC Outside Source', u'screen_name': u'BBCOS'}]}, u'favorite_count': 0, u'favorited': False, u'filter_level': u'medium', u'geo': None, u'id': 515410856616935425, u'id_str': u'515410856616935425', u'in_reply_to_screen_name': None, u'in_reply_to_status_id': None, u'in_reply_to_status_id_str': None, u'in_reply_to_user_id': None, u'in_reply_to_user_id_str': None, u'lang': u'en', u'place': None, u'possibly_sensitive': False, u'retweet_count': 0, u'retweeted': False, u'retweeted_status': {u'contributors': None, u'coordinates': None, u'created_at': u'Fri Sep 26 07:44:43 +0000 2014', u'entities': {u'hashtags': [], u'symbols': [], u'trends': [], u'urls': [{u'display_url': u'bbc.in/1qBcZ4G', u'expanded_url': u'http://bbc.in/1qBcZ4G', u'indices': [115, 137], u'url': u'http://t.co/8kPs90syqo'}], u'user_mentions': []}, u'favorite_count': 0, u'favorited': False, u'filter_level': u'low', u'geo': None, u'id': 515406597829693440, u'id_str': u'515406597829693440', u'in_reply_to_screen_name': None, u'in_reply_to_status_id': None, u'in_reply_to_status_id_str': None, u'in_reply_to_user_id': None, u'in_reply_to_user_id_str': None, u'lang': u'en', u'place': None, u'possibly_sensitive': False, u'retweet_count': 3, u'retweeted': False, u'source': u'<a href=\"http://www.socialflow.com\" rel=\"nofollow\">SocialFlow</a>', u'text': u\"EU's anti-terrorism chief tells BBC the number of Europeans joining Islamist fighters in Syria and Iraq now 3,000+ http://t.co/8kPs90syqo\", u'truncated': False, u'user': {u'contributors_enabled': False, u'created_at': u'Tue Nov 12 10:17:10 +0000 2013', u'default_profile': False, u'default_profile_image': False, u'description': u'Real-time reports from inside the BBC newsroom with @BBCRosAtkins. Combining your sources and ours. BBC World Service radio 10GMT, BBC World News TV 17GMT.', u'favourites_count': 734, u'follow_request_sent': None, u'followers_count': 16500, u'following': None, u'friends_count': 750, u'geo_enabled': False, u'id': 2190056023, u'id_str': u'2190056023', u'is_translator': False, u'lang': u'en-gb', u'listed_count': 340, u'location': u'London', u'name': u'BBC Outside Source', u'notifications': None, u'profile_background_color': u'131516', u'profile_background_image_url': u'http://abs.twimg.com/images/themes/theme14/bg.gif', u'profile_background_image_url_https': u'https://abs.twimg.com/images/themes/theme14/bg.gif', u'profile_background_tile': True, u'profile_banner_url': u'https://pbs.twimg.com/profile_banners/2190056023/1399384694', u'profile_image_url': u'http://pbs.twimg.com/profile_images/421268342767222784/17yQM0_d_normal.jpeg', u'profile_image_url_https': u'https://pbs.twimg.com/profile_images/421268342767222784/17yQM0_d_normal.jpeg', u'profile_link_color': u'009999', u'profile_sidebar_border_color': u'EEEEEE', u'profile_sidebar_fill_color': u'EFEFEF', u'profile_text_color': u'333333', u'profile_use_background_image': True, u'protected': False, u'screen_name': u'BBCOS', u'statuses_count': 6602, u'time_zone': u'London', u'url': u'http://www.bbc.co.uk/programmes/p01k2bx3', u'utc_offset': 3600, u'verified': True}}, u'source': u'<a href=\"http://twitter.com/#!/download/ipad\" rel=\"nofollow\">Twitter for iPad</a>', u'text': u\"RT @BBCOS: EU's anti-terrorism chief tells BBC the number of Europeans joining Islamist fighters in Syria and Iraq now 3,000+ http://t.co/8\\u2026\", u'timestamp_ms': u'1411718498766', u'truncated': False, u'user': {u'contributors_enabled': False, u'created_at': u'Sun Nov 13 20:46:00 +0000 2011', u'default_profile': True, u'default_profile_image': False, u'description': u'The more I do nothing, the less time I have to do anything', u'favourites_count': 27, u'follow_request_sent': None, u'followers_count': 216, u'following': None, u'friends_count': 747, u'geo_enabled': True, u'id': 411752020, u'id_str': u'411752020', u'is_translator': False, u'lang': u'en', u'listed_count': 1, u'location': u'', u'name': u'Gerald Quinlan', u'notifications': None, u'profile_background_color': u'C0DEED', u'profile_background_image_url': u'http://abs.twimg.com/images/themes/theme1/bg.png', u'profile_background_image_url_https': u'https://abs.twimg.com/images/themes/theme1/bg.png', u'profile_background_tile': False, u'profile_image_url': u'http://pbs.twimg.com/profile_images/1661078970/image_normal.jpg', u'profile_image_url_https': u'https://pbs.twimg.com/profile_images/1661078970/image_normal.jpg', u'profile_link_color': u'0084B4', u'profile_sidebar_border_color': u'C0DEED', u'profile_sidebar_fill_color': u'DDEEF6', u'profile_text_color': u'333333', u'profile_use_background_image': True, u'protected': False, u'screen_name': u'QuinlanQuinlan', u'statuses_count': 9223, u'time_zone': None, u'url': None, u'utc_offset': None, u'verified': False}}\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that some of the lines might be **corrupt** -- i.e., they do not contain a tweet, but other information, e.g., a logging message. It is your responsibility to deal with corrupt lines and make sure they do not affect your computation.\n",
"\n",
"If `tweet_string` is the string representation of one tweet, you can load it into a Python dictionary with the following statement.\n",
"\n",
"```\n",
"tweet = ast.literal_eval(tweet_string)\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem 4 (20 points)\n",
"\n",
"Use PySpark to write and execute queries for the following tasks (1 - 10).\n",
"Generate one cell per task.\n",
"\n",
"You are free to reuse (possibly persisted) RDDs and other code from earlier tasks.\n",
"\n",
"**Tasks**\n",
"1. Find out how many lines (tweets or corrupt) there are there in the data.\n",
"\n",
"2. Inspect any three tweets and infer the tweet schema from them, as completely as you can.\n",
"* Each tweet is identified by its unique `id` value. Some tweets might appear in more than one lines in the data (i.e., the same tweet id might appear in more than one lines). How many tweets are there that appear in *only* one line?\n",
"\n",
"* Each tweet is associated with a `lang` value, that denotes the language the tweet was written in. How many different languages are there in the dataset?\n",
"\n",
"* How many *lines* are there in the data for each language? (Ignore tweet ids for this task).\n",
"\n",
"* How many *tweets* are there in the data for the english language (*lang = 'en'*)?\n",
"\n",
"* Each tweet is associated with a `text` value that stores the content of the message. What is the minimum and maximum message length in the data? *Note*: you should compute both values in a single pass over the data.\n",
"\n",
"* Consider the `text` message that appears in a tweet. We define a *word* to be a maximal sequence of alphanumeric characters found in the text, after the text has been converted to *lowercase*. For example, if the text is \n",
"```\n",
"My username is spark123 & my password is spar!.Kk\n",
"```\n",
"then the words contained in it are the following.\n",
"```\n",
"my, username, is, spark123, my, password, is, spar, kk\n",
"```\n",
"Find the 1000 most frequent words in the `text` messages of all english tweets (*lang = 'en'*) along with the number of their occurances.\n",
"\n",
"* Find the 100 most frequent words in english tweets (*lang = 'en'*) that start with each character of the latin alphabet ('a', 'b', ..., 'z'). Use the *lowercase* version of messages.\n",
"\n",
"* Each tweet is associated with a `user` who generated it; and the user is associated with a unique `screen_name`. Find the `screen_name` of the $10$ users with the most english tweets (with distinct tweet ids) in the data.\n",
"\n",
"**Note** Twitter users are also associated with an `id` value, different than the tweet `id`. Make sure you do not confuse the two.\n",
"\n",
"\n",
"### Solutions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## 1\n",
"\n",
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this cell to explain / expand your answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## 2 \n",
"\n",
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this cell to explain / expand your answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## 3\n",
"\n",
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this cell to explain / expand your answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## 4\n",
"\n",
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this cell to explain / expand your answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## 5\n",
"\n",
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this cell to explain / expand your answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## 6\n",
"\n",
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this cell to explain / expand your answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## 7\n",
"\n",
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this cell to explain / expand your answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## 8\n",
"\n",
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this cell to explain / expand your answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## 9\n",
"\n",
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this cell to explain / expand your answer."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"## 10\n",
"\n",
"# Your code goes here"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Use this cell to explain / expand your answer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problem 5: Pagerank (30 points)\n",
"\n",
"Some tweets are `replies` to tweets written by other Twitter users. You can tell which tweets are replies by checking the `in_reply_to_screen_name` value of a tweet: if it is `None`, then it is not a reply - otherwise, it is a reply to the user with the `screen_name` mentioned therein.\n",
"\n",
"For example, `in_reply_to_screen_name: None` signifies that the tweet is not a reply to another tweet, but `in_reply_to_screen_name: northern_bytes` signifies that the tweet is a reply to another tweet generated by user `northern_bytes`.\n",
"\n",
"We are going to construct a graph in the following steps:\n",
"1. We place one node in the graph for each user who has produced more than $20$ english tweets (with distinct ids) in the data;\n",
"2. We place one directed edge from node $u$ to node $v$ iff the total number of english replies from user $u$ to user $v$ in the data is more than $10$. In that case, we will say that '$u$ is connected to $v$'.\n",
"\n",
"### 1. Graph construction\n",
"Construct a pair RDD named **linksRDD** to store the adjacency list of each node.\n",
"Specifically, for each node (user) $u$ in the graph, you should have one element of the following form\n",
"```\n",
"(screen_name, [screen_name_1, screen_name_2, ..., screen_name_v, ...])\n",
"```\n",
"where `screen_name` corresponds to user $u$ and `screen_name_v` corresponds to a user $v$ that $u$ is connected to."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Your code goes here\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"How many nodes and edges are there in the graph?"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Your code goes here\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 2. Pagerank Computation\n",
"\n",
"Implement and employ PageRank on the graph you constructed above, using $10$ iterations and parameter $\\alpha = 0.15$. Store the pagerank scores in an RDD named **ranksRDD**."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"## Pagerank implementation\n",
"\n",
"# Your code goes here\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Report the $5$ nodes with highest pagerank values."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Your code goes here\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"***\n",
"***\n",
"\n",
"## Credits\n",
"\n",
"We are grateful to: \n",
"* Apurva Nandan, Olli Tourunen and Aleksi Kallio from CSC, for providing infrastructure and support with setting up Spark clusters.\n",
"* Kiran Garimella, doctoral student at Aalto, for providing the twitter data.\n",
"\n",
"## Updates\n",
"\n",
"If the file is updated (e.g., to add a clarification), a short description of the update will be listed here, as well as on *mycourses*.\n",
"\n",
"* The big dataset is posted (\"hdfs:///moderndb/input_2.5m.input\"). To analyze this dataset, you will connect to a *bigger cluster* than the one you've been using for the small dataset. You do that by providing some additional environment specifications -- see the cell with the _os.environ()_ calls in Section **PySpark Setup**. The bigger cluster consists of 8 nodes, with 4 cores, 15gb ram, and 230gb disk each.\n",
"* Added link in Section **Server Availability** (https://86.50.170.119/resources) to resources webpage for the big cluster.\n",
"* Minor: The order of statements in **PySpark Setup** has changed a little. Also changed the wording at a couple of places ('big' dataset instead of ~~large~~)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
IBM/differential-privacy-library | notebooks/naive_bayes.ipynb | 1 | 8227 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Naive Bayes with differential privacy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We start by importing the required libraries and modules and collecting the data that we need from the [Adult dataset](https://archive.ics.uci.edu/ml/datasets/adult)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import diffprivlib.models as dp\n",
"import numpy as np\n",
"from sklearn.naive_bayes import GaussianNB"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"X_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
" usecols=(0, 4, 10, 11, 12), delimiter=\", \")\n",
"\n",
"y_train = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data\",\n",
" usecols=14, dtype=str, delimiter=\", \")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's also collect the test data from Adult to test our models once they're trained."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"X_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
" usecols=(0, 4, 10, 11, 12), delimiter=\", \", skiprows=1)\n",
"\n",
"y_test = np.loadtxt(\"https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test\",\n",
" usecols=14, dtype=str, delimiter=\", \", skiprows=1)\n",
"# Must trim trailing period \".\" from label\n",
"y_test = np.array([a[:-1] for a in y_test])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Naive Bayes with no privacy"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To begin, let's first train a regular (non-private) naive Bayes classifier, and test its accuracy."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GaussianNB()"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nonprivate_clf = GaussianNB()\n",
"nonprivate_clf.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Non-private test accuracy: 79.64%\n"
]
}
],
"source": [
"print(\"Non-private test accuracy: %.2f%%\" % \n",
" (nonprivate_clf.score(X_test, y_test) * 100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Differentially private naive Bayes classification"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the `models.GaussianNB` module of diffprivlib, we can train a naive Bayes classifier while satisfying differential privacy.\n",
"\n",
"If we don't specify any parameters, the model defaults to `epsilon = 1` and selects the model's feature bounds from the data. This throws a warning with `.fit()` is first called, as it leaks additional privacy. To ensure no additional privacy loss, we should specify the bounds as an argument, and choose the bounds indepedently of the data (i.e. using domain knowledge)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"dp_clf = dp.GaussianNB()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you re-evaluate this cell, the test accuracy will change. This is due to the randomness introduced by differential privacy. Nevertheless, the accuracy should be in the range of 87–93%."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Differentially private test accuracy (epsilon=1.00): 79.33%\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
".../site-packages/diffprivlib/models/naive_bayes.py:100: PrivacyLeakWarning: Bounds have not been specified and will be calculated on the data provided. This will result in additional privacy leakage. To ensure differential privacy and no additional privacy leakage, specify bounds for each dimension.\n",
" \"privacy leakage, specify bounds for each dimension.\", PrivacyLeakWarning)\n"
]
}
],
"source": [
"dp_clf.fit(X_train, y_train)\n",
"\n",
"print(\"Differentially private test accuracy (epsilon=%.2f): %.2f%%\" % \n",
" (dp_clf.epsilon, dp_clf.score(X_test, y_test) * 100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By setting `epsilon=float(\"inf\")` we get an identical model to the non-private naive Bayes classifier."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Agreement between non-private and differentially private (epsilon=inf) classifiers: 100.00%\n"
]
}
],
"source": [
"dp_clf = dp.GaussianNB(epsilon=float(\"inf\"), bounds=(-1e5, 1e5))\n",
"dp_clf.fit(X_train, y_train)\n",
"\n",
"print(\"Agreement between non-private and differentially private (epsilon=inf) classifiers: %.2f%%\" % \n",
" (dp_clf.score(X_test, nonprivate_clf.predict(X_test)) * 100))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Changing `epsilon`"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"On this occasion, we're going to specify the `bounds` parameter as a list of tuples, indicating the ranges in which we expect each feature to lie."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"bounds = ([17, 1, 0, 0, 1], [100, 16, 100000, 4500, 100])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We will also specify a value for `epsilon`. High `epsilon` (i.e. greater than 1) gives better and more consistent accuracy, but less privacy. Small `epsilon` (i.e. less than 1) gives better privacy but worse and less consistent accuracy."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"GaussianNB(accountant=BudgetAccountant(spent_budget=[(1.0, 0), (inf, 0), (0.1, 0)]),\n",
" bounds=(array([17., 1., 0., 0., 1.]),\n",
" array([1.0e+02, 1.6e+01, 1.0e+05, 4.5e+03, 1.0e+02])),\n",
" epsilon=0.1)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dp_clf2 = dp.GaussianNB(epsilon=0.1, bounds=bounds)\n",
"\n",
"dp_clf2.fit(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Differentially private test accuracy (epsilon=0.10): 79.34%\n"
]
}
],
"source": [
"print(\"Differentially private test accuracy (epsilon=%.2f): %.2f%%\" % \n",
" (dp_clf2.epsilon, dp_clf2.score(X_test, y_test) * 100))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
gsd-ufal/Juliabox | container/interactive/IJulia/tutorial/00 - Start Tutorial.ipynb | 1 | 2529 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# An IJulia Notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is an **IJulia notebook**. It can mix code, multimedia results, headings, documentation, equations like $\\sqrt{\\int x^2 dx}$, and even interactive widgets."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Running Julia code\n",
"\n",
"In order to run Julia code, select the cell below and press Ctrl-Enter. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"1+1 # Try changing the numbers and press Ctrl-Enter again."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.9361759817195194"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rand() # Press Ctrl-Enter repeatedly to generate new random numbers."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Julia and IJulia examples:"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [Julia Basics](Basics.ipynb)\n",
"* [Plotting in Julia](Plotting in Julia.ipynb)\n",
"* [Multiple Dispatch](Multiple Dispatch.ipynb)\n",
"* [Calling C and Python](Calling C and Python.ipynb)\n",
"* [Interactive Widgets](Interactive Widgets.ipynb)\n",
"* [Metaprogramming](Metaprogramming.ipynb)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"These examples have been taken from [Steve Johnson's talk at EuroSciPy 2014](https://github.com/stevengj/Julia-EuroSciPy14).\n",
"\n",
"Go ahead and modify the tutorial notebooks to try out different examples. The entire tutorial folder gets refreshed with every login."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 0.3.11",
"language": "julia",
"name": "julia-0.3"
},
"language": "Julia",
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "0.3.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
NYUDataBootcamp/Projects | MBA_S16/Torosian-Craft-Breweries.ipynb | 1 | 344228 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Peter Torosian \n",
"Data Bootcamp MBA \n",
"5/12/16\n",
"<h3 align=\"center\">Craft Brewery Project</h3> "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Author: Peter Torosian\n",
"\n",
"I was interested in investigating the increase of craft beer production and brewery openings over the past decade. In particular, I wanted to visualize clusters of breweries and where the growth of craft brewing has been over the past few years.\n",
"\n",
"The final graph shows surprising growth in states like Pennsylvania, Wisconsin and Florida. It is also interesting to see the states that produce the most craft beer per resident."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import Internet Data\n",
"Some open source brewery data is found on the https://openbeerdb.com/ website.\n",
"\n",
"The dataframes that I have uploaded for my project include: production data for all microbreweries in the US, provided by Brewers Association and an open source directory of microbreweries that include their location data."
]
},
{
"cell_type": "code",
"execution_count": 295,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Today is 2016-05-12\n",
"What version of Python are we running? \n",
"3.5.1 |Anaconda 4.0.0 (x86_64)| (default, Dec 7 2015, 11:24:55) \n",
"[GCC 4.2.1 (Apple Inc. build 5577)]\n",
"Plotly version: 1.9.7\n",
"Pandas version: 0.18.0\n",
"Peter Torosian\n"
]
}
],
"source": [
"# import packages \n",
"import pandas as pd # data management\n",
"import matplotlib.pyplot as plt # graphics \n",
"import numpy as np # foundation for Pandas\n",
"import seaborn.apionly as sns # fancy matplotlib graphics (no styling)\n",
"\n",
"import requests, io # internet and input tools \n",
"import zipfile as zf # zip file tools \n",
"\n",
"from geopy.geocoders import Nominatim #import geopy for getting geocodes\n",
"geolocator = Nominatim() #first must install geopy using \"pip install geopy\"\n",
"\n",
"# plotly imports\n",
"from plotly.offline import iplot, iplot_mpl # plotting functions\n",
"import plotly.graph_objs as go # plotting functions\n",
"import plotly # just to print version and init notebook\n",
"import cufflinks as cf # gives us df.iplot that feels like df.plot\n",
"cf.set_config_file(offline=True, offline_show_link=False)\n",
"\n",
"# these lines make our graphics show up in the notebook\n",
"%matplotlib inline \n",
"plotly.offline.init_notebook_mode()\n",
"\n",
"\n",
"# check Python version \n",
"import datetime as dt \n",
"import sys\n",
"print('Today is', dt.date.today())\n",
"print('What version of Python are we running? \\n', sys.version, sep='')\n",
"print('Plotly version: ', plotly.__version__)\n",
"print('Pandas version: ', pd.__version__)\n",
"\n",
"print('Peter Torosian')"
]
},
{
"cell_type": "code",
"execution_count": 296,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Response status code: 200\n",
"Response type: <class 'requests.models.Response'>\n",
"Response .content: <class 'bytes'>\n",
"Response headers:\n",
"{'Last-Modified': 'Mon, 25 Jan 2016 23:09:03 GMT', 'ETag': '\"8cb9a-52a30a7adeea1\"', 'Content-Length': '576410', 'Keep-Alive': 'timeout=5, max=100', 'Connection': 'Keep-Alive', 'Content-Type': 'application/zip', 'Server': 'Apache/2.4.10 (Debian)', 'Accept-Ranges': 'bytes', 'Date': 'Fri, 13 May 2016 02:56:56 GMT'}\n"
]
}
],
"source": [
"# get \"response\" from url \n",
"url = 'http://openbeerdb.com/files/openbeerdb_csv.zip'\n",
"r = requests.get(url) \n",
"\n",
"# describe response \n",
"print('Response status code:', r.status_code)\n",
"print('Response type:', type(r))\n",
"print('Response .content:', type(r.content)) \n",
"print('Response headers:\\n', r.headers, sep='')\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 297,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Type of zipfile object: <class 'zipfile.ZipFile'>\n"
]
}
],
"source": [
"# convert bytes to zip file \n",
"br = zf.ZipFile(io.BytesIO(r.content)) \n",
"print('Type of zipfile object:', type(br))"
]
},
{
"cell_type": "code",
"execution_count": 298,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"['openbeerdb_csv/beers.csv',\n",
" 'openbeerdb_csv/breweries.csv',\n",
" 'openbeerdb_csv/breweries_geocode.csv',\n",
" 'openbeerdb_csv/categories.csv',\n",
" 'openbeerdb_csv/styles.csv',\n",
" 'openbeerdb_csv/']"
]
},
"execution_count": 298,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# what's in the zip file?\n",
"br.namelist()"
]
},
{
"cell_type": "code",
"execution_count": 299,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# set csv files imported to dataframes\n",
"breweries = pd.read_csv(br.open(br.namelist()[1])) #this file is a list of breweries and addresses, etc\n",
"geocode = pd.read_csv(br.open(br.namelist()[2])) #this file is only brewery IDs and Lat/Long positions"
]
},
{
"cell_type": "code",
"execution_count": 300,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# rename id column to brewery id so it matches with geocode file\n",
"breweries = breweries.rename(columns={'id': 'brewery_id'})"
]
},
{
"cell_type": "code",
"execution_count": 301,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# merge the two dataframes\n",
"geobrew = pd.merge(breweries, geocode,\n",
" how = 'left',\n",
" on='brewery_id')"
]
},
{
"cell_type": "code",
"execution_count": 302,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [],
"source": [
"#reduce dataframe to only breweries in the United States\n",
"USA = geobrew['country'] == 'United States'\n",
"geobrewUSA = geobrew[USA]\n",
"#clean up bad data: bad data found during cleanup later in the notebook\n",
"geobrewUSA = geobrewUSA[geobrewUSA.brewery_id != 353]\n",
"geobrewUSA = geobrewUSA[geobrewUSA.brewery_id != 1184]"
]
},
{
"cell_type": "code",
"execution_count": 303,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>brewery_id</th>\n",
" <th>name</th>\n",
" <th>address1</th>\n",
" <th>address2</th>\n",
" <th>city</th>\n",
" <th>state</th>\n",
" <th>code</th>\n",
" <th>country</th>\n",
" <th>phone</th>\n",
" <th>website</th>\n",
" <th>filepath</th>\n",
" <th>descript</th>\n",
" <th>last_mod</th>\n",
" <th>id</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>accuracy</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>273</th>\n",
" <td>1421</td>\n",
" <td>DC Brau</td>\n",
" <td>3178-B Bladensburg Rd. NE</td>\n",
" <td>NaN</td>\n",
" <td>Washington</td>\n",
" <td>DC</td>\n",
" <td>20018</td>\n",
" <td>United States</td>\n",
" <td>NaN</td>\n",
" <td>http://www.dcbrau.com/</td>\n",
" <td>logo.png</td>\n",
" <td>The first brewery to open in the nation's capi...</td>\n",
" <td>2011-08-08 19:02:40</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>380</th>\n",
" <td>381</td>\n",
" <td>City Brewing Company, LLC</td>\n",
" <td>925 South Third Street</td>\n",
" <td>NaN</td>\n",
" <td>La Crosse</td>\n",
" <td>Wisconsin</td>\n",
" <td>54601</td>\n",
" <td>United States</td>\n",
" <td>1-608-785-4200</td>\n",
" <td>http://www.citybrewery.com/</td>\n",
" <td>NaN</td>\n",
" <td>City Brewing Company is a premier, state-of-th...</td>\n",
" <td>2010-07-22 20:00:20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>419</th>\n",
" <td>1420</td>\n",
" <td>Devil's Canyon</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Belmont</td>\n",
" <td>CA</td>\n",
" <td>NaN</td>\n",
" <td>United States</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2011-07-28 19:03:35</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>475</th>\n",
" <td>475</td>\n",
" <td>Eagle Brewing</td>\n",
" <td>625 Fourth Street</td>\n",
" <td>NaN</td>\n",
" <td>Mukilteo</td>\n",
" <td>Washington</td>\n",
" <td>98275</td>\n",
" <td>United States</td>\n",
" <td>1-425-348-8088</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2010-07-22 20:00:20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>702</th>\n",
" <td>702</td>\n",
" <td>Island Brewing Company</td>\n",
" <td>5049 Sixth Street</td>\n",
" <td>NaN</td>\n",
" <td>Carpinteria</td>\n",
" <td>California</td>\n",
" <td>93013</td>\n",
" <td>United States</td>\n",
" <td>1-805-745-8272</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2010-07-22 20:00:20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1033</th>\n",
" <td>1030</td>\n",
" <td>Pyramid Alehouse, Brewery and Restaurant - Sea...</td>\n",
" <td>1201 First Avenue South</td>\n",
" <td>NaN</td>\n",
" <td>Seattle</td>\n",
" <td>Washington</td>\n",
" <td>98134</td>\n",
" <td>United States</td>\n",
" <td>1-206-682-3377</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2010-07-22 20:00:20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1153</th>\n",
" <td>1150</td>\n",
" <td>Ska Brewing Company</td>\n",
" <td>545 Turner Drive</td>\n",
" <td>NaN</td>\n",
" <td>Durango</td>\n",
" <td>Colorado</td>\n",
" <td>81301</td>\n",
" <td>United States</td>\n",
" <td>970.247.5792</td>\n",
" <td>http://www.skabrewing.com/</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2010-07-22 20:00:20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1387</th>\n",
" <td>1384</td>\n",
" <td>Marshall Wharf Brewing Company</td>\n",
" <td>2 Pinchy Lane</td>\n",
" <td>NaN</td>\n",
" <td>Belfast</td>\n",
" <td>Maine</td>\n",
" <td>4915</td>\n",
" <td>United States</td>\n",
" <td>207-338-1707</td>\n",
" <td>http://www.marshallwharf.com</td>\n",
" <td>mwLogo1.png</td>\n",
" <td>NaN</td>\n",
" <td>2010-07-22 20:00:20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1398</th>\n",
" <td>1396</td>\n",
" <td>Aspen Brewing Company</td>\n",
" <td>555 North Mill Street</td>\n",
" <td>NaN</td>\n",
" <td>Aspen</td>\n",
" <td>Colorado</td>\n",
" <td>81611</td>\n",
" <td>United States</td>\n",
" <td>NaN</td>\n",
" <td>http://aspenbrewingcompany.com</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2010-11-08 08:40:44</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1399</th>\n",
" <td>1397</td>\n",
" <td>Tallgrass Brewing Co.</td>\n",
" <td>8845 Quail Lane</td>\n",
" <td>NaN</td>\n",
" <td>Manhattan</td>\n",
" <td>KS</td>\n",
" <td>66502</td>\n",
" <td>United States</td>\n",
" <td>785-537-1131</td>\n",
" <td>http://www.tallgrassbeer.com</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2010-11-11 19:21:21</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1400</th>\n",
" <td>1398</td>\n",
" <td>Half Acre Beer Company</td>\n",
" <td>4257 North Lincoln Avenue</td>\n",
" <td>NaN</td>\n",
" <td>Chicago</td>\n",
" <td>IL</td>\n",
" <td>60618</td>\n",
" <td>United States</td>\n",
" <td>NaN</td>\n",
" <td>http://inyourguts.blogspot.com/</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2010-11-14 11:17:07</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1401</th>\n",
" <td>1399</td>\n",
" <td>New Jersey Beer Company</td>\n",
" <td>4201 Tonnelle Avenue</td>\n",
" <td>NaN</td>\n",
" <td>North Bergen</td>\n",
" <td>NJ</td>\n",
" <td>07047</td>\n",
" <td>United States</td>\n",
" <td>NaN</td>\n",
" <td>http://www.njbeerco.com</td>\n",
" <td>NJBClogo1.jpg</td>\n",
" <td>New Jersey Beer Co. is dedicated to crafting q...</td>\n",
" <td>2010-11-17 14:11:59</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1402</th>\n",
" <td>1400</td>\n",
" <td>FINNEGANS</td>\n",
" <td>619 10th St. South</td>\n",
" <td>Suite 100</td>\n",
" <td>Minneapolis</td>\n",
" <td>Minnesota</td>\n",
" <td>55404</td>\n",
" <td>United States</td>\n",
" <td>NaN</td>\n",
" <td>http://www.finnegans.org</td>\n",
" <td>finn_dlyc.jpg</td>\n",
" <td>FINNEGANS was founded in 2000 in Minneapolis, ...</td>\n",
" <td>2010-12-03 17:53:48</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1404</th>\n",
" <td>1402</td>\n",
" <td>Warwick Valley Wine Co.</td>\n",
" <td>114 Little York Rd</td>\n",
" <td>NaN</td>\n",
" <td>Warwick</td>\n",
" <td>NY</td>\n",
" <td>10990</td>\n",
" <td>United States</td>\n",
" <td>(845) 258-4858</td>\n",
" <td>http://www.wvwinery.com</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2010-12-20 16:17:52</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1406</th>\n",
" <td>1404</td>\n",
" <td>The St. Louis Brewrey</td>\n",
" <td>2100 Locust Street</td>\n",
" <td>NaN</td>\n",
" <td>St. Louis</td>\n",
" <td>MO</td>\n",
" <td>63103</td>\n",
" <td>United States</td>\n",
" <td>314-241-2337</td>\n",
" <td>http://http://www.schlafly.com</td>\n",
" <td>schlafly.3color_.jpg</td>\n",
" <td>The Schlafly Tap Room first opened its doors i...</td>\n",
" <td>2011-02-10 07:49:03</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1409</th>\n",
" <td>1407</td>\n",
" <td>Catawba Valley Brewing Company</td>\n",
" <td>P.O. Box 1154</td>\n",
" <td>NaN</td>\n",
" <td>Glen Alpine</td>\n",
" <td>NC</td>\n",
" <td>28628</td>\n",
" <td>United States</td>\n",
" <td>828-430-6883</td>\n",
" <td>http://www.catawbavalleybrewingcompany.com/</td>\n",
" <td>catawba-valley-logo.jpg</td>\n",
" <td>The Catawba Valley Brewing Company grew out of...</td>\n",
" <td>2011-02-16 12:36:49</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1410</th>\n",
" <td>1408</td>\n",
" <td>Walnut Brewery</td>\n",
" <td>1123 Walnut Street</td>\n",
" <td>NaN</td>\n",
" <td>Boulder</td>\n",
" <td>Colorado</td>\n",
" <td>80302</td>\n",
" <td>United States</td>\n",
" <td>(303) 447-1345</td>\n",
" <td>http://www.walnutbrewery.com/</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2011-03-01 13:26:24</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1411</th>\n",
" <td>1409</td>\n",
" <td>Columbus Brewing Company</td>\n",
" <td>525 Short St.</td>\n",
" <td>NaN</td>\n",
" <td>Columbus</td>\n",
" <td>OH</td>\n",
" <td>43215</td>\n",
" <td>United States</td>\n",
" <td>614-464-2739</td>\n",
" <td>www.columbusbrewingco.com</td>\n",
" <td>columbus_brewing_company.jpg</td>\n",
" <td>Located just south of downtown Columbus, Ohio,...</td>\n",
" <td>2011-03-08 12:19:07</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1412</th>\n",
" <td>1410</td>\n",
" <td>Oso</td>\n",
" <td>1812 Post Road</td>\n",
" <td>NaN</td>\n",
" <td>Plover</td>\n",
" <td>WI</td>\n",
" <td>54467</td>\n",
" <td>United States</td>\n",
" <td>715-254-2163</td>\n",
" <td>http://www.osobrewing.com/Home.php</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2011-03-16 09:05:15</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1413</th>\n",
" <td>1411</td>\n",
" <td>Element Brewing Company</td>\n",
" <td>30 Bridge St</td>\n",
" <td>NaN</td>\n",
" <td>Millers Falls</td>\n",
" <td>MA</td>\n",
" <td>01349</td>\n",
" <td>United States</td>\n",
" <td>413-835-6340</td>\n",
" <td>http://www.elementbeer.com</td>\n",
" <td>NaN</td>\n",
" <td>Nano Brewery specializing in bottle conditione...</td>\n",
" <td>2011-04-18 05:17:29</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1415</th>\n",
" <td>1413</td>\n",
" <td>Wedge Brewery</td>\n",
" <td>125B Roberts St</td>\n",
" <td>NaN</td>\n",
" <td>Asheville</td>\n",
" <td>NC</td>\n",
" <td>28801-3128</td>\n",
" <td>United States</td>\n",
" <td>828-505-2792</td>\n",
" <td>http://wedgebrewing.com/</td>\n",
" <td>wedge.png</td>\n",
" <td>The Wedge Brewing Co. is located in the lower ...</td>\n",
" <td>2011-05-23 10:34:59</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1418</th>\n",
" <td>1416</td>\n",
" <td>Brewery Vivant</td>\n",
" <td>925 Cherry Street SE</td>\n",
" <td>NaN</td>\n",
" <td>Grand Rapids</td>\n",
" <td>MI</td>\n",
" <td>49506</td>\n",
" <td>United States</td>\n",
" <td>616 719 1604</td>\n",
" <td>http://breweryvivant.com</td>\n",
" <td>bv-logo.png</td>\n",
" <td>Brewery Vivant is the realization of years of ...</td>\n",
" <td>2011-06-22 12:19:30</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1419</th>\n",
" <td>1417</td>\n",
" <td>Oakshire</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Eugene</td>\n",
" <td>Or</td>\n",
" <td>NaN</td>\n",
" <td>United States</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2011-07-07 07:42:42</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1420</th>\n",
" <td>1418</td>\n",
" <td>Oakshire</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Eugene</td>\n",
" <td>Or</td>\n",
" <td>NaN</td>\n",
" <td>United States</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2011-07-07 07:44:13</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" brewery_id name \\\n",
"273 1421 DC Brau \n",
"380 381 City Brewing Company, LLC \n",
"419 1420 Devil's Canyon \n",
"475 475 Eagle Brewing \n",
"702 702 Island Brewing Company \n",
"1033 1030 Pyramid Alehouse, Brewery and Restaurant - Sea... \n",
"1153 1150 Ska Brewing Company \n",
"1387 1384 Marshall Wharf Brewing Company \n",
"1398 1396 Aspen Brewing Company \n",
"1399 1397 Tallgrass Brewing Co. \n",
"1400 1398 Half Acre Beer Company \n",
"1401 1399 New Jersey Beer Company \n",
"1402 1400 FINNEGANS \n",
"1404 1402 Warwick Valley Wine Co. \n",
"1406 1404 The St. Louis Brewrey \n",
"1409 1407 Catawba Valley Brewing Company \n",
"1410 1408 Walnut Brewery \n",
"1411 1409 Columbus Brewing Company \n",
"1412 1410 Oso \n",
"1413 1411 Element Brewing Company \n",
"1415 1413 Wedge Brewery \n",
"1418 1416 Brewery Vivant \n",
"1419 1417 Oakshire \n",
"1420 1418 Oakshire \n",
"\n",
" address1 address2 city state \\\n",
"273 3178-B Bladensburg Rd. NE NaN Washington DC \n",
"380 925 South Third Street NaN La Crosse Wisconsin \n",
"419 NaN NaN Belmont CA \n",
"475 625 Fourth Street NaN Mukilteo Washington \n",
"702 5049 Sixth Street NaN Carpinteria California \n",
"1033 1201 First Avenue South NaN Seattle Washington \n",
"1153 545 Turner Drive NaN Durango Colorado \n",
"1387 2 Pinchy Lane NaN Belfast Maine \n",
"1398 555 North Mill Street NaN Aspen Colorado \n",
"1399 8845 Quail Lane NaN Manhattan KS \n",
"1400 4257 North Lincoln Avenue NaN Chicago IL \n",
"1401 4201 Tonnelle Avenue NaN North Bergen NJ \n",
"1402 619 10th St. South Suite 100 Minneapolis Minnesota \n",
"1404 114 Little York Rd NaN Warwick NY \n",
"1406 2100 Locust Street NaN St. Louis MO \n",
"1409 P.O. Box 1154 NaN Glen Alpine NC \n",
"1410 1123 Walnut Street NaN Boulder Colorado \n",
"1411 525 Short St. NaN Columbus OH \n",
"1412 1812 Post Road NaN Plover WI \n",
"1413 30 Bridge St NaN Millers Falls MA \n",
"1415 125B Roberts St NaN Asheville NC \n",
"1418 925 Cherry Street SE NaN Grand Rapids MI \n",
"1419 NaN NaN Eugene Or \n",
"1420 NaN NaN Eugene Or \n",
"\n",
" code country phone \\\n",
"273 20018 United States NaN \n",
"380 54601 United States 1-608-785-4200 \n",
"419 NaN United States NaN \n",
"475 98275 United States 1-425-348-8088 \n",
"702 93013 United States 1-805-745-8272 \n",
"1033 98134 United States 1-206-682-3377 \n",
"1153 81301 United States 970.247.5792 \n",
"1387 4915 United States 207-338-1707 \n",
"1398 81611 United States NaN \n",
"1399 66502 United States 785-537-1131 \n",
"1400 60618 United States NaN \n",
"1401 07047 United States NaN \n",
"1402 55404 United States NaN \n",
"1404 10990 United States (845) 258-4858 \n",
"1406 63103 United States 314-241-2337 \n",
"1409 28628 United States 828-430-6883 \n",
"1410 80302 United States (303) 447-1345 \n",
"1411 43215 United States 614-464-2739 \n",
"1412 54467 United States 715-254-2163 \n",
"1413 01349 United States 413-835-6340 \n",
"1415 28801-3128 United States 828-505-2792 \n",
"1418 49506 United States 616 719 1604 \n",
"1419 NaN United States NaN \n",
"1420 NaN United States NaN \n",
"\n",
" website \\\n",
"273 http://www.dcbrau.com/ \n",
"380 http://www.citybrewery.com/ \n",
"419 NaN \n",
"475 NaN \n",
"702 NaN \n",
"1033 NaN \n",
"1153 http://www.skabrewing.com/ \n",
"1387 http://www.marshallwharf.com \n",
"1398 http://aspenbrewingcompany.com \n",
"1399 http://www.tallgrassbeer.com \n",
"1400 http://inyourguts.blogspot.com/ \n",
"1401 http://www.njbeerco.com \n",
"1402 http://www.finnegans.org \n",
"1404 http://www.wvwinery.com \n",
"1406 http://http://www.schlafly.com \n",
"1409 http://www.catawbavalleybrewingcompany.com/ \n",
"1410 http://www.walnutbrewery.com/ \n",
"1411 www.columbusbrewingco.com \n",
"1412 http://www.osobrewing.com/Home.php \n",
"1413 http://www.elementbeer.com \n",
"1415 http://wedgebrewing.com/ \n",
"1418 http://breweryvivant.com \n",
"1419 NaN \n",
"1420 NaN \n",
"\n",
" filepath \\\n",
"273 logo.png \n",
"380 NaN \n",
"419 NaN \n",
"475 NaN \n",
"702 NaN \n",
"1033 NaN \n",
"1153 NaN \n",
"1387 mwLogo1.png \n",
"1398 NaN \n",
"1399 NaN \n",
"1400 NaN \n",
"1401 NJBClogo1.jpg \n",
"1402 finn_dlyc.jpg \n",
"1404 NaN \n",
"1406 schlafly.3color_.jpg \n",
"1409 catawba-valley-logo.jpg \n",
"1410 NaN \n",
"1411 columbus_brewing_company.jpg \n",
"1412 NaN \n",
"1413 NaN \n",
"1415 wedge.png \n",
"1418 bv-logo.png \n",
"1419 NaN \n",
"1420 NaN \n",
"\n",
" descript last_mod \\\n",
"273 The first brewery to open in the nation's capi... 2011-08-08 19:02:40 \n",
"380 City Brewing Company is a premier, state-of-th... 2010-07-22 20:00:20 \n",
"419 NaN 2011-07-28 19:03:35 \n",
"475 NaN 2010-07-22 20:00:20 \n",
"702 NaN 2010-07-22 20:00:20 \n",
"1033 NaN 2010-07-22 20:00:20 \n",
"1153 NaN 2010-07-22 20:00:20 \n",
"1387 NaN 2010-07-22 20:00:20 \n",
"1398 NaN 2010-11-08 08:40:44 \n",
"1399 NaN 2010-11-11 19:21:21 \n",
"1400 NaN 2010-11-14 11:17:07 \n",
"1401 New Jersey Beer Co. is dedicated to crafting q... 2010-11-17 14:11:59 \n",
"1402 FINNEGANS was founded in 2000 in Minneapolis, ... 2010-12-03 17:53:48 \n",
"1404 NaN 2010-12-20 16:17:52 \n",
"1406 The Schlafly Tap Room first opened its doors i... 2011-02-10 07:49:03 \n",
"1409 The Catawba Valley Brewing Company grew out of... 2011-02-16 12:36:49 \n",
"1410 NaN 2011-03-01 13:26:24 \n",
"1411 Located just south of downtown Columbus, Ohio,... 2011-03-08 12:19:07 \n",
"1412 NaN 2011-03-16 09:05:15 \n",
"1413 Nano Brewery specializing in bottle conditione... 2011-04-18 05:17:29 \n",
"1415 The Wedge Brewing Co. is located in the lower ... 2011-05-23 10:34:59 \n",
"1418 Brewery Vivant is the realization of years of ... 2011-06-22 12:19:30 \n",
"1419 NaN 2011-07-07 07:42:42 \n",
"1420 NaN 2011-07-07 07:44:13 \n",
"\n",
" id latitude longitude accuracy \n",
"273 NaN NaN NaN NaN \n",
"380 NaN NaN NaN NaN \n",
"419 NaN NaN NaN NaN \n",
"475 NaN NaN NaN NaN \n",
"702 NaN NaN NaN NaN \n",
"1033 NaN NaN NaN NaN \n",
"1153 NaN NaN NaN NaN \n",
"1387 NaN NaN NaN NaN \n",
"1398 NaN NaN NaN NaN \n",
"1399 NaN NaN NaN NaN \n",
"1400 NaN NaN NaN NaN \n",
"1401 NaN NaN NaN NaN \n",
"1402 NaN NaN NaN NaN \n",
"1404 NaN NaN NaN NaN \n",
"1406 NaN NaN NaN NaN \n",
"1409 NaN NaN NaN NaN \n",
"1410 NaN NaN NaN NaN \n",
"1411 NaN NaN NaN NaN \n",
"1412 NaN NaN NaN NaN \n",
"1413 NaN NaN NaN NaN \n",
"1415 NaN NaN NaN NaN \n",
"1418 NaN NaN NaN NaN \n",
"1419 NaN NaN NaN NaN \n",
"1420 NaN NaN NaN NaN "
]
},
"execution_count": 303,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#find entries with missing latitude/longitude information\n",
"missing = geobrewUSA['latitude'].isnull()\n",
"missingGEO = geobrewUSA[missing]\n",
"missingGEO"
]
},
{
"cell_type": "code",
"execution_count": 304,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/Pete/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: SettingWithCopyWarning:\n",
"\n",
"\n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"\n"
]
}
],
"source": [
"#the Lat/Long lookup algorithm incorrectly handles Aspen, Colorado. Colorado was replaced by CO\n",
"missingGEO['state'] = missingGEO['state'].replace(['Colorado'], 'CO')"
]
},
{
"cell_type": "code",
"execution_count": 305,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/Pete/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning:\n",
"\n",
"\n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
"\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>brewery_id</th>\n",
" <th>name</th>\n",
" <th>address1</th>\n",
" <th>address2</th>\n",
" <th>city</th>\n",
" <th>state</th>\n",
" <th>code</th>\n",
" <th>country</th>\n",
" <th>phone</th>\n",
" <th>website</th>\n",
" <th>filepath</th>\n",
" <th>descript</th>\n",
" <th>last_mod</th>\n",
" <th>id</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>accuracy</th>\n",
" <th>city_state</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>273</th>\n",
" <td>1421</td>\n",
" <td>DC Brau</td>\n",
" <td>3178-B Bladensburg Rd. NE</td>\n",
" <td>NaN</td>\n",
" <td>Washington</td>\n",
" <td>DC</td>\n",
" <td>20018</td>\n",
" <td>United States</td>\n",
" <td>NaN</td>\n",
" <td>http://www.dcbrau.com/</td>\n",
" <td>logo.png</td>\n",
" <td>The first brewery to open in the nation's capi...</td>\n",
" <td>2011-08-08 19:02:40</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>Washington DC</td>\n",
" </tr>\n",
" <tr>\n",
" <th>380</th>\n",
" <td>381</td>\n",
" <td>City Brewing Company, LLC</td>\n",
" <td>925 South Third Street</td>\n",
" <td>NaN</td>\n",
" <td>La Crosse</td>\n",
" <td>Wisconsin</td>\n",
" <td>54601</td>\n",
" <td>United States</td>\n",
" <td>1-608-785-4200</td>\n",
" <td>http://www.citybrewery.com/</td>\n",
" <td>NaN</td>\n",
" <td>City Brewing Company is a premier, state-of-th...</td>\n",
" <td>2010-07-22 20:00:20</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>La Crosse Wisconsin</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" brewery_id name address1 \\\n",
"273 1421 DC Brau 3178-B Bladensburg Rd. NE \n",
"380 381 City Brewing Company, LLC 925 South Third Street \n",
"\n",
" address2 city state code country phone \\\n",
"273 NaN Washington DC 20018 United States NaN \n",
"380 NaN La Crosse Wisconsin 54601 United States 1-608-785-4200 \n",
"\n",
" website filepath \\\n",
"273 http://www.dcbrau.com/ logo.png \n",
"380 http://www.citybrewery.com/ NaN \n",
"\n",
" descript last_mod \\\n",
"273 The first brewery to open in the nation's capi... 2011-08-08 19:02:40 \n",
"380 City Brewing Company is a premier, state-of-th... 2010-07-22 20:00:20 \n",
"\n",
" id latitude longitude accuracy city_state \n",
"273 NaN NaN NaN NaN Washington DC \n",
"380 NaN NaN NaN NaN La Crosse Wisconsin "
]
},
"execution_count": 305,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#combine city and state for find lat/long for the missing rows later on\n",
"missingGEO['city_state'] = missingGEO['city'] + ' ' + missingGEO['state']\n",
"missingGEO.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 306,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"273 Washington DC\n",
"380 La Crosse Wisconsin\n",
"419 Belmont CA\n",
"475 Mukilteo Washington\n",
"702 Carpinteria California\n",
"1033 Seattle Washington\n",
"1153 Durango CO\n",
"1387 Belfast Maine\n",
"1398 Aspen CO\n",
"1399 Manhattan KS\n",
"1400 Chicago IL\n",
"1401 North Bergen NJ\n",
"1402 Minneapolis Minnesota\n",
"1404 Warwick NY\n",
"1406 St. Louis MO\n",
"1409 Glen Alpine NC\n",
"1410 Boulder CO\n",
"1411 Columbus OH\n",
"1412 Plover WI\n",
"1413 Millers Falls MA\n",
"1415 Asheville NC\n",
"1418 Grand Rapids MI\n",
"1419 Eugene Or\n",
"1420 Eugene Or\n",
"Name: city_state, dtype: object"
]
},
"execution_count": 306,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# make an array of cities/states that need lat/long\n",
"spot = []\n",
"spot = missingGEO.iloc[:,17]\n",
"spot"
]
},
{
"cell_type": "code",
"execution_count": 307,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# keep track of index values for corresponding cities for recombining later on\n",
"join = []\n",
"join = missingGEO.index.values\n",
"idIndex = pd.DataFrame(join, columns =['index'])"
]
},
{
"cell_type": "code",
"execution_count": 308,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Belmont, San Mateo County, California, United States of America\n",
"(37.5202145, -122.2758007)\n"
]
}
],
"source": [
"# test code to see if geolocator algorithm works for a particular city/state\n",
"location = geolocator.geocode('belmont CA')\n",
"print(location.address)\n",
"print((location.latitude, location.longitude))"
]
},
{
"cell_type": "code",
"execution_count": 309,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>index</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>38.894955</td>\n",
" <td>-77.036646</td>\n",
" <td>273</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>43.801356</td>\n",
" <td>-91.239581</td>\n",
" <td>380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>37.520215</td>\n",
" <td>-122.275801</td>\n",
" <td>419</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" latitude longitude index\n",
"0 38.894955 -77.036646 273\n",
"1 43.801356 -91.239581 380\n",
"2 37.520215 -122.275801 419"
]
},
"execution_count": 309,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# loop through the list to get both latitude and longitude\n",
"locations = []\n",
"for row in spot:\n",
" location = geolocator.geocode([row])\n",
" locations.append({'latitude' : location.latitude,'longitude' : location.longitude})\n",
" \n",
"coord = pd.DataFrame(locations, columns=['latitude', 'longitude'])\n",
"coord = pd.concat([coord, idIndex], axis = 1)\n",
"coord.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 310,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# set index to match the previous index from the original USA brewery list\n",
"coord = coord.set_index('index')"
]
},
{
"cell_type": "code",
"execution_count": 311,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>brewery_id</th>\n",
" <th>name</th>\n",
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" <td>1421</td>\n",
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" <th>380</th>\n",
" <td>381</td>\n",
" <td>City Brewing Company, LLC</td>\n",
" <td>925 South Third Street</td>\n",
" <td>NaN</td>\n",
" <td>La Crosse</td>\n",
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" <td>City Brewing Company is a premier, state-of-th...</td>\n",
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" <td>NaN</td>\n",
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],
"text/plain": [
" brewery_id name address1 \\\n",
"273 1421 DC Brau 3178-B Bladensburg Rd. NE \n",
"380 381 City Brewing Company, LLC 925 South Third Street \n",
"\n",
" address2 city state code country phone \\\n",
"273 NaN Washington DC 20018 United States NaN \n",
"380 NaN La Crosse Wisconsin 54601 United States 1-608-785-4200 \n",
"\n",
" website filepath \\\n",
"273 http://www.dcbrau.com/ logo.png \n",
"380 http://www.citybrewery.com/ NaN \n",
"\n",
" descript last_mod \\\n",
"273 The first brewery to open in the nation's capi... 2011-08-08 19:02:40 \n",
"380 City Brewing Company is a premier, state-of-th... 2010-07-22 20:00:20 \n",
"\n",
" id \n",
"273 NaN \n",
"380 NaN "
]
},
"execution_count": 311,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# drop the columns of missing data so that new lat/long data can be merged in\n",
"missingGEO = missingGEO.drop(missingGEO.columns[[14,15,16,17]], axis =1)\n",
"missingGEO.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 312,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# concat in new lat/long data\n",
"FixedmissingGEO = pd.concat([missingGEO, coord], axis = 1)"
]
},
{
"cell_type": "code",
"execution_count": 313,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# get rid of any remaining breweries with NaN for lat/long. these were determined to not be relevant\n",
"notmissing = geobrewUSA['latitude'].notnull()\n",
"geobrewUSAfixed = geobrewUSA[notmissing]"
]
},
{
"cell_type": "code",
"execution_count": 314,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# make a new geobrewUSA dataframe that is complete with every brewery having latitude and longitude\n",
"geobrewUSA = pd.concat([geobrewUSAfixed, FixedmissingGEO], axis = 0)"
]
},
{
"cell_type": "code",
"execution_count": 315,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#drop the last redundant row\n",
"geobrewUSA = geobrewUSA[:-1]"
]
},
{
"cell_type": "code",
"execution_count": 316,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>accuracy</th>\n",
" <th>address1</th>\n",
" <th>address2</th>\n",
" <th>brewery_id</th>\n",
" <th>city</th>\n",
" <th>code</th>\n",
" <th>country</th>\n",
" <th>descript</th>\n",
" <th>filepath</th>\n",
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" <th>last_mod</th>\n",
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" </thead>\n",
" <tbody>\n",
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"</table>\n",
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"text/plain": [
"Empty DataFrame\n",
"Columns: [accuracy, address1, address2, brewery_id, city, code, country, descript, filepath, id, last_mod, latitude, longitude, name, phone, state, website]\n",
"Index: []"
]
},
"execution_count": 316,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Find stray data point in Europe. This was corrected by changing \"Colorado\" to \"CO\"\n",
"euro = geobrewUSA['longitude'] > 0\n",
"europoint = geobrewUSA[euro]\n",
"europoint"
]
},
{
"cell_type": "code",
"execution_count": 317,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>accuracy</th>\n",
" <th>address1</th>\n",
" <th>address2</th>\n",
" <th>brewery_id</th>\n",
" <th>city</th>\n",
" <th>code</th>\n",
" <th>country</th>\n",
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" <th>state</th>\n",
" <th>website</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>752</th>\n",
" <td>RANGE_INTERPOLATED</td>\n",
" <td>75-5629 Kuakini Highway</td>\n",
" <td>NaN</td>\n",
" <td>751</td>\n",
" <td>Kailua-Kona</td>\n",
" <td>96740</td>\n",
" <td>United States</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>705.0</td>\n",
" <td>2010-07-22 20:00:20</td>\n",
" <td>19.642</td>\n",
" <td>-155.996</td>\n",
" <td>Kona Brewing</td>\n",
" <td>1-808-334-1133</td>\n",
" <td>Hawaii</td>\n",
" <td>http://www.konabrewingco.com</td>\n",
" </tr>\n",
" <tr>\n",
" <th>847</th>\n",
" <td>ROOFTOP</td>\n",
" <td>275 East Kawili Street</td>\n",
" <td>NaN</td>\n",
" <td>846</td>\n",
" <td>Hilo</td>\n",
" <td>96720</td>\n",
" <td>United States</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>797.0</td>\n",
" <td>2010-07-22 20:00:20</td>\n",
" <td>19.706</td>\n",
" <td>-155.069</td>\n",
" <td>Mehana Brewing</td>\n",
" <td>1-808-934-8211</td>\n",
" <td>Hawaii</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" accuracy address1 address2 brewery_id \\\n",
"752 RANGE_INTERPOLATED 75-5629 Kuakini Highway NaN 751 \n",
"847 ROOFTOP 275 East Kawili Street NaN 846 \n",
"\n",
" city code country descript filepath id \\\n",
"752 Kailua-Kona 96740 United States NaN NaN 705.0 \n",
"847 Hilo 96720 United States NaN NaN 797.0 \n",
"\n",
" last_mod latitude longitude name phone \\\n",
"752 2010-07-22 20:00:20 19.642 -155.996 Kona Brewing 1-808-334-1133 \n",
"847 2010-07-22 20:00:20 19.706 -155.069 Mehana Brewing 1-808-934-8211 \n",
"\n",
" state website \n",
"752 Hawaii http://www.konabrewingco.com \n",
"847 Hawaii NaN "
]
},
"execution_count": 317,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Find stray data point in USVI, should not be included\n",
"USVI = geobrewUSA['latitude'] < 20\n",
"straypoint = geobrewUSA[USVI]\n",
"straypoint"
]
},
{
"cell_type": "code",
"execution_count": 318,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# remove data that is not needed from dataframe\n",
"geobrewUSA = geobrewUSA.drop(geobrewUSA.columns[[0,1,2,4,5,6,7,8,9,10,14,15,16]], axis =1)"
]
},
{
"cell_type": "code",
"execution_count": 319,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# create marker for map\n",
"marker = {\"color\": \"crimson\",\n",
" \"size\": 5,\n",
" \"colorscale\": \"Red\"}"
]
},
{
"cell_type": "code",
"execution_count": 335,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# create layout for map\n",
"layout = dict(title = '2008 US Breweries<br>(hover over point for brewery name)', #title\n",
" showlegend = False, #remove legend\n",
" geo = dict( \n",
" scope='usa', \n",
" projection=dict( type='albers usa' ), #use built in USA map\n",
" showland = True,\n",
" landcolor = 'rgb(217, 217, 217)', #set map/border colors annd sizes\n",
" subunitwidth=1,\n",
" countrywidth=1,\n",
" subunitcolor=\"rgb(255, 255, 255)\",\n",
" countrycolor=\"rgb(255, 255, 255)\"),\n",
" width=1050, height=750) "
]
},
{
"cell_type": "code",
"execution_count": 336,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# using lat/lon mode for map\n",
"trace = dict(type=\"scattergeo\", # trace type\n",
" mode=\"markers\", # draw dots\n",
" lat=geobrewUSA[\"latitude\"], # latitude coordinate\n",
" lon=geobrewUSA[\"longitude\"], # longitude coordinate\n",
" text = geobrewUSA['name'], # what shows up with hovering over data point\n",
" marker=marker # marker settings (color, size...)\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 338,
"metadata": {
"collapsed": false
},
"outputs": [
{
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-123.0950505]}], {\"title\": \"2008 US Breweries<br>(hover over point for brewery name)\", \"height\": 750, \"width\": 1050, \"showlegend\": false, \"geo\": {\"showland\": true, \"subunitcolor\": \"rgb(255, 255, 255)\", \"countrywidth\": 1, \"projection\": {\"type\": \"albers usa\"}, \"landcolor\": \"rgb(217, 217, 217)\", \"subunitwidth\": 1, \"countrycolor\": \"rgb(255, 255, 255)\", \"scope\": \"usa\"}}, {\"showLink\": true, \"linkText\": \"\"})</script>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot all the breweries in the US from data\n",
"iplot(go.Figure(data=[trace], layout=layout), link_text=\"\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Import Remaining Data\n",
"The remainder of the data I am using is provided only to dues paying members of the Brewer's Association, I downloaded the data in .xls files and uploaded them into my project. I have attached the files in my email."
]
},
{
"cell_type": "code",
"execution_count": 323,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>State</th>\n",
" <th>Estimate</th>\n",
" <th>2014 Barrels</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>#FREEDOM Craft Brewery</td>\n",
" <td>NY</td>\n",
" <td>y</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>(512) Brewing Co</td>\n",
" <td>TX</td>\n",
" <td>NaN</td>\n",
" <td>10500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name State Estimate 2014 Barrels\n",
"0 #FREEDOM Craft Brewery NY y 25\n",
"1 (512) Brewing Co TX NaN 10500"
]
},
"execution_count": 323,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# import full list of brewery production data: file name = Brewers_Association_Data_2014.xls\n",
"localURL = 'http://localhost:8888/files/Documents/NYU%20Stern/Spring%202016/Data%20Boot%20Camp/Brewers_Association_Data_2014.xls'\n",
"BAdata = pd.read_excel(localURL)\n",
"BAdata = BAdata.rename(columns={'Craft Brewer Name': 'name'}) #rename 'name' column to match other dataframes\n",
"BAdata.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 324,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# import data on craft beer production by state: file name = Brewers_Association_Data_2014_States.xls\n",
"localURL = 'http://localhost:8888/files/Documents/NYU%20Stern/Spring%202016/Data%20Boot%20Camp/Brewers_Association_Data_2014_States.xls'\n",
"BAdata_states = pd.read_excel(localURL)"
]
},
{
"cell_type": "code",
"execution_count": 325,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Rank</th>\n",
" <th></th>\n",
" <th>2014 Craft Barrels</th>\n",
" <th>2013 Craft Barrels</th>\n",
" <th>2012 Craft Barrels</th>\n",
" <th>2011 Craft Barrels</th>\n",
" <th>Unnamed: 7</th>\n",
" <th>21+ Population</th>\n",
" <th>Gallons Produced /21+ Adult</th>\n",
" </tr>\n",
" <tr>\n",
" <th>State</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>West Virginia</th>\n",
" <td>49</td>\n",
" <td>NaN</td>\n",
" <td>7923.31</td>\n",
" <td>5147.0</td>\n",
" <td>3752.0</td>\n",
" <td>3055.0</td>\n",
" <td>NaN</td>\n",
" <td>1.388300e+06</td>\n",
" <td>0.176923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Arkansas</th>\n",
" <td>48</td>\n",
" <td>NaN</td>\n",
" <td>14640.75</td>\n",
" <td>10417.0</td>\n",
" <td>5639.0</td>\n",
" <td>4079.0</td>\n",
" <td>NaN</td>\n",
" <td>2.115911e+06</td>\n",
" <td>0.214500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Rank 2014 Craft Barrels 2013 Craft Barrels \\\n",
"State \n",
"West Virginia 49 NaN 7923.31 5147.0 \n",
"Arkansas 48 NaN 14640.75 10417.0 \n",
"\n",
" 2012 Craft Barrels 2011 Craft Barrels Unnamed: 7 \\\n",
"State \n",
"West Virginia 3752.0 3055.0 NaN \n",
"Arkansas 5639.0 4079.0 NaN \n",
"\n",
" 21+ Population Gallons Produced /21+ Adult \n",
"State \n",
"West Virginia 1.388300e+06 0.176923 \n",
"Arkansas 2.115911e+06 0.214500 "
]
},
"execution_count": 325,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# clean up data by state for plotting: sort by production, drop rows without data, set index for labeling\n",
"BAdata_states.drop(BAdata_states.index[52:57], inplace=True)\n",
"BAdata_states = BAdata_states.sort_values(by=['Gallons Produced /21+ Adult'], ascending=[True])\n",
"BAdata_states = BAdata_states[BAdata_states.State != 'U.S. Territories']\n",
"BAdata_states = BAdata_states.set_index('State')\n",
"BAdata_states.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 326,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x11ba88a90>"
]
},
"execution_count": 326,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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DuLq64uHhwYYNG+jbty9nz56lT58+9OzZ840/j8Tr+tvWWx9aeueE5NQtyak7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QQFxcHNbW1qhUKs6cOaNe4OzFixdcvXoVT09PAIoVK0ZsbCyvXr2iYMGCAFy6dEnjmPny5SMx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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x122a89400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot beer produced with labels and titles\n",
"plt.style.use('fivethirtyeight')\n",
"fig, ax = plt.subplots()\n",
"BAdata_states['Gallons Produced /21+ Adult'].plot(ax=ax, kind='barh', alpha=0.5, figsize=(6,13))\n",
"ax.set_title('Beer Produced per Capita', loc='left', fontsize=16)\n",
"ax.set_xlabel('Beer Gallons per 21+ Adult')\n",
"ax.set_ylabel('')"
]
},
{
"cell_type": "code",
"execution_count": 339,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x124511cc0>"
]
},
"execution_count": 339,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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xSszJkyfJycmhbdu2Skx8fDypqalKzOHDhzExMeHNN99UYk6cOMHDhw+1Ymxt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\nEEIIIYTIjIJKQgghhBBCCCEy+z91I6vGM6bYdAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x123ebf7b8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# rearrange production data for plotting total production information\n",
"BAdata_states = BAdata_states.sort_values(by=['2014 Craft Barrels'], ascending=[False])\n",
"\n",
"plt.style.use('fivethirtyeight')\n",
"fig, ax = plt.subplots()\n",
"BAdata_states['2014 Craft Barrels'].plot(ax=ax, kind='bar', alpha=0.5, figsize=(13,5),color='m') #2014 data\n",
"BAdata_states['2013 Craft Barrels'].plot(ax=ax, kind='bar', alpha=1, figsize=(13,5)) #2013 data\n",
"ax.set_title('Total Craft Beer Produced by State', loc='center', fontsize=20) #set title\n",
"ax.set_xlabel('') #x-axis label\n",
"ax.set_ylabel('Beer Gallons per Year') #y-axis label\n",
"ax.legend(['2014 Craft Barrels', '2013 Craft Barrels']) #legend"
]
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| mit |
mne-tools/mne-tools.github.io | 0.17/_downloads/ff48d5930a7d9b839b81ad4f2979cfc3/plot_background_statistics.ipynb | 1 | 33319 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Statistical inference\n\n\nHere we will briefly cover multiple concepts of inferential statistics in an\nintroductory manner, and demonstrate how to use some MNE statistical functions.\n :depth: 3\n\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Authors: Eric Larson <[email protected]>\n# License: BSD (3-clause)\n\nfrom functools import partial\n\nimport numpy as np\nfrom scipy import stats\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D # noqa, analysis:ignore\n\nimport mne\nfrom mne.stats import (ttest_1samp_no_p, bonferroni_correction, fdr_correction,\n permutation_t_test, permutation_cluster_1samp_test)\n\nprint(__doc__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hypothesis testing\n------------------\nNull hypothesis\n^^^^^^^^^^^^^^^\nFrom `Wikipedia <https://en.wikipedia.org/wiki/Null_hypothesis>`__:\n\n In inferential statistics, a general statement or default position that\n there is no relationship between two measured phenomena, or no\n association among groups.\n\nWe typically want to reject a **null hypothesis** with\nsome probability (e.g., p < 0.05). This probability is also called the\nsignificance level $\\alpha$.\nTo think about what this means, let's follow the illustrative example from\n[1]_ and construct a toy dataset consisting of a 40 x 40 square with a\n\"signal\" present in the center with white noise added and a Gaussian\nsmoothing kernel applied.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"width = 40\nn_subjects = 10\nsignal_mean = 100\nsignal_sd = 100\nnoise_sd = 0.01\ngaussian_sd = 5\nalpha = 0.05\nsigma = 1e-3 # sigma for the \"hat\" method\nn_permutations = 'all' # run an exact test\nn_src = width * width\n\n# For each \"subject\", make a smoothed noisy signal with a centered peak\nrng = np.random.RandomState(2)\nX = noise_sd * rng.randn(n_subjects, width, width)\n# Add a signal at the center\nX[:, width // 2, width // 2] = signal_mean + rng.randn(n_subjects) * signal_sd\n# Spatially smooth with a 2D Gaussian kernel\nsize = width // 2 - 1\ngaussian = np.exp(-(np.arange(-size, size + 1) ** 2 / float(gaussian_sd ** 2)))\nfor si in range(X.shape[0]):\n for ri in range(X.shape[1]):\n X[si, ri, :] = np.convolve(X[si, ri, :], gaussian, 'same')\n for ci in range(X.shape[2]):\n X[si, :, ci] = np.convolve(X[si, :, ci], gaussian, 'same')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data averaged over all subjects looks like this:\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig, ax = plt.subplots()\nax.imshow(X.mean(0), cmap='inferno')\nax.set(xticks=[], yticks=[], title=\"Data averaged over subjects\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case, a null hypothesis we could test for each voxel is:\n\n There is no difference between the mean value and zero\n ($H_0 \\colon \\mu = 0$).\n\nThe alternative hypothesis, then, is that the voxel has a non-zero mean\n($H_1 \\colon \\mu \\neq 0$).\nThis is a *two-tailed* test because the mean could be less than\nor greater than zero, whereas a *one-tailed* test would test only one of\nthese possibilities, i.e. $H_1 \\colon \\mu \\geq 0$ or\n$H_1 \\colon \\mu \\leq 0$.\n\n<div class=\"alert alert-info\"><h4>Note</h4><p>Here we will refer to each spatial location as a \"voxel\".\n In general, though, it could be any sort of data value,\n including cortical vertex at a specific time, pixel in a\n time-frequency decomposition, etc.</p></div>\n\nParametric tests\n^^^^^^^^^^^^^^^^\nLet's start with a **paired t-test**, which is a standard test\nfor differences in paired samples. Mathematically, it is equivalent\nto a 1-sample t-test on the difference between the samples in each condition.\nThe paired t-test is **parametric**\nbecause it assumes that the underlying sample distribution is Gaussian, and\nis only valid in this case. This happens to be satisfied by our toy dataset,\nbut is not always satisfied for neuroimaging data.\n\nIn the context of our toy dataset, which has many voxels\n($40 \\cdot 40 = 1600$), applying the paired t-test is called a\n*mass-univariate* approach as it treats each voxel independently.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titles = ['t']\nout = stats.ttest_1samp(X, 0, axis=0)\nts = [out[0]]\nps = [out[1]]\nmccs = [False] # these are not multiple-comparisons corrected\n\n\ndef plot_t_p(t, p, title, mcc, axes=None):\n if axes is None:\n fig = plt.figure(figsize=(6, 3))\n axes = [fig.add_subplot(121, projection='3d'), fig.add_subplot(122)]\n show = True\n else:\n fig = axes[0].figure\n show = False\n p_lims = [0.1, 0.001]\n t_lims = -stats.distributions.t.ppf(p_lims, n_subjects - 1)\n p_lims = [-np.log10(p) for p in p_lims]\n # t plot\n x, y = np.mgrid[0:width, 0:width]\n surf = axes[0].plot_surface(x, y, np.reshape(t, (width, width)),\n rstride=1, cstride=1, linewidth=0,\n vmin=t_lims[0], vmax=t_lims[1], cmap='viridis')\n axes[0].set(xticks=[], yticks=[], zticks=[],\n xlim=[0, width - 1], ylim=[0, width - 1])\n axes[0].view_init(30, 15)\n cbar = plt.colorbar(ax=axes[0], shrink=0.75, orientation='horizontal',\n fraction=0.1, pad=0.025, mappable=surf)\n cbar.set_ticks(t_lims)\n cbar.set_ticklabels(['%0.1f' % t_lim for t_lim in t_lims])\n cbar.set_label('t-value')\n cbar.ax.get_xaxis().set_label_coords(0.5, -0.3)\n if not show:\n axes[0].set(title=title)\n if mcc:\n axes[0].title.set_weight('bold')\n # p plot\n use_p = -np.log10(np.reshape(np.maximum(p, 1e-5), (width, width)))\n img = axes[1].imshow(use_p, cmap='inferno', vmin=p_lims[0], vmax=p_lims[1],\n interpolation='nearest')\n axes[1].set(xticks=[], yticks=[])\n cbar = plt.colorbar(ax=axes[1], shrink=0.75, orientation='horizontal',\n fraction=0.1, pad=0.025, mappable=img)\n cbar.set_ticks(p_lims)\n cbar.set_ticklabels(['%0.1f' % p_lim for p_lim in p_lims])\n cbar.set_label(r'$-\\log_{10}(p)$')\n cbar.ax.get_xaxis().set_label_coords(0.5, -0.3)\n if show:\n text = fig.suptitle(title)\n if mcc:\n text.set_weight('bold')\n plt.subplots_adjust(0, 0.05, 1, 0.9, wspace=0, hspace=0)\n mne.viz.utils.plt_show()\n\n\nplot_t_p(ts[-1], ps[-1], titles[-1], mccs[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\"Hat\" variance adjustment\n~~~~~~~~~~~~~~~~~~~~~~~~~\nThe \"hat\" technique regularizes the variance values used in the t-test\ncalculation [1]_ to compensate for implausibly small variances.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"ts.append(ttest_1samp_no_p(X, sigma=sigma))\nps.append(stats.distributions.t.sf(np.abs(ts[-1]), len(X) - 1) * 2)\ntitles.append(r'$\\mathrm{t_{hat}}$')\nmccs.append(False)\nplot_t_p(ts[-1], ps[-1], titles[-1], mccs[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Non-parametric tests\n^^^^^^^^^^^^^^^^^^^^\nInstead of assuming an underlying Gaussian distribution, we could instead\nuse a **non-parametric resampling** method. In the case of a paired t-test\nbetween two conditions A and B, which is mathematically equivalent to a\none-sample t-test between the difference in the conditions A-B, under the\nnull hypothesis we have the principle of **exchangeability**. This means\nthat, if the null is true, we can exchange conditions and not change\nthe distribution of the test statistic.\n\nWhen using a paired t-test, exchangeability thus means that we can flip the\nsigns of the difference between A and B. Therefore, we can construct the\n**null distribution** values for each voxel by taking random subsets of\nsamples (subjects), flipping the sign of their difference, and recording the\nabsolute value of the resulting statistic (we record the absolute value\nbecause we conduct a two-tailed test). The absolute value of the statistic\nevaluated on the veridical data can then be compared to this distribution,\nand the p-value is simply the proportion of null distribution values that\nare smaller.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>In the case of a true one-sample t-test, i.e. analyzing a single\n condition rather than the difference between two conditions,\n it is not clear where/how exchangeability applies; see\n `this FieldTrip discussion <ft_exch_>`_.</p></div>\n\nIn the case where ``n_permutations`` is large enough (or \"all\") so\nthat the complete set of unique resampling exchanges can be done\n(which is $2^{N_{samp}}-1$ for a one-tailed and\n$2^{N_{samp}-1}-1$ for a two-tailed test, not counting the\nveridical distribution), instead of randomly exchanging conditions\nthe null is formed from using all possible exchanges. This is known\nas a permutation test (or exact test).\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Here we have to do a bit of gymnastics to get our function to do\n# a permutation test without correcting for multiple comparisons:\n\nX.shape = (n_subjects, n_src) # flatten the array for simplicity\ntitles.append('Permutation')\nts.append(np.zeros(width * width))\nps.append(np.zeros(width * width))\nmccs.append(False)\nfor ii in range(n_src):\n ts[-1][ii], ps[-1][ii] = permutation_t_test(X[:, [ii]], verbose=False)[:2]\nplot_t_p(ts[-1], ps[-1], titles[-1], mccs[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Multiple comparisons\n--------------------\nSo far, we have done no correction for multiple comparisons. This is\npotentially problematic for these data because there are\n$40 \\cdot 40 = 1600$ tests being performed. If we use a threshold\np < 0.05 for each individual test, we would expect many voxels to be declared\nsignificant even if there were no true effect. In other words, we would make\nmany **type I errors** (adapted from `here <errors_>`_):\n\n.. rst-class:: skinnytable\n\n +----------+--------+------------------+------------------+\n | | Null hypothesis |\n | +------------------+------------------+\n | | True | False |\n +==========+========+==================+==================+\n | | | Type I error | Correct |\n | | Yes | False positive | True positive |\n + Reject +--------+------------------+------------------+\n | | | Correct | Type II error |\n | | No | True Negative | False negative |\n +----------+--------+------------------+------------------+\n\nTo see why, consider a standard $\\alpha = 0.05$.\nFor a single test, our probability of making a type I error is 0.05.\nThe probability of making at least one type I error in\n$N_{\\mathrm{test}}$ independent tests is then given by\n$1 - (1 - \\alpha)^{N_{\\mathrm{test}}}$:\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"N = np.arange(1, 80)\nalpha = 0.05\np_type_I = 1 - (1 - alpha) ** N\nfig, ax = plt.subplots(figsize=(4, 3))\nax.scatter(N, p_type_I, 3)\nax.set(xlim=N[[0, -1]], ylim=[0, 1], xlabel=r'$N_{\\mathrm{test}}$',\n ylabel=u'Probability of at least\\none type I error')\nax.grid(True)\nfig.tight_layout()\nfig.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To combat this problem, several methods exist. Typically these\nprovide control over either one of the following two measures:\n\n1. `Familywise error rate (FWER) <fwer_>`_\n The probability of making one or more type I errors:\n\n .. math::\n \\mathrm{P}(N_{\\mathrm{type\\ I}} >= 1 \\mid H_0)\n\n2. `False discovery rate (FDR) <fdr_>`_\n The expected proportion of rejected null hypotheses that are\n actually true:\n\n .. math::\n \\mathrm{E}(\\frac{N_{\\mathrm{type\\ I}}}{N_{\\mathrm{reject}}}\n \\mid N_{\\mathrm{reject}} > 0) \\cdot\n \\mathrm{P}(N_{\\mathrm{reject}} > 0 \\mid H_0)\n\nWe cover some techniques that control FWER and FDR below.\n\nBonferroni correction\n^^^^^^^^^^^^^^^^^^^^^\nPerhaps the simplest way to deal with multiple comparisons, `Bonferroni\ncorrection <https://en.wikipedia.org/wiki/Bonferroni_correction>`__\nconservatively multiplies the p-values by the number of comparisons to\ncontrol the FWER.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titles.append('Bonferroni')\nts.append(ts[-1])\nps.append(bonferroni_correction(ps[0])[1])\nmccs.append(True)\nplot_t_p(ts[-1], ps[-1], titles[-1], mccs[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"False discovery rate (FDR) correction\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nTypically FDR is performed with the Benjamini-Hochberg procedure, which\nis less restrictive than Bonferroni correction for large numbers of\ncomparisons (fewer type II errors), but provides less strict control of type\nI errors.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titles.append('FDR')\nts.append(ts[-1])\nps.append(fdr_correction(ps[0])[1])\nmccs.append(True)\nplot_t_p(ts[-1], ps[-1], titles[-1], mccs[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Non-parametric resampling test with a maximum statistic\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n**Non-parametric resampling tests** can also be used to correct for multiple\ncomparisons. In its simplest form, we again do permutations using\nexchangeability under the null hypothesis, but this time we take the\n*maximum statistic across all voxels* in each permutation to form the\nnull distribution. The p-value for each voxel from the veridical data\nis then given by the proportion of null distribution values\nthat were smaller.\n\nThis method has two important features:\n\n1. It controls FWER.\n2. It is non-parametric. Even though our initial test statistic\n (here a 1-sample t-test) is parametric, the null\n distribution for the null hypothesis rejection (the mean value across\n subjects is indistinguishable from zero) is obtained by permutations.\n This means that it makes no assumptions of Gaussianity\n (which do hold for this example, but do not in general for some types\n of processed neuroimaging data).\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titles.append(r'$\\mathbf{Perm_{max}}$')\nout = permutation_t_test(X, verbose=False)[:2]\nts.append(out[0])\nps.append(out[1])\nmccs.append(True)\nplot_t_p(ts[-1], ps[-1], titles[-1], mccs[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Clustering\n^^^^^^^^^^\nEach of the aforementioned multiple comparisons corrections have the\ndisadvantage of not fully incorporating the correlation structure of the\ndata, namely that points close to one another (e.g., in space or time) tend\nto be correlated. However, by defining the connectivity/adjacency/neighbor\nstructure in our data, we can use **clustering** to compensate.\n\nTo use this, we need to rethink our null hypothesis. Instead\nof thinking about a null hypothesis about means per voxel (with one\nindependent test per voxel), we consider a null hypothesis about sizes\nof clusters in our data, which could be stated like:\n\n The distribution of spatial cluster sizes observed in two experimental\n conditions are drawn from the same probability distribution.\n\nHere we only have a single condition and we contrast to zero, which can\nbe thought of as:\n\n The distribution of spatial cluster sizes is independent of the sign\n of the data.\n\nIn this case, we again do permutations with a maximum statistic, but, under\neach permutation, we:\n\n1. Compute the test statistic for each voxel individually.\n2. Threshold the test statistic values.\n3. Cluster voxels that exceed this threshold (with the same sign) based on\n adjacency.\n4. Retain the size of the largest cluster (measured, e.g., by a simple voxel\n count, or by the sum of voxel t-values within the cluster) to build the\n null distribution.\n\nAfter doing these permutations, the cluster sizes in our veridical data\nare compared to this null distribution. The p-value associated with each\ncluster is again given by the proportion of smaller null distribution\nvalues. This can then be subjected to a standard p-value threshold\n(e.g., p < 0.05) to reject the null hypothesis (i.e., find an effect of\ninterest).\n\nThis reframing to consider *cluster sizes* rather than *individual means*\nmaintains the advantages of the standard non-parametric permutation\ntest -- namely controlling FWER and making no assumptions of parametric\ndata distribution.\nCritically, though, it also accounts for the correlation structure in the\ndata -- which in this toy case is spatial but in general can be\nmultidimensional (e.g., spatio-temporal) -- because the null distribution\nwill be derived from data in a way that preserves these correlations.\n\nHowever, there is a drawback. If a cluster significantly deviates from\nthe null, no further inference on the cluster (e.g., peak location) can be\nmade, as the entire cluster as a whole is used to reject the null.\nMoreover, because the test statistic concerns the full data, the null\nhypothesis (and our rejection of it) refers to the structure of the full\ndata. For more information, see also the comprehensive\n`FieldTrip tutorial <ft_cluster_>`_.\n\nDefining the connectivity/neighbor/adjacency matrix\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nFirst we need to define our connectivity/neighbor/adjacency matrix.\nThis is a square array (or sparse matrix) of shape ``(n_src, n_src)`` that\ncontains zeros and ones to define which spatial points are connected, i.e.,\nwhich voxels are adjacent to each other. In our case this\nis quite simple, as our data are aligned on a rectangular grid.\n\nLet's pretend that our data were smaller -- a 3 x 3 grid. Thinking about\neach voxel as being connected to the other voxels it touches, we would\nneed a 9 x 9 connectivity matrix. The first row of this matrix contains the\nvoxels in the flattened data that the first voxel touches. Since it touches\nthe second element in the first row and the first element in the second row\n(and is also a neighbor to itself), this would be::\n\n [1, 1, 0, 1, 0, 0, 0, 0, 0]\n\n:mod:`sklearn.feature_extraction` provides a convenient function for this:\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from sklearn.feature_extraction.image import grid_to_graph # noqa: E402\nmini_connectivity = grid_to_graph(3, 3).toarray()\nassert mini_connectivity.shape == (9, 9)\nprint(mini_connectivity[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In general the connectivity between voxels can be more complex, such as\nthose between sensors in 3D space, or time-varying activation at brain\nvertices on a cortical surface. MNE provides several convenience functions\nfor computing connectivity/neighbor/adjacency matrices (see the\n`Statistics API <api_reference_statistics>`).\n\nStandard clustering\n~~~~~~~~~~~~~~~~~~~\nHere, since our data are on a grid, we can use ``connectivity=None`` to\ntrigger optimized grid-based code, and run the clustering algorithm.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titles.append('Clustering')\n# Reshape data to what is equivalent to (n_samples, n_space, n_time)\nX.shape = (n_subjects, width, width)\n# Compute threshold from t distribution (this is also the default)\nthreshold = stats.distributions.t.ppf(1 - alpha, n_subjects - 1)\nt_clust, clusters, p_values, H0 = permutation_cluster_1samp_test(\n X, n_jobs=1, threshold=threshold, connectivity=None,\n n_permutations=n_permutations)\n# Put the cluster data in a viewable format\np_clust = np.ones((width, width))\nfor cl, p in zip(clusters, p_values):\n p_clust[cl] = p\nts.append(t_clust)\nps.append(p_clust)\nmccs.append(True)\nplot_t_p(ts[-1], ps[-1], titles[-1], mccs[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\"Hat\" variance adjustment\n~~~~~~~~~~~~~~~~~~~~~~~~~\nThis method can also be used in this context to correct for small\nvariances [1]_:\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titles.append(r'$\\mathbf{C_{hat}}$')\nstat_fun_hat = partial(ttest_1samp_no_p, sigma=sigma)\nt_hat, clusters, p_values, H0 = permutation_cluster_1samp_test(\n X, n_jobs=1, threshold=threshold, connectivity=None,\n n_permutations=n_permutations, stat_fun=stat_fun_hat, buffer_size=None)\np_hat = np.ones((width, width))\nfor cl, p in zip(clusters, p_values):\n p_hat[cl] = p\nts.append(t_hat)\nps.append(p_hat)\nmccs.append(True)\nplot_t_p(ts[-1], ps[-1], titles[-1], mccs[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\nThreshold-free cluster enhancement (TFCE)\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nTFCE eliminates the free parameter initial ``threshold`` value that\ndetermines which points are included in clustering by approximating\na continuous integration across possible threshold values with a standard\n`Riemann sum <https://en.wikipedia.org/wiki/Riemann_sum>`__ [2]_.\nThis requires giving a starting threshold ``start`` and a step\nsize ``step``, which in MNE is supplied as a dict.\nThe smaller the ``step`` and closer to 0 the ``start`` value,\nthe better the approximation, but the longer it takes.\n\nA significant advantage of TFCE is that, rather than modifying the\nstatistical null hypothesis under test (from one about individual voxels\nto one about the distribution of clusters in the data), it modifies the *data\nunder test* while still controlling for multiple comparisons.\nThe statistical test is then done at the level of individual voxels rather\nthan clusters. This allows for evaluation of each point\nindependently for significance rather than only as cluster groups.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titles.append(r'$\\mathbf{C_{TFCE}}$')\nthreshold_tfce = dict(start=0, step=0.2)\nt_tfce, _, p_tfce, H0 = permutation_cluster_1samp_test(\n X, n_jobs=1, threshold=threshold_tfce, connectivity=None,\n n_permutations=n_permutations)\nts.append(t_tfce)\nps.append(p_tfce)\nmccs.append(True)\nplot_t_p(ts[-1], ps[-1], titles[-1], mccs[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also combine TFCE and the \"hat\" correction:\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"titles.append(r'$\\mathbf{C_{hat,TFCE}}$')\nt_tfce_hat, _, p_tfce_hat, H0 = permutation_cluster_1samp_test(\n X, n_jobs=1, threshold=threshold_tfce, connectivity=None,\n n_permutations=n_permutations, stat_fun=stat_fun_hat, buffer_size=None)\nts.append(t_tfce_hat)\nps.append(p_tfce_hat)\nmccs.append(True)\nplot_t_p(ts[-1], ps[-1], titles[-1], mccs[-1])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Visualize and compare methods\n-----------------------------\nLet's take a look at these statistics. The top row shows each test statistic,\nand the bottom shows p-values for various statistical tests, with the ones\nwith proper control over FWER or FDR with bold titles.\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"fig = plt.figure(facecolor='w', figsize=(14, 3))\nassert len(ts) == len(titles) == len(ps)\nfor ii in range(len(ts)):\n ax = [fig.add_subplot(2, 10, ii + 1, projection='3d'),\n fig.add_subplot(2, 10, 11 + ii)]\n plot_t_p(ts[ii], ps[ii], titles[ii], mccs[ii], ax)\nfig.tight_layout(pad=0, w_pad=0.05, h_pad=0.1)\nplt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The first three columns show the parametric and non-parametric statistics\nthat are not corrected for multiple comparisons:\n\n- Mass univariate **t-tests** result in jagged edges.\n- **\"Hat\" variance correction** of the t-tests produces less peaky edges,\n correcting for sharpness in the statistic driven by low-variance voxels.\n- **Non-parametric resampling tests** are very similar to t-tests. This is to\n be expected: the data are drawn from a Gaussian distribution, and thus\n satisfy parametric assumptions.\n\nThe next three columns show multiple comparison corrections of the\nmass univariate tests (parametric and non-parametric). These\ntoo conservatively correct for multiple comparisons because neighboring\nvoxels in our data are correlated:\n\n- **Bonferroni correction** eliminates any significant activity.\n- **FDR correction** is less conservative than Bonferroni.\n- A **permutation test with a maximum statistic** also eliminates any\n significant activity.\n\nThe final four columns show the non-parametric cluster-based permutation\ntests with a maximum statistic:\n\n- **Standard clustering** identifies the correct region. However, the whole\n area must be declared significant, so no peak analysis can be done.\n Also, the peak is broad.\n- **Clustering with \"hat\" variance adjustment** tightens the estimate of\n significant activity.\n- **Clustering with TFCE** allows analyzing each significant point\n independently, but still has a broadened estimate.\n- **Clustering with TFCE and \"hat\" variance adjustment** tightens the area\n declared significant (again FWER corrected).\n\nStatistical functions in MNE\n----------------------------\nThe complete listing of statistical functions provided by MNE are in\nthe `Statistics API list <api_reference_statistics>`, but we will give\na brief overview here.\n\nMNE provides several convenience parametric testing functions that can be\nused in conjunction with the non-parametric clustering methods. However,\nthe set of functions we provide is not meant to be exhaustive.\n\nIf the univariate statistical contrast of interest is not listed here\n(e.g., interaction term in an unbalanced ANOVA), consider checking out the\n:mod:`statsmodels` package. It offers many functions for computing\nstatistical contrasts, e.g., :func:`statsmodels.stats.anova.anova_lm`.\nTo use these functions in clustering:\n\n1. Determine which test statistic (e.g., t-value, F-value) you would use\n in a univariate context to compute your contrast of interest. In other\n words, if there were only a single output such as reaction times, what\n test statistic might you compute on the data?\n2. Wrap the call to that function within a function that takes an input of\n the same shape that is expected by your clustering function,\n and returns an array of the same shape without the \"samples\" dimension\n (e.g., :func:`mne.stats.permutation_cluster_1samp_test` takes an array\n of shape ``(n_samples, p, q)`` and returns an array of shape ``(p, q)``).\n3. Pass this wrapped function to the ``stat_fun`` argument to the clustering\n function.\n4. Set an appropriate ``threshold`` value (float or dict) based on the\n values your statistical contrast function returns.\n\nParametric methods provided by MNE\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n\n- :func:`mne.stats.ttest_1samp_no_p`\n Paired t-test, optionally with hat adjustment.\n This is used by default for contrast enhancement in paired cluster tests.\n\n- :func:`mne.stats.f_oneway`\n One-way ANOVA for independent samples.\n This can be used to compute various F-contrasts. It is used by default\n for contrast enhancement in non-paired cluster tests.\n\n- :func:`mne.stats.f_mway_rm`\n M-way ANOVA for repeated measures and balanced designs.\n This returns F-statistics and p-values. The associated helper function\n :func:`mne.stats.f_threshold_mway_rm` can be used to determine the\n F-threshold at a given significance level.\n\n- :func:`mne.stats.linear_regression`\n Compute ordinary least square regressions on multiple targets, e.g.,\n sensors, time points across trials (samples).\n For each regressor it returns the beta value, t-statistic, and\n uncorrected p-value. While it can be used as a test, it is\n particularly useful to compute weighted averages or deal with\n continuous predictors.\n\nNon-parametric methods\n^^^^^^^^^^^^^^^^^^^^^^\n\n- :func:`mne.stats.permutation_cluster_test`\n Unpaired contrasts with connectivity.\n\n- :func:`mne.stats.spatio_temporal_cluster_test`\n Unpaired contrasts with spatio-temporal connectivity.\n\n- :func:`mne.stats.permutation_t_test`\n Paired contrast with no connectivity.\n\n- :func:`mne.stats.permutation_cluster_1samp_test`\n Paired contrasts with connectivity.\n\n- :func:`mne.stats.spatio_temporal_cluster_1samp_test`\n Paired contrasts with spatio-temporal connectivity.\n\n<div class=\"alert alert-danger\"><h4>Warning</h4><p>In most MNE functions, data has shape\n ``(..., n_space, n_time)``, where the spatial dimension can\n be e.g. sensors or source vertices. But for our spatio-temporal\n clustering functions, the spatial dimensions need to be **last**\n for computational efficiency reasons. For example, for\n :func:`mne.stats.spatio_temporal_cluster_1samp_test`, ``X``\n needs to be of shape ``(n_samples, n_time, n_space)``. You can\n use :func:`numpy.transpose` to transpose axes if necessary.</p></div>\n\nReferences\n----------\n.. [1] Ridgway et al. 2012, \"The problem of low variance voxels in\n statistical parametric mapping; a new hat avoids a 'haircut'\",\n NeuroImage. 2012 Feb 1;59(3):2131-41.\n\n.. [2] Smith and Nichols 2009, \"Threshold-free cluster enhancement:\n addressing problems of smoothing, threshold dependence, and\n localisation in cluster inference\", NeuroImage 44 (2009) 83-98.\n\n.. include:: ../tutorial_links.inc\n\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | bsd-3-clause |
cedadev/ipython_project | notebooks/[testing] ncserialisable.ipynb | 1 | 4073 | {
"metadata": {
"name": "[testing] ncserialisable"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import IPython\n",
"c = IPython.parallel.Client()\n",
"dv = c[0]\n",
"dv.block = True\n",
"dv.activate()\n",
"\n",
"with dv.sync_imports():\n",
" import pickle\n",
" import numpy\n",
" from nc_ipython import ncserialisable"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"importing pickle on engine(s)\n",
"importing numpy on engine(s)\n",
"importing ncserialisable from nc_ipython on engine(s)"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from nc_ipython import ncserialisable"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d = ncserialisable.Dataset('data', 'w', format = 'NETCDF4')\n",
"try:\n",
" g = d.createGroup('g')\n",
" t = numpy.dtype(numpy.float64)\n",
" t = g.createVLType(t, 't')\n",
" x, y, z = 50, 30, 20\n",
" g.createDimension('x', x)\n",
" g.createDimension('y', y)\n",
" g.createDimension('z', z)\n",
" w = g.createVariable('w', t, ('x'))\n",
" v = g.createVariable('v', 'f8', ('x', 'y', 'z'))\n",
" for i in xrange(x):\n",
" for j in xrange(y):\n",
" v[i,j,:] = numpy.random.random(z)\n",
"finally:\n",
" d.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"d = ncserialisable.Dataset('data', 'w', format = 'NETCDF3_64BIT')\n",
"try:\n",
" x, y, z = 5, 3, 2\n",
" d.createDimension('x', x)\n",
" d.createDimension('y', y)\n",
" d.createDimension('z', z)\n",
" v = d.createVariable('v', 'f8', ('x', 'y', 'z'))\n",
" for i in xrange(x):\n",
" for j in xrange(y):\n",
" v[i,j,:] = numpy.random.random(z)\n",
"finally:\n",
" d.close()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"reload(ncserialisable)\n",
"%px reload(ncserialisable)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"text": [
"\u001b[0;31mOut[0:10]: \u001b[0m<module 'nc_ipython.ncserialisable' from '/home/users/jmlansdowne/modules/nc_ipython/ncserialisable.pyc'>"
]
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def f (w):\n",
" v = w.group().variables['v']\n",
" rtn = (v[:].sum() / v.size, v.size, w.dtype)\n",
" w.group().parent.close()\n",
" return rtn\n",
"\n",
"d = ncserialisable.Dataset('data', 'r')\n",
"try:\n",
" g = d.groups['g']\n",
" w = g.variables['w']\n",
" print dv.apply(f, w)\n",
"finally:\n",
" try:\n",
" d.close()\n",
" except RuntimeError:\n",
" pass"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[0.50039141107065677, 30000, dtype('float64')]\n"
]
}
],
"prompt_number": 33
}
],
"metadata": {}
}
]
} | bsd-3-clause |
catalystcomputing/DSIoT-Python-sessions | Session4/code/03 Improving outcomes - including text.ipynb | 1 | 6077 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Improving outcomes - including text\n",
"\n",
"We finshed the last section with an accuracy score of 0.471204188482 47% (or ~10%).\n",
"\n",
"Lets see if we can improve that by using the standard features of Sckit to look at the text as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Imports\n",
"from sklearn import metrics\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.feature_extraction import DictVectorizer\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Training Data\n",
"training_raw = pd.read_table(\"../data/training_data.dat\")\n",
"df_training = pd.DataFrame(training_raw)\n",
"\n",
"# test Data\n",
"test_raw = pd.read_table(\"../data/test_data.dat\")\n",
"df_test = pd.DataFrame(test_raw)\n",
"df_test.head()\n",
"\n",
"# target names\n",
"target_categories = ['Unclassified','Art','Aviation','Boating','Camping /Walking /Climbing','Collecting']\n",
"target_values = ['1','528','529','530','531','532']\n",
"\n",
"# features\n",
"feature_names = ['Barcode','Description','UnitRRP']"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Extract features from panda and convert into a dictionary\n",
"x_df_training = df_training[feature_names].T.to_dict().values()\n",
"x_df_training[:2]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create vectorizer class\n",
"# We use sparse = False so we get an array rather than scipy.sparse matrix\n",
"# This is so we can see the values\n",
"vectorizer = DictVectorizer( sparse = False )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Create a feature to indices mapping\n",
"vectorizer.fit( x_df_training )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# vectorizer all the dictionaries\n",
"vec_x_df_training = vectorizer.transform( x_df_training )\n",
"vec_x_df_training[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Extract target results from panda\n",
"target = df_training[\"CategoryID\"].values"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Create classifier class\n",
"model = DecisionTreeClassifier(random_state=511)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# train model\n",
"model.fit(vec_x_df_training, target)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Extract test data\n",
"x_df_test = df_test[feature_names].T.to_dict().values()\n",
"x_df_test[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# vectorizer test data\n",
"vec_x_df_test = vectorizer.transform( x_df_test )\n",
"vec_x_df_test[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Test the model\n",
"expected = df_test[\"CategoryID\"].values\n",
"predicted = model.predict(vec_x_df_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(metrics.classification_report(expected, predicted, target_names=target_categories))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(metrics.confusion_matrix(expected, predicted))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"metrics.accuracy_score(expected, predicted, normalize=True, sample_weight=None)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So we have failed to improve going from 47% to 46%.\n",
"\n",
"Looking at the data why so little change."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_training[df_training[\"CategoryID\"]==529][:15]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"df_test[df_test[\"CategoryID\"]==529][:15]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.11"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
jskDr/jamespy_py3 | wireless/hamming_nb/hamming.ipynb | 2 | 11554 | {
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import numba as nb"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [4, 5, 6]])"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([[1,2,3],[4,5,6]])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 4],\n",
" [2, 5],\n",
" [3, 6]])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.T"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from wireless import hamming"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"N_iter = 2\n",
"u_array = np.random.randint(2, size=(N_iter, 4))"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0, 1, 1, 1],\n",
" [1, 1, 0, 0]])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u_array"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/sjkim/data/github_download/jamespy_py3/wireless/hamming.py:1041: NumbaWarning: \u001b[1m\n",
"Compilation is falling back to object mode WITH looplifting enabled because Function \"encode_array_n_hamming\" failed type inference due to: \u001b[1m\u001b[1m\u001b[1mInvalid use of Function(<function zeros_like at 0x7f77382320e0>) with argument(s) of type(s): (array(int64, 2d, C), shape=UniTuple(int64 x 2))\n",
" * parameterized\n",
"\u001b[1mIn definition 0:\u001b[0m\n",
"\u001b[1m TypeError: typer() got an unexpected keyword argument 'shape'\u001b[0m\n",
" raised from /home/sjkim/anaconda3/envs/tf2/lib/python3.7/site-packages/numba/typing/templates.py:283\n",
"\u001b[1mIn definition 1:\u001b[0m\n",
"\u001b[1m TypeError: typer() got an unexpected keyword argument 'shape'\u001b[0m\n",
" raised from /home/sjkim/anaconda3/envs/tf2/lib/python3.7/site-packages/numba/typing/templates.py:283\n",
"\u001b[1mThis error is usually caused by passing an argument of a type that is unsupported by the named function.\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[1m[1] During: resolving callee type: Function(<function zeros_like at 0x7f77382320e0>)\u001b[0m\n",
"\u001b[0m\u001b[1m[2] During: typing of call at /home/sjkim/data/github_download/jamespy_py3/wireless/hamming.py (1050)\n",
"\u001b[0m\n",
"\u001b[1m\n",
"File \"../hamming.py\", line 1050:\u001b[0m\n",
"\u001b[1mdef encode_array_n_hamming(u_array):\n",
" <source elided>\n",
" [1,1,0,1]]) \n",
"\u001b[1m x_array = np.zeros_like(u_array, shape=(u_array.shape[0], G.shape[0]))\n",
"\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
" @nb.jit\n",
"/home/sjkim/data/github_download/jamespy_py3/wireless/hamming.py:1041: NumbaWarning: \u001b[1m\n",
"Compilation is falling back to object mode WITHOUT looplifting enabled because Function \"encode_array_n_hamming\" failed type inference due to: \u001b[1m\u001b[1mcannot determine Numba type of <class 'numba.dispatcher.LiftedLoop'>\u001b[0m\n",
"\u001b[1m\n",
"File \"../hamming.py\", line 1051:\u001b[0m\n",
"\u001b[1mdef encode_array_n_hamming(u_array):\n",
" <source elided>\n",
" x_array = np.zeros_like(u_array, shape=(u_array.shape[0], G.shape[0]))\n",
"\u001b[1m for i in range(x_array.shape[0]):\n",
"\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0m\n",
" @nb.jit\n",
"/home/sjkim/anaconda3/envs/tf2/lib/python3.7/site-packages/numba/object_mode_passes.py:178: NumbaWarning: \u001b[1mFunction \"encode_array_n_hamming\" was compiled in object mode without forceobj=True, but has lifted loops.\n",
"\u001b[1m\n",
"File \"../hamming.py\", line 1043:\u001b[0m\n",
"\u001b[1mdef encode_array_n_hamming(u_array):\n",
"\u001b[1m G = np.array([[1,0,0,0],\n",
"\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
" state.func_ir.loc))\n",
"/home/sjkim/anaconda3/envs/tf2/lib/python3.7/site-packages/numba/object_mode_passes.py:188: NumbaDeprecationWarning: \u001b[1m\n",
"Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
"\n",
"For more information visit http://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
"\u001b[1m\n",
"File \"../hamming.py\", line 1043:\u001b[0m\n",
"\u001b[1mdef encode_array_n_hamming(u_array):\n",
"\u001b[1m G = np.array([[1,0,0,0],\n",
"\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
" state.func_ir.loc))\n"
]
},
{
"data": {
"text/plain": [
"array([[0, 1, 1, 1, 3, 2, 2],\n",
" [1, 1, 0, 0, 1, 1, 2]])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hamming.encode_array_n_hamming(u_array)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"G = np.array([[1,0,0,0],\n",
" [0,1,0,0],\n",
" [0,0,1,0],\n",
" [0,0,0,1],\n",
" [0,1,1,1],\n",
" [1,0,1,1],\n",
" [1,1,0,1]]) \n",
"u_array = np.random.randint(2, size=(10, 4))\n",
"x_array = np.zeros_like(u_array, shape=(u_array.shape[0], G.shape[0]))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(10, 7)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_array.shape"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"@nb.jit\n",
"def encode_array_n_hamming(u_array):\n",
" G = np.array([[1,0,0,0],\n",
" [0,1,0,0],\n",
" [0,0,1,0],\n",
" [0,0,0,1],\n",
" [0,1,1,1],\n",
" [1,0,1,1],\n",
" [1,1,0,1]]) \n",
" x_array = np.zeros_like(u_array, shape=(u_array.shape[0], G.shape[0]))\n",
" for i in range(x_array.shape[0]):\n",
" for j in range(x_array.shape[1]):\n",
" x_array[i,j] = np.sum(u_array[i,:] * G[j,:])\n",
" return x_array"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([[0, 0, 0, 0],\n",
" [1, 0, 1, 1],\n",
" [0, 1, 0, 0],\n",
" [0, 1, 0, 0],\n",
" [0, 1, 1, 1]]),\n",
" array([[0, 0, 0, 0],\n",
" [1, 0, 1, 1],\n",
" [0, 1, 0, 0],\n",
" [0, 1, 0, 0],\n",
" [0, 1, 1, 1]]))"
]
},
"execution_count": 70,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@nb.jit\n",
"def _encode_array_n_hamming(u_array):\n",
" G = np.array([[1,0,0,0],\n",
" [0,1,0,0],\n",
" [0,0,1,0],\n",
" [0,0,0,1],\n",
" [0,1,1,1],\n",
" [1,0,1,1],\n",
" [1,1,0,1]]) \n",
" x_array = np.zeros(shape=(u_array.shape[0], G.shape[0]), dtype=u_array.dtype)\n",
" for i in range(x_array.shape[0]):\n",
" for j in range(x_array.shape[1]):\n",
" x_array[i,j] = np.mod(np.sum(u_array[i,:] * G[j,:]),2) \n",
" return x_array\n",
"\n",
"def encode_array_n_hamming(u_array):\n",
" G = np.array([[1,0,0,0],\n",
" [0,1,0,0],\n",
" [0,0,1,0],\n",
" [0,0,0,1],\n",
" [0,1,1,1],\n",
" [1,0,1,1],\n",
" [1,1,0,1]]) \n",
" return np.mod(np.dot(u_array, G.T),2)\n",
"\n",
"def channel_numpy_awgn(x_array, SNRdB):\n",
" \"\"\"\n",
" 출력을 (0,1) --> (1,-1)로 바꾸고 가우시안 노이즈를 더함.\n",
" \"\"\"\n",
" #y_array = np.zeros(x_array.shape, dtype=nb.float_)\n",
" SNR = np.power(10, SNRdB/10)\n",
" noise_sig = 1/np.sqrt(SNR)\n",
" n_array = np.random.normal(0.0, noise_sig, size=x_array.shape)\n",
" y_array = 1.0 - x_array*2 + n_array\n",
" return y_array \n",
"\n",
"def decode_array_n_hamming(y_array, K_code:int=4): \n",
" G = np.array([[1,0,0,0],\n",
" [0,1,0,0],\n",
" [0,0,1,0],\n",
" [0,0,0,1],\n",
" [0,1,1,1],\n",
" [1,0,1,1],\n",
" [1,1,0,1]]) \n",
" a = np.arange(0,2**K_code,dtype='uint8')\n",
" m = np.unpackbits(a.reshape(-1,1), axis=1)[:,-4:]\n",
" # m을 uint8에서 int 즉 int64로 바꾸기 위한 코드임.\n",
" m = np.dot(m, np.eye(K_code, dtype=int))\n",
" # print(m.dtype)\n",
" x = 1.0 - 2.0*np.mod(np.dot(m, G.T),2) # 16 x 7\n",
" r_array = np.dot(y_array, x.T)\n",
" argmax_r_arry = np.argmax(r_array,axis=1)\n",
" ud_array = m[argmax_r_arry,:]\n",
" return ud_array\n",
" #return argmax_r_arry\n",
"\n",
"u_array = np.random.randint(2, size=(5, 4))\n",
"x_array = encode_array_n_hamming(u_array)\n",
"y_array = channel_numpy_awgn(x_array, 10)\n",
"ud_array = decode_array_n_hamming(y_array)\n",
"u_array, ud_array"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([8.00300711, 7.43678122])"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.max(r_array,axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "tf2",
"language": "python",
"name": "tf2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
| mit |
stereoboy/Study | papers/External_Memory/External Memory.ipynb | 1 | 5807 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# References\n",
"* Stanford NLP Lecture\n",
" * http://cs224d.stanford.edu/syllabus.html\n",
" \n",
"# Data\n",
"\n",
"* babi data for\n",
" * http://www.thespermwhale.com/jaseweston/\n",
" * http://www.thespermwhale.com/jaseweston/babi/tasks_1-20_v1-2.tar.gz"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Memory Networks Facebook AI\n",
"## Memory Networks (2014)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## End-To-End Memory Networks\n",
"* Source Code\n",
" * https://github.com/facebook/MemNN\n",
"* Differential Version Of Memory Networks\n",
"* Two grand challenges in artifical intelligence research\n",
" * Multiple computational steps in the service of answering a question or completing a task\n",
" * Long term dependencies in sequential data\n",
"* Because the function from input to output is smooth, we can easily compute gradients and back-propagate through it.\n",
"<img src=\"C2Q7W6NKPYUTPK448GGVG6E7W1VXQ408.png\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# NTM DeepMind\n",
"## Neural Turing Machine (2014)\n",
"\n",
"* Human Cognition VS Computing\n",
" * Rule-based manipulation VS Simple Program\n",
" * Short-term storage of information VS Program arguments \n",
" * -> Working Memormy VS \"NTM\""
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"<img src=\"LKTTEJA7F9N1D58DRU1G1Q1AF3X0JUTB.png\"/>\n",
"<img src=\"YPIE6LGFCS12CPEWOSRG7IB2GXC0C831.png\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"* !!! bit #7, bit #8 in input are delimiter bits\n",
" * On bit #7 means \"input start\"\n",
" * On bit #8 means \"input end and start result\" such as <go> word in seq2seq\n",
" * insert zero bits in inputs while outputs are generated\n",
"<img src=\"B61HT3HBUA6KGAIV6NGR3HLLVRVAOAGL.png\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## REINFORCEMENT LEARNING NEURAL TURING MACHINES\n",
"* source code \n",
" * https://github.com/ilyasu123/rlntm"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Hybrid computing using a neural network with dynamic external memory (DNC) (2016)\n",
"* Unofficial Source code\n",
" * https://github.com/Mostafa-Samir/DNC-tensorflow\n",
"* NTM vs DNC\n",
" * Same at target level\n",
" * Implementation of addressing method + introduction of memory allocation method\n",
" * DNC is better for accuracy (comparison in bAbI task)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes\n",
"\n",
"* SAM (Sparse Access Model)\n",
" * Upgrade NTM using ANN for large size external moemory \n",
" * \"Scaling\" = Large size\n",
" * Approximate Nearest Neighbor (ANN) $\\mathcal{O}(\\log N)$ instead of linear search $\\mathcal{O}(N)$\n",
" * K-Nearest Neighbor(KNN)\n",
" * K sparse number\n",
" * c.f. Sparse Differentiable Neural Computer (SDNC) is upgrade version of DNC"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"<img src=\"ENHTL692NDDOTXHP6K151W8513729XEC.png\"/>"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Pointer Networks (2015)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Ask Me Anything: Dynamic Memory Networks for Natural Language Processing (DMN, Dynamic Memory Networks) (2015)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## \"Hierarchical Memory Networks\" (ICLR 2017)\n",
"Reduce memory seen by softmax by expressing memory hierarchically\n",
"\n",
"## \"Dynamic NTM with Continuous and Discrete Addressing Schemes\" ICLR 2017\n",
"Based on REINFORCE hard attention and softmax\n",
"Soft attention is used in combination\n",
"I do not really understand\n",
"\n",
"## \"Lie Access Neural Turing Machine\" ICLR 2017\n",
"Addressing using Lee group\n",
"I can move the head naturally"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
jpallas/beakerx | doc/groovy/TableAPI.ipynb | 1 | 15347 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Interactive Tables and their API\n",
"\n",
"The table UI allows column drag/drop, hide, sorting, formatting, searching, selecting/export as CSV. This makes it easy to paste into a spreadsheet like Excel.\n",
"\n",
"There is a menu in the top-left for the whole table, and each column has a menu that appears on hover.\n",
"\n",
"There are also keyboard commands: digits change the precision of all columns, shift-digit changes the precision of the current column. Arrow keys navigate. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"new TableDisplay( new CSV().read(\"../resources/data/interest-rates.csv\"))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import com.twosigma.beakerx.table.*\n",
"import com.twosigma.beakerx.table.format.TableDisplayStringFormat\n",
"\n",
"display = new TableDisplay(new CSV().read(\"../resources/data/interest-rates.csv\"))\n",
"//show all time columns in days\n",
"display.setStringFormatForTimes(TimeUnit.DAYS)\n",
"//min 4, max 6 decimal places for all doubles\n",
"display.setStringFormatForType(ColumnType.Double, TableDisplayStringFormat.getDecimalFormat(4,6))\n",
"//setting for a column takes precidence over the type\n",
"display.setStringFormatForColumn(\"m3\", TableDisplayStringFormat.getDecimalFormat(0, 0))\n",
"//set the alignment\n",
"display.setAlignmentProviderForType(ColumnType.Double, TableDisplayAlignmentProvider.RIGHT_ALIGNMENT)\n",
"display.setAlignmentProviderForColumn('m3', TableDisplayAlignmentProvider.CENTER_ALIGNMENT)\n",
"\n",
"//using a closure\n",
"display.setStringFormatForColumn(\"y3\") { value, row, col, tableDisplay ->\n",
" if(value < 8) {\n",
" \":(\"\n",
" } else {\n",
" \":)\"\n",
" } \n",
"}\n",
"\n",
"display"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This changes the format of the table above in the previous output cell. It's updated because the object is synchronized between the kernel and the client."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display.setStringFormatForTimes(TimeUnit.HOURS)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import com.twosigma.beakerx.table.*\n",
"import com.twosigma.beakerx.table.renderer.TableDisplayCellRenderer\n",
"\n",
"def display2 = new TableDisplay(new CSV().read(\"../resources/data/interest-rates.csv\"))\n",
"//right now, the only renderer option is for data bars\n",
"display2.setRendererForType(ColumnType.Double, TableDisplayCellRenderer.getDataBarsRenderer())\n",
"//use the false parameter to hide the String value\n",
"display2.setRendererForColumn(\"y10\", TableDisplayCellRenderer.getDataBarsRenderer(false))\n",
"display2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import com.twosigma.beakerx.table.*\n",
"import com.twosigma.beakerx.fileloader.CSV\n",
"import com.twosigma.beakerx.table.format.TableDisplayStringFormat\n",
"\n",
"def display3 = new TableDisplay(new CSV().read(\"../resources/data/interest-rates.csv\"))\n",
"display3.setStringFormatForType(ColumnType.Double, TableDisplayStringFormat.getDecimalFormat(9,9))\n",
"//freeze a column\n",
"display3.setColumnFrozen(\"y1\", true)\n",
"//freeze a column to the right\n",
"display3.setColumnFrozenRight(\"y10\", true)\n",
"//hide a column\n",
"display3.setColumnVisible(\"y30\", false)\n",
"\n",
"//explicitly set column order/visiblity\n",
"display3.setColumnOrder([\"m3\", \"y1\", \"y5\", \"time\", \"y2\"]) //Columns in the list will be shown in the provided order. Columns not in the list will be hidden.\n",
"display3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import com.twosigma.beakerx.table.*\n",
"import com.twosigma.beakerx.table.highlight.TableDisplayCellHighlighter\n",
"\n",
"def display4 = new TableDisplay(new CSV().read(\"../resources/data/interest-rates.csv\"))\n",
"display4.addCellHighlighter(TableDisplayCellHighlighter.getHeatmapHighlighter(\"m3\", TableDisplayCellHighlighter.FULL_ROW))\n",
"\n",
"//the following two overloads should also be supported\n",
"//set the min and max used for calculating the color\n",
"//display4.addCellHighlighter(TableDisplayCellHighlighter.getHeatmapHighlighter(\"y1\", TableDisplayCellHighlighter.FULL_ROW, 0, 5))\n",
"//set the colors used for the min and max\n",
"//display4.addCellHighlighter(TableDisplayCellHighlighter.getHeatmapHighlighter(\"m6\", TableDisplayCellHighlighter.SINGLE_COLUMN, null, null, Color.YELLOW, Color.BLUE))\n",
"\n",
"display4"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def map = [\n",
" [a:1, b:2, c:3],\n",
" [a:4, b:5, c:6],\n",
" [a:7, b:8, c:5]\n",
"]\n",
"def display5 = new TableDisplay(map)\n",
"display5.addCellHighlighter { row, column, tableDisplay ->\n",
" if (column == 2) {\n",
" display5.values[row][column] < 5 ? Color.RED : Color.GREEN\n",
" }\n",
"}\n",
"display5"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import com.twosigma.beakerx.table.*\n",
"import com.twosigma.beakerx.table.highlight.*\n",
"\n",
"display6 = new TableDisplay(new CSV().read(\"../resources/data/interest-rates.csv\"))\n",
"display6.addCellHighlighter(TableDisplayCellHighlighter.getHeatmapHighlighter(\"m3\", 0, 8, Color.ORANGE, Color.PINK))\n",
"display6.addCellHighlighter(TableDisplayCellHighlighter.getHeatmapHighlighter(\"m6\", TableDisplayCellHighlighter.SINGLE_COLUMN, 6, 8, Color.BLACK, Color.PINK))\n",
"\n",
"display6.addCellHighlighter(new ThreeColorHeatmapHighlighter(\"y1\", TableDisplayCellHighlighter.SINGLE_COLUMN, 4, 6, 8, new Color(247,106,106), new Color(239,218,82), new Color(100,189,122)))\n",
"\n",
"display6"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display6.removeAllCellHighlighters()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import com.twosigma.beakerx.table.highlight.*\n",
"\n",
"def table = new TableDisplay([[1,2,3], \n",
" [3,4,5], \n",
" [6,2,8], \n",
" [6,2,8], \n",
" [6,2,8], \n",
" [6,4,8], \n",
" [6,2,8], \n",
" [6,2,8], \n",
" [6,5,8]], \n",
" ['a', 'b', 'c'], \n",
" ['double', 'double', 'double'])\n",
"table.addCellHighlighter(TableDisplayCellHighlighter.getUniqueEntriesHighlighter(\"b\", TableDisplayCellHighlighter.FULL_ROW))\n",
"table"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def mapList4 = [\n",
" [a:1, b:2, c:3],\n",
" [a:4, b:5, c:6],\n",
" [a:7, b:8, c:5]\n",
"]\n",
"def display7 = new TableDisplay(mapList4)\n",
"\n",
"//set what happens on a double click\n",
"display7.setDoubleClickAction { row, col, tableDisplay ->\n",
" tableDisplay.values[row][col] = tableDisplay.values[row].sum()\n",
"}\n",
"//run tagged cell on action\n",
"//display7.setDoubleClickAction(\"misc_formatting\");\n",
"\n",
"\n",
"//add a context menu item\n",
"display7.addContextMenuItem(\"negate\") { row, col, tableDisplay ->\n",
" tableDisplay.values[row][col] = -tableDisplay.values[row][col]\n",
"}\n",
"\n",
"//run tagged cell on action\n",
"//display7.addContextMenuItem(\"negate\", \"misc_formatting\");\n",
"\n",
"display7"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"misc_formatting"
]
},
"outputs": [],
"source": [
"Math.random()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def mapList5 = [\n",
" [firstCol:1, secondCol:2, thirdCol:3],\n",
" [firstCol:4, secondCol:5, thirdCol:6],\n",
" [firstCol:9, secondCol:8, thirdCol:9]\n",
"]\n",
"def td4 = new TableDisplay(mapList5)\n",
"\n",
"//tool tip can be set with a closure\n",
"td4.setToolTip { row, col, display ->\n",
" \"The value is: \" + display.values[row][col]\n",
"}\n",
"td4\n",
"\n",
"//set the font size and color\n",
"td4.dataFontSize = 15\n",
"td4.headerFontSize = 30\n",
"\n",
"def colors = [[Color.LIGHT_GRAY, Color.GRAY, Color.RED],\n",
" [Color.YELLOW, Color.ORANGE, Color.RED],\n",
" [Color.MAGENTA, Color.BLUE, Color.BLACK]]\n",
"\n",
"td4.setFontColorProvider { row, col, td ->\n",
" colors[row][col]\n",
"}\n",
"\n",
"//try different filter options\n",
"td4.setRowFilter { row, model ->\n",
" //model[row][1] == 8\n",
" true\n",
" //false\n",
" //model[row][0] == model[row][2]\n",
"}\n",
"\n",
"//set vertical headers\n",
"//you can also do this in the right-click menu\n",
"td4.setHeadersVertical(true)\n",
"\n",
"td4"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"abc = 0; // test variable\n",
"mapList = [\n",
" [a:1, b:2, c:3],\n",
" [a:4, b:5, c:6],\n",
" [a:7, b:8, c:5]\n",
"]\n",
"OutputCell.HIDDEN"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def display1 = new TableDisplay(mapList)\n",
"//set what happens on a double click\n",
"display1.setDoubleClickAction { row, col, tableDisplay ->\n",
" tableDisplay.values[row][col] = tableDisplay.values[row].sum()\n",
"}\n",
"\n",
"//add a context menu item\n",
"display1.addContextMenuItem(\"negate\") { row, col, tableDisplay ->\n",
" tableDisplay.values[row][col] = -tableDisplay.values[row][col]\n",
"}\n",
"\n",
"display1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def display2 = new TableDisplay(mapList)\n",
"\n",
"//run tagged cell on action\n",
"display2.addContextMenuItem(\"run print cell\", \"print_cell\");\n",
"display2.setDoubleClickAction(\"print_cell\");\n",
"\n",
"display2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"print_cell"
]
},
"outputs": [],
"source": [
"abc++\n",
"println abc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## API to run cells by tag"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"beakerx.runByTag(\"print_cell\") "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def display3 = new TableDisplay(mapList)\n",
"display3.setDoubleClickAction {beakerx.runByTag(\"print_cell2\")}\n",
"display3.addContextMenuItem(\"print_cell2\") { beakerx.runByTag(\"print_cell2\") }\n",
"display3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"print_cell2"
]
},
"outputs": [],
"source": [
"abc++\n",
"println abc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Action API\n",
"\n",
"Bind running a cell to the context menu or double click action on the table."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"display4 = new TableDisplay(mapList)\n",
"\n",
"//run tagged cell on action\n",
"display4.addContextMenuItem(\"run tagged_cell cell\", \"tagged_cell\");\n",
"display4.setDoubleClickAction(\"tagged_cell\");\n",
"\n",
"display4"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"tags": [
"tagged_cell"
]
},
"outputs": [],
"source": [
"def details = display4.details\n",
"\n",
"if(details != null){\n",
" switch(details.actionType){\n",
" case 'DOUBLE_CLICK':\n",
" print (\"You clicked on the cell [\" + details.row + \", \" + details.col + \"]\")\n",
" break;\n",
" case 'CONTEXT_MENU_CLICK':\n",
" print (\"You selected context menu '\" + details.contextMenuItem + \"' on the cell [\" + details.row + \", \" + details.col + \"]\")\n",
" break;\n",
" }\n",
"}else{\n",
" println \"no table tag action performed.\"\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"displayHtml = new TableDisplay([[col1: \"This & that\", col2: \"This / that\", col3: \"This > that\"]]);\n",
"displayHtml"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Update cell"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def mapListToUpdate = [\n",
" [a:1, b:2, c:3],\n",
" [a:4, b:5, c:6],\n",
" [a:7, b:8, c:9]\n",
"]\n",
"tableToUpdate = new TableDisplay(mapListToUpdate)\n",
"\n",
"tableToUpdate"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tableToUpdate.values[0][0] = 99\n",
"tableToUpdate.sendModel()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"tableToUpdate.updateCell(2,\"c\",121)\n",
"tableToUpdate.sendModel()"
]
}
],
"metadata": {
"anaconda-cloud": {},
"beakerx_kernel_parameters": {},
"celltoolbar": "Tags",
"kernelspec": {
"display_name": "Groovy",
"language": "groovy",
"name": "groovy"
},
"language_info": {
"codemirror_mode": "groovy",
"file_extension": ".groovy",
"mimetype": "",
"name": "Groovy",
"nbconverter_exporter": "",
"version": "2.4.3"
},
"widgets": {
"state": {
"f7d30807-6d2d-414d-b604-fcb0ed748612": {
"views": [
{
"cell_index": 0
},
{
"cell_index": 1
}
]
}
},
"version": "1.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| apache-2.0 |
wcmckee/wcmckee-notebook | tpb.ipynb | 1 | 35152 | {
"metadata": {
"name": "",
"signature": "sha256:e7be1ef8aa52ac83091887010a30fbf8c16eaf8a782ef986c579666aec10fe7c"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"The Pirate Bay"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from tpb import TPB\n",
"from tpb import CATEGORIES, ORDERS"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "ImportError",
"evalue": "No module named tpb",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mImportError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-1-760329d428b8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtpb\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mTPB\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtpb\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mCATEGORIES\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mORDERS\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mImportError\u001b[0m: No module named tpb"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t = TPB('https://thepriatebay.sx')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'TPB' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-2-6fd7c726bffa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTPB\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'https://thepriatebay.sx'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'TPB' is not defined"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"search = t.search('homeland', category=CATEGORIES.VIDEO.MOVIES)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 't' is not defined",
"output_type": "pyerr",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-3-0f8cb848836a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msearch\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'homeland'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcategory\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mCATEGORIES\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mVIDEO\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMOVIES\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 't' is not defined"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pagSea = search.page(0).multipage()\n",
"\n",
"for pag in pagSea:\n",
" print pag"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "URLError",
"evalue": "<urlopen error [Errno 110] Connection timed out>",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mURLError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-51-e22ab9fb44b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mpagSea\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msearch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmultipage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mpag\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpagSea\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mpag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tpb/tpb.pyc\u001b[0m in \u001b[0;36mitems\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 130\u001b[0m \u001b[0mitems\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPaginated\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 131\u001b[0;31m \u001b[0mfirst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 132\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfirst\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 133\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tpb/tpb.pyc\u001b[0m in \u001b[0;36mitems\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mtorrent\u001b[0m \u001b[0mon\u001b[0m \u001b[0mpage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \"\"\"\n\u001b[0;32m---> 55\u001b[0;31m \u001b[0mrequest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 56\u001b[0m \u001b[0mcontent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0mpage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBeautifulSoup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"lxml\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_opener\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0m_opener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 402\u001b[0m \u001b[0mreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 403\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 406\u001b[0m \u001b[0;31m# post-process response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36m_open\u001b[0;34m(self, req, data)\u001b[0m\n\u001b[1;32m 420\u001b[0m \u001b[0mprotocol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 421\u001b[0m result = self._call_chain(self.handle_open, protocol, protocol +\n\u001b[0;32m--> 422\u001b[0;31m '_open', req)\n\u001b[0m\u001b[1;32m 423\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 424\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m 380\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 381\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 383\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 384\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36mhttps_open\u001b[0;34m(self, req)\u001b[0m\n\u001b[1;32m 1220\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1221\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhttps_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1222\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhttplib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mHTTPSConnection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1224\u001b[0m \u001b[0mhttps_request\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAbstractHTTPHandler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_request_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req)\u001b[0m\n\u001b[1;32m 1182\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# XXX what error?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1183\u001b[0m \u001b[0mh\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1184\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1185\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1186\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mURLError\u001b[0m: <urlopen error [Errno 110] Connection timed out>"
]
}
],
"prompt_number": 51
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t.top().category(CATEGORIES.VIDEO.MOVIES)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 52,
"text": [
"<tpb.tpb.Top at 0x41de690>"
]
}
],
"prompt_number": 52
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"t.search('24').order(ORDERS.SEEDERS.ASC).page(3)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 53,
"text": [
"<tpb.tpb.Search at 0x41de7d0>"
]
}
],
"prompt_number": 53
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"search = t.search('breaking bad', category=CATEGORIES.VIDEO.MOVIES)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 54
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"search.page(2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 55,
"text": [
"<tpb.tpb.Search at 0x41de610>"
]
}
],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 55
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"search.order(ORDERS.SEEDERS.ASC).multipage()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 56,
"text": [
"<tpb.tpb.Search at 0x41de610>"
]
}
],
"prompt_number": 56
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"breaking = t.search('breaking bad').order(ORDERS.SEEDERS.ASC).page(0)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 57
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for brea in breaking:\n",
" print brea"
],
"language": "python",
"metadata": {},
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "pyerr",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-58-b8b30db6642f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mbrea\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mbreaking\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;32mprint\u001b[0m \u001b[0mbrea\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tpb/tpb.pyc\u001b[0m in \u001b[0;36mitems\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 140\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mitem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mPaginated\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 141\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python2.7/dist-packages/tpb/tpb.pyc\u001b[0m in \u001b[0;36mitems\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[0mtorrent\u001b[0m \u001b[0mon\u001b[0m \u001b[0mpage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 54\u001b[0m \"\"\"\n\u001b[0;32m---> 55\u001b[0;31m \u001b[0mrequest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 56\u001b[0m \u001b[0mcontent\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 57\u001b[0m \u001b[0mpage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBeautifulSoup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"lxml\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0m_opener\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m \u001b[0m_opener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbuild_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 128\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python2.7/urllib2.pyc\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m 402\u001b[0m \u001b[0mreq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 403\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 404\u001b[0;31m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_open\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 406\u001b[0m \u001b[0;31m# post-process response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body)\u001b[0m\n\u001b[1;32m 827\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mmessage_body\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 828\u001b[0m \u001b[0mmessage_body\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 829\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 830\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmessage_body\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 831\u001b[0m \u001b[0;31m#message_body was not a string (i.e. it is a file) and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m 789\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 790\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 791\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 792\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 793\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mNotConnected\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python2.7/httplib.pyc\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1171\u001b[0m sock = socket.create_connection((self.host, self.port),\n\u001b[0;32m-> 1172\u001b[0;31m self.timeout, self.source_address)\n\u001b[0m\u001b[1;32m 1173\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tunnel_host\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1174\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python2.7/socket.pyc\u001b[0m in \u001b[0;36mcreate_connection\u001b[0;34m(address, timeout, source_address)\u001b[0m\n\u001b[1;32m 560\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msource_address\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 561\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msource_address\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 562\u001b[0;31m \u001b[0msock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msa\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 563\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msock\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 564\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/lib/python2.7/socket.pyc\u001b[0m in \u001b[0;36mmeth\u001b[0;34m(name, self, *args)\u001b[0m\n\u001b[1;32m 222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 226\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_m\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_socketmethods\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"prompt_number": 58
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"blah = t.search('babylon 5').page(0).multipage()"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for bad in blah:\n",
" print bad"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
wllmtrng/udacity_data_analyst_nanodegree | P3 Data Munge/Audit_And_Shape.ipynb | 1 | 744528 | {
"cells": [
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import codecs\n",
"import json\n",
"import re\n",
"\n",
"import IPython.display as disp\n",
"#from lxml import etree as ET\n",
"import xml.etree.cElementTree as ET\n",
"\n",
"from collections import defaultdict\n",
"from enum import IntEnum\n",
"from pprint import pprint"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# P3: Wrangle OpenStreetMap Data"
]
},
{
"cell_type": "code",
"execution_count": 188,
"metadata": {
"code_folding": [],
"collapsed": true
},
"outputs": [],
"source": [
"class AuditXml(object):\n",
" \"\"\"\n",
" Used to audit openstreetmaps xml files.\n",
" \n",
" Examples\n",
" ----------\n",
" example_audit = AuditXml(\"example1.osm\")\n",
" example_audit.run()\n",
" example_audit.summary()\n",
" \"\"\"\n",
" class Options(IntEnum):\n",
" \"\"\"\n",
" Enum options to pass into AuditXml during instantiation.\n",
" The following options can be OR'd together.\n",
" \"\"\"\n",
" tag_frequency = 1,\n",
" key_frequency = 2,\n",
" key_names = 4,\n",
" street_analysis = 8\n",
" \n",
" \n",
" lower = re.compile(r'^([a-z]|_)*$')\n",
" lower_colon = re.compile(r'^([a-z]|_)*:([a-z]|_)*$')\n",
" problemchars = re.compile(r'[=\\+/&<>;\\'\"\\?%#$@\\,\\. \\t\\r\\n]+')\n",
" street_type_re = re.compile(r'\\b\\S+\\.?$', re.IGNORECASE)\n",
" \n",
" street_expected = ['Alley', 'Avenue', 'Boulevard', 'Center', 'Circle',\n",
" 'Commons', 'Court', 'Drive', 'Highway', 'Lane',\n",
" 'Parkway', 'Place', 'Plaza', 'Road', 'Square',\n",
" 'Street', 'Terrace', 'Trail', 'Vista', 'Walk', 'Way']\n",
" \n",
" def __init__(self, filename):\n",
" self.filename = filename\n",
" self.tags_found_dict = {}\n",
" self.keys_found_dict = {}\n",
" self.key_names_audit = {\"lower\": 0,\n",
" \"lower_colon\": 0,\n",
" \"problemchars\": 0,\n",
" \"other\": 0}\n",
" self.street_types = defaultdict(set)\n",
" \n",
" self.all_options = 0\n",
" \n",
" for option in self.Options:\n",
" self.all_options += option\n",
" \n",
" def run(self, options=None):\n",
" if options is None:\n",
" options = self.all_options\n",
"\n",
" for _, elem in ET.iterparse(self.filename):\n",
" if options & self.Options.tag_frequency:\n",
" self.option_tag_frequency(elem)\n",
" \n",
" if options & self.Options.key_frequency:\n",
" self.option_key_frequency(elem)\n",
" \n",
" if options & self.Options.key_names:\n",
" self.option_key_names(elem)\n",
" \n",
" if options & self.Options.street_analysis:\n",
" self.option_street_analysis(elem)\n",
"\n",
" elem.clear()\n",
" \n",
" def option_tag_frequency(self, elem):\n",
" \"\"\"\n",
" Find out how what kind of tags and how frequent do they occur.\n",
" \"\"\"\n",
" self.count_names(elem.tag, self.tags_found_dict)\n",
" \n",
" def option_key_frequency(self, elem):\n",
" \"\"\"\n",
" Find out what kind of key value pairs are their according to the \n",
" Open Street Maps Standard.\n",
" \"\"\"\n",
" if elem.tag == \"tag\":\n",
" key = elem.attrib[\"k\"]\n",
" self.count_names(key, self.keys_found_dict)\n",
" \n",
" def option_key_names(self, elem):\n",
" \"\"\"\n",
" Find out what types of keys are out there and if they pose any threat\n",
" to converting into JSON.\n",
" \"\"\"\n",
" if elem.tag == \"tag\":\n",
" match = False\n",
" key = elem.attrib[\"k\"]\n",
" if self.lower.search(key):\n",
" self.key_names_audit[\"lower\"] += 1\n",
" match = True\n",
"\n",
" if self.lower_colon.search(key):\n",
" self.key_names_audit[\"lower_colon\"] += 1\n",
" match = True\n",
"\n",
" if self.problemchars.search(key):\n",
" self.key_names_audit[\"problemchars\"] += 1\n",
" match = True\n",
"\n",
" if not match:\n",
" self.key_names_audit[\"other\"] += 1\n",
" \n",
" def option_street_analysis(self, elem):\n",
" \"\"\"\n",
" Inspect values associated with the addr:street key and find out what type\n",
" of unexpected street types are out there.\n",
" \"\"\"\n",
" if elem.tag == \"tag\":\n",
" if elem.attrib['k'] == \"addr:street\":\n",
" street_name = elem.attrib['v']\n",
" m = self.street_type_re.search(street_name)\n",
" if m:\n",
" street_type = m.group()\n",
" if street_type not in self.street_expected:\n",
" self.street_types[street_type].add(street_name)\n",
"\n",
" def summary(self, options=None):\n",
" \n",
" if options is None:\n",
" options = self.all_options\n",
"\n",
" if options & self.Options.tag_frequency:\n",
" print(\"Tags found and their frequencies:----------------------\")\n",
" pprint(sorted(self.tags_found_dict.items(), key=lambda t: -t[1]))\n",
" print(\"\")\n",
"\n",
" if options & self.Options.key_frequency:\n",
" print(\"Keys found and their frequencies:--------------------\\n\")\n",
" print(\"Sorted by Name:----------------------------------------\")\n",
" pprint(sorted(self.keys_found_dict.items(), key=lambda t: t[0].lower()))\n",
" print(\"\")\n",
" print(\"Sorted by Frequency------------------------------------\")\n",
" pprint(sorted(audit.keys_found_dict.items(), key=lambda t: -t[1]))\n",
" print(\"\")\n",
"\n",
" if options & self.Options.key_names:\n",
" print(\"Types of keys:---------------------------------------\")\n",
" pprint(self.key_names_audit)\n",
" print(\"\")\n",
"\n",
" if options & self.Options.street_analysis:\n",
" print(\"\\nStreet name analysis:--------------------------------\")\n",
" pprint(sorted(self.street_types.items(), key=lambda t: t[0].lower()))\n",
" print(\"\")\n",
" \n",
" @staticmethod\n",
" def count_names(name, mydict):\n",
" \"\"\"Builds a frequency dictionary of names passed in\"\"\"\n",
" if name in mydict:\n",
" mydict[name] += 1\n",
" else:\n",
" mydict[name] = 1\n"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tags found and their frequencies:----------------------\n",
"[('tag', 64),\n",
" ('node', 25),\n",
" ('nd', 11),\n",
" ('member', 3),\n",
" ('way', 2),\n",
" ('bounds', 1),\n",
" ('relation', 1),\n",
" ('osm', 1)]\n",
"\n",
"Keys found and their frequencies:--------------------\n",
"\n",
"Sorted by Name:----------------------------------------\n",
"[('addr:city', 4),\n",
" ('addr:country', 1),\n",
" ('addr:housename', 1),\n",
" ('addr:housenumber', 6),\n",
" ('addr:postcode', 5),\n",
" ('addr:state', 1),\n",
" ('addr:street', 6),\n",
" ('addr:street:name', 1),\n",
" ('addr:street:prefix', 1),\n",
" ('addr:street:type', 1),\n",
" ('amenity', 5),\n",
" ('building', 1),\n",
" ('building:levels', 1),\n",
" ('chicago:building_id', 1),\n",
" ('cuisine', 4),\n",
" ('highway', 2),\n",
" ('name', 6),\n",
" ('outdoor_seating', 3),\n",
" ('phone', 4),\n",
" ('restriction', 1),\n",
" ('shop', 1),\n",
" ('smoking', 3),\n",
" ('source', 1),\n",
" ('takeaway', 3),\n",
" ('type', 1)]\n",
"\n",
"Sorted by Frequency------------------------------------\n",
"[('building', 485012),\n",
" ('highway', 373635),\n",
" ('name', 255138),\n",
" ('addr:housenumber', 197174),\n",
" ('addr:street', 184889),\n",
" ('tiger:county', 179036),\n",
" ('addr:city', 174577),\n",
" ('tiger:cfcc', 172993),\n",
" ('source', 165759),\n",
" ('tiger:name_base', 153158),\n",
" ('tiger:name_type', 144636),\n",
" ('tiger:reviewed', 139251),\n",
" ('tiger:zip_left', 121605),\n",
" ('tiger:zip_right', 115990),\n",
" ('tiger:tlid', 107670),\n",
" ('tiger:source', 106881),\n",
" ('tiger:separated', 101151),\n",
" ('addr:state', 81845),\n",
" ('created_by', 50420),\n",
" ('addr:postcode', 49234),\n",
" ('amenity', 43500),\n",
" ('service', 42926),\n",
" ('oneway', 41906),\n",
" ('waterway', 38818),\n",
" ('tiger:upload_uuid', 32527),\n",
" ('landuse', 31434),\n",
" ('power', 28585),\n",
" ('paloalto_ca:id', 25298),\n",
" ('natural', 19577),\n",
" ('bicycle', 19173),\n",
" ('nhd:reach_code', 19089),\n",
" ('redwood_city_ca:bld_gid', 18697),\n",
" ('nhd:com_id', 18443),\n",
" ('gnis:fcode', 18442),\n",
" ('nhd:fdate', 18435),\n",
" ('surface', 17986),\n",
" ('redwood_city_ca:addr_id', 17164),\n",
" ('leisure', 16335),\n",
" ('access', 15922),\n",
" ('gnis:ftype', 15465),\n",
" ('layer', 15349),\n",
" ('addr:county', 14921),\n",
" ('ref', 14774),\n",
" ('maxspeed', 14676),\n",
" ('lanes', 14665),\n",
" ('boat', 14505),\n",
" ('scvwd:ROUTEID', 14253),\n",
" ('foot', 13709),\n",
" ('ele', 13349),\n",
" ('attribution', 13099),\n",
" ('gnis:feature_id', 11440),\n",
" ('railway', 11375),\n",
" ('barrier', 9891),\n",
" ('gnis:created', 9728),\n",
" ('shop', 9608),\n",
" ('gnis:county_id', 9392),\n",
" ('gnis:state_id', 9390),\n",
" ('addr:country', 9062),\n",
" ('operator', 8957),\n",
" ('description', 8403),\n",
" ('bridge', 8350),\n",
" ('cycleway', 7452),\n",
" ('type', 7384),\n",
" ('tiger:name_base_1', 7323),\n",
" ('FMMP_modified', 7277),\n",
" ('FMMP_reviewed', 7214),\n",
" ('intermittent', 7050),\n",
" ('emergency', 6830),\n",
" ('sbc_id', 6676),\n",
" ('sbc_parcel', 6676),\n",
" ('sbc_apn', 6676),\n",
" ('website', 6112),\n",
" ('tiger:name_direction_prefix', 6083),\n",
" ('hgv', 6042),\n",
" ('sidewalk', 5841),\n",
" ('crossing', 5760),\n",
" ('generator:source', 5434),\n",
" ('sport', 5415),\n",
" ('area', 5402),\n",
" ('acres', 5360),\n",
" ('note:mk408', 5082),\n",
" ('cuisine', 4881),\n",
" ('name_1', 4847),\n",
" ('phone', 4654),\n",
" ('massgis:cat', 4561),\n",
" ('man_made', 4521),\n",
" ('tiger:name_type_1', 4337),\n",
" ('horse', 4231),\n",
" ('scvwd:FACILITY', 4229),\n",
" ('note', 4076),\n",
" ('tunnel', 3938),\n",
" ('tiger:zip_left_1', 3923),\n",
" ('power_source', 3526),\n",
" ('tourism', 3505),\n",
" ('hgv:national_network', 3268),\n",
" ('wheelchair', 3144),\n",
" ('parking', 3017),\n",
" ('taxon', 3009),\n",
" ('religion', 2973),\n",
" ('species:en', 2954),\n",
" ('building:levels', 2922),\n",
" ('source:hgv:national_network', 2917),\n",
" ('Yr_Planted', 2890),\n",
" ('source:maxspeed', 2881),\n",
" ('Type', 2856),\n",
" ('gnis:id', 2754),\n",
" ('NO_PRMT', 2674),\n",
" ('COMM_CODE', 2674),\n",
" ('NO_SITE', 2674),\n",
" ('fields_ID', 2674),\n",
" ('len', 2674),\n",
" ('fields', 2674),\n",
" ('addr:full', 2671),\n",
" ('gauge', 2634),\n",
" ('NO_PRMT_SI', 2622),\n",
" ('footway', 2406),\n",
" ('crop', 2332),\n",
" ('ID', 2259),\n",
" ('source:addr', 2173),\n",
" ('Shape_Area', 2072),\n",
" ('Shape_Leng', 2070),\n",
" ('landcover', 2063),\n",
" ('restriction', 2023),\n",
" ('place', 2000),\n",
" ('traffic_calming', 1977),\n",
" ('motor_vehicle', 1964),\n",
" ('electrified', 1917),\n",
" ('source:name', 1858),\n",
" ('NHS', 1825),\n",
" ('gnis:county_name', 1776),\n",
" ('denomination', 1764),\n",
" ('leaf_cycle', 1763),\n",
" ('opening_hours', 1718),\n",
" ('aeroway', 1710),\n",
" ('SHAPE_len', 1631),\n",
" ('redwood_city_ca:bldg_id', 1616),\n",
" ('usage', 1589),\n",
" ('DT_ADD', 1582),\n",
" ('is_in', 1570),\n",
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" ('STATEFP', 1164),\n",
" ('public_transport', 1160),\n",
" ('SHAPE_STLe', 1157),\n",
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" ('gnis:ST_num', 1143),\n",
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" ('gnis:County_num', 1139),\n",
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" ('AWATER', 795),\n",
" ('ALAND', 795),\n",
" ('owner', 784),\n",
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" ('tiger:PCICBSA', 319),\n",
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" ('tiger:name_direction_prefix_1', 283),\n",
" ('cycleway:right', 282),\n",
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" ('location', 268),\n",
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" ('email', 266),\n",
" ('incline', 265),\n",
" ('takeaway', 264),\n",
" ('segregated', 258),\n",
" ('history', 256),\n",
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" ('bicycle_parking', 248),\n",
" ('irrigated', 247),\n",
" ('importuuid', 244),\n",
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" ('animal', 243),\n",
" ('nps:length', 242),\n",
" ('pipe_type', 239),\n",
" ('backrest', 235),\n",
" ('contact:phone', 233),\n",
" ('generator:method', 233),\n",
" ('denotation', 231),\n",
" ('quantity', 229),\n",
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" ('url', 220),\n",
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" ('nps:condition', 198),\n",
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" ('fax', 187),\n",
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" ('microbrewery', 106),\n",
" ('delivery', 106),\n",
" ('kerb', 105),\n",
" ('camp_site', 104),\n",
" ('smoothness', 104),\n",
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" ('scvwd:AREA_FT', 101),\n",
" ('scvwd:AREA_AC', 100),\n",
" ('start_date', 99),\n",
" ('id', 99),\n",
" ('noaa:lnam', 99),\n",
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" ('noaa:taghash', 99),\n",
" ('tiger:zip_left_4', 99),\n",
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" ('via', 99),\n",
" ('payment:litecoin', 98),\n",
" ('utility_wires', 98),\n",
" ('day_off', 96),\n",
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" ('outside', 95),\n",
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" ('caltrans:type', 94),\n",
" ('caltrans:dynsegpm', 92),\n",
" ('nextbus:route', 92),\n",
" ('species', 92),\n",
" ('roof:shape', 92),\n",
" ('cycle_network', 90),\n",
" ('proposed', 89),\n",
" ('ford', 89),\n",
" ('seamark:light:sequence', 87),\n",
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" ('short_name', 87),\n",
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" ('maxspeed:towing', 83),\n",
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" ('addr:2:housenumber', 81),\n",
" ('local_ref', 81),\n",
" ('trail_visibility', 80),\n",
" ('drive_in', 80),\n",
" ('scvwd:OWNER', 79),\n",
" ('hgv:minweight', 79),\n",
" ('scvwd:SYS_NAME', 79),\n",
" ('scvwd:GIS_ID', 79),\n",
" ('seamark:light:range', 78),\n",
" ('source_id', 78),\n",
" ('loc_name', 77),\n",
" ('monitoring:water_level', 77),\n",
" ('except', 77),\n",
" ('source:website', 77),\n",
" ('scvwd:NAME', 75),\n",
" ('seamark:light:exhibition', 75),\n",
" ('seamark:light:height', 73),\n",
" ('lot_no', 72),\n",
" ('todo', 72),\n",
" ('caltrans:pctuse', 72),\n",
" ('recommended_speed', 71),\n",
" ('noref', 70),\n",
" ('psv', 70),\n",
" ('scvwd:MAXIMO_ID', 70),\n",
" ('maxheight', 70),\n",
" ('train', 70),\n",
" ('mrosd:name_2', 68),\n",
" ('address', 68),\n",
" ('seamark:daymark:colour_pattern', 68),\n",
" ('seamark:daymark:colour', 68),\n",
" ('protect_class', 68),\n",
" ('seamark:daymark:shape', 68),\n",
" ('street', 66),\n",
" ('board_type', 66),\n",
" ('ANSICODE', 66),\n",
" ('place_name', 65),\n",
" ('minweight:hgv', 65),\n",
" ('tiger:name_base_3', 65),\n",
" ('camp_pitch:electric', 64),\n",
" ('hiking', 64),\n",
" ('camp_pitch:drinking_water', 64),\n",
" ('camp_pitch:drain', 64),\n",
" ('camp_pitch:fire', 64),\n",
" ('camp_pitch:picnic_table', 64),\n",
" ('camp_pitch:type', 64),\n",
" ('camp_pitch:parking', 64),\n",
" ('wetap:photo', 63),\n",
" ('building:part', 62),\n",
" ('bottle', 62),\n",
" ('scvwd:SHAPE_AREA', 62),\n",
" ('toilets', 62),\n",
" ('toilets:position', 61),\n",
" ('verified:name', 59),\n",
" ('protection_title', 59),\n",
" ('scvwd:WB_TYPE', 59),\n",
" ('embankment', 58),\n",
" ('dogs', 58),\n",
" ('step_count', 55),\n",
" ('internet_access:fee', 55),\n",
" ('bldgid', 54),\n",
" ('city', 54),\n",
" ('addrid', 54),\n",
" ('hnumber', 54),\n",
" ('artwork_type', 53),\n",
" ('mtb:scale', 53),\n",
" ('cutting', 50),\n",
" ('open_date', 49),\n",
" ('amenity:historic', 49),\n",
" ('name:es', 48),\n",
" ('seamark:buoy_lateral:category', 48),\n",
" ('seamark:buoy_lateral:shape', 48),\n",
" ('seamark:buoy_lateral:colour', 48),\n",
" ('collection_times', 48),\n",
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" ('cmt', 47),\n",
" ('unisex', 47),\n",
" ('sym', 47),\n",
" ('seamark:daymark:construction', 47),\n",
" ('capacity:disabled_v', 46),\n",
" ('capacity:loading_zn', 46),\n",
" ('attraction', 46),\n",
" ('capacity:bike_locke', 46),\n",
" ('capacity:standard', 46),\n",
" ('capacity:compact', 46),\n",
" ('vta:owner', 46),\n",
" ('capacity:bike_rack', 46),\n",
" ('capacity:kiss_n_rid', 46),\n",
" ('capacity:motorcycle', 46),\n",
" ('cycleway:left', 45),\n",
" ('cooperative', 45),\n",
" ('crossing_ref', 45),\n",
" ('stop_id', 45),\n",
" ('mooring', 45),\n",
" ('addr:3:housenumber', 44),\n",
" ('roof:height', 43),\n",
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" ('recycling_type', 43),\n",
" ('nist:fips_code', 41),\n",
" ('tiger:zip_right_4', 41),\n",
" ('county', 40),\n",
" ('ref:right', 40),\n",
" ('outside_atm', 40),\n",
" ('exit_to:left', 39),\n",
" ('maxcyclewidth', 39),\n",
" ('mincyclewidth', 39),\n",
" ('new', 38),\n",
" ('toilets:wheelchair', 38),\n",
" ('disused:amenity', 38),\n",
" ('service:bicycle:pump', 37),\n",
" ('male', 37),\n",
" ('seamark:fog_signal:category', 37),\n",
" ('military', 36),\n",
" ('addr:place', 36),\n",
" ('sfgov.org:OBJECTID', 36),\n",
" ('seamark', 36),\n",
" ('sfgov.org:OFFICE_TYP', 36),\n",
" ('wpt_description', 36),\n",
" ('exit_to:right', 35),\n",
" ('official_name', 35),\n",
" ('female', 35),\n",
" ('seamark:beacon_special_purpose:category', 34),\n",
" ('seamark:beacon_special_purpose:shape', 34),\n",
" ('wetap:statusnote', 34),\n",
" ('crossing:bell', 33),\n",
" ('seamark:reference', 33),\n",
" ('tiger:AREAID', 33),\n",
" ('tiger:COUNTYFP', 33),\n",
" ('tiger:AWATER', 33),\n",
" ('vehicle', 33),\n",
" ('tiger:ALAND', 33),\n",
" ('wetap:quality', 33),\n",
" ('social_facility:for', 32),\n",
" ('ref:blklot', 32),\n",
" ('seamark:light:group', 32),\n",
" ('operator:type', 32),\n",
" ('indoor', 32),\n",
" ('notes3', 31),\n",
" ('stop', 31),\n",
" ('tiger:name_direction_prefix_2', 31),\n",
" ('service:bicycle:chain_tool', 31),\n",
" ('symbol', 31),\n",
" ('name:ar', 31),\n",
" ('fireplace', 30),\n",
" ('grade', 30),\n",
" ('wetap:temperature', 30),\n",
" ('roof:material', 30),\n",
" ('wetap:flow', 30),\n",
" ('bridge:ref', 30),\n",
" ('note:address', 30),\n",
" ('addr:4:housenumber', 29),\n",
" ('Comments', 29),\n",
" ('contact:email', 29),\n",
" ('parking:condition:left:2:reason', 28),\n",
" ('parking:condition:right:2:reason', 28),\n",
" ('passenger', 28),\n",
" ('depository', 28),\n",
" ('parking:condition:left:2:time_interval', 28),\n",
" ('parking:condition:left:2', 28),\n",
" ('artist_name', 28),\n",
" ('parking:condition:right:2', 28),\n",
" ('hazmat', 28),\n",
" ('roof:slope:direction', 28),\n",
" ('nps:comment', 28),\n",
" ('parking:condition:right:2:time_interval', 28),\n",
" ('seamark:buoy_lateral:system', 28),\n",
" ('rwc_ca:address_id', 27),\n",
" ('camp_site:type', 27),\n",
" ('camp_site:fire', 27),\n",
" ('name:zh', 27),\n",
" ('camp_site:table', 27),\n",
" ('crossing:light', 27),\n",
" ('maxlength', 27),\n",
" ('golf_cart', 27),\n",
" ('busway', 26),\n",
" ('maxwidth', 26),\n",
" ('Id', 26),\n",
" ('service:bicycle:repair', 26),\n",
" ('addr:5:housenumber', 26),\n",
" ('hoops', 25),\n",
" ('recycling:plastic_bottles', 25),\n",
" ('recycling:glass_bottles', 25),\n",
" ('boundary:type', 25),\n",
" ('fuel', 25),\n",
" ('buoy', 25),\n",
" ('seamark:beacon_special_purpose:colour', 24),\n",
" ('difficulty', 24),\n",
" ('source_import', 24),\n",
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" ('service:bicycle:retail', 24),\n",
" ('tram', 24),\n",
" ('courts', 24),\n",
" ('stars', 23),\n",
" ('traffic_signals', 23),\n",
" ('icao', 23),\n",
" ('note:hgv', 23),\n",
" ('name:ru', 23),\n",
" ('iata', 22),\n",
" ('gosm:sig:8CBDE645', 22),\n",
" ('recycling:cans', 22),\n",
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" ('grades', 21),\n",
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" ('image', 21),\n",
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" ('closest_town', 20),\n",
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" ('note:highway', 20),\n",
" ('camp_site:water', 20),\n",
" ('camp_site:electric', 20),\n",
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" ('culvert', 20),\n",
" ('camp_site:parking', 20),\n",
" ('extensions', 20),\n",
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" ('organic', 20),\n",
" ('rcn', 20),\n",
" ('dry_clean', 19),\n",
" ('sanitary_dump_station', 19),\n",
" ('baby_hatch', 19),\n",
" ('nudism', 19),\n",
" ('horses', 19),\n",
" ('fuel:octane_91', 19),\n",
" ('payment:visa', 19),\n",
" ('station_name', 19),\n",
" ('railway:preserved', 18),\n",
" ('parking:lane:both', 18),\n",
" ('wheelchair:description', 18),\n",
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" ('seamark:buoy_special_purpose:colour', 18),\n",
" ('source:position', 18),\n",
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" ('payment:cash', 18),\n",
" ('parking:condition:both', 18),\n",
" ('source:tracer', 17),\n",
" ('seamark:light:reference', 17),\n",
" ('source:zoomlevel', 17),\n",
" ('self_service', 17),\n",
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" ('addr:floor', 17),\n",
" ('bridge:name', 17),\n",
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" ('architect', 17),\n",
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" ('CertID', 16),\n",
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" ('public', 16),\n",
" ('Acres', 16),\n",
" ('contact:fax', 16),\n",
" ('payment:mastercard', 16),\n",
" ('snowmobile', 16),\n",
" ('noname', 16),\n",
" ('ramp', 16),\n",
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" ('source:outline', 15),\n",
" ('recycling:glass', 15),\n",
" ('name:ja', 15),\n",
" ('map_type', 15),\n",
" ('public_transport:version', 15),\n",
" ('surveillance:type', 15),\n",
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" ('service:bicycle:diy', 15),\n",
" ('ship:type', 14),\n",
" ('coin_op', 14),\n",
" ('seamark:fog_signal:group', 14),\n",
" ('recycling:paper', 14),\n",
" ('payment:diners_club', 14),\n",
" ('enterance', 14),\n",
" ('mrosd:point_of_interest', 14),\n",
" ('parking:condition:both:time_interval', 14),\n",
" ('name_3', 14),\n",
" ('parking:condition:both:residents', 14),\n",
" ('heritage', 14),\n",
" ('Open_Date', 14),\n",
" ('healthcare', 14),\n",
" ('parking:condition:both:maxstay', 14),\n",
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" ('site', 14),\n",
" ('seats', 14),\n",
" ('golf:par', 13),\n",
" ('website:official', 13),\n",
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" ('source:alt_name', 1),\n",
" ('name:an', 1),\n",
" ('name:ab', 1),\n",
" ('res', 1),\n",
" ('ramp_speed', 1),\n",
" ('name:so', 1),\n",
" ('name:sn', 1),\n",
" ('name:sm', 1),\n",
" ('name:si', 1),\n",
" ('access:boat', 1),\n",
" ('name:se', 1),\n",
" ('name:sd', 1),\n",
" ('name:sc', 1),\n",
" ('name:sa', 1),\n",
" ('vestibule_atm_fee', 1),\n",
" ('name:su', 1),\n",
" ('name:tpi', 1),\n",
" ('quality', 1),\n",
" ('disused:contact:website', 1),\n",
" ('myspace', 1),\n",
" ('name:mhr', 1),\n",
" ('socket:USB:Micro-B', 1),\n",
" ('name:eml', 1),\n",
" ('name:srn', 1),\n",
" ('craft:historic', 1),\n",
" ('vehicle_test_centre', 1),\n",
" ('payment:solarcoin', 1),\n",
" ('sanitary_dump_station:bilge_water', 1),\n",
" ('store_ref', 1),\n",
" ('alt_name2', 1),\n",
" ('Lydian Academy', 1),\n",
" ('STREET', 1),\n",
" ('shoes', 1),\n",
" ('name:frr', 1),\n",
" ('name:frp', 1),\n",
" ('name:ts', 1),\n",
" ('recycling:waste_oil', 1),\n",
" ('occupant', 1),\n",
" ('ele89gnis:county_id', 1),\n",
" ('official_name:vi', 1),\n",
" ('place:historic', 1),\n",
" ('name:to', 1),\n",
" ('name:rm', 1),\n",
" ('name:ro', 1),\n",
" ('Source', 1),\n",
" ('computer', 1),\n",
" ('name:rw', 1),\n",
" ('topmark:colour', 1),\n",
" ('stop:direction', 1),\n",
" ('ROUTEID', 1),\n",
" ('courtyard', 1),\n",
" ('operator:website', 1),\n",
" ('old_man_made', 1),\n",
" ('name:bat-smg', 1),\n",
" ('future:name', 1),\n",
" ('volunteer', 1),\n",
" ('name:new', 1),\n",
" ('restaurant', 1),\n",
" ('striping_condition', 1),\n",
" ('isced:level', 1),\n",
" ('disused:tiger:cfcc', 1),\n",
" ('happy_hours', 1),\n",
" ('foodshed', 1),\n",
" ('really_has_a_road_through_it', 1),\n",
" ('animal_boarding', 1),\n",
" ('name:wuu', 1),\n",
" ('name:mwl', 1),\n",
" ('name:simple', 1),\n",
" ('add', 1),\n",
" ('radar_transponder', 1),\n",
" ('name:dsb', 1),\n",
" ('maxcyclwidth', 1),\n",
" ('garage', 1),\n",
" ('sanitary_dump_station:rinse_water', 1),\n",
" ('name:nah', 1),\n",
" ('name:nap', 1),\n",
" ('name:zea', 1),\n",
" ('newaesthetic', 1),\n",
" ('health_specialty:physiotherapy', 1),\n",
" ('fuel:octane_100', 1),\n",
" ('memorial:type', 1),\n",
" ('ref_heritage', 1),\n",
" ('name:nso', 1),\n",
" ('waste:number', 1),\n",
" ('backcountry', 1),\n",
" ('website:searchstr', 1),\n",
" ('camera:type', 1),\n",
" ('recycling:wood', 1),\n",
" ('name:nov', 1),\n",
" ('function', 1),\n",
" ('source:height', 1),\n",
" ('name:qu', 1),\n",
" ('basin', 1),\n",
" ('rooms', 1),\n",
" ('monument', 1),\n",
" ('name:fiu-vro', 1),\n",
" ('name:stq', 1),\n",
" ('name:nds-nl', 1),\n",
" ('note_3', 1),\n",
" ('seamark:light:sector_end', 1),\n",
" ('stay', 1),\n",
" ('name:ga', 1),\n",
" ('socket:AC:bs_1363', 1),\n",
" ('reception_desk', 1),\n",
" ('name:gn', 1),\n",
" ('name:gl', 1),\n",
" ('name:gv', 1),\n",
" ('name:gu', 1)]\n",
"\n",
"Types of keys:---------------------------------------\n",
"{'lower': 35, 'lower_colon': 26, 'other': 3, 'problemchars': 0}\n",
"\n",
"\n",
"Street name analysis:--------------------------------\n",
"[('Ave', set(['N. Lincoln Ave', 'North Lincoln Ave'])),\n",
" ('blvd.', set(['North Lincoln blvd.'])),\n",
" ('bouleva.', set(['North Lincoln bouleva.'])),\n",
" ('Rd.', set(['Baldwin Rd.'])),\n",
" ('St.', set(['West Lexington St.']))]\n",
"\n"
]
}
],
"source": [
"# Run on sample set to verify functionality.\n",
"example_audit = AuditXml(\"example1.osm\")\n",
"example_audit.run()\n",
"example_audit.summary()"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {
"code_folding": [
48,
51,
56
],
"collapsed": true
},
"outputs": [],
"source": [
"# Run on large set\n",
"audit = AuditXml(\"san-francisco-bay_california.osm\")\n",
"audit.run()"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tags found and their frequencies:----------------------\n",
"[('nd', 11237495),\n",
" ('node', 9572721),\n",
" ('tag', 4551092),\n",
" ('way', 928003),\n",
" ('member', 61917),\n",
" ('relation', 6975),\n",
" ('bounds', 1),\n",
" ('osm', 1)]\n",
"\n"
]
}
],
"source": [
"audit.summary(audit.Options.tag_frequency)"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Types of keys:---------------------------------------\n",
"{'lower': 2052979,\n",
" 'lower_colon': 2352583,\n",
" 'other': 145349,\n",
" 'problemchars': 181}\n",
"\n"
]
}
],
"source": [
"audit.summary(audit.Options.key_names)"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Keys found and their frequencies:--------------------\n",
"\n",
"Sorted by Name:----------------------------------------\n",
"[('', 1),\n",
" ('145', 1),\n",
" ('24h', 2),\n",
" ('3', 1),\n",
" ('_Acres_', 8),\n",
" ('_OBJNAME_', 3),\n",
" ('_Shape_Area_', 8),\n",
" ('_Shape_Leng_', 8),\n",
" ('abandoned', 7),\n",
" ('abandoned:aeroway', 8),\n",
" ('abandoned:amenity', 2),\n",
" ('abandoned:highway', 9),\n",
" ('abutters', 116),\n",
" ('Access', 3),\n",
" ('access', 15922),\n",
" ('access:backward', 4),\n",
" ('access:bicycle', 12),\n",
" ('access:bicycles', 1),\n",
" ('access:boat', 1),\n",
" ('access:conditional', 7),\n",
" ('access:dog', 1),\n",
" ('access:dogs', 6),\n",
" ('access:foot', 7),\n",
" ('access:horse', 3),\n",
" ('access:lanes', 1),\n",
" ('access:motor_vehicle', 1),\n",
" ('access:vehicle', 1),\n",
" ('accuracy:east', 2),\n",
" ('accuracy:ellipsoid', 2),\n",
" ('accuracy:north', 2),\n",
" ('Acres', 16),\n",
" ('acres', 5360),\n",
" ('add', 1),\n",
" ('addr.source:housenumber', 82),\n",
" ('addr:1:housenumber', 192),\n",
" ('addr:1:street', 5),\n",
" ('addr:2:housenumber', 81),\n",
" ('addr:3:housenumber', 44),\n",
" ('addr:4:housenumber', 29),\n",
" ('addr:4:street', 1),\n",
" ('addr:5:housenumber', 26),\n",
" ('addr:5:street', 1),\n",
" ('addr:6:housenumber', 20),\n",
" ('addr:7:housenumber', 11),\n",
" ('addr:8:housenumber', 1),\n",
" ('addr:city', 174577),\n",
" ('addr:country', 9062),\n",
" ('addr:county', 14921),\n",
" ('addr:door', 1),\n",
" ('addr:flats', 1),\n",
" ('addr:floor', 17),\n",
" ('addr:full', 2671),\n",
" ('addr:housename', 646),\n",
" ('addr:housenumber', 197174),\n",
" ('addr:housenumber:source', 3),\n",
" ('addr:housenumber_1', 4),\n",
" ('addr:housenumber_2', 4),\n",
" ('addr:housenumber_3', 2),\n",
" ('addr:housenumber_4', 1),\n",
" ('addr:housenumber_5', 1),\n",
" ('addr:housenumber_6', 1),\n",
" ('addr:inclusion', 160),\n",
" ('addr:interpolation', 623),\n",
" ('addr:paloaltoca_id', 1318),\n",
" ('addr:phone', 2),\n",
" ('addr:pier', 1),\n",
" ('addr:place', 36),\n",
" ('addr:postcode', 49234),\n",
" ('addr:province', 2),\n",
" ('addr:side', 480),\n",
" ('addr:state', 81845),\n",
" ('addr:street', 184889),\n",
" ('addr:street:source', 4),\n",
" ('addr:street:suffix', 2),\n",
" ('addr:street:type', 1),\n",
" ('addr:street_1', 1),\n",
" ('addr:suite', 8),\n",
" ('addr:unit', 885),\n",
" ('address', 68),\n",
" ('ADDRESS_ID', 1),\n",
" ('addrid', 54),\n",
" ('admin_level', 589),\n",
" ('admin_value', 2),\n",
" ('advertising', 2),\n",
" ('aed', 4),\n",
" ('aed:description', 2),\n",
" ('aed:opening_hours', 2),\n",
" ('aerialway', 3),\n",
" ('aerodrome', 5),\n",
" ('aeroway', 1710),\n",
" ('airmark', 1),\n",
" ('ALAND', 795),\n",
" ('alcohol', 1),\n",
" ('alt_name', 1120),\n",
" ('alt_name2', 1),\n",
" ('alt_name:am', 1),\n",
" ('alt_name:en', 3),\n",
" ('alt_name:es', 1),\n",
" ('alt_name:vi', 7),\n",
" ('alt_name_1', 2),\n",
" ('alt_ref', 8),\n",
" ('alt_url', 1),\n",
" ('alterations', 16),\n",
" ('amenity', 43500),\n",
" ('amenity:historic', 49),\n",
" ('amenity_1', 7),\n",
" ('amperage', 1),\n",
" ('amtrak_drop_off_only', 1),\n",
" ('animal', 243),\n",
" ('animal_boarding', 1),\n",
" ('animal_keeping', 1),\n",
" ('animal_keeping:type', 1),\n",
" ('animal_shelter', 1),\n",
" ('animal_shelter:adoption', 4),\n",
" ('ANSICODE', 66),\n",
" ('architect', 17),\n",
" ('area', 5402),\n",
" ('area:highway', 9),\n",
" ('area:railway', 5),\n",
" ('Area_12_13', 7),\n",
" ('AREA_12_13', 3),\n",
" ('AREA_M2', 395),\n",
" ('AREAID', 655),\n",
" ('artist', 5),\n",
" ('artist_name', 28),\n",
" ('artwork', 1),\n",
" ('artwork_type', 53),\n",
" ('Asset', 121),\n",
" ('atm', 435),\n",
" ('atm:fee', 1),\n",
" ('atm:opening_hours', 1),\n",
" ('atm:operator', 1),\n",
" ('atm_inside', 2),\n",
" ('Atribution', 983),\n",
" ('attraction', 46),\n",
" ('Attribution', 1003),\n",
" ('attribution', 13099),\n",
" ('attribution:url', 2),\n",
" ('attribution:wikipedia', 1),\n",
" ('authentication:membership_card', 1),\n",
" ('automated', 6),\n",
" ('avgspeed', 12),\n",
" ('award:michelin', 1),\n",
" ('AWATER', 795),\n",
" ('baby', 1),\n",
" ('baby_hatch', 19),\n",
" ('backcountry', 1),\n",
" ('backrest', 235),\n",
" ('bagel', 2),\n",
" ('bakery', 1),\n",
" ('bank', 1),\n",
" ('bar', 1),\n",
" ('barbecue_grill', 13),\n",
" ('barometer', 2),\n",
" ('barrier', 9891),\n",
" ('barrier:personnel', 3),\n",
" ('barrier:personnel:operator', 1),\n",
" ('basin', 1),\n",
" ('beacon', 11),\n",
" ('beacon:colour', 8),\n",
" ('bearing', 11),\n",
" ('beauty', 2),\n",
" ('beer_garden', 1),\n",
" ('bench', 441),\n",
" ('bench:backrest', 1),\n",
" ('bicycle', 19173),\n",
" ('bicycle:backward', 1),\n",
" ('bicycle:forward', 1),\n",
" ('bicycle:suitability', 3),\n",
" ('bicycle_parking', 248),\n",
" ('bicycle_speed', 1),\n",
" ('bike', 1),\n",
" ('bikelane', 6),\n",
" ('biodiesel', 2),\n",
" ('bldgid', 54),\n",
" ('BLDGID', 419),\n",
" ('board_type', 66),\n",
" ('boat', 14505),\n",
" ('boat_rental', 1),\n",
" ('boating', 1),\n",
" ('bollard', 6),\n",
" ('books', 5),\n",
" ('border_type', 338),\n",
" ('bottle', 62),\n",
" ('boundary', 842),\n",
" ('boundary:type', 25),\n",
" ('boundary_type', 8),\n",
" ('boxes', 4),\n",
" ('brand', 357),\n",
" ('bread', 2),\n",
" ('bridge', 8350),\n",
" ('bridge:alt_name', 3),\n",
" ('bridge:movable', 5),\n",
" ('bridge:name', 17),\n",
" ('bridge:old_name', 2),\n",
" ('bridge:ref', 30),\n",
" ('bridge:structure', 12),\n",
" ('bridge:support', 2),\n",
" ('BRIDGE_NO', 133),\n",
" ('bubbler', 2),\n",
" ('builder', 1),\n",
" ('builders', 2),\n",
" ('building', 485012),\n",
" ('building.source:levels', 1),\n",
" ('building:color', 1),\n",
" ('building:colour', 2),\n",
" ('building:condition', 2),\n",
" ('building:height', 9),\n",
" ('building:level', 7),\n",
" ('building:levels', 2922),\n",
" ('building:levels:underground', 13),\n",
" ('building:material', 360),\n",
" ('building:min_level', 5),\n",
" ('building:min_levels', 1),\n",
" ('building:part', 62),\n",
" ('building:use', 5),\n",
" ('building_1', 2),\n",
" ('building_2', 1),\n",
" ('building_no', 1),\n",
" ('built', 133),\n",
" ('bulb', 293),\n",
" ('bunker_type', 1),\n",
" ('buoy', 25),\n",
" ('buoy:colour', 22),\n",
" ('buoy:shape', 1),\n",
" ('bus', 561),\n",
" ('business', 2),\n",
" ('Business', 1),\n",
" ('busway', 26),\n",
" ('button_operated', 222),\n",
" ('cables', 777),\n",
" ('calming', 2),\n",
" ('caltrans:district', 173),\n",
" ('caltrans:dynsegpm', 92),\n",
" ('caltrans:pctuse', 72),\n",
" ('caltrans:type', 94),\n",
" ('camera:mount', 1),\n",
" ('camera:type', 1),\n",
" ('camp_pitch:drain', 64),\n",
" ('camp_pitch:drinking_water', 64),\n",
" ('camp_pitch:electric', 64),\n",
" ('camp_pitch:fire', 64),\n",
" ('camp_pitch:parking', 64),\n",
" ('camp_pitch:picnic_table', 64),\n",
" ('camp_pitch:type', 64),\n",
" ('camp_site', 104),\n",
" ('camp_site:drain', 20),\n",
" ('camp_site:electric', 20),\n",
" ('camp_site:fire', 27),\n",
" ('camp_site:parking', 20),\n",
" ('camp_site:surface', 20),\n",
" ('camp_site:table', 27),\n",
" ('camp_site:type', 27),\n",
" ('camp_site:water', 20),\n",
" ('camping', 1),\n",
" ('canoe', 1),\n",
" ('capacity', 1248),\n",
" ('capacity:bike_locke', 46),\n",
" ('capacity:bike_rack', 46),\n",
" ('capacity:compact', 46),\n",
" ('capacity:disabled', 187),\n",
" ('capacity:disabled_v', 46),\n",
" ('capacity:kiss_n_rid', 46),\n",
" ('capacity:loading_zn', 46),\n",
" ('capacity:motorcycle', 46),\n",
" ('capacity:parent', 1),\n",
" ('capacity:standard', 46),\n",
" ('capacity:women', 2),\n",
" ('capital', 1),\n",
" ('capture', 1364),\n",
" ('car', 9),\n",
" ('car_wash', 8),\n",
" ('caravans', 4),\n",
" ('cargo', 3),\n",
" ('cash_in', 2),\n",
" ('CATEGORY', 2),\n",
" ('census:population', 108),\n",
" ('center_turn_lane', 2),\n",
" ('centre_turn_lane', 2),\n",
" ('CertID', 16),\n",
" ('CertStat', 16),\n",
" ('change:lanes:backward', 3),\n",
" ('change:lanes:both_ways', 1),\n",
" ('change:lanes:forward', 3),\n",
" ('check_date', 2),\n",
" ('cinema:3D', 2),\n",
" ('circuits', 1),\n",
" ('circumference', 7),\n",
" ('city', 54),\n",
" ('CITY_OWNED', 2),\n",
" ('city_served', 1),\n",
" ('class:bicycle', 7),\n",
" ('closest_town', 20),\n",
" ('clothes', 21),\n",
" ('club', 3),\n",
" ('cmt', 47),\n",
" ('coastline', 1),\n",
" ('coin_op', 14),\n",
" ('collection_time', 1),\n",
" ('collection_times', 48),\n",
" ('color', 1),\n",
" ('colour', 137),\n",
" ('comedy', 1),\n",
" ('COMM_CODE', 2674),\n",
" ('comment', 17),\n",
" ('Comments', 29),\n",
" ('common', 121),\n",
" ('communication:mobile_phone', 1),\n",
" ('community', 4),\n",
" ('community:en', 2),\n",
" ('community:gender', 4),\n",
" ('community_resource', 4),\n",
" ('company', 1),\n",
" ('compressed_air', 1),\n",
" ('computer', 1),\n",
" ('condition', 12),\n",
" ('construction', 354),\n",
" ('construction:lanes', 7),\n",
" ('contact:email', 29),\n",
" ('contact:facebook', 1),\n",
" ('contact:fax', 16),\n",
" ('contact:google_plus', 1),\n",
" ('contact:instagram', 1),\n",
" ('contact:name', 1),\n",
" ('contact:phone', 233),\n",
" ('contact:website', 152),\n",
" ('content', 3),\n",
" ('contents', 10),\n",
" ('conveying', 2),\n",
" ('cooperative', 45),\n",
" ('cost:coffee', 6),\n",
" ('country', 4),\n",
" ('county', 40),\n",
" ('county:abbrev', 21),\n",
" ('county:ansi', 21),\n",
" ('county:name', 21),\n",
" ('COUNTYFP', 1165),\n",
" ('courts', 24),\n",
" ('courtyard', 1),\n",
" ('covered', 563),\n",
" ('craft', 177),\n",
" ('craft:historic', 1),\n",
" ('crane:maxload', 1),\n",
" ('created_by', 50420),\n",
" ('crop', 2332),\n",
" ('crossing', 5760),\n",
" ('crossing:barrier', 5),\n",
" ('crossing:bell', 33),\n",
" ('crossing:for', 2),\n",
" ('crossing:island', 1),\n",
" ('crossing:light', 27),\n",
" ('crossing_ref', 45),\n",
" ('csp:globalid', 167),\n",
" ('csp:unitcode', 167),\n",
" ('cui', 1),\n",
" ('cuisine', 4881),\n",
" ('cuisine_1', 2),\n",
" ('culvert', 20),\n",
" ('currency:USD', 1),\n",
" ('cutting', 50),\n",
" ('cycle_network', 90),\n",
" ('cycleway', 7452),\n",
" ('cycleway:left', 45),\n",
" ('cycleway:right', 282),\n",
" ('dance:style', 1),\n",
" ('dance:teaching', 4),\n",
" ('dance:type', 1),\n",
" ('danger', 1),\n",
" ('date', 3),\n",
" ('date_off', 4),\n",
" ('date_on', 4),\n",
" ('datum:ele', 3),\n",
" ('day_off', 96),\n",
" ('day_on', 112),\n",
" ('default_angle', 1),\n",
" ('definition', 3),\n",
" ('defunct:amenity', 1),\n",
" ('deli', 1),\n",
" ('delivery', 106),\n",
" ('demolished:building', 3),\n",
" ('demolished:railway', 3),\n",
" ('denomination', 1764),\n",
" ('denotation', 231),\n",
" ('dentist', 2),\n",
" ('depository', 28),\n",
" ('DEPT', 2),\n",
" ('depth', 6),\n",
" ('depth:dredged', 1),\n",
" ('description', 8403),\n",
" ('designation', 337),\n",
" ('designer', 1),\n",
" ('destination', 799),\n",
" ('destination:lanes', 2),\n",
" ('destination:ref', 146),\n",
" ('diesel', 4),\n",
" ('diet:gluten_free', 1),\n",
" ('diet:halal', 3),\n",
" ('diet:kosher', 1),\n",
" ('diet:raw', 1),\n",
" ('diet:vegan', 11),\n",
" ('diet:vegetarian', 20),\n",
" ('difficulty', 24),\n",
" ('diplomatic', 1),\n",
" ('Direction', 7),\n",
" ('direction', 823),\n",
" ('disabled', 1),\n",
" ('disabled:capacity', 2),\n",
" ('dismount', 2),\n",
" ('dispensing', 118),\n",
" ('display', 5),\n",
" ('dist:blue', 9),\n",
" ('dist:red', 9),\n",
" ('dist:white', 9),\n",
" ('distance', 6),\n",
" ('disused', 87),\n",
" ('disused:aeroway', 4),\n",
" ('disused:amenity', 38),\n",
" ('disused:contact:phone', 1),\n",
" ('disused:contact:website', 1),\n",
" ('disused:cuisine', 3),\n",
" ('disused:highway', 3),\n",
" ('disused:leisure', 5),\n",
" ('disused:name', 11),\n",
" ('disused:opening_hours', 1),\n",
" ('disused:operator', 3),\n",
" ('disused:railway', 2),\n",
" ('disused:religion', 2),\n",
" ('disused:shop', 9),\n",
" ('disused:sport', 1),\n",
" ('disused:tiger:cfcc', 1),\n",
" ('disused:tourism', 1),\n",
" ('disused:website', 2),\n",
" ('doctor', 1),\n",
" ('doctors', 1),\n",
" ('dog', 439),\n",
" ('dog_waste_bin', 1),\n",
" ('dogs', 58),\n",
" ('donate', 1),\n",
" ('donut', 2),\n",
" ('door:levels', 1),\n",
" ('DOT #', 2),\n",
" ('drainagetype', 6),\n",
" ('dress_code', 1),\n",
" ('drink', 1),\n",
" ('drink:beer', 1),\n",
" ('drink:wine', 1),\n",
" ('drinkable', 6),\n",
" ('drinking_water', 121),\n",
" ('drinkwater', 7),\n",
" ('drive_in', 80),\n",
" ('drive_through', 157),\n",
" ('drive_through_atm', 2),\n",
" ('drive_through_atm_capacity', 1),\n",
" ('drive_thru', 1),\n",
" ('drop_off_only', 2),\n",
" ('dry_clean', 19),\n",
" ('dry_weather_only', 9),\n",
" ('DT_ADD', 1582),\n",
" ('DT_MANT', 1346),\n",
" ('education', 2),\n",
" ('ele', 13349),\n",
" ('ele89gnis:county_id', 1),\n",
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" ('seamark:topmark:colour_pattern', 2),\n",
" ('seamark:topmark:shape', 12),\n",
" ('seamark:type', 329),\n",
" ('seamark:wreck:category', 1),\n",
" ('seamark:wreck:water_level', 1),\n",
" ('seasonal', 11),\n",
" ('seasonal:dry_season', 5),\n",
" ('seasonal:wet_season', 5),\n",
" ('Seasonal_', 121),\n",
" ('seating', 1),\n",
" ('seats', 14),\n",
" ('second_hand', 22),\n",
" ('security_zone', 13),\n",
" ('segregated', 258),\n",
" ('self_service', 17),\n",
" ('separated', 5),\n",
" ('service', 42926),\n",
" ('service:bicycle', 1),\n",
" ('service:bicycle:chain_tool', 31),\n",
" ('service:bicycle:cleaning', 1),\n",
" ('service:bicycle:diy', 15),\n",
" ('service:bicycle:pump', 37),\n",
" ('service:bicycle:rental', 9),\n",
" ('service:bicycle:repair', 26),\n",
" ('service:bicycle:retail', 24),\n",
" ('service:bicycle:second_hand', 6),\n",
" ('service:press', 2),\n",
" ('service_1', 1),\n",
" ('service_times', 3),\n",
" ('services', 1),\n",
" ('SFBayTrail:agency', 1),\n",
" ('SFBayTrail:seg_num', 1),\n",
" ('SFBayTrail:trail_type', 1),\n",
" ('sfgov.org:OBJECTID', 36),\n",
" ('sfgov.org:OFFICE_TYP', 36),\n",
" ('sfgov:OBJNAME', 268),\n",
" ('Shape_area', 1366),\n",
" ('Shape_Area', 2072),\n",
" ('SHAPE_area', 1490),\n",
" ('SHAPE_len', 1631),\n",
" ('Shape_len', 1367),\n",
" ('SHAPE_LENG', 3),\n",
" ('Shape_Leng', 2070),\n",
" ('SHAPE_length', 1),\n",
" ('SHAPE_STAr', 1154),\n",
" ('SHAPE_STLe', 1157),\n",
" ('share_taxi', 1),\n",
" ('shelter', 1281),\n",
" ('shelter_type', 21),\n",
" ('ship', 1),\n",
" ('ship:type', 14),\n",
" ('shoes', 1),\n",
" ('shooting', 3),\n",
" ('shop', 9608),\n",
" ('shop:historic', 7),\n",
" ('shop_1', 1),\n",
" ('short_name', 87),\n",
" ('short_name:vi', 1),\n",
" ('short_name_1', 1),\n",
" ('shortName', 4),\n",
" ('shower', 1),\n",
" ('showers', 1),\n",
" ('sidewalk', 5841),\n",
" ('sign', 17),\n",
" ('Sign Legen', 7),\n",
" ('sign_condition', 1),\n",
" ('signcondition', 5),\n",
" ('site', 14),\n",
" ('site_ownership', 1),\n",
" ('size', 213),\n",
" ('ski', 17),\n",
" ('slipway:type', 1),\n",
" ('sloped_curb', 123),\n",
" ('sloped_curve', 1),\n",
" ('smokefree', 1),\n",
" ('smoking', 518),\n",
" ('smoothness', 104),\n",
" ('snowmobile', 16),\n",
" ('social', 1),\n",
" ('social_facility', 17),\n",
" ('social_facility:for', 32),\n",
" ('social_facility:type', 1),\n",
" ('social_facility_for', 1),\n",
" ('socket:30_pin_dock', 1),\n",
" ('socket:AC', 1),\n",
" ('socket:AC:bs_1363', 1),\n",
" ('socket:AC:nema_5_15', 1),\n",
" ('socket:chademo', 11),\n",
" ('socket:tesla_supercharger', 2),\n",
" ('socket:type1', 11),\n",
" ('socket:type1_chademo', 1),\n",
" ('socket:type1_combo', 4),\n",
" ('socket:USB', 1),\n",
" ('socket:USB:lightning', 1),\n",
" ('socket:USB:Micro-B', 1),\n",
" ('socket:USB:Type-A', 1),\n",
" ('socket:USB:Type-C', 1),\n",
" ('solar_powered', 1),\n",
" ('something', 2),\n",
" ('sound', 2),\n",
" ('source', 165759),\n",
" ('Source', 1),\n",
" ('source:addr', 2173),\n",
" ('source:alt_name', 1),\n",
" ('source:amenity', 3),\n",
" ('source:building', 1),\n",
" ('source:date', 10),\n",
" ('source:destination', 10),\n",
" ('source:geometry', 684),\n",
" ('source:height', 1),\n",
" ('source:hgv:national_network', 2917),\n",
" ('source:hgv:state_network', 719),\n",
" ('source:highway', 2),\n",
" ('source:id', 3),\n",
" ('source:lit', 4),\n",
" ('source:maxspeed', 2881),\n",
" ('source:name', 1858),\n",
" ('source:outline', 15),\n",
" ('source:pkey', 301),\n",
" ('source:population', 3),\n",
" ('source:position', 18),\n",
" ('source:tracer', 17),\n",
" ('source:website', 77),\n",
" ('source:zoomlevel', 17),\n",
" ('source_1', 13),\n",
" ('source_2', 2),\n",
" ('source_id', 78),\n",
" ('source_import', 24),\n",
" ('source_ref', 325),\n",
" ('source_url', 2),\n",
" ('sourceng', 1),\n",
" ('soure', 1),\n",
" ('species', 92),\n",
" ('species:en', 2954),\n",
" ('spikes', 1),\n",
" ('spline:link', 2),\n",
" ('sport', 5415),\n",
" ('src:id', 1483),\n",
" ('staffed', 1),\n",
" ('stairs', 1),\n",
" ('stars', 23),\n",
" ('start_date', 99),\n",
" ('state', 255),\n",
" ('state_capital', 1),\n",
" ('STATEFP', 1164),\n",
" ('stateofrepair', 17),\n",
" ('stateofrepair:note', 2),\n",
" ('station', 3),\n",
" ('station_id', 2),\n",
" ('station_name', 19),\n",
" ('status', 130),\n",
" ('STATUS', 1),\n",
" ('stay', 1),\n",
" ('step_count', 55),\n",
" ('steps', 7),\n",
" ('stile', 3),\n",
" ('stop', 31),\n",
" ('stop:direction', 1),\n",
" ('stop_id', 45),\n",
" ('storage', 1),\n",
" ('store', 3),\n",
" ('store_number', 1),\n",
" ('store_ref', 1),\n",
" ('STR_NAME', 1),\n",
" ('street', 66),\n",
" ('STREET', 1),\n",
" ('Street Fro', 7),\n",
" ('Street Ont', 7),\n",
" ('streetlight', 1),\n",
" ('STREETR_TY', 1),\n",
" ('striping_condition', 1),\n",
" ('stripingcondition', 5),\n",
" ('structure', 4),\n",
" ('studio', 3),\n",
" ('style', 1),\n",
" ('Subclass', 121),\n",
" ('subject:wikidata', 2),\n",
" ('substation', 7),\n",
" ('subway', 126),\n",
" ('suite', 1),\n",
" ('supervised', 115),\n",
" ('support', 4),\n",
" ('surface', 17986),\n",
" ('surface_condition', 1),\n",
" ('surveillance', 17),\n",
" ('surveillance:type', 15),\n",
" ('survey:date', 1146),\n",
" ('swimming', 4),\n",
" ('swing_gate:type', 2),\n",
" ('sym', 47),\n",
" ('symbol', 31),\n",
" ('tactile_paving', 148),\n",
" ('tag', 1),\n",
" ('Tag_Num', 121),\n",
" ('tailgaiting', 12),\n",
" ('takeaway', 264),\n",
" ('takeout', 1),\n",
" ('tasting', 2),\n",
" ('taxi', 13),\n",
" ('taxiway', 9),\n",
" ('taxon', 3009),\n",
" ('telescope:diameter', 8),\n",
" ('telescope:type', 11),\n",
" ('tenant', 7),\n",
" ('tents', 5),\n",
" ('terminal', 10),\n",
" ('terminus', 1),\n",
" ('terrace', 4),\n",
" ('tesla:ref', 1),\n",
" ('theatre', 1),\n",
" ('theatre:genre', 5),\n",
" ('thermometer', 2),\n",
" ('ticker', 620),\n",
" ('tidal', 3),\n",
" ('tiger:ALAND', 33),\n",
" ('tiger:AREAID', 33),\n",
" ('tiger:arid', 160),\n",
" ('tiger:AWATER', 33),\n",
" ('tiger:base', 1),\n",
" ('tiger:cfcc', 172993),\n",
" ('tiger:CLASSFP', 317),\n",
" ('tiger:county', 179036),\n",
" ('tiger:COUNTYFP', 33),\n",
" ('tiger:CPI', 318),\n",
" ('tiger:FUNCSTAT', 317),\n",
" ('Tiger:HYDROID', 158),\n",
" ('tiger:LINEARID', 662),\n",
" ('tiger:LSAD', 317),\n",
" ('tiger:mtfcc', 407),\n",
" ('Tiger:MTFCC', 1216),\n",
" ('tiger:MTFCC', 987),\n",
" ('tiger:NAME', 318),\n",
" ('tiger:name_base', 153158),\n",
" ('tiger:name_base_1', 7323),\n",
" ('tiger:name_base_2', 514),\n",
" ('tiger:name_base_3', 65),\n",
" ('tiger:name_base_4', 1),\n",
" ('tiger:name_direction_prefix', 6083),\n",
" ('tiger:name_direction_prefix_1', 283),\n",
" ('tiger:name_direction_prefix_2', 31),\n",
" ('tiger:name_direction_prefix_3', 1),\n",
" ('tiger:name_direction_suffix', 398),\n",
" ('tiger:name_direction_suffix_1', 83),\n",
" ('tiger:name_direction_suffix_2', 10),\n",
" ('tiger:name_full', 134),\n",
" ('tiger:name_type', 144636),\n",
" ('tiger:name_type_1', 4337),\n",
" ('tiger:name_type_2', 211),\n",
" ('tiger:name_type_3', 10),\n",
" ('tiger:name_type_4', 1),\n",
" ('tiger:NAMELSAD', 319),\n",
" ('tiger:PCICBSA', 319),\n",
" ('tiger:PCINECTA', 318),\n",
" ('tiger:PLACEFP', 319),\n",
" ('tiger:PLACENS', 319),\n",
" ('tiger:PLCIDFP', 319),\n",
" ('tiger:reviewed', 139251),\n",
" ('tiger:RTTYP', 479),\n",
" ('tiger:separated', 101151),\n",
" ('tiger:source', 106881),\n",
" ('tiger:STATEFP', 330),\n",
" ('tiger:tlid', 107670),\n",
" ('tiger:type', 1),\n",
" ('tiger:upload_uuid', 32527),\n",
" ('tiger:zip', 1),\n",
" ('tiger:zip_left', 121605),\n",
" ('tiger:zip_left_1', 3923),\n",
" ('tiger:zip_left_2', 1020),\n",
" ('tiger:zip_left_3', 321),\n",
" ('tiger:zip_left_4', 99),\n",
" ('tiger:zip_left_5', 2),\n",
" ('tiger:zip_right', 115990),\n",
" ('tiger:zip_right_1', 1559),\n",
" ('tiger:zip_right_2', 413),\n",
" ('tiger:zip_right_3', 115),\n",
" ('tiger:zip_right_4', 41),\n",
" ('timezone', 1),\n",
" ('to', 327),\n",
" ('todo', 72),\n",
" ('toilets', 62),\n",
" ('toilets:access', 5),\n",
" ('toilets:disposal', 207),\n",
" ('toilets:female', 2),\n",
" ('toilets:handwashing', 2),\n",
" ('toilets:male', 2),\n",
" ('toilets:opening_hours', 1),\n",
" ('toilets:position', 61),\n",
" ('toilets:wheelchair', 38),\n",
" ('toll', 184),\n",
" ('toll:backward', 1),\n",
" ('toll:forward', 1),\n",
" ('tomb', 48),\n",
" ('topmark', 10),\n",
" ('topmark:colour', 1),\n",
" ('topmark:shape', 1),\n",
" ('tourism', 3505),\n",
" ('tower:construction', 153),\n",
" ('tower:type', 211),\n",
" ('townhall:type', 2),\n",
" ('tracks', 6),\n",
" ('tracktype', 1006),\n",
" ('trade', 13),\n",
" ('traffic_calming', 1977),\n",
" ('traffic_sign', 207),\n",
" ('traffic_signal:sound', 3),\n",
" ('traffic_signals', 23),\n",
" ('traffic_signals:direction', 427),\n",
" ('traffic_signals:sound', 305),\n",
" ('traffic_signals:vibration', 5),\n",
" ('trail', 2),\n",
" ('trail_visibility', 80),\n",
" ('trailhead', 1),\n",
" ('train', 70),\n",
" ('training', 9),\n",
" ('tram', 24),\n",
" ('transit_access', 2),\n",
" ('tree', 1),\n",
" ('trees', 163),\n",
" ('trolley_wire', 741),\n",
" ('truck', 5),\n",
" ('Trunk_Diam', 121),\n",
" ('tunnel', 3938),\n",
" ('turn:lanes', 386),\n",
" ('turn:lanes:backward', 119),\n",
" ('turn:lanes:both_ways', 1),\n",
" ('turn:lanes:forward', 132),\n",
" ('turn_restrictions', 267),\n",
" ('turnlane', 6),\n",
" ('type', 7384),\n",
" ('Type', 2856),\n",
" ('type:park', 2),\n",
" ('undefined', 1),\n",
" ('unisex', 47),\n",
" ('unmarked', 1),\n",
" ('unsigned_ref', 164),\n",
" ('url', 220),\n",
" ('usage', 1589),\n",
" ('usdot:ref', 6),\n",
" ('usdot:source', 6),\n",
" ('Use', 1),\n",
" ('user_defined_other', 7),\n",
" ('utility_wires', 98),\n",
" ('vacant', 1),\n",
" ('valet', 5),\n",
" ('valves', 1),\n",
" ('vehicle', 33),\n",
" ('vehicle:conditional', 9),\n",
" ('vehicle_test_centre', 1),\n",
" ('vending', 83),\n",
" ('vendor_model', 1),\n",
" ('verified', 16),\n",
" ('verified:name', 59),\n",
" ('vestibule', 1),\n",
" ('vestibule_atm', 10),\n",
" ('vestibule_atm_capacity', 7),\n",
" ('vestibule_atm_fee', 1),\n",
" ('vestibule_atm_op', 1),\n",
" ('vestibule_atm_operator', 6),\n",
" ('vestibule_depository', 1),\n",
" ('via', 99),\n",
" ('visibility', 11),\n",
" ('voltage', 1436),\n",
" ('voltage-high', 15),\n",
" ('voltage-low', 9),\n",
" ('volunteer', 1),\n",
" ('vta:id', 389),\n",
" ('vta:owner', 46),\n",
" ('wall', 4),\n",
" ('waste', 6),\n",
" ('waste:number', 1),\n",
" ('waste_basket', 1),\n",
" ('water', 771),\n",
" ('water_point', 3),\n",
" ('waterway', 38818),\n",
" ('web', 3),\n",
" ('website', 6112),\n",
" ('website2', 1),\n",
" ('website:es', 1),\n",
" ('website:official', 13),\n",
" ('website:searchstr', 1),\n",
" ('website:searchstring', 2),\n",
" ('website_1', 3),\n",
" ('wetap:bottle', 5),\n",
" ('wetap:credit', 16),\n",
" ('wetap:dog', 1),\n",
" ('wetap:flow', 30),\n",
" ('wetap:photo', 63),\n",
" ('wetap:quality', 33),\n",
" ('wetap:status', 163),\n",
" ('wetap:status_note', 4),\n",
" ('wetap:statusnote', 34),\n",
" ('wetap:temperature', 30),\n",
" ('wetland', 704),\n",
" ('wheelchair', 3144),\n",
" ('wheelchair:description', 18),\n",
" ('wheelchair:note', 2),\n",
" ('wholesale', 2),\n",
" ('width', 599),\n",
" ('widthcycle', 5),\n",
" ('wifi', 116),\n",
" ('wikidata', 13),\n",
" ('wikipedia', 1040),\n",
" ('Windmill', 1),\n",
" ('wires', 473),\n",
" ('woe:id', 7),\n",
" ('women', 2),\n",
" ('wood', 11),\n",
" ('works', 2),\n",
" ('wpt_description', 36),\n",
" ('wpt_symbol', 4),\n",
" ('X_COORD', 1),\n",
" ('x_coordinate', 11),\n",
" ('Y_COORD', 1),\n",
" ('y_coordinate', 11),\n",
" ('year', 1),\n",
" ('year_built', 1),\n",
" ('yelp', 2),\n",
" ('Yr_Planted', 2890),\n",
" ('z_order', 4),\n",
" ('zlevel', 8),\n",
" ('zone', 1),\n",
" ('ZONE', 8),\n",
" ('Zone', 1147),\n",
" ('zoning', 1),\n",
" ('Zoning', 1165),\n",
" ('zoning_code', 4)]\n",
"\n",
"Sorted by Frequency------------------------------------\n",
"[('building', 485012),\n",
" ('highway', 373635),\n",
" ('name', 255138),\n",
" ('addr:housenumber', 197174),\n",
" ('addr:street', 184889),\n",
" ('tiger:county', 179036),\n",
" ('addr:city', 174577),\n",
" ('tiger:cfcc', 172993),\n",
" ('source', 165759),\n",
" ('tiger:name_base', 153158),\n",
" ('tiger:name_type', 144636),\n",
" ('tiger:reviewed', 139251),\n",
" ('tiger:zip_left', 121605),\n",
" ('tiger:zip_right', 115990),\n",
" ('tiger:tlid', 107670),\n",
" ('tiger:source', 106881),\n",
" ('tiger:separated', 101151),\n",
" ('addr:state', 81845),\n",
" ('created_by', 50420),\n",
" ('addr:postcode', 49234),\n",
" ('amenity', 43500),\n",
" ('service', 42926),\n",
" ('oneway', 41906),\n",
" ('waterway', 38818),\n",
" ('tiger:upload_uuid', 32527),\n",
" ('landuse', 31434),\n",
" ('power', 28585),\n",
" ('paloalto_ca:id', 25298),\n",
" ('natural', 19577),\n",
" ('bicycle', 19173),\n",
" ('nhd:reach_code', 19089),\n",
" ('redwood_city_ca:bld_gid', 18697),\n",
" ('nhd:com_id', 18443),\n",
" ('gnis:fcode', 18442),\n",
" ('nhd:fdate', 18435),\n",
" ('surface', 17986),\n",
" ('redwood_city_ca:addr_id', 17164),\n",
" ('leisure', 16335),\n",
" ('access', 15922),\n",
" ('gnis:ftype', 15465),\n",
" ('layer', 15349),\n",
" ('addr:county', 14921),\n",
" ('ref', 14774),\n",
" ('maxspeed', 14676),\n",
" ('lanes', 14665),\n",
" ('boat', 14505),\n",
" ('scvwd:ROUTEID', 14253),\n",
" ('foot', 13709),\n",
" ('ele', 13349),\n",
" ('attribution', 13099),\n",
" ('gnis:feature_id', 11440),\n",
" ('railway', 11375),\n",
" ('barrier', 9891),\n",
" ('gnis:created', 9728),\n",
" ('shop', 9608),\n",
" ('gnis:county_id', 9392),\n",
" ('gnis:state_id', 9390),\n",
" ('addr:country', 9062),\n",
" ('operator', 8957),\n",
" ('description', 8403),\n",
" ('bridge', 8350),\n",
" ('cycleway', 7452),\n",
" ('type', 7384),\n",
" ('tiger:name_base_1', 7323),\n",
" ('FMMP_modified', 7277),\n",
" ('FMMP_reviewed', 7214),\n",
" ('intermittent', 7050),\n",
" ('emergency', 6830),\n",
" ('sbc_id', 6676),\n",
" ('sbc_parcel', 6676),\n",
" ('sbc_apn', 6676),\n",
" ('website', 6112),\n",
" ('tiger:name_direction_prefix', 6083),\n",
" ('hgv', 6042),\n",
" ('sidewalk', 5841),\n",
" ('crossing', 5760),\n",
" ('generator:source', 5434),\n",
" ('sport', 5415),\n",
" ('area', 5402),\n",
" ('acres', 5360),\n",
" ('note:mk408', 5082),\n",
" ('cuisine', 4881),\n",
" ('name_1', 4847),\n",
" ('phone', 4654),\n",
" ('massgis:cat', 4561),\n",
" ('man_made', 4521),\n",
" ('tiger:name_type_1', 4337),\n",
" ('horse', 4231),\n",
" ('scvwd:FACILITY', 4229),\n",
" ('note', 4076),\n",
" ('tunnel', 3938),\n",
" ('tiger:zip_left_1', 3923),\n",
" ('power_source', 3526),\n",
" ('tourism', 3505),\n",
" ('hgv:national_network', 3268),\n",
" ('wheelchair', 3144),\n",
" ('parking', 3017),\n",
" ('taxon', 3009),\n",
" ('religion', 2973),\n",
" ('species:en', 2954),\n",
" ('building:levels', 2922),\n",
" ('source:hgv:national_network', 2917),\n",
" ('Yr_Planted', 2890),\n",
" ('source:maxspeed', 2881),\n",
" ('Type', 2856),\n",
" ('gnis:id', 2754),\n",
" ('NO_PRMT', 2674),\n",
" ('COMM_CODE', 2674),\n",
" ('NO_SITE', 2674),\n",
" ('fields_ID', 2674),\n",
" ('len', 2674),\n",
" ('fields', 2674),\n",
" ('addr:full', 2671),\n",
" ('gauge', 2634),\n",
" ('NO_PRMT_SI', 2622),\n",
" ('footway', 2406),\n",
" ('crop', 2332),\n",
" ('ID', 2259),\n",
" ('source:addr', 2173),\n",
" ('Shape_Area', 2072),\n",
" ('Shape_Leng', 2070),\n",
" ('landcover', 2063),\n",
" ('restriction', 2023),\n",
" ('place', 2000),\n",
" ('traffic_calming', 1977),\n",
" ('motor_vehicle', 1964),\n",
" ('electrified', 1917),\n",
" ('source:name', 1858),\n",
" ('NHS', 1825),\n",
" ('gnis:county_name', 1776),\n",
" ('denomination', 1764),\n",
" ('leaf_cycle', 1763),\n",
" ('opening_hours', 1718),\n",
" ('aeroway', 1710),\n",
" ('SHAPE_len', 1631),\n",
" ('redwood_city_ca:bldg_id', 1616),\n",
" ('usage', 1589),\n",
" ('DT_ADD', 1582),\n",
" ('is_in', 1570),\n",
" ('tiger:zip_right_1', 1559),\n",
" ('lcn_ref', 1539),\n",
" ('SHAPE_area', 1490),\n",
" ('src:id', 1483),\n",
" ('route', 1468),\n",
" ('maxspeed:trailer', 1455),\n",
" ('voltage', 1436),\n",
" ('old_ref', 1392),\n",
" ('gnis:import_uuid', 1387),\n",
" ('OPER_ADD', 1384),\n",
" ('maxspeed:hgv', 1384),\n",
" ('Shape_len', 1367),\n",
" ('Shape_area', 1366),\n",
" ('capture', 1364),\n",
" ('DT_MANT', 1346),\n",
" ('addr:paloaltoca_id', 1318),\n",
" ('network', 1309),\n",
" ('shelter', 1281),\n",
" ('gnis:reviewed', 1262),\n",
" ('capacity', 1248),\n",
" ('Tiger:MTFCC', 1216),\n",
" ('PMT_SITE', 1190),\n",
" ('P_S_COMM', 1190),\n",
" ('OBJECTID', 1176),\n",
" ('entrance', 1176),\n",
" ('GENERIC', 1166),\n",
" ('COUNTYFP', 1165),\n",
" ('Zoning', 1165),\n",
" ('STATEFP', 1164),\n",
" ('public_transport', 1160),\n",
" ('SHAPE_STLe', 1157),\n",
" ('golf', 1155),\n",
" ('SHAPE_STAr', 1154),\n",
" ('Zone', 1147),\n",
" ('survey:date', 1146),\n",
" ('gnis:ST_num', 1143),\n",
" ('gnis:ST_alpha', 1143),\n",
" ('gnis:County', 1140),\n",
" ('gnis:Class', 1140),\n",
" ('gnis:County_num', 1139),\n",
" ('import_uuid', 1126),\n",
" ('alt_name', 1120),\n",
" ('noexit', 1086),\n",
" ('exit_to', 1077),\n",
" ('fee', 1076),\n",
" ('frequency', 1054),\n",
" ('wikipedia', 1040),\n",
" ('tiger:zip_left_2', 1020),\n",
" ('tracktype', 1006),\n",
" ('Attribution', 1003),\n",
" ('tiger:MTFCC', 987),\n",
" ('Atribution', 983),\n",
" ('height', 937),\n",
" ('hov', 901),\n",
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" ('owner', 784),\n",
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" ('email', 266),\n",
" ('incline', 265),\n",
" ('takeaway', 264),\n",
" ('segregated', 258),\n",
" ('history', 256),\n",
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" ('irrigated', 247),\n",
" ('importuuid', 244),\n",
" ('nps:pets', 243),\n",
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" ('backrest', 235),\n",
" ('contact:phone', 233),\n",
" ('generator:method', 233),\n",
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" ('quantity', 229),\n",
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" ('rwc_ca:id', 184),\n",
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" ('contact:website', 152),\n",
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" ('microbrewery', 106),\n",
" ('delivery', 106),\n",
" ('kerb', 105),\n",
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" ('scvwd:AREA_FT', 101),\n",
" ('scvwd:AREA_AC', 100),\n",
" ('start_date', 99),\n",
" ('id', 99),\n",
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" ('outside', 95),\n",
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" ('caltrans:type', 94),\n",
" ('caltrans:dynsegpm', 92),\n",
" ('nextbus:route', 92),\n",
" ('species', 92),\n",
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" ('local_ref', 81),\n",
" ('trail_visibility', 80),\n",
" ('drive_in', 80),\n",
" ('scvwd:OWNER', 79),\n",
" ('hgv:minweight', 79),\n",
" ('scvwd:SYS_NAME', 79),\n",
" ('scvwd:GIS_ID', 79),\n",
" ('seamark:light:range', 78),\n",
" ('source_id', 78),\n",
" ('loc_name', 77),\n",
" ('monitoring:water_level', 77),\n",
" ('except', 77),\n",
" ('source:website', 77),\n",
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" ('seamark:light:exhibition', 75),\n",
" ('seamark:light:height', 73),\n",
" ('lot_no', 72),\n",
" ('todo', 72),\n",
" ('caltrans:pctuse', 72),\n",
" ('recommended_speed', 71),\n",
" ('noref', 70),\n",
" ('psv', 70),\n",
" ('scvwd:MAXIMO_ID', 70),\n",
" ('maxheight', 70),\n",
" ('train', 70),\n",
" ('mrosd:name_2', 68),\n",
" ('address', 68),\n",
" ('seamark:daymark:colour_pattern', 68),\n",
" ('seamark:daymark:colour', 68),\n",
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" ('street', 66),\n",
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" ('minweight:hgv', 65),\n",
" ('tiger:name_base_3', 65),\n",
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" ('camp_pitch:type', 64),\n",
" ('camp_pitch:parking', 64),\n",
" ('wetap:photo', 63),\n",
" ('building:part', 62),\n",
" ('bottle', 62),\n",
" ('scvwd:SHAPE_AREA', 62),\n",
" ('toilets', 62),\n",
" ('toilets:position', 61),\n",
" ('verified:name', 59),\n",
" ('protection_title', 59),\n",
" ('scvwd:WB_TYPE', 59),\n",
" ('embankment', 58),\n",
" ('dogs', 58),\n",
" ('step_count', 55),\n",
" ('internet_access:fee', 55),\n",
" ('bldgid', 54),\n",
" ('city', 54),\n",
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" ('hnumber', 54),\n",
" ('artwork_type', 53),\n",
" ('mtb:scale', 53),\n",
" ('cutting', 50),\n",
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" ('name:es', 48),\n",
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" ('seamark:buoy_lateral:colour', 48),\n",
" ('collection_times', 48),\n",
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" ('capacity:bike_locke', 46),\n",
" ('capacity:standard', 46),\n",
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" ('capacity:bike_rack', 46),\n",
" ('capacity:kiss_n_rid', 46),\n",
" ('capacity:motorcycle', 46),\n",
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" ('crossing_ref', 45),\n",
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" ('mincyclewidth', 39),\n",
" ('new', 38),\n",
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" ('male', 37),\n",
" ('seamark:fog_signal:category', 37),\n",
" ('military', 36),\n",
" ('addr:place', 36),\n",
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" ('wpt_description', 36),\n",
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" ('official_name', 35),\n",
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" ('wetap:statusnote', 34),\n",
" ('crossing:bell', 33),\n",
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" ('vehicle', 33),\n",
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" ('wetap:quality', 33),\n",
" ('social_facility:for', 32),\n",
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" ('operator:type', 32),\n",
" ('indoor', 32),\n",
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" ('service:bicycle:chain_tool', 31),\n",
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" ('fireplace', 30),\n",
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" ('roof:material', 30),\n",
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" ('note:address', 30),\n",
" ('addr:4:housenumber', 29),\n",
" ('Comments', 29),\n",
" ('contact:email', 29),\n",
" ('parking:condition:left:2:reason', 28),\n",
" ('parking:condition:right:2:reason', 28),\n",
" ('passenger', 28),\n",
" ('depository', 28),\n",
" ('parking:condition:left:2:time_interval', 28),\n",
" ('parking:condition:left:2', 28),\n",
" ('artist_name', 28),\n",
" ('parking:condition:right:2', 28),\n",
" ('hazmat', 28),\n",
" ('roof:slope:direction', 28),\n",
" ('nps:comment', 28),\n",
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" ('seamark:buoy_lateral:system', 28),\n",
" ('rwc_ca:address_id', 27),\n",
" ('camp_site:type', 27),\n",
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" ('name:zh', 27),\n",
" ('camp_site:table', 27),\n",
" ('crossing:light', 27),\n",
" ('maxlength', 27),\n",
" ('golf_cart', 27),\n",
" ('busway', 26),\n",
" ('maxwidth', 26),\n",
" ('Id', 26),\n",
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" ('addr:5:housenumber', 26),\n",
" ('hoops', 25),\n",
" ('recycling:plastic_bottles', 25),\n",
" ('recycling:glass_bottles', 25),\n",
" ('boundary:type', 25),\n",
" ('fuel', 25),\n",
" ('buoy', 25),\n",
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" ('stars', 23),\n",
" ('traffic_signals', 23),\n",
" ('icao', 23),\n",
" ('note:hgv', 23),\n",
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" ('iata', 22),\n",
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" ('recycling:cans', 22),\n",
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" ('grades', 21),\n",
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" ('sanitary_dump_station', 19),\n",
" ('baby_hatch', 19),\n",
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" ('horses', 19),\n",
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" ('payment:visa', 19),\n",
" ('station_name', 19),\n",
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" ('source:position', 18),\n",
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" ('CertID', 16),\n",
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" ('Acres', 16),\n",
" ('contact:fax', 16),\n",
" ('payment:mastercard', 16),\n",
" ('snowmobile', 16),\n",
" ('noname', 16),\n",
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" ('recycling:paper', 14),\n",
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" ('parking:condition:both:time_interval', 14),\n",
" ('name_3', 14),\n",
" ('parking:condition:both:residents', 14),\n",
" ('heritage', 14),\n",
" ('Open_Date', 14),\n",
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" ('site', 14),\n",
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" ('future:name', 1),\n",
" ('volunteer', 1),\n",
" ('name:new', 1),\n",
" ('restaurant', 1),\n",
" ('striping_condition', 1),\n",
" ('isced:level', 1),\n",
" ('disused:tiger:cfcc', 1),\n",
" ('happy_hours', 1),\n",
" ('foodshed', 1),\n",
" ('really_has_a_road_through_it', 1),\n",
" ('animal_boarding', 1),\n",
" ('name:wuu', 1),\n",
" ('name:mwl', 1),\n",
" ('name:simple', 1),\n",
" ('add', 1),\n",
" ('radar_transponder', 1),\n",
" ('name:dsb', 1),\n",
" ('maxcyclwidth', 1),\n",
" ('garage', 1),\n",
" ('sanitary_dump_station:rinse_water', 1),\n",
" ('name:nah', 1),\n",
" ('name:nap', 1),\n",
" ('name:zea', 1),\n",
" ('newaesthetic', 1),\n",
" ('health_specialty:physiotherapy', 1),\n",
" ('fuel:octane_100', 1),\n",
" ('memorial:type', 1),\n",
" ('ref_heritage', 1),\n",
" ('name:nso', 1),\n",
" ('waste:number', 1),\n",
" ('backcountry', 1),\n",
" ('website:searchstr', 1),\n",
" ('camera:type', 1),\n",
" ('recycling:wood', 1),\n",
" ('name:nov', 1),\n",
" ('function', 1),\n",
" ('source:height', 1),\n",
" ('name:qu', 1),\n",
" ('basin', 1),\n",
" ('rooms', 1),\n",
" ('monument', 1),\n",
" ('name:fiu-vro', 1),\n",
" ('name:stq', 1),\n",
" ('name:nds-nl', 1),\n",
" ('note_3', 1),\n",
" ('seamark:light:sector_end', 1),\n",
" ('stay', 1),\n",
" ('name:ga', 1),\n",
" ('socket:AC:bs_1363', 1),\n",
" ('reception_desk', 1),\n",
" ('name:gn', 1),\n",
" ('name:gl', 1),\n",
" ('name:gv', 1),\n",
" ('name:gu', 1)]\n",
"\n"
]
}
],
"source": [
"audit.summary(audit.Options.key_frequency)"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Street name analysis:--------------------------------\n",
"[('0.1', set(['Ala 680 PM 0.1'])),\n",
" ('1',\n",
" set(['10795 Hwy 1',\n",
" '2030 Hwy 1',\n",
" 'California Highway 16, House No. 1',\n",
" 'State Hwy 1',\n",
" 'Stewart Drive Suite #1',\n",
" 'W Of Us 101 @ Jct Sr 1'])),\n",
" ('10', set(['San Mateo 35 PM 10', 'South St #10'])),\n",
" ('100', set(['Woodside Road, Suite 100'])),\n",
" ('101',\n",
" set(['Highway 101',\n",
" 'Nw Quad Lincoln Ave / Us 101',\n",
" 'Se Quad Smith Ranch Rd / Us 101'])),\n",
" ('10675', set(['10675'])),\n",
" ('11', set(['Fairview Rd #11'])),\n",
" ('110', set(['West Angela Street, Suite 110'])),\n",
" ('110,', set(['Promenade Circle #110,'])),\n",
" ('114', set(['West Evelyn Avenue Suite #114'])),\n",
" ('116',\n",
" set(['Hwy 116',\n",
" 'In \"Y\" Jct Of Sr 121 / Sr 116',\n",
" 'W Side Of Us 101 @ Sr 116'])),\n",
" ('12',\n",
" set(['480 Highway 12',\n",
" 'Hawkins St #12',\n",
" 'Main Street At Sr 12',\n",
" 'Rustic St #12'])),\n",
" ('120', set(['E. Hwy 120', 'East Highway 120'])),\n",
" ('12180142', set(['12180142'])),\n",
" (u'122\\xb029\\'07.1\"W', set([u'37\\xb042\\'46.0\"N 122\\xb029\\'07.1\"W'])),\n",
" ('128', set(['Highway 128'])),\n",
" ('13', set(['North Port Rd 13'])),\n",
" ('140', set(['West Highway 140'])),\n",
" ('15', set(['Doolittle Dr, Suite 15'])),\n",
" ('155', set(['Woodside Road, Suite 155'])),\n",
" ('15th', set(['15th'])),\n",
" ('16360', set(['16360'])),\n",
" ('18C', set(['County Road 18C'])),\n",
" ('1st', set(['Thomas Lane Off 1st'])),\n",
" ('2',\n",
" set(['College St #2',\n",
" 'Port Rd 2',\n",
" 'San Francisco Bicycle Route 2',\n",
" 'Showers Drive STE 2'])),\n",
" ('2.0', set(['Hwy 780 PM 2.0'])),\n",
" ('200', set(['Sutter Street, STE #200'])),\n",
" ('203', set(['Bartlett Street #203'])),\n",
" ('22', set(['Port Rd 22'])),\n",
" ('23', set(['Port Rd 23', 'Via Padre #23'])),\n",
" ('245', set(['245'])),\n",
" ('24th', set(['24th'])),\n",
" ('25', set(['Via Padre #25'])),\n",
" ('26', set(['East State Route 26'])),\n",
" ('27', set(['Via Padre #27'])),\n",
" ('29', set(['Fairview Rd #29', 'Via Padre #29'])),\n",
" ('3.2', set(['ALA 84 PM 3.2'])),\n",
" ('31', set(['Via Padre #31'])),\n",
" ('310', set(['Greenback Lane, Suite 310'])),\n",
" ('3658', set(['Market Street Suite 3658'])),\n",
" ('37', set(['Off Glen Rd @ Atherton Ave; N Of Sr 37'])),\n",
" ('4',\n",
" set(['4',\n",
" 'East State Route 4',\n",
" 'Pacheco Blvd @ Blum Rd N Of Sr 4',\n",
" 'State Highway 4',\n",
" 'West State Route 4'])),\n",
" ('4.5', set(['SF 80 PM 4.5'])),\n",
" ('41276', set(['Upton St 41276'])),\n",
" ('43.6', set(['Hwy 29 PM 43.6'])),\n",
" ('438', set(['South St #438'])),\n",
" ('488', set(['Market Street #488'])),\n",
" ('5', set(['2nd St/Rte 5'])),\n",
" ('50', set(['3rd St / Rte 50'])),\n",
" ('56', set(['Fairview Road Sp 56'])),\n",
" ('63', set(['Fairview Rd #63'])),\n",
" ('7', set(['Showers Drive STE 7'])),\n",
" ('7.1', set(['Hwy 17 PM 7.1'])),\n",
" ('7193', set(['7193'])),\n",
" ('730', set(['Sansome Street Ste 730', 'Sansome Street Suite 730'])),\n",
" ('803', set(['Line St #803'])),\n",
" ('88', set(['East State Route 88', 'North Highway 88'])),\n",
" ('9', set(['Highway 9', 'Hwy 9', 'Hwy. 9'])),\n",
" ('94B', set(['County Road 94B'])),\n",
" ('95', set(['Sally St #95'])),\n",
" ('95628-7416', set(['Fair Oaks Boulevard, Fair Oaks, CA 95628-7416'])),\n",
" ('95683', set(['Pera Dr at Escuela Drive, Rancho Murieta, Ca 95683'])),\n",
" ('99', set(['13th Ave Rte 99', '47th Avenue / Rte 99'])),\n",
" ('9th', set(['9th'])),\n",
" ('A',\n",
" set(['Adrian Court, Suite A',\n",
" 'Avenue A',\n",
" 'Lane A',\n",
" 'Pier 50 A',\n",
" 'Upton St #A'])),\n",
" ('a',\n",
" set(['1st St #a', 'Monterey St #a', 'Pacheco Pass Hwy #a', 'Powell St #a'])),\n",
" ('A201', set(['S De Anza Blvd., Ste A201'])),\n",
" ('AA', set(['Showers Drive BLDG AA'])),\n",
" ('Abenue', set(['Columbus Abenue'])),\n",
" ('AL', set(['Bieghle AL'])),\n",
" ('Alameda', set(['Alameda', 'The Alameda'])),\n",
" ('Alemeda', set(['The Alemeda'])),\n",
" ('Alto', set(['Camino Alto'])),\n",
" ('Aly', set(['Elmore Aly', 'Hawkins Aly', 'Park Aly', 'Smith Aly'])),\n",
" ('Antonio', set(['Calle San Antonio'])),\n",
" ('Arena', set(['Arena'])),\n",
" ('Arguello', set(['Arguello'])),\n",
" ('arrowhead', set(['lake arrowhead'])),\n",
" ('Arroyo', set(['Camino Arroyo'])),\n",
" ('Ashfield', set(['Ashfield'])),\n",
" ('Ave',\n",
" set([' Grant Ave',\n",
" '1425 E Dunne Ave',\n",
" '2000 Trower Ave',\n",
" '220 Sylvania Ave',\n",
" '41st Ave',\n",
" '441 Snyder Ave',\n",
" '45th Ave',\n",
" '7401 Solano Ave',\n",
" 'A Rose Ave',\n",
" 'Afton Ave',\n",
" 'Allerton Ave',\n",
" 'Argonne Ave',\n",
" 'Bayshore Ave',\n",
" 'Bernal Ave',\n",
" 'Blake Ave',\n",
" 'Blenheim Ave',\n",
" 'Cabrillo Ave',\n",
" 'California Ave',\n",
" 'Carpenteria Ave',\n",
" 'Carr Ave',\n",
" 'Central Ave',\n",
" 'Chanticleer Ave',\n",
" 'Cherry Ave',\n",
" 'Corte Madera Ave',\n",
" 'Day Ave',\n",
" 'E Louise Ave',\n",
" 'Earl Ave',\n",
" 'El Monte Ave',\n",
" 'Esplanade Ave',\n",
" 'Flora Ave',\n",
" 'Floribunda Ave',\n",
" 'Forest Ave',\n",
" 'Foxworthy Ave',\n",
" 'Geneva Ave',\n",
" 'Greenwood Ave',\n",
" 'Hamilton Ave',\n",
" 'Healdsburg Ave',\n",
" 'Homestead Ave',\n",
" 'Huntington Ave',\n",
" 'Jan Ave',\n",
" 'Jerrold Ave',\n",
" 'Locust Ave',\n",
" 'Loma Vista Ave',\n",
" 'Lorton Ave',\n",
" 'Magnolia Ave',\n",
" 'Manzanita Ave',\n",
" 'Mapleton Ave',\n",
" 'Meridian Ave',\n",
" 'Morena Ave',\n",
" 'Mowry Ave',\n",
" 'N Blaney Ave',\n",
" 'Ne & Sw Quads I-80 / Willow Ave',\n",
" 'Ne Quad Enterprise Dr / I-80 Ic @ Capitol Ave',\n",
" 'Palmetto Ave',\n",
" 'Paloma Ave',\n",
" 'Pennsylvania Ave',\n",
" 'Phelan Ave',\n",
" 'Pine Tree Ave',\n",
" 'Portage Ave',\n",
" 'Prospect Ave',\n",
" 'Railroad Ave',\n",
" 'Rose Ave',\n",
" 'S California Ave',\n",
" 'San Pablo Ave',\n",
" 'Saratoga Ave',\n",
" 'Scott Ave',\n",
" 'Seaboard Ave',\n",
" 'Seely Ave',\n",
" 'Shattuck Ave',\n",
" 'Snyder Ave',\n",
" 'Somme Ave',\n",
" 'Sperry Ave',\n",
" 'Tehama Ave',\n",
" 'The Alameda Ave',\n",
" 'Thorton Ave',\n",
" 'University Ave',\n",
" 'Van Ness Ave',\n",
" 'Verdun Ave',\n",
" 'W & E Of Us 101 N Of Seminary Ave',\n",
" 'W 25th Ave',\n",
" 'Walsh Ave',\n",
" 'Western Ave'])),\n",
" ('ave', set(['wilcox ave'])),\n",
" ('Ave.',\n",
" set(['Dartmouth Ave.',\n",
" 'E. Cotati Ave.',\n",
" 'Edes Ave.',\n",
" 'Fairmount Ave.',\n",
" 'Hamilton Ave.',\n",
" 'Jefferson Ave.',\n",
" 'Lincoln Ave.',\n",
" 'Menalto Ave.',\n",
" 'San Carlos Ave.',\n",
" 'Tunstead Ave.',\n",
" 'Watt Ave.',\n",
" 'West Edmundson Ave.',\n",
" 'Willie Stargell Ave.',\n",
" 'Yulupa Ave.'])),\n",
" ('Aveenue', set(['Market Aveenue'])),\n",
" ('Avenie', set(['Garvin Avenie'])),\n",
" ('avenue', set(['Santa Cruz avenue'])),\n",
" ('Axis', set(['North-South Axis'])),\n",
" ('b', set(['Kane Dr #b', 'Technology Pkwy #b'])),\n",
" ('B', set(['Pier 50 B'])),\n",
" ('Bascom', set(['S. Bascom'])),\n",
" ('Bay', set(['Bay'])),\n",
" ('Bellomy', set(['Bellomy'])),\n",
" ('Bikeway', set(['Sprockett Bikeway'])),\n",
" ('Bluxome', set(['Bluxome'])),\n",
" ('blvd', set(['foothill blvd'])),\n",
" ('Blvd',\n",
" set(['10500 Foothill Blvd',\n",
" '1485 South Petaluma Blvd',\n",
" '1535 Airport Blvd',\n",
" '1555 Airport Blvd',\n",
" '21300 San Ramon Valley Blvd',\n",
" '2501 East Bayshore Blvd',\n",
" '25555 Hesperian Blvd',\n",
" '296 Airport Blvd',\n",
" '380 Foster City Blvd',\n",
" '5055 Farmhill Blvd',\n",
" '600 Leweling Blvd',\n",
" '6010 Folsom Blvd',\n",
" '800 E Airway Blvd',\n",
" '9660 Stockton Blvd',\n",
" 'Airport Blvd',\n",
" 'Anza Blvd',\n",
" 'Ardenwood Blvd',\n",
" 'Arena Blvd',\n",
" 'Auburn Blvd',\n",
" 'Bellam Blvd',\n",
" 'Brookhurst Blvd',\n",
" 'California Blvd',\n",
" 'Center Blvd',\n",
" 'Century Blvd',\n",
" 'Cipriani Blvd',\n",
" 'De Anza Blvd',\n",
" 'Diamond Blvd',\n",
" 'Dublin Blvd',\n",
" 'E. Airway Blvd',\n",
" 'Folsom Blvd',\n",
" 'Franklin Blvd',\n",
" 'Fremont Blvd',\n",
" 'Geary Blvd',\n",
" 'International Blvd',\n",
" 'Junipero Serra & Westborough Blvd',\n",
" 'Lewelling Blvd',\n",
" 'Los Gatos Blvd',\n",
" 'MacArthur Blvd',\n",
" 'Mace Blvd',\n",
" 'McCarthy Blvd',\n",
" 'Mission Blvd',\n",
" 'Mission College Blvd',\n",
" 'Monterey Blvd',\n",
" 'Monument Blvd',\n",
" 'N California Blvd',\n",
" 'N McCarthy Blvd',\n",
" 'N. Market Blvd',\n",
" 'Newark Blvd',\n",
" 'Northgate Blvd',\n",
" 'Pacific Commons Blvd',\n",
" 'Palm Valley Blvd',\n",
" 'Petaluma Blvd',\n",
" 'Russell Blvd',\n",
" 'S Tracy Blvd',\n",
" 'S. Mcdowell Blvd',\n",
" 'Santa Teresa Blvd',\n",
" 'Sawyer Camp Trail & Hillcrest Blvd',\n",
" 'Se Quad Us 101 @ South Petaluma Blvd',\n",
" 'Sir Francis Drake Blvd',\n",
" 'Skyline Blvd',\n",
" 'Stevens Creek Blvd',\n",
" 'Tice Valley Blvd',\n",
" 'Tracy Blvd',\n",
" 'Under Ramp Sw Quad Of Us 101 / Sr 92 Ic Off 19th Ave & Fashion Island Blvd',\n",
" 'Veterans Blvd',\n",
" 'W. Las Positas Blvd',\n",
" 'Warm Springs Blvd',\n",
" 'Washington Blvd',\n",
" 'Westside Blvd'])),\n",
" ('Blvd,',\n",
" set(['Nw Quad I-280 / Sr 35 Ic @ Jct Hayne Rd, Golf Course Dr, Skyline Blvd,'])),\n",
" ('Blvd.',\n",
" set(['Arena Blvd.',\n",
" 'East Francisco Blvd.',\n",
" 'Fair Oaks Blvd.',\n",
" 'Freedmond Blvd.',\n",
" 'Northgate Blvd.',\n",
" 'Sir Francis Drake Blvd.'])),\n",
" ('BLVD.', set(['SOUTH TRACY BLVD.'])),\n",
" ('Boulevar', set(['Lake Washington Boulevar'])),\n",
" ('Bradshaw', set(['Bradshaw'])),\n",
" ('Brannan', set(['Brannan'])),\n",
" ('Bridge', set(['Pier 1 SM- Hayward Bridge'])),\n",
" ('Bridgeway', set(['Bridgeway'])),\n",
" ('broadway', set(['broadway'])),\n",
" ('Broadway', set(['Broadway', 'North Broadway'])),\n",
" ('Building', set(['Ferry Building', 'Multi Use Building'])),\n",
" ('C', set(['Plymouth Street #C'])),\n",
" ('c', set(['San Juan Rd #c'])),\n",
" ('CA', set(['Zanker Rd., San Jose, CA', 'Zanker Road, San Jose, CA'])),\n",
" ('California', set(['California'])),\n",
" ('Calle', set(['La Calle'])),\n",
" ('Cedar', set(['Cedar'])),\n",
" ('Central', set(['Plaza Central'])),\n",
" ('Chestnut', set(['Chestnut'])),\n",
" ('Cir',\n",
" set(['Alpine Cir',\n",
" 'Amador Cir',\n",
" 'Ashford Cir',\n",
" 'Celadon Cir',\n",
" 'Doris Cir',\n",
" 'Dots Cir',\n",
" 'Foxhill Cir',\n",
" 'Franklin Cir',\n",
" 'Franklins Cir',\n",
" 'Greenwood Cir',\n",
" 'Heather Glen Cir',\n",
" 'Holland Cir',\n",
" 'Holly Tree Cir',\n",
" 'Ione Cir',\n",
" 'Jacaranda Cir',\n",
" 'Meadow Way Cir',\n",
" 'Poppy Lane Cir',\n",
" 'Rays Cir',\n",
" 'Shoshone Cir',\n",
" 'Verde Cir'])),\n",
" ('Circle:', set(['Arroyo Circle:'])),\n",
" ('Clemente', set(['San Clemente'])),\n",
" ('Cnr', set(['@ Sunrise Blvd Nw Cnr'])),\n",
" ('Columbus', set(['Columbus'])),\n",
" ('Common', set(['Hemingway Common', 'Kiwi Common', 'Pamela Common'])),\n",
" ('Corcoran', set(['425 Corcoran'])),\n",
" ('Corte', set(['Bella Corte'])),\n",
" ('Courtyard', set(['The Courtyard'])),\n",
" ('Crati', set(['Piane Crati'])),\n",
" ('Cres', set(['Wellesley Cres'])),\n",
" ('Crescent', set(['Clarendon Crescent', 'Wellesley Crescent'])),\n",
" ('Cross', set(['1345 Wooden Vly Cross', 'Hwy 29 at Ruthf Cross'])),\n",
" ('Cruz', set(['Calle Cruz'])),\n",
" ('CT', set(['Isles CT'])),\n",
" ('Ct',\n",
" set(['Acacia Ct',\n",
" 'Adrian Ct',\n",
" 'Alissa Ct',\n",
" 'Almond Ct',\n",
" 'Amber Ct',\n",
" 'American Ct',\n",
" 'Apple Ct',\n",
" 'Arbor Ct',\n",
" 'Azul Ct',\n",
" 'Ball Ct',\n",
" 'Barbras Ct',\n",
" 'Belmont Ct',\n",
" 'Beth Ct',\n",
" 'Blake Ct',\n",
" 'Brandy Ct',\n",
" 'Butterfly Ct',\n",
" 'Conrad Ct',\n",
" 'Corrib Ct',\n",
" 'Cosco Ct',\n",
" 'Del Rio Ct',\n",
" 'Eastview Ct',\n",
" 'Ellis Ct',\n",
" 'Ervin Ct',\n",
" 'Feather Ct',\n",
" 'Florence Ct',\n",
" 'Franklin Ct',\n",
" 'Freya Ct',\n",
" 'Gabriele Ct',\n",
" 'Gia Ct',\n",
" 'Guiness Ct',\n",
" 'Hamilton Ct',\n",
" 'Helen Ct',\n",
" 'Hillock Ct',\n",
" 'Howard Ct',\n",
" 'Ian Ct',\n",
" 'Jeanette Ct',\n",
" 'Jess Ct',\n",
" 'Jills Ct',\n",
" 'Julian Ct',\n",
" 'Karen Ct',\n",
" 'Ken Ct',\n",
" 'Kimberly Ct',\n",
" 'La Macchia Ct',\n",
" 'Laguna Ct',\n",
" 'Lang Ct',\n",
" 'Lassen Ct',\n",
" 'Laurel Ct',\n",
" 'Leisure Ct',\n",
" 'Lemmon Ct',\n",
" 'Lemos Ct',\n",
" 'Lima Ct',\n",
" 'Madera Ct',\n",
" 'Marcus Ct',\n",
" 'Mariposa Ct',\n",
" 'Marseille Ct',\n",
" 'Matulich Ct',\n",
" 'Mayme Ct',\n",
" 'Meadow Ct',\n",
" 'Melinda Ct',\n",
" 'Merintas Ct',\n",
" 'Mica Ct',\n",
" 'Monica Ct',\n",
" 'Monte Bello Ct',\n",
" 'Monte Cristo Ct',\n",
" 'Ortiz Ct',\n",
" 'Peach Ct',\n",
" 'Pear Ct',\n",
" 'Peridot Ct',\n",
" 'Perivale Ct',\n",
" 'Perrien Ct',\n",
" 'Plum Ct',\n",
" 'Prescott Ct',\n",
" 'Ranchito Ct',\n",
" 'Renton Ct',\n",
" 'Ricardo Ct',\n",
" 'Rossi Ct',\n",
" 'Ruger Ct',\n",
" 'Saddle Ct',\n",
" 'Santa Ana Ct',\n",
" 'Sierra Ct',\n",
" 'Stephanie Ct',\n",
" 'Stierlin Ct',\n",
" 'Swan Ct',\n",
" 'Tamara Ct',\n",
" 'Teresita Ct',\n",
" 'Terrys Ct',\n",
" 'Tree Ct',\n",
" 'Trente Ct',\n",
" 'Ventura Ct',\n",
" 'Villa Pacheco Ct',\n",
" 'Vista Park Hill Ct',\n",
" 'Western Ct'])),\n",
" ('Ct.', set(['Stierlin Ct.'])),\n",
" ('Ctr', set(['Linda Mar Shppng Ctr', 'Tanforan Shopping Ctr'])),\n",
" ('Cut', set(['Short Cut'])),\n",
" ('Cut-Off', set(['Butano Cut-Off'])),\n",
" ('D', set(['Avenue D'])),\n",
" ('d1', set(['Nash Rd #d1'])),\n",
" ('Dr',\n",
" set(['Adrian Dr',\n",
" 'Alissa Dr',\n",
" 'Alpine Dr',\n",
" 'Arlington Dr',\n",
" 'Arriba Dr',\n",
" 'Audubon Dr',\n",
" 'Avichi Knoll Dr',\n",
" 'Banfield Dr',\n",
" 'Bert Dr',\n",
" 'Black Forest Dr',\n",
" 'Bonnie View Dr',\n",
" 'Brigantino Dr',\n",
" 'California Dr',\n",
" 'Chabot Dr',\n",
" 'Chateau Dr',\n",
" 'Christopher Dr',\n",
" 'Clearview Dr',\n",
" 'Corriente Point Dr',\n",
" 'Courthouse Dr',\n",
" 'Crescent Dr',\n",
" 'Daffodil Dr',\n",
" 'Dan Dr',\n",
" 'Del Mar Dr',\n",
" 'Del Monte Dr',\n",
" 'Diablo Hills Dr',\n",
" 'Dixie Dr',\n",
" 'Donald Dr',\n",
" 'Duffin Dr',\n",
" 'Eastview Dr',\n",
" 'Edgewood Dr',\n",
" 'El Cerro Dr',\n",
" 'El Toro Dr',\n",
" 'Eldene Dr',\n",
" 'Evelyns Dr',\n",
" 'Everest Dr',\n",
" 'Ewen Dr',\n",
" 'Felice Dr',\n",
" 'Forest Creek Dr',\n",
" 'Fountain Oaks Dr',\n",
" 'Four Corners Dr',\n",
" 'Franks Dr',\n",
" 'Gateway Dr',\n",
" 'Georges Dr',\n",
" 'Glenmoor Dr',\n",
" 'Gonzalez Dr',\n",
" 'Helen Dr',\n",
" 'Hemlock Dr',\n",
" 'Hillock Dr',\n",
" 'Hillside Dr',\n",
" 'Hilltop Dr',\n",
" 'Hitch Dr',\n",
" 'Horizon Dr',\n",
" 'Irma Dr',\n",
" 'Jacqueline Dr',\n",
" 'Joe Borovich Dr',\n",
" 'Johnson Dr',\n",
" 'Joshua Dr',\n",
" 'Kane Dr',\n",
" 'Kathryn Dr',\n",
" 'Kelly Dr',\n",
" 'Kirkpatrick Dr',\n",
" 'Kurasaki Dr',\n",
" 'La Baig Dr',\n",
" 'Lanini Dr',\n",
" 'Larios Dr',\n",
" 'Las Palmas Dr',\n",
" 'Lasuen Dr',\n",
" 'Le Chateau Dr',\n",
" 'Le Mans Dr',\n",
" 'Liege Dr',\n",
" 'Linwood Dr',\n",
" 'Lorene Dr',\n",
" 'Madrone Dr',\n",
" 'Majestic Dr',\n",
" 'Maranatha Dr',\n",
" 'Marguerite Dr',\n",
" 'Marks Dr',\n",
" 'Marne Dr',\n",
" 'Marseille Dr',\n",
" 'Mary Dr',\n",
" 'Matador Dr',\n",
" 'Mccary Dr',\n",
" 'Memorial Dr',\n",
" 'Minto Dr',\n",
" 'Monica Dr',\n",
" 'Monte Bello Dr',\n",
" 'Monte Carlo Dr',\n",
" 'Monte Verde Dr',\n",
" 'Morning Glory Dr',\n",
" 'Morris Dr',\n",
" 'Ne Quad I-80 / Hilltop Dr',\n",
" 'Nicholson Dr',\n",
" 'Nora Dr',\n",
" 'Olga Dr',\n",
" 'Olive Dr',\n",
" 'Park Center Dr',\n",
" 'Paseo Dr',\n",
" 'Paul Dr',\n",
" 'Paullus Dr',\n",
" 'Pleasant Valley Dr',\n",
" 'Poppy Lane Dr',\n",
" 'Primavera Dr',\n",
" 'Ranchito Dr',\n",
" 'Regal Dr',\n",
" 'Ricardo Dr',\n",
" 'Ridge Dr',\n",
" 'Ridgemark Dr',\n",
" 'Riviera Dr',\n",
" 'Robert Dr',\n",
" 'Rose Dr',\n",
" 'Ross Dr',\n",
" 'S Ridgemark Dr',\n",
" 'Samaritan Dr',\n",
" 'San Juan Dr',\n",
" 'San Lorenzo Dr',\n",
" 'San Tropez Dr',\n",
" 'Santa Rosa Dr',\n",
" 'Sawtooth Dr',\n",
" 'Serene Dr',\n",
" 'Shelton Dr',\n",
" 'Sherwood Dr',\n",
" 'Sky Ranch Dr',\n",
" 'Southridge Dr',\n",
" 'Spring Dr',\n",
" 'State University Dr',\n",
" 'Steinbeck Dr',\n",
" 'Stephens Dr',\n",
" 'Summer Dr',\n",
" 'Sunset Dr',\n",
" 'Talbot Dr',\n",
" 'Tiffany Dr',\n",
" 'Tina Dr',\n",
" 'Trieste Dr',\n",
" 'Versailles Dr',\n",
" 'Vineyard Dr',\n",
" 'Virginia Dr',\n",
" 'Willow Dr',\n",
" 'Wilma Dr',\n",
" 'Windmill Dr'])),\n",
" ('Dr.',\n",
" set(['Campus Dr.',\n",
" 'Fairway Dr.',\n",
" 'Grandview Dr.',\n",
" 'Monarch Bay Dr.',\n",
" 'Zinfandel Dr.'])),\n",
" ('dunne', set(['San Felipe Rd #dunne'])),\n",
" ('E', set(['911 Donaldson Way E', 'Avenue E', 'Tribute Road, Suite E'])),\n",
" ('East',\n",
" set(['1st Street East',\n",
" '2nd Street East',\n",
" '8th Street East',\n",
" 'Buena Vista Avenue East',\n",
" 'Fourth Street East',\n",
" 'Francisco Blvd East',\n",
" 'Francisco Boulevard East',\n",
" 'Ne Quad Benicia Rd / I-80 Ic @ Lincoln Rd East',\n",
" 'Rio Robles East',\n",
" 'Sir Francis Drake Boulevard East',\n",
" 'Vanderbilt Court East'])),\n",
" ('Embarcadero', set(['Embarcadero', 'The Embarcadero'])),\n",
" ('Escarpado', set(['El Escarpado'])),\n",
" ('Escuela', set(['Camina Escuela'])),\n",
" ('Esplanade', set(['Esplanade'])),\n",
" ('Estates', set(['Heatherwood Estates'])),\n",
" ('Evelyn', set(['West Evelyn'])),\n",
" ('Expressway',\n",
" set(['Almaden Expressway',\n",
" 'Alta Arden Expressway',\n",
" 'Central Expressway',\n",
" 'E. Capitol Expressway',\n",
" 'East Capitol Expressway',\n",
" 'Foothill Expressway',\n",
" 'Lawrence Expressway',\n",
" 'Montague Expressway',\n",
" 'Oregon Expressway',\n",
" 'San Tomas Expressway',\n",
" 'West Capitol Expressway'])),\n",
" ('Expwy', set(['N E & Sw Quad Of Us 101 / Rohnert Park Expwy'])),\n",
" ('Extension',\n",
" set(['I Street Extension',\n",
" 'Mission Street Extension',\n",
" 'Old Davis Road Extension'])),\n",
" ('F', set(['Avenue F', 'Port Rd F'])),\n",
" ('Fillmore', set(['Fillmore'])),\n",
" ('Floor',\n",
" set(['11th Street, Fifth Floor',\n",
" 'Montgomery Street, 2nd Floor',\n",
" 'Twin Dolphin Drive 6th Floor'])),\n",
" ('Folsom', set(['Folsom'])),\n",
" ('Franklin', set(['Franklin'])),\n",
" ('Freeway', set(['MacArthur Freeway'])),\n",
" ('front', set(['front'])),\n",
" ('Front', set(['Front'])),\n",
" ('Galvez', set(['Galvez'])),\n",
" ('Garden', set(['Scott Avenue Garden'])),\n",
" ('Gardens', set(['Wildwood Gardens'])),\n",
" ('Grande', set(['Via Grande'])),\n",
" ('Green', set(['Miranda Green'])),\n",
" ('Grove', set(['Aspen Grove'])),\n",
" ('Gularte', set(['Paseo Gularte'])),\n",
" ('Gulch', set(['Rodeo Creek Gulch'])),\n",
" ('H', set(['Avenue H'])),\n",
" ('Hall', set(['McCone Hall'])),\n",
" ('Hamilton', set(['Hamilton', 'Mount Hamilton'])),\n",
" ('Harrison', set(['Harrison'])),\n",
" ('Hawkins', set(['A B C D E F Hawkins'])),\n",
" ('Hill',\n",
" set(['Blossom Hill',\n",
" 'California Street ( tra Polk St & Larkin St ) a Nob Hill'])),\n",
" ('Hwy',\n",
" set(['2003 Cabrillo Hwy',\n",
" '2100 Napa-Vallejo Hwy',\n",
" '21265 Coast Hwy',\n",
" '3111 N St Helena Hwy',\n",
" '3535 N St Helena Hwy',\n",
" '40 Shoreline Hwy',\n",
" '4201 Old Sonoma Hwy',\n",
" 'Airline Hwy',\n",
" 'Gravenstein Hwy',\n",
" 'Monterey Hwy',\n",
" 'Redwood Hwy',\n",
" 'San Juan Hwy'])),\n",
" ('I-280', set(['Page Mill Rd @ Arastradero Rd Int S Of I-280'])),\n",
" ('I-580', set(['E Of Center St @ I-580'])),\n",
" ('I-580)',\n",
" set(['N Side Of Foothill Blvd @ John Dr (Near I-580)',\n",
" 'Portola Near Alviso Place, (1/2 Mi From I-580)'])),\n",
" ('I-80',\n",
" set(['Nw Cnr Peabody Rd / Cliffside Dr Int @ I-80',\n",
" 'Richmond Pkwy @ I-80'])),\n",
" ('Ic',\n",
" set(['Ne & Se Quads Us 101 / Atherton Ave Ic',\n",
" 'Ne & Se Quads Us 101 / Rowland Blvd Ic',\n",
" 'Ne Quad I-280 / Edgewood Rd Ic',\n",
" 'Nw Cnr Of Curtola Pkwy & Lemon St @ I-780 025 Mi W Of I-780 / I-80 Ic',\n",
" 'Nw Quad Green Valley Rd / I-80 Ic',\n",
" 'Nw Quad Sr 84 / Ardenwood Blvd Ic',\n",
" 'Se Quad Of E 2nd St / I-780 Ic',\n",
" 'Se Quad Sr 238 (Mission Blvd) / I-680 Ic',\n",
" 'Se Quad Sr 92 / Ralston Ic',\n",
" 'Sw Cnr Of Curtola Pkwy & Lemon St @ I-780 025 Mi W Of I-780 / I-80 Ic',\n",
" 'Sw Of Sr 29 / Imola Ave Ic',\n",
" 'Sw Quad Enterprise Dr / I-80 Ic'])),\n",
" ('Ingoglia', set(['Via Ingoglia'])),\n",
" ('Int',\n",
" set(['012 Mi Ne Of Sr 1 / Linda Mar Blvd Int',\n",
" 'Main St & Rr Ave Int',\n",
" 'S/E Quad I-5 / Sr 12 Int',\n",
" 'S/E Quad Sr 99 / Sr 12 Int',\n",
" 'Se Cnr Sheldon Rd Int',\n",
" 'Se Quad Sr 1 / Crespi Dr Int',\n",
" 'Sw Quad I-280 / Sr 84 Ic Woodside Rd & Linderbrook Rd Int'])),\n",
" ('Irwin',\n",
" set(['Under Us 101 Btwn 3rd St & Mission Ave Btwn Hetherton & Irwin'])),\n",
" ('Jefferson', set(['Third & Jefferson'])),\n",
" ('Julian', set(['West Julian'])),\n",
" ('King', set(['King'])),\n",
" ('Knxville', set(['4454 Berryessa Knxville'])),\n",
" ('Landing', set(['Hamilton Landing'])),\n",
" ('Las', set(['Alameda De Las'])),\n",
" ('Latrobe', set(['S Of Us 50, E Side Of Latrobe'])),\n",
" ('Leslie', set(['Leslie'])),\n",
" ('Lindbergh', set(['Ne Quad Us 101 / 3rd Ave Off Lindbergh'])),\n",
" ('Ln',\n",
" set(['A Marshall Ln',\n",
" 'Apricot Ln',\n",
" 'Arbour Ln',\n",
" 'Aubrey Ln',\n",
" 'Barnes Ln',\n",
" 'Beresini Ln',\n",
" 'Blossom Ln',\n",
" 'Crittenden Ln',\n",
" 'Dealy Ln',\n",
" 'Elan Village Ln',\n",
" 'Gardenia Ln',\n",
" 'Gaundabert Ln',\n",
" 'Heatherwood Ln',\n",
" 'Hunter Ln',\n",
" 'Joes Ln',\n",
" 'Jonquil Ln',\n",
" 'Lovers Ln',\n",
" 'Marshall Ln',\n",
" 'Pearce Ln',\n",
" 'Peartree Ln',\n",
" 'Skillman Ln',\n",
" 'Sundown Ln',\n",
" 'Thomas Ln',\n",
" 'Vista Ln',\n",
" 'Walnut Ln',\n",
" 'West March Ln',\n",
" 'Weyburn Ln'])),\n",
" ('Ln.', set(['Bont Ln.'])),\n",
" ('Loma', set(['La Loma'])),\n",
" ('Loop',\n",
" set(['Cedar Pointe Loop',\n",
" 'Engineering Loop',\n",
" 'Hamlin Loop',\n",
" 'Infinite Loop',\n",
" 'Village View Loop'])),\n",
" ('Lugano', set(['Via Lugano'])),\n",
" ('Luna', set(['Calle de Luna'])),\n",
" ('M', set(['Avenue M', 'Port Rd M'])),\n",
" ('Maclane', set(['Maclane'])),\n",
" ('Mall',\n",
" set(['Capitol Mall',\n",
" 'Escondido Mall',\n",
" 'Galvez Mall',\n",
" 'Lasuen Mall',\n",
" 'Lomita Mall',\n",
" 'Pacific Avenue Mall',\n",
" 'Panama Mall',\n",
" 'Physical Sciences Mall',\n",
" 'Sam McDonald Mall',\n",
" 'Serra Mall',\n",
" 'Via Pueblo Mall'])),\n",
" ('Manzanita', set(['Manzanita'])),\n",
" ('Mar', set(['Camino del Mar', 'Rancho Del Mar'])),\n",
" ('Marina', set(['Pacific Marina', 'San Leandro Marina'])),\n",
" ('Market', set(['Market'])),\n",
" ('Market/Castro', set(['Market/Castro'])),\n",
" ('Market/Noe', set(['Market/Noe'])),\n",
" ('Marketplace', set(['The Marketplace'])),\n",
" ('Mason', set(['Fort Mason'])),\n",
" ('Meadowood', set(['Meadowood'])),\n",
" ('Merrill', set(['Merrill'])),\n",
" ('Mission', set(['Mission'])),\n",
" ('Monte', set(['North Via Monte'])),\n",
" ('N', set(['El Camino Real N', 'Petaluma Blvd N'])),\n",
" ('Ness', set(['Van Ness'])),\n",
" ('Norte', set(['Via Vaquero Norte'])),\n",
" ('North',\n",
" set(['Cabrillo Highway North',\n",
" 'Magnolia Drive North',\n",
" 'Mission Bay Boulevard North',\n",
" 'Petaluma Blvd. North',\n",
" 'Petaluma Boulevard North'])),\n",
" ('Northgate', set(['Northgate'])),\n",
" ('Oakridge', set(['Oakridge'])),\n",
" ('Oaks', set(['Waverley Oaks'])),\n",
" ('Ora', set(['Avenue Del Ora'])),\n",
" ('Oro', set(['Vista De Oro'])),\n",
" ('Ortega', set(['Via Ortega'])),\n",
" ('Pablo', set(['Camino Pablo', 'Via Juan Pablo'])),\n",
" ('Pacific', set(['Pacific'])),\n",
" ('Padre', set(['Via Padre'])),\n",
" ('Palms', set(['Avenue of the Palms'])),\n",
" ('Paraiso', set(['El Camino Paraiso'])),\n",
" ('Park',\n",
" set(['College Park', 'South Park', 'Sr 238 @ Mission San Jose Park'])),\n",
" ('parkway', set(['Bridge parkway'])),\n",
" ('Path',\n",
" set(['Arden Path',\n",
" 'Indian Rock Path',\n",
" 'Mendocino Path',\n",
" 'Oak Street Path',\n",
" 'Parnassus Path'])),\n",
" ('PH', set(['Bridle PH'])),\n",
" ('Piero', set(['Avenida Del Piero'])),\n",
" ('PK', set(['Trinity PK', 'Val Dervin PK'])),\n",
" ('Pkwy',\n",
" set(['Community Pkwy',\n",
" 'Del Valle Pkwy',\n",
" 'Oak Ave Pkwy',\n",
" 'Technology Pkwy'])),\n",
" ('PKWY', set(['Southport P PKWY'])),\n",
" ('Pl',\n",
" set(['Bordeaux Pl',\n",
" 'N Gettysburg Pl',\n",
" 'San Francisco/Oakland Bridge Toll Pl',\n",
" 'Tuscany Pl',\n",
" 'Verona Pl',\n",
" 'Vicksburg Pl'])),\n",
" ('PL',\n",
" set(['Alexandria PL',\n",
" 'Arabian PL',\n",
" 'Beverly PL',\n",
" 'Buckskin PL',\n",
" 'Burns PL',\n",
" 'Chambord PL',\n",
" 'Cumberland PL',\n",
" 'Fredericksburg PL',\n",
" 'Gettysburg PL',\n",
" 'Grigsby PL',\n",
" 'Harrisburg PL',\n",
" 'Herndon PL',\n",
" 'Janet PL',\n",
" 'Le Mans PL',\n",
" 'Leesburg PL',\n",
" 'Morgan PL',\n",
" 'Morning Dew PL',\n",
" 'Mustang PL',\n",
" 'Richmond PL',\n",
" 'Rothesay PL',\n",
" 'Vicksburg PL',\n",
" 'Williamsburg PL'])),\n",
" ('PlaceZ', set(['Elkhorn PlaceZ', 'Town Center PlaceZ'])),\n",
" ('Plz', set(['Toro Plz', 'Woodside Plz'])),\n",
" ('Point', set(['Portsmouth Point'])),\n",
" ('Post', set(['Post'])),\n",
" ('Powell', set(['Bay and Powell', 'Powell'])),\n",
" ('Prado', set(['Nw Of Us 101 @ Nave Dr Oc & Alameda Del Prado'])),\n",
" ('Presada', set(['Paseo Presada'])),\n",
" ('PT', set(['Crescent PT', 'Freshwater PT', 'Griffin PT', 'Salmon PT'])),\n",
" ('Pueblo', set(['Via Pueblo'])),\n",
" ('Pulgas',\n",
" set(['Alamed de las Pulgas',\n",
" 'Alameda De Las Pulgas',\n",
" 'Alameda de Las Pulgas',\n",
" 'Alameda de las Pulgas'])),\n",
" ('PZ', set(['Hunter Square PZ', 'Portage PZ', 'St Marks PZ'])),\n",
" ('Quad', set(['North Quad'])),\n",
" ('Ramon', set(['815 Camino Ramon'])),\n",
" ('Ramps', set(['@ Pasatiempo Sb Ramps'])),\n",
" ('Raod', set(['Orchard Raod'])),\n",
" ('Rd',\n",
" set(['100 Howell Mountain Rd',\n",
" '1553 Colony Rd',\n",
" '1820 Monticello Rd',\n",
" '5520 Berryessa Knox. Rd',\n",
" '99 Frontage Rd',\n",
" 'A & B San Felipe Rd',\n",
" 'Anzar Rd',\n",
" 'Aromitas Rd',\n",
" 'Ascot Rd',\n",
" 'Best Rd',\n",
" 'Blue Ravine Rd',\n",
" 'Bollinger Rd',\n",
" 'Bolsa Rd',\n",
" 'Bonnie View Rd',\n",
" 'Bridge Rd',\n",
" 'Bridgevale Rd',\n",
" 'Briggs Rd',\n",
" 'Brookwood Ave / Sr 12 Ic N Of Bennett Valley Rd',\n",
" 'Bruceville Rd',\n",
" 'Buena Vista Rd',\n",
" 'Carpenteria Rd',\n",
" 'Chittenden Rd',\n",
" 'Cole Rd',\n",
" 'Cowden Rd',\n",
" 'Diablo Hills Rd',\n",
" 'Embarcadero Rd',\n",
" 'Failing Rd',\n",
" 'Fairview Rd',\n",
" 'Fallon Rd',\n",
" 'Florin Rd',\n",
" 'Forest Rd',\n",
" 'Gateway Rd',\n",
" 'Graf Rd',\n",
" 'Hidden Valley Rd',\n",
" 'Hillcrest Rd',\n",
" 'Hillside Rd',\n",
" 'Homestead Rd',\n",
" 'Hospital Rd',\n",
" 'Jackson Rd',\n",
" 'Leisure Town Rd',\n",
" 'Lone Tree Rd',\n",
" 'Magladry Rd',\n",
" 'Mansfield Rd',\n",
" 'Marshlands Rd',\n",
" 'Mccloskey Rd',\n",
" 'Mecartney Rd',\n",
" 'Menzel Rd',\n",
" 'Merrill Rd',\n",
" 'Miller Rd',\n",
" 'Mount Hermon Rd',\n",
" 'N Chappell Rd',\n",
" 'Nash Rd',\n",
" 'Nw Quad I-80 / Magazine St Ic Off Lincoln Rd @ San Miguel Rd',\n",
" 'Old Ranch Rd',\n",
" 'Old School Rd',\n",
" 'Payne Rd',\n",
" 'Quarry Rd',\n",
" 'Quinn Canyon Rd',\n",
" 'Rocks Rd',\n",
" 'Rollins Rd',\n",
" 'S Chappell Rd',\n",
" 'S. Power Inn Rd',\n",
" 'Salinas Rd',\n",
" 'San Felipe Rd',\n",
" 'San Jua Rd',\n",
" 'San Juan Canyon Rd',\n",
" 'San Juan Rd',\n",
" 'San Mateo Rd',\n",
" 'Sand Hill Rd',\n",
" 'Santa Ana Rd',\n",
" 'Santa Ana Valley Rd',\n",
" 'Saratoga Los Gatos Rd',\n",
" 'School Rd',\n",
" 'Se Quad I-680 / Rudgear Rd',\n",
" 'Searle Rd',\n",
" 'Shore Rd',\n",
" 'South Chrisman Rd',\n",
" 'Southside Rd',\n",
" 'Stoneridge Mall Rd',\n",
" 'Sunnyslope Rd',\n",
" 'Sw Quad I-680 / Bollinger Canyon Rd',\n",
" 'Ursuline Rd',\n",
" 'W Graf Rd',\n",
" 'W Lincoln Rd',\n",
" 'W Middlefield Rd',\n",
" 'Willow Rd',\n",
" 'Woodside Rd',\n",
" 'Wright Rd',\n",
" 'Ygnacio Valley Rd'])),\n",
" ('RD', set(['3132 MT VEEDER RD'])),\n",
" ('Rd.',\n",
" set(['Alpine Rd.',\n",
" 'E. French Camp Rd.',\n",
" 'Millerick Rd.',\n",
" 'Monterey Rd.',\n",
" 'N. Union Rd.',\n",
" 'Roslea Rd.'])),\n",
" ('Real',\n",
" set(['E El Camino Real',\n",
" 'Easst El Camino Real',\n",
" 'East El Camino Real',\n",
" 'El Camino Real',\n",
" 'El Circulo Del Real',\n",
" 'S El Camino Real',\n",
" 'South El Camino Real',\n",
" 'South el Camino Real',\n",
" 'Villa Real',\n",
" 'W. El Camino Real',\n",
" 'West El Camino Real'])),\n",
" ('Rey', set(['9225 Calle Del Rey'])),\n",
" ('Rhein', set(['Stein Am Rhein'])),\n",
" ('Ridge', set(['Roble Ridge'])),\n",
" ('road', set(['680 Sanitarium road'])),\n",
" ('Robles', set(['Dante Robles'])),\n",
" ('robles', set(['rio robles'])),\n",
" ('Rock', set(['Mission Rock'])),\n",
" ('Rodriguez', set(['Via Rodriguez'])),\n",
" ('Row', set(['Alvarado Row', 'Santana Row'])),\n",
" ('Run', set(['Martini Run', 'Quail Run', 'Whippett Run'])),\n",
" ('Russell', set(['Russell'])),\n",
" ('S', set(['Monterey St 402 6t S', 'Third & 310 Fourth S'])),\n",
" ('Schwerin', set(['Schwerin'])),\n",
" ('Seabright', set(['Seabright'])),\n",
" ('Serra', set(['Via Serra'])),\n",
" ('side', set(['San Juan Rd #side'])),\n",
" ('Side',\n",
" set(['Elkhorn Blvd Sw Side',\n",
" 'Twin Cities Rd (Sr 104) / Sr /99 Int Se Side'])),\n",
" ('Siding', set(['Bonair Siding'])),\n",
" ('Sobrante', set(['Camino Sobrante'])),\n",
" ('Sol', set(['Tierra Del Sol'])),\n",
" ('South',\n",
" set(['Brooks Road South',\n",
" 'Cabrillo Highway South',\n",
" 'Governors Avenue South',\n",
" 'Gravenstein Highway South',\n",
" 'Magnolia Drive South',\n",
" 'Saint Helena Highway South'])),\n",
" ('south', set(['Petaluma Blvd south'])),\n",
" ('Southgate', set(['Southgate'])),\n",
" ('Spencer', set(['E & W Of Us 101 @ Monte Mar & Spencer'])),\n",
" ('Sq', set(['Evergreen Village Sq'])),\n",
" ('square', set(['33 union square'])),\n",
" ('St',\n",
" set([' Laguna St',\n",
" '&402 A & B 3rd St',\n",
" '1113 Washington St',\n",
" '1125 Third St',\n",
" '1234 Washington St',\n",
" '1480 Main St',\n",
" '1539 First St',\n",
" '162 N. Main St',\n",
" '170 S Market St',\n",
" '19th St',\n",
" '1st St',\n",
" '225 Rooney St',\n",
" '2nd St',\n",
" '3rd St',\n",
" '4th St',\n",
" '5th St',\n",
" '6th St',\n",
" '7th St',\n",
" '9th St',\n",
" 'A 4th St',\n",
" 'Ahwahnee St',\n",
" 'Alvarado St',\n",
" 'Arthur St',\n",
" 'Brannan St',\n",
" 'Bryant St',\n",
" 'Burbank St',\n",
" 'Bush St',\n",
" 'Casa Verde St',\n",
" 'Cesar Chavez St St',\n",
" 'Church St',\n",
" 'College St',\n",
" 'Connecticut St',\n",
" 'Cumberland St',\n",
" 'Cushman St',\n",
" 'D St',\n",
" 'D St & 51 4th St',\n",
" 'Delancey St',\n",
" 'Donner St',\n",
" 'E Bidwell St',\n",
" 'E Haydon St',\n",
" 'E Of Sr 242/ S Of Willow Pass Rd/ W Of Market St',\n",
" 'E Park St',\n",
" 'East St',\n",
" 'Embarcadero St',\n",
" 'Folsom St',\n",
" 'Franklin St',\n",
" 'Garden St',\n",
" 'Granada St',\n",
" 'Green St',\n",
" 'Hawkins St',\n",
" 'Haydon St',\n",
" 'Hayes St',\n",
" 'Hill St',\n",
" 'Howard St',\n",
" 'Hyde St',\n",
" 'J St',\n",
" 'Jefferson St',\n",
" 'Kearny St',\n",
" 'Keller St',\n",
" 'King St',\n",
" 'Lakeville St',\n",
" 'Lang St',\n",
" 'Laurel St',\n",
" 'Leavenworth St',\n",
" 'Line St',\n",
" 'Lyon St',\n",
" 'Madison St',\n",
" 'Maple St',\n",
" 'Market St',\n",
" 'Mccarthy St',\n",
" 'Mccray St',\n",
" 'Mendell St',\n",
" 'Meridian St',\n",
" 'Mission St',\n",
" 'Missouri St',\n",
" 'Monroe St',\n",
" 'Monte Carlo St',\n",
" 'Monterey St',\n",
" 'N Monterey St',\n",
" 'N Sally St',\n",
" 'Nevada St',\n",
" 'Noe St',\n",
" 'North St',\n",
" 'Park St',\n",
" 'Pear St',\n",
" 'Peralta St',\n",
" 'Polk St',\n",
" 'Powell St',\n",
" 'Prune St',\n",
" 'Ramona St',\n",
" 'Recht St',\n",
" 'Rustic St',\n",
" 'S Delaware St',\n",
" 'Sally St',\n",
" 'San Benito St',\n",
" 'San Jose St',\n",
" 'Severinsen St',\n",
" 'South St',\n",
" 'Tahualami & 4th St',\n",
" 'Tahualami St',\n",
" 'Thompson St',\n",
" 'Trancas St',\n",
" 'Turk St',\n",
" 'Under I-580 Btwn Fruitvale / Champion St',\n",
" 'Under I-880 @ 7th St & Linden St',\n",
" 'Velado St',\n",
" 'Victoria St',\n",
" 'W 2nd St',\n",
" 'W Dana St',\n",
" 'Washington St',\n",
" 'Williams St'])),\n",
" ('ST', set(['L ST'])),\n",
" ('st', set(['8th st', 'Crane st', 'Dyer st'])),\n",
" ('St.',\n",
" set(['5th St.',\n",
" '9th St.',\n",
" 'California St.',\n",
" 'East Bidwell St.',\n",
" 'Halleck St.',\n",
" 'Market St.',\n",
" 'McKinley St.',\n",
" 'PurpleLeaf St.',\n",
" 'Sutter St.',\n",
" 'Valencia St.',\n",
" 'Washington St.',\n",
" 'Webster St.'])),\n",
" ('Stanford', set(['Stanford'])),\n",
" ('Station', set(['Tennant Station'])),\n",
" ('Steps', set(['Bancroft Steps'])),\n",
" ('Stockton', set(['Geneva Pt Dr @ Stockton'])),\n",
" ('Stores', set(['Across Street From Factory Outlet Stores'])),\n",
" ('street',\n",
" set(['Center street',\n",
" 'N 1st street',\n",
" 'Plaza street',\n",
" 'S street',\n",
" 'Vallejo street',\n",
" 'laguna street',\n",
" 'market street',\n",
" 'townsend street'])),\n",
" ('sundale', set(['sundale'])),\n",
" ('Sur', set(['Via Vaquero Sur'])),\n",
" ('Tahualami', set(['Tahualami'])),\n",
" ('Tassajara', set(['Camino Tassajara'])),\n",
" ('Telegraph', set(['3605 Telegraph'])),\n",
" ('terrace', set(['Ice House terrace'])),\n",
" ('Townsend', set(['Townsend'])),\n",
" ('Toyon', set(['Upper Toyon'])),\n",
" ('Uppr', set(['Freeport Blvd Uppr'])),\n",
" ('Ventana', set(['Via Ventana'])),\n",
" ('Vera', set(['Villa Vera'])),\n",
" ('Verda', set(['Valle Verda'])),\n",
" ('Via', set(['La Casa Via'])),\n",
" ('View', set(['Norwood View'])),\n",
" ('Village', set(['Seascape Village', 'Town and Country Village'])),\n",
" ('way', set(['san lorenzo way'])),\n",
" ('Way)', set(['Congress Spring Road (Big Basin Way)'])),\n",
" ('Wedemeyer', set(['Wedemeyer'])),\n",
" ('West',\n",
" set(['1st Street West',\n",
" '2nd Street West',\n",
" '3rd Street West',\n",
" '5th Street West',\n",
" '7th Street West',\n",
" 'Buena Vista Avenue West',\n",
" 'Campus Dr. West',\n",
" 'Campus Drive West',\n",
" 'Lincoln Road West',\n",
" 'Portage Bay West',\n",
" 'Vanderbilt Court West',\n",
" 'West'])),\n",
" ('Wharf', set(['Fishermans Wharf'])),\n",
" ('Winchester', set(['Winchester']))]\n",
"\n"
]
}
],
"source": [
"audit.summary(audit.Options.street_analysis)"
]
},
{
"cell_type": "markdown",
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"source": [
"## Summary of Audit:\n",
"\n",
"From the audit, I am choosing to continue addressing street abbreviations by adding more conversions to the work done in Lesson 6. I am also choosing to convert additional \"addr:(?i)[a-z].\" keys under the \"address\" name.\n",
"\n",
"I have also noticed that there is abundunt amount of keys prefixed with \"tiger\". After further research, I have discovered that it is data that was imported into openstreetmap provided by the US Census during the early stages in openstreet development. \"tiger:county\" will be accepted as \"address:county\" and \"tiger:zip_left\" will be accepted as \"address:postcode\" if no values for those keys exist already."
]
},
{
"cell_type": "code",
"execution_count": 183,
"metadata": {
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"collapsed": false
},
"outputs": [],
"source": [
"class ShapeXmlToJson(object):\n",
" \"\"\"Shapes passed in Open Street Maps xml file to JSON\"\"\"\n",
" \n",
" attrtovar_toplevel = set([\"id\", \"visible\"])\n",
" \"\"\"Attributes to convert into elements under the constructed member\"\"\"\n",
" \n",
" attrtovar_created = set([\"version\", \"changeset\", \"timestamp\", \"user\", \"uid\"])\n",
" \"\"\"Attributes to convert to elements under the created name inside the member\"\"\"\n",
"\n",
" re_c = re.compile\n",
" problemchars = re_c(r'[=\\+/&<>;\\'\"\\?%#$@\\,\\. \\t\\r\\n]')\n",
" street_type_re = re_c(r'\\b\\S+\\.?$', re.IGNORECASE)\n",
" address_type = re_c(r'^addr:')\n",
" \n",
" \n",
" corrections = [(re_c(\"a[bv]e\\.?n?[ui]?e?$\", re.IGNORECASE), \"Avenue\"),\n",
" (re_c(\"blvd\\.?$\", re.IGNORECASE), \"Boulevard\"),\n",
" (re_c(\"bouleva.*$\", re.IGNORECASE), \"Boulevard\"),\n",
" (re_c(\"circ?l?e?\\.?$\", re.IGNORECASE), \"Circle\"),\n",
" (re_c(\"ct\\.?$\", re.IGNORECASE), \"Court\"),\n",
" (re_c(\"court$\", re.IGNORECASE), \"Court\"),\n",
" (re_c(\"ctr\\.?$\", re.IGNORECASE), \"Center\"),\n",
" (re_c(\"center$\", re.IGNORECASE), \"Center\"),\n",
" (re_c(\"dr\\.?$\", re.IGNORECASE), \"Drive\"),\n",
" (re_c(\"drive$\", re.IGNORECASE), \"Drive\"),\n",
" (re_c(\"expwy\\.?$\", re.IGNORECASE), \"Expressway\"),\n",
" (re_c(\"expressway$\", re.IGNORECASE), \"Expressway\"),\n",
" (re_c(\"hwy\\.?$\", re.IGNORECASE), \"Highway\"),\n",
" (re_c(\"highway$\", re.IGNORECASE), \"Highway\"),\n",
" (re_c(\"ln\\.?$\", re.IGNORECASE), \"Lane\"),\n",
" (re_c(\"lane$\", re.IGNORECASE), \"Lane\"),\n",
" (re_c(\"pkwy\\.?$\", re.IGNORECASE), \"Parkway\"),\n",
" (re_c(\"pl\\.?$\", re.IGNORECASE), \"Place\"),\n",
" (re_c(\"rd\\.?$\", re.IGNORECASE), \"Road\"),\n",
" (re_c(\"road$\", re.IGNORECASE), \"Road\"),\n",
" (re_c(\"st\\.?$\", re.IGNORECASE), \"Street\"),\n",
" (re_c(\"street$\", re.IGNORECASE), \"Street\"),\n",
" (re_c(\"terrace$\", re.IGNORECASE), \"Terrace\"),\n",
" (re_c(\"way$\", re.IGNORECASE), \"Way\")]\n",
" \"\"\"Mapping of corrections of street types\"\"\"\n",
" \n",
" def __init__(self, infile, outfile=None, store_to_var=False, pretty=False):\n",
" self.source = infile\n",
" self.store_to_var = store_to_var\n",
" self.pretty = pretty\n",
" self.data = []\n",
" if outfile is None:\n",
" self.outfile = \"{0}.json\".format(self.source)\n",
" \n",
" def shape(self):\n",
" \"\"\"Converts XML to JSON and writes it to file suffixed with .json\"\"\"\n",
" with codecs.open(self.outfile, \"w\") as fo:\n",
" for _, elem in ET.iterparse(self.source):\n",
" el = self.shape_element(elem)\n",
" if el is not None:\n",
" if self.store_to_var:\n",
" self.data.append(el)\n",
" if self.pretty:\n",
" fo.write(json.dumps(el, indent=2)+\"\\n\")\n",
" else:\n",
" fo.write(json.dumps(el) + \"\\n\")\n",
" \n",
" @staticmethod\n",
" def shape_element(element):\n",
" '''\n",
" Converts passed in XML tag to JSON with each member following\n",
" the structure:\n",
" {\n",
" \"id\": \"261114295\", \n",
" \"visible\": \"true\", \n",
" \"type\": \"node\", \n",
" \"pos\": [41.9730791, -87.6866303], # Optional\n",
" \"created\": {\n",
" \"changeset\": \"11129782\", \n",
" \"user\": \"bbmiller\", \n",
" \"version\": \"7\", \n",
" \"uid\": \"451048\", \n",
" \"timestamp\": \"2012-03-28T18:31:23Z\"\n",
" }\n",
" }\n",
" '''\n",
" if element.tag == \"node\" or element.tag == \"way\":\n",
" node = {}\n",
" created = {}\n",
" pos = [None,None]\n",
" node_refs = []\n",
"\n",
" node[\"created\"] = created\n",
"\n",
" node[\"type\"] = element.tag\n",
"\n",
" for k,v in element.attrib.iteritems():\n",
" if k in ShapeXmlToJson.attrtovar_created:\n",
" node[\"created\"][k] = v\n",
" elif k == \"lat\":\n",
" pos[0] = float(v)\n",
" elif k == \"lon\":\n",
" pos[1] = float(v)\n",
" elif k in ShapeXmlToJson.attrtovar_toplevel:\n",
" node[k] = v\n",
" else:\n",
" raise KeyError(k)\n",
" if (pos[0] is not None) and (pos[1] is not None):\n",
" node[\"pos\"] = pos\n",
"\n",
" for tag in element.iter(\"tag\"):\n",
" k,v = tag.attrib['k'], tag.attrib['v']\n",
" k = k.lower().strip()\n",
" v = v.strip()\n",
"\n",
" if ShapeXmlToJson.problemchars.search(k):\n",
" print(\"------------Problem inserting {}:{}--------------\".format(k,v))\n",
" print(\"Node:\")\n",
" pprint(node)\n",
" print(\"-------------------------------------------------\")\n",
" continue\n",
" elif k == \"address\":\n",
" print(\"------------Ignoring Address Key---------------\")\n",
" print(\"{}:{}\".format(k,v))\n",
" pprint(node)\n",
" continue\n",
" elif k == \"addr:street\":\n",
" if \"address\" not in node:\n",
" node[\"address\"] = {}\n",
" val = ShapeXmlToJson.street_name_convert(v)\n",
" node[\"address\"][\"street\"] = val\n",
" elif k == \"addr:housenumber\":\n",
" if \"address\" not in node:\n",
" node[\"address\"] = {}\n",
" node[\"address\"][\"housenumber\"] = v\n",
" elif k.count(':') == 1 and ShapeXmlToJson.address_type.match(k):\n",
" # Convert only keys that have one colon and ignore the rest for now\n",
" if \"address\" not in node:\n",
" node[\"address\"] = {}\n",
" node[\"address\"][k.split(':')[1]] = v\n",
" elif k == \"tiger:county\":\n",
" # Convert old tiger data\n",
" if \"address\" not in node:\n",
" node[\"address\"] = {}\n",
" elif \"county\" in node[\"address\"]:\n",
" continue\n",
" if v.count(',') > 0:\n",
" v = v.split(',')[0]\n",
"\n",
" node[\"address\"][\"county\"] = v\n",
" elif k == \"tiger:zip_left\":\n",
" # Convert old tiger data\n",
" if \"address\" not in node:\n",
" node[\"address\"] = {}\n",
" elif \"postcode\" in node[\"address\"]:\n",
" continue\n",
"\n",
" node[\"address\"][\"postcode\"] = v \n",
" else:\n",
" node[k] = v\n",
"\n",
" for tag in element.iter(\"nd\"):\n",
" ref = tag.attrib[\"ref\"]\n",
" node_refs.append(ref)\n",
"\n",
" if len(node_refs) != 0:\n",
" node[\"node_refs\"] = node_refs\n",
" \n",
" element.clear()\n",
" return node\n",
" else:\n",
" return None\n",
" \n",
" @staticmethod\n",
" def street_name_convert(orig):\n",
" \"\"\"Checks and converts street names with abbreviated\n",
" street types to the full spelling\n",
" \"\"\"\n",
" corrected = orig\n",
" m = ShapeXmlToJson.street_type_re.search(orig)\n",
" if m:\n",
" street_type = m.group()\n",
" for re_tuple in ShapeXmlToJson.corrections:\n",
" match = re_tuple[0].match(street_type)\n",
" if match:\n",
" # Don't change if string to be replaced is the same\n",
" if street_type != re_tuple[1]:\n",
" corrected = ShapeXmlToJson.street_type_re.sub(re_tuple[1], orig)\n",
" print(\"{} -> {}\".format(orig, corrected))\n",
" break\n",
" return corrected"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"North Lincoln Ave -> North Lincoln Avenue\n",
"North Lincoln blvd. -> North Lincoln Boulevard\n",
"North Lincoln bouleva. -> North Lincoln Boulevard\n",
"N. Lincoln Ave -> N. Lincoln Avenue\n",
"Baldwin Rd. -> Baldwin Road\n",
"West Lexington St. -> West Lexington Street\n",
"[{'created': {'changeset': '11129782',\n",
" 'timestamp': '2012-03-28T18:31:23Z',\n",
" 'uid': '451048',\n",
" 'user': 'bbmiller',\n",
" 'version': '7'},\n",
" 'id': '261114295',\n",
" 'pos': [41.9730791, -87.6866303],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '8448766',\n",
" 'timestamp': '2011-06-15T17:04:54Z',\n",
" 'uid': '451048',\n",
" 'user': 'bbmiller',\n",
" 'version': '6'},\n",
" 'id': '261114296',\n",
" 'pos': [41.9730416, -87.6878512],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '8581395',\n",
" 'timestamp': '2011-06-29T14:14:14Z',\n",
" 'uid': '451048',\n",
" 'user': 'bbmiller',\n",
" 'version': '5'},\n",
" 'id': '261114299',\n",
" 'pos': [41.9729565, -87.6939548],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '8581395',\n",
" 'timestamp': '2011-06-29T14:14:14Z',\n",
" 'uid': '451048',\n",
" 'user': 'bbmiller',\n",
" 'version': '5'},\n",
" 'id': '261146436',\n",
" 'pos': [41.970738, -87.6976025],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '8581395',\n",
" 'timestamp': '2011-06-29T14:14:15Z',\n",
" 'uid': '451048',\n",
" 'user': 'bbmiller',\n",
" 'version': '7'},\n",
" 'id': '261147304',\n",
" 'pos': [41.9740068, -87.6988576],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '8581395',\n",
" 'timestamp': '2011-06-29T14:14:14Z',\n",
" 'uid': '451048',\n",
" 'user': 'bbmiller',\n",
" 'version': '5'},\n",
" 'id': '261224274',\n",
" 'pos': [41.9707656, -87.6938669],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '8448766',\n",
" 'timestamp': '2011-06-15T16:55:37Z',\n",
" 'uid': '451048',\n",
" 'user': 'bbmiller',\n",
" 'version': '47'},\n",
" 'id': '293816175',\n",
" 'pos': [41.9730154, -87.6890403],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '15348240',\n",
" 'timestamp': '2013-03-13T07:46:29Z',\n",
" 'uid': '567034',\n",
" 'user': 'Umbugbene',\n",
" 'version': '37'},\n",
" 'id': '305896090',\n",
" 'pos': [41.9749225, -87.6891198],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '15348240',\n",
" 'timestamp': '2013-03-13T08:02:56Z',\n",
" 'uid': '567034',\n",
" 'user': 'Umbugbene',\n",
" 'version': '12'},\n",
" 'id': '317636974',\n",
" 'pos': [41.9740292, -87.701243],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '15348240',\n",
" 'timestamp': '2013-03-13T08:08:01Z',\n",
" 'uid': '567034',\n",
" 'user': 'Umbugbene',\n",
" 'version': '13'},\n",
" 'id': '317636971',\n",
" 'pos': [41.9740556, -87.6979712],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '14927972',\n",
" 'timestamp': '2013-02-05T22:43:49Z',\n",
" 'uid': '567034',\n",
" 'user': 'Umbugbene',\n",
" 'version': '2'},\n",
" 'id': '317637399',\n",
" 'pos': [41.9705609, -87.7012048],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '14927972',\n",
" 'timestamp': '2013-02-05T22:43:49Z',\n",
" 'uid': '567034',\n",
" 'user': 'Umbugbene',\n",
" 'version': '2'},\n",
" 'id': '317637398',\n",
" 'pos': [41.9706972, -87.7012109],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '8448766',\n",
" 'timestamp': '2011-06-15T17:04:54Z',\n",
" 'uid': '451048',\n",
" 'user': 'bbmiller',\n",
" 'version': '3'},\n",
" 'id': '365214872',\n",
" 'pos': [41.973113, -87.6847998],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '8581395',\n",
" 'timestamp': '2011-06-29T14:14:15Z',\n",
" 'uid': '451048',\n",
" 'user': 'bbmiller',\n",
" 'version': '6'},\n",
" 'id': '261299091',\n",
" 'pos': [41.9747482, -87.6988886],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '8448766',\n",
" 'timestamp': '2011-06-15T17:04:54Z',\n",
" 'uid': '451048',\n",
" 'user': 'bbmiller',\n",
" 'version': '6'},\n",
" 'id': '261114294',\n",
" 'pos': [41.9731219, -87.6841979],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '3359748',\n",
" 'timestamp': '2009-12-13T00:36:09Z',\n",
" 'uid': '147510',\n",
" 'user': 'woodpeck_fixbot',\n",
" 'version': '4'},\n",
" 'id': '261210804',\n",
" 'pos': [41.9707217, -87.7000019],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '8581395',\n",
" 'timestamp': '2011-06-29T14:14:15Z',\n",
" 'uid': '451048',\n",
" 'user': 'bbmiller',\n",
" 'version': '7'},\n",
" 'id': '261221422',\n",
" 'pos': [41.9748542, -87.6922652],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '8581395',\n",
" 'timestamp': '2011-06-29T14:14:15Z',\n",
" 'uid': '451048',\n",
" 'user': 'bbmiller',\n",
" 'version': '7'},\n",
" 'highway': 'traffic_signals',\n",
" 'id': '261221424',\n",
" 'pos': [41.9758794, -87.6923639],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'address': {'city': 'Chicago',\n",
" 'housenumber': '5157',\n",
" 'postcode': '60625',\n",
" 'street': 'North Lincoln Avenue'},\n",
" 'amenity': 'restaurant',\n",
" 'created': {'changeset': '17206049',\n",
" 'timestamp': '2013-08-03T16:43:42Z',\n",
" 'uid': '1219059',\n",
" 'user': 'linuxUser16',\n",
" 'version': '2'},\n",
" 'cuisine': 'mexican',\n",
" 'id': '2406124091',\n",
" 'name': 'La Cabana De Don Luis',\n",
" 'outdoor_seating': 'no',\n",
" 'phone': '1 (773)-271-5176',\n",
" 'pos': [41.975703, -87.6921867],\n",
" 'smoking': 'no',\n",
" 'takeaway': 'yes',\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'address': {'city': 'Chicago',\n",
" 'housenumber': '5157',\n",
" 'postcode': '60625',\n",
" 'street': 'North Lincoln Boulevard'},\n",
" 'amenity': 'restaurant',\n",
" 'created': {'changeset': '17206049',\n",
" 'timestamp': '2013-08-03T16:43:42Z',\n",
" 'uid': '1219059',\n",
" 'user': 'linuxUser16',\n",
" 'version': '2'},\n",
" 'cuisine': 'mexican',\n",
" 'id': '2406124091',\n",
" 'name': 'La Cabana De Don Luis',\n",
" 'outdoor_seating': 'no',\n",
" 'phone': '1 (773)-271-5176',\n",
" 'pos': [41.975703, -87.6921867],\n",
" 'smoking': 'no',\n",
" 'takeaway': 'yes',\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'address': {'city': 'Chicago',\n",
" 'housenumber': '5157',\n",
" 'postcode': '60625',\n",
" 'street': 'North Lincoln Boulevard'},\n",
" 'amenity': 'restaurant',\n",
" 'created': {'changeset': '17206049',\n",
" 'timestamp': '2013-08-03T16:43:42Z',\n",
" 'uid': '1219059',\n",
" 'user': 'linuxUser16',\n",
" 'version': '2'},\n",
" 'cuisine': 'mexican',\n",
" 'id': '2406124091',\n",
" 'name': 'La Cabana De Don Luis',\n",
" 'outdoor_seating': 'no',\n",
" 'phone': '1 (773)-271-5176',\n",
" 'pos': [41.975703, -87.6921867],\n",
" 'smoking': 'no',\n",
" 'takeaway': 'yes',\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'address': {'city': 'Chicago',\n",
" 'country': 'US',\n",
" 'housenumber': '4874',\n",
" 'postcode': '60625',\n",
" 'state': 'Illinois',\n",
" 'street': 'N. Lincoln Avenue'},\n",
" 'created': {'changeset': '20187349',\n",
" 'timestamp': '2014-01-25T01:56:10Z',\n",
" 'uid': '1219059',\n",
" 'user': 'linuxUser16',\n",
" 'version': '1'},\n",
" 'id': '2636084635',\n",
" 'name': 'Matty Ks',\n",
" 'phone': '(773)-654-1347',\n",
" 'pos': [41.9705219, -87.6900344],\n",
" 'shop': 'doityourself',\n",
" 'source': 'GPS',\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '8581395',\n",
" 'timestamp': '2011-06-29T14:14:13Z',\n",
" 'uid': '451048',\n",
" 'user': 'bbmiller',\n",
" 'version': '6'},\n",
" 'id': '261198953',\n",
" 'pos': [41.9707413, -87.6963097],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'amenity': 'fast_food',\n",
" 'created': {'changeset': '5288876',\n",
" 'timestamp': '2010-07-22T16:16:51Z',\n",
" 'uid': '26299',\n",
" 'user': 'uboot',\n",
" 'version': '2'},\n",
" 'cuisine': 'sausage',\n",
" 'id': '757860928',\n",
" 'name': \"Shelly's Tasty Freeze\",\n",
" 'pos': [41.9747374, -87.6920102],\n",
" 'type': 'node',\n",
" 'visible': 'true'},\n",
" {'created': {'changeset': '20187382',\n",
" 'timestamp': '2014-01-25T02:01:54Z',\n",
" 'uid': '1219059',\n",
" 'user': 'linuxUser16',\n",
" 'version': '1'},\n",
" 'highway': 'service',\n",
" 'id': '258219703',\n",
" 'node_refs': ['2636086179', '2636086178', '2636086177', '2636086176'],\n",
" 'type': 'way',\n",
" 'visible': 'true'},\n",
" {'address': {'housename': 'Village Hall',\n",
" 'housenumber': '1400',\n",
" 'postcode': '60067',\n",
" 'street': 'Baldwin Road'},\n",
" 'amenity': 'townhall',\n",
" 'created': {'changeset': '11043902',\n",
" 'timestamp': '2012-03-20T18:56:44Z',\n",
" 'uid': '634589',\n",
" 'user': 'Jacobs Studios',\n",
" 'version': '2'},\n",
" 'id': '1683602133',\n",
" 'name': 'Village Hall',\n",
" 'pos': [42.1251718, -88.0780576],\n",
" 'type': 'node'},\n",
" {'addr:street:name': 'Lexington',\n",
" 'addr:street:prefix': 'West',\n",
" 'addr:street:type': 'Street',\n",
" 'address': {'housenumber': '1412', 'street': 'West Lexington Street'},\n",
" 'building': 'yes',\n",
" 'building:levels': '1',\n",
" 'chicago:building_id': '366409',\n",
" 'created': {'changeset': '15353317',\n",
" 'timestamp': '2013-03-13T15:58:04Z',\n",
" 'uid': '674454',\n",
" 'user': 'chicago-buildings',\n",
" 'version': '1'},\n",
" 'id': '209809850',\n",
" 'node_refs': ['2199822281',\n",
" '2199822390',\n",
" '2199822392',\n",
" '2199822369',\n",
" '2199822370',\n",
" '2199822284',\n",
" '2199822281'],\n",
" 'type': 'way',\n",
" 'visible': 'true'}]\n"
]
}
],
"source": [
"\n",
"example1osm_shape = ShapeXmlToJson(\"example1.osm\", pretty=True, store_to_var=True)\n",
"example1osm_shape.shape()\n",
"\n",
"correct_first_elem = {\n",
" \"id\": \"261114295\", \n",
" \"visible\": \"true\", \n",
" \"type\": \"node\", \n",
" \"pos\": [41.9730791, -87.6866303], \n",
" \"created\": {\n",
" \"changeset\": \"11129782\", \n",
" \"user\": \"bbmiller\", \n",
" \"version\": \"7\", \n",
" \"uid\": \"451048\", \n",
" \"timestamp\": \"2012-03-28T18:31:23Z\"\n",
" }\n",
"}\n",
"\n",
"data = example1osm_shape.data\n",
"pprint(data)\n",
"\n",
"assert data[0] == correct_first_elem\n",
"assert data[-1][\"address\"] == {\n",
" \"street\": \"West Lexington Street\", \n",
" \"housenumber\": \"1412\"\n",
" }\n",
"assert data[-1][\"node_refs\"] == [ \"2199822281\", \"2199822390\", \"2199822392\", \"2199822369\", \n",
" \"2199822370\", \"2199822284\", \"2199822281\"]\n"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"------------Problem inserting sfgov.org:objectid:16--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '1390',\n",
" 'postcode': '94102',\n",
" 'state': 'CA',\n",
" 'street': 'Market Street'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '22611423',\n",
" 'timestamp': '2014-05-29T04:29:32Z',\n",
" 'uid': '501715',\n",
" 'user': 'rkuris',\n",
" 'version': '7'},\n",
" 'id': '61689054',\n",
" 'name': 'Fox Plaza',\n",
" 'phone': '(415) 931-1053',\n",
" 'pos': [37.7774401, -122.4169146],\n",
" 'postal_code': '94102',\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '1390',\n",
" 'postcode': '94102',\n",
" 'state': 'CA',\n",
" 'street': 'Market Street'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '22611423',\n",
" 'timestamp': '2014-05-29T04:29:32Z',\n",
" 'uid': '501715',\n",
" 'user': 'rkuris',\n",
" 'version': '7'},\n",
" 'id': '61689054',\n",
" 'name': 'Fox Plaza',\n",
" 'phone': '(415) 931-1053',\n",
" 'pos': [37.7774401, -122.4169146],\n",
" 'postal_code': '94102',\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:32--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '1600',\n",
" 'postcode': '94103',\n",
" 'state': 'CA',\n",
" 'street': 'Bryant Street'},\n",
" 'amenity': 'post_office',\n",
" 'collection_times': 'Mo-Fr 09:00-18:00,17:00; Sa 09:00-14:00',\n",
" 'created': {'changeset': '24263865',\n",
" 'timestamp': '2014-07-20T23:35:34Z',\n",
" 'uid': '28775',\n",
" 'user': 'StellanL',\n",
" 'version': '5'},\n",
" 'id': '266903742',\n",
" 'name': 'Bryant Street Post Office',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 861-8130',\n",
" 'pos': [37.7666648, -122.4107311],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '1600',\n",
" 'postcode': '94103',\n",
" 'state': 'CA',\n",
" 'street': 'Bryant Street'},\n",
" 'amenity': 'post_office',\n",
" 'collection_times': 'Mo-Fr 09:00-18:00,17:00; Sa 09:00-14:00',\n",
" 'created': {'changeset': '24263865',\n",
" 'timestamp': '2014-07-20T23:35:34Z',\n",
" 'uid': '28775',\n",
" 'user': 'StellanL',\n",
" 'version': '5'},\n",
" 'id': '266903742',\n",
" 'name': 'Bryant Street Post Office',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 861-8130',\n",
" 'pos': [37.7666648, -122.4107311],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"Chateau Dr -> Chateau Drive\n",
"Park St -> Park Street\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '415'},\n",
" 'amenity': 'restaurant',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:50Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '4'},\n",
" 'id': '317124811',\n",
" 'name': 'Koh Samui and the Monkey Restaurand and Bar',\n",
" 'pos': [37.7796465, -122.39465],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '490'},\n",
" 'amenity': 'bank',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:51Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '6'},\n",
" 'id': '317124814',\n",
" 'name': 'Wells Fargo',\n",
" 'operator': 'Wells Fargo',\n",
" 'outside_atm': 'yes',\n",
" 'outside_atm_capacity': '3',\n",
" 'pos': [37.7786807, -122.3965033],\n",
" 'type': 'node',\n",
" 'vestibule_atm': 'yes',\n",
" 'vestibule_atm_capacity': '1'}\n",
"-------------------------------------------------\n",
"------------Ignoring Address Key---------------\n",
"address:400 Valencia Street\n",
"{'created': {'changeset': '7780343',\n",
" 'timestamp': '2011-04-05T23:37:42Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '4'},\n",
" 'id': '319020714',\n",
" 'name': 'Little Star Pizza',\n",
" 'phone': '14155517827',\n",
" 'pos': [37.7664478, -122.4222039],\n",
" 'type': 'node'}\n",
"Floribunda Ave -> Floribunda Avenue\n",
"------------Ignoring Address Key---------------\n",
"address:1115 Solano Avenue\n",
"{'created': {'changeset': '16653701',\n",
" 'timestamp': '2013-06-22T09:17:58Z',\n",
" 'uid': '933797',\n",
" 'user': 'oba510',\n",
" 'version': '8'},\n",
" 'id': '343610998',\n",
" 'name': 'Albany Twin Theatre',\n",
" 'phone': '15104645980',\n",
" 'pos': [37.890459, -122.2982037],\n",
" 'type': 'node'}\n",
"------------Ignoring Address Key---------------\n",
"address:421 40th Street\n",
"{'created': {'changeset': '19047326',\n",
" 'timestamp': '2013-11-22T00:09:28Z',\n",
" 'uid': '1812078',\n",
" 'user': 'barbearian',\n",
" 'version': '5'},\n",
" 'id': '346581846',\n",
" 'name': 'Manifesto Bicycles',\n",
" 'pos': [37.8283848, -122.2607361],\n",
" 'shop': 'bicycle',\n",
" 'type': 'node'}\n",
"------------Ignoring Address Key---------------\n",
"address:220 Middlefield Drive\n",
"{'created': {'changeset': '23036288',\n",
" 'timestamp': '2014-06-20T02:23:36Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '3'},\n",
" 'ele': '33',\n",
" 'id': '358768991',\n",
" 'name': 'Lakeshore Alternative Elementary School',\n",
" 'phone': '+1 (415) 759-2825',\n",
" 'pos': [37.7301624, -122.4859713],\n",
" 'type': 'node'}\n",
"Paloma Ave -> Paloma Avenue\n",
"S Delaware St -> S Delaware Street\n",
"Ursuline Rd -> Ursuline Road\n",
"Skyline Blvd -> Skyline Boulevard\n",
"2nd St -> 2nd Street\n",
"------------Problem inserting sfgov.org:objectid:3--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94124', 'state': 'CA'},\n",
" 'alt_name': 'Bayview Station San Francisco Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:04Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '2'},\n",
" 'ele': '13',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087577',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855325',\n",
" 'name': 'Bayview',\n",
" 'phone': '(415) 822-7157',\n",
" 'pos': [37.7288889, -122.3925],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94124', 'state': 'CA'},\n",
" 'alt_name': 'Bayview Station San Francisco Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:04Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '2'},\n",
" 'ele': '13',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087577',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855325',\n",
" 'name': 'Bayview',\n",
" 'phone': '(415) 822-7157',\n",
" 'pos': [37.7288889, -122.3925],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:4--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94110', 'state': 'CA'},\n",
" 'alt_name': 'Bernal Heights Station San Francisco Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '17386357',\n",
" 'timestamp': '2013-08-17T17:24:51Z',\n",
" 'uid': '235835',\n",
" 'user': 'lizhenry',\n",
" 'version': '5'},\n",
" 'ele': '31',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087578',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855329',\n",
" 'name': 'Bernal Heights post office',\n",
" 'phone': '(415) 550-7538',\n",
" 'pos': [37.7442585, -122.4216677],\n",
" 'type': 'node',\n",
" 'wheelchair': 'limited'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94110', 'state': 'CA'},\n",
" 'alt_name': 'Bernal Heights Station San Francisco Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '17386357',\n",
" 'timestamp': '2013-08-17T17:24:51Z',\n",
" 'uid': '235835',\n",
" 'user': 'lizhenry',\n",
" 'version': '5'},\n",
" 'ele': '31',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087578',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855329',\n",
" 'name': 'Bernal Heights post office',\n",
" 'phone': '(415) 550-7538',\n",
" 'pos': [37.7442585, -122.4216677],\n",
" 'type': 'node',\n",
" 'wheelchair': 'limited'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:6--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '867',\n",
" 'postcode': '94108',\n",
" 'state': 'CA',\n",
" 'street': 'Stockton Street'},\n",
" 'alt_name': 'Chinatown Station Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:05Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '5'},\n",
" 'ele': '41',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087579',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855332',\n",
" 'name': 'Chinatown',\n",
" 'phone': '(415) 433-1202',\n",
" 'pos': [37.7938952, -122.4080329],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '867',\n",
" 'postcode': '94108',\n",
" 'state': 'CA',\n",
" 'street': 'Stockton Street'},\n",
" 'alt_name': 'Chinatown Station Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:05Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '5'},\n",
" 'ele': '41',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087579',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855332',\n",
" 'name': 'Chinatown',\n",
" 'phone': '(415) 433-1202',\n",
" 'pos': [37.7938952, -122.4080329],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:14--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94112', 'state': 'CA'},\n",
" 'alt_name': 'Excelsior Station San Francisco Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '20683290',\n",
" 'timestamp': '2014-02-20T21:08:55Z',\n",
" 'uid': '436419',\n",
" 'user': 'wvdp',\n",
" 'version': '4'},\n",
" 'ele': '51',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087582',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855342',\n",
" 'name': 'Excelsior Station',\n",
" 'phone': '(415) 334-1057',\n",
" 'pos': [37.7212182, -122.4380828],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94112', 'state': 'CA'},\n",
" 'alt_name': 'Excelsior Station San Francisco Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '20683290',\n",
" 'timestamp': '2014-02-20T21:08:55Z',\n",
" 'uid': '436419',\n",
" 'user': 'wvdp',\n",
" 'version': '4'},\n",
" 'ele': '51',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087582',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855342',\n",
" 'name': 'Excelsior Station',\n",
" 'phone': '(415) 334-1057',\n",
" 'pos': [37.7212182, -122.4380828],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:18--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '5654',\n",
" 'postcode': '94121',\n",
" 'state': 'CA',\n",
" 'street': 'Geary Boulevard'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '33327662',\n",
" 'timestamp': '2015-08-14T01:48:19Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '3'},\n",
" 'ele': '43',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087584',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855349',\n",
" 'name': 'Geary Station',\n",
" 'phone': '(415) 665-1355',\n",
" 'pos': [37.7805556, -122.4802778],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '5654',\n",
" 'postcode': '94121',\n",
" 'state': 'CA',\n",
" 'street': 'Geary Boulevard'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '33327662',\n",
" 'timestamp': '2015-08-14T01:48:19Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '3'},\n",
" 'ele': '43',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087584',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855349',\n",
" 'name': 'Geary Station',\n",
" 'phone': '(415) 665-1355',\n",
" 'pos': [37.7805556, -122.4802778],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:19--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94118', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:06Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '3'},\n",
" 'ele': '68',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087585',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855351',\n",
" 'name': 'Golden Gate',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 751-9739',\n",
" 'pos': [37.7813918, -122.4538958],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94118', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:06Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '3'},\n",
" 'ele': '68',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087585',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855351',\n",
" 'name': 'Golden Gate',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 751-9739',\n",
" 'pos': [37.7813918, -122.4538958],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:22--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '2055',\n",
" 'postcode': '94123',\n",
" 'state': 'CA',\n",
" 'street': 'Lombard Street'},\n",
" 'alt_name': 'Marina Station San Francisco Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '19850735',\n",
" 'timestamp': '2014-01-06T19:46:43Z',\n",
" 'uid': '1213904',\n",
" 'user': 'mpmckenna8',\n",
" 'version': '4'},\n",
" 'ele': '12',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087587',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855358',\n",
" 'name': 'Marina',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 351-1875',\n",
" 'pos': [37.7997929, -122.4352048],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '2055',\n",
" 'postcode': '94123',\n",
" 'state': 'CA',\n",
" 'street': 'Lombard Street'},\n",
" 'alt_name': 'Marina Station San Francisco Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '19850735',\n",
" 'timestamp': '2014-01-06T19:46:43Z',\n",
" 'uid': '1213904',\n",
" 'user': 'mpmckenna8',\n",
" 'version': '4'},\n",
" 'ele': '12',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087587',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855358',\n",
" 'name': 'Marina',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 351-1875',\n",
" 'pos': [37.7997929, -122.4352048],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:24--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94134', 'state': 'CA'},\n",
" 'alt_name': 'McLaren Station San Francisco Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:06Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '3'},\n",
" 'ele': '17',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087588',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855362',\n",
" 'name': 'McLaren Station',\n",
" 'phone': '(415) 467-5026',\n",
" 'pos': [37.7268815, -122.4031604],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94134', 'state': 'CA'},\n",
" 'alt_name': 'McLaren Station San Francisco Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:06Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '3'},\n",
" 'ele': '17',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087588',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855362',\n",
" 'name': 'McLaren Station',\n",
" 'phone': '(415) 467-5026',\n",
" 'pos': [37.7268815, -122.4031604],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:26--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '4083',\n",
" 'postcode': '94114',\n",
" 'state': 'CA',\n",
" 'street': '24th Street'},\n",
" 'alt_name': 'Noe Valley Station Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:06Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '6'},\n",
" 'ele': '62',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087590',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855368',\n",
" 'name': 'NOE Valley',\n",
" 'operator': 'United States Postal Service',\n",
" 'phone': '(415) 821-3863',\n",
" 'pos': [37.7512444, -122.4336206],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '4083',\n",
" 'postcode': '94114',\n",
" 'state': 'CA',\n",
" 'street': '24th Street'},\n",
" 'alt_name': 'Noe Valley Station Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:06Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '6'},\n",
" 'ele': '62',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087590',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855368',\n",
" 'name': 'NOE Valley',\n",
" 'operator': 'United States Postal Service',\n",
" 'phone': '(415) 821-3863',\n",
" 'pos': [37.7512444, -122.4336206],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:28--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '1640',\n",
" 'postcode': '94133',\n",
" 'state': 'CA',\n",
" 'street': 'Stockton Street'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '32792509',\n",
" 'timestamp': '2015-07-22T03:24:47Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '7'},\n",
" 'ele': '26',\n",
" 'id': '358855370',\n",
" 'name': 'North Beach',\n",
" 'phone': '(415) 362-3128',\n",
" 'pos': [37.8009696, -122.4091792],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '1640',\n",
" 'postcode': '94133',\n",
" 'state': 'CA',\n",
" 'street': 'Stockton Street'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '32792509',\n",
" 'timestamp': '2015-07-22T03:24:47Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '7'},\n",
" 'ele': '26',\n",
" 'id': '358855370',\n",
" 'name': 'North Beach',\n",
" 'phone': '(415) 362-3128',\n",
" 'pos': [37.8009696, -122.4091792],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:30--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94116', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:07Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '3'},\n",
" 'ele': '75',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087593',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855376',\n",
" 'name': 'Parkside',\n",
" 'phone': '(415) 759-0150',\n",
" 'pos': [37.7427669, -122.4855404],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94116', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:07Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '3'},\n",
" 'ele': '75',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087593',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855376',\n",
" 'name': 'Parkside',\n",
" 'phone': '(415) 759-0150',\n",
" 'pos': [37.7427669, -122.4855404],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:35--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94105', 'state': 'CA'},\n",
" 'alt_name': 'Rincon Station San Francisco Post Office;Rincon Annex',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:07Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '3'},\n",
" 'ele': '2',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087595',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855383',\n",
" 'name': 'Rincon Finance Center',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 896-0762',\n",
" 'pos': [37.7919866, -122.3919984],\n",
" 'postal_code': '94105',\n",
" 'source': 'Survey',\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94105', 'state': 'CA'},\n",
" 'alt_name': 'Rincon Station San Francisco Post Office;Rincon Annex',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:07Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '3'},\n",
" 'ele': '2',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087595',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855383',\n",
" 'name': 'Rincon Finance Center',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 896-0762',\n",
" 'pos': [37.7919866, -122.3919984],\n",
" 'postal_code': '94105',\n",
" 'source': 'Survey',\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:39--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1849',\n",
" 'postcode': '94115',\n",
" 'state': 'CA',\n",
" 'street': 'Geary Boulevard'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '33283862',\n",
" 'timestamp': '2015-08-12T06:42:54Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '5'},\n",
" 'ele': '40',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:feature_id': '2087596',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855386',\n",
" 'name': 'Steiner Street Station',\n",
" 'phone': '(415) 931-1053',\n",
" 'pos': [37.7839348, -122.4335835],\n",
" 'type': 'node',\n",
" 'wheelchair': 'yes'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1849',\n",
" 'postcode': '94115',\n",
" 'state': 'CA',\n",
" 'street': 'Geary Boulevard'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '33283862',\n",
" 'timestamp': '2015-08-12T06:42:54Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '5'},\n",
" 'ele': '40',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:feature_id': '2087596',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855386',\n",
" 'name': 'Steiner Street Station',\n",
" 'phone': '(415) 931-1053',\n",
" 'pos': [37.7839348, -122.4335835],\n",
" 'type': 'node',\n",
" 'wheelchair': 'yes'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:41--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94122', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:08Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '2'},\n",
" 'ele': '68',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087597',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855389',\n",
" 'name': 'Sunset Station',\n",
" 'phone': '(415) 665-1355',\n",
" 'pos': [37.7630556, -122.4802778],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94122', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:08Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '2'},\n",
" 'ele': '68',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087597',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855389',\n",
" 'name': 'Sunset Station',\n",
" 'phone': '(415) 665-1355',\n",
" 'pos': [37.7630556, -122.4802778],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:43--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94134', 'state': 'CA'},\n",
" 'alt_name': 'Visitacion Station San Francisco Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:08Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '2'},\n",
" 'ele': '19',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087598',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855392',\n",
" 'name': 'Visitacion Station',\n",
" 'phone': '(415) 333-4629',\n",
" 'pos': [37.7122222, -122.4058333],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94134', 'state': 'CA'},\n",
" 'alt_name': 'Visitacion Station San Francisco Post Office',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:08Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '2'},\n",
" 'ele': '19',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087598',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855392',\n",
" 'name': 'Visitacion Station',\n",
" 'phone': '(415) 333-4629',\n",
" 'pos': [37.7122222, -122.4058333],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:46--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '317',\n",
" 'postcode': '94102',\n",
" 'state': 'CA',\n",
" 'street': 'West Portal Avenue'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '31409236',\n",
" 'timestamp': '2015-05-23T23:42:49Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '8'},\n",
" 'ele': '96',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087599',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855395',\n",
" 'name': 'West Portal Station',\n",
" 'opening_hours': 'Mo-Fr 09:00-17:00 Sa 09:00-15:00',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 550-5534',\n",
" 'pos': [37.7376642, -122.4691782],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:United States Government - Postal Service--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '317',\n",
" 'postcode': '94102',\n",
" 'state': 'CA',\n",
" 'street': 'West Portal Avenue'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '31409236',\n",
" 'timestamp': '2015-05-23T23:42:49Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '8'},\n",
" 'ele': '96',\n",
" 'gnis:county_id': '075',\n",
" 'gnis:created': '05/22/2006',\n",
" 'gnis:edited': '09/18/2007',\n",
" 'gnis:feature_id': '2087599',\n",
" 'gnis:state_id': '06',\n",
" 'id': '358855395',\n",
" 'name': 'West Portal Station',\n",
" 'opening_hours': 'Mo-Fr 09:00-17:00 Sa 09:00-15:00',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 550-5534',\n",
" 'pos': [37.7376642, -122.4691782],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"Hayes St -> Hayes Street\n",
"Laurel St -> Laurel Street\n",
"California Dr -> California Drive\n",
"Rollins Rd -> Rollins Road\n",
"Bryant St -> Bryant Street\n",
"Williams St -> Williams Street\n",
"Washington St. -> Washington Street\n",
"------------Problem inserting old location:No Longer a Riply Museum--------------\n",
"Node:\n",
"{'created': {'changeset': '22873304',\n",
" 'timestamp': '2014-06-11T15:02:29Z',\n",
" 'uid': '2116437',\n",
" 'user': 'Sonoma County Historical Society',\n",
" 'version': '2'},\n",
" 'id': '368174132',\n",
" 'pos': [38.4363009, -122.712487],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"San Carlos Ave. -> San Carlos Avenue\n",
"------------Problem inserting sfgov.org:objectid:42--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94104', 'state': 'CA'},\n",
" 'alt_name': 'Sutter Station',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:08Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '4'},\n",
" 'id': '392204997',\n",
" 'name': 'Sutter Street',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 765-1761',\n",
" 'pos': [37.7900298, -122.4031376],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94104', 'state': 'CA'},\n",
" 'alt_name': 'Sutter Station',\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:08Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '4'},\n",
" 'id': '392204997',\n",
" 'name': 'Sutter Street',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 765-1761',\n",
" 'pos': [37.7900298, -122.4031376],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"Geary Blvd -> Geary Boulevard\n",
"Hillside Dr -> Hillside Drive\n",
"El Monte Ave -> El Monte Avenue\n",
"Redwood Hwy -> Redwood Highway\n",
"Meridian Ave -> Meridian Avenue\n",
"Washington Blvd -> Washington Boulevard\n",
"Vicksburg Pl -> Vicksburg Place\n",
"Vicksburg Pl -> Vicksburg Place\n",
"N Gettysburg Pl -> N Gettysburg Place\n",
"N Gettysburg Pl -> N Gettysburg Place\n",
"Vicksburg Pl -> Vicksburg Place\n",
"Cipriani Blvd -> Cipriani Boulevard\n",
"N Gettysburg Pl -> N Gettysburg Place\n",
"Vicksburg Pl -> Vicksburg Place\n",
"Palmetto Ave -> Palmetto Avenue\n",
"3132 MT VEEDER RD -> 3132 MT VEEDER Road\n",
"7401 Solano Ave -> 7401 Solano Avenue\n",
"7401 Solano Ave -> 7401 Solano Avenue\n",
"1113 Washington St -> 1113 Washington Street\n",
"1234 Washington St -> 1234 Washington Street\n",
"3111 N St Helena Hwy -> 3111 N St Helena Highway\n",
"1480 Main St -> 1480 Main Street\n",
"1480 Main St -> 1480 Main Street\n",
"3535 N St Helena Hwy -> 3535 N St Helena Highway\n",
"680 Sanitarium road -> 680 Sanitarium Road\n",
"100 Howell Mountain Rd -> 100 Howell Mountain Road\n",
"1535 Airport Blvd -> 1535 Airport Boulevard\n",
"1555 Airport Blvd -> 1555 Airport Boulevard\n",
"4201 Old Sonoma Hwy -> 4201 Old Sonoma Highway\n",
"1539 First St -> 1539 First Street\n",
"1125 Third St -> 1125 Third Street\n",
"2100 Napa-Vallejo Hwy -> 2100 Napa-Vallejo Highway\n",
"2000 Trower Ave -> 2000 Trower Avenue\n",
"1820 Monticello Rd -> 1820 Monticello Road\n",
"5520 Berryessa Knox. Rd -> 5520 Berryessa Knox. Road\n",
"------------Ignoring Address Key---------------\n",
"address:3692 18th Street\n",
"{'created': {'changeset': '10657823',\n",
" 'timestamp': '2012-02-11T21:39:27Z',\n",
" 'uid': '236172',\n",
" 'user': 'saikofish',\n",
" 'version': '6'},\n",
" 'id': '540456131',\n",
" 'name': 'Bi-Rite Creamery',\n",
" 'phone': '+1 (415) 626-5600',\n",
" 'pos': [37.7615705, -122.425662],\n",
" 'type': 'node'}\n",
"------------Ignoring Address Key---------------\n",
"address:6114 Highway 9, Felton, CA 95018-9704\n",
"{'created': {'changeset': '3818390',\n",
" 'timestamp': '2010-02-07T20:08:31Z',\n",
" 'uid': '36694',\n",
" 'user': 'neufeind',\n",
" 'version': '2'},\n",
" 'id': '550619432',\n",
" 'name': \"Long's Cabinet Shop, Inc.\",\n",
" 'pos': [37.0497161, -122.0726529],\n",
" 'type': 'node'}\n",
"Santa Cruz avenue -> Santa Cruz Avenue\n",
"Santa Cruz avenue -> Santa Cruz Avenue\n",
"Santa Cruz avenue -> Santa Cruz Avenue\n",
"Santa Cruz avenue -> Santa Cruz Avenue\n",
"Santa Cruz avenue -> Santa Cruz Avenue\n",
"Santa Cruz avenue -> Santa Cruz Avenue\n",
"Santa Cruz avenue -> Santa Cruz Avenue\n",
"Santa Cruz avenue -> Santa Cruz Avenue\n",
"Santa Cruz avenue -> Santa Cruz Avenue\n",
"Santa Cruz avenue -> Santa Cruz Avenue\n",
"Santa Cruz avenue -> Santa Cruz Avenue\n",
"Santa Cruz avenue -> Santa Cruz Avenue\n",
"Ascot Rd -> Ascot Road\n",
"Crane st -> Crane Street\n",
"Glenmoor Dr -> Glenmoor Drive\n",
"Glenmoor Dr -> Glenmoor Drive\n",
"Mowry Ave -> Mowry Avenue\n",
"Mowry Ave -> Mowry Avenue\n",
"Mowry Ave -> Mowry Avenue\n",
"Mowry Ave -> Mowry Avenue\n",
"Mowry Ave -> Mowry Avenue\n",
"Mowry Ave -> Mowry Avenue\n",
"Menalto Ave. -> Menalto Avenue\n",
"Mowry Ave -> Mowry Avenue\n",
"Thorton Ave -> Thorton Avenue\n",
"Rose Dr -> Rose Drive\n",
"------------Problem inserting dot #:751686L--------------\n",
"Node:\n",
"{'created': {'changeset': '11056966',\n",
" 'timestamp': '2012-03-21T20:59:56Z',\n",
" 'uid': '179942',\n",
" 'user': 'Felix Ko',\n",
" 'version': '3'},\n",
" 'id': '602391114',\n",
" 'pos': [37.9512018, -122.3637321],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting dot #:751687T--------------\n",
"Node:\n",
"{'created': {'changeset': '11056966',\n",
" 'timestamp': '2012-03-21T20:48:26Z',\n",
" 'uid': '179942',\n",
" 'user': 'Felix Ko',\n",
" 'version': '3'},\n",
" 'id': '602391118',\n",
" 'pos': [37.9499199, -122.3664849],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:5--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94107', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:04Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '3'},\n",
" 'id': '603370896',\n",
" 'name': 'Brannan Street Station',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 536-6413',\n",
" 'pos': [37.7790275, -122.3958707],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94107', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:04Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '3'},\n",
" 'id': '603370896',\n",
" 'name': 'Brannan Street Station',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 536-6413',\n",
" 'pos': [37.7790275, -122.3958707],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"Halleck St. -> Halleck Street\n",
"------------Problem inserting sfgov.org:objectid:29--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94102', 'state': 'CA'},\n",
" 'alt_name': \"Macy's Station\",\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:07Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '2'},\n",
" 'description': \"In the basement of Macy's.\",\n",
" 'id': '633151410',\n",
" 'name': 'Number Fifty Seven',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 956-0131',\n",
" 'pos': [37.7866487, -122.4072447],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94102', 'state': 'CA'},\n",
" 'alt_name': \"Macy's Station\",\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:07Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '2'},\n",
" 'description': \"In the basement of Macy's.\",\n",
" 'id': '633151410',\n",
" 'name': 'Number Fifty Seven',\n",
" 'operator': 'USPS',\n",
" 'phone': '(415) 956-0131',\n",
" 'pos': [37.7866487, -122.4072447],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"California Ave -> California Avenue\n",
"Woodside Rd -> Woodside Road\n",
"------------Ignoring Address Key---------------\n",
"address:1748 Church Street\n",
"{'created': {'changeset': '27027510',\n",
" 'timestamp': '2014-11-25T17:59:38Z',\n",
" 'uid': '481533',\n",
" 'user': 'dbaron',\n",
" 'version': '7'},\n",
" 'id': '677681773',\n",
" 'name': 'Toast Eatery',\n",
" 'phone': '+1 (415) 282-4328',\n",
" 'pos': [37.7429854, -122.4267774],\n",
" 'type': 'node'}\n",
"------------Ignoring Address Key---------------\n",
"address:3054 Taraval Street\n",
"{'created': {'changeset': '32486052',\n",
" 'timestamp': '2015-07-08T01:01:09Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '13'},\n",
" 'id': '702434594',\n",
" 'name': 'North Beach Pizza',\n",
" 'phone': '+1 (415) 242-9100',\n",
" 'pos': [37.7421568, -122.4990647],\n",
" 'type': 'node'}\n",
"Mission Blvd -> Mission Boulevard\n",
"------------Ignoring Address Key---------------\n",
"address:1304 Lincoln Avenue\n",
"{'created': {'changeset': '16503999',\n",
" 'timestamp': '2013-06-11T00:54:12Z',\n",
" 'uid': '1535927',\n",
" 'user': 'curiousscholar',\n",
" 'version': '4'},\n",
" 'id': '712627724',\n",
" 'name': 'Forbidden Island',\n",
" 'phone': '+1 (510) 749-0332',\n",
" 'pos': [37.7746069, -122.2629548],\n",
" 'type': 'node'}\n",
"Watt Ave. -> Watt Avenue\n",
"San Pablo Ave -> San Pablo Avenue\n",
"Santa Teresa Blvd -> Santa Teresa Boulevard\n",
"Park St -> Park Street\n",
"Earl Ave -> Earl Avenue\n",
"------------Ignoring Address Key---------------\n",
"address:2555 Main Street\n",
"{'created': {'changeset': '8133061',\n",
" 'timestamp': '2011-05-13T16:18:39Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '4'},\n",
" 'id': '934054869',\n",
" 'name': 'Wine Spectator Greystone Restaurant',\n",
" 'phone': '+1 (707) 967-1010',\n",
" 'pos': [38.5149649, -122.485214],\n",
" 'type': 'node'}\n",
"Arena Blvd -> Arena Boulevard\n",
"------------Ignoring Address Key---------------\n",
"address:1396 La Playa Street\n",
"{'created': {'changeset': '8910283',\n",
" 'timestamp': '2011-08-03T13:50:13Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '5'},\n",
" 'id': '960932055',\n",
" 'name': 'Java Beach Cafe',\n",
" 'phone': '+1 (415) 665-5282',\n",
" 'pos': [37.7604494, -122.5089783],\n",
" 'type': 'node'}\n",
"Portage Ave -> Portage Avenue\n",
"California Ave -> California Avenue\n",
"Tanforan Shopping Ctr -> Tanforan Shopping Center\n",
"Magnolia Ave -> Magnolia Avenue\n",
"Monterey Hwy -> Monterey Highway\n",
"------------Ignoring Address Key---------------\n",
"address:4184 Piedmont Avenue\n",
"{'created': {'changeset': '16968876',\n",
" 'timestamp': '2013-07-15T23:17:36Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '3'},\n",
" 'id': '1086779748',\n",
" 'name': 'Lush Gelato',\n",
" 'phone': '+1 (510) 547-1299',\n",
" 'pos': [37.8270187, -122.2510477],\n",
" 'type': 'node'}\n",
"Mission Blvd -> Mission Boulevard\n",
"Mission Blvd -> Mission Boulevard\n",
"Mission Blvd -> Mission Boulevard\n",
"Ardenwood Blvd -> Ardenwood Boulevard\n",
"296 Airport Blvd -> 296 Airport Boulevard\n",
"------------Ignoring Address Key---------------\n",
"address:2760 24th Street\n",
"{'created': {'changeset': '17386357',\n",
" 'timestamp': '2013-08-17T18:19:17Z',\n",
" 'uid': '235835',\n",
" 'user': 'lizhenry',\n",
" 'version': '5'},\n",
" 'id': '1130796532',\n",
" 'name': 'Dynamo Donut & Coffee',\n",
" 'phone': '+1 (415) 920-1978',\n",
" 'pos': [37.7529654, -122.4076372],\n",
" 'type': 'node'}\n",
"------------Ignoring Address Key---------------\n",
"address:2832 Mission Street\n",
"{'created': {'changeset': '20385818',\n",
" 'timestamp': '2014-02-05T05:16:05Z',\n",
" 'uid': '481533',\n",
" 'user': 'dbaron',\n",
" 'version': '4'},\n",
" 'id': '1168850885',\n",
" 'name': 'Rosamunde Sausage Grill',\n",
" 'phone': '14159709015',\n",
" 'pos': [37.7516328, -122.418583],\n",
" 'type': 'node'}\n",
"W Graf Rd -> W Graf Road\n",
"Graf Rd -> Graf Road\n",
"San Juan Rd -> San Juan Road\n",
"San Juan Rd -> San Juan Road\n",
"Gateway Dr -> Gateway Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"1st St -> 1st Street\n",
"Mccloskey Rd -> Mccloskey Road\n",
"N Monterey St -> N Monterey Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"3rd St -> 3rd Street\n",
"N Monterey St -> N Monterey Street\n",
"N Monterey St -> N Monterey Street\n",
"N Chappell Rd -> N Chappell Road\n",
"San Benito St -> San Benito Street\n",
"San Felipe Rd -> San Felipe Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"N Chappell Rd -> N Chappell Road\n",
"San Benito St -> San Benito Street\n",
"1st St -> 1st Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Felipe Rd -> San Felipe Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"San Felipe Rd -> San Felipe Road\n",
"Gateway Dr -> Gateway Drive\n",
"Gateway Dr -> Gateway Drive\n",
"San Felipe Rd -> San Felipe Road\n",
"N Chappell Rd -> N Chappell Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"3rd St -> 3rd Street\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Kirkpatrick Dr -> Kirkpatrick Drive\n",
"N Monterey St -> N Monterey Street\n",
"3rd St -> 3rd Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Benito St -> San Benito Street\n",
"N Monterey St -> N Monterey Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Benito St -> San Benito Street\n",
"1st St -> 1st Street\n",
"San Benito St -> San Benito Street\n",
"San Benito St -> San Benito Street\n",
"San Benito St -> San Benito Street\n",
"San Felipe Rd -> San Felipe Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Fairview Rd -> Fairview Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Fairview Rd -> Fairview Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Fairview Rd -> Fairview Road\n",
"Magladry Rd -> Magladry Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Fairview Rd -> Fairview Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Magladry Rd -> Magladry Road\n",
"Magladry Rd -> Magladry Road\n",
"Fairview Rd -> Fairview Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Magladry Rd -> Magladry Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Fairview Rd -> Fairview Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Magladry Rd -> Magladry Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Fairview Rd -> Fairview Road\n",
"Fairview Rd -> Fairview Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Fairview Rd -> Fairview Road\n",
"Magladry Rd -> Magladry Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Fairview Rd -> Fairview Road\n",
"Dixie Dr -> Dixie Drive\n",
"Magladry Rd -> Magladry Road\n",
"Dixie Dr -> Dixie Drive\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Fairview Rd -> Fairview Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Fairview Rd -> Fairview Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Fairview Rd -> Fairview Road\n",
"Fairview Rd -> Fairview Road\n",
"Fairview Rd -> Fairview Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Magladry Rd -> Magladry Road\n",
"Gardenia Ln -> Gardenia Lane\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Gardenia Ln -> Gardenia Lane\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Barnes Ln -> Barnes Lane\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Gardenia Ln -> Gardenia Lane\n",
"Junipero Serra & Westborough Blvd -> Junipero Serra & Westborough Boulevard\n",
"W Graf Rd -> W Graf Road\n",
"San Jua Rd -> San Jua Road\n",
"San Juan Rd -> San Juan Road\n",
"San Jua Rd -> San Jua Road\n",
"San Jua Rd -> San Jua Road\n",
"San Juan Rd -> San Juan Road\n",
"San Jua Rd -> San Jua Road\n",
"North St -> North Street\n",
"Mccray St -> Mccray Street\n",
"Mccray St -> Mccray Street\n",
"------------Ignoring Address Key---------------\n",
"address:400 40th Street\n",
"{'created': {'changeset': '16981620',\n",
" 'timestamp': '2013-07-17T01:20:06Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '10'},\n",
" 'id': '1186424529',\n",
" 'name': 'Homeroom',\n",
" 'phone': '+1 (510) 597-0400',\n",
" 'pos': [37.8285776, -122.2599099],\n",
" 'type': 'node'}\n",
"Fairview Rd -> Fairview Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Fairview Rd -> Fairview Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Fairview Rd -> Fairview Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Ross Dr -> Ross Drive\n",
"Hillside Rd -> Hillside Road\n",
"Quinn Canyon Rd -> Quinn Canyon Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Ross Dr -> Ross Drive\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Ross Dr -> Ross Drive\n",
"Quinn Canyon Rd -> Quinn Canyon Road\n",
"Shore Rd -> Shore Road\n",
"West March Ln -> West March Lane\n",
"1st St -> 1st Street\n",
"Larios Dr -> Larios Drive\n",
"N Monterey St -> N Monterey Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"1st St -> 1st Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"Larios Dr -> Larios Drive\n",
"1485 South Petaluma Blvd -> 1485 South Petaluma Boulevard\n",
"------------Ignoring Address Key---------------\n",
"address:846 Divisadero Street\n",
"{'created': {'changeset': '7856491',\n",
" 'timestamp': '2011-04-13T22:05:14Z',\n",
" 'uid': '20587',\n",
" 'user': 'balrog-kun',\n",
" 'version': '2'},\n",
" 'id': '1234225439',\n",
" 'name': 'Little Star Pizza',\n",
" 'phone': '14154411118',\n",
" 'pos': [37.777497, -122.4379613],\n",
" 'type': 'node'}\n",
"------------Ignoring Address Key---------------\n",
"address:1175 Solano Ave\n",
"{'created': {'changeset': '16653701',\n",
" 'timestamp': '2013-06-22T09:17:57Z',\n",
" 'uid': '933797',\n",
" 'user': 'oba510',\n",
" 'version': '5'},\n",
" 'id': '1234228225',\n",
" 'name': 'Little Star Pizza',\n",
" 'phone': '15105267827',\n",
" 'pos': [37.8905114, -122.2970148],\n",
" 'type': 'node'}\n",
"------------Ignoring Address Key---------------\n",
"address:464 3rd Street\n",
"{'created': {'changeset': '26745862',\n",
" 'timestamp': '2014-11-12T22:30:41Z',\n",
" 'uid': '1213904',\n",
" 'user': 'mpmckenna8',\n",
" 'version': '6'},\n",
" 'id': '1234603544',\n",
" 'name': 'Beer Revolution',\n",
" 'name_1': 'Beer Rev',\n",
" 'phone': '15104522337',\n",
" 'pos': [37.7971044, -122.2762711],\n",
" 'type': 'node',\n",
" 'wifi': 'no'}\n",
"Manzanita Ave -> Manzanita Avenue\n",
"------------Ignoring Address Key---------------\n",
"address:4401 Piedmont Avenue\n",
"{'created': {'changeset': '21392096',\n",
" 'timestamp': '2014-03-29T23:14:09Z',\n",
" 'uid': '321578',\n",
" 'user': 'rabbitface',\n",
" 'version': '4'},\n",
" 'id': '1241641683',\n",
" 'name': 'Kona Club',\n",
" 'phone': '+1 (510) 654-7100',\n",
" 'pos': [37.8304351, -122.2472872],\n",
" 'type': 'node'}\n",
"------------Ignoring Address Key---------------\n",
"address:4290 Piedmont Avenue\n",
"{'created': {'changeset': '21824540',\n",
" 'timestamp': '2014-04-20T21:42:05Z',\n",
" 'uid': '2032478',\n",
" 'user': 'SarahStierch',\n",
" 'version': '3'},\n",
" 'id': '1241643749',\n",
" 'name': 'Shimizu',\n",
" 'phone': '+1 (510) 653-7672',\n",
" 'pos': [37.828533, -122.249387],\n",
" 'type': 'node'}\n",
"------------Ignoring Address Key---------------\n",
"address:2390 E. Bidwell St. Suite 100, Folsom Ca 95630\n",
"{'created': {'changeset': '7908667',\n",
" 'timestamp': '2011-04-19T17:52:57Z',\n",
" 'uid': '70696',\n",
" 'user': 'xybot',\n",
" 'version': '2'},\n",
" 'id': '1251326857',\n",
" 'name': 'Folsom Chiropractor | Tom Gibson, DC',\n",
" 'phone': '(916) 259-5000',\n",
" 'pos': [38.6649462, -121.1380901],\n",
" 'source': 'Google Places',\n",
" 'type': 'node'}\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Recht St -> Recht Street\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Recht St -> Recht Street\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Meridian St -> Meridian Street\n",
"San Felipe Rd -> San Felipe Road\n",
"------------Ignoring Address Key---------------\n",
"address:356 7th Street\n",
"{'created': {'changeset': '16303392',\n",
" 'timestamp': '2013-05-27T01:32:02Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '3'},\n",
" 'id': '1260285974',\n",
" 'name': 'SF City Clinic',\n",
" 'phone': '+1 (415) 487-5500',\n",
" 'pos': [37.7759896, -122.4070756],\n",
" 'type': 'node'}\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Fairview Rd -> Fairview Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Memorial Dr -> Memorial Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Sunnyslope Rd -> Sunnyslope Road\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"Westside Blvd -> Westside Boulevard\n",
"Fairview Rd -> Fairview Road\n",
"Lemmon Ct -> Lemmon Court\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lemmon Ct -> Lemmon Court\n",
"Lemmon Ct -> Lemmon Court\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Fairview Rd -> Fairview Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Lemmon Ct -> Lemmon Court\n",
"Fairview Rd -> Fairview Road\n",
"Fairview Rd -> Fairview Road\n",
"Menzel Rd -> Menzel Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Fairview Rd -> Fairview Road\n",
"Fairview Rd -> Fairview Road\n",
"Fairview Rd -> Fairview Road\n",
"Fairview Rd -> Fairview Road\n",
"------------Ignoring Address Key---------------\n",
"address:201 9th Street\n",
"{'created': {'changeset': '18555221',\n",
" 'timestamp': '2013-10-26T17:48:48Z',\n",
" 'uid': '1775756',\n",
" 'user': 'ets_2016',\n",
" 'version': '5'},\n",
" 'id': '1275243849',\n",
" 'name': 'AsiaSF',\n",
" 'phone': '+1 (415) 255-2742',\n",
" 'pos': [37.7750027, -122.4129992],\n",
" 'type': 'node'}\n",
"Airline Hwy -> Airline Highway\n",
"Prospect Ave -> Prospect Avenue\n",
"E Park St -> E Park Street\n",
"Prospect Ave -> Prospect Avenue\n",
"Prospect Ave -> Prospect Avenue\n",
"Hayes St -> Hayes Street\n",
"6th St -> 6th Street\n",
"5th St -> 5th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"7th St -> 7th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Joes Ln -> Joes Lane\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Marks Dr -> Marks Drive\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Villa Pacheco Ct -> Villa Pacheco Court\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Ct -> Helen Court\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Ct -> Helen Court\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Ct -> Helen Court\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Ct -> Helen Court\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Ct -> Helen Court\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Ct -> Helen Court\n",
"Helen Ct -> Helen Court\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Ct -> Helen Court\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Ct -> Helen Court\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Ct -> Helen Court\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Ct -> Helen Court\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Ct -> Helen Court\n",
"Helen Ct -> Helen Court\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Ct -> Helen Court\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Ct -> Helen Court\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Ct -> Helen Court\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Ct -> Helen Court\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Ct -> Helen Court\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Ct -> Helen Court\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"Helen Ct -> Helen Court\n",
"Helen Dr -> Helen Drive\n",
"Duffin Dr -> Duffin Drive\n",
"S California Ave -> S California Avenue\n",
"Sunset Dr -> Sunset Drive\n",
"Hillock Ct -> Hillock Court\n",
"Sunset Dr -> Sunset Drive\n",
"Sunset Dr -> Sunset Drive\n",
"East Francisco Blvd. -> East Francisco Boulevard\n",
"Adrian Dr -> Adrian Drive\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Corrib Ct -> Corrib Court\n",
"Corrib Ct -> Corrib Court\n",
"Guiness Ct -> Guiness Court\n",
"Adrian Dr -> Adrian Drive\n",
"Bollinger Rd -> Bollinger Road\n",
"Carpenteria Ave -> Carpenteria Avenue\n",
"Northgate Blvd -> Northgate Boulevard\n",
"Northgate Blvd -> Northgate Boulevard\n",
"Petaluma Blvd -> Petaluma Boulevard\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"School Rd -> School Road\n",
"Northgate Blvd -> Northgate Boulevard\n",
"Hawkins St -> Hawkins Street\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"Shelton Dr -> Shelton Drive\n",
"4th St -> 4th Street\n",
"Mccray St -> Mccray Street\n",
"Mccray St -> Mccray Street\n",
"Sally St -> Sally Street\n",
"San Benito St -> San Benito Street\n",
"Sally St -> Sally Street\n",
"San Benito St -> San Benito Street\n",
"Mccray St -> Mccray Street\n",
"Mccray St -> Mccray Street\n",
"3rd St -> 3rd Street\n",
"Gravenstein Hwy -> Gravenstein Highway\n",
"Gravenstein Hwy -> Gravenstein Highway\n",
"Gravenstein Hwy -> Gravenstein Highway\n",
"41st Ave -> 41st Avenue\n",
"Embarcadero St -> Embarcadero Street\n",
"Brannan St -> Brannan Street\n",
"Grandview Dr. -> Grandview Drive\n",
"Allerton Ave -> Allerton Avenue\n",
"19th St -> 19th Street\n",
"Florin Rd -> Florin Road\n",
"Newark Blvd -> Newark Boulevard\n",
"wilcox ave -> wilcox Avenue\n",
"Fair Oaks Blvd. -> Fair Oaks Boulevard\n",
"------------Ignoring Address Key---------------\n",
"address:289 8th Street\n",
"{'created': {'changeset': '11173847',\n",
" 'timestamp': '2012-04-01T07:58:44Z',\n",
" 'uid': '123633',\n",
" 'user': 'stevea',\n",
" 'version': '1'},\n",
" 'id': '1700641977',\n",
" 'name': 'Wicked Grounds',\n",
" 'phone': '+1 (415) 503-0405',\n",
" 'pos': [37.7752496, -122.4101688],\n",
" 'type': 'node',\n",
" 'wifi': 'yes'}\n",
"Railroad Ave -> Railroad Avenue\n",
"------------Ignoring Address Key---------------\n",
"address:3991 24th Street\n",
"{'created': {'changeset': '11280655',\n",
" 'timestamp': '2012-04-12T20:14:07Z',\n",
" 'uid': '28775',\n",
" 'user': 'StellanL',\n",
" 'version': '1'},\n",
" 'id': '1713269609',\n",
" 'name': 'Toast Eatery',\n",
" 'phone': '+1 (415) 642-6328',\n",
" 'pos': [37.7513266, -122.4315999],\n",
" 'type': 'node'}\n",
"S California Ave -> S California Avenue\n",
"S California Ave -> S California Avenue\n",
"S California Ave -> S California Avenue\n",
"SOUTH TRACY BLVD. -> SOUTH TRACY Boulevard\n",
"S Tracy Blvd -> S Tracy Boulevard\n",
"Tracy Blvd -> Tracy Boulevard\n",
"Tracy Blvd -> Tracy Boulevard\n",
"Laurel St -> Laurel Street\n",
"Magnolia Ave -> Magnolia Avenue\n",
"Magnolia Ave -> Magnolia Avenue\n",
"Corte Madera Ave -> Corte Madera Avenue\n",
"Magnolia Ave -> Magnolia Avenue\n",
"Corte Madera Ave -> Corte Madera Avenue\n",
"Magnolia Ave -> Magnolia Avenue\n",
"Magnolia Ave -> Magnolia Avenue\n",
"Magnolia Ave -> Magnolia Avenue\n",
"Magnolia Ave -> Magnolia Avenue\n",
"Magnolia Ave -> Magnolia Avenue\n",
"Magnolia Ave -> Magnolia Avenue\n",
"Magnolia Ave -> Magnolia Avenue\n",
"Magnolia Ave -> Magnolia Avenue\n",
"Magnolia Ave -> Magnolia Avenue\n",
"Van Ness Ave -> Van Ness Avenue\n",
"S California Ave -> S California Avenue\n",
"S California Ave -> S California Avenue\n",
"S California Ave -> S California Avenue\n",
"S California Ave -> S California Avenue\n",
"S California Ave -> S California Avenue\n",
"Bruceville Rd -> Bruceville Road\n",
"Sand Hill Rd -> Sand Hill Road\n",
"Sand Hill Rd -> Sand Hill Road\n",
"Valencia St. -> Valencia Street\n",
"6th St -> 6th Street\n",
"Folsom St -> Folsom Street\n",
"Delancey St -> Delancey Street\n",
"Minto Dr -> Minto Drive\n",
"E. Cotati Ave. -> E. Cotati Avenue\n",
"Noe St -> Noe Street\n",
"Market St -> Market Street\n",
"Washington Blvd -> Washington Boulevard\n",
"Cherry Ave -> Cherry Avenue\n",
"Mission Blvd -> Mission Boulevard\n",
"Marshlands Rd -> Marshlands Road\n",
"Center Blvd -> Center Boulevard\n",
"Sir Francis Drake Blvd -> Sir Francis Drake Boulevard\n",
"Sir Francis Drake Blvd -> Sir Francis Drake Boulevard\n",
"Sir Francis Drake Blvd -> Sir Francis Drake Boulevard\n",
"Sir Francis Drake Blvd. -> Sir Francis Drake Boulevard\n",
"Tunstead Ave. -> Tunstead Avenue\n",
"Bellam Blvd -> Bellam Boulevard\n",
"townsend street -> townsend Street\n",
"Mission St -> Mission Street\n",
"E. Airway Blvd -> E. Airway Boulevard\n",
"Arena Blvd -> Arena Boulevard\n",
"Arena Blvd -> Arena Boulevard\n",
"Arena Blvd -> Arena Boulevard\n",
"Los Gatos Blvd -> Los Gatos Boulevard\n",
"Arena Blvd -> Arena Boulevard\n",
"Arena Blvd -> Arena Boulevard\n",
"Arena Blvd. -> Arena Boulevard\n",
"9th St -> 9th Street\n",
"Shattuck Ave -> Shattuck Avenue\n",
"Northgate Blvd -> Northgate Boulevard\n",
"Northgate Blvd. -> Northgate Boulevard\n",
"Northgate Blvd -> Northgate Boulevard\n",
"Diamond Blvd -> Diamond Boulevard\n",
"De Anza Blvd -> De Anza Boulevard\n",
"S street -> S Street\n",
"Columbus Abenue -> Columbus Avenue\n",
"Southport P PKWY -> Southport P Parkway\n",
"Southport P PKWY -> Southport P Parkway\n",
"Southport P PKWY -> Southport P Parkway\n",
"Southport P PKWY -> Southport P Parkway\n",
"Southport P PKWY -> Southport P Parkway\n",
"Southport P PKWY -> Southport P Parkway\n",
"Southport P PKWY -> Southport P Parkway\n",
"Southport P PKWY -> Southport P Parkway\n",
"Southport P PKWY -> Southport P Parkway\n",
"Southport P PKWY -> Southport P Parkway\n",
"Southport P PKWY -> Southport P Parkway\n",
"Southport P PKWY -> Southport P Parkway\n",
"W 25th Ave -> W 25th Avenue\n",
"Ygnacio Valley Rd -> Ygnacio Valley Road\n",
"Ygnacio Valley Rd -> Ygnacio Valley Road\n",
"Howard St -> Howard Street\n",
"Sir Francis Drake Blvd. -> Sir Francis Drake Boulevard\n",
"Sir Francis Drake Blvd. -> Sir Francis Drake Boulevard\n",
"Bernal Ave -> Bernal Avenue\n",
"Healdsburg Ave -> Healdsburg Avenue\n",
"Healdsburg Ave -> Healdsburg Avenue\n",
"Walsh Ave -> Walsh Avenue\n",
"Hyde St -> Hyde Street\n",
"Mendell St -> Mendell Street\n",
"Geneva Ave -> Geneva Avenue\n",
"Mission St -> Mission Street\n",
"Hayes St -> Hayes Street\n",
"Grant Ave -> Grant Avenue\n",
"------------Ignoring Address Key---------------\n",
"address:419 40th Street\n",
"{'created': {'changeset': '19047326',\n",
" 'timestamp': '2013-11-22T00:09:28Z',\n",
" 'uid': '1812078',\n",
" 'user': 'barbearian',\n",
" 'version': '3'},\n",
" 'id': '2274545053',\n",
" 'name': 'SubRosa Coffee',\n",
" 'pos': [37.8283775, -122.2606884],\n",
" 'type': 'node'}\n",
"E Bidwell St -> E Bidwell Street\n",
"market street -> market Street\n",
"E Bidwell St -> E Bidwell Street\n",
"Blue Ravine Rd -> Blue Ravine Road\n",
"E Bidwell St -> E Bidwell Street\n",
"Meridian Ave -> Meridian Avenue\n",
"45th Ave -> 45th Avenue\n",
"Fairmount Ave. -> Fairmount Avenue\n",
"Jefferson Ave. -> Jefferson Avenue\n",
"Chanticleer Ave -> Chanticleer Avenue\n",
"E Bidwell St -> E Bidwell Street\n",
"220 Sylvania Ave -> 220 Sylvania Avenue\n",
"41st Ave -> 41st Avenue\n",
"Fremont Blvd -> Fremont Boulevard\n",
"------------Problem inserting sfgov.org:objectid:7--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94102', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:02Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254140',\n",
" 'name': 'Civic CNTR P O Box Unit',\n",
" 'phone': '(415) 563-7284',\n",
" 'pos': [37.781871, -122.4156773],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94102', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:02Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254140',\n",
" 'name': 'Civic CNTR P O Box Unit',\n",
" 'phone': '(415) 563-7284',\n",
" 'pos': [37.781871, -122.4156773],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:9--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94118', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:02Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254141',\n",
" 'name': 'Contract Station #11',\n",
" 'phone': '(415) 422-6323',\n",
" 'pos': [37.7773621, -122.4484428],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94118', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:02Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254141',\n",
" 'name': 'Contract Station #11',\n",
" 'phone': '(415) 422-6323',\n",
" 'pos': [37.7773621, -122.4484428],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:10--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94103', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '18555221',\n",
" 'timestamp': '2013-10-26T17:48:48Z',\n",
" 'uid': '1775756',\n",
" 'user': 'ets_2016',\n",
" 'version': '2'},\n",
" 'id': '2383254142',\n",
" 'name': 'Contract Station #36',\n",
" 'phone': '(415) 431-7340',\n",
" 'pos': [37.7808858, -122.4117298],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94103', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '18555221',\n",
" 'timestamp': '2013-10-26T17:48:48Z',\n",
" 'uid': '1775756',\n",
" 'user': 'ets_2016',\n",
" 'version': '2'},\n",
" 'id': '2383254142',\n",
" 'name': 'Contract Station #36',\n",
" 'phone': '(415) 431-7340',\n",
" 'pos': [37.7808858, -122.4117298],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:11--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94124', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:02Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254143',\n",
" 'name': 'Diamond Heights Carrier ANNX',\n",
" 'phone': '(415) 641-0158',\n",
" 'pos': [37.7446387, -122.3861526],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94124', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:02Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254143',\n",
" 'name': 'Diamond Heights Carrier ANNX',\n",
" 'phone': '(415) 641-0158',\n",
" 'pos': [37.7446387, -122.3861526],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:15--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94102', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '32778013',\n",
" 'timestamp': '2015-07-21T13:58:38Z',\n",
" 'uid': '152074',\n",
" 'user': 'beweta',\n",
" 'version': '2'},\n",
" 'id': '2383254145',\n",
" 'name': 'Federal Building San Fran',\n",
" 'phone': '+1 415 487 8981',\n",
" 'pos': [37.7814398, -122.4180054],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94102', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '32778013',\n",
" 'timestamp': '2015-07-21T13:58:38Z',\n",
" 'uid': '152074',\n",
" 'user': 'beweta',\n",
" 'version': '2'},\n",
" 'id': '2383254145',\n",
" 'name': 'Federal Building San Fran',\n",
" 'phone': '+1 415 487 8981',\n",
" 'pos': [37.7814398, -122.4180054],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:20--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '821',\n",
" 'postcode': '94122',\n",
" 'state': 'CA',\n",
" 'street': 'Irving Street'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '32997142',\n",
" 'timestamp': '2015-07-31T01:33:24Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '3'},\n",
" 'id': '2383254146',\n",
" 'name': 'Irving Street Station',\n",
" 'phone': '(415) 665-1355',\n",
" 'pos': [37.7638987, -122.4667417],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '821',\n",
" 'postcode': '94122',\n",
" 'state': 'CA',\n",
" 'street': 'Irving Street'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '32997142',\n",
" 'timestamp': '2015-07-31T01:33:24Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '3'},\n",
" 'id': '2383254146',\n",
" 'name': 'Irving Street Station',\n",
" 'phone': '(415) 665-1355',\n",
" 'pos': [37.7638987, -122.4667417],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:21--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94132', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '31216083',\n",
" 'timestamp': '2015-05-17T02:05:26Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '2'},\n",
" 'id': '2383254147',\n",
" 'name': 'Lakeshore Plaza Postal Store',\n",
" 'phone': '(415) 564-0258',\n",
" 'pos': [37.7338247, -122.4892211],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94132', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '31216083',\n",
" 'timestamp': '2015-05-17T02:05:26Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '2'},\n",
" 'id': '2383254147',\n",
" 'name': 'Lakeshore Plaza Postal Store',\n",
" 'phone': '(415) 564-0258',\n",
" 'pos': [37.7338247, -122.4892211],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:23--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94123', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '20772957',\n",
" 'timestamp': '2014-02-25T15:49:51Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '2'},\n",
" 'id': '2383254148',\n",
" 'name': 'Marina Green Retail',\n",
" 'phone': '(415) 440-4390',\n",
" 'pos': [37.8045051, -122.4337479],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94123', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '20772957',\n",
" 'timestamp': '2014-02-25T15:49:51Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '2'},\n",
" 'id': '2383254148',\n",
" 'name': 'Marina Green Retail',\n",
" 'phone': '(415) 440-4390',\n",
" 'pos': [37.8045051, -122.4337479],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:25--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94110', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:03Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254149',\n",
" 'name': 'Mission San Francisco',\n",
" 'phone': '(415) 648-0155',\n",
" 'pos': [37.7866698, -122.4214531],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94110', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:03Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254149',\n",
" 'name': 'Mission San Francisco',\n",
" 'phone': '(415) 648-0155',\n",
" 'pos': [37.7866698, -122.4214531],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:31--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94109', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:03Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254152',\n",
" 'name': 'Pine Street',\n",
" 'phone': '(415) 351-2435',\n",
" 'pos': [37.7898768, -122.4192753],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94109', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:03Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254152',\n",
" 'name': 'Pine Street',\n",
" 'phone': '(415) 351-2435',\n",
" 'pos': [37.7898768, -122.4192753],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:33--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94129', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:03Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254153',\n",
" 'name': 'Presidio',\n",
" 'phone': '(415) 563-0126',\n",
" 'pos': [37.7884232, -122.4595198],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94129', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:03Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254153',\n",
" 'name': 'Presidio',\n",
" 'phone': '(415) 563-0126',\n",
" 'pos': [37.7884232, -122.4595198],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:36--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94108', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:03Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254154',\n",
" 'name': 'Sacramento Street Contract Station',\n",
" 'phone': '(415) 397-0786',\n",
" 'pos': [37.7936124, -122.405058],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94108', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:03Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254154',\n",
" 'name': 'Sacramento Street Contract Station',\n",
" 'phone': '(415) 397-0786',\n",
" 'pos': [37.7936124, -122.405058],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:38--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94188', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:04Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254155',\n",
" 'name': 'San Francisco P&Dc Finance',\n",
" 'phone': '(415) 550-5134',\n",
" 'pos': [37.7399729, -122.3826923],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94188', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:04Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254155',\n",
" 'name': 'San Francisco P&Dc Finance',\n",
" 'phone': '(415) 550-5134',\n",
" 'pos': [37.7399729, -122.3826923],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:40--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94124', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:04Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254156',\n",
" 'name': 'Stonestown Station',\n",
" 'phone': '(415) 285-0872',\n",
" 'pos': [37.7471813, -122.3980567],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94124', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:04Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254156',\n",
" 'name': 'Stonestown Station',\n",
" 'phone': '(415) 285-0872',\n",
" 'pos': [37.7471813, -122.3980567],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:objectid:1--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94114', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:04Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254157',\n",
" 'name': '18th Street Station',\n",
" 'phone': '(415) 431-2701',\n",
" 'pos': [37.7680508, -122.4279355],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sfgov.org:office_typ:Post Office--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco', 'postcode': '94114', 'state': 'CA'},\n",
" 'amenity': 'post_office',\n",
" 'created': {'changeset': '16937177',\n",
" 'timestamp': '2013-07-13T13:48:04Z',\n",
" 'uid': '210173',\n",
" 'user': 'osmmaker',\n",
" 'version': '1'},\n",
" 'id': '2383254157',\n",
" 'name': '18th Street Station',\n",
" 'phone': '(415) 431-2701',\n",
" 'pos': [37.7680508, -122.4279355],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"Monte Verde Dr -> Monte Verde Drive\n",
"Monte Verde Dr -> Monte Verde Drive\n",
"Monte Verde Dr -> Monte Verde Drive\n",
"Lincoln Ave. -> Lincoln Avenue\n",
"Webster St. -> Webster Street\n",
"MacArthur Blvd -> MacArthur Boulevard\n",
"N McCarthy Blvd -> N McCarthy Boulevard\n",
"McKinley St. -> McKinley Street\n",
"Mission Blvd -> Mission Boulevard\n",
"International Blvd -> International Boulevard\n",
"Foxworthy Ave -> Foxworthy Avenue\n",
"University Ave -> University Avenue\n",
"Jackson Rd -> Jackson Road\n",
"Folsom Blvd -> Folsom Boulevard\n",
"San Mateo Rd -> San Mateo Road\n",
"El Monte Ave -> El Monte Avenue\n",
"N. Market Blvd -> N. Market Boulevard\n",
"------------Problem inserting lydian academy:bicycle_parking--------------\n",
"Node:\n",
"{'address': {'floor': '2'},\n",
" 'amenity': 'school',\n",
" 'created': {'changeset': '20204821',\n",
" 'timestamp': '2014-01-26T05:30:08Z',\n",
" 'uid': '169004',\n",
" 'user': 'oldtopos',\n",
" 'version': '1'},\n",
" 'id': '2637767685',\n",
" 'pos': [37.4519169, -122.1801198],\n",
" 'source': 'photograph',\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"L ST -> L Street\n",
"Lakeville St -> Lakeville Street\n",
"162 N. Main St -> 162 N. Main Street\n",
"Saratoga Los Gatos Rd -> Saratoga Los Gatos Road\n",
"Saratoga Los Gatos Rd -> Saratoga Los Gatos Road\n",
"Warm Springs Blvd -> Warm Springs Boulevard\n",
"Mission Blvd -> Mission Boulevard\n",
"Mission Blvd -> Mission Boulevard\n",
"Mission Blvd -> Mission Boulevard\n",
"Mission Blvd -> Mission Boulevard\n",
"Mission Blvd -> Mission Boulevard\n",
"Mission Blvd -> Mission Boulevard\n",
"Mission Blvd -> Mission Boulevard\n",
"Warm Springs Blvd -> Warm Springs Boulevard\n",
"Mission Blvd -> Mission Boulevard\n",
"King St -> King Street\n",
"Crescent Dr -> Crescent Drive\n",
"Crescent Dr -> Crescent Drive\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '689'},\n",
" 'amenity': 'marketplace',\n",
" 'created': {'changeset': '21676806',\n",
" 'timestamp': '2014-04-14T00:47:51Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '1'},\n",
" 'id': '2789555933',\n",
" 'pos': [37.7839545, -122.3984353],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '590',\n",
" 'postcode': '94107',\n",
" 'state': 'CA',\n",
" 'street': 'Brannan Street'},\n",
" 'created': {'changeset': '31995962',\n",
" 'timestamp': '2015-06-16T00:49:03Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '3'},\n",
" 'id': '2789570259',\n",
" 'name': 'K9 Playtime',\n",
" 'pos': [37.776885, -122.3986153],\n",
" 'shop': 'pet',\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"Ice House terrace -> Ice House Terrace\n",
"Perivale Ct -> Perivale Court\n",
"S. Mcdowell Blvd -> S. Mcdowell Boulevard\n",
"Sky Ranch Dr -> Sky Ranch Drive\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1273',\n",
" 'state': 'CA',\n",
" 'street': 'Fulton Street'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '32792613',\n",
" 'timestamp': '2015-07-22T03:39:42Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '2'},\n",
" 'id': '2863463502',\n",
" 'pos': [37.7768027, -122.4376796],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1610',\n",
" 'postcode': '94115',\n",
" 'state': 'CA',\n",
" 'street': 'Golden Gate Avenue'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '32792613',\n",
" 'timestamp': '2015-07-22T03:39:42Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '2'},\n",
" 'id': '2863465302',\n",
" 'pos': [37.7790312, -122.437174],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '1401, 1403'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '22382341',\n",
" 'timestamp': '2014-05-17T04:00:47Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '1'},\n",
" 'id': '2863465502',\n",
" 'pos': [37.7780919, -122.435183],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1610',\n",
" 'postcode': '94115',\n",
" 'state': 'CA',\n",
" 'street': 'Golden Gate Avenue'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '32792613',\n",
" 'timestamp': '2015-07-22T03:39:42Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '2'},\n",
" 'id': '2863466301',\n",
" 'pos': [37.7790365, -122.4373098],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '925'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '22382341',\n",
" 'timestamp': '2014-05-17T04:00:47Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '1'},\n",
" 'id': '2863466302',\n",
" 'pos': [37.778954, -122.4353483],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '915'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '22382341',\n",
" 'timestamp': '2014-05-17T04:00:47Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '1'},\n",
" 'id': '2863466401',\n",
" 'pos': [37.7787017, -122.4353147],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1267',\n",
" 'state': 'CA',\n",
" 'street': 'Fulton Street'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '32792613',\n",
" 'timestamp': '2015-07-22T03:39:42Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '2'},\n",
" 'id': '2863469201',\n",
" 'pos': [37.776809, -122.4375985],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1247',\n",
" 'state': 'CA',\n",
" 'street': 'Fulton Street'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '32792613',\n",
" 'timestamp': '2015-07-22T03:39:41Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '2'},\n",
" 'id': '2863469301',\n",
" 'pos': [37.7768506, -122.4373375],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"Van Ness Ave -> Van Ness Avenue\n",
"------------Problem inserting sign legen:NO LEFT TURN (SYM)--------------\n",
"Node:\n",
"{'created': {'changeset': '22758364',\n",
" 'timestamp': '2014-06-05T15:58:14Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'SOUTH WEST CORNER, WESTBOUND TRAFFIC',\n",
" 'highway': 'turning_circle',\n",
" 'id': '2901312124',\n",
" 'num_row': '334',\n",
" 'pos': [37.7231893, -122.4531936],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street fro:OCEAN AVE--------------\n",
"Node:\n",
"{'created': {'changeset': '22758364',\n",
" 'timestamp': '2014-06-05T15:58:14Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'SOUTH WEST CORNER, WESTBOUND TRAFFIC',\n",
" 'highway': 'turning_circle',\n",
" 'id': '2901312124',\n",
" 'num_row': '334',\n",
" 'pos': [37.7231893, -122.4531936],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street ont:HAROLD AVE--------------\n",
"Node:\n",
"{'created': {'changeset': '22758364',\n",
" 'timestamp': '2014-06-05T15:58:14Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'SOUTH WEST CORNER, WESTBOUND TRAFFIC',\n",
" 'highway': 'turning_circle',\n",
" 'id': '2901312124',\n",
" 'num_row': '334',\n",
" 'pos': [37.7231893, -122.4531936],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sign legen:NO LEFT TURN (SYM)--------------\n",
"Node:\n",
"{'created': {'changeset': '22758364',\n",
" 'timestamp': '2014-06-05T15:58:14Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'NORTH SIDE, WESTBOUND TRAFFIC',\n",
" 'highway': 'turning_circle',\n",
" 'id': '2901312125',\n",
" 'num_row': '321',\n",
" 'pos': [37.7233646, -122.4533091],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street fro:OCEAN AVE--------------\n",
"Node:\n",
"{'created': {'changeset': '22758364',\n",
" 'timestamp': '2014-06-05T15:58:14Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'NORTH SIDE, WESTBOUND TRAFFIC',\n",
" 'highway': 'turning_circle',\n",
" 'id': '2901312125',\n",
" 'num_row': '321',\n",
" 'pos': [37.7233646, -122.4533091],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street ont:HAROLD AVE--------------\n",
"Node:\n",
"{'created': {'changeset': '22758364',\n",
" 'timestamp': '2014-06-05T15:58:14Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'NORTH SIDE, WESTBOUND TRAFFIC',\n",
" 'highway': 'turning_circle',\n",
" 'id': '2901312125',\n",
" 'num_row': '321',\n",
" 'pos': [37.7233646, -122.4533091],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sign legen:NO LEFT TURN (SYM)--------------\n",
"Node:\n",
"{'created': {'changeset': '22763498',\n",
" 'timestamp': '2014-06-05T20:49:38Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'CENTER ISLAND, WESTBOUND TRAFFIC',\n",
" 'id': '2901836011',\n",
" 'num_row': '1155',\n",
" 'pos': [37.7473945, -122.4072605],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street fro:PERALTA AVE--------------\n",
"Node:\n",
"{'created': {'changeset': '22763498',\n",
" 'timestamp': '2014-06-05T20:49:38Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'CENTER ISLAND, WESTBOUND TRAFFIC',\n",
" 'id': '2901836011',\n",
" 'num_row': '1155',\n",
" 'pos': [37.7473945, -122.4072605],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street ont:YORK ST--------------\n",
"Node:\n",
"{'created': {'changeset': '22763498',\n",
" 'timestamp': '2014-06-05T20:49:38Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'CENTER ISLAND, WESTBOUND TRAFFIC',\n",
" 'id': '2901836011',\n",
" 'num_row': '1155',\n",
" 'pos': [37.7473945, -122.4072605],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sign legen:NO LEFT TURN (SYM)--------------\n",
"Node:\n",
"{'created': {'changeset': '22782677',\n",
" 'timestamp': '2014-06-06T20:32:49Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'CENTER ISLAND, NORTHBOUND TRAFFIC',\n",
" 'id': '2903408235',\n",
" 'num_row': '315',\n",
" 'pos': [37.7517922, -122.4250501],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street fro:DOLORES ST--------------\n",
"Node:\n",
"{'created': {'changeset': '22782677',\n",
" 'timestamp': '2014-06-06T20:32:49Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'CENTER ISLAND, NORTHBOUND TRAFFIC',\n",
" 'id': '2903408235',\n",
" 'num_row': '315',\n",
" 'pos': [37.7517922, -122.4250501],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street ont:24TH ST--------------\n",
"Node:\n",
"{'created': {'changeset': '22782677',\n",
" 'timestamp': '2014-06-06T20:32:49Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'CENTER ISLAND, NORTHBOUND TRAFFIC',\n",
" 'id': '2903408235',\n",
" 'num_row': '315',\n",
" 'pos': [37.7517922, -122.4250501],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sign legen:NO LEFT TURN (SYM)--------------\n",
"Node:\n",
"{'created': {'changeset': '22782677',\n",
" 'timestamp': '2014-06-06T20:32:49Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'CENTER ISLAND, NORTHBOUND TRAFFIC',\n",
" 'id': '2903408236',\n",
" 'num_row': '403',\n",
" 'pos': [37.7517997, -122.4250415],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street fro:DOLORES ST--------------\n",
"Node:\n",
"{'created': {'changeset': '22782677',\n",
" 'timestamp': '2014-06-06T20:32:49Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'CENTER ISLAND, NORTHBOUND TRAFFIC',\n",
" 'id': '2903408236',\n",
" 'num_row': '403',\n",
" 'pos': [37.7517997, -122.4250415],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street ont:24TH ST--------------\n",
"Node:\n",
"{'created': {'changeset': '22782677',\n",
" 'timestamp': '2014-06-06T20:32:49Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'CENTER ISLAND, NORTHBOUND TRAFFIC',\n",
" 'id': '2903408236',\n",
" 'num_row': '403',\n",
" 'pos': [37.7517997, -122.4250415],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sign legen:NO LEFT TURN (SYM)--------------\n",
"Node:\n",
"{'created': {'changeset': '22783496',\n",
" 'timestamp': '2014-06-06T21:17:21Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'CENTER ISLAND, NORTHBOUND TRAFFIC',\n",
" 'id': '2903454215',\n",
" 'num_row': '490',\n",
" 'pos': [37.7596623, -122.4281944],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street fro:CHURCH ST--------------\n",
"Node:\n",
"{'created': {'changeset': '22783496',\n",
" 'timestamp': '2014-06-06T21:17:21Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'CENTER ISLAND, NORTHBOUND TRAFFIC',\n",
" 'id': '2903454215',\n",
" 'num_row': '490',\n",
" 'pos': [37.7596623, -122.4281944],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street ont:19TH ST--------------\n",
"Node:\n",
"{'created': {'changeset': '22783496',\n",
" 'timestamp': '2014-06-06T21:17:21Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'CENTER ISLAND, NORTHBOUND TRAFFIC',\n",
" 'id': '2903454215',\n",
" 'num_row': '490',\n",
" 'pos': [37.7596623, -122.4281944],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting sign legen:NO LEFT TURN (SYM)--------------\n",
"Node:\n",
"{'created': {'changeset': '22783496',\n",
" 'timestamp': '2014-06-06T21:17:21Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'SOUTH EAST CORNER, NORTHBOUND TRAFFIC',\n",
" 'id': '2903454216',\n",
" 'num_row': '552',\n",
" 'pos': [37.7596804, -122.4281979],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street fro:CHURCH ST--------------\n",
"Node:\n",
"{'created': {'changeset': '22783496',\n",
" 'timestamp': '2014-06-06T21:17:21Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'SOUTH EAST CORNER, NORTHBOUND TRAFFIC',\n",
" 'id': '2903454216',\n",
" 'num_row': '552',\n",
" 'pos': [37.7596804, -122.4281979],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"------------Problem inserting street ont:19TH ST--------------\n",
"Node:\n",
"{'created': {'changeset': '22783496',\n",
" 'timestamp': '2014-06-06T21:17:21Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'direction': 'SOUTH EAST CORNER, NORTHBOUND TRAFFIC',\n",
" 'id': '2903454216',\n",
" 'num_row': '552',\n",
" 'pos': [37.7596804, -122.4281979],\n",
" 'type': 'node'}\n",
"-------------------------------------------------\n",
"Leavenworth St -> Leavenworth Street\n",
"Hayes St -> Hayes Street\n",
"Hayes St -> Hayes Street\n",
"PurpleLeaf St. -> PurpleLeaf Street\n",
"Herndon PL -> Herndon Place\n",
"Herndon PL -> Herndon Place\n",
"Herndon PL -> Herndon Place\n",
"Herndon PL -> Herndon Place\n",
"Herndon PL -> Herndon Place\n",
"Herndon PL -> Herndon Place\n",
"Herndon PL -> Herndon Place\n",
"Herndon PL -> Herndon Place\n",
"Herndon PL -> Herndon Place\n",
"Herndon PL -> Herndon Place\n",
"Morgan PL -> Morgan Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morgan PL -> Morgan Place\n",
"Grigsby PL -> Grigsby Place\n",
"Grigsby PL -> Grigsby Place\n",
"Grigsby PL -> Grigsby Place\n",
"Grigsby PL -> Grigsby Place\n",
"Grigsby PL -> Grigsby Place\n",
"Grigsby PL -> Grigsby Place\n",
"Grigsby PL -> Grigsby Place\n",
"Grigsby PL -> Grigsby Place\n",
"Grigsby PL -> Grigsby Place\n",
"Grigsby PL -> Grigsby Place\n",
"Grigsby PL -> Grigsby Place\n",
"Grigsby PL -> Grigsby Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Le Mans PL -> Le Mans Place\n",
"Le Mans PL -> Le Mans Place\n",
"Le Mans PL -> Le Mans Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Morning Dew PL -> Morning Dew Place\n",
"Le Mans PL -> Le Mans Place\n",
"Le Mans PL -> Le Mans Place\n",
"Le Mans PL -> Le Mans Place\n",
"Le Mans PL -> Le Mans Place\n",
"Le Mans PL -> Le Mans Place\n",
"Le Mans PL -> Le Mans Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Janet PL -> Janet Place\n",
"Beverly PL -> Beverly Place\n",
"Janet PL -> Janet Place\n",
"Richmond PL -> Richmond Place\n",
"Arabian PL -> Arabian Place\n",
"Mustang PL -> Mustang Place\n",
"Mustang PL -> Mustang Place\n",
"Arabian PL -> Arabian Place\n",
"Arabian PL -> Arabian Place\n",
"Mustang PL -> Mustang Place\n",
"Arabian PL -> Arabian Place\n",
"Mustang PL -> Mustang Place\n",
"Buckskin PL -> Buckskin Place\n",
"Buckskin PL -> Buckskin Place\n",
"Buckskin PL -> Buckskin Place\n",
"Buckskin PL -> Buckskin Place\n",
"Arabian PL -> Arabian Place\n",
"Mustang PL -> Mustang Place\n",
"Mustang PL -> Mustang Place\n",
"Arabian PL -> Arabian Place\n",
"Arabian PL -> Arabian Place\n",
"Mustang PL -> Mustang Place\n",
"Mustang PL -> Mustang Place\n",
"Arabian PL -> Arabian Place\n",
"Arabian PL -> Arabian Place\n",
"Mustang PL -> Mustang Place\n",
"Arabian PL -> Arabian Place\n",
"Mustang PL -> Mustang Place\n",
"Buckskin PL -> Buckskin Place\n",
"Buckskin PL -> Buckskin Place\n",
"Mustang PL -> Mustang Place\n",
"Arabian PL -> Arabian Place\n",
"Mustang PL -> Mustang Place\n",
"Arabian PL -> Arabian Place\n",
"Buckskin PL -> Buckskin Place\n",
"Mustang PL -> Mustang Place\n",
"Arabian PL -> Arabian Place\n",
"Buckskin PL -> Buckskin Place\n",
"Mustang PL -> Mustang Place\n",
"Arabian PL -> Arabian Place\n",
"Mustang PL -> Mustang Place\n",
"Mustang PL -> Mustang Place\n",
"Buckskin PL -> Buckskin Place\n",
"Buckskin PL -> Buckskin Place\n",
"Buckskin PL -> Buckskin Place\n",
"Buckskin PL -> Buckskin Place\n",
"Buckskin PL -> Buckskin Place\n",
"Arabian PL -> Arabian Place\n",
"Arabian PL -> Arabian Place\n",
"Buckskin PL -> Buckskin Place\n",
"Mustang PL -> Mustang Place\n",
"Mustang PL -> Mustang Place\n",
"Buckskin PL -> Buckskin Place\n",
"Mustang PL -> Mustang Place\n",
"Buckskin PL -> Buckskin Place\n",
"Mustang PL -> Mustang Place\n",
"Arabian PL -> Arabian Place\n",
"Arabian PL -> Arabian Place\n",
"Mustang PL -> Mustang Place\n",
"Buckskin PL -> Buckskin Place\n",
"Mustang PL -> Mustang Place\n",
"Buckskin PL -> Buckskin Place\n",
"Buckskin PL -> Buckskin Place\n",
"Leesburg PL -> Leesburg Place\n",
"Alexandria PL -> Alexandria Place\n",
"Williamsburg PL -> Williamsburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Arabian PL -> Arabian Place\n",
"Arabian PL -> Arabian Place\n",
"Buckskin PL -> Buckskin Place\n",
"Mustang PL -> Mustang Place\n",
"Mustang PL -> Mustang Place\n",
"Arabian PL -> Arabian Place\n",
"Arabian PL -> Arabian Place\n",
"Arabian PL -> Arabian Place\n",
"Mustang PL -> Mustang Place\n",
"Gettysburg PL -> Gettysburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Vicksburg PL -> Vicksburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Gettysburg PL -> Gettysburg Place\n",
"Arabian PL -> Arabian Place\n",
"Arabian PL -> Arabian Place\n",
"Arabian PL -> Arabian Place\n",
"Arabian PL -> Arabian Place\n",
"Leesburg PL -> Leesburg Place\n",
"Vicksburg PL -> Vicksburg Place\n",
"Vicksburg PL -> Vicksburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Vicksburg PL -> Vicksburg Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Vicksburg PL -> Vicksburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Vicksburg PL -> Vicksburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Gettysburg PL -> Gettysburg Place\n",
"Williamsburg PL -> Williamsburg Place\n",
"Williamsburg PL -> Williamsburg Place\n",
"Gettysburg PL -> Gettysburg Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Williamsburg PL -> Williamsburg Place\n",
"Williamsburg PL -> Williamsburg Place\n",
"Gettysburg PL -> Gettysburg Place\n",
"Vicksburg PL -> Vicksburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Vicksburg PL -> Vicksburg Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Gettysburg PL -> Gettysburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Williamsburg PL -> Williamsburg Place\n",
"Alexandria PL -> Alexandria Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Fredericksburg PL -> Fredericksburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Williamsburg PL -> Williamsburg Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Alexandria PL -> Alexandria Place\n",
"Williamsburg PL -> Williamsburg Place\n",
"Williamsburg PL -> Williamsburg Place\n",
"Alexandria PL -> Alexandria Place\n",
"Leesburg PL -> Leesburg Place\n",
"Alexandria PL -> Alexandria Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Alexandria PL -> Alexandria Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Fredericksburg PL -> Fredericksburg Place\n",
"Harrisburg PL -> Harrisburg Place\n",
"Alexandria PL -> Alexandria Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Fredericksburg PL -> Fredericksburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Fredericksburg PL -> Fredericksburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Fredericksburg PL -> Fredericksburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Fredericksburg PL -> Fredericksburg Place\n",
"Fredericksburg PL -> Fredericksburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Fredericksburg PL -> Fredericksburg Place\n",
"Fredericksburg PL -> Fredericksburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Fredericksburg PL -> Fredericksburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Fredericksburg PL -> Fredericksburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Leesburg PL -> Leesburg Place\n",
"Richmond PL -> Richmond Place\n",
"Richmond PL -> Richmond Place\n",
"Leesburg PL -> Leesburg Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Leesburg PL -> Leesburg Place\n",
"Richmond PL -> Richmond Place\n",
"Alexandria PL -> Alexandria Place\n",
"Richmond PL -> Richmond Place\n",
"Richmond PL -> Richmond Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Alexandria PL -> Alexandria Place\n",
"Chambord PL -> Chambord Place\n",
"Chambord PL -> Chambord Place\n",
"Chambord PL -> Chambord Place\n",
"Chambord PL -> Chambord Place\n",
"Chambord PL -> Chambord Place\n",
"Chambord PL -> Chambord Place\n",
"Chambord PL -> Chambord Place\n",
"Rothesay PL -> Rothesay Place\n",
"Rothesay PL -> Rothesay Place\n",
"Rothesay PL -> Rothesay Place\n",
"Rothesay PL -> Rothesay Place\n",
"Rothesay PL -> Rothesay Place\n",
"Rothesay PL -> Rothesay Place\n",
"Rothesay PL -> Rothesay Place\n",
"Rothesay PL -> Rothesay Place\n",
"Rothesay PL -> Rothesay Place\n",
"Rothesay PL -> Rothesay Place\n",
"Burns PL -> Burns Place\n",
"Rothesay PL -> Rothesay Place\n",
"Rothesay PL -> Rothesay Place\n",
"Burns PL -> Burns Place\n",
"Burns PL -> Burns Place\n",
"Rothesay PL -> Rothesay Place\n",
"Rothesay PL -> Rothesay Place\n",
"Rothesay PL -> Rothesay Place\n",
"Burns PL -> Burns Place\n",
"Rothesay PL -> Rothesay Place\n",
"Burns PL -> Burns Place\n",
"Burns PL -> Burns Place\n",
"Burns PL -> Burns Place\n",
"Burns PL -> Burns Place\n",
"Burns PL -> Burns Place\n",
"Burns PL -> Burns Place\n",
"Rothesay PL -> Rothesay Place\n",
"Burns PL -> Burns Place\n",
"Rothesay PL -> Rothesay Place\n",
"Burns PL -> Burns Place\n",
"Burns PL -> Burns Place\n",
"Burns PL -> Burns Place\n",
"Burns PL -> Burns Place\n",
"Burns PL -> Burns Place\n",
"Burns PL -> Burns Place\n",
"Burns PL -> Burns Place\n",
"Burns PL -> Burns Place\n",
"Monroe St -> Monroe Street\n",
"Blenheim Ave -> Blenheim Avenue\n",
"------------Ignoring Address Key---------------\n",
"address:510 S Murphy Avenue, Sunnyvale, CA 94086\n",
"{'created': {'changeset': '25254734',\n",
" 'timestamp': '2014-09-05T19:02:43Z',\n",
" 'uid': '816433',\n",
" 'user': 'f2003104',\n",
" 'version': '1'},\n",
" 'id': '3061406142',\n",
" 'name': 'Sunnyvale Optometric Center',\n",
" 'pos': [37.3707738, -122.0320404],\n",
" 'type': 'node'}\n",
"Pacific Commons Blvd -> Pacific Commons Boulevard\n",
"Dublin Blvd -> Dublin Boulevard\n",
"Garvin Avenie -> Garvin Avenue\n",
"Garvin Avenie -> Garvin Avenue\n",
"Linda Mar Shppng Ctr -> Linda Mar Shppng Center\n",
"Sir Francis Drake Blvd. -> Sir Francis Drake Boulevard\n",
"8th st -> 8th Street\n",
"Auburn Blvd -> Auburn Boulevard\n",
"foothill blvd -> foothill Boulevard\n",
"Cumberland St -> Cumberland Street\n",
"Tice Valley Blvd -> Tice Valley Boulevard\n",
"Railroad Ave -> Railroad Avenue\n",
"Dyer st -> Dyer Street\n",
"California Blvd -> California Boulevard\n",
"Trancas St -> Trancas Street\n",
"Trancas St -> Trancas Street\n",
"Conrad Ct -> Conrad Court\n",
"Pleasant Valley Dr -> Pleasant Valley Drive\n",
"------------Ignoring Address Key---------------\n",
"address:3100 Telegraph Ave\n",
"{'created': {'changeset': '15299117',\n",
" 'timestamp': '2013-03-09T08:36:33Z',\n",
" 'uid': '933797',\n",
" 'user': 'oba510',\n",
" 'version': '7'},\n",
" 'id': '23006785',\n",
" 'name': 'Peralta Medical Office Building',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:3700 Mandela Parkway\n",
"{'created': {'changeset': '24683754',\n",
" 'timestamp': '2014-08-11T17:28:42Z',\n",
" 'uid': '153669',\n",
" 'user': 'dchiles',\n",
" 'version': '10'},\n",
" 'id': '23014312',\n",
" 'name': 'Best Buy',\n",
" 'phone': '+1 510 4200323',\n",
" 'ref': '499',\n",
" 'shop': 'electronics',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:3650 Mandela Parkway\n",
"{'created': {'changeset': '24683754',\n",
" 'timestamp': '2014-08-11T17:28:43Z',\n",
" 'uid': '153669',\n",
" 'user': 'dchiles',\n",
" 'version': '9'},\n",
" 'id': '23014315',\n",
" 'name': 'Extended Stay America Hotel',\n",
" 'phone': '+1 510 9231481',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:3938 Horton Street\n",
"{'created': {'changeset': '11770924',\n",
" 'timestamp': '2012-06-01T19:27:11Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '8'},\n",
" 'id': '23014325',\n",
" 'name': 'Toys R Us / Babies R Us',\n",
" 'phone': '+1 (510) 637-7003',\n",
" 'shop': 'toys',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:1712 85th Ave\n",
"{'created': {'changeset': '410506',\n",
" 'timestamp': '2008-03-28T03:20:53Z',\n",
" 'uid': '22946',\n",
" 'user': 'David Muir Sharnoff',\n",
" 'version': '2'},\n",
" 'id': '23488820',\n",
" 'name': '85th Avenue Mini Park',\n",
" 'type': 'way'}\n",
"Crittenden Ln -> Crittenden Lane\n",
"Crittenden Ln -> Crittenden Lane\n",
"Crittenden Ln -> Crittenden Lane\n",
"Stierlin Ct -> Stierlin Court\n",
"Airport Blvd -> Airport Boulevard\n",
"Anza Blvd -> Anza Boulevard\n",
"Airport Blvd -> Airport Boulevard\n",
"Dartmouth Ave. -> Dartmouth Avenue\n",
"Ne Quad I-80 / Hilltop Dr -> Ne Quad I-80 / Hilltop Drive\n",
"N. Union Rd. -> N. Union Road\n",
"5th St. -> 5th Street\n",
"Mace Blvd -> Mace Boulevard\n",
"Fairway Dr. -> Fairway Drive\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '795'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676806',\n",
" 'timestamp': '2014-04-14T00:47:52Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '4'},\n",
" 'id': '25759093',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"Crittenden Ln -> Crittenden Lane\n",
"San Juan Hwy -> San Juan Highway\n",
"Airline Hwy -> Airline Highway\n",
"1425 E Dunne Ave -> 1425 E Dunne Avenue\n",
"Fountain Oaks Dr -> Fountain Oaks Drive\n",
"Airline Hwy -> Airline Highway\n",
"West Edmundson Ave. -> West Edmundson Avenue\n",
"Monterey Rd. -> Monterey Road\n",
"Bert Dr -> Bert Drive\n",
"Bert Dr -> Bert Drive\n",
"Bert Dr -> Bert Drive\n",
"Bert Dr -> Bert Drive\n",
"Technology Pkwy -> Technology Parkway\n",
"Bert Dr -> Bert Drive\n",
"San Felipe Rd -> San Felipe Road\n",
"Maple St -> Maple Street\n",
"Maple St -> Maple Street\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"Gateway Dr -> Gateway Drive\n",
"Gateway Dr -> Gateway Drive\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"4th St -> 4th Street\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"Bolsa Rd -> Bolsa Road\n",
"Wright Rd -> Wright Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"Bert Dr -> Bert Drive\n",
"Bert Dr -> Bert Drive\n",
"Bert Dr -> Bert Drive\n",
"Technology Pkwy -> Technology Parkway\n",
"Technology Pkwy -> Technology Parkway\n",
"Technology Pkwy -> Technology Parkway\n",
"Technology Pkwy -> Technology Parkway\n",
"Bolsa Rd -> Bolsa Road\n",
"N 1st street -> N 1st Street\n",
"Mccray St -> Mccray Street\n",
"Mccray St -> Mccray Street\n",
"Virginia Dr -> Virginia Drive\n",
"Monterey St -> Monterey Street\n",
"Prospect Ave -> Prospect Avenue\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"1st St -> 1st Street\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Juan Rd -> San Juan Road\n",
"Felice Dr -> Felice Drive\n",
"Buena Vista Rd -> Buena Vista Road\n",
"Marshlands Rd -> Marshlands Road\n",
"Sunnyslope Rd -> Sunnyslope Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"Shelton Dr -> Shelton Drive\n",
"Shelton Dr -> Shelton Drive\n",
"Shelton Dr -> Shelton Drive\n",
"Shelton Dr -> Shelton Drive\n",
"Hamilton Ct -> Hamilton Court\n",
"Hamilton Ct -> Hamilton Court\n",
"Shelton Dr -> Shelton Drive\n",
"Shelton Dr -> Shelton Drive\n",
"Park Center Dr -> Park Center Drive\n",
"Park Center Dr -> Park Center Drive\n",
"Shelton Dr -> Shelton Drive\n",
"Shelton Dr -> Shelton Drive\n",
"Fallon Rd -> Fallon Road\n",
"Shelton Dr -> Shelton Drive\n",
"------------Ignoring Address Key---------------\n",
"address:730 Taraval Street\n",
"{'created': {'changeset': '18104852',\n",
" 'timestamp': '2013-09-30T06:55:34Z',\n",
" 'uid': '416346',\n",
" 'user': 'Brian@Brea',\n",
" 'version': '13'},\n",
" 'id': '28941473',\n",
" 'name': 'Safeway 0909',\n",
" 'phone': '+1 (415) 665-4136',\n",
" 'ref': '909',\n",
" 'shop': 'supermarket',\n",
" 'type': 'way'}\n",
"A 4th St -> A 4th Street\n",
"Homestead Rd -> Homestead Road\n",
"4th St -> 4th Street\n",
"San Benito St -> San Benito Street\n",
"1st St -> 1st Street\n",
"San Benito St -> San Benito Street\n",
"Hamilton Ave -> Hamilton Avenue\n",
"Hamilton Ave -> Hamilton Avenue\n",
"N E & Sw Quad Of Us 101 / Rohnert Park Expwy -> N E & Sw Quad Of Us 101 / Rohnert Park Expressway\n",
"N E & Sw Quad Of Us 101 / Rohnert Park Expwy -> N E & Sw Quad Of Us 101 / Rohnert Park Expressway\n",
"Diamond Blvd -> Diamond Boulevard\n",
"Prescott Ct -> Prescott Court\n",
"Prescott Ct -> Prescott Court\n",
"Vallejo street -> Vallejo Street\n",
"Madison St -> Madison Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"W Middlefield Rd -> W Middlefield Road\n",
"9th St. -> 9th Street\n",
"Freedmond Blvd. -> Freedmond Boulevard\n",
"Mission St -> Mission Street\n",
"225 Rooney St -> 225 Rooney Street\n",
"Franklin Blvd -> Franklin Boulevard\n",
"Green St -> Green Street\n",
"Green St -> Green Street\n",
"Green St -> Green Street\n",
"Green St -> Green Street\n",
"Kearny St -> Kearny Street\n",
"Kearny St -> Kearny Street\n",
"Blake Ave -> Blake Avenue\n",
"Gaundabert Ln -> Gaundabert Lane\n",
"N Blaney Ave -> N Blaney Avenue\n",
"Leisure Town Rd -> Leisure Town Road\n",
"------------Ignoring Address Key---------------\n",
"address:1199 Jacklin Road\n",
"{'created': {'changeset': '9091428',\n",
" 'timestamp': '2011-08-22T07:34:44Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '2'},\n",
" 'id': '39259353',\n",
" 'name': 'Golfland',\n",
" 'phone': '+1 (408) 263-6855',\n",
" 'type': 'way'}\n",
"Phelan Ave -> Phelan Avenue\n",
"Vicksburg Pl -> Vicksburg Place\n",
"N Gettysburg Pl -> N Gettysburg Place\n",
"Mount Hermon Rd -> Mount Hermon Road\n",
"Vicksburg Pl -> Vicksburg Place\n",
"Samaritan Dr -> Samaritan Drive\n",
"Vicksburg Pl -> Vicksburg Place\n",
"W Lincoln Rd -> W Lincoln Road\n",
"Dealy Ln -> Dealy Lane\n",
"Huntington Ave -> Huntington Avenue\n",
"Peralta St -> Peralta Street\n",
"2nd St -> 2nd Street\n",
"Courthouse Dr -> Courthouse Drive\n",
"Chabot Dr -> Chabot Drive\n",
"Chabot Dr -> Chabot Drive\n",
"Johnson Dr -> Johnson Drive\n",
"Johnson Dr -> Johnson Drive\n",
"W. Las Positas Blvd -> W. Las Positas Boulevard\n",
"Stoneridge Mall Rd -> Stoneridge Mall Road\n",
"Flora Ave -> Flora Avenue\n",
"Flora Ave -> Flora Avenue\n",
"Flora Ave -> Flora Avenue\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Benito St -> San Benito Street\n",
"San Benito St -> San Benito Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Benito St -> San Benito Street\n",
"East St -> East Street\n",
"1st St -> 1st Street\n",
"San Benito St -> San Benito Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"7th St -> 7th Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"Sundown Ln -> Sundown Lane\n",
"Sundown Ln -> Sundown Lane\n",
"Sundown Ln -> Sundown Lane\n",
"Sundown Ln -> Sundown Lane\n",
"Sundown Ln -> Sundown Lane\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Sundown Ln -> Sundown Lane\n",
"Sundown Ln -> Sundown Lane\n",
"Sundown Ln -> Sundown Lane\n",
"Sundown Ln -> Sundown Lane\n",
"Sundown Ln -> Sundown Lane\n",
"Sundown Ln -> Sundown Lane\n",
"Sundown Ln -> Sundown Lane\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Saddle Ct -> Saddle Court\n",
"Saddle Ct -> Saddle Court\n",
"Saddle Ct -> Saddle Court\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Dr -> Diablo Hills Drive\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Horizon Dr -> Horizon Drive\n",
"Horizon Dr -> Horizon Drive\n",
"Horizon Dr -> Horizon Drive\n",
"Horizon Dr -> Horizon Drive\n",
"Meadow Ct -> Meadow Court\n",
"Meadow Ct -> Meadow Court\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Serene Dr -> Serene Drive\n",
"Serene Dr -> Serene Drive\n",
"Serene Dr -> Serene Drive\n",
"Serene Dr -> Serene Drive\n",
"Serene Dr -> Serene Drive\n",
"Serene Dr -> Serene Drive\n",
"Serene Dr -> Serene Drive\n",
"Serene Dr -> Serene Drive\n",
"Serene Dr -> Serene Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Olga Dr -> Olga Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Majestic Dr -> Majestic Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Regal Dr -> Regal Drive\n",
"Nash Rd -> Nash Road\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Nevada St -> Nevada Street\n",
"Nevada St -> Nevada Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Eastview Dr -> Eastview Drive\n",
"Eastview Dr -> Eastview Drive\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Eastview Dr -> Eastview Drive\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Velado St -> Velado Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Cushman St -> Cushman Street\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Olga Dr -> Olga Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Eastview Ct -> Eastview Court\n",
"Eastview Ct -> Eastview Court\n",
"Eastview Ct -> Eastview Court\n",
"Eastview Ct -> Eastview Court\n",
"Eastview Ct -> Eastview Court\n",
"Eastview Ct -> Eastview Court\n",
"Eastview Ct -> Eastview Court\n",
"Eastview Ct -> Eastview Court\n",
"Eastview Ct -> Eastview Court\n",
"Eastview Ct -> Eastview Court\n",
"Eastview Ct -> Eastview Court\n",
"Eastview Dr -> Eastview Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Irma Dr -> Irma Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Mary Dr -> Mary Drive\n",
"Nora Dr -> Nora Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Cushman St -> Cushman Street\n",
"Velado St -> Velado Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Severinsen St -> Severinsen Street\n",
"Velado St -> Velado Street\n",
"Velado St -> Velado Street\n",
"Velado St -> Velado Street\n",
"Tina Dr -> Tina Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Paul Dr -> Paul Drive\n",
"Nora Dr -> Nora Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Irma Dr -> Irma Drive\n",
"Southridge Dr -> Southridge Drive\n",
"Southridge Dr -> Southridge Drive\n",
"Paul Dr -> Paul Drive\n",
"Paul Dr -> Paul Drive\n",
"Paul Dr -> Paul Drive\n",
"Paul Dr -> Paul Drive\n",
"Paul Dr -> Paul Drive\n",
"Paul Dr -> Paul Drive\n",
"Paul Dr -> Paul Drive\n",
"Paul Dr -> Paul Drive\n",
"Nora Dr -> Nora Drive\n",
"Nora Dr -> Nora Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Southridge Dr -> Southridge Drive\n",
"Southridge Dr -> Southridge Drive\n",
"Paul Dr -> Paul Drive\n",
"Paul Dr -> Paul Drive\n",
"Paul Dr -> Paul Drive\n",
"Paul Dr -> Paul Drive\n",
"Paul Dr -> Paul Drive\n",
"Paul Dr -> Paul Drive\n",
"Paul Dr -> Paul Drive\n",
"Southridge Dr -> Southridge Drive\n",
"Southridge Dr -> Southridge Drive\n",
"Southridge Dr -> Southridge Drive\n",
"Southridge Dr -> Southridge Drive\n",
"Southridge Dr -> Southridge Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Tina Dr -> Tina Drive\n",
"Nevada St -> Nevada Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Nevada St -> Nevada Street\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Morning Glory Dr -> Morning Glory Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Joshua Dr -> Joshua Drive\n",
"Joshua Dr -> Joshua Drive\n",
"Joshua Dr -> Joshua Drive\n",
"Joshua Dr -> Joshua Drive\n",
"Joshua Dr -> Joshua Drive\n",
"Joshua Dr -> Joshua Drive\n",
"Joshua Dr -> Joshua Drive\n",
"Hillock Ct -> Hillock Court\n",
"Hillock Ct -> Hillock Court\n",
"Hillock Ct -> Hillock Court\n",
"Joshua Dr -> Joshua Drive\n",
"Joshua Dr -> Joshua Drive\n",
"Joshua Dr -> Joshua Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Hillock Dr -> Hillock Drive\n",
"Southside Rd -> Southside Road\n",
"Hillock Ct -> Hillock Court\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Hillock Ct -> Hillock Court\n",
"Hillock Ct -> Hillock Court\n",
"Hillock Ct -> Hillock Court\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Leisure Ct -> Leisure Court\n",
"Leisure Ct -> Leisure Court\n",
"Leisure Ct -> Leisure Court\n",
"Leisure Ct -> Leisure Court\n",
"Leisure Ct -> Leisure Court\n",
"Leisure Ct -> Leisure Court\n",
"Leisure Ct -> Leisure Court\n",
"Leisure Ct -> Leisure Court\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Ervin Ct -> Ervin Court\n",
"Ervin Ct -> Ervin Court\n",
"Ervin Ct -> Ervin Court\n",
"Ervin Ct -> Ervin Court\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Ervin Ct -> Ervin Court\n",
"Ervin Ct -> Ervin Court\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Leisure Ct -> Leisure Court\n",
"Leisure Ct -> Leisure Court\n",
"Leisure Ct -> Leisure Court\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Evelyns Dr -> Evelyns Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Gia Ct -> Gia Court\n",
"Gia Ct -> Gia Court\n",
"Gia Ct -> Gia Court\n",
"Gia Ct -> Gia Court\n",
"Gia Ct -> Gia Court\n",
"Gia Ct -> Gia Court\n",
"Gia Ct -> Gia Court\n",
"Ruger Ct -> Ruger Court\n",
"Ruger Ct -> Ruger Court\n",
"Ruger Ct -> Ruger Court\n",
"Ruger Ct -> Ruger Court\n",
"Ruger Ct -> Ruger Court\n",
"Ruger Ct -> Ruger Court\n",
"Ruger Ct -> Ruger Court\n",
"Ruger Ct -> Ruger Court\n",
"Ruger Ct -> Ruger Court\n",
"Ruger Ct -> Ruger Court\n",
"Alissa Ct -> Alissa Court\n",
"Alissa Ct -> Alissa Court\n",
"Alissa Ct -> Alissa Court\n",
"Alissa Ct -> Alissa Court\n",
"Alissa Ct -> Alissa Court\n",
"Alissa Ct -> Alissa Court\n",
"Alissa Ct -> Alissa Court\n",
"Alissa Ct -> Alissa Court\n",
"Alissa Ct -> Alissa Court\n",
"Alissa Dr -> Alissa Drive\n",
"Alissa Dr -> Alissa Drive\n",
"Alissa Dr -> Alissa Drive\n",
"Alissa Dr -> Alissa Drive\n",
"Alissa Dr -> Alissa Drive\n",
"Alissa Dr -> Alissa Drive\n",
"Alissa Dr -> Alissa Drive\n",
"Alissa Dr -> Alissa Drive\n",
"Alissa Dr -> Alissa Drive\n",
"Alissa Dr -> Alissa Drive\n",
"Alissa Dr -> Alissa Drive\n",
"Alissa Dr -> Alissa Drive\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Black Forest Dr -> Black Forest Drive\n",
"Black Forest Dr -> Black Forest Drive\n",
"Black Forest Dr -> Black Forest Drive\n",
"Black Forest Dr -> Black Forest Drive\n",
"Black Forest Dr -> Black Forest Drive\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Memorial Dr -> Memorial Drive\n",
"Verdun Ave -> Verdun Avenue\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Willow Rd -> Willow Road\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Lewelling Blvd -> Lewelling Boulevard\n",
"Hill St -> Hill Street\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Liege Dr -> Liege Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Black Forest Dr -> Black Forest Drive\n",
"Trente Ct -> Trente Court\n",
"Trente Ct -> Trente Court\n",
"Black Forest Dr -> Black Forest Drive\n",
"Trente Ct -> Trente Court\n",
"Black Forest Dr -> Black Forest Drive\n",
"Trente Ct -> Trente Court\n",
"Black Forest Dr -> Black Forest Drive\n",
"Black Forest Dr -> Black Forest Drive\n",
"Trente Ct -> Trente Court\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Black Forest Dr -> Black Forest Drive\n",
"Black Forest Dr -> Black Forest Drive\n",
"Trente Ct -> Trente Court\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Versailles Dr -> Versailles Drive\n",
"Trente Ct -> Trente Court\n",
"Trente Ct -> Trente Court\n",
"Trente Ct -> Trente Court\n",
"Trente Ct -> Trente Court\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Verdun Ave -> Verdun Avenue\n",
"Verdun Ave -> Verdun Avenue\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Argonne Ave -> Argonne Avenue\n",
"Argonne Ave -> Argonne Avenue\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Marne Dr -> Marne Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Argonne Ave -> Argonne Avenue\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Trieste Dr -> Trieste Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Versailles Dr -> Versailles Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Marne Dr -> Marne Drive\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Somme Ave -> Somme Avenue\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Paseo Dr -> Paseo Drive\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Laguna Ct -> Laguna Court\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Joe Borovich Dr -> Joe Borovich Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Del Mar Dr -> Del Mar Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Versailles Dr -> Versailles Drive\n",
"Del Monte Dr -> Del Monte Drive\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Memorial Dr -> Memorial Drive\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Memorial Dr -> Memorial Drive\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Memorial Dr -> Memorial Drive\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Memorial Dr -> Memorial Drive\n",
"Pear St -> Pear Street\n",
"Pear St -> Pear Street\n",
"Pear St -> Pear Street\n",
"Pear St -> Pear Street\n",
"Pear St -> Pear Street\n",
"Pear St -> Pear Street\n",
"Pear St -> Pear Street\n",
"Pear St -> Pear Street\n",
"Pear St -> Pear Street\n",
"Pear St -> Pear Street\n",
"Pear St -> Pear Street\n",
"Pear St -> Pear Street\n",
"Pear St -> Pear Street\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Memorial Dr -> Memorial Drive\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Plum Ct -> Plum Court\n",
"Memorial Dr -> Memorial Drive\n",
"Memorial Dr -> Memorial Drive\n",
"Pear Ct -> Pear Court\n",
"Pear Ct -> Pear Court\n",
"Pear Ct -> Pear Court\n",
"Pear Ct -> Pear Court\n",
"Pear Ct -> Pear Court\n",
"Peach Ct -> Peach Court\n",
"Pear Ct -> Pear Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Pear Ct -> Pear Court\n",
"Pear Ct -> Pear Court\n",
"Pear Ct -> Pear Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Peach Ct -> Peach Court\n",
"Memorial Dr -> Memorial Drive\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Memorial Dr -> Memorial Drive\n",
"Apple Ct -> Apple Court\n",
"Apple Ct -> Apple Court\n",
"Memorial Dr -> Memorial Drive\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Brigantino Dr -> Brigantino Drive\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"El Toro Dr -> El Toro Drive\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Clearview Dr -> Clearview Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Meridian St -> Meridian Street\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"Matador Dr -> Matador Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"Barnes Ln -> Barnes Lane\n",
"Barnes Ln -> Barnes Lane\n",
"Barnes Ln -> Barnes Lane\n",
"Barnes Ln -> Barnes Lane\n",
"Meridian St -> Meridian Street\n",
"Barnes Ln -> Barnes Lane\n",
"Barnes Ln -> Barnes Lane\n",
"Barnes Ln -> Barnes Lane\n",
"Gardenia Ln -> Gardenia Lane\n",
"Barnes Ln -> Barnes Lane\n",
"Barnes Ln -> Barnes Lane\n",
"Barnes Ln -> Barnes Lane\n",
"Gardenia Ln -> Gardenia Lane\n",
"Barnes Ln -> Barnes Lane\n",
"Barnes Ln -> Barnes Lane\n",
"Barnes Ln -> Barnes Lane\n",
"Gardenia Ln -> Gardenia Lane\n",
"Gardenia Ln -> Gardenia Lane\n",
"Gardenia Ln -> Gardenia Lane\n",
"Gardenia Ln -> Gardenia Lane\n",
"Gardenia Ln -> Gardenia Lane\n",
"Gardenia Ln -> Gardenia Lane\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Gardenia Ln -> Gardenia Lane\n",
"Gardenia Ln -> Gardenia Lane\n",
"Gardenia Ln -> Gardenia Lane\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Gardenia Ln -> Gardenia Lane\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Barnes Ln -> Barnes Lane\n",
"Hillcrest Rd -> Hillcrest Road\n",
"------------Ignoring Address Key---------------\n",
"address:855 East El Camino Real\n",
"{'created': {'changeset': '9044962',\n",
" 'timestamp': '2011-08-17T07:38:13Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '7'},\n",
" 'id': '49186561',\n",
" 'name': 'Golfland USA',\n",
" 'phone': '+1 (408) 245-8434',\n",
" 'type': 'way'}\n",
"Old School Rd -> Old School Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Airport Blvd -> Airport Boulevard\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"Poppy Lane Cir -> Poppy Lane Circle\n",
"Poppy Lane Cir -> Poppy Lane Circle\n",
"Poppy Lane Cir -> Poppy Lane Circle\n",
"Poppy Lane Cir -> Poppy Lane Circle\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"Poppy Lane Cir -> Poppy Lane Circle\n",
"Poppy Lane Cir -> Poppy Lane Circle\n",
"Poppy Lane Cir -> Poppy Lane Circle\n",
"Poppy Lane Dr -> Poppy Lane Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"Holly Tree Cir -> Holly Tree Circle\n",
"Holly Tree Cir -> Holly Tree Circle\n",
"Poppy Lane Dr -> Poppy Lane Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Sawtooth Dr -> Sawtooth Drive\n",
"Sawtooth Dr -> Sawtooth Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Clearview Dr -> Clearview Drive\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Clearview Dr -> Clearview Drive\n",
"Alpine Cir -> Alpine Circle\n",
"Alpine Cir -> Alpine Circle\n",
"Alpine Cir -> Alpine Circle\n",
"Alpine Cir -> Alpine Circle\n",
"Alpine Cir -> Alpine Circle\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Sawtooth Dr -> Sawtooth Drive\n",
"Sawtooth Dr -> Sawtooth Drive\n",
"Sawtooth Dr -> Sawtooth Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Sawtooth Dr -> Sawtooth Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Lorton Ave -> Lorton Avenue\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Audubon Dr -> Audubon Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Century Blvd -> Century Boulevard\n",
"Century Blvd -> Century Boulevard\n",
"Century Blvd -> Century Boulevard\n",
"Century Blvd -> Century Boulevard\n",
"Century Blvd -> Century Boulevard\n",
"Century Blvd -> Century Boulevard\n",
"Century Blvd -> Century Boulevard\n",
"Century Blvd -> Century Boulevard\n",
"Ridge Dr -> Ridge Drive\n",
"Ridge Dr -> Ridge Drive\n",
"Ridge Dr -> Ridge Drive\n",
"Ridge Dr -> Ridge Drive\n",
"Ridge Dr -> Ridge Drive\n",
"Ridge Dr -> Ridge Drive\n",
"Ridge Dr -> Ridge Drive\n",
"Ridge Dr -> Ridge Drive\n",
"Ridge Dr -> Ridge Drive\n",
"Ridge Dr -> Ridge Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Ridge Dr -> Ridge Drive\n",
"Ridge Dr -> Ridge Drive\n",
"Ridge Dr -> Ridge Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Vineyard Dr -> Vineyard Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Alpine Dr -> Alpine Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Hemlock Dr -> Hemlock Drive\n",
"Tree Ct -> Tree Court\n",
"Tree Ct -> Tree Court\n",
"Tree Ct -> Tree Court\n",
"Tree Ct -> Tree Court\n",
"Tree Ct -> Tree Court\n",
"Arbor Ct -> Arbor Court\n",
"Arbor Ct -> Arbor Court\n",
"Arbor Ct -> Arbor Court\n",
"Tree Ct -> Tree Court\n",
"Tree Ct -> Tree Court\n",
"Arbor Ct -> Arbor Court\n",
"Arbor Ct -> Arbor Court\n",
"Arbor Ct -> Arbor Court\n",
"Arbor Ct -> Arbor Court\n",
"Arbor Ct -> Arbor Court\n",
"Arbor Ct -> Arbor Court\n",
"Arbor Ct -> Arbor Court\n",
"Arbor Ct -> Arbor Court\n",
"Arbor Ct -> Arbor Court\n",
"Arbor Ct -> Arbor Court\n",
"Kearny St -> Kearny Street\n",
"Kearny St -> Kearny Street\n",
"Cabrillo Ave -> Cabrillo Avenue\n",
"Sawtooth Dr -> Sawtooth Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Bonnie View Dr -> Bonnie View Drive\n",
"Sawtooth Dr -> Sawtooth Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Mission College Blvd -> Mission College Boulevard\n",
"Briggs Rd -> Briggs Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Fairview Rd -> Fairview Road\n",
"Fairview Rd -> Fairview Road\n",
"Old Ranch Rd -> Old Ranch Road\n",
"Poppy Lane Dr -> Poppy Lane Drive\n",
"Poppy Lane Dr -> Poppy Lane Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Holly Tree Cir -> Holly Tree Circle\n",
"Holly Tree Cir -> Holly Tree Circle\n",
"Holly Tree Cir -> Holly Tree Circle\n",
"Holly Tree Cir -> Holly Tree Circle\n",
"Holly Tree Cir -> Holly Tree Circle\n",
"Holly Tree Cir -> Holly Tree Circle\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"Meadow Way Cir -> Meadow Way Circle\n",
"Meadow Way Cir -> Meadow Way Circle\n",
"Meadow Way Cir -> Meadow Way Circle\n",
"Meadow Way Cir -> Meadow Way Circle\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Meadow Way Cir -> Meadow Way Circle\n",
"El Cerro Dr -> El Cerro Drive\n",
"Meadow Way Cir -> Meadow Way Circle\n",
"Meadow Way Cir -> Meadow Way Circle\n",
"Meadow Way Cir -> Meadow Way Circle\n",
"Meadow Way Cir -> Meadow Way Circle\n",
"Forest Creek Dr -> Forest Creek Drive\n",
"Forest Creek Dr -> Forest Creek Drive\n",
"Forest Creek Dr -> Forest Creek Drive\n",
"Forest Creek Dr -> Forest Creek Drive\n",
"Forest Creek Dr -> Forest Creek Drive\n",
"Forest Creek Dr -> Forest Creek Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Seaboard Ave -> Seaboard Avenue\n",
"Forest Creek Dr -> Forest Creek Drive\n",
"El Toro Dr -> El Toro Drive\n",
"Forest Creek Dr -> Forest Creek Drive\n",
"Forest Creek Dr -> Forest Creek Drive\n",
"Forest Creek Dr -> Forest Creek Drive\n",
"Forest Creek Dr -> Forest Creek Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Heather Glen Cir -> Heather Glen Circle\n",
"Heather Glen Cir -> Heather Glen Circle\n",
"Heather Glen Cir -> Heather Glen Circle\n",
"Heather Glen Cir -> Heather Glen Circle\n",
"Heather Glen Cir -> Heather Glen Circle\n",
"Heather Glen Cir -> Heather Glen Circle\n",
"Heather Glen Cir -> Heather Glen Circle\n",
"Heather Glen Cir -> Heather Glen Circle\n",
"El Toro Dr -> El Toro Drive\n",
"Greenwood Cir -> Greenwood Circle\n",
"Greenwood Cir -> Greenwood Circle\n",
"Greenwood Cir -> Greenwood Circle\n",
"Greenwood Cir -> Greenwood Circle\n",
"Greenwood Cir -> Greenwood Circle\n",
"Greenwood Cir -> Greenwood Circle\n",
"Greenwood Cir -> Greenwood Circle\n",
"Greenwood Cir -> Greenwood Circle\n",
"Greenwood Cir -> Greenwood Circle\n",
"El Toro Dr -> El Toro Drive\n",
"Sunnyslope Rd -> Sunnyslope Road\n",
"Sunnyslope Rd -> Sunnyslope Road\n",
"Sunnyslope Rd -> Sunnyslope Road\n",
"Sunnyslope Rd -> Sunnyslope Road\n",
"Sunnyslope Rd -> Sunnyslope Road\n",
"Sunnyslope Rd -> Sunnyslope Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Felipe Rd -> San Felipe Road\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"Sally St -> Sally Street\n",
"Mccray St -> Mccray Street\n",
"Mccray St -> Mccray Street\n",
"Sally St -> Sally Street\n",
"Mccray St -> Mccray Street\n",
"Mccray St -> Mccray Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Mccray St -> Mccray Street\n",
"Sally St -> Sally Street\n",
"Mccray St -> Mccray Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Mccray St -> Mccray Street\n",
"Mccray St -> Mccray Street\n",
"Mccray St -> Mccray Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Mccray St -> Mccray Street\n",
"Mccray St -> Mccray Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Mccray St -> Mccray Street\n",
"Mccray St -> Mccray Street\n",
"Sally St -> Sally Street\n",
"Mccray St -> Mccray Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"North St -> North Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"1st St -> 1st Street\n",
"Monterey St -> Monterey Street\n",
"Monterey St -> Monterey Street\n",
"Monterey St -> Monterey Street\n",
"7th St -> 7th Street\n",
"Monterey St -> Monterey Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"Monterey St -> Monterey Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"Monterey St -> Monterey Street\n",
"San Juan Rd -> San Juan Road\n",
"N Chappell Rd -> N Chappell Road\n",
"N Chappell Rd -> N Chappell Road\n",
"N Chappell Rd -> N Chappell Road\n",
"N Chappell Rd -> N Chappell Road\n",
"N Chappell Rd -> N Chappell Road\n",
"N Chappell Rd -> N Chappell Road\n",
"Rustic St -> Rustic Street\n",
"Primavera Dr -> Primavera Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Rustic St -> Rustic Street\n",
"Rustic St -> Rustic Street\n",
"Primavera Dr -> Primavera Drive\n",
"Rustic St -> Rustic Street\n",
"Primavera Dr -> Primavera Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Rustic St -> Rustic Street\n",
"Primavera Dr -> Primavera Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Rustic St -> Rustic Street\n",
"Donald Dr -> Donald Drive\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Airline Hwy -> Airline Highway\n",
"Georges Dr -> Georges Drive\n",
"Georges Dr -> Georges Drive\n",
"Georges Dr -> Georges Drive\n",
"Georges Dr -> Georges Drive\n",
"Georges Dr -> Georges Drive\n",
"Georges Dr -> Georges Drive\n",
"Georges Dr -> Georges Drive\n",
"Georges Dr -> Georges Drive\n",
"Franks Dr -> Franks Drive\n",
"Franks Dr -> Franks Drive\n",
"Franks Dr -> Franks Drive\n",
"Franks Dr -> Franks Drive\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Donald Dr -> Donald Drive\n",
"Rays Cir -> Rays Circle\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Franks Dr -> Franks Drive\n",
"Franks Dr -> Franks Drive\n",
"Everest Dr -> Everest Drive\n",
"Everest Dr -> Everest Drive\n",
"Everest Dr -> Everest Drive\n",
"Everest Dr -> Everest Drive\n",
"Everest Dr -> Everest Drive\n",
"Everest Dr -> Everest Drive\n",
"Everest Dr -> Everest Drive\n",
"Everest Dr -> Everest Drive\n",
"Donald Dr -> Donald Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Franks Dr -> Franks Drive\n",
"Franks Dr -> Franks Drive\n",
"Dots Cir -> Dots Circle\n",
"Florence Ct -> Florence Court\n",
"Marks Dr -> Marks Drive\n",
"Florence Ct -> Florence Court\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Barbras Ct -> Barbras Court\n",
"Barbras Ct -> Barbras Court\n",
"Barbras Ct -> Barbras Court\n",
"Barbras Ct -> Barbras Court\n",
"Barbras Ct -> Barbras Court\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Terrys Ct -> Terrys Court\n",
"Terrys Ct -> Terrys Court\n",
"Terrys Ct -> Terrys Court\n",
"Terrys Ct -> Terrys Court\n",
"Terrys Ct -> Terrys Court\n",
"Terrys Ct -> Terrys Court\n",
"Terrys Ct -> Terrys Court\n",
"Florence Ct -> Florence Court\n",
"Florence Ct -> Florence Court\n",
"Florence Ct -> Florence Court\n",
"Florence Ct -> Florence Court\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Marks Dr -> Marks Drive\n",
"Ridgemark Dr -> Ridgemark Drive\n",
"Donald Dr -> Donald Drive\n",
"Ridgemark Dr -> Ridgemark Drive\n",
"Ridgemark Dr -> Ridgemark Drive\n",
"Ridgemark Dr -> Ridgemark Drive\n",
"Ridgemark Dr -> Ridgemark Drive\n",
"Ridgemark Dr -> Ridgemark Drive\n",
"Ridgemark Dr -> Ridgemark Drive\n",
"Ridgemark Dr -> Ridgemark Drive\n",
"Ridgemark Dr -> Ridgemark Drive\n",
"Ridgemark Dr -> Ridgemark Drive\n",
"Ridgemark Dr -> Ridgemark Drive\n",
"Ridgemark Dr -> Ridgemark Drive\n",
"Ridgemark Dr -> Ridgemark Drive\n",
"Lanini Dr -> Lanini Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Ken Ct -> Ken Court\n",
"Ken Ct -> Ken Court\n",
"Ken Ct -> Ken Court\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Jess Ct -> Jess Court\n",
"Jess Ct -> Jess Court\n",
"Mayme Ct -> Mayme Court\n",
"Mayme Ct -> Mayme Court\n",
"Mayme Ct -> Mayme Court\n",
"Paullus Dr -> Paullus Drive\n",
"Mayme Ct -> Mayme Court\n",
"Mayme Ct -> Mayme Court\n",
"Jess Ct -> Jess Court\n",
"Paullus Dr -> Paullus Drive\n",
"Jess Ct -> Jess Court\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Marcus Ct -> Marcus Court\n",
"Marcus Ct -> Marcus Court\n",
"Marcus Ct -> Marcus Court\n",
"Marcus Ct -> Marcus Court\n",
"Marcus Ct -> Marcus Court\n",
"Marcus Ct -> Marcus Court\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Dan Dr -> Dan Drive\n",
"Jeanette Ct -> Jeanette Court\n",
"Jeanette Ct -> Jeanette Court\n",
"Jeanette Ct -> Jeanette Court\n",
"Dan Dr -> Dan Drive\n",
"Dan Dr -> Dan Drive\n",
"Dan Dr -> Dan Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Paullus Dr -> Paullus Drive\n",
"Doris Cir -> Doris Circle\n",
"Doris Cir -> Doris Circle\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"Doris Cir -> Doris Circle\n",
"Doris Cir -> Doris Circle\n",
"Lanini Dr -> Lanini Drive\n",
"Lanini Dr -> Lanini Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"S Ridgemark Dr -> S Ridgemark Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Rays Cir -> Rays Circle\n",
"Donald Dr -> Donald Drive\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Donald Dr -> Donald Drive\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Rays Cir -> Rays Circle\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Donald Dr -> Donald Drive\n",
"Georges Dr -> Georges Drive\n",
"Mapleton Ave -> Mapleton Avenue\n",
"Mapleton Ave -> Mapleton Avenue\n",
"5th St -> 5th Street\n",
"4th St -> 4th Street\n",
"Mapleton Ave -> Mapleton Avenue\n",
"4th St -> 4th Street\n",
"Locust Ave -> Locust Avenue\n",
"College St -> College Street\n",
"Line St -> Line Street\n",
"Locust Ave -> Locust Avenue\n",
"Line St -> Line Street\n",
"4th St -> 4th Street\n",
"W 2nd St -> W 2nd Street\n",
"Line St -> Line Street\n",
"Mapleton Ave -> Mapleton Avenue\n",
"Powell St -> Powell Street\n",
"W 2nd St -> W 2nd Street\n",
"Mapleton Ave -> Mapleton Avenue\n",
"Mapleton Ave -> Mapleton Avenue\n",
"4th St -> 4th Street\n",
"5th St -> 5th Street\n",
"College St -> College Street\n",
"College St -> College Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"College St -> College Street\n",
"College St -> College Street\n",
"4th St -> 4th Street\n",
"Locust Ave -> Locust Avenue\n",
"4th St -> 4th Street\n",
"Mapleton Ave -> Mapleton Avenue\n",
"Powell St -> Powell Street\n",
"Hawkins St -> Hawkins Street\n",
"Hawkins St -> Hawkins Street\n",
"Hawkins St -> Hawkins Street\n",
"Hawkins St -> Hawkins Street\n",
"Hawkins St -> Hawkins Street\n",
"Hawkins St -> Hawkins Street\n",
"Hawkins St -> Hawkins Street\n",
"Hawkins St -> Hawkins Street\n",
"Hawkins St -> Hawkins Street\n",
"Hawkins St -> Hawkins Street\n",
"Hawkins St -> Hawkins Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"E Haydon St -> E Haydon Street\n",
"E Haydon St -> E Haydon Street\n",
"E Haydon St -> E Haydon Street\n",
"Prune St -> Prune Street\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"Sherwood Dr -> Sherwood Drive\n",
"E Park St -> E Park Street\n",
"E Park St -> E Park Street\n",
"Prune St -> Prune Street\n",
"E Park St -> E Park Street\n",
"San Benito St -> San Benito Street\n",
"San Benito St -> San Benito Street\n",
"San Benito St -> San Benito Street\n",
"San Benito St -> San Benito Street\n",
"San Benito St -> San Benito Street\n",
"San Benito St -> San Benito Street\n",
"San Benito St -> San Benito Street\n",
"Victoria St -> Victoria Street\n",
"Victoria St -> Victoria Street\n",
"Victoria St -> Victoria Street\n",
"Victoria St -> Victoria Street\n",
"Victoria St -> Victoria Street\n",
"Victoria St -> Victoria Street\n",
"Victoria St -> Victoria Street\n",
"Haydon St -> Haydon Street\n",
"Haydon St -> Haydon Street\n",
"Haydon St -> Haydon Street\n",
"Victoria St -> Victoria Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Victoria St -> Victoria Street\n",
"Sally St -> Sally Street\n",
"Victoria St -> Victoria Street\n",
"Sally St -> Sally Street\n",
"Park St -> Park Street\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '680'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676806',\n",
" 'timestamp': '2014-04-14T00:47:53Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '80296718',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '690'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676806',\n",
" 'timestamp': '2014-04-14T00:47:53Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '80296749',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"Park St -> Park Street\n",
"Park St -> Park Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Prune St -> Prune Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"S Chappell Rd -> S Chappell Road\n",
"Alvarado St -> Alvarado Street\n",
"Alvarado St -> Alvarado Street\n",
"Alvarado St -> Alvarado Street\n",
"Alvarado St -> Alvarado Street\n",
"Alvarado St -> Alvarado Street\n",
"Alvarado St -> Alvarado Street\n",
"Alvarado St -> Alvarado Street\n",
"Alvarado St -> Alvarado Street\n",
"Recht St -> Recht Street\n",
"Recht St -> Recht Street\n",
"Recht St -> Recht Street\n",
"S Chappell Rd -> S Chappell Road\n",
"S Chappell Rd -> S Chappell Road\n",
"Mccarthy St -> Mccarthy Street\n",
"Mccarthy St -> Mccarthy Street\n",
"Mccarthy St -> Mccarthy Street\n",
"Mccarthy St -> Mccarthy Street\n",
"Mccarthy St -> Mccarthy Street\n",
"Mccarthy St -> Mccarthy Street\n",
"Recht St -> Recht Street\n",
"Recht St -> Recht Street\n",
"Recht St -> Recht Street\n",
"Holland Cir -> Holland Circle\n",
"Holland Cir -> Holland Circle\n",
"Recht St -> Recht Street\n",
"Holland Cir -> Holland Circle\n",
"Holland Cir -> Holland Circle\n",
"Holland Cir -> Holland Circle\n",
"Recht St -> Recht Street\n",
"Recht St -> Recht Street\n",
"Holland Cir -> Holland Circle\n",
"Recht St -> Recht Street\n",
"Holland Cir -> Holland Circle\n",
"Recht St -> Recht Street\n",
"Howard Ct -> Howard Court\n",
"Recht St -> Recht Street\n",
"Howard Ct -> Howard Court\n",
"Recht St -> Recht Street\n",
"Holland Cir -> Holland Circle\n",
"Howard Ct -> Howard Court\n",
"Howard Ct -> Howard Court\n",
"Recht St -> Recht Street\n",
"Holland Cir -> Holland Circle\n",
"Holland Cir -> Holland Circle\n",
"Howard Ct -> Howard Court\n",
"Holland Cir -> Holland Circle\n",
"Howard Ct -> Howard Court\n",
"Holland Cir -> Holland Circle\n",
"Recht St -> Recht Street\n",
"Howard Ct -> Howard Court\n",
"Holland Cir -> Holland Circle\n",
"Holland Cir -> Holland Circle\n",
"Recht St -> Recht Street\n",
"San Felipe Rd -> San Felipe Road\n",
"Powell St -> Powell Street\n",
"Powell St -> Powell Street\n",
"Homestead Ave -> Homestead Avenue\n",
"Homestead Ave -> Homestead Avenue\n",
"Homestead Ave -> Homestead Avenue\n",
"Homestead Ave -> Homestead Avenue\n",
"Powell St -> Powell Street\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Wilma Dr -> Wilma Drive\n",
"Stephanie Ct -> Stephanie Court\n",
"Walnut Ln -> Walnut Lane\n",
"6th St -> 6th Street\n",
"4th St -> 4th Street\n",
"Powell St -> Powell Street\n",
"Powell St -> Powell Street\n",
"5th St -> 5th Street\n",
"Del Rio Ct -> Del Rio Court\n",
"Stephanie Ct -> Stephanie Court\n",
"7th St -> 7th Street\n",
"5th St -> 5th Street\n",
"Powell St -> Powell Street\n",
"6th St -> 6th Street\n",
"South St -> South Street\n",
"Apricot Ln -> Apricot Lane\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"South St -> South Street\n",
"5th St -> 5th Street\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"5th St -> 5th Street\n",
"6th St -> 6th Street\n",
"South St -> South Street\n",
"6th St -> 6th Street\n",
"5th St -> 5th Street\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Powell St -> Powell Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Homestead Ave -> Homestead Avenue\n",
"Walnut Ln -> Walnut Lane\n",
"Powell St -> Powell Street\n",
"5th St -> 5th Street\n",
"Del Rio Ct -> Del Rio Court\n",
"Apricot Ln -> Apricot Lane\n",
"Powell St -> Powell Street\n",
"Line St -> Line Street\n",
"Jan Ave -> Jan Avenue\n",
"Walnut Ln -> Walnut Lane\n",
"Gabriele Ct -> Gabriele Court\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"South St -> South Street\n",
"Wilma Dr -> Wilma Drive\n",
"5th St -> 5th Street\n",
"Powell St -> Powell Street\n",
"6th St -> 6th Street\n",
"4th St -> 4th Street\n",
"Peridot Ct -> Peridot Court\n",
"South St -> South Street\n",
"Powell St -> Powell Street\n",
"5th St -> 5th Street\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Stephanie Ct -> Stephanie Court\n",
"Powell St -> Powell Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"4th St -> 4th Street\n",
"Powell St -> Powell Street\n",
"4th St -> 4th Street\n",
"Peridot Ct -> Peridot Court\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Walnut Ln -> Walnut Lane\n",
"7th St -> 7th Street\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Apricot Ln -> Apricot Lane\n",
"Stephanie Ct -> Stephanie Court\n",
"South St -> South Street\n",
"Del Rio Ct -> Del Rio Court\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Julian Ct -> Julian Court\n",
"Wilma Dr -> Wilma Drive\n",
"Monica Dr -> Monica Drive\n",
"Tamara Ct -> Tamara Court\n",
"Tamara Ct -> Tamara Court\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Wilma Dr -> Wilma Drive\n",
"Beth Ct -> Beth Court\n",
"Gabriele Ct -> Gabriele Court\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"4th St -> 4th Street\n",
"Powell St -> Powell Street\n",
"6th St -> 6th Street\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Peridot Ct -> Peridot Court\n",
"Stephanie Ct -> Stephanie Court\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"Beth Ct -> Beth Court\n",
"Stephanie Ct -> Stephanie Court\n",
"7th St -> 7th Street\n",
"6th St -> 6th Street\n",
"Powell St -> Powell Street\n",
"Powell St -> Powell Street\n",
"4th St -> 4th Street\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"5th St -> 5th Street\n",
"Julian Ct -> Julian Court\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Apricot Ln -> Apricot Lane\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"5th St -> 5th Street\n",
"South St -> South Street\n",
"5th St -> 5th Street\n",
"6th St -> 6th Street\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"South St -> South Street\n",
"5th St -> 5th Street\n",
"Julian Ct -> Julian Court\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Julian Ct -> Julian Court\n",
"Wilma Dr -> Wilma Drive\n",
"Del Rio Ct -> Del Rio Court\n",
"6th St -> 6th Street\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Gabriele Ct -> Gabriele Court\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Cosco Ct -> Cosco Court\n",
"Vista Ln -> Vista Lane\n",
"Western Ct -> Western Court\n",
"Western Ct -> Western Court\n",
"Cosco Ct -> Cosco Court\n",
"W 2nd St -> W 2nd Street\n",
"La Macchia Ct -> La Macchia Court\n",
"4th St -> 4th Street\n",
"Cosco Ct -> Cosco Court\n",
"Nash Rd -> Nash Road\n",
"Nash Rd -> Nash Road\n",
"Nash Rd -> Nash Road\n",
"Nash Rd -> Nash Road\n",
"Nash Rd -> Nash Road\n",
"Nash Rd -> Nash Road\n",
"Nash Rd -> Nash Road\n",
"South St -> South Street\n",
"Kathryn Dr -> Kathryn Drive\n",
"San Lorenzo Dr -> San Lorenzo Drive\n",
"La Macchia Ct -> La Macchia Court\n",
"Miller Rd -> Miller Road\n",
"Summer Dr -> Summer Drive\n",
"La Macchia Ct -> La Macchia Court\n",
"Matulich Ct -> Matulich Court\n",
"South St -> South Street\n",
"Teresita Ct -> Teresita Court\n",
"Miller Rd -> Miller Road\n",
"Kimberly Ct -> Kimberly Court\n",
"San Lorenzo Dr -> San Lorenzo Drive\n",
"Kimberly Ct -> Kimberly Court\n",
"San Lorenzo Dr -> San Lorenzo Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Graf Rd -> Graf Road\n",
"Kimberly Ct -> Kimberly Court\n",
"Kathryn Dr -> Kathryn Drive\n",
"Teresita Ct -> Teresita Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Peridot Ct -> Peridot Court\n",
"San Lorenzo Dr -> San Lorenzo Drive\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Summer Dr -> Summer Drive\n",
"Matulich Ct -> Matulich Court\n",
"Kimberly Ct -> Kimberly Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"South St -> South Street\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Miller Rd -> Miller Road\n",
"Teresita Ct -> Teresita Court\n",
"Peridot Ct -> Peridot Court\n",
"Miller Rd -> Miller Road\n",
"South St -> South Street\n",
"South St -> South Street\n",
"Miller Rd -> Miller Road\n",
"Kimberly Ct -> Kimberly Court\n",
"South St -> South Street\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Kimberly Ct -> Kimberly Court\n",
"Teresita Ct -> Teresita Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Mica Ct -> Mica Court\n",
"Kimberly Ct -> Kimberly Court\n",
"Kathryn Dr -> Kathryn Drive\n",
"Gabriele Ct -> Gabriele Court\n",
"Matulich Ct -> Matulich Court\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"South St -> South Street\n",
"Matulich Ct -> Matulich Court\n",
"South St -> South Street\n",
"Kimberly Ct -> Kimberly Court\n",
"La Macchia Ct -> La Macchia Court\n",
"Matulich Ct -> Matulich Court\n",
"Gabriele Ct -> Gabriele Court\n",
"Summer Dr -> Summer Drive\n",
"South St -> South Street\n",
"South St -> South Street\n",
"Matulich Ct -> Matulich Court\n",
"Kimberly Ct -> Kimberly Court\n",
"Graf Rd -> Graf Road\n",
"San Lorenzo Dr -> San Lorenzo Drive\n",
"Teresita Ct -> Teresita Court\n",
"Teresita Ct -> Teresita Court\n",
"Gabriele Ct -> Gabriele Court\n",
"Kimberly Ct -> Kimberly Court\n",
"Kimberly Ct -> Kimberly Court\n",
"San Lorenzo Dr -> San Lorenzo Drive\n",
"La Macchia Ct -> La Macchia Court\n",
"Summer Dr -> Summer Drive\n",
"Matulich Ct -> Matulich Court\n",
"Summer Dr -> Summer Drive\n",
"San Lorenzo Dr -> San Lorenzo Drive\n",
"Peridot Ct -> Peridot Court\n",
"San Lorenzo Dr -> San Lorenzo Drive\n",
"Ellis Ct -> Ellis Court\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"San Lorenzo Dr -> San Lorenzo Drive\n",
"South St -> South Street\n",
"San Lorenzo Dr -> San Lorenzo Drive\n",
"San Lorenzo Dr -> San Lorenzo Drive\n",
"Miller Rd -> Miller Road\n",
"South St -> South Street\n",
"San Lorenzo Dr -> San Lorenzo Drive\n",
"Kimberly Ct -> Kimberly Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Miller Rd -> Miller Road\n",
"South St -> South Street\n",
"South St -> South Street\n",
"Kimberly Ct -> Kimberly Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Gabriele Ct -> Gabriele Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Teresita Ct -> Teresita Court\n",
"South St -> South Street\n",
"Summer Dr -> Summer Drive\n",
"Miller Rd -> Miller Road\n",
"Summer Dr -> Summer Drive\n",
"South St -> South Street\n",
"Matulich Ct -> Matulich Court\n",
"Nash Rd -> Nash Road\n",
"Nash Rd -> Nash Road\n",
"Nash Rd -> Nash Road\n",
"Westside Blvd -> Westside Boulevard\n",
"Westside Blvd -> Westside Boulevard\n",
"Del Rio Ct -> Del Rio Court\n",
"Del Rio Ct -> Del Rio Court\n",
"Monica Ct -> Monica Court\n",
"Monica Ct -> Monica Court\n",
"Monica Ct -> Monica Court\n",
"Monica Ct -> Monica Court\n",
"Monica Ct -> Monica Court\n",
"Monica Ct -> Monica Court\n",
"Monica Ct -> Monica Court\n",
"Julian Ct -> Julian Court\n",
"Julian Ct -> Julian Court\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Monica Dr -> Monica Drive\n",
"Monica Dr -> Monica Drive\n",
"Monica Dr -> Monica Drive\n",
"Monica Dr -> Monica Drive\n",
"Monica Dr -> Monica Drive\n",
"Monica Dr -> Monica Drive\n",
"Westside Blvd -> Westside Boulevard\n",
"Westside Blvd -> Westside Boulevard\n",
"Ione Cir -> Ione Circle\n",
"Monica Ct -> Monica Court\n",
"Monica Ct -> Monica Court\n",
"Monica Ct -> Monica Court\n",
"Monica Ct -> Monica Court\n",
"Monica Ct -> Monica Court\n",
"Monica Ct -> Monica Court\n",
"Monica Ct -> Monica Court\n",
"Beth Ct -> Beth Court\n",
"Beth Ct -> Beth Court\n",
"Monica Ct -> Monica Court\n",
"Westside Blvd -> Westside Boulevard\n",
"Del Rio Ct -> Del Rio Court\n",
"Westside Blvd -> Westside Boulevard\n",
"Westside Blvd -> Westside Boulevard\n",
"Ione Cir -> Ione Circle\n",
"Ione Cir -> Ione Circle\n",
"Ione Cir -> Ione Circle\n",
"Monica Dr -> Monica Drive\n",
"Monica Dr -> Monica Drive\n",
"Monica Dr -> Monica Drive\n",
"Monica Dr -> Monica Drive\n",
"Monica Dr -> Monica Drive\n",
"Monica Dr -> Monica Drive\n",
"Monica Dr -> Monica Drive\n",
"Monica Dr -> Monica Drive\n",
"Westside Blvd -> Westside Boulevard\n",
"Westside Blvd -> Westside Boulevard\n",
"Westside Blvd -> Westside Boulevard\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Line St -> Line Street\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"Apricot Ln -> Apricot Lane\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"Powell St -> Powell Street\n",
"South St -> South Street\n",
"Powell St -> Powell Street\n",
"7th St -> 7th Street\n",
"South St -> South Street\n",
"San Benito St -> San Benito Street\n",
"Hill St -> Hill Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"Hill St -> Hill Street\n",
"Hill St -> Hill Street\n",
"Hill St -> Hill Street\n",
"Hill St -> Hill Street\n",
"Hill St -> Hill Street\n",
"Hill St -> Hill Street\n",
"Vista Park Hill Ct -> Vista Park Hill Court\n",
"Vista Park Hill Ct -> Vista Park Hill Court\n",
"Vista Park Hill Ct -> Vista Park Hill Court\n",
"Vista Park Hill Ct -> Vista Park Hill Court\n",
"Hill St -> Hill Street\n",
"Hill St -> Hill Street\n",
"Hill St -> Hill Street\n",
"Hill St -> Hill Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"Powell St -> Powell Street\n",
"Powell St -> Powell Street\n",
"Powell St -> Powell Street\n",
"6th St -> 6th Street\n",
"College St -> College Street\n",
"6th St -> 6th Street\n",
"5th St -> 5th Street\n",
"College St -> College Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"6th St -> 6th Street\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Steinbeck Dr -> Steinbeck Drive\n",
"Peridot Ct -> Peridot Court\n",
"Peridot Ct -> Peridot Court\n",
"Peridot Ct -> Peridot Court\n",
"Mica Ct -> Mica Court\n",
"Mica Ct -> Mica Court\n",
"Mica Ct -> Mica Court\n",
"Mica Ct -> Mica Court\n",
"Mica Ct -> Mica Court\n",
"Mica Ct -> Mica Court\n",
"Mica Ct -> Mica Court\n",
"Mica Ct -> Mica Court\n",
"Mica Ct -> Mica Court\n",
"Mica Ct -> Mica Court\n",
"Peridot Ct -> Peridot Court\n",
"Peridot Ct -> Peridot Court\n",
"Peridot Ct -> Peridot Court\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"College St -> College Street\n",
"7th St -> 7th Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"College St -> College Street\n",
"College St -> College Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Westside Blvd -> Westside Boulevard\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Westside Blvd -> Westside Boulevard\n",
"Line St -> Line Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"Westside Blvd -> Westside Boulevard\n",
"Westside Blvd -> Westside Boulevard\n",
"Westside Blvd -> Westside Boulevard\n",
"Westside Blvd -> Westside Boulevard\n",
"Westside Blvd -> Westside Boulevard\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Jan Ave -> Jan Avenue\n",
"Spring Dr -> Spring Drive\n",
"Tamara Ct -> Tamara Court\n",
"Tamara Ct -> Tamara Court\n",
"Tamara Ct -> Tamara Court\n",
"Tiffany Dr -> Tiffany Drive\n",
"Jan Ave -> Jan Avenue\n",
"Marguerite Dr -> Marguerite Drive\n",
"5th St -> 5th Street\n",
"Spring Dr -> Spring Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"Miller Rd -> Miller Road\n",
"5th St -> 5th Street\n",
"Line St -> Line Street\n",
"Westside Blvd -> Westside Boulevard\n",
"W 2nd St -> W 2nd Street\n",
"Mariposa Ct -> Mariposa Court\n",
"Westside Blvd -> Westside Boulevard\n",
"South St -> South Street\n",
"Ball Ct -> Ball Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"5th St -> 5th Street\n",
"Line St -> Line Street\n",
"Stephanie Ct -> Stephanie Court\n",
"Line St -> Line Street\n",
"Tiffany Dr -> Tiffany Drive\n",
"Summer Dr -> Summer Drive\n",
"Jan Ave -> Jan Avenue\n",
"Jan Ave -> Jan Avenue\n",
"Summer Dr -> Summer Drive\n",
"College St -> College Street\n",
"Westside Blvd -> Westside Boulevard\n",
"Graf Rd -> Graf Road\n",
"4th St -> 4th Street\n",
"5th St -> 5th Street\n",
"Line St -> Line Street\n",
"Marguerite Dr -> Marguerite Drive\n",
"Westside Blvd -> Westside Boulevard\n",
"Line St -> Line Street\n",
"7th St -> 7th Street\n",
"5th St -> 5th Street\n",
"Westside Blvd -> Westside Boulevard\n",
"Jan Ave -> Jan Avenue\n",
"Mapleton Ave -> Mapleton Avenue\n",
"Kimberly Ct -> Kimberly Court\n",
"Line St -> Line Street\n",
"Jan Ave -> Jan Avenue\n",
"Line St -> Line Street\n",
"Spring Dr -> Spring Drive\n",
"Summer Dr -> Summer Drive\n",
"Willow Dr -> Willow Drive\n",
"South St -> South Street\n",
"Marguerite Dr -> Marguerite Drive\n",
"Brandy Ct -> Brandy Court\n",
"Beresini Ln -> Beresini Lane\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"South St -> South Street\n",
"Beresini Ln -> Beresini Lane\n",
"Beresini Ln -> Beresini Lane\n",
"Line St -> Line Street\n",
"W Graf Rd -> W Graf Road\n",
"W 2nd St -> W 2nd Street\n",
"Marguerite Dr -> Marguerite Drive\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"Line St -> Line Street\n",
"Summer Dr -> Summer Drive\n",
"Line St -> Line Street\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"Westside Blvd -> Westside Boulevard\n",
"Line St -> Line Street\n",
"Westside Blvd -> Westside Boulevard\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"W 2nd St -> W 2nd Street\n",
"Line St -> Line Street\n",
"Summer Dr -> Summer Drive\n",
"4th St -> 4th Street\n",
"Summer Dr -> Summer Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"W Graf Rd -> W Graf Road\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Summer Dr -> Summer Drive\n",
"Marguerite Dr -> Marguerite Drive\n",
"5th St -> 5th Street\n",
"Spring Dr -> Spring Drive\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"College St -> College Street\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"4th St -> 4th Street\n",
"Bridgevale Rd -> Bridgevale Road\n",
"5th St -> 5th Street\n",
"Felice Dr -> Felice Drive\n",
"Summer Dr -> Summer Drive\n",
"Jan Ave -> Jan Avenue\n",
"Marguerite Dr -> Marguerite Drive\n",
"Marguerite Dr -> Marguerite Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"Marguerite Dr -> Marguerite Drive\n",
"5th St -> 5th Street\n",
"Westside Blvd -> Westside Boulevard\n",
"Spring Dr -> Spring Drive\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Brandy Ct -> Brandy Court\n",
"Ranchito Dr -> Ranchito Drive\n",
"Graf Rd -> Graf Road\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Marguerite Dr -> Marguerite Drive\n",
"Graf Rd -> Graf Road\n",
"Marguerite Dr -> Marguerite Drive\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"Summer Dr -> Summer Drive\n",
"Beresini Ln -> Beresini Lane\n",
"Marguerite Dr -> Marguerite Drive\n",
"Marguerite Dr -> Marguerite Drive\n",
"South St -> South Street\n",
"Beresini Ln -> Beresini Lane\n",
"4th St -> 4th Street\n",
"Teresita Ct -> Teresita Court\n",
"Mapleton Ave -> Mapleton Avenue\n",
"Marguerite Dr -> Marguerite Drive\n",
"Westside Blvd -> Westside Boulevard\n",
"Beresini Ln -> Beresini Lane\n",
"Mica Ct -> Mica Court\n",
"Spring Dr -> Spring Drive\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"5th St -> 5th Street\n",
"Westside Blvd -> Westside Boulevard\n",
"4th St -> 4th Street\n",
"Jacqueline Dr -> Jacqueline Drive\n",
"South St -> South Street\n",
"Jan Ave -> Jan Avenue\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"Line St -> Line Street\n",
"Wilma Dr -> Wilma Drive\n",
"Line St -> Line Street\n",
"W Graf Rd -> W Graf Road\n",
"W Graf Rd -> W Graf Road\n",
"W Graf Rd -> W Graf Road\n",
"Westside Blvd -> Westside Boulevard\n",
"Westside Blvd -> Westside Boulevard\n",
"South St -> South Street\n",
"5th St -> 5th Street\n",
"Locust Ave -> Locust Avenue\n",
"4th St -> 4th Street\n",
"W 2nd St -> W 2nd Street\n",
"Line St -> Line Street\n",
"Jan Ave -> Jan Avenue\n",
"W 2nd St -> W 2nd Street\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"W Graf Rd -> W Graf Road\n",
"South St -> South Street\n",
"South St -> South Street\n",
"South St -> South Street\n",
"Monterey St -> Monterey Street\n",
"South St -> South Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"Powell St -> Powell Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"Stephanie Ct -> Stephanie Court\n",
"Tamara Ct -> Tamara Court\n",
"Tamara Ct -> Tamara Court\n",
"South St -> South Street\n",
"South St -> South Street\n",
"Summer Dr -> Summer Drive\n",
"Kimberly Ct -> Kimberly Court\n",
"Kathryn Dr -> Kathryn Drive\n",
"Kathryn Dr -> Kathryn Drive\n",
"Kimberly Ct -> Kimberly Court\n",
"Kimberly Ct -> Kimberly Court\n",
"Kimberly Ct -> Kimberly Court\n",
"Kimberly Ct -> Kimberly Court\n",
"Kimberly Ct -> Kimberly Court\n",
"Kimberly Ct -> Kimberly Court\n",
"Kimberly Ct -> Kimberly Court\n",
"Kimberly Ct -> Kimberly Court\n",
"Robert Dr -> Robert Drive\n",
"Robert Dr -> Robert Drive\n",
"Robert Dr -> Robert Drive\n",
"Robert Dr -> Robert Drive\n",
"Robert Dr -> Robert Drive\n",
"Robert Dr -> Robert Drive\n",
"Robert Dr -> Robert Drive\n",
"Robert Dr -> Robert Drive\n",
"Gabriele Ct -> Gabriele Court\n",
"Gabriele Ct -> Gabriele Court\n",
"Gabriele Ct -> Gabriele Court\n",
"Gabriele Ct -> Gabriele Court\n",
"Gabriele Ct -> Gabriele Court\n",
"Gabriele Ct -> Gabriele Court\n",
"Gabriele Ct -> Gabriele Court\n",
"Gabriele Ct -> Gabriele Court\n",
"Gabriele Ct -> Gabriele Court\n",
"San Juan Rd -> San Juan Road\n",
"4th St -> 4th Street\n",
"Tiffany Dr -> Tiffany Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"Tiffany Dr -> Tiffany Drive\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Ball Ct -> Ball Court\n",
"Ball Ct -> Ball Court\n",
"Ball Ct -> Ball Court\n",
"Felice Dr -> Felice Drive\n",
"Felice Dr -> Felice Drive\n",
"Felice Dr -> Felice Drive\n",
"Felice Dr -> Felice Drive\n",
"Felice Dr -> Felice Drive\n",
"Cosco Ct -> Cosco Court\n",
"San Juan Rd -> San Juan Road\n",
"Cosco Ct -> Cosco Court\n",
"Ellis Ct -> Ellis Court\n",
"Ellis Ct -> Ellis Court\n",
"Ellis Ct -> Ellis Court\n",
"Ellis Ct -> Ellis Court\n",
"Ellis Ct -> Ellis Court\n",
"Ellis Ct -> Ellis Court\n",
"Ellis Ct -> Ellis Court\n",
"Felice Dr -> Felice Drive\n",
"Rossi Ct -> Rossi Court\n",
"Rossi Ct -> Rossi Court\n",
"Rossi Ct -> Rossi Court\n",
"Rossi Ct -> Rossi Court\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"Tiffany Dr -> Tiffany Drive\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"Mapleton Ave -> Mapleton Avenue\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Westside Blvd -> Westside Boulevard\n",
"Rossi Ct -> Rossi Court\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"College St -> College Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"Mapleton Ave -> Mapleton Avenue\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"Powell St -> Powell Street\n",
"Powell St -> Powell Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"4th St -> 4th Street\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Virginia Dr -> Virginia Drive\n",
"Virginia Dr -> Virginia Drive\n",
"Virginia Dr -> Virginia Drive\n",
"Virginia Dr -> Virginia Drive\n",
"Virginia Dr -> Virginia Drive\n",
"Virginia Dr -> Virginia Drive\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Mapleton Ave -> Mapleton Avenue\n",
"College St -> College Street\n",
"College St -> College Street\n",
"College St -> College Street\n",
"Mapleton Ave -> Mapleton Avenue\n",
"College St -> College Street\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"College St -> College Street\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"College St -> College Street\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Locust Ave -> Locust Avenue\n",
"Acacia Ct -> Acacia Court\n",
"Acacia Ct -> Acacia Court\n",
"Acacia Ct -> Acacia Court\n",
"Acacia Ct -> Acacia Court\n",
"Acacia Ct -> Acacia Court\n",
"Acacia Ct -> Acacia Court\n",
"Acacia Ct -> Acacia Court\n",
"Acacia Ct -> Acacia Court\n",
"Acacia Ct -> Acacia Court\n",
"Acacia Ct -> Acacia Court\n",
"Acacia Ct -> Acacia Court\n",
"Acacia Ct -> Acacia Court\n",
"Almond Ct -> Almond Court\n",
"Almond Ct -> Almond Court\n",
"Almond Ct -> Almond Court\n",
"Almond Ct -> Almond Court\n",
"Almond Ct -> Almond Court\n",
"Almond Ct -> Almond Court\n",
"Almond Ct -> Almond Court\n",
"Almond Ct -> Almond Court\n",
"Almond Ct -> Almond Court\n",
"Almond Ct -> Almond Court\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"W 2nd St -> W 2nd Street\n",
"Westside Blvd -> Westside Boulevard\n",
"Westside Blvd -> Westside Boulevard\n",
"Jills Ct -> Jills Court\n",
"Jills Ct -> Jills Court\n",
"Jills Ct -> Jills Court\n",
"Jills Ct -> Jills Court\n",
"Jills Ct -> Jills Court\n",
"Jills Ct -> Jills Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Line St -> Line Street\n",
"Vista Ln -> Vista Lane\n",
"Vista Ln -> Vista Lane\n",
"Vista Ln -> Vista Lane\n",
"Vista Ln -> Vista Lane\n",
"Vista Ln -> Vista Lane\n",
"Vista Ln -> Vista Lane\n",
"Westside Blvd -> Westside Boulevard\n",
"Vista Ln -> Vista Lane\n",
"La Macchia Ct -> La Macchia Court\n",
"La Macchia Ct -> La Macchia Court\n",
"La Macchia Ct -> La Macchia Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Westside Blvd -> Westside Boulevard\n",
"Vista Ln -> Vista Lane\n",
"Westside Blvd -> Westside Boulevard\n",
"Vista Ln -> Vista Lane\n",
"Vista Ln -> Vista Lane\n",
"Vista Ln -> Vista Lane\n",
"Felice Dr -> Felice Drive\n",
"Cosco Ct -> Cosco Court\n",
"Felice Dr -> Felice Drive\n",
"Cosco Ct -> Cosco Court\n",
"Cosco Ct -> Cosco Court\n",
"Western Ct -> Western Court\n",
"Western Ct -> Western Court\n",
"Western Ct -> Western Court\n",
"Western Ct -> Western Court\n",
"Western Ct -> Western Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Madera Ct -> Madera Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Lassen Ct -> Lassen Court\n",
"Miller Rd -> Miller Road\n",
"Miller Rd -> Miller Road\n",
"Miller Rd -> Miller Road\n",
"Miller Rd -> Miller Road\n",
"Miller Rd -> Miller Road\n",
"Miller Rd -> Miller Road\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Matulich Ct -> Matulich Court\n",
"Matulich Ct -> Matulich Court\n",
"Matulich Ct -> Matulich Court\n",
"Matulich Ct -> Matulich Court\n",
"Matulich Ct -> Matulich Court\n",
"Matulich Ct -> Matulich Court\n",
"Teresita Ct -> Teresita Court\n",
"San Lorenzo Dr -> San Lorenzo Drive\n",
"Teresita Ct -> Teresita Court\n",
"Teresita Ct -> Teresita Court\n",
"Teresita Ct -> Teresita Court\n",
"Teresita Ct -> Teresita Court\n",
"Teresita Ct -> Teresita Court\n",
"Graf Rd -> Graf Road\n",
"Graf Rd -> Graf Road\n",
"Rustic St -> Rustic Street\n",
"Rustic St -> Rustic Street\n",
"Rustic St -> Rustic Street\n",
"Maple St -> Maple Street\n",
"Maple St -> Maple Street\n",
"Amber Ct -> Amber Court\n",
"Amber Ct -> Amber Court\n",
"Amber Ct -> Amber Court\n",
"Rustic St -> Rustic Street\n",
"Lorene Dr -> Lorene Drive\n",
"Lorene Dr -> Lorene Drive\n",
"Lorene Dr -> Lorene Drive\n",
"Lorene Dr -> Lorene Drive\n",
"Amber Ct -> Amber Court\n",
"Amber Ct -> Amber Court\n",
"Amber Ct -> Amber Court\n",
"Adrian Ct -> Adrian Court\n",
"Verona Pl -> Verona Place\n",
"Maple St -> Maple Street\n",
"Adrian Ct -> Adrian Court\n",
"Arbour Ln -> Arbour Lane\n",
"Verona Pl -> Verona Place\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Holland Cir -> Holland Circle\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Holland Cir -> Holland Circle\n",
"Arbour Ln -> Arbour Lane\n",
"Sally St -> Sally Street\n",
"Sally St -> Sally Street\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Tuscany Pl -> Tuscany Place\n",
"Holland Cir -> Holland Circle\n",
"Holland Cir -> Holland Circle\n",
"Howard Ct -> Howard Court\n",
"Verona Pl -> Verona Place\n",
"Arbour Ln -> Arbour Lane\n",
"Tuscany Pl -> Tuscany Place\n",
"Rustic St -> Rustic Street\n",
"Adrian Ct -> Adrian Court\n",
"Tuscany Pl -> Tuscany Place\n",
"Recht St -> Recht Street\n",
"Verona Pl -> Verona Place\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Recht St -> Recht Street\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Adrian Ct -> Adrian Court\n",
"Recht St -> Recht Street\n",
"Recht St -> Recht Street\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Recht St -> Recht Street\n",
"Howard Ct -> Howard Court\n",
"Verona Pl -> Verona Place\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Tuscany Pl -> Tuscany Place\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"S Chappell Rd -> S Chappell Road\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"N Sally St -> N Sally Street\n",
"Tuscany Pl -> Tuscany Place\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Tuscany Pl -> Tuscany Place\n",
"Freya Ct -> Freya Court\n",
"Verona Pl -> Verona Place\n",
"Freya Ct -> Freya Court\n",
"Sally St -> Sally Street\n",
"Verona Pl -> Verona Place\n",
"Recht St -> Recht Street\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Adrian Ct -> Adrian Court\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Arbour Ln -> Arbour Lane\n",
"Verona Pl -> Verona Place\n",
"Tuscany Pl -> Tuscany Place\n",
"Tuscany Pl -> Tuscany Place\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Verona Pl -> Verona Place\n",
"Freya Ct -> Freya Court\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Melinda Ct -> Melinda Court\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Sally St -> Sally Street\n",
"Holland Cir -> Holland Circle\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Holland Cir -> Holland Circle\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Verona Pl -> Verona Place\n",
"Adrian Ct -> Adrian Court\n",
"Verona Pl -> Verona Place\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Verona Pl -> Verona Place\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Verona Pl -> Verona Place\n",
"Melinda Ct -> Melinda Court\n",
"Verona Pl -> Verona Place\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Mccarthy St -> Mccarthy Street\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Recht St -> Recht Street\n",
"Holland Cir -> Holland Circle\n",
"Mccarthy St -> Mccarthy Street\n",
"Verona Pl -> Verona Place\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Recht St -> Recht Street\n",
"Holland Cir -> Holland Circle\n",
"Verona Pl -> Verona Place\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Recht St -> Recht Street\n",
"Verona Pl -> Verona Place\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Howard Ct -> Howard Court\n",
"Melinda Ct -> Melinda Court\n",
"Tuscany Pl -> Tuscany Place\n",
"Howard Ct -> Howard Court\n",
"Arbour Ln -> Arbour Lane\n",
"Recht St -> Recht Street\n",
"Holland Cir -> Holland Circle\n",
"Sally St -> Sally Street\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Sally St -> Sally Street\n",
"Holland Cir -> Holland Circle\n",
"Arbour Ln -> Arbour Lane\n",
"Tuscany Pl -> Tuscany Place\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Verona Pl -> Verona Place\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Tuscany Pl -> Tuscany Place\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Holland Cir -> Holland Circle\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Sally St -> Sally Street\n",
"Ian Ct -> Ian Court\n",
"Verona Pl -> Verona Place\n",
"Recht St -> Recht Street\n",
"Sally St -> Sally Street\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Tuscany Pl -> Tuscany Place\n",
"Sally St -> Sally Street\n",
"Tuscany Pl -> Tuscany Place\n",
"Sally St -> Sally Street\n",
"Ian Ct -> Ian Court\n",
"Holland Cir -> Holland Circle\n",
"Holland Cir -> Holland Circle\n",
"Tuscany Pl -> Tuscany Place\n",
"Holland Cir -> Holland Circle\n",
"Holland Cir -> Holland Circle\n",
"Arbour Ln -> Arbour Lane\n",
"Tuscany Pl -> Tuscany Place\n",
"Tuscany Pl -> Tuscany Place\n",
"Arbour Ln -> Arbour Lane\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Verona Pl -> Verona Place\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Sally St -> Sally Street\n",
"Verona Pl -> Verona Place\n",
"Recht St -> Recht Street\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Holland Cir -> Holland Circle\n",
"Tuscany Pl -> Tuscany Place\n",
"Tuscany Pl -> Tuscany Place\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Verona Pl -> Verona Place\n",
"Ian Ct -> Ian Court\n",
"Recht St -> Recht Street\n",
"Tuscany Pl -> Tuscany Place\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Recht St -> Recht Street\n",
"Marseille Dr -> Marseille Drive\n",
"Riviera Dr -> Riviera Drive\n",
"Recht St -> Recht Street\n",
"San Tropez Dr -> San Tropez Drive\n",
"Riviera Dr -> Riviera Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"Riviera Dr -> Riviera Drive\n",
"Tuscany Pl -> Tuscany Place\n",
"La Baig Dr -> La Baig Drive\n",
"Le Chateau Dr -> Le Chateau Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"Le Chateau Dr -> Le Chateau Drive\n",
"Recht St -> Recht Street\n",
"La Baig Dr -> La Baig Drive\n",
"Holland Cir -> Holland Circle\n",
"Riviera Dr -> Riviera Drive\n",
"La Baig Dr -> La Baig Drive\n",
"Riviera Dr -> Riviera Drive\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Arbour Ln -> Arbour Lane\n",
"Le Mans Dr -> Le Mans Drive\n",
"Holland Cir -> Holland Circle\n",
"Riviera Dr -> Riviera Drive\n",
"Mccarthy St -> Mccarthy Street\n",
"San Tropez Dr -> San Tropez Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Tropez Dr -> San Tropez Drive\n",
"La Baig Dr -> La Baig Drive\n",
"Recht St -> Recht Street\n",
"Recht St -> Recht Street\n",
"Bordeaux Pl -> Bordeaux Place\n",
"San Tropez Dr -> San Tropez Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Le Chateau Dr -> Le Chateau Drive\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Riviera Dr -> Riviera Drive\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"Le Mans Dr -> Le Mans Drive\n",
"Marseille Dr -> Marseille Drive\n",
"Arbour Ln -> Arbour Lane\n",
"La Baig Dr -> La Baig Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"La Baig Dr -> La Baig Drive\n",
"Riviera Dr -> Riviera Drive\n",
"La Baig Dr -> La Baig Drive\n",
"Verona Pl -> Verona Place\n",
"Tuscany Pl -> Tuscany Place\n",
"Marseille Ct -> Marseille Court\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Tropez Dr -> San Tropez Drive\n",
"Holland Cir -> Holland Circle\n",
"Mccarthy St -> Mccarthy Street\n",
"Le Mans Dr -> Le Mans Drive\n",
"Bordeaux Pl -> Bordeaux Place\n",
"San Tropez Dr -> San Tropez Drive\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"Recht St -> Recht Street\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"Holland Cir -> Holland Circle\n",
"Le Chateau Dr -> Le Chateau Drive\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Holland Cir -> Holland Circle\n",
"Recht St -> Recht Street\n",
"Holland Cir -> Holland Circle\n",
"Marseille Ct -> Marseille Court\n",
"Riviera Dr -> Riviera Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Bordeaux Pl -> Bordeaux Place\n",
"San Tropez Dr -> San Tropez Drive\n",
"Arbour Ln -> Arbour Lane\n",
"Tuscany Pl -> Tuscany Place\n",
"Holland Cir -> Holland Circle\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Arbour Ln -> Arbour Lane\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Riviera Dr -> Riviera Drive\n",
"Verona Pl -> Verona Place\n",
"Howard Ct -> Howard Court\n",
"Marseille Ct -> Marseille Court\n",
"Holland Cir -> Holland Circle\n",
"Arbour Ln -> Arbour Lane\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Recht St -> Recht Street\n",
"Recht St -> Recht Street\n",
"San Tropez Dr -> San Tropez Drive\n",
"Holland Cir -> Holland Circle\n",
"Riviera Dr -> Riviera Drive\n",
"Recht St -> Recht Street\n",
"Marseille Dr -> Marseille Drive\n",
"Riviera Dr -> Riviera Drive\n",
"Recht St -> Recht Street\n",
"Bordeaux Pl -> Bordeaux Place\n",
"La Baig Dr -> La Baig Drive\n",
"Marseille Dr -> Marseille Drive\n",
"Howard Ct -> Howard Court\n",
"Holland Cir -> Holland Circle\n",
"Le Chateau Dr -> Le Chateau Drive\n",
"La Baig Dr -> La Baig Drive\n",
"Recht St -> Recht Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Monte Carlo St -> Monte Carlo Street\n",
"La Baig Dr -> La Baig Drive\n",
"La Baig Dr -> La Baig Drive\n",
"Arbour Ln -> Arbour Lane\n",
"Le Mans Dr -> Le Mans Drive\n",
"Howard Ct -> Howard Court\n",
"Riviera Dr -> Riviera Drive\n",
"Mccarthy St -> Mccarthy Street\n",
"Arbour Ln -> Arbour Lane\n",
"Recht St -> Recht Street\n",
"Le Chateau Dr -> Le Chateau Drive\n",
"Riviera Dr -> Riviera Drive\n",
"La Baig Dr -> La Baig Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Recht St -> Recht Street\n",
"San Tropez Dr -> San Tropez Drive\n",
"La Baig Dr -> La Baig Drive\n",
"La Baig Dr -> La Baig Drive\n",
"Recht St -> Recht Street\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Arbour Ln -> Arbour Lane\n",
"La Baig Dr -> La Baig Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"Le Mans Dr -> Le Mans Drive\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Arbour Ln -> Arbour Lane\n",
"Mccarthy St -> Mccarthy Street\n",
"Verona Pl -> Verona Place\n",
"Le Mans Dr -> Le Mans Drive\n",
"Recht St -> Recht Street\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Recht St -> Recht Street\n",
"San Tropez Dr -> San Tropez Drive\n",
"Tuscany Pl -> Tuscany Place\n",
"Holland Cir -> Holland Circle\n",
"Recht St -> Recht Street\n",
"Recht St -> Recht Street\n",
"Le Chateau Dr -> Le Chateau Drive\n",
"Arbour Ln -> Arbour Lane\n",
"Tuscany Pl -> Tuscany Place\n",
"Holland Cir -> Holland Circle\n",
"La Baig Dr -> La Baig Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"Le Mans Dr -> Le Mans Drive\n",
"Tuscany Pl -> Tuscany Place\n",
"La Baig Dr -> La Baig Drive\n",
"Recht St -> Recht Street\n",
"Recht St -> Recht Street\n",
"Le Chateau Dr -> Le Chateau Drive\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Howard Ct -> Howard Court\n",
"Holland Cir -> Holland Circle\n",
"Riviera Dr -> Riviera Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Tropez Dr -> San Tropez Drive\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"Recht St -> Recht Street\n",
"Marseille Dr -> Marseille Drive\n",
"Howard Ct -> Howard Court\n",
"Marseille Dr -> Marseille Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"La Baig Dr -> La Baig Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"Howard Ct -> Howard Court\n",
"Mccarthy St -> Mccarthy Street\n",
"Recht St -> Recht Street\n",
"Le Mans Dr -> Le Mans Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Tropez Dr -> San Tropez Drive\n",
"Arbour Ln -> Arbour Lane\n",
"Tuscany Pl -> Tuscany Place\n",
"Verona Pl -> Verona Place\n",
"Marseille Ct -> Marseille Court\n",
"Arbour Ln -> Arbour Lane\n",
"Marseille Ct -> Marseille Court\n",
"Le Mans Dr -> Le Mans Drive\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Holland Cir -> Holland Circle\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Recht St -> Recht Street\n",
"Adrian Ct -> Adrian Court\n",
"Arbour Ln -> Arbour Lane\n",
"Riviera Dr -> Riviera Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"Mccarthy St -> Mccarthy Street\n",
"Marseille Dr -> Marseille Drive\n",
"Howard Ct -> Howard Court\n",
"La Baig Dr -> La Baig Drive\n",
"Le Chateau Dr -> Le Chateau Drive\n",
"Recht St -> Recht Street\n",
"Marseille Ct -> Marseille Court\n",
"San Tropez Dr -> San Tropez Drive\n",
"La Baig Dr -> La Baig Drive\n",
"Holland Cir -> Holland Circle\n",
"Le Chateau Dr -> Le Chateau Drive\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Bordeaux Pl -> Bordeaux Place\n",
"Holland Cir -> Holland Circle\n",
"La Baig Dr -> La Baig Drive\n",
"La Baig Dr -> La Baig Drive\n",
"Riviera Dr -> Riviera Drive\n",
"Howard Ct -> Howard Court\n",
"San Tropez Dr -> San Tropez Drive\n",
"Holland Cir -> Holland Circle\n",
"Recht St -> Recht Street\n",
"Las Palmas Dr -> Las Palmas Drive\n",
"La Baig Dr -> La Baig Drive\n",
"Riviera Dr -> Riviera Drive\n",
"Marseille Dr -> Marseille Drive\n",
"Le Chateau Dr -> Le Chateau Drive\n",
"La Baig Dr -> La Baig Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Tropez Dr -> San Tropez Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Tuscany Pl -> Tuscany Place\n",
"Riviera Dr -> Riviera Drive\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"Le Mans Dr -> Le Mans Drive\n",
"San Tropez Dr -> San Tropez Drive\n",
"Le Mans Dr -> Le Mans Drive\n",
"Cherry Ave -> Cherry Avenue\n",
"Mariposa Ct -> Mariposa Court\n",
"W Graf Rd -> W Graf Road\n",
"Amador Cir -> Amador Circle\n",
"Mariposa Ct -> Mariposa Court\n",
"Marguerite Dr -> Marguerite Drive\n",
"W Graf Rd -> W Graf Road\n",
"Marguerite Dr -> Marguerite Drive\n",
"Mariposa Ct -> Mariposa Court\n",
"Marguerite Dr -> Marguerite Drive\n",
"Amador Cir -> Amador Circle\n",
"Beresini Ln -> Beresini Lane\n",
"Mariposa Ct -> Mariposa Court\n",
"Miller Rd -> Miller Road\n",
"Marguerite Dr -> Marguerite Drive\n",
"Marguerite Dr -> Marguerite Drive\n",
"Marguerite Dr -> Marguerite Drive\n",
"Marguerite Dr -> Marguerite Drive\n",
"Marguerite Dr -> Marguerite Drive\n",
"Marguerite Dr -> Marguerite Drive\n",
"Marguerite Dr -> Marguerite Drive\n",
"Beresini Ln -> Beresini Lane\n",
"Miller Rd -> Miller Road\n",
"Beresini Ln -> Beresini Lane\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Marguerite Dr -> Marguerite Drive\n",
"Marguerite Dr -> Marguerite Drive\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Miller Rd -> Miller Road\n",
"Beresini Ln -> Beresini Lane\n",
"Mariposa Ct -> Mariposa Court\n",
"Beresini Ln -> Beresini Lane\n",
"Mariposa Ct -> Mariposa Court\n",
"Mariposa Ct -> Mariposa Court\n",
"Amador Cir -> Amador Circle\n",
"Mariposa Ct -> Mariposa Court\n",
"Marguerite Dr -> Marguerite Drive\n",
"Beresini Ln -> Beresini Lane\n",
"Marguerite Dr -> Marguerite Drive\n",
"W Graf Rd -> W Graf Road\n",
"W Graf Rd -> W Graf Road\n",
"Mariposa Ct -> Mariposa Court\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Amador Cir -> Amador Circle\n",
"Willow Dr -> Willow Drive\n",
"W Graf Rd -> W Graf Road\n",
"Bridge Rd -> Bridge Road\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Bridge Rd -> Bridge Road\n",
"Graf Rd -> Graf Road\n",
"Bridge Rd -> Bridge Road\n",
"Willow Dr -> Willow Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"Willow Dr -> Willow Drive\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Willow Dr -> Willow Drive\n",
"Willow Dr -> Willow Drive\n",
"Willow Dr -> Willow Drive\n",
"Bridge Rd -> Bridge Road\n",
"Ranchito Dr -> Ranchito Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"W Graf Rd -> W Graf Road\n",
"Willow Dr -> Willow Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Graf Rd -> Graf Road\n",
"Graf Rd -> Graf Road\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Azul Ct -> Azul Court\n",
"Bridge Rd -> Bridge Road\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Ranchito Dr -> Ranchito Drive\n",
"Azul Ct -> Azul Court\n",
"Ranchito Ct -> Ranchito Court\n",
"Ranchito Dr -> Ranchito Drive\n",
"W Graf Rd -> W Graf Road\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Willow Dr -> Willow Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"Azul Ct -> Azul Court\n",
"Ranchito Dr -> Ranchito Drive\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Willow Dr -> Willow Drive\n",
"Willow Dr -> Willow Drive\n",
"Willow Dr -> Willow Drive\n",
"Willow Dr -> Willow Drive\n",
"Willow Dr -> Willow Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"Bridge Rd -> Bridge Road\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Ranchito Dr -> Ranchito Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"Willow Dr -> Willow Drive\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Willow Dr -> Willow Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"Bridge Rd -> Bridge Road\n",
"Ranchito Dr -> Ranchito Drive\n",
"Willow Dr -> Willow Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"Azul Ct -> Azul Court\n",
"Ranchito Dr -> Ranchito Drive\n",
"Azul Ct -> Azul Court\n",
"Willow Dr -> Willow Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Graf Rd -> Graf Road\n",
"Willow Dr -> Willow Drive\n",
"Azul Ct -> Azul Court\n",
"Azul Ct -> Azul Court\n",
"Ranchito Dr -> Ranchito Drive\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"W Graf Rd -> W Graf Road\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Willow Dr -> Willow Drive\n",
"Azul Ct -> Azul Court\n",
"Willow Dr -> Willow Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"Willow Dr -> Willow Drive\n",
"Bridgevale Rd -> Bridgevale Road\n",
"W Graf Rd -> W Graf Road\n",
"Ranchito Dr -> Ranchito Drive\n",
"Willow Dr -> Willow Drive\n",
"Azul Ct -> Azul Court\n",
"Graf Rd -> Graf Road\n",
"Willow Dr -> Willow Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"Ranchito Dr -> Ranchito Drive\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Bridge Rd -> Bridge Road\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Ranchito Ct -> Ranchito Court\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Ranchito Ct -> Ranchito Court\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Ranchito Ct -> Ranchito Court\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Ranchito Ct -> Ranchito Court\n",
"Ranchito Ct -> Ranchito Court\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Ranchito Ct -> Ranchito Court\n",
"Ranchito Ct -> Ranchito Court\n",
"Ranchito Ct -> Ranchito Court\n",
"Ranchito Ct -> Ranchito Court\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Bridge Rd -> Bridge Road\n",
"Ranchito Ct -> Ranchito Court\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Ranchito Ct -> Ranchito Court\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Ranchito Ct -> Ranchito Court\n",
"Jacaranda Cir -> Jacaranda Circle\n",
"Bridge Rd -> Bridge Road\n",
"Ranchito Ct -> Ranchito Court\n",
"Bridgevale Rd -> Bridgevale Road\n",
"Ortiz Ct -> Ortiz Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Ventura Ct -> Ventura Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Verde Cir -> Verde Circle\n",
"Verde Cir -> Verde Circle\n",
"Graf Rd -> Graf Road\n",
"Miller Rd -> Miller Road\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Verde Cir -> Verde Circle\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Ortiz Ct -> Ortiz Court\n",
"Brandy Ct -> Brandy Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Miller Rd -> Miller Road\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Graf Rd -> Graf Road\n",
"Miller Rd -> Miller Road\n",
"Ortiz Ct -> Ortiz Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Ventura Ct -> Ventura Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Ortiz Ct -> Ortiz Court\n",
"Ventura Ct -> Ventura Court\n",
"Verde Cir -> Verde Circle\n",
"Ventura Ct -> Ventura Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Verde Cir -> Verde Circle\n",
"Verde Cir -> Verde Circle\n",
"Ortiz Ct -> Ortiz Court\n",
"Miller Rd -> Miller Road\n",
"Verde Cir -> Verde Circle\n",
"Ortiz Ct -> Ortiz Court\n",
"Ventura Ct -> Ventura Court\n",
"Miller Rd -> Miller Road\n",
"Ventura Ct -> Ventura Court\n",
"Ventura Ct -> Ventura Court\n",
"Verde Cir -> Verde Circle\n",
"Verde Cir -> Verde Circle\n",
"Ventura Ct -> Ventura Court\n",
"Miller Rd -> Miller Road\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Brandy Ct -> Brandy Court\n",
"Ortiz Ct -> Ortiz Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Ventura Ct -> Ventura Court\n",
"Brandy Ct -> Brandy Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Miller Rd -> Miller Road\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Miller Rd -> Miller Road\n",
"Ventura Ct -> Ventura Court\n",
"Ventura Ct -> Ventura Court\n",
"Verde Cir -> Verde Circle\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Brandy Ct -> Brandy Court\n",
"Brandy Ct -> Brandy Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Verde Cir -> Verde Circle\n",
"Brandy Ct -> Brandy Court\n",
"Ventura Ct -> Ventura Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Ventura Ct -> Ventura Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Brandy Ct -> Brandy Court\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"Ortiz Ct -> Ortiz Court\n",
"Ortiz Ct -> Ortiz Court\n",
"Miller Rd -> Miller Road\n",
"Miller Rd -> Miller Road\n",
"Graf Rd -> Graf Road\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"N Sally St -> N Sally Street\n",
"East St -> East Street\n",
"N Monterey St -> N Monterey Street\n",
"N Chappell Rd -> N Chappell Road\n",
"Rustic St -> Rustic Street\n",
"Sierra Ct -> Sierra Court\n",
"N Sally St -> N Sally Street\n",
"Maple St -> Maple Street\n",
"Sally St -> Sally Street\n",
"Sierra Ct -> Sierra Court\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Thompson St -> Thompson Street\n",
"Mccarthy St -> Mccarthy Street\n",
"Lorene Dr -> Lorene Drive\n",
"San Juan Dr -> San Juan Drive\n",
"Rustic St -> Rustic Street\n",
"San Juan Dr -> San Juan Drive\n",
"N Chappell Rd -> N Chappell Road\n",
"San Juan Dr -> San Juan Drive\n",
"San Juan Dr -> San Juan Drive\n",
"San Juan Dr -> San Juan Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Alvarado St -> Alvarado Street\n",
"N Chappell Rd -> N Chappell Road\n",
"San Juan Dr -> San Juan Drive\n",
"Thompson St -> Thompson Street\n",
"Sally St -> Sally Street\n",
"San Juan Dr -> San Juan Drive\n",
"N Chappell Rd -> N Chappell Road\n",
"San Juan Dr -> San Juan Drive\n",
"Rustic St -> Rustic Street\n",
"San Juan Dr -> San Juan Drive\n",
"Sierra Ct -> Sierra Court\n",
"San Juan Dr -> San Juan Drive\n",
"Maple St -> Maple Street\n",
"San Juan Dr -> San Juan Drive\n",
"Lorene Dr -> Lorene Drive\n",
"San Juan Dr -> San Juan Drive\n",
"Sierra Ct -> Sierra Court\n",
"North St -> North Street\n",
"Maple St -> Maple Street\n",
"Sierra Ct -> Sierra Court\n",
"Sierra Ct -> Sierra Court\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Juan Dr -> San Juan Drive\n",
"San Juan Dr -> San Juan Drive\n",
"N Sally St -> N Sally Street\n",
"Sally St -> Sally Street\n",
"Mccarthy St -> Mccarthy Street\n",
"San Juan Dr -> San Juan Drive\n",
"Lorene Dr -> Lorene Drive\n",
"Thompson St -> Thompson Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Bolsa Rd -> Bolsa Road\n",
"1st St -> 1st Street\n",
"Rustic St -> Rustic Street\n",
"North St -> North Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Juan Dr -> San Juan Drive\n",
"N Sally St -> N Sally Street\n",
"N Sally St -> N Sally Street\n",
"San Juan Dr -> San Juan Drive\n",
"Thompson St -> Thompson Street\n",
"Rustic St -> Rustic Street\n",
"San Juan Dr -> San Juan Drive\n",
"San Juan Dr -> San Juan Drive\n",
"Sally St -> Sally Street\n",
"N Chappell Rd -> N Chappell Road\n",
"Maple St -> Maple Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Rustic St -> Rustic Street\n",
"Lorene Dr -> Lorene Drive\n",
"Rustic St -> Rustic Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Thompson St -> Thompson Street\n",
"San Juan Dr -> San Juan Drive\n",
"Sally St -> Sally Street\n",
"San Juan Dr -> San Juan Drive\n",
"Rustic St -> Rustic Street\n",
"Mccray St -> Mccray Street\n",
"El Toro Dr -> El Toro Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Rustic St -> Rustic Street\n",
"N Sally St -> N Sally Street\n",
"Rustic St -> Rustic Street\n",
"N Sally St -> N Sally Street\n",
"San Juan Dr -> San Juan Drive\n",
"San Juan Dr -> San Juan Drive\n",
"Rustic St -> Rustic Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Juan Dr -> San Juan Drive\n",
"San Juan Dr -> San Juan Drive\n",
"San Juan Dr -> San Juan Drive\n",
"Rustic St -> Rustic Street\n",
"N Sally St -> N Sally Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"San Juan Dr -> San Juan Drive\n",
"Maple St -> Maple Street\n",
"Rustic St -> Rustic Street\n",
"Mccarthy St -> Mccarthy Street\n",
"San Juan Dr -> San Juan Drive\n",
"Rustic St -> Rustic Street\n",
"Rustic St -> Rustic Street\n",
"Alvarado St -> Alvarado Street\n",
"Alvarado St -> Alvarado Street\n",
"Mccarthy St -> Mccarthy Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Rustic St -> Rustic Street\n",
"Rustic St -> Rustic Street\n",
"Maple St -> Maple Street\n",
"Rustic St -> Rustic Street\n",
"Maple St -> Maple Street\n",
"Maple St -> Maple Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"N Sally St -> N Sally Street\n",
"Rustic St -> Rustic Street\n",
"San Juan Dr -> San Juan Drive\n",
"Alvarado St -> Alvarado Street\n",
"Rustic St -> Rustic Street\n",
"N Sally St -> N Sally Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"N Sally St -> N Sally Street\n",
"Madrone Dr -> Madrone Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Madrone Dr -> Madrone Drive\n",
"N Chappell Rd -> N Chappell Road\n",
"N Chappell Rd -> N Chappell Road\n",
"N Chappell Rd -> N Chappell Road\n",
"Madrone Dr -> Madrone Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Rustic St -> Rustic Street\n",
"Primavera Dr -> Primavera Drive\n",
"N Chappell Rd -> N Chappell Road\n",
"Primavera Dr -> Primavera Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Primavera Dr -> Primavera Drive\n",
"N Chappell Rd -> N Chappell Road\n",
"Primavera Dr -> Primavera Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Rustic St -> Rustic Street\n",
"Primavera Dr -> Primavera Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Sierra Ct -> Sierra Court\n",
"Sierra Ct -> Sierra Court\n",
"Sierra Ct -> Sierra Court\n",
"Sierra Ct -> Sierra Court\n",
"Sierra Ct -> Sierra Court\n",
"Sierra Ct -> Sierra Court\n",
"Sierra Ct -> Sierra Court\n",
"Sierra Ct -> Sierra Court\n",
"Sierra Ct -> Sierra Court\n",
"Primavera Dr -> Primavera Drive\n",
"Rustic St -> Rustic Street\n",
"Madrone Dr -> Madrone Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Madrone Dr -> Madrone Drive\n",
"N Chappell Rd -> N Chappell Road\n",
"Rustic St -> Rustic Street\n",
"Madrone Dr -> Madrone Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Rustic St -> Rustic Street\n",
"Lorene Dr -> Lorene Drive\n",
"N Chappell Rd -> N Chappell Road\n",
"Rustic St -> Rustic Street\n",
"N Chappell Rd -> N Chappell Road\n",
"N Chappell Rd -> N Chappell Road\n",
"Madrone Dr -> Madrone Drive\n",
"N Chappell Rd -> N Chappell Road\n",
"Primavera Dr -> Primavera Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Primavera Dr -> Primavera Drive\n",
"N Chappell Rd -> N Chappell Road\n",
"N Chappell Rd -> N Chappell Road\n",
"Rustic St -> Rustic Street\n",
"Rustic St -> Rustic Street\n",
"Madrone Dr -> Madrone Drive\n",
"Rustic St -> Rustic Street\n",
"Primavera Dr -> Primavera Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Primavera Dr -> Primavera Drive\n",
"Madrone Dr -> Madrone Drive\n",
"N Chappell Rd -> N Chappell Road\n",
"Primavera Dr -> Primavera Drive\n",
"N Chappell Rd -> N Chappell Road\n",
"Primavera Dr -> Primavera Drive\n",
"Rustic St -> Rustic Street\n",
"N Chappell Rd -> N Chappell Road\n",
"Primavera Dr -> Primavera Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Rustic St -> Rustic Street\n",
"Madrone Dr -> Madrone Drive\n",
"Madrone Dr -> Madrone Drive\n",
"Newark Blvd -> Newark Boulevard\n",
"Meridian St -> Meridian Street\n",
"Meridian St -> Meridian Street\n",
"Fairview Rd -> Fairview Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Meridian St -> Meridian Street\n",
"Santa Ana Ct -> Santa Ana Court\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Meridian St -> Meridian Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Meridian St -> Meridian Street\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Fairview Rd -> Fairview Road\n",
"Meridian St -> Meridian Street\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Daffodil Dr -> Daffodil Drive\n",
"Arlington Dr -> Arlington Drive\n",
"Belmont Ct -> Belmont Court\n",
"Kane Dr -> Kane Drive\n",
"Kane Dr -> Kane Drive\n",
"Arlington Dr -> Arlington Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Daffodil Dr -> Daffodil Drive\n",
"Laurel Ct -> Laurel Court\n",
"Laurel Ct -> Laurel Court\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Jonquil Ln -> Jonquil Lane\n",
"Kane Dr -> Kane Drive\n",
"Jonquil Ln -> Jonquil Lane\n",
"Daffodil Dr -> Daffodil Drive\n",
"Jonquil Ln -> Jonquil Lane\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Belmont Ct -> Belmont Court\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Meridian St -> Meridian Street\n",
"Santa Ana Ct -> Santa Ana Court\n",
"Jonquil Ln -> Jonquil Lane\n",
"Daffodil Dr -> Daffodil Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Daffodil Dr -> Daffodil Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Arlington Dr -> Arlington Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Jonquil Ln -> Jonquil Lane\n",
"Kane Dr -> Kane Drive\n",
"Laurel Ct -> Laurel Court\n",
"Daffodil Dr -> Daffodil Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Kane Dr -> Kane Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Daffodil Dr -> Daffodil Drive\n",
"Jonquil Ln -> Jonquil Lane\n",
"Daffodil Dr -> Daffodil Drive\n",
"Meridian St -> Meridian Street\n",
"Jonquil Ln -> Jonquil Lane\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Laurel Ct -> Laurel Court\n",
"Fairview Rd -> Fairview Road\n",
"Kane Dr -> Kane Drive\n",
"Daffodil Dr -> Daffodil Drive\n",
"Edgewood Dr -> Edgewood Drive\n",
"Kane Dr -> Kane Drive\n",
"Kane Dr -> Kane Drive\n",
"Daffodil Dr -> Daffodil Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Daffodil Dr -> Daffodil Drive\n",
"Fairview Rd -> Fairview Road\n",
"Daffodil Dr -> Daffodil Drive\n",
"Jonquil Ln -> Jonquil Lane\n",
"Daffodil Dr -> Daffodil Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Kane Dr -> Kane Drive\n",
"Kane Dr -> Kane Drive\n",
"Arlington Dr -> Arlington Drive\n",
"Lemmon Ct -> Lemmon Court\n",
"Kane Dr -> Kane Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Kane Dr -> Kane Drive\n",
"Daffodil Dr -> Daffodil Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Kane Dr -> Kane Drive\n",
"Kane Dr -> Kane Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Belmont Ct -> Belmont Court\n",
"Daffodil Dr -> Daffodil Drive\n",
"Fairview Rd -> Fairview Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Edgewood Dr -> Edgewood Drive\n",
"Daffodil Dr -> Daffodil Drive\n",
"Fairview Rd -> Fairview Road\n",
"Belmont Ct -> Belmont Court\n",
"Daffodil Dr -> Daffodil Drive\n",
"Meridian St -> Meridian Street\n",
"Kane Dr -> Kane Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Fairview Rd -> Fairview Road\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Laurel Ct -> Laurel Court\n",
"Fairview Rd -> Fairview Road\n",
"Daffodil Dr -> Daffodil Drive\n",
"Kane Dr -> Kane Drive\n",
"Belmont Ct -> Belmont Court\n",
"Daffodil Dr -> Daffodil Drive\n",
"Kane Dr -> Kane Drive\n",
"Daffodil Dr -> Daffodil Drive\n",
"Arlington Dr -> Arlington Drive\n",
"Kane Dr -> Kane Drive\n",
"Jonquil Ln -> Jonquil Lane\n",
"Arlington Dr -> Arlington Drive\n",
"Belmont Ct -> Belmont Court\n",
"Laurel Ct -> Laurel Court\n",
"Kane Dr -> Kane Drive\n",
"Mansfield Rd -> Mansfield Road\n",
"Kane Dr -> Kane Drive\n",
"Arlington Dr -> Arlington Drive\n",
"Arlington Dr -> Arlington Drive\n",
"Kane Dr -> Kane Drive\n",
"Daffodil Dr -> Daffodil Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Kane Dr -> Kane Drive\n",
"Kane Dr -> Kane Drive\n",
"Laurel Ct -> Laurel Court\n",
"Fairview Rd -> Fairview Road\n",
"Laurel Ct -> Laurel Court\n",
"Kane Dr -> Kane Drive\n",
"Jonquil Ln -> Jonquil Lane\n",
"Daffodil Dr -> Daffodil Drive\n",
"Mansfield Rd -> Mansfield Road\n",
"Jonquil Ln -> Jonquil Lane\n",
"Jonquil Ln -> Jonquil Lane\n",
"Kane Dr -> Kane Drive\n",
"Daffodil Dr -> Daffodil Drive\n",
"Kane Dr -> Kane Drive\n",
"South St -> South Street\n",
"Jan Ave -> Jan Avenue\n",
"South St -> South Street\n",
"College St -> College Street\n",
"Tiffany Dr -> Tiffany Drive\n",
"Tamara Ct -> Tamara Court\n",
"2nd St -> 2nd Street\n",
"Sally St -> Sally Street\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Walnut Ln -> Walnut Lane\n",
"Powell St -> Powell Street\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Victoria St -> Victoria Street\n",
"Powell St -> Powell Street\n",
"Powell St -> Powell Street\n",
"Walnut Ln -> Walnut Lane\n",
"Walnut Ln -> Walnut Lane\n",
"Powell St -> Powell Street\n",
"Keller St -> Keller Street\n",
"Fremont Blvd -> Fremont Boulevard\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Peartree Ln -> Peartree Lane\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Lemmon Ct -> Lemmon Court\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Lemmon Ct -> Lemmon Court\n",
"Lemmon Ct -> Lemmon Court\n",
"Lemmon Ct -> Lemmon Court\n",
"Fairview Rd -> Fairview Road\n",
"Fairview Rd -> Fairview Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Lemmon Ct -> Lemmon Court\n",
"Lemmon Ct -> Lemmon Court\n",
"Fairview Rd -> Fairview Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Lemmon Ct -> Lemmon Court\n",
"Lemmon Ct -> Lemmon Court\n",
"Lemmon Ct -> Lemmon Court\n",
"Granada St -> Granada Street\n",
"Menzel Rd -> Menzel Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Menzel Rd -> Menzel Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Hunter Ln -> Hunter Lane\n",
"Mansfield Rd -> Mansfield Road\n",
"Fairview Rd -> Fairview Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Hunter Ln -> Hunter Lane\n",
"Mansfield Rd -> Mansfield Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Santa Ana Valley Rd -> Santa Ana Valley Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Mansfield Rd -> Mansfield Road\n",
"Hunter Ln -> Hunter Lane\n",
"Mansfield Rd -> Mansfield Road\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '400'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676806',\n",
" 'timestamp': '2014-04-14T00:47:57Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '93817356',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '657'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676806',\n",
" 'timestamp': '2014-04-14T00:47:58Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '93817362',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '653'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676806',\n",
" 'timestamp': '2014-04-14T00:47:58Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '93817365',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '665'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676806',\n",
" 'timestamp': '2014-04-14T00:47:59Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '93817369',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Aubrey Ln -> Aubrey Lane\n",
"Aubrey Ln -> Aubrey Lane\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Lemos Ct -> Lemos Court\n",
"Lemos Ct -> Lemos Court\n",
"Lemos Ct -> Lemos Court\n",
"Perrien Ct -> Perrien Court\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Blake Ct -> Blake Court\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Aubrey Ln -> Aubrey Lane\n",
"Lemos Ct -> Lemos Court\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"Lemos Ct -> Lemos Court\n",
"San Felipe Rd -> San Felipe Road\n",
"Mccloskey Rd -> Mccloskey Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"Community Pkwy -> Community Parkway\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Bonnie View Rd -> Bonnie View Road\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Bonnie View Rd -> Bonnie View Road\n",
"El Cerro Dr -> El Cerro Drive\n",
"Bonnie View Rd -> Bonnie View Road\n",
"El Cerro Dr -> El Cerro Drive\n",
"Bonnie View Rd -> Bonnie View Road\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Bonnie View Rd -> Bonnie View Road\n",
"El Toro Dr -> El Toro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Bonnie View Rd -> Bonnie View Road\n",
"El Toro Dr -> El Toro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Poppy Lane Dr -> Poppy Lane Drive\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Bonnie View Rd -> Bonnie View Road\n",
"El Cerro Dr -> El Cerro Drive\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Sunnyslope Rd -> Sunnyslope Road\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Poppy Lane Dr -> Poppy Lane Drive\n",
"Bonnie View Rd -> Bonnie View Road\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Poppy Lane Dr -> Poppy Lane Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Bonnie View Rd -> Bonnie View Road\n",
"El Cerro Dr -> El Cerro Drive\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Bonnie View Rd -> Bonnie View Road\n",
"El Cerro Dr -> El Cerro Drive\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Bonnie View Rd -> Bonnie View Road\n",
"El Toro Dr -> El Toro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"9660 Stockton Blvd -> 9660 Stockton Boulevard\n",
"Shoshone Cir -> Shoshone Circle\n",
"Shoshone Cir -> Shoshone Circle\n",
"Shoshone Cir -> Shoshone Circle\n",
"Shoshone Cir -> Shoshone Circle\n",
"Clearview Dr -> Clearview Drive\n",
"Clearview Dr -> Clearview Drive\n",
"Sawtooth Dr -> Sawtooth Drive\n",
"Sawtooth Dr -> Sawtooth Drive\n",
"Sawtooth Dr -> Sawtooth Drive\n",
"Bonnie View Rd -> Bonnie View Road\n",
"Clearview Dr -> Clearview Drive\n",
"Shoshone Cir -> Shoshone Circle\n",
"2501 East Bayshore Blvd -> 2501 East Bayshore Boulevard\n",
"McCarthy Blvd -> McCarthy Boulevard\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Maranatha Dr -> Maranatha Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Heatherwood Ln -> Heatherwood Lane\n",
"Maranatha Dr -> Maranatha Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Maranatha Dr -> Maranatha Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Maranatha Dr -> Maranatha Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Maranatha Dr -> Maranatha Drive\n",
"Sundown Ln -> Sundown Lane\n",
"Airline Hwy -> Airline Highway\n",
"Maranatha Dr -> Maranatha Drive\n",
"Foxhill Cir -> Foxhill Circle\n",
"Foxhill Cir -> Foxhill Circle\n",
"Foxhill Cir -> Foxhill Circle\n",
"Foxhill Cir -> Foxhill Circle\n",
"Best Rd -> Best Road\n",
"Foxhill Cir -> Foxhill Circle\n",
"Foxhill Cir -> Foxhill Circle\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Foxhill Cir -> Foxhill Circle\n",
"Foxhill Cir -> Foxhill Circle\n",
"Foxhill Cir -> Foxhill Circle\n",
"Foxhill Cir -> Foxhill Circle\n",
"Sundown Ln -> Sundown Lane\n",
"Best Rd -> Best Road\n",
"Foxhill Cir -> Foxhill Circle\n",
"Foxhill Cir -> Foxhill Circle\n",
"Foxhill Cir -> Foxhill Circle\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Maranatha Dr -> Maranatha Drive\n",
"Maranatha Dr -> Maranatha Drive\n",
"Foxhill Cir -> Foxhill Circle\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Foxhill Cir -> Foxhill Circle\n",
"Foxhill Cir -> Foxhill Circle\n",
"Sundown Ln -> Sundown Lane\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Lima Ct -> Lima Court\n",
"Foxhill Cir -> Foxhill Circle\n",
"Best Rd -> Best Road\n",
"Foxhill Cir -> Foxhill Circle\n",
"Diablo Hills Rd -> Diablo Hills Road\n",
"Best Rd -> Best Road\n",
"Sundown Ln -> Sundown Lane\n",
"Sundown Ln -> Sundown Lane\n",
"Foxhill Cir -> Foxhill Circle\n",
"Under Ramp Sw Quad Of Us 101 / Sr 92 Ic Off 19th Ave & Fashion Island Blvd -> Under Ramp Sw Quad Of Us 101 / Sr 92 Ic Off 19th Ave & Fashion Island Boulevard\n",
"Sundown Ln -> Sundown Lane\n",
"Meadow Ct -> Meadow Court\n",
"D St & 51 4th St -> D St & 51 4th Street\n",
"Airline Hwy -> Airline Highway\n",
"Failing Rd -> Failing Road\n",
"Airline Hwy -> Airline Highway\n",
"Airline Hwy -> Airline Highway\n",
"Airline Hwy -> Airline Highway\n",
"D St -> D Street\n",
"Jerrold Ave -> Jerrold Avenue\n",
"Missouri St -> Missouri Street\n",
"Missouri St -> Missouri Street\n",
"Missouri St -> Missouri Street\n",
"Connecticut St -> Connecticut Street\n",
"Cesar Chavez St St -> Cesar Chavez St Street\n",
"Missouri St -> Missouri Street\n",
"Connecticut St -> Connecticut Street\n",
"Connecticut St -> Connecticut Street\n",
"------------Ignoring Address Key---------------\n",
"address:900 Pennsylvania Ave\n",
"{'created': {'changeset': '21688172',\n",
" 'timestamp': '2014-04-14T15:36:39Z',\n",
" 'uid': '1829683',\n",
" 'user': 'Luis36995',\n",
" 'version': '5'},\n",
" 'id': '97638171',\n",
" 'name': 'San Francisco Food Bank',\n",
" 'phone': '+1 (415) 282-1900',\n",
" 'type': 'way'}\n",
"Pennsylvania Ave -> Pennsylvania Avenue\n",
"School Rd -> School Road\n",
"Anzar Rd -> Anzar Road\n",
"Church St -> Church Street\n",
"N Monterey St -> N Monterey Street\n",
"Monterey St -> Monterey Street\n",
"3rd St -> 3rd Street\n",
"Church St -> Church Street\n",
"Salinas Rd -> Salinas Road\n",
"Franklin St -> Franklin Street\n",
"Tahualami & 4th St -> Tahualami & 4th Street\n",
"Franklin Cir -> Franklin Circle\n",
"Washington St -> Washington Street\n",
"Washington St -> Washington Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"Church St -> Church Street\n",
"Washington St -> Washington Street\n",
"Franklin St -> Franklin Street\n",
"6th St -> 6th Street\n",
"Church St -> Church Street\n",
"Church St -> Church Street\n",
"Lasuen Dr -> Lasuen Drive\n",
"3rd St -> 3rd Street\n",
"Franklins Cir -> Franklins Circle\n",
"Monterey St -> Monterey Street\n",
"Monterey St -> Monterey Street\n",
"Franklin Cir -> Franklin Circle\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"6th St -> 6th Street\n",
"Mission St -> Mission Street\n",
"Franklin St -> Franklin Street\n",
"Washington St -> Washington Street\n",
"Mission St -> Mission Street\n",
"Thomas Ln -> Thomas Lane\n",
"Franklin Ct -> Franklin Court\n",
"Church St -> Church Street\n",
"N Monterey St -> N Monterey Street\n",
"Washington St -> Washington Street\n",
"Merintas Ct -> Merintas Court\n",
"Thomas Ln -> Thomas Lane\n",
"3rd St -> 3rd Street\n",
"Tahualami St -> Tahualami Street\n",
"Franklin St -> Franklin Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"Franklin Ct -> Franklin Court\n",
"3rd St -> 3rd Street\n",
"Washington St -> Washington Street\n",
"Franklin Ct -> Franklin Court\n",
"3rd St -> 3rd Street\n",
"Washington St -> Washington Street\n",
"Franklin Cir -> Franklin Circle\n",
"Church St -> Church Street\n",
"Tahualami St -> Tahualami Street\n",
"3rd St -> 3rd Street\n",
"Washington St -> Washington Street\n",
"Washington St -> Washington Street\n",
"Franklin St -> Franklin Street\n",
"Washington St -> Washington Street\n",
"Franklin St -> Franklin Street\n",
"Franklin St -> Franklin Street\n",
"Pearce Ln -> Pearce Lane\n",
"Franklin St -> Franklin Street\n",
"Washington St -> Washington Street\n",
"Pearce Ln -> Pearce Lane\n",
"Pearce Ln -> Pearce Lane\n",
"Pearce Ln -> Pearce Lane\n",
"Franklin St -> Franklin Street\n",
"Washington St -> Washington Street\n",
"6th St -> 6th Street\n",
"Franklin St -> Franklin Street\n",
"Washington St -> Washington Street\n",
"6th St -> 6th Street\n",
"1553 Colony Rd -> 1553 Colony Road\n",
"5055 Farmhill Blvd -> 5055 Farmhill Boulevard\n",
"2003 Cabrillo Hwy -> 2003 Cabrillo Highway\n",
"Washington St -> Washington Street\n",
"Polk St -> Polk Street\n",
"Washington St -> Washington Street\n",
"Polk St -> Polk Street\n",
"380 Foster City Blvd -> 380 Foster City Boulevard\n",
"800 E Airway Blvd -> 800 E Airway Boulevard\n",
"San Benito St -> San Benito Street\n",
"San Felipe Rd -> San Felipe Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Lone Tree Rd -> Lone Tree Road\n",
"Dixie Dr -> Dixie Drive\n",
"Fairview Rd -> Fairview Road\n",
"Jonquil Ln -> Jonquil Lane\n",
"Kane Dr -> Kane Drive\n",
"Santa Ana Rd -> Santa Ana Road\n",
"Sawyer Camp Trail & Hillcrest Blvd -> Sawyer Camp Trail & Hillcrest Boulevard\n",
"3rd St -> 3rd Street\n",
"Mccarthy St -> Mccarthy Street\n",
"Santa Ana Valley Rd -> Santa Ana Valley Road\n",
"Santa Ana Valley Rd -> Santa Ana Valley Road\n",
"Fairview Rd -> Fairview Road\n",
"Santa Ana Valley Rd -> Santa Ana Valley Road\n",
"Santa Ana Valley Rd -> Santa Ana Valley Road\n",
"Santa Ana Valley Rd -> Santa Ana Valley Road\n",
"Santa Ana Valley Rd -> Santa Ana Valley Road\n",
"American Ct -> American Court\n",
"American Ct -> American Court\n",
"Feather Ct -> Feather Court\n",
"Feather Ct -> Feather Court\n",
"American Ct -> American Court\n",
"Feather Ct -> Feather Court\n",
"Feather Ct -> Feather Court\n",
"Feather Ct -> Feather Court\n",
"American Ct -> American Court\n",
"American Ct -> American Court\n",
"Feather Ct -> Feather Court\n",
"American Ct -> American Court\n",
"Feather Ct -> Feather Court\n",
"American Ct -> American Court\n",
"American Ct -> American Court\n",
"600 Leweling Blvd -> 600 Leweling Boulevard\n",
"Christopher Dr -> Christopher Drive\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Hillside Rd -> Hillside Road\n",
"Quinn Canyon Rd -> Quinn Canyon Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Hillside Rd -> Hillside Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Ross Dr -> Ross Drive\n",
"Hillside Rd -> Hillside Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Hillside Rd -> Hillside Road\n",
"Hillside Rd -> Hillside Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Hillside Rd -> Hillside Road\n",
"San Juan Rd -> San Juan Road\n",
"Hillside Rd -> Hillside Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Quinn Canyon Rd -> Quinn Canyon Road\n",
"Hillside Rd -> Hillside Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Hillside Rd -> Hillside Road\n",
"Quinn Canyon Rd -> Quinn Canyon Road\n",
"Hillside Rd -> Hillside Road\n",
"Hillside Rd -> Hillside Road\n",
"Christopher Dr -> Christopher Drive\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Hillside Rd -> Hillside Road\n",
"Hillside Rd -> Hillside Road\n",
"Hillside Rd -> Hillside Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Ross Dr -> Ross Drive\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Christopher Dr -> Christopher Drive\n",
"San Juan Rd -> San Juan Road\n",
"San Juan Rd -> San Juan Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Hillside Rd -> Hillside Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"Four Corners Dr -> Four Corners Drive\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"Four Corners Dr -> Four Corners Drive\n",
"------------Ignoring Address Key---------------\n",
"address:4226 Piedmont Avenue\n",
"{'created': {'changeset': '16968876',\n",
" 'timestamp': '2013-07-15T23:15:06Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '2'},\n",
" 'id': '104437305',\n",
" 'name': 'Fentons Creamery',\n",
" 'phone': '+1 (510) 658-7000',\n",
" 'type': 'way'}\n",
"21300 San Ramon Valley Blvd -> 21300 San Ramon Valley Boulevard\n",
"San Francisco/Oakland Bridge Toll Pl -> San Francisco/Oakland Bridge Toll Place\n",
"Under I-880 @ 7th St & Linden St -> Under I-880 @ 7th St & Linden Street\n",
"Under I-580 Btwn Fruitvale / Champion St -> Under I-580 Btwn Fruitvale / Champion Street\n",
"Sw Quad I-680 / Bollinger Canyon Rd -> Sw Quad I-680 / Bollinger Canyon Road\n",
"Under I-580 Btwn Fruitvale / Champion St -> Under I-580 Btwn Fruitvale / Champion Street\n",
"10500 Foothill Blvd -> 10500 Foothill Boulevard\n",
"San Juan Rd -> San Juan Road\n",
"Gonzalez Dr -> Gonzalez Drive\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"3rd St -> 3rd Street\n",
"San Jose St -> San Jose Street\n",
"Donner St -> Donner Street\n",
"Donner St -> Donner Street\n",
"Monterey St -> Monterey Street\n",
"6th St -> 6th Street\n",
"Franklin St -> Franklin Street\n",
"Franklin St -> Franklin Street\n",
"Donner St -> Donner Street\n",
"Donner St -> Donner Street\n",
"Donner St -> Donner Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"6th St -> 6th Street\n",
"6th St -> 6th Street\n",
"Franklin St -> Franklin Street\n",
"Donner St -> Donner Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"Donner St -> Donner Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"1st St -> 1st Street\n",
"Mission St -> Mission Street\n",
"1st St -> 1st Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"Washington St -> Washington Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"2nd St -> 2nd Street\n",
"1st St -> 1st Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"2nd St -> 2nd Street\n",
"Monterey St -> Monterey Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"Donner St -> Donner Street\n",
"Franklin St -> Franklin Street\n",
"Washington St -> Washington Street\n",
"1st St -> 1st Street\n",
"Monterey St -> Monterey Street\n",
"Franklin St -> Franklin Street\n",
"Washington St -> Washington Street\n",
"Franklin St -> Franklin Street\n",
"Jefferson St -> Jefferson Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"Donner St -> Donner Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"1st St -> 1st Street\n",
"1st St -> 1st Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"6th St -> 6th Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"1st St -> 1st Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"Monterey St -> Monterey Street\n",
"Ahwahnee St -> Ahwahnee Street\n",
"Franklin St -> Franklin Street\n",
"Franklin Cir -> Franklin Circle\n",
"2nd St -> 2nd Street\n",
"Stevens Creek Blvd -> Stevens Creek Boulevard\n",
"Lang St -> Lang Street\n",
"Lang Ct -> Lang Court\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Stephens Dr -> Stephens Drive\n",
"Lang Ct -> Lang Court\n",
"Kurasaki Dr -> Kurasaki Drive\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Salinas Rd -> Salinas Road\n",
"Lang St -> Lang Street\n",
"Lang Ct -> Lang Court\n",
"Lang St -> Lang Street\n",
"Salinas Rd -> Salinas Road\n",
"Lang Ct -> Lang Court\n",
"Lang St -> Lang Street\n",
"Church St -> Church Street\n",
"Lang St -> Lang Street\n",
"Lang Ct -> Lang Court\n",
"Lang Ct -> Lang Court\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Lang Ct -> Lang Court\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Lang Ct -> Lang Court\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Stephens Dr -> Stephens Drive\n",
"Lang St -> Lang Street\n",
"Lang Ct -> Lang Court\n",
"Lang St -> Lang Street\n",
"Stephens Dr -> Stephens Drive\n",
"Washington St -> Washington Street\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Lang St -> Lang Street\n",
"Olga Dr -> Olga Drive\n",
"Olga Dr -> Olga Drive\n",
"Olga Dr -> Olga Drive\n",
"Olga Dr -> Olga Drive\n",
"Olga Dr -> Olga Drive\n",
"W & E Of Us 101 N Of Seminary Ave -> W & E Of Us 101 N Of Seminary Avenue\n",
"W & E Of Us 101 N Of Seminary Ave -> W & E Of Us 101 N Of Seminary Avenue\n",
"40 Shoreline Hwy -> 40 Shoreline Highway\n",
"Se Quad I-680 / Rudgear Rd -> Se Quad I-680 / Rudgear Road\n",
"Ne & Sw Quads I-80 / Willow Ave -> Ne & Sw Quads I-80 / Willow Avenue\n",
"Nw Quad I-80 / Magazine St Ic Off Lincoln Rd @ San Miguel Rd -> Nw Quad I-80 / Magazine St Ic Off Lincoln Rd @ San Miguel Road\n",
"E Of Sr 242/ S Of Willow Pass Rd/ W Of Market St -> E Of Sr 242/ S Of Willow Pass Rd/ W Of Market Street\n",
"Brookwood Ave / Sr 12 Ic N Of Bennett Valley Rd -> Brookwood Ave / Sr 12 Ic N Of Bennett Valley Road\n",
"Se Quad Us 101 @ South Petaluma Blvd -> Se Quad Us 101 @ South Petaluma Boulevard\n",
"------------Ignoring Address Key---------------\n",
"address:3400 Telegraph Avenue\n",
"{'created': {'changeset': '7751553',\n",
" 'timestamp': '2011-04-03T08:56:24Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '3'},\n",
" 'id': '107011813',\n",
" 'name': 'Walgreens',\n",
" 'phone': '15105970458',\n",
" 'type': 'way'}\n",
"6010 Folsom Blvd -> 6010 Folsom Boulevard\n",
"Ne Quad Enterprise Dr / I-80 Ic @ Capitol Ave -> Ne Quad Enterprise Dr / I-80 Ic @ Capitol Avenue\n",
"21265 Coast Hwy -> 21265 Coast Highway\n",
"Talbot Dr -> Talbot Drive\n",
"Freya Ct -> Freya Court\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Fallon Rd -> Fallon Road\n",
"Shelton Dr -> Shelton Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Santa Rosa Dr -> Santa Rosa Drive\n",
"Fallon Rd -> Fallon Road\n",
"Fallon Rd -> Fallon Road\n",
"Fallon Rd -> Fallon Road\n",
"Hillcrest Rd -> Hillcrest Road\n",
"El Toro Dr -> El Toro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"El Cerro Dr -> El Cerro Drive\n",
"El Toro Dr -> El Toro Drive\n",
"Marne Dr -> Marne Drive\n",
"Hillcrest Rd -> Hillcrest Road\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"A & B San Felipe Rd -> A & B San Felipe Road\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"Lovers Ln -> Lovers Lane\n",
"5th St -> 5th Street\n",
"College St -> College Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"5th St -> 5th Street\n",
"Sundown Ln -> Sundown Lane\n",
"Sundown Ln -> Sundown Lane\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '608', 'street': 'Folsom Street'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '23006977',\n",
" 'timestamp': '2014-06-18T15:00:04Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '5'},\n",
" 'id': '113347423',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Ignoring Address Key---------------\n",
"address:901 Gilman Street\n",
"{'created': {'changeset': '15719691',\n",
" 'timestamp': '2013-04-14T03:00:30Z',\n",
" 'uid': '153669',\n",
" 'user': 'dchiles',\n",
" 'version': '7'},\n",
" 'id': '113625442',\n",
" 'name': 'Pyramid Brewery & Alehouse',\n",
" 'phone': '+1 (510) 528-9880',\n",
" 'type': 'way',\n",
" 'wifi': 'free'}\n",
"Weyburn Ln -> Weyburn Lane\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '470'},\n",
" 'amenity': 'parking',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:05:00Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '4'},\n",
" 'id': '114711558',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"South St -> South Street\n",
"7th St -> 7th Street\n",
"7th St -> 7th Street\n",
"Rays Cir -> Rays Circle\n",
"Florence Ct -> Florence Court\n",
"------------Ignoring Address Key---------------\n",
"address:3111 International Boulevard\n",
"{'created': {'changeset': '8357750',\n",
" 'timestamp': '2011-06-06T08:46:57Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '2'},\n",
" 'id': '116564776',\n",
" 'name': \"Wendy's\",\n",
" 'phone': '+1 (510) 533-0100',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:47131 Bayside Parkway\n",
"{'created': {'changeset': '16073308',\n",
" 'timestamp': '2013-05-11T00:42:43Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '2'},\n",
" 'id': '116904860',\n",
" 'name': 'Mattson Technology',\n",
" 'type': 'way'}\n",
"Arthur St -> Arthur Street\n",
"Russell Blvd -> Russell Boulevard\n",
"Ramona St -> Ramona Street\n",
"California Ave -> California Avenue\n",
"Lake Washington Boulevar -> Lake Washington Boulevard\n",
"Lake Washington Boulevar -> Lake Washington Boulevard\n",
"Lake Washington Boulevar -> Lake Washington Boulevard\n",
"Lake Washington Boulevar -> Lake Washington Boulevard\n",
"Lake Washington Boulevar -> Lake Washington Boulevard\n",
"Lake Washington Boulevar -> Lake Washington Boulevard\n",
"Lake Washington Boulevar -> Lake Washington Boulevard\n",
"Mace Blvd -> Mace Boulevard\n",
"Monte Cristo Ct -> Monte Cristo Court\n",
"Monte Cristo Ct -> Monte Cristo Court\n",
"Monte Cristo Ct -> Monte Cristo Court\n",
"Monte Cristo Ct -> Monte Cristo Court\n",
"Monte Cristo Ct -> Monte Cristo Court\n",
"Monte Bello Dr -> Monte Bello Drive\n",
"Monte Bello Dr -> Monte Bello Drive\n",
"Monte Bello Dr -> Monte Bello Drive\n",
"Monte Bello Dr -> Monte Bello Drive\n",
"Monte Bello Dr -> Monte Bello Drive\n",
"Monte Bello Dr -> Monte Bello Drive\n",
"Monte Bello Dr -> Monte Bello Drive\n",
"Monte Bello Dr -> Monte Bello Drive\n",
"Monte Bello Ct -> Monte Bello Court\n",
"Monte Bello Ct -> Monte Bello Court\n",
"Monte Bello Ct -> Monte Bello Court\n",
"Monte Bello Ct -> Monte Bello Court\n",
"Monte Bello Ct -> Monte Bello Court\n",
"Monte Bello Ct -> Monte Bello Court\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Corrib Ct -> Corrib Court\n",
"Corrib Ct -> Corrib Court\n",
"Ashford Cir -> Ashford Circle\n",
"Corrib Ct -> Corrib Court\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Corrib Ct -> Corrib Court\n",
"Corrib Ct -> Corrib Court\n",
"Corrib Ct -> Corrib Court\n",
"Ashford Cir -> Ashford Circle\n",
"Hidden Valley Rd -> Hidden Valley Road\n",
"Ashford Cir -> Ashford Circle\n",
"Eldene Dr -> Eldene Drive\n",
"Eldene Dr -> Eldene Drive\n",
"Mccary Dr -> Mccary Drive\n",
"Mccary Dr -> Mccary Drive\n",
"Mccary Dr -> Mccary Drive\n",
"Hilltop Dr -> Hilltop Drive\n",
"Kelly Dr -> Kelly Drive\n",
"Hilltop Dr -> Hilltop Drive\n",
"Hilltop Dr -> Hilltop Drive\n",
"Hilltop Dr -> Hilltop Drive\n",
"Hilltop Dr -> Hilltop Drive\n",
"Hilltop Dr -> Hilltop Drive\n",
"Kelly Dr -> Kelly Drive\n",
"Hidden Valley Rd -> Hidden Valley Road\n",
"Hidden Valley Rd -> Hidden Valley Road\n",
"Hidden Valley Rd -> Hidden Valley Road\n",
"Morris Dr -> Morris Drive\n",
"Hidden Valley Rd -> Hidden Valley Road\n",
"Hidden Valley Rd -> Hidden Valley Road\n",
"Nicholson Dr -> Nicholson Drive\n",
"Nicholson Dr -> Nicholson Drive\n",
"Morris Dr -> Morris Drive\n",
"Morris Dr -> Morris Drive\n",
"Morris Dr -> Morris Drive\n",
"Hitch Dr -> Hitch Drive\n",
"Hitch Dr -> Hitch Drive\n",
"Hitch Dr -> Hitch Drive\n",
"Hitch Dr -> Hitch Drive\n",
"Ashford Cir -> Ashford Circle\n",
"Guiness Ct -> Guiness Court\n",
"Guiness Ct -> Guiness Court\n",
"Guiness Ct -> Guiness Court\n",
"Guiness Ct -> Guiness Court\n",
"Guiness Ct -> Guiness Court\n",
"Guiness Ct -> Guiness Court\n",
"Guiness Ct -> Guiness Court\n",
"Ewen Dr -> Ewen Drive\n",
"Hidden Valley Rd -> Hidden Valley Road\n",
"Mccary Dr -> Mccary Drive\n",
"Mccary Dr -> Mccary Drive\n",
"Ewen Dr -> Ewen Drive\n",
"Adrian Dr -> Adrian Drive\n",
"Hidden Valley Rd -> Hidden Valley Road\n",
"Hidden Valley Rd -> Hidden Valley Road\n",
"Kelly Dr -> Kelly Drive\n",
"Kelly Dr -> Kelly Drive\n",
"Kelly Dr -> Kelly Drive\n",
"Kelly Dr -> Kelly Drive\n",
"Windmill Dr -> Windmill Drive\n",
"Windmill Dr -> Windmill Drive\n",
"Windmill Dr -> Windmill Drive\n",
"Windmill Dr -> Windmill Drive\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Ashford Cir -> Ashford Circle\n",
"Hidden Valley Rd -> Hidden Valley Road\n",
"Adrian Dr -> Adrian Drive\n",
"Hidden Valley Rd -> Hidden Valley Road\n",
"Adrian Dr -> Adrian Drive\n",
"Adrian Dr -> Adrian Drive\n",
"Cowden Rd -> Cowden Road\n",
"Cowden Rd -> Cowden Road\n",
"Cowden Rd -> Cowden Road\n",
"Cowden Rd -> Cowden Road\n",
"Hospital Rd -> Hospital Road\n",
"Cowden Rd -> Cowden Road\n",
"Hospital Rd -> Hospital Road\n",
"Cowden Rd -> Cowden Road\n",
"Cowden Rd -> Cowden Road\n",
"Cowden Rd -> Cowden Road\n",
"Cowden Rd -> Cowden Road\n",
"Cowden Rd -> Cowden Road\n",
"Cowden Rd -> Cowden Road\n",
"Hospital Rd -> Hospital Road\n",
"Cowden Rd -> Cowden Road\n",
"Cowden Rd -> Cowden Road\n",
"Cowden Rd -> Cowden Road\n",
"Hospital Rd -> Hospital Road\n",
"Aromitas Rd -> Aromitas Road\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Anzar Rd -> Anzar Road\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Anzar Rd -> Anzar Road\n",
"Anzar Rd -> Anzar Road\n",
"Cole Rd -> Cole Road\n",
"Quarry Rd -> Quarry Road\n",
"Carr Ave -> Carr Avenue\n",
"Anzar Rd -> Anzar Road\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Anzar Rd -> Anzar Road\n",
"Cole Rd -> Cole Road\n",
"Aromitas Rd -> Aromitas Road\n",
"Cole Rd -> Cole Road\n",
"------------Ignoring Address Key---------------\n",
"address:3250 Highway 128\n",
"{'created': {'changeset': '8825104',\n",
" 'timestamp': '2011-07-25T11:11:00Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '2'},\n",
" 'id': '123049299',\n",
" 'name': 'River Rock Casino',\n",
" 'phone': '+1 (707) 857-2777',\n",
" 'type': 'way'}\n",
"Gateway Rd -> Gateway Road\n",
"Pine Tree Ave -> Pine Tree Avenue\n",
"Pine Tree Ave -> Pine Tree Avenue\n",
"Seely Ave -> Seely Avenue\n",
"A Marshall Ln -> A Marshall Lane\n",
"Karen Ct -> Karen Court\n",
"Rose Ave -> Rose Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Karen Ct -> Karen Court\n",
"Seely Ave -> Seely Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Rose Ave -> Rose Avenue\n",
"Karen Ct -> Karen Court\n",
"Seely Ave -> Seely Avenue\n",
"Pine Tree Ave -> Pine Tree Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Pine Tree Ave -> Pine Tree Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Karen Ct -> Karen Court\n",
"Scott Ave -> Scott Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Seely Ave -> Seely Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Karen Ct -> Karen Court\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Karen Ct -> Karen Court\n",
"Pine Tree Ave -> Pine Tree Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Seely Ave -> Seely Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Karen Ct -> Karen Court\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Karen Ct -> Karen Court\n",
"Pine Tree Ave -> Pine Tree Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Pine Tree Ave -> Pine Tree Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Pine Tree Ave -> Pine Tree Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Pine Tree Ave -> Pine Tree Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Pine Tree Ave -> Pine Tree Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Seely Ave -> Seely Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Pine Tree Ave -> Pine Tree Avenue\n",
"A Rose Ave -> A Rose Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Carr Ave -> Carr Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Hamilton Ave -> Hamilton Avenue\n",
"Hamilton Ave. -> Hamilton Avenue\n",
"Willow Rd -> Willow Road\n",
"Snyder Ave -> Snyder Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Scott Ave -> Scott Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Garden St -> Garden Street\n",
"Scott Ave -> Scott Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"441 Snyder Ave -> 441 Snyder Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Garden St -> Garden Street\n",
"Snyder Ave -> Snyder Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Pine Tree Ave -> Pine Tree Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Carpenteria Ave -> Carpenteria Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Scott Ave -> Scott Avenue\n",
"Scott Ave -> Scott Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Scott Ave -> Scott Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Pine Tree Ave -> Pine Tree Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Marshall Ln -> Marshall Lane\n",
"Rose Ave -> Rose Avenue\n",
"Garden St -> Garden Street\n",
"Snyder Ave -> Snyder Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Rose Ave -> Rose Avenue\n",
"Hamilton Ave -> Hamilton Avenue\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Arriba Dr -> Arriba Drive\n",
"Seely Ave -> Seely Avenue\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Cole Rd -> Cole Road\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Arriba Dr -> Arriba Drive\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Snyder Ave -> Snyder Avenue\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Arriba Dr -> Arriba Drive\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Arriba Dr -> Arriba Drive\n",
"Carr Ave -> Carr Avenue\n",
"Seely Ave -> Seely Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Cole Rd -> Cole Road\n",
"Snyder Ave -> Snyder Avenue\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Cole Rd -> Cole Road\n",
"Seely Ave -> Seely Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Snyder Ave -> Snyder Avenue\n",
"Carpenteria Rd -> Carpenteria Road\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Carr Ave -> Carr Avenue\n",
"Ricardo Dr -> Ricardo Drive\n",
"Ricardo Ct -> Ricardo Court\n",
"Ricardo Dr -> Ricardo Drive\n",
"Ricardo Ct -> Ricardo Court\n",
"Ricardo Ct -> Ricardo Court\n",
"Ricardo Dr -> Ricardo Drive\n",
"Cole Rd -> Cole Road\n",
"Ricardo Dr -> Ricardo Drive\n",
"Ricardo Ct -> Ricardo Court\n",
"Cole Rd -> Cole Road\n",
"Ricardo Dr -> Ricardo Drive\n",
"Ricardo Dr -> Ricardo Drive\n",
"Cole Rd -> Cole Road\n",
"Arriba Dr -> Arriba Drive\n",
"Ricardo Dr -> Ricardo Drive\n",
"Ricardo Dr -> Ricardo Drive\n",
"Ricardo Dr -> Ricardo Drive\n",
"Ricardo Dr -> Ricardo Drive\n",
"Ricardo Dr -> Ricardo Drive\n",
"Ricardo Dr -> Ricardo Drive\n",
"Ricardo Dr -> Ricardo Drive\n",
"Ricardo Ct -> Ricardo Court\n",
"Ricardo Dr -> Ricardo Drive\n",
"Ricardo Dr -> Ricardo Drive\n",
"Seely Ave -> Seely Avenue\n",
"25555 Hesperian Blvd -> 25555 Hesperian Boulevard\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '166,168'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:54Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '124884342',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '158'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:55Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '124884344',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '156'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:55Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '124884346',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '150'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:56Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '124884352',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '164'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:55Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '124884357',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '140'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:56Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '124884359',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '171'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:56Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '124889458',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '551'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:57Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '124889461',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '181'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:57Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '124889463',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '424'},\n",
" 'amenity': 'parking',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:59Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '4'},\n",
" 'id': '124889469',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '550'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:54Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '4'},\n",
" 'id': '124889472',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '599'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:58Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '124890326',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '560'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:58Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '124903642',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"Anzar Rd -> Anzar Road\n",
"Cole Rd -> Cole Road\n",
"Anzar Rd -> Anzar Road\n",
"Rocks Rd -> Rocks Road\n",
"Rocks Rd -> Rocks Road\n",
"Rocks Rd -> Rocks Road\n",
"Salinas Rd -> Salinas Road\n",
"&402 A & B 3rd St -> &402 A & B 3rd Street\n",
"Renton Ct -> Renton Court\n",
"Salinas Rd -> Salinas Road\n",
"Salinas Rd -> Salinas Road\n",
"Salinas Rd -> Salinas Road\n",
"Salinas Rd -> Salinas Road\n",
"Salinas Rd -> Salinas Road\n",
"Renton Ct -> Renton Court\n",
"Salinas Rd -> Salinas Road\n",
"Salinas Rd -> Salinas Road\n",
"Salinas Rd -> Salinas Road\n",
"Stierlin Ct. -> Stierlin Court\n",
"Snyder Ave -> Snyder Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Snyder Ave -> Snyder Avenue\n",
"Forest Rd -> Forest Road\n",
"Carr Ave -> Carr Avenue\n",
"Anzar Rd -> Anzar Road\n",
"Forest Rd -> Forest Road\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"Forest Rd -> Forest Road\n",
"Payne Rd -> Payne Road\n",
"Payne Rd -> Payne Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Payne Rd -> Payne Road\n",
"Payne Rd -> Payne Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Payne Rd -> Payne Road\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"Payne Rd -> Payne Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Payne Rd -> Payne Road\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Payne Rd -> Payne Road\n",
"Payne Rd -> Payne Road\n",
"Payne Rd -> Payne Road\n",
"Payne Rd -> Payne Road\n",
"Chittenden Rd -> Chittenden Road\n",
"School Rd -> School Road\n",
"Butterfly Ct -> Butterfly Court\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"Payne Rd -> Payne Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Anzar Rd -> Anzar Road\n",
"School Rd -> School Road\n",
"Butterfly Ct -> Butterfly Court\n",
"Chittenden Rd -> Chittenden Road\n",
"School Rd -> School Road\n",
"Butterfly Ct -> Butterfly Court\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"Anzar Rd -> Anzar Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Anzar Rd -> Anzar Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Anzar Rd -> Anzar Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Anzar Rd -> Anzar Road\n",
"Anzar Rd -> Anzar Road\n",
"Anzar Rd -> Anzar Road\n",
"Merrill Rd -> Merrill Road\n",
"School Rd -> School Road\n",
"Anzar Rd -> Anzar Road\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"Anzar Rd -> Anzar Road\n",
"Searle Rd -> Searle Road\n",
"Searle Rd -> Searle Road\n",
"Searle Rd -> Searle Road\n",
"Searle Rd -> Searle Road\n",
"Searle Rd -> Searle Road\n",
"Searle Rd -> Searle Road\n",
"Searle Rd -> Searle Road\n",
"Searle Rd -> Searle Road\n",
"School Rd -> School Road\n",
"Merrill Rd -> Merrill Road\n",
"Chittenden Rd -> Chittenden Road\n",
"Anzar Rd -> Anzar Road\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"School Rd -> School Road\n",
"Anzar Rd -> Anzar Road\n",
"School Rd -> School Road\n",
"Anzar Rd -> Anzar Road\n",
"------------Ignoring Address Key---------------\n",
"address:2533 Castro Valley Boulevard\n",
"{'created': {'changeset': '9044952',\n",
" 'timestamp': '2011-08-17T07:37:10Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '5'},\n",
" 'id': '126334975',\n",
" 'name': 'Golfland Golden Tee',\n",
" 'phone': '+1 (510) 537-2168',\n",
" 'type': 'way'}\n",
"Rocks Rd -> Rocks Road\n",
"Salinas Rd -> Salinas Road\n",
"Salinas Rd -> Salinas Road\n",
"Victoria St -> Victoria Street\n",
"Victoria St -> Victoria Street\n",
"Blossom Ln -> Blossom Lane\n",
"Southside Rd -> Southside Road\n",
"Swan Ct -> Swan Court\n",
"Swan Ct -> Swan Court\n",
"Southside Rd -> Southside Road\n",
"Southside Rd -> Southside Road\n",
"Southside Rd -> Southside Road\n",
"Southside Rd -> Southside Road\n",
"Blossom Ln -> Blossom Lane\n",
"Blossom Ln -> Blossom Lane\n",
"Blossom Ln -> Blossom Lane\n",
"Cowden Rd -> Cowden Road\n",
"Cowden Rd -> Cowden Road\n",
"Blossom Ln -> Blossom Lane\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"Tehama Ave -> Tehama Avenue\n",
"------------Ignoring Address Key---------------\n",
"address:1095 Rollins Road\n",
"{'created': {'changeset': '24730757',\n",
" 'timestamp': '2014-08-13T19:32:04Z',\n",
" 'uid': '2229190',\n",
" 'user': 'kylewm',\n",
" 'version': '2'},\n",
" 'id': '131356774',\n",
" 'name': 'Fattoria E Mare',\n",
" 'phone': '650-342-4922',\n",
" 'type': 'way'}\n",
"N California Blvd -> N California Boulevard\n",
"Sperry Ave -> Sperry Avenue\n",
"Embarcadero Rd -> Embarcadero Road\n",
"Embarcadero Rd -> Embarcadero Road\n",
"Heather Glen Cir -> Heather Glen Circle\n",
"Day Ave -> Day Avenue\n",
"Day Ave -> Day Avenue\n",
"Day Ave -> Day Avenue\n",
"Corriente Point Dr -> Corriente Point Drive\n",
"Sally St -> Sally Street\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '487'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:53Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '3'},\n",
" 'id': '147689546',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"Zinfandel Dr. -> Zinfandel Drive\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '100'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '20892758',\n",
" 'timestamp': '2014-03-03T17:27:18Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '148259784',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"California St. -> California Street\n",
"Folsom St -> Folsom Street\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '655'},\n",
" 'amenity': 'restaurant',\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676806',\n",
" 'timestamp': '2014-04-14T00:47:54Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '3'},\n",
" 'cuisine': 'chinese',\n",
" 'email': '[email protected]',\n",
" 'fax': '415-495-0815',\n",
" 'id': '149102132',\n",
" 'name': 'Canton',\n",
" 'phone': '415-495-3064',\n",
" 'source': 'Survey, website, Bing',\n",
" 'type': 'way',\n",
" 'website': 'http://www.cantonsf.com/'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '747', 'street': 'Folsom Street'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '23014847',\n",
" 'timestamp': '2014-06-18T23:05:37Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '3'},\n",
" 'id': '149102513',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Ignoring Address Key---------------\n",
"address:19 Hacker Way\n",
"{'created': {'changeset': '11084649',\n",
" 'timestamp': '2012-03-24T14:40:25Z',\n",
" 'uid': '92274',\n",
" 'user': 'adjuva',\n",
" 'version': '2'},\n",
" 'id': '149394973',\n",
" 'name': 'Building 19',\n",
" 'type': 'way'}\n",
"Sutter St. -> Sutter Street\n",
"Folsom St -> Folsom Street\n",
"Pacific Commons Blvd -> Pacific Commons Boulevard\n",
"Elan Village Ln -> Elan Village Lane\n",
"Bont Ln. -> Bont Lane\n",
"Alpine Rd. -> Alpine Road\n",
"East Bidwell St. -> East Bidwell Street\n",
"Campus Dr. -> Campus Drive\n",
"California Ave -> California Avenue\n",
"S California Ave -> S California Avenue\n",
"S California Ave -> S California Avenue\n",
"S California Ave -> S California Avenue\n",
"S California Ave -> S California Avenue\n",
"S California Ave -> S California Avenue\n",
"Oak Ave Pkwy -> Oak Ave Parkway\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '470'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:53Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '159953365',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '466'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:05:00Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '160892691',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '440'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:59Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '160892692',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '458'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:59Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '160892694',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"S Tracy Blvd -> S Tracy Boulevard\n",
"------------Ignoring Address Key---------------\n",
"address:2051 Market Street\n",
"{'created': {'changeset': '16148272',\n",
" 'timestamp': '2013-05-16T06:49:47Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '2'},\n",
" 'id': '167176657',\n",
" 'name': 'Eros',\n",
" 'phone': '+1 (415) 255-4921',\n",
" 'type': 'way'}\n",
"san lorenzo way -> san lorenzo Way\n",
"Mission Blvd -> Mission Boulevard\n",
"------------Ignoring Address Key---------------\n",
"address:3001 Taraval Street\n",
"{'created': {'changeset': '18658764',\n",
" 'timestamp': '2013-11-01T20:54:04Z',\n",
" 'uid': '14293',\n",
" 'user': 'KindredCoda',\n",
" 'version': '5'},\n",
" 'id': '173642176',\n",
" 'name': 'Walgreens',\n",
" 'phone': '+1 (415) 759-0572',\n",
" 'ref': '4570',\n",
" 'source': 'data.sfgov.org',\n",
" 'type': 'way'}\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '685'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676806',\n",
" 'timestamp': '2014-04-14T00:48:01Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '173886691',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '689'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676806',\n",
" 'timestamp': '2014-04-14T00:48:00Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '173886701',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '428'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:52Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '173886710',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '674'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676806',\n",
" 'timestamp': '2014-04-14T00:47:59Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '3'},\n",
" 'id': '173886711',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '401'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676945',\n",
" 'timestamp': '2014-04-14T01:04:52Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '173886724',\n",
" 'source': 'Bing',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"Roslea Rd. -> Roslea Road\n",
"Laguna St -> Laguna Street\n",
"99 Frontage Rd -> 99 Frontage Road\n",
"Brookhurst Blvd -> Brookhurst Boulevard\n",
"Western Ave -> Western Avenue\n",
"N. Market Blvd -> N. Market Boulevard\n",
"Lake Washington Boulevar -> Lake Washington Boulevard\n",
"Lake Washington Boulevar -> Lake Washington Boulevard\n",
"Lake Washington Boulevar -> Lake Washington Boulevard\n",
"Saratoga Ave -> Saratoga Avenue\n",
"Linwood Dr -> Linwood Drive\n",
"Monument Blvd -> Monument Boulevard\n",
"laguna street -> laguna Street\n",
"laguna street -> laguna Street\n",
"Del Valle Pkwy -> Del Valle Parkway\n",
"Isles CT -> Isles Court\n",
"Stoneridge Mall Rd -> Stoneridge Mall Road\n",
"Stoneridge Mall Rd -> Stoneridge Mall Road\n",
"Avichi Knoll Dr -> Avichi Knoll Drive\n",
"Avichi Knoll Dr -> Avichi Knoll Drive\n",
"Avichi Knoll Dr -> Avichi Knoll Drive\n",
"Mission St -> Mission Street\n",
"Mission St -> Mission Street\n",
"Market St. -> Market Street\n",
"Monterey Blvd -> Monterey Boulevard\n",
"Edes Ave. -> Edes Avenue\n",
"Celadon Cir -> Celadon Circle\n",
"California Ave -> California Avenue\n",
"Campus Dr. -> Campus Drive\n",
"Greenwood Ave -> Greenwood Avenue\n",
"S. Power Inn Rd -> S. Power Inn Road\n",
"------------Problem inserting rose gate common:yes--------------\n",
"Node:\n",
"{'created': {'changeset': '20256001',\n",
" 'timestamp': '2014-01-28T22:50:12Z',\n",
" 'uid': '33612',\n",
" 'user': 'kz7',\n",
" 'version': '2'},\n",
" 'highway': 'residential',\n",
" 'id': '235188043',\n",
" 'name': 'Rose Gate Common',\n",
" 'source': 'bing',\n",
" 'source:name': 'TIGER2012',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"Loma Vista Ave -> Loma Vista Avenue\n",
"------------Ignoring Address Key---------------\n",
"address:3561 El Camino Real, Ste 75\n",
"{'created': {'changeset': '31396752',\n",
" 'timestamp': '2015-05-23T12:41:05Z',\n",
" 'uid': '68982',\n",
" 'user': 'kisaa',\n",
" 'version': '3'},\n",
" 'id': '236989191',\n",
" 'name': 'Paris Baguette',\n",
" 'phone': '408-260-0404',\n",
" 'type': 'way'}\n",
"Plaza street -> Plaza Street\n",
"Plaza street -> Plaza Street\n",
"Plaza street -> Plaza Street\n",
"Plaza street -> Plaza Street\n",
"Center street -> Center Street\n",
"Plaza street -> Plaza Street\n",
"Plaza street -> Plaza Street\n",
"Plaza street -> Plaza Street\n",
"Plaza street -> Plaza Street\n",
"Center street -> Center Street\n",
"Center street -> Center Street\n",
"Center street -> Center Street\n",
"Center street -> Center Street\n",
"Center street -> Center Street\n",
"Center street -> Center Street\n",
"Yulupa Ave. -> Yulupa Avenue\n",
"The Alameda Ave -> The Alameda Avenue\n",
"N McCarthy Blvd -> N McCarthy Boulevard\n",
"------------Ignoring Address Key---------------\n",
"address:375 7th Street\n",
"{'created': {'changeset': '31973551',\n",
" 'timestamp': '2015-06-15T01:02:46Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '4'},\n",
" 'id': '243357392',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:1101 Eucalyptus Drive\n",
"{'created': {'changeset': '22301322',\n",
" 'timestamp': '2014-05-12T21:35:04Z',\n",
" 'uid': '1981678',\n",
" 'user': 'aweverka',\n",
" 'version': '3'},\n",
" 'id': '247534019',\n",
" 'name': 'Lowell High School',\n",
" 'phone': '+1 (415) 759-2730',\n",
" 'type': 'way'}\n",
"Hayes St -> Hayes Street\n",
"Auburn Blvd -> Auburn Boulevard\n",
"Folsom Blvd -> Folsom Boulevard\n",
"Folsom Blvd -> Folsom Boulevard\n",
"Mission Blvd -> Mission Boulevard\n",
"Mission Blvd -> Mission Boulevard\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '205'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '20892758',\n",
" 'timestamp': '2014-03-03T17:27:17Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257543712',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '235'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '20892758',\n",
" 'timestamp': '2014-03-03T17:27:16Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257543713',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '520'},\n",
" 'building': 'house',\n",
" 'created': {'changeset': '22157579',\n",
" 'timestamp': '2014-05-06T01:09:25Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257543715',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '540'},\n",
" 'building': 'house',\n",
" 'created': {'changeset': '22157579',\n",
" 'timestamp': '2014-05-06T01:09:26Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '3'},\n",
" 'id': '257543719',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '530'},\n",
" 'building': 'house',\n",
" 'created': {'changeset': '22157579',\n",
" 'timestamp': '2014-05-06T01:09:26Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257543720',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '500,510'},\n",
" 'building': 'house',\n",
" 'created': {'changeset': '22157579',\n",
" 'timestamp': '2014-05-06T01:09:25Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257543722',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '555'},\n",
" 'building': 'house',\n",
" 'created': {'changeset': '22157579',\n",
" 'timestamp': '2014-05-06T01:09:23Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257546057',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '322'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '20892758',\n",
" 'timestamp': '2014-03-03T17:27:14Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257546067',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '545'},\n",
" 'building': 'house',\n",
" 'created': {'changeset': '22157579',\n",
" 'timestamp': '2014-05-06T01:09:24Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257546074',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '505'},\n",
" 'building': 'house',\n",
" 'created': {'changeset': '22157579',\n",
" 'timestamp': '2014-05-06T01:09:24Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257546080',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '551'},\n",
" 'building': 'house',\n",
" 'created': {'changeset': '22157579',\n",
" 'timestamp': '2014-05-06T01:09:24Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257546114',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"W Dana St -> W Dana Street\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '403'},\n",
" 'building': 'house',\n",
" 'created': {'changeset': '21822566',\n",
" 'timestamp': '2014-04-20T19:59:28Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257622452',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '439'},\n",
" 'building': 'house',\n",
" 'created': {'changeset': '21822566',\n",
" 'timestamp': '2014-04-20T19:59:28Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257622464',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '483'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21822566',\n",
" 'timestamp': '2014-04-20T19:59:29Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257622467',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '531'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21822566',\n",
" 'timestamp': '2014-04-20T19:59:31Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '3'},\n",
" 'id': '257623430',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '581'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21822566',\n",
" 'timestamp': '2014-04-20T19:59:32Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257623438',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '505'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21822566',\n",
" 'timestamp': '2014-04-20T19:59:30Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '3'},\n",
" 'id': '257623448',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '571'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21822566',\n",
" 'timestamp': '2014-04-20T19:59:32Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257623463',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '539'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21822566',\n",
" 'timestamp': '2014-04-20T19:59:31Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '3'},\n",
" 'id': '257623487',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '807,811'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21822566',\n",
" 'timestamp': '2014-04-20T19:59:35Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257682616',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '761'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21822566',\n",
" 'timestamp': '2014-04-20T19:59:34Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257683580',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '750'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21822566',\n",
" 'timestamp': '2014-04-20T19:59:35Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257683630',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '715'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21822566',\n",
" 'timestamp': '2014-04-20T19:59:33Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257683635',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '609,635'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21822566',\n",
" 'timestamp': '2014-04-20T19:59:33Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '257683639',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Ignoring Address Key---------------\n",
"address:2790 Harrison Street\n",
"{'created': {'changeset': '20179024',\n",
" 'timestamp': '2014-01-24T15:38:57Z',\n",
" 'uid': '1240849',\n",
" 'user': 'ediyes',\n",
" 'version': '2'},\n",
" 'id': '258121512',\n",
" 'name': 'Humphry Slocombe',\n",
" 'phone': '+1 (415) 550-6971',\n",
" 'type': 'way'}\n",
"Turk St -> Turk Street\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '374'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676678',\n",
" 'timestamp': '2014-04-14T00:32:06Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '260329412',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '396'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21673796',\n",
" 'timestamp': '2014-04-13T20:26:04Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '260329430',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'housenumber': '360'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '21676678',\n",
" 'timestamp': '2014-04-14T00:32:07Z',\n",
" 'uid': '25663',\n",
" 'user': 'CoreyFarwell',\n",
" 'version': '2'},\n",
" 'id': '260331685',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Ignoring Address Key---------------\n",
"address:345 7th Street\n",
"{'created': {'changeset': '20430810',\n",
" 'timestamp': '2014-02-07T13:43:40Z',\n",
" 'uid': '510836',\n",
" 'user': 'Rub21',\n",
" 'version': '1'},\n",
" 'ele': '5',\n",
" 'id': '260459201',\n",
" 'name': 'Ukrainian Orthodox Church of Saint Michael',\n",
" 'phone': '+1 (415) 861-4066',\n",
" 'type': 'way'}\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1640',\n",
" 'postcode': '94115',\n",
" 'state': 'CA',\n",
" 'street': 'Golden Gate Avenue'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '32792613',\n",
" 'timestamp': '2015-07-22T03:39:40Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '3'},\n",
" 'id': '261270941',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1008',\n",
" 'postcode': '94115',\n",
" 'state': 'CA',\n",
" 'street': 'Divisadero Street'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '32792613',\n",
" 'timestamp': '2015-07-22T03:39:39Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '3'},\n",
" 'id': '261272204',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1670',\n",
" 'postcode': '94115',\n",
" 'state': 'CA',\n",
" 'street': 'Golden Gate Avenue'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '32792613',\n",
" 'timestamp': '2015-07-22T03:39:40Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '3'},\n",
" 'id': '261282295',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1531',\n",
" 'postcode': '94115',\n",
" 'state': 'CA',\n",
" 'street': 'Golden Gate Avenue'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '32792613',\n",
" 'timestamp': '2015-07-22T03:39:40Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '3'},\n",
" 'id': '261283637',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1521',\n",
" 'postcode': '94115',\n",
" 'state': 'CA',\n",
" 'street': 'Golden Gate Avenue'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '32792613',\n",
" 'timestamp': '2015-07-22T03:39:40Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '3'},\n",
" 'id': '261283652',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1509',\n",
" 'postcode': '94115',\n",
" 'state': 'CA',\n",
" 'street': 'Golden Gate Avenue'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '32792613',\n",
" 'timestamp': '2015-07-22T03:39:39Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '3'},\n",
" 'id': '261284835',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"------------Problem inserting addr.source:housenumber:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'country': 'US',\n",
" 'housenumber': '1013',\n",
" 'postcode': '94115',\n",
" 'state': 'CA',\n",
" 'street': 'Divisadero Street'},\n",
" 'building': 'yes',\n",
" 'created': {'changeset': '32792613',\n",
" 'timestamp': '2015-07-22T03:39:39Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '3'},\n",
" 'id': '262243109',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"Banfield Dr -> Banfield Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"Talbot Dr -> Talbot Drive\n",
"------------Problem inserting building.source:levels:survey--------------\n",
"Node:\n",
"{'address': {'city': 'San Francisco',\n",
" 'housenumber': '624',\n",
" 'postcode': '94122',\n",
" 'street': 'Irving Street'},\n",
" 'amenity': 'restaurant',\n",
" 'building': 'yes',\n",
" 'building:levels': '2',\n",
" 'created': {'changeset': '31216188',\n",
" 'timestamp': '2015-05-17T02:20:54Z',\n",
" 'uid': '371121',\n",
" 'user': 'AndrewSnow',\n",
" 'version': '4'},\n",
" 'cuisine': 'crepes;omelettes;salads;pasta',\n",
" 'id': '267038704',\n",
" 'internet_access': 'yes',\n",
" 'name': 'Crepevine',\n",
" 'operator': 'Crepevine',\n",
" 'type': 'way'}\n",
"-------------------------------------------------\n",
"Ursuline Rd -> Ursuline Road\n",
"Millerick Rd. -> Millerick Road\n",
"Monte Carlo Dr -> Monte Carlo Drive\n",
"------------Ignoring Address Key---------------\n",
"address:27\n",
"{'created': {'changeset': '25715268',\n",
" 'timestamp': '2014-09-27T22:52:41Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '4'},\n",
" 'id': '277432537',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:39\n",
"{'created': {'changeset': '26034363',\n",
" 'timestamp': '2014-10-12T20:12:04Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '5'},\n",
" 'id': '277432719',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:41\n",
"{'created': {'changeset': '25736881',\n",
" 'timestamp': '2014-09-29T00:03:09Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '4'},\n",
" 'id': '277432978',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:30\n",
"{'created': {'changeset': '25715316',\n",
" 'timestamp': '2014-09-27T22:56:20Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '3'},\n",
" 'id': '277440213',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:24\n",
"{'created': {'changeset': '26266778',\n",
" 'timestamp': '2014-10-22T21:40:13Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '4'},\n",
" 'id': '277440215',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:3\n",
"{'created': {'changeset': '26266778',\n",
" 'timestamp': '2014-10-22T21:40:14Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '4'},\n",
" 'id': '277463204',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:4\n",
"{'created': {'changeset': '25715268',\n",
" 'timestamp': '2014-09-27T22:52:42Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '3'},\n",
" 'id': '277463232',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:15\n",
"{'created': {'changeset': '25715268',\n",
" 'timestamp': '2014-09-27T22:52:42Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '3'},\n",
" 'id': '277463259',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:6\n",
"{'created': {'changeset': '25715268',\n",
" 'timestamp': '2014-09-27T22:52:42Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '3'},\n",
" 'id': '277463288',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:19\n",
"{'created': {'changeset': '25715268',\n",
" 'timestamp': '2014-09-27T22:52:42Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '3'},\n",
" 'id': '277463307',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:23\n",
"{'created': {'changeset': '26266778',\n",
" 'timestamp': '2014-10-22T21:40:13Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '4'},\n",
" 'id': '277463332',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:10\n",
"{'created': {'changeset': '25932237',\n",
" 'timestamp': '2014-10-08T05:25:57Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '4'},\n",
" 'id': '277463340',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:20\n",
"{'created': {'changeset': '25715316',\n",
" 'timestamp': '2014-09-27T22:56:20Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '3'},\n",
" 'id': '277463416',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:2\n",
"{'created': {'changeset': '25715268',\n",
" 'timestamp': '2014-09-27T22:52:42Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '3'},\n",
" 'id': '277463457',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:1\n",
"{'created': {'changeset': '25715268',\n",
" 'timestamp': '2014-09-27T22:52:42Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '3'},\n",
" 'id': '277463458',\n",
" 'type': 'way'}\n",
"------------Ignoring Address Key---------------\n",
"address:5\n",
"{'created': {'changeset': '26266778',\n",
" 'timestamp': '2014-10-22T21:40:14Z',\n",
" 'uid': '1249504',\n",
" 'user': 'EranChazan',\n",
" 'version': '4'},\n",
" 'id': '277463491',\n",
" 'type': 'way'}\n",
"Skillman Ln -> Skillman Lane\n",
"Monarch Bay Dr. -> Monarch Bay Drive\n",
"Mission St -> Mission Street\n",
"Olive Dr -> Olive Drive\n",
"Morena Ave -> Morena Avenue\n",
"Lyon St -> Lyon Street\n",
"Bush St -> Bush Street\n",
"Van Ness Ave -> Van Ness Avenue\n",
"Cumberland PL -> Cumberland Place\n",
"Palm Valley Blvd -> Palm Valley Boulevard\n",
"Palm Valley Blvd -> Palm Valley Boulevard\n",
"Palm Valley Blvd -> Palm Valley Boulevard\n",
"Palm Valley Blvd -> Palm Valley Boulevard\n",
"State University Dr -> State University Drive\n",
"J St -> J Street\n",
"Casa Verde St -> Casa Verde Street\n",
"Forest Ave -> Forest Avenue\n",
"Bayshore Ave -> Bayshore Avenue\n",
"Central Ave -> Central Avenue\n",
"E Louise Ave -> E Louise Avenue\n",
"Veterans Blvd -> Veterans Boulevard\n",
"Veterans Blvd -> Veterans Boulevard\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"San Felipe Rd -> San Felipe Road\n",
"Afton Ave -> Afton Avenue\n",
"Afton Ave -> Afton Avenue\n",
"Mowry Ave -> Mowry Avenue\n",
"San Juan Canyon Rd -> San Juan Canyon Road\n",
"Esplanade Ave -> Esplanade Avenue\n",
"Esplanade Ave -> Esplanade Avenue\n",
"Willie Stargell Ave. -> Willie Stargell Avenue\n",
"Mecartney Rd -> Mecartney Road\n",
"Monroe St -> Monroe Street\n",
"Palm Valley Blvd -> Palm Valley Boulevard\n",
"Burbank St -> Burbank Street\n"
]
}
],
"source": [
"sf_bay_area = ShapeXmlToJson(\"san-francisco-bay_california.osm\")\n",
"sf_bay_area.shape()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"<style>\n",
" @font-face {\n",
" font-family: \"Computer Modern\";\n",
" src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunss.otf');\n",
" }\n",
" @font-face {\n",
" font-family: \"Computer Modern\";\n",
" font-weight: bold;\n",
" src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsx.otf');\n",
" }\n",
" @font-face {\n",
" font-family: \"Computer Modern\";\n",
" font-style: oblique;\n",
" src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunsi.otf');\n",
" }\n",
" @font-face {\n",
" font-family: \"Computer Modern\";\n",
" font-weight: bold;\n",
" font-style: oblique;\n",
" src: url('http://9dbb143991406a7c655e-aa5fcb0a5a4ec34cff238a2d56ca4144.r56.cf5.rackcdn.com/cmunso.otf');\n",
" }\n",
"\n",
" div.CodeMirror code{ /* code font */\n",
" font-family: \"Consolas\", monospace;\n",
" font-size: 10pt;\n",
" }\n",
"\n",
" div.text_cell_render{\n",
" font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
" font-size: 115%;\n",
" padding: 0;\n",
" }\n",
" /* header colours and fonts */\n",
" h1 {\n",
" color: #444;\n",
" }\n",
" h2 { color: #444; }\n",
" h3\n",
" {\n",
" color: #444;\n",
" font-style: italic;\n",
" font-weight: bold;\n",
" font-size: 120% !important;\n",
" margin-top: 0.6em !important;\n",
" }\n",
" h4\n",
" {\n",
" margin-top: 0.5em !important;\n",
" color: #444;\n",
" }\n",
" h5 { color: #444; }\n",
" h6 { color: #444; }\n",
"\n",
" ul {margin-top: 0em !important}\n",
" ol {margin-top: 0em !important}\n",
" p {margin-top: 0.4em !important}\n",
"\n",
" div.output_subarea\n",
" {\n",
" padding: 1em;\n",
" }\n",
"</style>\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {\n",
" extensions: [\"AMSmath.js\"]\n",
" },\n",
" tex2jax: {\n",
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
"/* displayIndent: \"4em\",*/\n",
" \"HTML-CSS\": {\n",
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
" }\n",
" });\n",
"</script>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def css_styling():\n",
" styles = open(\"../css/custom.css\", \"r\").read()\n",
" return disp.HTML(styles)\n",
"css_styling()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
},
"latex_envs": {
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 0
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
radhikapc/foundation-homework | homework_sql/twitterbot/botactonmagic.ipynb | 1 | 4356 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import requests\n",
"from bs4 import BeautifulSoup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"response = requests.get(\"https://www.facebook.com/actonmagic\")\n",
"doc = BeautifulSoup(response.text, 'html.parser')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"stories = doc.find_all(\"div\", { 'class': '_5pbx userContent' })\n",
"len(stories)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import re\n",
"all_stories = []\n",
"\n",
"for story in stories:\n",
" \n",
" status = story.find(\"p\")\n",
" if status:\n",
" status_text = status.text.strip()\n",
" #print(status_text)\n",
" new_status = re.sub(r\"[^A-Za-z0-9\\-\\?\\:\\.\\/]\", \" \", status_text)\n",
" clean_status = re.sub(r\"\\s\\s+\",\" \", new_status)\n",
" #print(new_status)\n",
" #print(clean_status)\n",
" all_stories.append(clean_status)\n",
"print(all_stories)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"all_stories[0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"api_key = \"my key\"\n",
"api_secret = \"my secret\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"!pip install twython"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import twython\n",
"twitter = twython.Twython(api_key, api_secret)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"auth = twitter.get_authentication_tokens()\n",
"print(\"Log into Twitter as the user you want to authorize and visit this URL:\")\n",
"print(\"\\t\" + auth['auth_url'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"\n",
"pin = \"8207618\"\n",
"\n",
"twitter = twython.Twython(api_key, api_secret, auth['oauth_token'], auth['oauth_token_secret'])\n",
"tokens = twitter.get_authorized_tokens(pin)\n",
"\n",
"new_access_token = tokens['oauth_token']\n",
"new_token_secret = tokens['oauth_token_secret']\n",
"print(\"your access token:\", new_access_token)\n",
"print(\"your token secret:\", new_token_secret)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"twitter = twython.Twython(api_key, api_secret, new_access_token, new_token_secret)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"story_1 = all_stories[0]\n",
"story_2 = all_stories[1]\n",
"if story_1 != story_2:\n",
" twitter.update_status(status=story_1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| mit |
rishuatgithub/MLPy | nlp/UPDATED_NLP_COURSE/05-Topic-Modeling/02-LDA-NMF-Assessment-Project.ipynb | 1 | 16843 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
"___"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Topic Modeling Assessment Project\n",
"\n",
"Welcome to your Topic Modeling Assessment! For this project you will be working with a dataset of over 400,000 quora questions that have no labeled cateogry, and attempting to find 20 cateogries to assign these questions to. The .csv file of these text questions can be found underneath the Topic-Modeling folder.\n",
"\n",
"Remember you can always check the solutions notebook and video lecture for any questions."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Task: Import pandas and read in the quora_questions.csv file."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Question</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>What is the step by step guide to invest in sh...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>What is the story of Kohinoor (Koh-i-Noor) Dia...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>How can I increase the speed of my internet co...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Why am I mentally very lonely? How can I solve...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Which one dissolve in water quikly sugar, salt...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Question\n",
"0 What is the step by step guide to invest in sh...\n",
"1 What is the story of Kohinoor (Koh-i-Noor) Dia...\n",
"2 How can I increase the speed of my internet co...\n",
"3 Why am I mentally very lonely? How can I solve...\n",
"4 Which one dissolve in water quikly sugar, salt..."
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preprocessing\n",
"\n",
"#### Task: Use TF-IDF Vectorization to create a vectorized document term matrix. You may want to explore the max_df and min_df parameters."
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<404289x38669 sparse matrix of type '<class 'numpy.float64'>'\n",
"\twith 2002912 stored elements in Compressed Sparse Row format>"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Non-negative Matrix Factorization\n",
"\n",
"#### TASK: Using Scikit-Learn create an instance of NMF with 20 expected components. (Use random_state=42).."
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NMF(alpha=0.0, beta_loss='frobenius', init=None, l1_ratio=0.0, max_iter=200,\n",
" n_components=20, random_state=42, shuffle=False, solver='cd', tol=0.0001,\n",
" verbose=0)"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### TASK: Print our the top 15 most common words for each of the 20 topics."
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"THE TOP 15 WORDS FOR TOPIC #0\n",
"['thing', 'read', 'place', 'visit', 'places', 'phone', 'buy', 'laptop', 'movie', 'ways', '2016', 'books', 'book', 'movies', 'best']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #1\n",
"['majors', 'recruit', 'sex', 'looking', 'differ', 'use', 'exist', 'really', 'compare', 'cost', 'long', 'feel', 'work', 'mean', 'does']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #2\n",
"['add', 'answered', 'needing', 'post', 'easily', 'improvement', 'delete', 'asked', 'google', 'answers', 'answer', 'ask', 'question', 'questions', 'quora']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #3\n",
"['using', 'website', 'investment', 'friends', 'black', 'internet', 'free', 'home', 'easy', 'youtube', 'ways', 'earn', 'online', 'make', 'money']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #4\n",
"['balance', 'earth', 'day', 'death', 'changed', 'live', 'want', 'change', 'moment', 'real', 'important', 'thing', 'meaning', 'purpose', 'life']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #5\n",
"['reservation', 'engineering', 'minister', 'president', 'company', 'china', 'business', 'country', 'olympics', 'available', 'job', 'spotify', 'war', 'pakistan', 'india']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #6\n",
"['beginners', 'online', 'english', 'book', 'did', 'hacking', 'want', 'python', 'languages', 'java', 'learning', 'start', 'language', 'programming', 'learn']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #7\n",
"['happen', 'presidency', 'think', 'presidential', '2016', 'vote', 'better', 'election', 'did', 'win', 'hillary', 'president', 'clinton', 'donald', 'trump']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #8\n",
"['russia', 'business', 'win', 'coming', 'countries', 'place', 'pakistan', 'happen', 'end', 'country', 'iii', 'start', 'did', 'war', 'world']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #9\n",
"['indian', 'companies', 'don', 'guy', 'men', 'culture', 'women', 'work', 'girls', 'live', 'girl', 'look', 'sex', 'feel', 'like']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #10\n",
"['ca', 'departments', 'positions', 'movies', 'songs', 'business', 'read', 'start', 'job', 'work', 'engineering', 'ways', 'bad', 'books', 'good']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #11\n",
"['money', 'modi', 'currency', 'economy', 'think', 'government', 'ban', 'banning', 'black', 'indian', 'rupee', 'rs', '1000', 'notes', '500']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #12\n",
"['blowing', 'resolutions', 'resolution', 'mind', 'likes', 'girl', '2017', 'year', 'don', 'employees', 'going', 'day', 'things', 'new', 'know']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #13\n",
"['aspects', 'fluent', 'skill', 'spoken', 'ways', 'language', 'fluently', 'speak', 'communication', 'pronunciation', 'speaking', 'writing', 'skills', 'improve', 'english']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #14\n",
"['diet', 'help', 'healthy', 'exercise', 'month', 'pounds', 'reduce', 'quickly', 'loss', 'fast', 'fat', 'ways', 'gain', 'lose', 'weight']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #15\n",
"['having', 'feel', 'long', 'spend', 'did', 'person', 'machine', 'movies', 'favorite', 'job', 'home', 'sex', 'possible', 'travel', 'time']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #16\n",
"['marriage', 'make', 'did', 'girlfriend', 'feel', 'tell', 'forget', 'really', 'friend', 'true', 'know', 'person', 'girl', 'fall', 'love']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #17\n",
"['easy', 'hack', 'prepare', 'quickest', 'facebook', 'increase', 'painless', 'instagram', 'account', 'best', 'commit', 'fastest', 'suicide', 'easiest', 'way']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #18\n",
"['web', 'java', 'scripting', 'phone', 'mechanical', 'better', 'job', 'use', 'account', 'data', 'software', 'science', 'computer', 'engineering', 'difference']\n",
"\n",
"\n",
"THE TOP 15 WORDS FOR TOPIC #19\n",
"['earth', 'blowing', 'stop', 'use', 'easily', 'mind', 'google', 'flat', 'questions', 'hate', 'believe', 'ask', 'don', 'think', 'people']\n",
"\n",
"\n"
]
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### TASK: Add a new column to the original quora dataframe that labels each question into one of the 20 topic categories."
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
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"2 How can I increase the speed of my internet co... 17\n",
"3 Why am I mentally very lonely? How can I solve... 11\n",
"4 Which one dissolve in water quikly sugar, salt... 14\n",
"5 Astrology: I am a Capricorn Sun Cap moon and c... 1\n",
"6 Should I buy tiago? 0\n",
"7 How can I be a good geologist? 10\n",
"8 When do you use シ instead of し? 19\n",
"9 Motorola (company): Can I hack my Charter Moto... 17"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Great job!"
]
}
],
"metadata": {
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"name": "python3"
},
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| apache-2.0 |
ondrejiayc/StatisticalMethods | examples/XrayImage/Summarizing.ipynb | 1 | 316418 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Summarizing Images"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Images are _high dimensional_ objects: our XMM image contains `648*648 = ` datapoints (the pixel values).\n",
"\n",
"Visualizing the data is an extremely important first step: the next is _summarizing_, which can be thought of as _dimensionality reduction_.\n",
"\n",
"Let's dust off some standard statistics and put them to good use in summarizing this X-ray image.\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/ondrej/anaconda/lib/python2.7/site-packages/IPython/kernel/__init__.py:13: ShimWarning: The `IPython.kernel` package has been deprecated. You should import from ipykernel or jupyter_client instead.\n",
" \"You should import from ipykernel or jupyter_client instead.\", ShimWarning)\n"
]
}
],
"source": [
"from __future__ import print_function\n",
"import astropy.io.fits as pyfits\n",
"import numpy as np\n",
"import astropy.visualization as viz\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"plt.rcParams['figure.figsize'] = (10.0, 10.0) "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"464K\ta1835_xmm//P0098010101M2U009EXPMAP3000.FTZ\r\n",
"48K\ta1835_xmm//P0098010101M2U009IMAGE_3000.FTZ\r\n",
"472K\ta1835_xmm//P0098010101M2X000BKGMAP3000.FTZ\r\n",
"984K\ttotal\r\n"
]
}
],
"source": [
"targdir = 'a1835_xmm/'\n",
"imagefile = targdir+'P0098010101M2U009IMAGE_3000.FTZ'\n",
"expmapfile = targdir+'P0098010101M2U009EXPMAP3000.FTZ'\n",
"bkgmapfile = targdir+'P0098010101M2X000BKGMAP3000.FTZ'\n",
"\n",
"!du -sch $targdir/*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## How Many Photons Came From the Cluster?\n",
"\n",
"Let's estimate the total counts due to the cluster.\n",
"\n",
"That means we need to somehow ignore\n",
"\n",
"* all the other objects in the field\n",
"\n",
"* the diffuse X-ray \"background\"\n",
"\n",
"Let's start by _masking_ various regions of the image to separate cluster from background."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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X8/k8jo+P44kTJzRGSrlcxuXlZd1GvXYliqLqvlhJz9CUri0SieDAwAAmk0nV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LOzk+rq6uiv/uqvaNWqVRQKhWjDhg20Zs0aWrx4MRmNRpo+fTpFIhH67ne/SyMjI7Rq1Spm+IVC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\nsCCq01YoFJDVasV//+x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WoVZ5dNra2oCiKN3ejGg0ih+02WxW4sXhOV9+v19CIBZ6UwKB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\nTSykiIiIiGz6/6aYuJybZnNcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f95b77db2d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"imfits = pyfits.open(imagefile)\n",
"im = imfits[0].data\n",
"plt.imshow(viz.scale_image(im, scale='log', max_cut=40), cmap='gray', origin='lower');"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Estimating the background\n",
"\n",
"Now let's look at the outer parts of the image, far from the cluster, and estimate the background level there."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# First make some coordinate arrays, including polar r from the cluster center:\n",
"ny, nx = im.shape\n",
"centroid = np.unravel_index(im.argmax(), im.shape)\n",
"x = np.linspace(0, nx-1, nx)\n",
"y = np.linspace(0, ny-1, ny)\n",
"dx, dy = np.meshgrid(x,y)\n",
"dx = dx - centroid[1]\n",
"dy = dy - centroid[0]\n",
"r = np.sqrt(dx*dx + dy*dy)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Now select an outer annulus, for the background,\n",
"# and an inner circle, for the cluster: \n",
"background = (r >= 100.0) & (r <= 150.0)\n",
"signal = (r < 100.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's visualize the background region by masking out everything else."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f95b7495d50>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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sLy/jgQcewEsvvbS+g6dt7Uc/+hF+/OMfj9Qn88pmszBNM3Q0VQYv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\nNV86nZ4YlAkhYBiGbx9RQdkHPvCBsds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2p04IlyvwprnQqNfrEEKgWCxiMBjE6lZAtF4M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\nGUi0XOJ0RPxCCH7NoDPOH/zBH2Dbtm0D7yeEQC6XQ7PZ9H14qLyppN/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fxnos0UbBQIpo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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f95b76eae50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"maskedimage = im.copy()\n",
"maskedimage[np.logical_not(background)] = -1\n",
"plt.imshow(viz.scale_image(maskedimage, scale='log', max_cut=40), cmap='gray', origin='lower')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's look at the _mean_ and _median_ of the pixels in the background annulus that have non-negative values."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean background counts per pixel = 0.0913094389573\n",
"Median background counts per pixel = 0.0\n"
]
}
],
"source": [
"meanbackground = np.mean(im[background])\n",
"medianbackground = np.median(im[background])\n",
"\n",
"print(\"Mean background counts per pixel = \",meanbackground)\n",
"print(\"Median background counts per pixel = \",medianbackground)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Q: Why do you think there's a difference? \n",
"Talk to your neighbor for a minute, and be ready to suggest an answer."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To understand the difference in these two estimates, lets look at a _pixel histogram_ for this annulus."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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SJEkDZs/cDLFnTpIk2TMnSZK0hljMDc6o7wnMFXs92jPTtsyzPTNtyzz7ZzEn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\nMIs5SZKkAfv/AXOsXZioQQWcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f95b76b2710>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,7))\n",
"n, bins, patches = plt.hist(im[background], bins=np.linspace(-3.5,29.5,34))\n",
"# plt.yscale('log', nonposy='clip')\n",
"plt.xlabel('Background annulus pixel value (counts)')\n",
"plt.ylabel('Frequency')\n",
"plt.axis([-3.0, 30.0, 0, 40000])\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Standard deviation: 0.659138624851\n"
]
}
],
"source": [
"stdevbackground = np.std(im[background])\n",
"print(\"Standard deviation: \",stdevbackground)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Exercise:\n",
"\n",
"_\"The background level in this image is approximately $0.09 \\pm 0.66$ counts\"_\n",
"\n",
"What's wrong with this statement?\n",
"\n",
"Talk to your neighbor for a few minutes, and see if you can come up with a better version."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Estimating the Cluster Counts\n",
"\n",
"Now let's summarize the circular region centered on the cluster, by making another masked image."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.image.AxesImage at 0x7f95b70d7690>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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b24tr164BAI4fP47x48dDr9ejr68PV69ehd1uR2lpKQCgvLxcvUZbWxsSExPV2jK9Xq+Z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\nFADAICEFADBISAEADBJSAACD/gHeLJQYilhQpwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f95d862fe50>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"maskedimage = im.copy()\n",
"maskedimage[np.logical_not(signal)] = 0\n",
"plt.imshow(viz.scale_image(maskedimage, scale='log', max_cut=40), cmap='gray', origin='lower')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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qBQBAi8Kvc2ZmPynp7ZL+Z7/r2yW9c770sFbKnUBjmJWIR01jUc941DQW9cxvmhMCfkbS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\nAFCQ/w8bdXlR5EducwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f95b7106490>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,7))\n",
"n, bins, patches = plt.hist(im[signal], bins=np.linspace(-3.5,29.5,34), color='red')\n",
"plt.yscale('log', nonposy='clip')\n",
"plt.xlabel('Signal region pixel value (counts)')\n",
"plt.ylabel('Frequency')\n",
"plt.axis([-3.0, 30.0, 0, 500000])\n",
"plt.grid(True)\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can make our estimates: "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Counts in signal region: 28195\n",
"Approximate counts due to background: 2866.84245494\n",
"Approximate counts due to cluster: 25328.1575451\n"
]
}
],
"source": [
"# Total counts in signal region:\n",
"Ntotal = np.sum(im[signal])\n",
"\n",
"# Background counts: the mean counts per pixel in the annulus, \n",
"# multiplied by the number of pixels in the signal region:\n",
"Nbackground = np.count_nonzero(signal)*meanbackground # Is this a good choice?\n",
"\n",
"# Difference is the cluster counts:\n",
"Ncluster = Ntotal - Nbackground\n",
"\n",
"print(\"Counts in signal region: \",Ntotal)\n",
"print(\"Approximate counts due to background: \",Nbackground)\n",
"print(\"Approximate counts due to cluster: \",Ncluster)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lessons\n",
"\n",
"Summary statistics are how we:\n",
"\n",
"* _reduce the dimensionality_ of datasets to a level where we can make sense of what is going on\n",
"\n",
"* make _rough estimates_ of important quantities we are interested in\n",
"\n",
"But:\n",
"\n",
"* their _uncertainty_ is non-trivial to estimate, and at least requires more information"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
CalebBell/fluids | docs/Data/Friction.ipynb | 1 | 18627 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Friction"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Experimental friction factors"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"from fluids.friction import friction_factor, oregon_Res, oregon_fd_smooth\n",
"import matplotlib.pyplot as plt\n",
"\n",
"Res = np.logspace(np.log10(oregon_Res[0]), np.log10(oregon_Res[-1]), 500)\n",
"fds_calc = [friction_factor(Re) for Re in Res]\n",
"plt.loglog(oregon_Res, oregon_fd_smooth, 'x', label='Oregon Data')\n",
"plt.loglog(Res, fds_calc, label='Colebrook')\n",
"plt.xlabel('Reynolds number')\n",
"plt.ylabel('Darcy friction factor')\n",
"plt.title(\"Experimental friction factor data for smooth pipe\")\n",
"plt.legend()\n",
"plt.plot()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[11.21, 20.22, 29.28, 43.19, 57.73, 64.58, 86.05, 113.3, 135.3, 157.5, 179.4, 206.4, 228.0, 270.9, 315.2, 358.9, 402.9, 450.2, 522.5, 583.1, 671.8, 789.8, 891.0, 1013.0, 1197.0, 1300.0, 1390.0, 1669.0, 1994.0, 2227.0, 2554.0, 2868.0, 2903.0, 2926.0, 2955.0, 2991.0, 2997.0, 3047.0, 3080.0, 3264.0, 3980.0, 4835.0, 5959.0, 8162.0, 10900.0, 13650.0, 18990.0, 29430.0, 40850.0, 59220.0, 84760.0, 120000.0, 176000.0, 237700.0, 298200.0, 467800.0, 587500.0, 824200.0, 1050000.0]\n",
"[5.537, 3.492, 2.329, 1.523, 1.173, 0.9863, 0.7826, 0.5709, 0.4815, 0.4182, 0.3655, 0.3237, 0.2884, 0.2433, 0.2077, 0.1834, 0.1656, 0.1475, 0.1245, 0.1126, 0.09917, 0.08501, 0.07722, 0.06707, 0.0588, 0.05328, 0.04815, 0.04304, 0.03739, 0.03405, 0.03091, 0.02804, 0.03182, 0.03846, 0.03363, 0.04124, 0.035, 0.03875, 0.04285, 0.0426, 0.03995, 0.03797, 0.0361, 0.03364, 0.03088, 0.02903, 0.0267, 0.02386, 0.02086, 0.02, 0.01805, 0.01686, 0.01594, 0.01511, 0.01462, 0.01365, 0.01313, 0.01244, 0.01198]\n"
]
}
],
"source": [
"print(oregon_Res)\n",
"print(oregon_fd_smooth)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Roughness data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'Brass': 1.52e-06,\n",
" 'Lead': 1.52e-06,\n",
" 'Glass': 1.52e-06,\n",
" 'Steel': 1.52e-06,\n",
" 'Asphalted cast iron': 0.000122,\n",
" 'Galvanized iron': 0.000152,\n",
" 'Cast iron': 0.000259,\n",
" 'Wood stave': 0.000183,\n",
" 'Rough wood stave': 0.000914,\n",
" 'Concrete': 0.000305,\n",
" 'Rough concrete': 0.00305,\n",
" 'Riveted steel': 0.000914,\n",
" 'Rough riveted steel': 0.00914}"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from fluids.friction import _roughness\n",
"# Material from Perry's handbook; roughness in meters.\n",
"_roughness"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Really good table from Idelʹchik, I. E, and A. S Ginevskiĭ. Handbook of Hydraulic \n",
"Resistance. Redding, CT: Begell House, 2007.**"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'Rough channels in rock, Blast-hewed, little jointing': (0.1, 0.14, None),\n",
" 'Rough channels in rock, Blast-hewed, substantial jointing': (0.13,\n",
" 0.5,\n",
" None),\n",
" 'Rough channels in rock, Roughly cut or very uneven surface': (0.5,\n",
" 1.5,\n",
" None),\n",
" 'Unlined tunnels, Rocks, gneiss, diameter 3-13.5 m': (0.3, 0.7, None),\n",
" 'Unlined tunnels, Rocks, granite, diameter 3-9 m': (0.2, 0.7, None),\n",
" 'Unlined tunnels, Shale, diameter, diameter 9-12 m': (0.25, 0.65, None),\n",
" 'Unlined tunnels, Shale, quartz, quartzile, diameter 7-10 m': (0.2,\n",
" 0.6,\n",
" None),\n",
" 'Unlined tunnels, Shale, sedimentary, diameter 4-7 m': (None, None, 0.4),\n",
" 'Unlined tunnels, Shale, nephrite bearing, diameter 3-8 m': (None, None, 0.2),\n",
" 'Wood tubes, Boards, thoroughly dressed': (None, None, 0.00015),\n",
" 'Wood tubes, Boards, well dressed': (None, None, 0.0003),\n",
" 'Wood tubes, Boards, undressed but fitted': (None, None, 0.0007),\n",
" 'Wood tubes, Boards, undressed': (None, None, 0.001),\n",
" 'Wood tubes, Staved': (None, None, 0.0006),\n",
" 'Plywood tubes, Birch plywood, transverse grain, good quality': (None,\n",
" None,\n",
" 0.00012),\n",
" 'Plywood tubes, Birch plywood, longitudal grain, good quality': (3e-05,\n",
" 5e-05,\n",
" None),\n",
" 'Glass tubes, Glass': (1.5e-06, 1e-05, None),\n",
" 'Concrete water conduits, no finish, New and finished with plater; excellent manufacture (joints aligned, prime coated and smoothed)': (5e-05,\n",
" 0.00015,\n",
" None),\n",
" 'Concrete water conduits, no finish, Used and corroded; with a wavy surface and wood framework': (0.001,\n",
" 0.004,\n",
" None),\n",
" 'Concrete water conduits, no finish, Old, poor fitting and manufacture; with an overgrown surface and deposits of sand and gravel': (0.001,\n",
" 0.004,\n",
" None),\n",
" 'Concrete water conduits, no finish, Very old; damaged surface, very overgrown': (0.005,\n",
" None,\n",
" None),\n",
" 'Concrete water conduits, no finish, Water conduit, finished with smoothed plaster': (0.005,\n",
" None,\n",
" None),\n",
" 'Concrete water conduits, no finish, New, very well manufactured, hand smoothed, prime-coated joints': (0.0001,\n",
" 0.0002,\n",
" None),\n",
" 'Concrete water conduits, no finish, Hand-smoothed cement finish and smoothed joints': (0.00015,\n",
" 0.00035,\n",
" None),\n",
" 'Concrete water conduits, no finish, Used, no deposits, moderately smooth, steel or wooden casing, joints prime coated but not smoothed': (0.0003,\n",
" 0.0006,\n",
" None),\n",
" 'Concrete water conduits, no finish, Used, prefabricated monoliths, cement plaster (wood floated), rough joints': (0.0005,\n",
" 0.001,\n",
" None),\n",
" 'Concrete water conduits, no finish, Conduits for water, sprayed surface of concrete': (0.0005,\n",
" 0.001,\n",
" None),\n",
" 'Concrete water conduits, no finish, Smoothed air-placed, either sprayed concrete or concrete on more concrete': (0.006,\n",
" 0.017,\n",
" None),\n",
" 'Concrete water conduits, no finish, Brushed air-placed, either sprayed concrete or concrete on more concrete': (None,\n",
" None,\n",
" 0.0023),\n",
" 'Concrete water conduits, no finish, Non-smoothed air-placed, either sprayed concrete or concrete on more concrete': (0.003,\n",
" 0.006,\n",
" None),\n",
" 'Reinforced concrete tubes, New': (0.00025, 0.00034, None),\n",
" 'Reinforced concrete tubes, Nonprocessed': (0.0025, None, None),\n",
" 'Asbestos cement tubes, New': (5e-05, 0.0001, None),\n",
" 'Asbestos cement tubes, Average': (0.0006, None, None),\n",
" 'Cement tubes, Smoothed': (0.0003, 0.0008, None),\n",
" 'Cement tubes, Non processed': (0.001, 0.002, None),\n",
" 'Cement tubes, Joints, non smoothed': (0.0019, 0.0064, None),\n",
" 'Cement-mortar plaster channels, Plaster, cement, smoothed joints and protrusions, and a casing': (5e-05,\n",
" 0.00022,\n",
" None),\n",
" 'Cement-mortar plaster channels, Steel trowled': (None, None, 0.0005),\n",
" 'Other, Plaster over a screen': (0.01, 0.015, None),\n",
" 'Other, Salt-glazed ceramic': (None, None, 0.0014),\n",
" 'Other, Slag-concrete': (None, None, 0.0015),\n",
" 'Other, Slag and alabaster-filling': (0.001, 0.0015, None),\n",
" 'Seamless tubes made from brass, copper, lead, aluminum, Commercially smooth': (1.5e-06,\n",
" 1e-05,\n",
" None),\n",
" 'Seamless steel tubes, New and unused': (2e-05, 0.0001, None),\n",
" 'Seamless steel tubes, Cleaned, following years of use': (None, 4e-05, None),\n",
" 'Seamless steel tubes, Bituminized': (None, 4e-05, None),\n",
" 'Seamless steel tubes, Heating systems piping; either superheated steam pipes, or just water pipes of systems with deaerators and chemical treatment': (None,\n",
" None,\n",
" 0.0001),\n",
" 'Seamless steel tubes, Following one year as a gas pipeline': (None,\n",
" None,\n",
" 0.00012),\n",
" 'Seamless steel tubes, Following multiple year as a gas pipeline': (4e-05,\n",
" 0.0002,\n",
" None),\n",
" 'Seamless steel tubes, Casings in gas wells, different conditions, several years of use': (6e-05,\n",
" 0.00022,\n",
" None),\n",
" 'Seamless steel tubes, Heating systems, saturated steam ducts or water pipes (with minor water leakage < 0.5%, and balance water deaerated)': (None,\n",
" None,\n",
" 0.0002),\n",
" 'Seamless steel tubes, Water heating system pipelines, any source': (None,\n",
" None,\n",
" 0.0002),\n",
" 'Seamless steel tubes, Oil pipelines, intermediate operating conditions ': (None,\n",
" None,\n",
" 0.0002),\n",
" 'Seamless steel tubes, Corroded, moderately ': (None, None, 0.0004),\n",
" 'Seamless steel tubes, Scale, small depositions only ': (None, None, 0.0004),\n",
" 'Seamless steel tubes, Condensate pipes in open systems or periodically operated steam pipelines': (None,\n",
" None,\n",
" 0.0005),\n",
" 'Seamless steel tubes, Compressed air piping': (None, None, 0.0008),\n",
" 'Seamless steel tubes, Following multiple years of operation, generally corroded or with small amounts of scale': (0.00015,\n",
" 0.001,\n",
" None),\n",
" 'Seamless steel tubes, Water heating piping without deaeration but with chemical treatment of water; leakage up to 3%; or condensate piping operated periodically': (None,\n",
" None,\n",
" 0.001),\n",
" 'Seamless steel tubes, Used water piping': (0.0012, 0.0015, None),\n",
" 'Seamless steel tubes, Poor condition': (0.005, None, None),\n",
" 'Welded steel tubes, Good condition': (4e-05, 0.0001, None),\n",
" 'Welded steel tubes, New and covered with bitumen': (None, None, 5e-05),\n",
" 'Welded steel tubes, Used and covered with partially dissolved bitumen; corroded': (None,\n",
" None,\n",
" 0.0001),\n",
" 'Welded steel tubes, Used, suffering general corrosion': (None,\n",
" None,\n",
" 0.00015),\n",
" 'Welded steel tubes, Surface looks like new, 10 mm lacquer inside, even joints': (0.0003,\n",
" 0.0004,\n",
" None),\n",
" 'Welded steel tubes, Used Gas mains': (None, None, 0.0005),\n",
" 'Welded steel tubes, Double or simple transverse riveted joints; with or without lacquer; without corrosion': (0.0006,\n",
" 0.0007,\n",
" None),\n",
" 'Welded steel tubes, Lacquered inside but rusted': (0.00095, 0.001, None),\n",
" 'Welded steel tubes, Gas mains, many years of use, with layered deposits': (None,\n",
" None,\n",
" 0.0011),\n",
" 'Welded steel tubes, Non-corroded and with double transverse riveted joints': (0.0012,\n",
" 0.0015,\n",
" None),\n",
" 'Welded steel tubes, Small deposits': (None, None, 0.0015),\n",
" 'Welded steel tubes, Heavily corroded and with double transverse riveted joints': (None,\n",
" None,\n",
" 0.002),\n",
" 'Welded steel tubes, Appreciable deposits': (0.002, 0.004, None),\n",
" 'Welded steel tubes, Gas mains, many years of use, deposits of resin/naphthalene': (None,\n",
" None,\n",
" 0.0024),\n",
" 'Welded steel tubes, Poor condition': (0.005, None, None),\n",
" 'Riveted steel tubes, Riveted laterally and longitudinally with one line; lacquered on the inside': (0.0003,\n",
" 0.0004,\n",
" None),\n",
" 'Riveted steel tubes, Riveted laterally and longitudinally with two lines; with or without lacquer on the inside and without corrosion': (0.0006,\n",
" 0.0007,\n",
" None),\n",
" 'Riveted steel tubes, Riveted laterally with one line and longitudinally with two lines; thickly lacquered or torred on the inside': (0.0012,\n",
" 0.0014,\n",
" None),\n",
" 'Riveted steel tubes, Riveted longitudinally with six lines, after extensive use': (None,\n",
" None,\n",
" 0.002),\n",
" 'Riveted steel tubes, Riveted laterally with four line and longitudinally with six lines; overlapping joints inside': (None,\n",
" None,\n",
" 0.004),\n",
" 'Riveted steel tubes, Extremely poor surface; overlapping and uneven joints': (0.005,\n",
" None,\n",
" None),\n",
" 'Roofing steel sheets, Oiled': (0.00015, 0.0011, None),\n",
" 'Roofing steel sheets, Not Oiled': (2e-05, 4e-05, None),\n",
" 'Galzanized steel tubes, Bright galvanization; new': (7e-05, 0.0001, None),\n",
" 'Galzanized steel tubes, Ordinary galvanization': (0.0001, 0.00015, None),\n",
" 'Galzanized sheet steel, New': (None, None, 0.00015),\n",
" 'Galzanized sheet steel, Used previously for water': (None, None, 0.00018),\n",
" 'Steel tubes, Glass enamel coat': (1e-06, 1e-05, None),\n",
" 'Steel tubes, New': (0.00025, 0.001, None),\n",
" 'Cast-iron tubes, New, bituminized': (0.0001, 0.00015, None),\n",
" 'Cast-iron tubes, Coated with asphalt': (0.00012, 0.0003, None),\n",
" 'Cast-iron tubes, Used water pipelines': (None, None, 0.0014),\n",
" 'Cast-iron tubes, Used and corroded': (0.001, 0.0015, None),\n",
" 'Cast-iron tubes, Deposits visible': (0.001, 0.0015, None),\n",
" 'Cast-iron tubes, Substantial deposits': (0.002, 0.004, None),\n",
" 'Cast-iron tubes, Cleaned after extensive use': (0.0003, 0.0015, None),\n",
" 'Cast-iron tubes, Severely corroded': (None, 0.003, None),\n",
" 'Steel water conduits in generating stations, New, clean, seamless (without joints), well fitted': (1.5e-05,\n",
" 4e-05,\n",
" None),\n",
" 'Steel water conduits in generating stations, New, clean, welded lengthwise and well fitted': (1.2e-05,\n",
" 3e-05,\n",
" None),\n",
" 'Steel water conduits in generating stations, New, clean, welded lengthwise and well fitted, with transverse welded joints': (8e-05,\n",
" 0.00017,\n",
" None),\n",
" 'Steel water conduits in generating stations, New, clean, coated, bituminized when manufactured': (1.4e-05,\n",
" 1.8e-05,\n",
" None),\n",
" 'Steel water conduits in generating stations, New, clean, coated, bituminized when manufactured, with transverse welded joints': (0.0002,\n",
" 0.0006,\n",
" None),\n",
" 'Steel water conduits in generating stations, New, clean, coated, galvanized': (0.0001,\n",
" 0.0002,\n",
" None),\n",
" 'Steel water conduits in generating stations, New, clean, coated, roughly galvanized': (0.0004,\n",
" 0.0007,\n",
" None),\n",
" 'Steel water conduits in generating stations, New, clean, coated, bituminized, curved': (0.0001,\n",
" 0.0014,\n",
" None),\n",
" 'Steel water conduits in generating stations, Used, clean, slight corrosion': (0.0001,\n",
" 0.0003,\n",
" None),\n",
" 'Steel water conduits in generating stations, Used, clean, moderate corrosion or slight deposits': (0.0003,\n",
" 0.0007,\n",
" None),\n",
" 'Steel water conduits in generating stations, Used, clean, severe corrosion': (0.0008,\n",
" 0.0015,\n",
" None),\n",
" 'Steel water conduits in generating stations, Used, clean, previously cleaned of either deposits or rust': (0.00015,\n",
" 0.0002,\n",
" None),\n",
" 'Used steel water conduits in generating stations, Used, all welded, <2 years use, no deposits': (0.00012,\n",
" 0.00024,\n",
" None),\n",
" 'Used steel water conduits in generating stations, Used, all welded, <20 years use, no deposits': (0.0006,\n",
" 0.005,\n",
" None),\n",
" 'Used steel water conduits in generating stations, Used, iron-bacterial corrosion': (0.003,\n",
" 0.004,\n",
" None),\n",
" 'Used steel water conduits in generating stations, Used, heavy corrosion, or with incrustation (deposit 1.5 - 9 mm deep)': (0.003,\n",
" 0.005,\n",
" None),\n",
" 'Used steel water conduits in generating stations, Used, heavy corrosion, or with incrustation (deposit 3 - 25 mm deep)': (0.006,\n",
" 0.0065,\n",
" None),\n",
" 'Used steel water conduits in generating stations, Used, inside coating, bituminized, < 2 years use': (0.0001,\n",
" 0.00035,\n",
" None)}"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from fluids.friction import HHR_roughness\n",
"HHR_roughness"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.5"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| mit |
RickBahague/Ambisyon2040_NEDA | Ambisyon 2040 Data Transformations.ipynb | 1 | 89235 | {
"cells": [
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import openpyxl\n",
"import xlrd\n",
"import os\n",
"%run ./AmbisyonETL.py"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data_path = \"./data/\""
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"['.DS_Store',\n",
" 'Ability-to-Pay-for-Medical-Expenses.xlsx',\n",
" 'ability-to-recover-from-unexpected-expenses.xls',\n",
" 'Absence-of-Constraints-in-different-aspects-of-life-SAVINGS-Confident.xlsx',\n",
" 'agreement-on-statements-on-corruption-in-local-offices-a-LOT.xls',\n",
" 'Agreement-on-Statements-on-Corruption-in-Local-Offices-ALL.xls',\n",
" 'Agreement-on-Statements-on-Corruption-in-Local-Offices-FEW.xls',\n",
" 'Agreement-on-Statements-on-Corruption-in-Local-Offices-NONE.xls',\n",
" 'Agreement-on-Statements-on-Corruption-in-National-Offices-A-LOT.xls',\n",
" 'Agreement-on-Statements-on-Corruption-in-National-Offices-ALL.xls',\n",
" 'Agreement-on-Statements-on-Corruption-in-National-Offices-FEW.xls',\n",
" 'Agreement-on-Statements-on-Corruption-in-National-Offices-NONE.xls',\n",
" 'Agreement-on-Statements-on-Employment-Agree.xls',\n",
" 'Agreement-on-Statements-on-Employment-Agree.xlsx',\n",
" 'Agreement-on-Statements-on-Employment-Disagree.xls',\n",
" 'Agreement-on-Statements-on-Employment-Disagree.xlsx',\n",
" 'Agreement-on-Statements-on-Governance-Agree.xls',\n",
" 'Agreement-on-Statements-on-Governance-Disagree.xls',\n",
" 'Agreement-on-Statements-on-Justice-Agree.xls',\n",
" 'Agreement-on-Statements-on-Justice-Disagree.xls',\n",
" 'Confidence-Achieving-Personal-Aspirations.xlsx',\n",
" 'Confidence-Family-Good-Health.xlsx',\n",
" 'Confidence-in-Achieving-Desired-Life-Status.xlsx',\n",
" 'Confidence-on-Ability-to-Buy-a-House.xlsx',\n",
" 'Confidence-on-Ability-to-Send-Children-to-School-Confident.xls',\n",
" 'Confidence-on-Ability-to-Send-Children-to-School-Not-Confident.xls',\n",
" 'Confidence-that-Filipinos-Will-Have-High-Standard-of-Living.xlsx',\n",
" 'Constraints-to-Education-BIG-HINDRANCE.xls',\n",
" 'Constraints-to-Education-NOT-HINDRANCE.xls',\n",
" 'Constraints-to-Education-SMALL-HINDRANCE.xls',\n",
" 'Current-Constraints-to-Saving-Money-BIG-HINDRANCE.xls',\n",
" 'Current-Constraints-to-Saving-Money-NOT-HINDRANCE.xls',\n",
" 'Current-Constraints-to-Saving-Money-SMALL-HINDRANCE.xls',\n",
" 'current-level-of-educationS5-educational-attainment.xlsx',\n",
" 'current-life-status.xls',\n",
" 'Current-Savings.xls',\n",
" 'Desired-Life-Status.xlsx',\n",
" 'Desired-Occupation.xlsx',\n",
" 'Educational-Attainment.xls',\n",
" 'Fairness-of-Treatment-from-Gov-Agencies-Fair.xls',\n",
" 'Fairness-of-Treatment-from-Gov-Agencies-Unfair.xls',\n",
" 'Fear-of-Insurgencies.xls',\n",
" 'Ideas-of-Life-Status-DWELLING-HOUSE.xlsx',\n",
" 'Ideas-of-Life-Status-EDUCATION-OF-CHILDREN.xlsx',\n",
" 'Ideas-of-Life-Status-FINANCES.xlsx',\n",
" 'Ideas-of-Life-Status-OCCUPATION.xlsx',\n",
" 'Ideas-of-Life-Status-VACATION.xlsx',\n",
" 'Ideas-of-Life-Status-VEHICLETRANSPORTATION.xlsx',\n",
" 'Importance-of-Education-for-Children.xlsx',\n",
" 'Importance-of-Peace-and-Sec-for-National-Development.xlsx',\n",
" 'Importance-of-Peace-and-Sec-for-Personal-Prosperity.xlsx',\n",
" 'Important-Economic-Attainment-Rank-1.xlsx',\n",
" 'Important-Gov-Services-for-Better-Future-Rank-1.xlsx',\n",
" 'Incidence-of-Saving-in-the-Past-Year.xls',\n",
" 'Level-of-education-currently-in.xls',\n",
" 'Outlook-for-Children-They-will-finish-their-studies.xlsx',\n",
" 'perceived-income-level-scale.xls',\n",
" 'Preferred-Community-to-Live-in.xlsx',\n",
" 'Preferred-Dwelling-type.xlsx',\n",
" 'Preferred-Type-of-Occupation-SCALE-02.xlsx',\n",
" 'Preferred-Type-of-Occupation-SCALE-03.xlsx',\n",
" 'Preferred-Type-of-Occupation-SCALE.xlsx',\n",
" 'Preferred-Work-Location.xlsx',\n",
" 'Presence-of-Opportunities-for-Employment-or-Business.xls',\n",
" 'satisfaction-on-health-offices-in-the-community-NOT-SATISFIED.xls',\n",
" 'satisfaction-on-health-offices-in-the-community-SATISFIED.xls',\n",
" 'Satisfaction-on-Schools-in-Community-DISSATISFIED.xls',\n",
" 'Satisfaction-on-Schools-in-Community-SATISFIED.xls',\n",
" 'Source-of-Financing-in-Buying-a-House.xlsx',\n",
" 'Support-Upon-Retirement.xlsx',\n",
" 'Whether-can-have-Savings-for-the-family.xlsx',\n",
" 'whether-have-access-to-hospital.xls',\n",
" 'Whether-Have-Job-Security.xls',\n",
" 'Whether-Have-Plan-to-Have-a-Business.xls',\n",
" 'Whether-Owns-House-Currently-Live-in.xls',\n",
" 'whether-worried-about-hospital-bills.xls',\n",
" 'Whether-Worried-on-Having-Job-or-Source-of-Income.xls']"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_dir = os.listdir(data_path+\"raw\")\n",
"list_dir"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data = pd.DataFrame()\n",
"fname_error = dict()\n",
"for fname in list_dir:\n",
" temp = pd.DataFrame()\n",
" if(fname[-5:]==\".xlsx\"):\n",
" try:\n",
" temp = get_data(fname,data_path)\n",
" temp.to_csv(data_path+\"csv/\"+fname[:-5]+\".csv\",sep=\"|\",mode=\"a\",index=False)\n",
" temp.to_csv(data_path+\"consolidated/\"+\"Ambisyon2040SurveyDataConsolidated.csv\",sep=\"|\",mode=\"a\",index=False)\n",
" data = data.append(temp)\n",
" except Exception as e:\n",
" fname_error[fname] = e\n",
" elif(fname[-5:]!=\".xlsx\"):\n",
" try:\n",
" temp = get_data_xls(fname,data_path)\n",
" temp.to_csv(data_path+\"csv/\"+fname[:-4]+\".csv\",sep=\"|\",mode=\"a\",index=False)\n",
" temp.to_csv(data_path+\"consolidated/\"+\"Ambisyon2040SurveyDataConsolidated.csv\",sep=\"|\",mode=\"a\",index=False)\n",
" data = data.append(temp)\n",
" except Exception as e:\n",
" fname_error[fname] = e"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"array(['10. Ability to Pay for Medical Expenses',\n",
" '40. Ability to Recover from Unexpected Expenses',\n",
" '4. Absence of Constraints in different aspects of life - Confident',\n",
" '61. Agreement on Statements on Corruption in Local Offices - A LOT',\n",
" '61. Agreement on Statements on Corruption in Local Offices - ALL',\n",
" '61. Agreement on Statements on Corruption in Local Offices - FEW',\n",
" '61. Agreement on Statements on Corruption in Local Offices - NONE',\n",
" '62. Agreement on Statements on Corruption in National Offices - A LOT',\n",
" '62. Agreement on Statements on Corruption in National Offices - ALL',\n",
" '62. Agreement on Statements on Corruption in National Offices - FEW',\n",
" '62. Agreement on Statements on Corruption in National Offices - NONE',\n",
" '51. Agreement on Statements on Employment - Agree',\n",
" '15. Agreement on Statements on Employment - Agree',\n",
" '51. Agreement on Statements on Employment - Disagree',\n",
" '15. Agreement on Statements on Employment - Disagree',\n",
" '60. Agreement on Statements on Governance - Agree',\n",
" '60. Agreement on Statements on Governance - Disagree',\n",
" '57. Agreement on Statements on Justice - Agree',\n",
" '57. Agreement on Statements on Justice - Disagree',\n",
" '6. Confidence Achieving Personal Aspirations',\n",
" '9. Confidence Family Good Health',\n",
" '7. Confidence in Achieving Desired Life Status',\n",
" '26. Confidence on Ability to Buy a House',\n",
" '48. Confidence on Ability to Send Children to School - Confident',\n",
" '48. Confidence on Ability to Send Children to School - Not Confident',\n",
" '50. Constraints to Education: BIG HINDRANCE',\n",
" '50. Constraints to Education: NOT HINDRANCE',\n",
" '50. Constraints to Education: SMALL HINDRANCE',\n",
" '43. Current Constraints to Saving Money: BIG HINDRANCE',\n",
" '43. Current Constraints to Saving Money: NOT HINDRANCE',\n",
" '43. Current Constraints to Saving Money: SMALL HINDRANCE',\n",
" '38. Current Life Status', '42. Current Savings',\n",
" '1. Desired Life Status', '16. Desired Occupation',\n",
" 'S5. Educational Attainment',\n",
" '58. Fairness of Treatment from Gov Agencies - Fair',\n",
" '58. Fairness of Treatment from Gov Agencies - Unfair',\n",
" '63. Fear of Insurgencies',\n",
" '2. Ideas of Life Status - DWELLING/ HOUSE',\n",
" '2. Ideas of Life Status - EDUCATION OF CHILDREN',\n",
" '2. Ideas of Life Status - FINANCES',\n",
" '2. Ideas of Life Status - OCCUPATION',\n",
" '2. Ideas of Life Status - VACATION',\n",
" '2. Ideas of Life Status - VEHICLE/TRANSPORTATION',\n",
" '13. Importance of Education for Children',\n",
" '35. Importance of Peace and Sec for National Development',\n",
" '30. Important Economic Attainment: Rank 1',\n",
" '33. Important Gov Services for Better Future: Rank 1',\n",
" '41. Incidence of Saving in the Past Year',\n",
" 'S4. Level of education currently in',\n",
" '5. Outlook for Children: They will finish their studies.',\n",
" '39. Perceived Income Level ---- SCALE',\n",
" '17. Preferred Type of Occupation ---- SCALE',\n",
" '52. Presence of Opportunities for Employment or Business',\n",
" '47. Satisfaction on Health Offices in the Community: NOT SATISFIED',\n",
" '47. Satisfaction on Health Offices in the Community: SATISFIED',\n",
" '49. Satisfaction on Schools in Community: DISSATISFIED',\n",
" '49. Satisfaction on Schools in Community: SATISFIED',\n",
" '18. Support Upon Retirement',\n",
" '8. Whether can have Savings for the family',\n",
" '46. Whether Have Access to Hospital',\n",
" '54. Whether Have Job Security',\n",
" '55. Whether Have Plan to Have a Business',\n",
" '44. Whether Owns House Currently Live in',\n",
" '45. Whether Worried About Hospital Bills',\n",
" '53. Whether Worried on Having Job or Source of Income'], dtype=object)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"questions = pd.unique(data['question'])\n",
"questions"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"77"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(list_dir)"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"67"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(questions)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are not able to read the following files:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'.DS_Store': xlrd.biffh.XLRDError(\"Unsupported format, or corrupt file: Expected BOF record; found b'\\\\x00\\\\x00\\\\x00\\\\x01Bud1'\"),\n",
" 'Confidence-that-Filipinos-Will-Have-High-Standard-of-Living.xlsx': ValueError('Plan shapes are not aligned'),\n",
" 'Importance-of-Peace-and-Sec-for-Personal-Prosperity.xlsx': ValueError('Plan shapes are not aligned'),\n",
" 'Preferred-Community-to-Live-in.xlsx': ValueError('Plan shapes are not aligned'),\n",
" 'Preferred-Dwelling-type.xlsx': ValueError('Plan shapes are not aligned'),\n",
" 'Preferred-Work-Location.xlsx': ValueError('Plan shapes are not aligned'),\n",
" 'Source-of-Financing-in-Buying-a-House.xlsx': ValueError('Plan shapes are not aligned'),\n",
" 'current-level-of-educationS5-educational-attainment.xlsx': zipfile.BadZipFile('File is not a zip file')}"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"fname_error"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data.columns = ['Age 15-19', 'Age 20-29', 'Age 30-39', 'Age 40-50', 'SEC AB', \\\n",
" 'SEC ABC (NET)', 'SEC C1', 'SEC C2', 'SEC D', 'SEC E', \\\n",
" 'Gender Female', 'Gender Male', 'Work Status Not Working', 'Response', 'Locale Rural', 'Work Status Student',\n",
" 'TOTAL', 'Total', 'Local Urban', 'Work Status Working', 'question']"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data[['question','Response','Total','Age 15-19', 'Age 20-29', 'Age 30-39', \\\n",
" 'Age 40-50', 'SEC AB', 'SEC ABC (NET)', 'SEC C1', 'SEC C2', 'SEC D', \\\n",
" 'SEC E', 'Gender Female', 'Gender Male', 'Work Status Not Working', \\\n",
" 'Work Status Student','Work Status Working',\\\n",
" 'Local Urban', 'Locale Rural']].\\\n",
"to_csv(data_path+\"consolidated/Ambisyon2040SurveyDataConsolidatedB.csv\",sep=\"|\",index=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Final Consolidated CSV file: "
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df2 = pd.read_csv(data_path+\"consolidated/Ambisyon2040SurveyDataConsolidatedB.csv\",sep=\"|\")"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>question</th>\n",
" <th>Response</th>\n",
" <th>Total</th>\n",
" <th>Age 15-19</th>\n",
" <th>Age 20-29</th>\n",
" <th>Age 30-39</th>\n",
" <th>Age 40-50</th>\n",
" <th>SEC AB</th>\n",
" <th>SEC ABC (NET)</th>\n",
" <th>SEC C1</th>\n",
" <th>SEC C2</th>\n",
" <th>SEC D</th>\n",
" <th>SEC E</th>\n",
" <th>Gender Female</th>\n",
" <th>Gender Male</th>\n",
" <th>Work Status Not Working</th>\n",
" <th>Work Status Student</th>\n",
" <th>Work Status Working</th>\n",
" <th>Local Urban</th>\n",
" <th>Locale Rural</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>10. Ability to Pay for Medical Expenses</td>\n",
" <td>Unweighted</td>\n",
" <td>10000</td>\n",
" <td>1511</td>\n",
" <td>3057</td>\n",
" <td>2967</td>\n",
" <td>2465</td>\n",
" <td>60</td>\n",
" <td>2280</td>\n",
" <td>493</td>\n",
" <td>1727</td>\n",
" <td>4784</td>\n",
" <td>2936</td>\n",
" <td>4998</td>\n",
" <td>5002</td>\n",
" <td>4058</td>\n",
" <td>1015</td>\n",
" <td>4927</td>\n",
" <td>4620</td>\n",
" <td>5380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>10. Ability to Pay for Medical Expenses</td>\n",
" <td>Weighted</td>\n",
" <td>10000</td>\n",
" <td>1978</td>\n",
" <td>3211</td>\n",
" <td>2594</td>\n",
" <td>2217</td>\n",
" <td>60</td>\n",
" <td>2260</td>\n",
" <td>482</td>\n",
" <td>1717</td>\n",
" <td>4820</td>\n",
" <td>2921</td>\n",
" <td>4968</td>\n",
" <td>5032</td>\n",
" <td>4042</td>\n",
" <td>1305</td>\n",
" <td>4653</td>\n",
" <td>4569</td>\n",
" <td>5431</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>10. Ability to Pay for Medical Expenses</td>\n",
" <td>NaN</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>10. Ability to Pay for Medical Expenses</td>\n",
" <td>CONFIDENT</td>\n",
" <td>78.6</td>\n",
" <td>81.7</td>\n",
" <td>81</td>\n",
" <td>77.5</td>\n",
" <td>73.8</td>\n",
" <td>98.3</td>\n",
" <td>87.6</td>\n",
" <td>89.8</td>\n",
" <td>86.6</td>\n",
" <td>79.7</td>\n",
" <td>70</td>\n",
" <td>77.8</td>\n",
" <td>79.5</td>\n",
" <td>76.9</td>\n",
" <td>84.6</td>\n",
" <td>78.5</td>\n",
" <td>81.4</td>\n",
" <td>76.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10. Ability to Pay for Medical Expenses</td>\n",
" <td>Definitely confident (4.00)</td>\n",
" <td>28.4</td>\n",
" <td>30.9</td>\n",
" <td>29.2</td>\n",
" <td>26.5</td>\n",
" <td>27.3</td>\n",
" <td>63.2</td>\n",
" <td>36.7</td>\n",
" <td>39.3</td>\n",
" <td>35</td>\n",
" <td>28.3</td>\n",
" <td>22.2</td>\n",
" <td>28</td>\n",
" <td>28.8</td>\n",
" <td>26.4</td>\n",
" <td>33.6</td>\n",
" <td>28.7</td>\n",
" <td>28.2</td>\n",
" <td>28.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>10. Ability to Pay for Medical Expenses</td>\n",
" <td>Somewhat confident (3.00)</td>\n",
" <td>50.2</td>\n",
" <td>50.8</td>\n",
" <td>51.8</td>\n",
" <td>50.9</td>\n",
" <td>46.5</td>\n",
" <td>35.1</td>\n",
" <td>50.9</td>\n",
" <td>50.5</td>\n",
" <td>51.6</td>\n",
" <td>51.4</td>\n",
" <td>47.7</td>\n",
" <td>49.8</td>\n",
" <td>50.7</td>\n",
" <td>50.5</td>\n",
" <td>51</td>\n",
" <td>49.7</td>\n",
" <td>53.2</td>\n",
" <td>47.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>10. Ability to Pay for Medical Expenses</td>\n",
" <td>NOT CONFIDENT</td>\n",
" <td>20.6</td>\n",
" <td>17.3</td>\n",
" <td>18.5</td>\n",
" <td>21.6</td>\n",
" <td>25.3</td>\n",
" <td>1.7</td>\n",
" <td>11.7</td>\n",
" <td>10</td>\n",
" <td>12.5</td>\n",
" <td>19.6</td>\n",
" <td>29</td>\n",
" <td>21.5</td>\n",
" <td>19.6</td>\n",
" <td>22.2</td>\n",
" <td>15.1</td>\n",
" <td>20.6</td>\n",
" <td>17.9</td>\n",
" <td>22.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>10. Ability to Pay for Medical Expenses</td>\n",
" <td>Somewhat not confident (2.00)</td>\n",
" <td>15.6</td>\n",
" <td>14.5</td>\n",
" <td>13.9</td>\n",
" <td>16.4</td>\n",
" <td>18.3</td>\n",
" <td>1.7</td>\n",
" <td>9.1</td>\n",
" <td>8.6</td>\n",
" <td>9.5</td>\n",
" <td>15.4</td>\n",
" <td>21.1</td>\n",
" <td>16.4</td>\n",
" <td>14.9</td>\n",
" <td>16.9</td>\n",
" <td>12.8</td>\n",
" <td>15.3</td>\n",
" <td>14.3</td>\n",
" <td>16.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>10. Ability to Pay for Medical Expenses</td>\n",
" <td>Definitely not confident (1.00)</td>\n",
" <td>4.9</td>\n",
" <td>2.8</td>\n",
" <td>4.6</td>\n",
" <td>5.2</td>\n",
" <td>6.9</td>\n",
" <td>-</td>\n",
" <td>2.6</td>\n",
" <td>1.4</td>\n",
" <td>3</td>\n",
" <td>4.2</td>\n",
" <td>7.9</td>\n",
" <td>5.1</td>\n",
" <td>4.8</td>\n",
" <td>5.3</td>\n",
" <td>2.3</td>\n",
" <td>5.3</td>\n",
" <td>3.6</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10. Ability to Pay for Medical Expenses</td>\n",
" <td>REFUSED</td>\n",
" <td>-</td>\n",
" <td>*</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>*</td>\n",
" <td>-</td>\n",
" <td>*</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>10. Ability to Pay for Medical Expenses</td>\n",
" <td>DON’T KNOW</td>\n",
" <td>0.8</td>\n",
" <td>1</td>\n",
" <td>0.5</td>\n",
" <td>0.9</td>\n",
" <td>0.9</td>\n",
" <td>-</td>\n",
" <td>0.7</td>\n",
" <td>*</td>\n",
" <td>0.8</td>\n",
" <td>0.7</td>\n",
" <td>1</td>\n",
" <td>0.7</td>\n",
" <td>0.9</td>\n",
" <td>0.9</td>\n",
" <td>*</td>\n",
" <td>0.9</td>\n",
" <td>0.7</td>\n",
" <td>0.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>AGE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>SEC</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>GENDER</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>WORK STATUS</td>\n",
" <td>LOCALE</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>NaN</td>\n",
" <td>Total</td>\n",
" <td>15-19</td>\n",
" <td>20-29</td>\n",
" <td>30-39</td>\n",
" <td>40-50</td>\n",
" <td>AB</td>\n",
" <td>ABC (NET)</td>\n",
" <td>C1</td>\n",
" <td>C2</td>\n",
" <td>D</td>\n",
" <td>E</td>\n",
" <td>Female</td>\n",
" <td>Male</td>\n",
" <td>Not Working</td>\n",
" <td>Student</td>\n",
" <td>Working</td>\n",
" <td>Urban</td>\n",
" <td>Rural</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>BASE- Total inteviews:</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>Unweighted</td>\n",
" <td>10000.0</td>\n",
" <td>1511.0</td>\n",
" <td>3057.0</td>\n",
" <td>2967.0</td>\n",
" <td>2465.0</td>\n",
" <td>60.0</td>\n",
" <td>2280.0</td>\n",
" <td>493.0</td>\n",
" <td>1727.0</td>\n",
" <td>4784.0</td>\n",
" <td>2936.0</td>\n",
" <td>4998.0</td>\n",
" <td>5002.0</td>\n",
" <td>4058.0</td>\n",
" <td>1015.0</td>\n",
" <td>4927.0</td>\n",
" <td>4620.0</td>\n",
" <td>5380.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>Weighted</td>\n",
" <td>10000.0</td>\n",
" <td>1978.0</td>\n",
" <td>3211.0</td>\n",
" <td>2594.0</td>\n",
" <td>2217.0</td>\n",
" <td>60.0</td>\n",
" <td>2260.0</td>\n",
" <td>482.0</td>\n",
" <td>1717.0</td>\n",
" <td>4820.0</td>\n",
" <td>2921.0</td>\n",
" <td>4968.0</td>\n",
" <td>5032.0</td>\n",
" <td>4042.0</td>\n",
" <td>1305.0</td>\n",
" <td>4653.0</td>\n",
" <td>4569.0</td>\n",
" <td>5431.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>NaN</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>Has the capacity to recover from small expense...</td>\n",
" <td>65.9</td>\n",
" <td>64.3</td>\n",
" <td>65.3</td>\n",
" <td>67.0</td>\n",
" <td>67.0</td>\n",
" <td>35.5</td>\n",
" <td>62.3</td>\n",
" <td>54.4</td>\n",
" <td>65.4</td>\n",
" <td>69.7</td>\n",
" <td>62.5</td>\n",
" <td>66.1</td>\n",
" <td>65.8</td>\n",
" <td>66.3</td>\n",
" <td>64.0</td>\n",
" <td>66.2</td>\n",
" <td>65.5</td>\n",
" <td>66.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>Has the capacity to recover from big expenses</td>\n",
" <td>22.3</td>\n",
" <td>23.5</td>\n",
" <td>23.6</td>\n",
" <td>21.6</td>\n",
" <td>20.1</td>\n",
" <td>62.9</td>\n",
" <td>33.6</td>\n",
" <td>42.3</td>\n",
" <td>30.1</td>\n",
" <td>21.2</td>\n",
" <td>15.3</td>\n",
" <td>22.5</td>\n",
" <td>22.1</td>\n",
" <td>21.5</td>\n",
" <td>25.0</td>\n",
" <td>22.3</td>\n",
" <td>24.9</td>\n",
" <td>20.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>Has no capacity to recover even from small exp...</td>\n",
" <td>11.3</td>\n",
" <td>11.5</td>\n",
" <td>10.6</td>\n",
" <td>11.1</td>\n",
" <td>12.6</td>\n",
" <td>1.6</td>\n",
" <td>3.6</td>\n",
" <td>2.5</td>\n",
" <td>4.0</td>\n",
" <td>8.6</td>\n",
" <td>21.8</td>\n",
" <td>11.1</td>\n",
" <td>11.6</td>\n",
" <td>11.8</td>\n",
" <td>10.3</td>\n",
" <td>11.2</td>\n",
" <td>9.0</td>\n",
" <td>13.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>Refused (vol.)</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>*</td>\n",
" <td>-</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>40. Ability to Recover from Unexpected Expenses</td>\n",
" <td>Don't Know (vol.)</td>\n",
" <td>*</td>\n",
" <td>0.6</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>-</td>\n",
" <td>0.5</td>\n",
" <td>0.8</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>0.6</td>\n",
" <td>*</td>\n",
" <td>0.6</td>\n",
" <td>*</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>4. Absence of Constraints in different aspects...</td>\n",
" <td>Unweighted</td>\n",
" <td>10000</td>\n",
" <td>1511</td>\n",
" <td>3057</td>\n",
" <td>2967</td>\n",
" <td>2465</td>\n",
" <td>60</td>\n",
" <td>2280</td>\n",
" <td>493</td>\n",
" <td>1727</td>\n",
" <td>4784</td>\n",
" <td>2936</td>\n",
" <td>4998</td>\n",
" <td>5002</td>\n",
" <td>4058</td>\n",
" <td>1015</td>\n",
" <td>4927</td>\n",
" <td>4620</td>\n",
" <td>5380</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>4. Absence of Constraints in different aspects...</td>\n",
" <td>Weighted</td>\n",
" <td>10000</td>\n",
" <td>1978</td>\n",
" <td>3211</td>\n",
" <td>2594</td>\n",
" <td>2217</td>\n",
" <td>60</td>\n",
" <td>2260</td>\n",
" <td>482</td>\n",
" <td>1717</td>\n",
" <td>4820</td>\n",
" <td>2921</td>\n",
" <td>4968</td>\n",
" <td>5032</td>\n",
" <td>4042</td>\n",
" <td>1305</td>\n",
" <td>4653</td>\n",
" <td>4569</td>\n",
" <td>5431</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1035</th>\n",
" <td>45. Whether Worried About Hospital Bills</td>\n",
" <td>WORRY</td>\n",
" <td>15.0</td>\n",
" <td>15.6</td>\n",
" <td>15.3</td>\n",
" <td>14.7</td>\n",
" <td>14.3</td>\n",
" <td>39.5</td>\n",
" <td>22.6</td>\n",
" <td>31.5</td>\n",
" <td>19.6</td>\n",
" <td>13.9</td>\n",
" <td>10.8</td>\n",
" <td>14.7</td>\n",
" <td>15.2</td>\n",
" <td>14.4</td>\n",
" <td>15.0</td>\n",
" <td>15.4</td>\n",
" <td>18.5</td>\n",
" <td>12.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1036</th>\n",
" <td>45. Whether Worried About Hospital Bills</td>\n",
" <td>Definitely will worry (1.00)</td>\n",
" <td>58.5</td>\n",
" <td>56.9</td>\n",
" <td>57.9</td>\n",
" <td>58.8</td>\n",
" <td>60.5</td>\n",
" <td>34.6</td>\n",
" <td>48.7</td>\n",
" <td>39.0</td>\n",
" <td>52.0</td>\n",
" <td>59.7</td>\n",
" <td>64.2</td>\n",
" <td>59.8</td>\n",
" <td>57.2</td>\n",
" <td>59.5</td>\n",
" <td>57.2</td>\n",
" <td>58.0</td>\n",
" <td>52.6</td>\n",
" <td>63.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1037</th>\n",
" <td>45. Whether Worried About Hospital Bills</td>\n",
" <td>Will worry (2.00)</td>\n",
" <td>26.4</td>\n",
" <td>27.4</td>\n",
" <td>26.5</td>\n",
" <td>26.4</td>\n",
" <td>25.2</td>\n",
" <td>25.9</td>\n",
" <td>28.5</td>\n",
" <td>29.3</td>\n",
" <td>28.4</td>\n",
" <td>26.1</td>\n",
" <td>25.0</td>\n",
" <td>25.2</td>\n",
" <td>27.5</td>\n",
" <td>25.8</td>\n",
" <td>27.8</td>\n",
" <td>26.4</td>\n",
" <td>28.7</td>\n",
" <td>24.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1038</th>\n",
" <td>45. Whether Worried About Hospital Bills</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1039</th>\n",
" <td>45. Whether Worried About Hospital Bills</td>\n",
" <td>WON'T WORRY</td>\n",
" <td>84.9</td>\n",
" <td>84.4</td>\n",
" <td>84.4</td>\n",
" <td>85.1</td>\n",
" <td>85.7</td>\n",
" <td>60.5</td>\n",
" <td>77.3</td>\n",
" <td>68.3</td>\n",
" <td>80.4</td>\n",
" <td>85.8</td>\n",
" <td>89.2</td>\n",
" <td>85.1</td>\n",
" <td>84.7</td>\n",
" <td>85.3</td>\n",
" <td>85.0</td>\n",
" <td>84.4</td>\n",
" <td>81.3</td>\n",
" <td>87.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1040</th>\n",
" <td>45. Whether Worried About Hospital Bills</td>\n",
" <td>Will worry a little (3.00)</td>\n",
" <td>13.4</td>\n",
" <td>13.8</td>\n",
" <td>13.9</td>\n",
" <td>13.3</td>\n",
" <td>12.6</td>\n",
" <td>29.2</td>\n",
" <td>19.7</td>\n",
" <td>27.5</td>\n",
" <td>17.2</td>\n",
" <td>12.6</td>\n",
" <td>9.8</td>\n",
" <td>13.2</td>\n",
" <td>13.6</td>\n",
" <td>13.1</td>\n",
" <td>13.3</td>\n",
" <td>13.7</td>\n",
" <td>16.6</td>\n",
" <td>10.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1041</th>\n",
" <td>45. Whether Worried About Hospital Bills</td>\n",
" <td>Will not worry (4.00)</td>\n",
" <td>1.6</td>\n",
" <td>1.8</td>\n",
" <td>1.4</td>\n",
" <td>1.4</td>\n",
" <td>1.7</td>\n",
" <td>10.3</td>\n",
" <td>2.9</td>\n",
" <td>4.0</td>\n",
" <td>2.4</td>\n",
" <td>1.3</td>\n",
" <td>0.9</td>\n",
" <td>1.5</td>\n",
" <td>1.6</td>\n",
" <td>1.3</td>\n",
" <td>1.7</td>\n",
" <td>1.7</td>\n",
" <td>1.9</td>\n",
" <td>1.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1042</th>\n",
" <td>45. Whether Worried About Hospital Bills</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1043</th>\n",
" <td>45. Whether Worried About Hospital Bills</td>\n",
" <td>REFUSED</td>\n",
" <td>*</td>\n",
" <td>-</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>-</td>\n",
" <td>*</td>\n",
" <td>-</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>-</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1044</th>\n",
" <td>45. Whether Worried About Hospital Bills</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1045</th>\n",
" <td>45. Whether Worried About Hospital Bills</td>\n",
" <td>DON’T KNOW</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>*</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1046</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>AGE</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>SEC</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>GENDER</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>WORK STATUS</td>\n",
" <td>LOCALE</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1047</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>NaN</td>\n",
" <td>Total</td>\n",
" <td>15-19</td>\n",
" <td>20-29</td>\n",
" <td>30-39</td>\n",
" <td>40-50</td>\n",
" <td>AB</td>\n",
" <td>ABC (NET)</td>\n",
" <td>C1</td>\n",
" <td>C2</td>\n",
" <td>D</td>\n",
" <td>E</td>\n",
" <td>Female</td>\n",
" <td>Male</td>\n",
" <td>Not Working</td>\n",
" <td>Student</td>\n",
" <td>Working</td>\n",
" <td>Urban</td>\n",
" <td>Rural</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1048</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>BASE- Total interviews:</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1049</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>Unweighted</td>\n",
" <td>10000.0</td>\n",
" <td>1511.0</td>\n",
" <td>3057.0</td>\n",
" <td>2967.0</td>\n",
" <td>2465.0</td>\n",
" <td>60.0</td>\n",
" <td>2280.0</td>\n",
" <td>493.0</td>\n",
" <td>1727.0</td>\n",
" <td>4784.0</td>\n",
" <td>2936.0</td>\n",
" <td>4998.0</td>\n",
" <td>5002.0</td>\n",
" <td>4058.0</td>\n",
" <td>1015.0</td>\n",
" <td>4927.0</td>\n",
" <td>4620.0</td>\n",
" <td>5380.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1050</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>Total</td>\n",
" <td>10000.0</td>\n",
" <td>1978.0</td>\n",
" <td>3211.0</td>\n",
" <td>2594.0</td>\n",
" <td>2217.0</td>\n",
" <td>60.0</td>\n",
" <td>2260.0</td>\n",
" <td>482.0</td>\n",
" <td>1717.0</td>\n",
" <td>4820.0</td>\n",
" <td>2921.0</td>\n",
" <td>4968.0</td>\n",
" <td>5032.0</td>\n",
" <td>4042.0</td>\n",
" <td>1305.0</td>\n",
" <td>4653.0</td>\n",
" <td>4569.0</td>\n",
" <td>5431.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1051</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1052</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>NaN</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" <td>%</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1053</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1054</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>I worry about my job or source of income alway...</td>\n",
" <td>36.6</td>\n",
" <td>31.1</td>\n",
" <td>35.8</td>\n",
" <td>37.4</td>\n",
" <td>41.6</td>\n",
" <td>21.7</td>\n",
" <td>29.2</td>\n",
" <td>26.6</td>\n",
" <td>30.2</td>\n",
" <td>34.7</td>\n",
" <td>45.4</td>\n",
" <td>37.1</td>\n",
" <td>36.1</td>\n",
" <td>38.8</td>\n",
" <td>28.9</td>\n",
" <td>36.8</td>\n",
" <td>30.8</td>\n",
" <td>41.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1055</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1056</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>I worry about my job or source of income often</td>\n",
" <td>30.0</td>\n",
" <td>29.6</td>\n",
" <td>30.3</td>\n",
" <td>31.0</td>\n",
" <td>28.8</td>\n",
" <td>18.9</td>\n",
" <td>24.1</td>\n",
" <td>18.6</td>\n",
" <td>25.8</td>\n",
" <td>33.1</td>\n",
" <td>29.6</td>\n",
" <td>29.7</td>\n",
" <td>30.3</td>\n",
" <td>31.1</td>\n",
" <td>26.5</td>\n",
" <td>30.1</td>\n",
" <td>30.5</td>\n",
" <td>29.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1057</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1058</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>I seldom worry about my job or source of income</td>\n",
" <td>25.1</td>\n",
" <td>26.6</td>\n",
" <td>26.2</td>\n",
" <td>24.2</td>\n",
" <td>23.1</td>\n",
" <td>25.9</td>\n",
" <td>33.3</td>\n",
" <td>39.7</td>\n",
" <td>31.8</td>\n",
" <td>24.0</td>\n",
" <td>20.4</td>\n",
" <td>25.0</td>\n",
" <td>25.2</td>\n",
" <td>23.2</td>\n",
" <td>29.6</td>\n",
" <td>25.4</td>\n",
" <td>27.5</td>\n",
" <td>23.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1059</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1060</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>I do not worry about my job or source of income</td>\n",
" <td>7.1</td>\n",
" <td>9.4</td>\n",
" <td>6.8</td>\n",
" <td>6.7</td>\n",
" <td>6.0</td>\n",
" <td>31.7</td>\n",
" <td>12.2</td>\n",
" <td>14.5</td>\n",
" <td>10.9</td>\n",
" <td>6.8</td>\n",
" <td>3.7</td>\n",
" <td>7.0</td>\n",
" <td>7.2</td>\n",
" <td>6.0</td>\n",
" <td>10.7</td>\n",
" <td>7.1</td>\n",
" <td>9.6</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1061</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1062</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>Refused (vol.)</td>\n",
" <td>0.8</td>\n",
" <td>1.5</td>\n",
" <td>0.7</td>\n",
" <td>0.7</td>\n",
" <td>*</td>\n",
" <td>1.7</td>\n",
" <td>0.6</td>\n",
" <td>*</td>\n",
" <td>0.6</td>\n",
" <td>1.0</td>\n",
" <td>*</td>\n",
" <td>0.8</td>\n",
" <td>0.8</td>\n",
" <td>0.6</td>\n",
" <td>1.8</td>\n",
" <td>0.6</td>\n",
" <td>0.9</td>\n",
" <td>0.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1063</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1064</th>\n",
" <td>53. Whether Worried on Having Job or Source of...</td>\n",
" <td>Don't Know (vol.)</td>\n",
" <td>*</td>\n",
" <td>1.9</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>-</td>\n",
" <td>0.6</td>\n",
" <td>*</td>\n",
" <td>0.7</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>*</td>\n",
" <td>2.4</td>\n",
" <td>-</td>\n",
" <td>0.6</td>\n",
" <td>*</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1065 rows × 20 columns</p>\n",
"</div>"
],
"text/plain": [
" question \\\n",
"0 10. Ability to Pay for Medical Expenses \n",
"1 10. Ability to Pay for Medical Expenses \n",
"2 10. Ability to Pay for Medical Expenses \n",
"3 10. Ability to Pay for Medical Expenses \n",
"4 10. Ability to Pay for Medical Expenses \n",
"5 10. Ability to Pay for Medical Expenses \n",
"6 10. Ability to Pay for Medical Expenses \n",
"7 10. Ability to Pay for Medical Expenses \n",
"8 10. Ability to Pay for Medical Expenses \n",
"9 10. Ability to Pay for Medical Expenses \n",
"10 10. Ability to Pay for Medical Expenses \n",
"11 40. Ability to Recover from Unexpected Expenses \n",
"12 40. Ability to Recover from Unexpected Expenses \n",
"13 40. Ability to Recover from Unexpected Expenses \n",
"14 40. Ability to Recover from Unexpected Expenses \n",
"15 40. Ability to Recover from Unexpected Expenses \n",
"16 40. Ability to Recover from Unexpected Expenses \n",
"17 40. Ability to Recover from Unexpected Expenses \n",
"18 40. Ability to Recover from Unexpected Expenses \n",
"19 40. Ability to Recover from Unexpected Expenses \n",
"20 40. Ability to Recover from Unexpected Expenses \n",
"21 40. Ability to Recover from Unexpected Expenses \n",
"22 40. Ability to Recover from Unexpected Expenses \n",
"23 40. Ability to Recover from Unexpected Expenses \n",
"24 40. Ability to Recover from Unexpected Expenses \n",
"25 40. Ability to Recover from Unexpected Expenses \n",
"26 40. Ability to Recover from Unexpected Expenses \n",
"27 40. Ability to Recover from Unexpected Expenses \n",
"28 4. Absence of Constraints in different aspects... \n",
"29 4. Absence of Constraints in different aspects... \n",
"... ... \n",
"1035 45. Whether Worried About Hospital Bills \n",
"1036 45. Whether Worried About Hospital Bills \n",
"1037 45. Whether Worried About Hospital Bills \n",
"1038 45. Whether Worried About Hospital Bills \n",
"1039 45. Whether Worried About Hospital Bills \n",
"1040 45. Whether Worried About Hospital Bills \n",
"1041 45. Whether Worried About Hospital Bills \n",
"1042 45. Whether Worried About Hospital Bills \n",
"1043 45. Whether Worried About Hospital Bills \n",
"1044 45. Whether Worried About Hospital Bills \n",
"1045 45. Whether Worried About Hospital Bills \n",
"1046 53. Whether Worried on Having Job or Source of... \n",
"1047 53. Whether Worried on Having Job or Source of... \n",
"1048 53. Whether Worried on Having Job or Source of... \n",
"1049 53. Whether Worried on Having Job or Source of... \n",
"1050 53. Whether Worried on Having Job or Source of... \n",
"1051 53. Whether Worried on Having Job or Source of... \n",
"1052 53. Whether Worried on Having Job or Source of... \n",
"1053 53. Whether Worried on Having Job or Source of... \n",
"1054 53. Whether Worried on Having Job or Source of... \n",
"1055 53. Whether Worried on Having Job or Source of... \n",
"1056 53. Whether Worried on Having Job or Source of... \n",
"1057 53. Whether Worried on Having Job or Source of... \n",
"1058 53. Whether Worried on Having Job or Source of... \n",
"1059 53. Whether Worried on Having Job or Source of... \n",
"1060 53. Whether Worried on Having Job or Source of... \n",
"1061 53. Whether Worried on Having Job or Source of... \n",
"1062 53. Whether Worried on Having Job or Source of... \n",
"1063 53. Whether Worried on Having Job or Source of... \n",
"1064 53. Whether Worried on Having Job or Source of... \n",
"\n",
" Response Total Age 15-19 \\\n",
"0 Unweighted 10000 1511 \n",
"1 Weighted 10000 1978 \n",
"2 NaN % % \n",
"3 CONFIDENT 78.6 81.7 \n",
"4 Definitely confident (4.00) 28.4 30.9 \n",
"5 Somewhat confident (3.00) 50.2 50.8 \n",
"6 NOT CONFIDENT 20.6 17.3 \n",
"7 Somewhat not confident (2.00) 15.6 14.5 \n",
"8 Definitely not confident (1.00) 4.9 2.8 \n",
"9 REFUSED - * \n",
"10 DON’T KNOW 0.8 1 \n",
"11 NaN NaN AGE \n",
"12 NaN Total 15-19 \n",
"13 BASE- Total inteviews: NaN NaN \n",
"14 Unweighted 10000.0 1511.0 \n",
"15 Weighted 10000.0 1978.0 \n",
"16 NaN NaN NaN \n",
"17 NaN % % \n",
"18 NaN NaN NaN \n",
"19 Has the capacity to recover from small expense... 65.9 64.3 \n",
"20 NaN NaN NaN \n",
"21 Has the capacity to recover from big expenses 22.3 23.5 \n",
"22 NaN NaN NaN \n",
"23 Has no capacity to recover even from small exp... 11.3 11.5 \n",
"24 NaN NaN NaN \n",
"25 Refused (vol.) * * \n",
"26 NaN NaN NaN \n",
"27 Don't Know (vol.) * 0.6 \n",
"28 Unweighted 10000 1511 \n",
"29 Weighted 10000 1978 \n",
"... ... ... ... \n",
"1035 WORRY 15.0 15.6 \n",
"1036 Definitely will worry (1.00) 58.5 56.9 \n",
"1037 Will worry (2.00) 26.4 27.4 \n",
"1038 NaN NaN NaN \n",
"1039 WON'T WORRY 84.9 84.4 \n",
"1040 Will worry a little (3.00) 13.4 13.8 \n",
"1041 Will not worry (4.00) 1.6 1.8 \n",
"1042 NaN NaN NaN \n",
"1043 REFUSED * - \n",
"1044 NaN NaN NaN \n",
"1045 DON’T KNOW - - \n",
"1046 NaN NaN AGE \n",
"1047 NaN Total 15-19 \n",
"1048 BASE- Total interviews: NaN NaN \n",
"1049 Unweighted 10000.0 1511.0 \n",
"1050 Total 10000.0 1978.0 \n",
"1051 NaN NaN NaN \n",
"1052 NaN % % \n",
"1053 NaN NaN NaN \n",
"1054 I worry about my job or source of income alway... 36.6 31.1 \n",
"1055 NaN NaN NaN \n",
"1056 I worry about my job or source of income often 30.0 29.6 \n",
"1057 NaN NaN NaN \n",
"1058 I seldom worry about my job or source of income 25.1 26.6 \n",
"1059 NaN NaN NaN \n",
"1060 I do not worry about my job or source of income 7.1 9.4 \n",
"1061 NaN NaN NaN \n",
"1062 Refused (vol.) 0.8 1.5 \n",
"1063 NaN NaN NaN \n",
"1064 Don't Know (vol.) * 1.9 \n",
"\n",
" Age 20-29 Age 30-39 Age 40-50 SEC AB SEC ABC (NET) SEC C1 SEC C2 \\\n",
"0 3057 2967 2465 60 2280 493 1727 \n",
"1 3211 2594 2217 60 2260 482 1717 \n",
"2 % % % % % % % \n",
"3 81 77.5 73.8 98.3 87.6 89.8 86.6 \n",
"4 29.2 26.5 27.3 63.2 36.7 39.3 35 \n",
"5 51.8 50.9 46.5 35.1 50.9 50.5 51.6 \n",
"6 18.5 21.6 25.3 1.7 11.7 10 12.5 \n",
"7 13.9 16.4 18.3 1.7 9.1 8.6 9.5 \n",
"8 4.6 5.2 6.9 - 2.6 1.4 3 \n",
"9 - - - - * - * \n",
"10 0.5 0.9 0.9 - 0.7 * 0.8 \n",
"11 NaN NaN NaN NaN SEC NaN NaN \n",
"12 20-29 30-39 40-50 AB ABC (NET) C1 C2 \n",
"13 NaN NaN NaN NaN NaN NaN NaN \n",
"14 3057.0 2967.0 2465.0 60.0 2280.0 493.0 1727.0 \n",
"15 3211.0 2594.0 2217.0 60.0 2260.0 482.0 1717.0 \n",
"16 NaN NaN NaN NaN NaN NaN NaN \n",
"17 % % % % % % % \n",
"18 NaN NaN NaN NaN NaN NaN NaN \n",
"19 65.3 67.0 67.0 35.5 62.3 54.4 65.4 \n",
"20 NaN NaN NaN NaN NaN NaN NaN \n",
"21 23.6 21.6 20.1 62.9 33.6 42.3 30.1 \n",
"22 NaN NaN NaN NaN NaN NaN NaN \n",
"23 10.6 11.1 12.6 1.6 3.6 2.5 4.0 \n",
"24 NaN NaN NaN NaN NaN NaN NaN \n",
"25 * * - - * - * \n",
"26 NaN NaN NaN NaN NaN NaN NaN \n",
"27 * * * - 0.5 0.8 * \n",
"28 3057 2967 2465 60 2280 493 1727 \n",
"29 3211 2594 2217 60 2260 482 1717 \n",
"... ... ... ... ... ... ... ... \n",
"1035 15.3 14.7 14.3 39.5 22.6 31.5 19.6 \n",
"1036 57.9 58.8 60.5 34.6 48.7 39.0 52.0 \n",
"1037 26.5 26.4 25.2 25.9 28.5 29.3 28.4 \n",
"1038 NaN NaN NaN NaN NaN NaN NaN \n",
"1039 84.4 85.1 85.7 60.5 77.3 68.3 80.4 \n",
"1040 13.9 13.3 12.6 29.2 19.7 27.5 17.2 \n",
"1041 1.4 1.4 1.7 10.3 2.9 4.0 2.4 \n",
"1042 NaN NaN NaN NaN NaN NaN NaN \n",
"1043 * * - - * * - \n",
"1044 NaN NaN NaN NaN NaN NaN NaN \n",
"1045 - * - - - - - \n",
"1046 NaN NaN NaN NaN SEC NaN NaN \n",
"1047 20-29 30-39 40-50 AB ABC (NET) C1 C2 \n",
"1048 NaN NaN NaN NaN NaN NaN NaN \n",
"1049 3057.0 2967.0 2465.0 60.0 2280.0 493.0 1727.0 \n",
"1050 3211.0 2594.0 2217.0 60.0 2260.0 482.0 1717.0 \n",
"1051 NaN NaN NaN NaN NaN NaN NaN \n",
"1052 % % % % % % % \n",
"1053 NaN NaN NaN NaN NaN NaN NaN \n",
"1054 35.8 37.4 41.6 21.7 29.2 26.6 30.2 \n",
"1055 NaN NaN NaN NaN NaN NaN NaN \n",
"1056 30.3 31.0 28.8 18.9 24.1 18.6 25.8 \n",
"1057 NaN NaN NaN NaN NaN NaN NaN \n",
"1058 26.2 24.2 23.1 25.9 33.3 39.7 31.8 \n",
"1059 NaN NaN NaN NaN NaN NaN NaN \n",
"1060 6.8 6.7 6.0 31.7 12.2 14.5 10.9 \n",
"1061 NaN NaN NaN NaN NaN NaN NaN \n",
"1062 0.7 0.7 * 1.7 0.6 * 0.6 \n",
"1063 NaN NaN NaN NaN NaN NaN NaN \n",
"1064 * * * - 0.6 * 0.7 \n",
"\n",
" SEC D SEC E Gender Female Gender Male Work Status Not Working \\\n",
"0 4784 2936 4998 5002 4058 \n",
"1 4820 2921 4968 5032 4042 \n",
"2 % % % % % \n",
"3 79.7 70 77.8 79.5 76.9 \n",
"4 28.3 22.2 28 28.8 26.4 \n",
"5 51.4 47.7 49.8 50.7 50.5 \n",
"6 19.6 29 21.5 19.6 22.2 \n",
"7 15.4 21.1 16.4 14.9 16.9 \n",
"8 4.2 7.9 5.1 4.8 5.3 \n",
"9 - - - - - \n",
"10 0.7 1 0.7 0.9 0.9 \n",
"11 NaN NaN NaN GENDER NaN \n",
"12 D E Female Male Not Working \n",
"13 NaN NaN NaN NaN NaN \n",
"14 4784.0 2936.0 4998.0 5002.0 4058.0 \n",
"15 4820.0 2921.0 4968.0 5032.0 4042.0 \n",
"16 NaN NaN NaN NaN NaN \n",
"17 % % % % % \n",
"18 NaN NaN NaN NaN NaN \n",
"19 69.7 62.5 66.1 65.8 66.3 \n",
"20 NaN NaN NaN NaN NaN \n",
"21 21.2 15.3 22.5 22.1 21.5 \n",
"22 NaN NaN NaN NaN NaN \n",
"23 8.6 21.8 11.1 11.6 11.8 \n",
"24 NaN NaN NaN NaN NaN \n",
"25 * * * * * \n",
"26 NaN NaN NaN NaN NaN \n",
"27 * * * * * \n",
"28 4784 2936 4998 5002 4058 \n",
"29 4820 2921 4968 5032 4042 \n",
"... ... ... ... ... ... \n",
"1035 13.9 10.8 14.7 15.2 14.4 \n",
"1036 59.7 64.2 59.8 57.2 59.5 \n",
"1037 26.1 25.0 25.2 27.5 25.8 \n",
"1038 NaN NaN NaN NaN NaN \n",
"1039 85.8 89.2 85.1 84.7 85.3 \n",
"1040 12.6 9.8 13.2 13.6 13.1 \n",
"1041 1.3 0.9 1.5 1.6 1.3 \n",
"1042 NaN NaN NaN NaN NaN \n",
"1043 * - * * * \n",
"1044 NaN NaN NaN NaN NaN \n",
"1045 - - - - - \n",
"1046 NaN NaN NaN GENDER NaN \n",
"1047 D E Female Male Not Working \n",
"1048 NaN NaN NaN NaN NaN \n",
"1049 4784.0 2936.0 4998.0 5002.0 4058.0 \n",
"1050 4820.0 2921.0 4968.0 5032.0 4042.0 \n",
"1051 NaN NaN NaN NaN NaN \n",
"1052 % % % % % \n",
"1053 NaN NaN NaN NaN NaN \n",
"1054 34.7 45.4 37.1 36.1 38.8 \n",
"1055 NaN NaN NaN NaN NaN \n",
"1056 33.1 29.6 29.7 30.3 31.1 \n",
"1057 NaN NaN NaN NaN NaN \n",
"1058 24.0 20.4 25.0 25.2 23.2 \n",
"1059 NaN NaN NaN NaN NaN \n",
"1060 6.8 3.7 7.0 7.2 6.0 \n",
"1061 NaN NaN NaN NaN NaN \n",
"1062 1.0 * 0.8 0.8 0.6 \n",
"1063 NaN NaN NaN NaN NaN \n",
"1064 * * * * * \n",
"\n",
" Work Status Student Work Status Working Local Urban Locale Rural \n",
"0 1015 4927 4620 5380 \n",
"1 1305 4653 4569 5431 \n",
"2 % % % % \n",
"3 84.6 78.5 81.4 76.3 \n",
"4 33.6 28.7 28.2 28.6 \n",
"5 51 49.7 53.2 47.7 \n",
"6 15.1 20.6 17.9 22.8 \n",
"7 12.8 15.3 14.3 16.8 \n",
"8 2.3 5.3 3.6 6 \n",
"9 - - - - \n",
"10 * 0.9 0.7 0.8 \n",
"11 NaN WORK STATUS LOCALE NaN \n",
"12 Student Working Urban Rural \n",
"13 NaN NaN NaN NaN \n",
"14 1015.0 4927.0 4620.0 5380.0 \n",
"15 1305.0 4653.0 4569.0 5431.0 \n",
"16 NaN NaN NaN NaN \n",
"17 % % % % \n",
"18 NaN NaN NaN NaN \n",
"19 64.0 66.2 65.5 66.3 \n",
"20 NaN NaN NaN NaN \n",
"21 25.0 22.3 24.9 20.1 \n",
"22 NaN NaN NaN NaN \n",
"23 10.3 11.2 9.0 13.4 \n",
"24 NaN NaN NaN NaN \n",
"25 * * * * \n",
"26 NaN NaN NaN NaN \n",
"27 0.6 * 0.6 * \n",
"28 1015 4927 4620 5380 \n",
"29 1305 4653 4569 5431 \n",
"... ... ... ... ... \n",
"1035 15.0 15.4 18.5 12.0 \n",
"1036 57.2 58.0 52.6 63.5 \n",
"1037 27.8 26.4 28.7 24.4 \n",
"1038 NaN NaN NaN NaN \n",
"1039 85.0 84.4 81.3 87.9 \n",
"1040 13.3 13.7 16.6 10.8 \n",
"1041 1.7 1.7 1.9 1.2 \n",
"1042 NaN NaN NaN NaN \n",
"1043 - * * * \n",
"1044 NaN NaN NaN NaN \n",
"1045 - - - - \n",
"1046 NaN WORK STATUS LOCALE NaN \n",
"1047 Student Working Urban Rural \n",
"1048 NaN NaN NaN NaN \n",
"1049 1015.0 4927.0 4620.0 5380.0 \n",
"1050 1305.0 4653.0 4569.0 5431.0 \n",
"1051 NaN NaN NaN NaN \n",
"1052 % % % % \n",
"1053 NaN NaN NaN NaN \n",
"1054 28.9 36.8 30.8 41.4 \n",
"1055 NaN NaN NaN NaN \n",
"1056 26.5 30.1 30.5 29.6 \n",
"1057 NaN NaN NaN NaN \n",
"1058 29.6 25.4 27.5 23.0 \n",
"1059 NaN NaN NaN NaN \n",
"1060 10.7 7.1 9.6 5.0 \n",
"1061 NaN NaN NaN NaN \n",
"1062 1.8 0.6 0.9 0.6 \n",
"1063 NaN NaN NaN NaN \n",
"1064 2.4 - 0.6 * \n",
"\n",
"[1065 rows x 20 columns]"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [conda root]",
"language": "python",
"name": "conda-root-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| gpl-3.0 |
pranavj1001/LearnLanguages | python/DataAnalysis/numpy/NumPy.ipynb | 1 | 26817 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NumPy\n",
"\n",
"NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.\n",
"\n",
"In this notebook we'll try various numpy methods and in the process learn more about NumPy.\n",
"\n",
"### Installation\n",
"\n",
"Please follow this [link](https://www.scipy.org/scipylib/download.html)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importing NumPy\n",
"\n",
"Once numpy is installed, we can import it in our file"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## NumPy Arrays\n",
"\n",
"In NumPy, strictly 1-D arrays are known as vectors and 2-D arrays are known as matrices (a matrix can also have only one row or one column). With NumPy arrays we can get access to various pre-written functions from NumPy.\n",
"\n",
"### Creating NumPy Arrays\n",
"\n",
"There are various ways to create NumPy Arrays. Some of them are listed below.\n",
"\n",
"1. **From a Python List**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['a', 'b', 'c', 'd']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list = ['a', 'b', 'c', 'd']\n",
"list"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['a', 'b', 'c', 'd'], dtype='<U1')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array(list)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[[1, 2, 3, 4, 5, 6, 7, 8, 9]]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list_matrix = [[1, 2, 3, 4, 5, 6, 7, 8, 9]]\n",
"list_matrix"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3, 4, 5, 6, 7, 8, 9]])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.array(list_matrix)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. **arange method**\n",
"\n",
" arange ( *starting_number*, *ending_number_plus_one*, *step?* )\n",
"\n",
" Returns evenly spaced values within a given interval. step is optional"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.arange(1, 11)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.arange(5, 60, 5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. **zeros method**\n",
"\n",
" zeros ( *shape* )\n",
" \n",
" Returns a new array of given shape and type, filled with zeros."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.zeros(10)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0.]])"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.zeros((5, 5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4. **ones method**\n",
"\n",
" ones ( *shape* )\n",
"\n",
" Returns a new array of given shape and type, filled with ones."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.ones(10)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 1., 1., 1., 1.],\n",
" [1., 1., 1., 1., 1.],\n",
" [1., 1., 1., 1., 1.],\n",
" [1., 1., 1., 1., 1.],\n",
" [1., 1., 1., 1., 1.]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.ones((5,5))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"5. **linspace**\n",
"\n",
" linspace ( *starting_number*, *ending_number*, *number_of_elements_in_array* )\n",
" \n",
" Returns evenly spaced numbers over a specified interval."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([10. , 12.5, 15. , 17.5, 20. ])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.linspace(10, 20, 5)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([100. , 100.11111111, 100.22222222, 100.33333333,\n",
" 100.44444444, 100.55555556, 100.66666667, 100.77777778,\n",
" 100.88888889, 101. ])"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.linspace(100, 101, 10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"6. **eye**\n",
"\n",
" eye ( *number_of_rows* )\n",
"\n",
" Returns a 2-D array with ones on the diagonal and zeros elsewhere. (Returns and identity matrix)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 0., 0., 0.],\n",
" [0., 1., 0., 0.],\n",
" [0., 0., 1., 0.],\n",
" [0., 0., 0., 1.]])"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.eye(4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Random\n",
"\n",
"1. **rand**\n",
"\n",
" rand( *shape* )\n",
" \n",
" Returns random values in a given shape."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.77319868, 0.26329287, 0.73144465, 0.84149091, 0.2036499 ])"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.rand(5)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.60726994, 0.42617981, 0.86102196],\n",
" [0.86543814, 0.30908303, 0.24178713],\n",
" [0.38308208, 0.61029731, 0.28254035]])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.rand(3,3)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[[0.16941154, 0.68912473, 0.43966093, 0.98221689],\n",
" [0.99432939, 0.42056898, 0.25871228, 0.3741148 ],\n",
" [0.30504666, 0.76178429, 0.71000702, 0.06467588]],\n",
"\n",
" [[0.24162657, 0.49613392, 0.97700371, 0.60832675],\n",
" [0.59610994, 0.95313682, 0.46022336, 0.37118844],\n",
" [0.73586936, 0.4774315 , 0.97240164, 0.55395152]]])"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.rand(2,3,4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. **randn**\n",
"\n",
" randn ( *shape* )\n",
" \n",
" Returns a sample (or samples) from the \"standard normal\" distribution."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.02496177, 0.08214048, 0.28732458])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.randn(3)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.22611994, -0.0807919 , -0.32115941, 0.33847872],\n",
" [-1.21235511, 0.73971392, -0.00640343, -0.58285227],\n",
" [-0.2409442 , 0.21120981, 0.8262523 , -1.034613 ],\n",
" [-0.55126158, 1.56535933, -0.686484 , 0.06669024]])"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.randn(4,4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. **randint**\n",
" \n",
" randint( *low*, *high?*, *size?* )\n",
" \n",
" Returns random integers from `low` (inclusive) to `high` (exclusive)."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.randint(5)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.randint(1,11)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([18, 25, 83, 41, 29, 99, 16, 24, 50, 75])"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.randint(1, 100, 10)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[74, 27, 76, 99],\n",
" [25, 92, 86, 45],\n",
" [28, 13, 82, 52],\n",
" [41, 32, 94, 84]])"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.random.randint(1, 100, (4, 4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Array Attributes\n",
"\n",
"max, min, argmax, argmin"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"list = np.arange(1,10)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list.max()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list.min()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list.argmax()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list.argmin()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reshape, Shape and Ravel\n",
"\n",
"1. **reshape**\n",
" \n",
" reshape ( *shape* )\n",
"\n",
" Returns an array containing the same data with a new shape."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reshape_list = np.arange(1,10)\n",
"reshape_list"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [4, 5, 6],\n",
" [7, 8, 9]])"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"reshape_list.reshape(3,3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. **Shape**\n",
"\n",
" Its an attribute which returns the shape (in a tuple) of the array."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5, 6, 7, 8, 9])"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shape_list = np.arange(1,10)\n",
"shape_list"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(9,)"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shape_list.shape"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1, 2, 3],\n",
" [4, 5, 6],\n",
" [7, 8, 9]])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shape_list.reshape(3,3)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3, 3)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shape_list.reshape(3,3).shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. **ravel**\n",
"\n",
" *ravel ( )*\n",
" \n",
" Returns a flattened array. (does not return a new copy)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 4, 5],\n",
" [ 6, 7, 8, 9, 10],\n",
" [11, 12, 13, 14, 15],\n",
" [16, 17, 18, 19, 20],\n",
" [21, 22, 23, 24, 25]])"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ravel_list = np.arange(1,26).reshape(5,5)\n",
"ravel_list"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20, 21, 22, 23, 24, 25])"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ravel_list = ravel_list.ravel()\n",
"ravel_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4. **flatten**\n",
"\n",
" *flatten ( )*\n",
" \n",
" Returns a copy of the array collapsed into one dimension."
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 4, 5],\n",
" [ 6, 7, 8, 9, 10],\n",
" [11, 12, 13, 14, 15],\n",
" [16, 17, 18, 19, 20],\n",
" [21, 22, 23, 24, 25]])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flatten_list = np.arange(1,26).reshape(5,5)\n",
"flatten_list"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20, 21, 22, 23, 24, 25])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"flatten_list = flatten_list.flatten()\n",
"flatten_list"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### dtype\n",
"\n",
"Its an attribute which returns the data type of the object"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('int64')"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list = np.arange(1,11)\n",
"list.dtype"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dtype('<U1')"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list = np.array(['a', 'b', 'c'])\n",
"list.dtype"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Selection\n",
"\n",
"1. **index-based selection**\n",
"\n",
" For normal 1-D lists"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20, 21, 22, 23, 24, 25])"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"selection_list = np.arange(1,26)\n",
"selection_list"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"selection_list[9]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"25"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"selection_list[24]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" For multi dimensional lists"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 1, 2, 3, 4, 5],\n",
" [ 6, 7, 8, 9, 10],\n",
" [11, 12, 13, 14, 15],\n",
" [16, 17, 18, 19, 20],\n",
" [21, 22, 23, 24, 25]])"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"selection_list = selection_list.reshape(5, 5)\n",
"selection_list"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[12, 13, 14, 15],\n",
" [17, 18, 19, 20],\n",
" [22, 23, 24, 25]])"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"selection_list[2:,1:]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 8, 9],\n",
" [13, 14],\n",
" [18, 19]])"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"selection_list[1:4,2:4]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. **comparison selectors**"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
" 18, 19, 20, 21, 22, 23, 24, 25])"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"selection_list = selection_list.ravel()\n",
"selection_list"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25])"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"selection_list[selection_list>10]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Universal Functions\n",
"\n",
"NumPy comes with many [universal functions](https://docs.scipy.org/doc/numpy/reference/ufuncs.html#available-ufuncs). Some of it are in the next cells."
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"uni_list = np.arange(1,11)\n",
"uni_list"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 1, 4, 9, 16, 25, 36, 49, 64, 81, 100])"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.square(uni_list)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.84147098, 0.90929743, 0.14112001, -0.7568025 , -0.95892427,\n",
" -0.2794155 , 0.6569866 , 0.98935825, 0.41211849, -0.54402111])"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.sin(uni_list)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0. , 0.69314718, 1.09861229, 1.38629436, 1.60943791,\n",
" 1.79175947, 1.94591015, 2.07944154, 2.19722458, 2.30258509])"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log(uni_list)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0. , 0.30103 , 0.47712125, 0.60205999, 0.69897 ,\n",
" 0.77815125, 0.84509804, 0.90308999, 0.95424251, 1. ])"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.log10(uni_list)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ True, True, True, True, True, True, True, True, True,\n",
" True])"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.isfinite(uni_list)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
google-research/google-research | mutual_information_representation_learning/mirl.ipynb | 1 | 75507 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"accelerator": "GPU",
"colab": {
"name": "Mutual_Information_Maximization_for_Representation_Learning.ipynb",
"version": "0.3.2",
"provenance": [],
"private_outputs": true,
"collapsed_sections": [
"3qHQzV6LhRsT",
"4FJW4sNNg6Vr",
"noACZM4kiBGq",
"LGCSUC4r-tfW",
"o16VFAvt_i_H",
"q35Q4nvrB_fJ",
"Ui2VAr9UBOns",
"PKcYpomeCoUI",
"5FbkpQ1dCZcq"
],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ULndVKL12doD"
},
"source": [
"##### Copyright 2019 Google LLC.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\")"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "9Mye5mzaTuxo",
"colab": {}
},
"source": [
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "h8Pcn3MPt4W6"
},
"source": [
"# Basic setup\n",
"\n",
"**About this Colab**<br /> This is a companion Colab for the paper:\n",
"\n",
"*On Mutual Information Maximization for Representation Learning*<br />\n",
"Michael Tschannen\\*, Josip Djolonga\\*, Paul Rubenstein, Sylvain Gelly, Mario Lucic\n",
"\n",
"The Colab can be used to visualize precomputed results or to rerun the experiments reported in the paper.\n",
"\n",
"**Running the experiments**<br />\n",
"By default, the precomputed results will be loaded, but individual experiments can be run with the Colab by checking the `RUN_EXPERIMENTS` checkbox below. The batch size used in the paper was 128 and we average over 20 runs. With one run, the entire set of experiments will complete in ~2 hours. For multiple runs we suggest copying the code and running a stand-alone version. If you wish to run the experiments within the Colab, make sure you execute all cells in the \"Setup\" section."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "YmT88ebujJHC",
"colab": {}
},
"source": [
"#@title Imports, configurations, and helper functions { display-mode: \"form\" }\n",
"from __future__ import division\n",
"from __future__ import print_function\n",
"\n",
"import collections\n",
"import copy\n",
"import functools\n",
"import itertools\n",
"import os\n",
"import pickle\n",
"\n",
"from matplotlib import pyplot as plt\n",
"from matplotlib.ticker import FuncFormatter\n",
"import numpy as np\n",
"import pandas as pd\n",
"from scipy.ndimage import gaussian_filter1d\n",
"import seaborn as sns\n",
"import tensorflow as tf\n",
"from tensorflow.python.ops.parallel_for import gradients\n",
"import tensorflow_datasets as tfds\n",
"import tensorflow_probability as tfp\n",
"import sklearn.linear_model as sk_linear\n",
"\n",
"\n",
"slim = tf.contrib.slim\n",
"tfb = tfp.bijectors\n",
"tfd = tfp.distributions\n",
"tfkl = tf.keras.layers\n",
"\n",
"tf.keras.backend.clear_session()\n",
"\n",
"ResultsConfig = collections.namedtuple(\n",
" \"ResultsConfig\", [\"nets\", \"critic\", \"loss\"])\n",
"\n",
"Results = collections.namedtuple(\n",
" 'Results',\n",
" ['iterations', 'training_losses', 'testing_losses',\n",
" 'classification_accuracies', 'singular_values'])\n",
"\n",
"ResultsAdversarial = collections.namedtuple(\n",
" \"ResultsAdversarial\",\n",
" [\"losses_e\", \"losses_c\", \"classification_accuracies\", \"iters\"]\n",
")\n",
"\n",
"ResultsSamplingIssues = collections.namedtuple(\n",
" \"ResultsSamplingIssues\", [\"mi_true\", \"nce_estimates_noniid\", \n",
" \"nce_estimates_iid\", \"nwj_estimates_noniid\", \n",
" \"nwj_estimates_iid\"])\n",
"\n",
"def convert_to_data_frame(result, exp_name, nets, critic, loss, seed):\n",
" \"\"\"Convert results class to a data frame.\"\"\"\n",
" label = \"{}, {}, {}\".format(nets, critic, loss)\n",
" rows = list(\n",
" zip(\n",
" itertools.repeat(exp_name),\n",
" itertools.repeat(nets),\n",
" itertools.repeat(critic),\n",
" itertools.repeat(loss),\n",
" itertools.repeat(seed),\n",
" result.iterations,\n",
" [-loss for loss in result.testing_losses], # Loss -> bound.\n",
" result.classification_accuracies,\n",
" itertools.repeat(label)))\n",
" df_eval = pd.DataFrame(\n",
" rows,\n",
" columns=(\"exp_name\", \"nets\", \"Critic\", \"Estimator\",\n",
" \"run\", \"iteration\", \"bound_value\", \"accuracy\", \"label\"))\n",
"\n",
" df_eval[\"Estimator\"] = df_eval[\"Estimator\"].replace(\n",
" to_replace={\n",
" \"nce\": \"$I_{NCE}$\",\n",
" \"nwj\": \"$I_{NWJ}$\"\n",
" })\n",
" df_eval[\"Critic\"] = df_eval[\"Critic\"].replace(\n",
" to_replace={\n",
" \"concat\": \"MLP\",\n",
" \"separable\": \"Separable\",\n",
" \"innerprod\": \"Inner product\",\n",
" \"bilinear\": \"Bilinear\"\n",
" })\n",
" return df_eval\n",
"\n",
"\n",
"def apply_default_style(ax):\n",
" ax.set_xlim([0, 20001])\n",
" ax.get_xaxis().set_major_formatter(\n",
" FuncFormatter(lambda x, p: format(int(x/1000), ',')))\n",
" ax.set_xlabel(\"Training steps (in thousands)\")\n",
" plt.tick_params(top=False, right=False, bottom=False, left=False)\n",
" handles, labels = ax.get_legend_handles_labels()\n",
" plt.legend(loc=\"lower right\", handles=handles[1:], labels=labels[1:])\n",
"\n",
"FONTSIZE = 15 \n",
"sns.set_style(\"whitegrid\")\n",
"plt.rcParams.update({'axes.labelsize': FONTSIZE,\n",
" 'xtick.labelsize': FONTSIZE,\n",
" 'ytick.labelsize': FONTSIZE,\n",
" 'legend.fontsize': FONTSIZE})\n",
"\n",
"NRUNS = 1 #@param { type: \"slider\", min: 1, max: 20, step: 1}\n",
"TRAIN_BATCH_SIZE = 128 #@param { type: \"slider\", min: 64, max: 128, step: 64}\n",
"RUN_EXPERIMENTS = False #@param { type: \"boolean\"}\n",
"DIMS = 784\n",
"\n",
"def get_testing_loss(x_array, session, loss, data_ph, dims, batch_size=512):\n",
" total_loss = 0\n",
" for i in range(0, x_array.shape[0], batch_size):\n",
" x_slice = x_array[i:i+batch_size, :dims]\n",
" total_loss += x_slice.shape[0] * session.run(loss,\n",
" feed_dict={data_ph: x_slice})\n",
" return total_loss / x_array.shape[0]\n",
"\n",
"def get_classification_accuracy(session, codes, data_ph, dims):\n",
" x_train_mapped = map_data(x_train, session, codes, data_ph, dims)\n",
" x_test_mapped = map_data(x_test, session, codes, data_ph, dims)\n",
" accuracy = logistic_fit(x_train_mapped, y_train, x_test_mapped, y_test)\n",
" return accuracy\n",
"\n",
"def map_data(x_array, session, codes, data_ph, dims, batch_size=512):\n",
" x_mapped = []\n",
" for i in range(0, x_array.shape[0], batch_size):\n",
" x_mapped.append(\n",
" session.run(codes,\n",
" feed_dict={data_ph: x_array[i:i+batch_size, :dims]}))\n",
" return np.concatenate(x_mapped, axis=0)\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "pLbfDqH5tRUQ",
"colab": {}
},
"source": [
"#@title Import bounds implemented by Poole et al. (2019) { display-mode: \"form\" }\n",
"# From https://colab.research.google.com/github/google-research/google-research/blob/master/vbmi/vbmi_demo.ipynb \n",
"\n",
"def reduce_logmeanexp_nodiag(x, axis=None):\n",
" batch_size = x.shape[0].value\n",
" logsumexp = tf.reduce_logsumexp(x - tf.linalg.tensor_diag(np.inf * tf.ones(batch_size)), axis=axis)\n",
" if axis:\n",
" num_elem = batch_size - 1.\n",
" else:\n",
" num_elem = batch_size * (batch_size - 1.)\n",
" return logsumexp - tf.math.log(num_elem)\n",
"\n",
"def tuba_lower_bound(scores, log_baseline=None):\n",
" if log_baseline is not None:\n",
" scores -= log_baseline[:, None]\n",
" batch_size = tf.cast(scores.shape[0], tf.float32)\n",
" # First term is an expectation over samples from the joint,\n",
" # which are the diagonal elmements of the scores matrix.\n",
" joint_term = tf.reduce_mean(tf.linalg.diag_part(scores))\n",
" # Second term is an expectation over samples from the marginal,\n",
" # which are the off-diagonal elements of the scores matrix.\n",
" marg_term = tf.exp(reduce_logmeanexp_nodiag(scores))\n",
" return 1. + joint_term - marg_term\n",
"\n",
"def nwj_lower_bound(scores):\n",
" # equivalent to: tuba_lower_bound(scores, log_baseline=1.)\n",
" return tuba_lower_bound(scores - 1.) \n",
"\n",
"def infonce_lower_bound(scores):\n",
" \"\"\"InfoNCE lower bound from van den Oord et al. (2018).\"\"\"\n",
" nll = tf.reduce_mean(tf.linalg.diag_part(scores) - tf.reduce_logsumexp(scores, axis=1))\n",
" # Alternative implementation:\n",
" # nll = -tf.nn.sparse_softmax_cross_entropy_with_logits(logits=scores, labels=tf.range(batch_size))\n",
" mi = tf.math.log(tf.cast(scores.shape[0].value, tf.float32)) + nll\n",
" return mi"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "pCuLIr7jcD_o",
"colab": {}
},
"source": [
"#@title Define the linear evaluation protocol { display-mode: \"form\" }\n",
"\n",
"def logistic_fit(x_train, y_train, x_test, y_test):\n",
" logistic_regressor = sk_linear.LogisticRegression(\n",
" solver='saga', multi_class='multinomial', tol=.1, C=10.)\n",
" from sklearn.preprocessing import MinMaxScaler\n",
" scaler = MinMaxScaler()\n",
" x_train = scaler.fit_transform(x_train)\n",
" x_test = scaler.transform(x_test)\n",
" logistic_regressor.fit(x_train, y_train.ravel())\n",
" return logistic_regressor.score(x_test, y_test.ravel())"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "NlcUu6NGkYAa",
"colab": {}
},
"source": [
"#@title Define and load the dataset, check baseline in pixel space { display-mode: \"form\" }\n",
"\n",
"tf.reset_default_graph()\n",
"\n",
"TFDS_NAME = \"mnist\"\n",
"FEATURE_INPUT = \"image\"\n",
"FEATURE_LABEL = \"label\"\n",
"N_CLASSES = 10\n",
"\n",
"DIMS = 784 # Total dimensions after flattening.\n",
"\n",
"def map_fn(example):\n",
" image = example[FEATURE_INPUT]\n",
" image = tf.cast(image, tf.float32) / 255.0\n",
" image = tf.reshape(image, [-1]) # Flatten.\n",
" label = example[FEATURE_LABEL]\n",
" return {FEATURE_INPUT: image, FEATURE_LABEL: label}\n",
"\n",
"def load_data(split):\n",
" return (tfds.load(TFDS_NAME, split=split)\n",
" .cache()\n",
" .map(map_func=map_fn)\n",
" .shuffle(1000))\n",
" \n",
"def tfds_to_np(dataset):\n",
" features = list(tfds.as_numpy(dataset))\n",
" images = np.stack([f[FEATURE_INPUT].ravel() for f in features])\n",
" labels = np.stack([f[FEATURE_LABEL].ravel() for f in features])\n",
" return images, labels\n",
"\n",
"dataset_train = load_data(\"train\")\n",
"dataset_test = load_data(\"test\")\n",
"x_train, y_train = tfds_to_np(dataset_train)\n",
"x_test, y_test = tfds_to_np(dataset_test)\n",
"tf.reset_default_graph()\n",
"\n",
"x_train_noisy = x_train + 0.05 * np.random.randn(*x_train.shape)\n",
"x_test_noisy = x_test + 0.05 * np.random.randn(*x_test.shape)\n",
"print(\"Fit on half the pixels: {}. It should be around 0.835.\".format(\n",
" logistic_fit(x_train_noisy[:, :DIMS//2], y_train,\n",
" x_test_noisy[:, :DIMS//2], y_test)))\n",
"\n",
"def processed_train_data(dims, batch_size):\n",
" dataset = load_data(\"train\")\n",
" dataset_batched = dataset.repeat().batch(batch_size, drop_remainder=True)\n",
" get_next = dataset_batched.make_one_shot_iterator().get_next()\n",
" features = get_next[FEATURE_INPUT]\n",
" labels = get_next[FEATURE_LABEL]\n",
" x_1, x_2 = tf.split(features, [dims, DIMS-dims], axis=-1)\n",
" return x_1, x_2, labels"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "3qHQzV6LhRsT"
},
"source": [
"## Encoders\n",
"\n",
"Here we define the encoder architectures, namely MLP and ConvNet."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "XqRJ4jSspi4o",
"colab": {}
},
"source": [
"class MLP(tf.keras.Model):\n",
" def __init__(self, layer_dimensions, shortcuts, dense_kwargs={}):\n",
" super(MLP, self).__init__()\n",
" self._layers = [tfkl.Dense(dimensions, **dense_kwargs)\n",
" for dimensions in layer_dimensions[:-1]]\n",
" dense_kwargs_copy = copy.deepcopy(dense_kwargs)\n",
" dense_kwargs_copy[\"activation\"] = None\n",
" self._layers.append(tfkl.Dense(layer_dimensions[-1], **dense_kwargs_copy))\n",
" self._shortcuts = shortcuts\n",
"\n",
" @property\n",
" def layers(self):\n",
" return self._layers\n",
"\n",
" def __call__(self, inputs):\n",
" x = inputs\n",
" for layer in self.layers:\n",
" x = layer(x) + x if self._shortcuts else layer(x)\n",
" return x\n",
"\n",
"\n",
"# LayerNorm implementation copied from\n",
"# https://stackoverflow.com/questions/39095252/fail-to-implement-layer-normalization-with-keras\n",
"class LayerNorm(tfkl.Layer):\n",
"\n",
" \"\"\" Layer Normalization in the style of https://arxiv.org/abs/1607.06450 \"\"\"\n",
" def __init__(self, scale_initializer='ones', bias_initializer='zeros',\n",
" axes=[1,2,3], epsilon=1e-6, **kwargs):\n",
" super(LayerNorm, self).__init__(**kwargs)\n",
" self.epsilon = epsilon\n",
" self.scale_initializer = tf.keras.initializers.get(scale_initializer)\n",
" self.bias_initializer = tf.keras.initializers.get(bias_initializer)\n",
" self.axes = axes\n",
"\n",
" def build(self, input_shape):\n",
" self.scale = self.add_weight(shape=(input_shape[-1],),\n",
" initializer=self.scale_initializer,\n",
" trainable=True,\n",
" name='{}_scale'.format(self.name))\n",
" self.bias = self.add_weight(shape=(input_shape[-1],),\n",
" initializer=self.bias_initializer,\n",
" trainable=True,\n",
" name='{}_bias'.format(self.name))\n",
" self.built = True\n",
"\n",
" def call(self, x, mask=None):\n",
" mean = tf.keras.backend.mean(x, axis=self.axes, keepdims=True)\n",
" std = tf.keras.backend.std(x, axis=self.axes, keepdims=True)\n",
" norm = (x - mean) * (1/(std + self.epsilon))\n",
" return norm * self.scale + self.bias\n",
"\n",
" def compute_output_shape(self, input_shape):\n",
" return input_shape\n",
"\n",
"\n",
"class ConvNet(tf.keras.Sequential):\n",
" def __init__(self, channels=64, kernel_size=5, input_dim=DIMS//2, output_dim=100,\n",
" activation=tf.nn.relu):\n",
" # Note: This works only for the specific data set considered here.\n",
" super(ConvNet, self).__init__([\n",
" tfkl.Reshape((14, 28, 1), input_shape=(input_dim,)),\n",
" tfkl.Conv2D(channels, kernel_size, strides=2,\n",
" padding=\"same\", activation=activation),\n",
" tfkl.Conv2D(2*channels, kernel_size, strides=2,\n",
" padding=\"same\", activation=activation),\n",
" LayerNorm(),\n",
" tfkl.GlobalAveragePooling2D(),\n",
" tfkl.Dense(output_dim),\n",
" ])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "4FJW4sNNg6Vr"
},
"source": [
"### Custom RealMVP\n",
"\n",
"We make two small modifications to the standard RealNVP implementation (highlighted with comments with **). Due to numerical instability we replace the exp with a softplus. The exp is normally used because it makes calculation of the log-det-jacobian simple, which is necessary for many applications of normalizing flows. In our setting, we only care about the architecture being invertible. This is still satisfied with our modification."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "VlNPS3KojJFa",
"colab": {}
},
"source": [
"from tensorflow_probability.python.internal import tensorshape_util\n",
"import tensorflow.compat.v1 as tf1\n",
"from tensorflow_probability.python.bijectors import affine_scalar\n",
"from tensorflow_probability.python.bijectors import bijector as bijector_lib\n",
"\n",
"# Modified from tensorflow_probability/python/bijectors/real_nvp.py\n",
"class RealNVP(bijector_lib.Bijector):\n",
" def __init__(self,\n",
" num_masked,\n",
" shift_and_log_scale_fn=None,\n",
" bijector_fn=None,\n",
" is_constant_jacobian=False,\n",
" validate_args=False,\n",
" name=None):\n",
" name = name or 'real_nvp'\n",
" if num_masked < 0:\n",
" raise ValueError('num_masked must be a non-negative integer.')\n",
" self._num_masked = num_masked\n",
" # At construction time, we don't know input_depth.\n",
" self._input_depth = None\n",
" if bool(shift_and_log_scale_fn) == bool(bijector_fn):\n",
" raise ValueError('Exactly one of `shift_and_log_scale_fn` and '\n",
" '`bijector_fn` should be specified.')\n",
" if shift_and_log_scale_fn:\n",
" def _bijector_fn(x0, input_depth, **condition_kwargs):\n",
" shift, log_scale = shift_and_log_scale_fn(x0, input_depth,\n",
" **condition_kwargs)\n",
" # ** First modification is here.\n",
" return affine_scalar.AffineScalar(shift=shift, scale=log_scale)\n",
"\n",
" bijector_fn = _bijector_fn\n",
"\n",
" if validate_args:\n",
" bijector_fn = _validate_bijector_fn(bijector_fn)\n",
"\n",
" # Still do this assignment for variable tracking.\n",
" self._shift_and_log_scale_fn = shift_and_log_scale_fn\n",
" self._bijector_fn = bijector_fn\n",
"\n",
" super(RealNVP, self).__init__(\n",
" forward_min_event_ndims=1,\n",
" is_constant_jacobian=is_constant_jacobian,\n",
" validate_args=validate_args,\n",
" name=name)\n",
"\n",
" def _cache_input_depth(self, x):\n",
" if self._input_depth is None:\n",
" self._input_depth = tf.compat.dimension_value(\n",
" tensorshape_util.with_rank_at_least(x.shape, 1)[-1])\n",
" if self._input_depth is None:\n",
" raise NotImplementedError(\n",
" 'Rightmost dimension must be known prior to graph execution.')\n",
" if self._num_masked >= self._input_depth:\n",
" raise ValueError(\n",
" 'Number of masked units must be smaller than the event size.')\n",
"\n",
" def _forward(self, x, **condition_kwargs):\n",
" self._cache_input_depth(x)\n",
" x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n",
" y1 = self._bijector_fn(x0, self._input_depth - self._num_masked,\n",
" **condition_kwargs).forward(x1)\n",
" y = tf.concat([x0, y1], axis=-1)\n",
" return y\n",
"\n",
" def _inverse(self, y, **condition_kwargs):\n",
" self._cache_input_depth(y)\n",
" y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n",
" x1 = self._bijector_fn(y0, self._input_depth - self._num_masked,\n",
" **condition_kwargs).inverse(y1)\n",
" x = tf.concat([y0, x1], axis=-1)\n",
" return x\n",
"\n",
" def _forward_log_det_jacobian(self, x, **condition_kwargs):\n",
" self._cache_input_depth(x)\n",
" x0, x1 = x[..., :self._num_masked], x[..., self._num_masked:]\n",
" return self._bijector_fn(x0, self._input_depth - self._num_masked,\n",
" **condition_kwargs).forward_log_det_jacobian(\n",
" x1, event_ndims=1)\n",
"\n",
" def _inverse_log_det_jacobian(self, y, **condition_kwargs):\n",
" self._cache_input_depth(y)\n",
" y0, y1 = y[..., :self._num_masked], y[..., self._num_masked:]\n",
" return self._bijector_fn(y0, self._input_depth - self._num_masked,\n",
" **condition_kwargs).inverse_log_det_jacobian(\n",
" y1, event_ndims=1)\n",
"\n",
"def real_nvp_default_template(hidden_layers,\n",
" shift_only=False,\n",
" activation=tf.nn.relu,\n",
" name=None,\n",
" *args, # pylint: disable=keyword-arg-before-vararg\n",
" **kwargs):\n",
" with tf.name_scope(name or 'real_nvp_default_template'):\n",
"\n",
" def _fn(x, output_units, **condition_kwargs):\n",
" \"\"\"Fully connected MLP parameterized via `real_nvp_template`.\"\"\"\n",
" if condition_kwargs:\n",
" raise NotImplementedError(\n",
" 'Conditioning not implemented in the default template.')\n",
"\n",
" if tensorshape_util.rank(x.shape) == 1:\n",
" x = x[tf.newaxis, ...]\n",
" reshape_output = lambda x: x[0]\n",
" else:\n",
" reshape_output = lambda x: x\n",
" for units in hidden_layers:\n",
" x = tf1.layers.dense(\n",
" inputs=x,\n",
" units=units,\n",
" activation=activation,\n",
" *args, # pylint: disable=keyword-arg-before-vararg\n",
" **kwargs)\n",
" x = tf1.layers.dense(\n",
" inputs=x,\n",
" units=(1 if shift_only else 2) * output_units,\n",
" activation=None,\n",
" *args, # pylint: disable=keyword-arg-before-vararg\n",
" **kwargs)\n",
" if shift_only:\n",
" return reshape_output(x), None\n",
" shift, log_scale = tf.split(x, 2, axis=-1)\n",
" # ** Here is the second modification.\n",
" return reshape_output(shift), 1e-7 + tf.nn.softplus(reshape_output(log_scale))\n",
"\n",
" return tf1.make_template('real_nvp_default_template', _fn)\n",
"\n",
"class RealNVPBijector(tf.keras.Model):\n",
" def __init__(self, dimensions, n_couplings, hidden_layers, dense_kwargs):\n",
" super(RealNVPBijector, self).__init__()\n",
" permutations = [np.random.permutation(dimensions)\n",
" for _ in range(n_couplings)]\n",
" bijectors = []\n",
" for permutation in permutations:\n",
" bijectors.append(RealNVP(\n",
" dimensions // 2,\n",
" real_nvp_default_template(hidden_layers, **dense_kwargs)))\n",
" bijectors.append(tfb.Permute(permutation))\n",
" self._bijector = tfb.Chain(bijectors)\n",
"\n",
" def call(self, inputs):\n",
" return self._bijector.forward(inputs)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Ax0hgIwkhplg"
},
"source": [
"## Critics\n",
"\n",
"Here we define the encoder architectures, namely Inner product, bilinear, concat, and separable critic."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "vRguW9Fyc7uO",
"colab": {}
},
"source": [
"class InnerProdCritic(tf.keras.Model):\n",
" def call(self, x, y):\n",
" return tf.matmul(x, y, transpose_b=True)\n",
"\n",
"class BilinearCritic(tf.keras.Model):\n",
" def __init__(self, feature_dim=100, **kwargs):\n",
" super(BilinearCritic, self).__init__(**kwargs)\n",
" self._W = tfkl.Dense(feature_dim, use_bias=False)\n",
"\n",
" def call(self, x, y):\n",
" return tf.matmul(x, self._W(y), transpose_b=True)\n",
"\n",
"# Copied from\n",
"# https://colab.research.google.com/github/google-research/google-research/blob/master/vbmi/vbmi_demo.ipynb\n",
"class ConcatCritic(tf.keras.Model):\n",
" def __init__(self, hidden_dim=200, layers=1, activation='relu', **kwargs):\n",
" super(ConcatCritic, self).__init__(**kwargs)\n",
" # output is scalar score\n",
" self._f = MLP([hidden_dim for _ in range(layers)]+[1], False, {\"activation\": \"relu\"})\n",
"\n",
" def call(self, x, y):\n",
" batch_size = tf.shape(x)[0]\n",
" # Tile all possible combinations of x and y\n",
" x_tiled = tf.tile(x[None, :], (batch_size, 1, 1))\n",
" y_tiled = tf.tile(y[:, None], (1, batch_size, 1))\n",
" # xy is [batch_size * batch_size, x_dim + y_dim]\n",
" xy_pairs = tf.reshape(tf.concat((x_tiled, y_tiled), axis=2),\n",
" [batch_size * batch_size, -1])\n",
" # Compute scores for each x_i, y_j pair.\n",
" scores = self._f(xy_pairs) \n",
" return tf.transpose(tf.reshape(scores, [batch_size, batch_size]))\n",
"\n",
"\n",
"class SeparableCritic(tf.keras.Model):\n",
" def __init__(self, hidden_dim=100, output_dim=100, layers=1,\n",
" activation='relu', **kwargs):\n",
" super(SeparableCritic, self).__init__(**kwargs)\n",
" self._f_x = MLP([hidden_dim for _ in range(layers)] + [output_dim], False, {\"activation\": activation})\n",
" self._f_y = MLP([hidden_dim for _ in range(layers)] + [output_dim], False, {\"activation\": activation})\n",
"\n",
" def call(self, x, y):\n",
" x_mapped = self._f_x(x)\n",
" y_mapped = self._f_y(y)\n",
" return tf.matmul(x_mapped, y_mapped, transpose_b=True)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "DKhwL9cdYQ3L"
},
"source": [
"# Experiments"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "noACZM4kiBGq"
},
"source": [
"## Training loop for Section 3.1 - 3.3 in the paper\n",
"\n",
"Classic training loop where we update the encoder (and possibly the critic) and evaluate the model on test data."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "B9sIQHE-znsE",
"colab": {}
},
"source": [
"def train(g1,\n",
" g2,\n",
" critic,\n",
" loss_fn,\n",
" learning_rate=1e-4,\n",
" batch_size=TRAIN_BATCH_SIZE,\n",
" n_iters=15000,\n",
" n_evals=15,\n",
" compute_jacobian=False,\n",
" noise_std=0.0,\n",
" data_dimensions=DIMS//2):\n",
" \"\"\"Runs the training loop for a fixed model.\n",
"\n",
" Args:\n",
" g1: Function, maps input1 to representation.\n",
" g2: Function, maps input2 to representation.\n",
" critic: Function, maps two representations to scalar.\n",
" loss_fn: Function, mutual information estimator.\n",
" learning_rate: Learning rate.\n",
" batch_size: Training batch size.\n",
" n_iters: Number of optimization iterations.\n",
" n_evals: Number of model evaluations.\n",
" compute_jacobian: Whether to estimate the singular values of the Jacobian.\n",
" noise_std: Standard deviation for the Gaussian noise. Default is 0.0.\n",
" data_dimensions: The dimension of the data. By default it's half of the\n",
" original data dimension.\n",
" Returns:\n",
" Returns and instance of `Results` tuple.\n",
" \"\"\"\n",
" x_1, x_2, _ = processed_train_data(data_dimensions, batch_size)\n",
"\n",
" if noise_std > 0.0:\n",
" assert x_1.shape == x_2.shape, \"X1 and X2 shapes must agree to add noise!\"\n",
" noise = noise_std * tf.random.normal(x_1.shape)\n",
" x_1 += noise\n",
" x_2 += noise\n",
"\n",
" # Compute the representations.\n",
" code_1, code_2 = g1(x_1), g2(x_2)\n",
" critic_matrix = critic(code_1, code_2)\n",
"\n",
" # Compute the Jacobian of g1 if needed.\n",
" if compute_jacobian:\n",
" jacobian = gradients.batch_jacobian(code_1, x_1, use_pfor=False)\n",
" singular_values = tf.linalg.svd(jacobian, compute_uv=False)\n",
"\n",
" # Optimizer setup.\n",
" loss = loss_fn(critic_matrix)\n",
" optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
" optimizer_op = optimizer.minimize(loss)\n",
"\n",
" with tf.Session() as session:\n",
" session.run(tf.global_variables_initializer())\n",
"\n",
" # Subgraph for eval (add noise to input if necessary)\n",
" data_ph = tf.placeholder(tf.float32, shape=[None, data_dimensions])\n",
" data_ph_noisy = data_ph + noise_std * tf.random.normal(tf.shape(data_ph))\n",
" codes = g1(data_ph_noisy)\n",
"\n",
" training_losses, testing_losses, classification_accuracies, iters, sigmas \\\n",
" = [], [], [], [], []\n",
" # Main training loop.\n",
" for iter_n in range(n_iters):\n",
" # Evaluate the model performance.\n",
" if iter_n % (n_iters // n_evals) == 0:\n",
" iters.append(iter_n)\n",
" accuracy = get_classification_accuracy(session, codes, data_ph, data_dimensions)\n",
" classification_accuracies.append(accuracy)\n",
" testing_losses.append(\n",
" get_testing_loss(x_test, session, loss, data_ph, data_dimensions))\n",
" if compute_jacobian:\n",
" sigmas.append(session.run(singular_values))\n",
" print(\"Step {:>10d} fit {:>.5f}\".format(iter_n, accuracy))\n",
" # Run one optimization step.\n",
" loss_np, _ = session.run([loss, optimizer_op])\n",
" training_losses.append(loss_np)\n",
"\n",
" return Results(iterations=iters,\n",
" training_losses=training_losses,\n",
" testing_losses=testing_losses,\n",
" classification_accuracies=classification_accuracies,\n",
" singular_values=sigmas)\n",
"\n",
"\n",
"def run_sweep(nets, critics, loss_fns, exp_name, **kwargs):\n",
" \"\"\"Runs the sweep across encoder networks, critics, and the estimators.\"\"\"\n",
" grid = itertools.product(nets, critics, loss_fns)\n",
" data_frames = []\n",
" results_with_singular_values = []\n",
" for nets_name, critic_name, loss_name in grid:\n",
" print(\"[New experiment] encoder: {}, critic: {}, loss: {}\".format(\n",
" nets_name, critic_name, loss_name))\n",
" with tf.Graph().as_default():\n",
" g1, g2 = nets[nets_name]()\n",
" critic = critics[critic_name]()\n",
" loss_fn = loss_fns[loss_name]\n",
" results_per_run = []\n",
" for n in range(NRUNS):\n",
" try:\n",
" results = train(g1, g2, critic, loss_fn, **kwargs)\n",
" results_per_run.append(results)\n",
" except Exception as ex:\n",
" print(\"Run {} failed! Error: {}\".format(n, ex))\n",
" for i, result in enumerate(results_per_run):\n",
" data_frames.append(convert_to_data_frame(\n",
" result, exp_name, nets_name, critic_name, loss_name, i))\n",
" if kwargs.get('compute_jacobian', False):\n",
" results_with_singular_values.append((\n",
" ResultsConfig(nets_name, critic_name, loss_name), results_per_run\n",
" ))\n",
" \n",
" return {\n",
" \"df\": pd.concat(data_frames), \n",
" \"singular_values\": results_with_singular_values\n",
" }"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "LGCSUC4r-tfW"
},
"source": [
"## Maximized MI and improved downstream performance"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "gUXTDLsq-xuF"
},
"source": [
"Reproduces the first experiment of Section 3.1 and the corresponding Figures 1 (a, b).\n",
"\n",
"In this experiment we use invertible architectures. We show that training to maximize the MI estimators results in improved downstream performance, even though MI is maximized for any parameter setting (due to invertibility)."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "5XTz-B4zs8d7",
"colab": {}
},
"source": [
"#@title Run experiment or load precomputed results { display-mode: \"form\" }\n",
"def run_all_experiments():\n",
" tf.reset_default_graph()\n",
" infonce_loss = lambda x: -infonce_lower_bound(x)\n",
" nwj_loss = lambda x: -nwj_lower_bound(x)\n",
" loss_fcts = {\n",
" \"nwj\": nwj_loss,\n",
" \"nce\": infonce_loss\n",
" }\n",
" kwargs = dict(\n",
" shift_only=True,\n",
" activation=lambda x: tf.nn.relu(x),\n",
" kernel_initializer=tf.initializers.truncated_normal(stddev=0.0001),\n",
" bias_initializer='zeros')\n",
" nets = {\n",
" \"realnvp\": lambda: (\n",
" RealNVPBijector(DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=kwargs),\n",
" RealNVPBijector(DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=kwargs)\n",
" )\n",
" }\n",
" critics = {\n",
" \"bilinear\": lambda: BilinearCritic(feature_dim=DIMS//2),\n",
" }\n",
" return run_sweep(nets, critics, loss_fcts, \"invertible\", n_iters=21000, n_evals=21)\n",
"\n",
"if RUN_EXPERIMENTS:\n",
" data_invertible = run_all_experiments()[\"df\"]\n",
"else:\n",
" !wget -q -N https://storage.googleapis.com/mi_for_rl_files/mi_results.pkl\n",
" data_invertible = pd.read_pickle('mi_results.pkl')\n",
" data_invertible = data_invertible[data_invertible.exp_name == \"invertible\"]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "8rp-GrKEli6j",
"colab": {}
},
"source": [
"#@title Downstream accuracy plot { display-mode: \"form\" }\n",
"data = data_invertible[data_invertible.Critic.isin([\"Bilinear\"])]\n",
"\n",
"plt.figure()\n",
"ax = sns.lineplot(data=data, x=\"iteration\", y=\"accuracy\", hue=\"Estimator\", ci=\"sd\");\n",
"apply_default_style(ax)\n",
"ax.set_ylabel(\"Accuracy\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "yAjY5DbSTmwa",
"colab": {}
},
"source": [
"#@title MI lower bound plot { display-mode: \"form\" }\n",
"plt.figure()\n",
"ax = sns.lineplot(data=data, x=\"iteration\", y=\"bound_value\", hue=\"Estimator\", ci=\"sd\",);\n",
"apply_default_style(ax)\n",
"ax.set_ylim(-5, 8)\n",
"ax.set_ylabel(\"$I_{EST}$\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "o16VFAvt_i_H"
},
"source": [
"## Maximized MI and worsened downstream performance"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "FvoPb7qV_nWB"
},
"source": [
"Reproduces the second experiment of Section 3.1 and the corresponding Figure 1 (c). \n",
"\n",
"In this experiment we use invertible architectures. By adversarially training an encoder, we show that it is possible to significantly deteriorate downstream performance, even though MI is maximized for any parameter setting (due to invertibility)."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "-KWO_-UEzf1u",
"colab": {}
},
"source": [
"#@title Define training loop { display-mode: \"form\" }\n",
"\n",
"def train_adversarial(net,\n",
" learning_rate=1e-3,\n",
" batch_size=TRAIN_BATCH_SIZE,\n",
" n_iters=4000,\n",
" record_every=400,\n",
" data_dimension=DIMS//2):\n",
" \"\"\"Runs the adversarial training loop for a fixed model.\n",
"\n",
" Args:\n",
" net: Function, maps input to representation.\n",
" learning_rate: Learning rate.\n",
" batch_size: Training batch size.\n",
" n_iters: Number of optimization iterations.\n",
" record_every: Evaluate the model every `record_every` steps.\n",
" data_dimensions: The dimension of the data. By default it's half of the\n",
" original data dimension.\n",
" Returns:\n",
" Returns and instance of `Results` tuple.\n",
" \"\"\"\n",
"\n",
"\n",
" if net.__class__ is not RealNVPBijector:\n",
" raise ValueError(\"Only implemented for the RealNVP class.\")\n",
"\n",
" # Get the data and compute the representation.\n",
" x_1, _, labels = processed_train_data(data_dimension, batch_size)\n",
" code = net(x_1)\n",
"\n",
" with tf.variable_scope(\"classifier\"):\n",
" logits = tf.layers.dense(code, N_CLASSES)\n",
"\n",
" # True classification loss for linear classifier.\n",
" loss_c = tf.nn.sparse_softmax_cross_entropy_with_logits(\n",
" logits=logits, labels=labels)\n",
" loss_c = tf.reduce_mean(loss_c)\n",
"\n",
" # Fake classification loss for the encoder.\n",
" labels_unif = (1 / N_CLASSES) * tf.ones(logits.shape)\n",
" loss_e = tf.nn.softmax_cross_entropy_with_logits(\n",
" logits=logits, labels=labels_unif)\n",
" loss_e = tf.reduce_mean(loss_e)\n",
"\n",
" # Setup the optimizers.\n",
" vars_e = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=\"real_nvp\")\n",
" vars_c = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=\"classifier\")\n",
"\n",
" optimizer_c = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
" optimizer_e = tf.train.AdamOptimizer(learning_rate=learning_rate*0.01)\n",
"\n",
" optimizer_op_c = optimizer_c.minimize(loss_c, var_list=vars_c)\n",
" optimizer_op_e = optimizer_e.minimize(loss_e, var_list=vars_e)\n",
"\n",
"\n",
" with tf.Session() as session:\n",
" session.run(tf.global_variables_initializer())\n",
" data_ph = tf.placeholder(tf.float32, shape=[None, data_dimension])\n",
" codes = net(data_ph)\n",
" losses_c, losses_e, classification_accuracies, iters = [], [], [], []\n",
"\n",
" # Warm-up the linear classifier.\n",
" for _ in range(1000):\n",
" session.run([loss_c, optimizer_op_c])\n",
"\n",
" # Run main loop.\n",
" for iter_n in range(n_iters):\n",
" if iter_n % record_every == 0:\n",
" iters.append(iter_n)\n",
" accuracy = get_classification_accuracy(\n",
" session, codes, data_ph, data_dimension)\n",
" classification_accuracies.append(accuracy)\n",
" print(\"Step {:>10d} fit {:>.5f}\".format(iter_n, accuracy))\n",
"\n",
" # Run 10 optimization steps for the classifier.\n",
" for _ in range(10):\n",
" loss_c_np, _ = session.run([loss_c, optimizer_op_c])\n",
" # Run 1 optimization steps for the encoder.\n",
" loss_e_np, _ = session.run([loss_e, optimizer_op_e])\n",
" losses_c.append(loss_c_np)\n",
" losses_e.append(loss_e_np)\n",
" if iter_n % 100 == 0:\n",
" print(\" loss_e {:>.5f} loss_c {:>.5f}\".format(loss_e_np, loss_c_np))\n",
"\n",
" return ResultsAdversarial(\n",
" losses_e=losses_e,\n",
" losses_c=losses_c,\n",
" classification_accuracies=classification_accuracies,\n",
" iters=iters)\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "KRJ-EdmiV7U2",
"colab": {}
},
"source": [
"#@title Run experiment or load precomputed results { display-mode: \"form\" }\n",
"\n",
"def run_all_experiments():\n",
" tf.reset_default_graph()\n",
" kwargs = dict(activation=lambda x: tf.nn.relu(x),\n",
" kernel_initializer=tf.initializers.truncated_normal(stddev=0.0001),\n",
" bias_initializer='zeros')\n",
" net = RealNVPBijector(DIMS // 2, n_couplings=30, hidden_layers=[512, 512], dense_kwargs=kwargs)\n",
" return train_adversarial(\n",
" net, learning_rate=1e-3, n_iters=4001, \n",
" record_every=400, data_dimension=DIMS//2, batch_size=128)\n",
"\n",
"if RUN_EXPERIMENTS:\n",
" data_adversarial = run_all_experiments()\n",
"else:\n",
" !wget -q -N https://storage.googleapis.com/mi_for_rl_files/adversarial_results.pkl\n",
" with tf.gfile.Open('adversarial_results.pkl', 'rb') as f:\n",
" data_adversarial = pickle.load(f, encoding='latin1')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "-JMOE6ca_HWy",
"colab": {}
},
"source": [
"#@title Downstream accuracy plot { display-mode: \"form\" }\n",
"plt.figure()\n",
"plt.plot(data_adversarial.iters, data_adversarial.classification_accuracies, linewidth=2)\n",
"ax = plt.gca()\n",
"ax.set_ylabel(\"Accuracy\")\n",
"apply_default_style(ax)\n",
"ax.set_xlim([0, 4001])\n",
"ax.set_xticklabels([0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4])\n",
"leg = plt.legend([\"Adversarially trained\\ninvertible encoder\"], prop={'size':14});"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "q35Q4nvrB_fJ"
},
"source": [
"## Bias towards hard-to-invert encoders"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8lwsU3qhB_fW"
},
"source": [
"Reproduces the third experiment of Section 3.1 and the corresponding Figures 2 (a, b, c).\n",
"\n",
"In this experiment we use architectures that can be both invertible and non-invertible. We show that training to maximize the MI estimators results in the encoders becoming hard to invert, in that they become locally ill-conditioned."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "-b2rZuh5WjPH",
"colab": {}
},
"source": [
"#@title Run experiment or load precomputed results { display-mode: \"form\" }\n",
"\n",
"def run_all_experiments():\n",
" tf.reset_default_graph()\n",
" infonce_loss = lambda x: -infonce_lower_bound(x)\n",
" nwj_loss = lambda x: -nwj_lower_bound(x)\n",
" loss_fcts = {\n",
" \"nwj\": nwj_loss,\n",
" \"nce\": infonce_loss\n",
" }\n",
" kwargs = dict(activation=\"relu\",\n",
" kernel_initializer=tf.initializers.truncated_normal(stddev=0.0001),\n",
" bias_initializer=\"zeros\")\n",
" nets = {\n",
" \"mlp\": lambda: (\n",
" MLP([DIMS // 2] * 5, shortcuts=True, dense_kwargs=kwargs),\n",
" MLP([DIMS // 2] * 5, shortcuts=True, dense_kwargs=kwargs))\n",
" }\n",
" critics = {\n",
" \"bilinear\": lambda: BilinearCritic(feature_dim=DIMS//2),\n",
" }\n",
" return run_sweep(nets, critics, loss_fcts,\"non_invertible\",\n",
" n_iters=21000, n_evals=21, compute_jacobian=True,\n",
" noise_std=0.05)\n",
"\n",
"if RUN_EXPERIMENTS:\n",
" all_results = run_all_experiments()\n",
" data_non_invertible = all_results[\"df\"]\n",
" non_invertible_singular_values = all_results[\"singular_values\"]\n",
"\n",
"else:\n",
" data_non_invertible = pd.read_pickle('mi_results.pkl')\n",
" data_non_invertible = data_non_invertible[data_non_invertible.exp_name == \"non_invertible\"]\n",
"\n",
" !wget -q -N https://storage.googleapis.com/mi_for_rl_files/condition_numbers_results.pkl\n",
" with tf.gfile.Open('condition_numbers_results.pkl', 'rb') as f:\n",
" non_invertible_singular_values = pickle.load(f, encoding='latin1')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "J5ebcwAP8Ssp",
"colab": {}
},
"source": [
"#@title Downstream accuracy plot { display-mode: \"form\" }\n",
"data = data_non_invertible[data_non_invertible.Critic.isin([\"Bilinear\"])]\n",
"plt.figure()\n",
"ax = sns.lineplot(data=data, x=\"iteration\", y=\"accuracy\", hue=\"Estimator\", ci=\"sd\");\n",
"apply_default_style(ax)\n",
"ax.set_ylim([0.83, 0.9])\n",
"ax.set_ylabel(\"Accuracy\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "kCz_wexf2WgK",
"colab": {}
},
"source": [
"#@title MI lower bound plot { display-mode: \"form\" }\n",
"plt.figure()\n",
"ax = sns.lineplot(data=data, x=\"iteration\", y=\"bound_value\", hue=\"Estimator\", ci=\"sd\");\n",
"apply_default_style(ax)\n",
"ax.set_ylim(-5, 8)\n",
"ax.set_ylabel(\"$I_{EST}$\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "d7LUJtv4rVqy",
"colab": {}
},
"source": [
"#@title Jacobian condition number plot { display-mode: \"form\" }\n",
"\n",
"colors = sns.color_palette()\n",
"\n",
"def percentile_plot(log_condition_number, iters, pcs = 5):\n",
" \"\"\"Create percentile plot.\"\"\"\n",
" sorted_log_condition_number = np.sort(log_condition_number, axis=1)\n",
" n_iters, n_condition_numbers = sorted_log_condition_number.shape\n",
" percentiles = [i / pcs for i in range(pcs + 1)]\n",
" pc_idx = [int(p * (n_condition_numbers - 1)) for p in percentiles]\n",
"\n",
" alpha = (\n",
" [i / (pcs / 2) for i in range(pcs//2 + 1)] +\n",
" [2 - (i / (pcs / 2)) for i in range(pcs//2 + 1, pcs + 1)]\n",
" )\n",
" alpha = [a + 0.05 for a in alpha]\n",
" alpha = [a / max(alpha) for a in alpha]\n",
"\n",
" plt.plot(\n",
" iters[: n_iters], \n",
" sorted_log_condition_number[:, pc_idx[0]],\n",
" color=\"gray\", alpha=1, lw=2,\n",
" label=\"Minimum\",\n",
" linestyle=\"--\"\n",
" )\n",
" for i in range(len(pc_idx) - 1):\n",
" p1, p2 = pc_idx[i], pc_idx[i + 1]\n",
" plt.fill_between(\n",
" iters[: n_iters], \n",
" sorted_log_condition_number[:, p1],\n",
" sorted_log_condition_number[:, p2],\n",
" color=colors[i], alpha=0.75)\n",
" if i != 4:\n",
" plt.plot(\n",
" iters[: n_iters], \n",
" sorted_log_condition_number[:, pc_idx[i + 1]],\n",
" color=colors[i], alpha=1, lw=2,\n",
" label=\"%.0fth perc.\" % ((100/pcs) * (i+1)),\n",
" linestyle=\"--\"\n",
" )\n",
" plt.plot(\n",
" iters[: n_iters], \n",
" sorted_log_condition_number[:, pc_idx[-1]],\n",
" color=colors[4], alpha=1, lw=2,\n",
" label=\"Maximum\",\n",
" linestyle=\"--\"\n",
" )\n",
"\n",
" apply_default_style(plt.gca())\n",
"\n",
"# As the Jacobian singular values are a batch_size x input_dim matrix per\n",
"# iteration, we need a separate routine to extract the corresponding data\n",
"# and aggregate it\n",
"def aggregate_singular_values(configs_and_results):\n",
" data_eval = {}\n",
" for (config, results_all_runs) in configs_and_results:\n",
" label = \"{}, {}, {}\".format(config.nets, config.critic, config.loss)\n",
" condition_numbers_runs = []\n",
" for run_number, results in enumerate(results_all_runs):\n",
" stacked_singular_values = np.stack(results.singular_values)\n",
" sorted_singular_values = np.sort(stacked_singular_values, axis=-1)\n",
" log_condition_numbers = np.log(sorted_singular_values[..., -1]) \\\n",
" - np.log(sorted_singular_values[..., 0])\n",
" condition_numbers_runs.append(log_condition_numbers)\n",
" if len(results_all_runs) > 0:\n",
" iterations = results_all_runs[0].iterations\n",
" condition_numbers = np.concatenate(condition_numbers_runs, axis=1)\n",
" data_eval[label] = (iterations, condition_numbers)\n",
" return data_eval\n",
"\n",
"results_dict = aggregate_singular_values(non_invertible_singular_values)\n",
"\n",
"for key in results_dict.keys():\n",
" if \"bilinear\" not in key:\n",
" continue\n",
" plt.figure()\n",
" its = results_dict[key][0]\n",
" condnbrs = results_dict[key][1]\n",
" percentile_plot(condnbrs, its)\n",
" if \"nce\" in key:\n",
" plt.ylabel(r\"Jacobian, $log\\ (\\sigma_1\\ /\\ \\sigma_{392})$\");\n",
" plt.legend(loc=\"lower right\", ncol=2)\n",
" plt.ylim([0, 8])\n",
" else:\n",
" plt.ylabel(\"Jacobian, $log\\ (\\sigma_1\\ /\\ \\sigma_{392})$\");\n",
" plt.legend(loc=\"upper left\", ncol=2)\n",
" plt.ylim([0, 8])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Ui2VAr9UBOns"
},
"source": [
"## Looser bounds with simpler critics can lead to better representations"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "Ysurx0zUBhs0"
},
"source": [
"Reproduces the experiment from Section 3.2 and the corresponding Figure 3.\n",
"\n",
"In this experiment we investigate the effect of the critic architecture on downstream performance. We find that simpler critics can result in better performance, despite leading to looser MI bounds."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "3yuppcWqXEBN",
"colab": {}
},
"source": [
"#@title Run experiment or load precomputed results { display-mode: \"form\" }\n",
"\n",
"def run_all_experiments():\n",
" tf.reset_default_graph()\n",
" infonce_loss = lambda x: -infonce_lower_bound(x)\n",
" nwj_loss = lambda x: -nwj_lower_bound(x)\n",
" loss_fcts = {\"nwj\": nwj_loss, \"nce\": infonce_loss}\n",
" nets = {\n",
" \"mlp\": lambda: (MLP([300, 300, 100], False, {\"activation\": \"relu\"}),\n",
" MLP([300, 300, 100], False, {\"activation\": \"relu\"})),\n",
" }\n",
" critics = {\n",
" \"concat\": lambda: ConcatCritic(),\n",
" \"bilinear\": lambda: BilinearCritic(),\n",
" \"separable\": lambda: SeparableCritic(layers=1),\n",
" }\n",
" return run_sweep(\n",
" nets, critics, loss_fcts, \"critic_impact\", n_iters=21000, n_evals=21)\n",
"\n",
"if RUN_EXPERIMENTS:\n",
" data_critic_impact = run_all_experiments()[\"df\"]\n",
"else:\n",
" data_critic_impact = pd.read_pickle('mi_results.pkl')\n",
" data_critic_impact = data_critic_impact[data_critic_impact.exp_name == \"critic_impact\"]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "TyJax82YWPpT",
"colab": {}
},
"source": [
"#@title Downstream accuracy plot { display-mode: \"form\" }\n",
"data = data_critic_impact\n",
"data = data[data.Critic.isin([\"Bilinear\", \"MLP\", \"Separable\"])]\n",
"data_nwj = data[data.Estimator.isin([\"$I_{NWJ}$\"])]\n",
"data_nce = data[data.Estimator.isin([\"$I_{NCE}$\"])]\n",
"\n",
"plt.figure()\n",
"ax = sns.lineplot(data=data_nwj, x=\"iteration\", y=\"accuracy\", hue=\"Critic\", ci=\"sd\");\n",
"apply_default_style(ax)\n",
"ax.set_ylabel(\"Accuracy with $I_{NWJ}$\")\n",
"ax.set_ylim([0.8, 0.9])\n",
"plt.figure()\n",
"ax = sns.lineplot(data=data_nce, x=\"iteration\", y=\"accuracy\", hue=\"Critic\", ci=\"sd\");\n",
"apply_default_style(ax)\n",
"ax.set_ylim([0.8, 0.9])\n",
"ax.set_ylabel(\"Accuracy with $I_{NCE}$\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "PjneQuoFjM1G",
"colab": {}
},
"source": [
"#@title MI lower bound plot { display-mode: \"form\" }\n",
"plt.figure()\n",
"ax = sns.lineplot(data=data_nwj, x=\"iteration\", y=\"bound_value\", hue=\"Critic\", ci=\"sd\");\n",
"apply_default_style(ax)\n",
"ax.set_ylim(2)\n",
"plt.ylabel(\"$I_{NWJ}$\")\n",
"plt.figure()\n",
"ax = sns.lineplot(data=data_nce, x=\"iteration\", y=\"bound_value\", hue=\"Critic\", ci=\"sd\");\n",
"apply_default_style(ax)\n",
"ax.set_ylim(2)\n",
"ax.set_ylabel(\"$I_{NCE}$\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PKcYpomeCoUI"
},
"source": [
"## Encoder architecture can be more important that the specific estimator"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "D7VEBx5UCpyp"
},
"source": [
"Reproduces the experiment from Section 3.3 and the corresponding Figures 4 (a, b).\n",
"\n",
"In this experiment we show that the choice of encoder architecture can have more impact on downstream performance than the specific estimator used."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "vbvS4q1vXMlo",
"colab": {}
},
"source": [
"#@title Run experiment or load precomputed results { display-mode: \"form\" }\n",
"\n",
"def run_all_experiments():\n",
" tf.reset_default_graph()\n",
" loss_fcts = {}\n",
" loss_fcts_est = {'nwj': nwj_lower_bound, 'nce': infonce_lower_bound}\n",
"\n",
" def loss_target_fn(x, fn, t):\n",
" return tf.abs(fn(x) - t)\n",
"\n",
" for target in [4, 2]:\n",
" for loss_name, loss_fn in loss_fcts_est.items():\n",
" loss_fcts['{}-{}'.format(loss_name, target)] = functools.partial(\n",
" loss_target_fn, fn=loss_fn, t=target)\n",
"\n",
" nets = {\n",
" \"convnet\": lambda: (ConvNet(), ConvNet()),\n",
" \"mlp\": lambda: (MLP([300, 300, 100], False, {\"activation\": \"relu\"}),\n",
" MLP([300, 300, 100], False, {\"activation\": \"relu\"})),\n",
" }\n",
"\n",
" critics = {\n",
" \"bilinear\": lambda: BilinearCritic(),\n",
" }\n",
" return run_sweep(nets, critics, loss_fcts, \"encoder_impact\", n_iters=21000,\n",
" n_evals=21)\n",
"\n",
"if RUN_EXPERIMENTS:\n",
" data_encoder_impact = run_all_experiments()[\"df\"]\n",
"else:\n",
" data_encoder_impact = pd.read_pickle('mi_results.pkl')\n",
" data_encoder_impact = data_encoder_impact[data_encoder_impact.exp_name == \"encoder_impact\"]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "VtgT2m2uwRfb",
"colab": {}
},
"source": [
"#@title Downstream accuracy and testing loss plots { display-mode: \"form\" }\n",
"data = data_encoder_impact\n",
"data = data[data.Critic == \"Bilinear\"].copy()\n",
"\n",
"data[\"label\"].replace(to_replace={\n",
" \"convnet, bilinear, nwj-2\": \"ConvNet $(I_{NWJ}, t=2)$\",\n",
" \"convnet, bilinear, nwj-4\": \"ConvNet $(I_{NWJ}, t=4)$\",\n",
" \"mlp, bilinear, nwj-2\": \"MLP $(I_{NWJ}, t=2)$\",\n",
" \"mlp, bilinear, nwj-4\": \"MLP $(I_{NWJ}, t=4)$\",\n",
" \"convnet, bilinear, nce-2\": \"ConvNet $(I_{NCE}, t=2)$\",\n",
" \"convnet, bilinear, nce-4\": \"ConvNet $(I_{NCE}, t=4)$\",\n",
" \"mlp, bilinear, nce-2\": \"MLP $(I_{NCE}, t=2)$\",\n",
" \"mlp, bilinear, nce-4\": \"MLP $(I_{NCE}, t=4)$\",\n",
" }, inplace=True)\n",
"\n",
"# We are trying to reach a given bound of t, hence it is minimized\n",
"data[\"bound_value\"] *= -1\n",
"data_nwj = data[data.Estimator.isin([\"nwj-2\", \"nwj-4\"])]\n",
"data_nce = data[data.Estimator.isin([\"nce-2\", \"nce-4\"])]\n",
"\n",
"del data # Make sure that `data` is not used by accident below.\n",
"\n",
"hue_ordering = np.unique(data_nwj.label.values)\n",
"\n",
"plt.rcParams.update({'legend.fontsize': 13})\n",
"plt.figure()\n",
"ax = sns.lineplot(data=data_nwj, x=\"iteration\", y=\"accuracy\", hue=\"label\", ci=\"sd\", hue_order=hue_ordering)\n",
"apply_default_style(ax)\n",
"ax.set_ylim([0.78, 0.92])\n",
"ax.set_ylabel(\"Accuracy with $I_{NWJ}$\")\n",
"\n",
"hue_ordering = np.unique(data_nce.label.values)\n",
"\n",
"plt.figure()\n",
"ax = sns.lineplot(data=data_nce, x=\"iteration\", y=\"accuracy\", hue=\"label\", ci=\"sd\", hue_order=hue_ordering)\n",
"apply_default_style(ax)\n",
"ax.set_ylim([0.78, 0.92])\n",
"ax.set_ylabel(\"Accuracy with $I_{NCE}$\");\n",
"plt.rcParams.update({'legend.fontsize': FONTSIZE})\n",
"\n",
"# Loss values\n",
"plt.figure()\n",
"hue_ordering = np.unique(data_nwj.label.values)\n",
"\n",
"ax = sns.lineplot(data=data_nwj, x=\"iteration\", y=\"bound_value\", hue=\"label\", ci=\"sd\",\n",
" hue_order=hue_ordering)\n",
"apply_default_style(ax)\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"plt.legend(loc=\"upper right\", handles=handles[1:], labels=labels[1:])\n",
"ax.set_ylabel(\"$L_t(g_1, g_2), I_{NWJ}$\")\n",
"\n",
"plt.figure()\n",
"hue_ordering = np.unique(data_nce.label.values)\n",
"ax = sns.lineplot(data=data_nce, x=\"iteration\", y=\"bound_value\", hue=\"label\", ci=\"sd\",\n",
" hue_order=hue_ordering)\n",
"apply_default_style(ax)\n",
"handles, labels = ax.get_legend_handles_labels()\n",
"plt.legend(loc=\"upper right\", handles=handles[1:], labels=labels[1:])\n",
"ax.set_ylabel(\"$L_t(g_1, g_2), I_{NCE}$\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5FbkpQ1dCZcq"
},
"source": [
"## InfoNCE and the importance of negative sampling"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "oFGeDtbYCaax"
},
"source": [
"Reproduces the experiment in Section 4 and the corresponding Figure 4 (c).\n",
"\n",
"In this experiment we show empirically that both $I_{NCE}$ and $I_{NWJ}$ estimators are not in general lower bounds on MI when samples are not drawn iid."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "ass3uDp7tUDG"
},
"source": [
"The InfoNCE objective is only provably a lower bound on the true mutual information if all of the samples $(X_i, Y_i)$ are drawn iid from the joint distribution $p(x,y)$. Here we demonstrate in with a simple synthetic example that when the $(X_i, Y_i)$ are drawn in a dependent fashion, InfoNCE can actually be larger than the true mutual information.\n",
"\n",
"We will draw a batch $(X_i, Y_i)$ as follows.\n",
"\n",
"First sample $Z \\sim \\mathcal{N}\\left(0, \\begin{bmatrix}1 & -0.5\\\\ -0.5 & 1\\end{bmatrix}\\right)$.\n",
"\n",
"Then sample $\\epsilon_i \\sim \\mathcal{N}\\left(0, \\begin{bmatrix}1 & 0.9\\\\ 0.9 & 1\\end{bmatrix}\\right)$ iid, and set $(X_i, Y_i) = Z + \\epsilon_i$.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "GRJtFmRptUDJ"
},
"source": [
"Then each $(X_i, Y_i)$ has marginal distribution $\\mathcal{N}\\left(0, \\begin{bmatrix}2 & 0.4\\\\ 0.4 & 2\\end{bmatrix}\\right)$, but note that the samples within a batch are dependent.\n",
"\n",
"For a bivariate Gaussian $(X,Y) \\sim \\mathcal{N}\\left(0, \\Sigma\\right)$ we have that $I(X,Y) = -0.5\\log(1-\\rho^2)$ where $\\rho^2 = \\Sigma_{12}\\Sigma_{21} / \\Sigma_{11}\\Sigma_{22}$."
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "EYPaInIktUDP",
"colab": {}
},
"source": [
"#@title Define training loop { display-mode: \"form\" }\n",
"\n",
"def train_bound(xy,\n",
" estimator=\"nce\",\n",
" hidden_dim=10,\n",
" layers=5,\n",
" learning_rate=1e-4,\n",
" n_iters=20000):\n",
" \"\"\"Estimates the MI lower-bound using a simple concat critic.\"\"\"\n",
"\n",
" if estimator not in [\"nce\", \"nwj\"]:\n",
" raise ValueError(\n",
" \"estimator must be one of 'nce', 'nwj', not: {}\".format(estimator))\n",
"\n",
" critic = ConcatCritic(hidden_dim=hidden_dim, layers=layers, activation='relu')\n",
" scores = critic(xy[:, 0, None], xy[:, 1, None])\n",
"\n",
" if estimator == \"nce\":\n",
" bound = infonce_lower_bound(scores)\n",
" else:\n",
" bound = nwj_lower_bound(scores)\n",
"\n",
" # Optimizer setup.\n",
" optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)\n",
" optimizer_op = optimizer.minimize(-bound)\n",
"\n",
" # Main training loop\n",
" with tf.Session() as session:\n",
" session.run(tf.global_variables_initializer())\n",
" bound_estimates = []\n",
" for iter_n in range(n_iters):\n",
" bound_np, _ = session.run([bound, optimizer_op])\n",
" bound_estimates.append(bound_np)\n",
" if iter_n % 1000 == 0:\n",
" print(\"Step {:>10d} {} {:>.5f}\".format(iter_n, estimator, bound_np))\n",
" return bound_estimates\n",
"\n",
"def mi_from_sigma(sigma):\n",
" rho_sq = sigma[0,1] * sigma[1,0] / (sigma[0,0] * sigma[1,1])\n",
" return -0.5 * np.log(1 - (rho_sq))\n",
"\n",
"def run_all_experiments():\n",
"\n",
" bs = TRAIN_BATCH_SIZE\n",
" sigma_z = np.array([[1.0, -0.5],[-0.5, 1.0]])\n",
" sigma_eps = np.array([[1.0, 0.9],[0.9, 1.0]], dtype=np.float64)\n",
"\n",
" z = tfd.MultivariateNormalFullCovariance(loc=0, covariance_matrix=sigma_z)\n",
" eps = tfd.MultivariateNormalFullCovariance(loc=0, covariance_matrix=sigma_eps)\n",
"\n",
" z_sample = tf.cast(z.sample(1), tf.float32)\n",
" eps_sample = tf.cast(eps.sample(bs), tf.float32)\n",
"\n",
" xy = z_sample + eps_sample\n",
"\n",
" mi_true = mi_from_sigma(sigma_eps + sigma_z)\n",
"\n",
" # Let's estimate the MI using InfoNCE with our non-iid samples\n",
" nce_estimates = train_bound(xy, 'nce')\n",
"\n",
" # We'll also estimate the MI using the NWJ estimator.\n",
" nwj_estimates = train_bound(xy, 'nwj')\n",
"\n",
" # Now as a sanity check, let's also evaluate the InfoNCE estimator using proper iid samples\n",
" sigma_xy = sigma_z + sigma_eps\n",
"\n",
" xy_iid = tfd.MultivariateNormalFullCovariance(\n",
" loc=0, covariance_matrix=sigma_xy).sample(bs)\n",
" xy_iid = tf.cast(xy_iid, tf.float32)\n",
"\n",
" # Compute the estimates using IID samples.\n",
" nce_estimates_iid = train_bound(xy_iid, 'nce')\n",
" nwj_estimates_iid = train_bound(xy_iid, 'nwj')\n",
"\n",
" return ResultsSamplingIssues(\n",
" mi_true=mi_true,\n",
" nce_estimates_noniid=nce_estimates,\n",
" nce_estimates_iid=nce_estimates_iid,\n",
" nwj_estimates_noniid=nwj_estimates,\n",
" nwj_estimates_iid=nwj_estimates_iid)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "-xeHsMXh05au",
"colab": {}
},
"source": [
"#@title Run experiment or load precomputed results { display-mode: \"form\" }\n",
"if RUN_EXPERIMENTS:\n",
" data_noniid_sampling = run_all_experiments()\n",
"else:\n",
" !wget -q -N https://storage.googleapis.com/mi_for_rl_files/noniid_results.pkl\n",
" with tf.gfile.Open('noniid_results.pkl', 'rb') as f:\n",
" data_noniid_sampling = pickle.load(f, encoding='latin1')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"id": "X84YlGbctUDl",
"colab": {}
},
"source": [
"#@title i.i.d. vs non-i.i.d. sampling plot { display-mode: \"form\" }\n",
"results = data_noniid_sampling\n",
"steps = [i for i in range(len(results.nce_estimates_iid))]\n",
"\n",
"plt.rcParams.update({'axes.labelsize': FONTSIZE,\n",
" 'xtick.labelsize': FONTSIZE,\n",
" 'ytick.labelsize': FONTSIZE,\n",
" 'legend.fontsize': FONTSIZE,\n",
" 'lines.linewidth': 2})\n",
"plt.figure()\n",
"ax = plt.gca()\n",
"plt.axhline(y=results.mi_true, ls='--', color='k', label=\"True MI\")\n",
"\n",
"ax.plot(steps, gaussian_filter1d(results.nce_estimates_noniid, 100),\n",
" label=\"$I_{NCE}$, non-i.i.d. samples\")\n",
"ax.plot(steps, gaussian_filter1d(results.nce_estimates_iid, 100),\n",
" label=\"$I_{NCE}$, I.i.d. samples\")\n",
"\n",
"steps = [i for i in range(len(results.nwj_estimates_iid))]\n",
"\n",
"ax.plot(steps, gaussian_filter1d(results.nwj_estimates_noniid, 100),\n",
" label=\"$I_{NWJ}$, non-i.i.d. samples\")\n",
"ax.plot(steps, gaussian_filter1d(results.nwj_estimates_iid, 100),\n",
" label=\"$I_{NWJ}$, i.i.d. samples\")\n",
"\n",
"apply_default_style(ax)\n",
"ax.set_ylabel('$I_{EST}$')\n",
"ax.set_ylim(-0.3, 0.25)\n",
"plt.legend(loc=\"lower right\", prop={'size':13})"
],
"execution_count": 0,
"outputs": []
}
]
} | apache-2.0 |
queirozfcom/python-sandbox | python3/notebooks/pandas-initialization/dataframe-initialization.ipynb | 2 | 11799 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import pprint\n",
"import json"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1.3.2'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## create from lists"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>names</th>\n",
" <th>ages</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>john</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>mary</td>\n",
" <td>22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>peter</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>gary</td>\n",
" <td>23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>anne</td>\n",
" <td>12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" names ages\n",
"0 john 33\n",
"1 mary 22\n",
"2 peter 45\n",
"3 gary 23\n",
"4 anne 12"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"names = ['john','mary','peter','gary','anne']\n",
"ages = [33,22,45,23,12]\n",
"\n",
"df = pd.DataFrame({\n",
" 'names':names,\n",
" 'ages':ages\n",
"})\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## create from list of dicts"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>john</td>\n",
" <td>male</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>mary</td>\n",
" <td>female</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>peter</td>\n",
" <td>male</td>\n",
" <td>34</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name gender age\n",
"0 john male 45\n",
"1 mary female 19\n",
"2 peter male 34"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_dicts = [\n",
" {'name':\"john\",\"gender\":'male','age':45},\n",
" {'name':\"mary\", 'gender':\"female\",'age':19},\n",
" {'name':\"peter\",'gender':'male', 'age':34}\n",
"]\n",
"\n",
"# must reassign since the append method does not work in place\n",
"df = pd.DataFrame.from_records(data_dicts)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## create from single dict use keys as index"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{\n",
" \"alice\": 12,\n",
" \"bob\": 20,\n",
" \"charlie\": 33\n",
"}\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>alice</th>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>bob</th>\n",
" <td>20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>charlie</th>\n",
" <td>33</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age\n",
"alice 12\n",
"bob 20\n",
"charlie 33"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = {\"alice\": 12, \"bob\": 20, \"charlie\": 33}\n",
"\n",
"print(json.dumps(d, indent=2))\n",
"\n",
"pd.DataFrame.from_dict(d, orient='index', columns=['age'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## create an empty dataframe and append rows"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>col_a</th>\n",
" <th>col_b</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>5.0</td>\n",
" <td>10.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.0</td>\n",
" <td>100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>32.0</td>\n",
" <td>999.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" col_a col_b\n",
"0 5.0 10.0\n",
"1 1.0 100.0\n",
"2 32.0 999.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame()\n",
"\n",
"# must reassign since the append method does not work in place\n",
"df = df.append({'col_a':5,'col_b':10}, ignore_index=True)\n",
"df = df.append({'col_a':1,'col_b':100}, ignore_index=True)\n",
"df = df.append({'col_a':32,'col_b':999}, ignore_index=True)\n",
"\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"## crate dataframe with specific types"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>name</th>\n",
" <th>gender</th>\n",
" <th>age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>john</td>\n",
" <td>male</td>\n",
" <td>45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>mary</td>\n",
" <td>female</td>\n",
" <td>19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>peter</td>\n",
" <td>male</td>\n",
" <td>34</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" name gender age\n",
"0 john male 45\n",
"1 mary female 19\n",
"2 peter male 34"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_dicts = [\n",
" {'name':\"john\",\"gender\":'male','age':45},\n",
" {'name':\"mary\", 'gender':\"female\",'age':19},\n",
" {'name':\"peter\",'gender':'male', 'age':34}\n",
"]\n",
"\n",
"# must reassign since the append method does not work in place\n",
"df = pd.DataFrame.from_records(data_dicts,)\n",
"df"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
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"title_sidebar": "Contents",
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},
"nbformat": 4,
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| mit |
mne-tools/mne-tools.github.io | 0.19/_downloads/69a53f341b5a9d09407d309924aa4d14/plot_source_power_spectrum.ipynb | 1 | 3408 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n======================================================\nCompute source power spectral density (PSD) in a label\n======================================================\n\nReturns an STC file containing the PSD (in dB) of each of the sources\nwithin a label.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Authors: Alexandre Gramfort <[email protected]>\n#\n# License: BSD (3-clause)\n\nimport matplotlib.pyplot as plt\n\nimport mne\nfrom mne import io\nfrom mne.datasets import sample\nfrom mne.minimum_norm import read_inverse_operator, compute_source_psd\n\nprint(__doc__)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set parameters\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"data_path = sample.data_path()\nraw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'\nfname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'\nfname_label = data_path + '/MEG/sample/labels/Aud-lh.label'\n\n# Setup for reading the raw data\nraw = io.read_raw_fif(raw_fname, verbose=False)\nevents = mne.find_events(raw, stim_channel='STI 014')\ninverse_operator = read_inverse_operator(fname_inv)\nraw.info['bads'] = ['MEG 2443', 'EEG 053']\n\n# picks MEG gradiometers\npicks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,\n stim=False, exclude='bads')\n\ntmin, tmax = 0, 120 # use the first 120s of data\nfmin, fmax = 4, 100 # look at frequencies between 4 and 100Hz\nn_fft = 2048 # the FFT size (n_fft). Ideally a power of 2\nlabel = mne.read_label(fname_label)\n\nstc = compute_source_psd(raw, inverse_operator, lambda2=1. / 9., method=\"dSPM\",\n tmin=tmin, tmax=tmax, fmin=fmin, fmax=fmax,\n pick_ori=\"normal\", n_fft=n_fft, label=label,\n dB=True)\n\nstc.save('psd_dSPM')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"View PSD of sources in label\n\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"plt.plot(1e3 * stc.times, stc.data.T)\nplt.xlabel('Frequency (Hz)')\nplt.ylabel('PSD (dB)')\nplt.title('Source Power Spectrum (PSD)')\nplt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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"nbformat": 4,
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} | bsd-3-clause |
igabr/Metis_Projects_Chicago_2017 | 03-Project-McNulty/feature_reduction_35.ipynb | 1 | 22263 | {
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pickle\n",
"%run helper_functions.py\n",
"%run s3.py\n",
"pd.options.display.max_columns = 1000\n",
"plt.rcParams[\"figure.figsize\"] = (15,10)\n",
"from datetime import datetime\n",
"\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.dummy import DummyClassifier\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sqlalchemy import create_engine\n",
"import psycopg2\n",
"import os"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook will select the top 35 features from out dataset.\n",
"\n",
"I will rescale the resulting columns - while I am keenly aware this makes no difference to the Random Forest Model, I am just doing it for consistency.\n",
"\n",
"I also pickle the scaler as we will be using this in our flask web app to transform the input data."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = unpickle_object(\"dummied_dataset.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#this logic will be important for flask data entry.\n",
"\n",
"float_columns = df.select_dtypes(include=['float64']).columns\n",
"\n",
"for col in float_columns:\n",
" if \"mths\" not in col:\n",
" df[col].fillna(df[col].median(), inplace=True)\n",
" else:\n",
" if col == \"inq_last_6mths\":\n",
" df[col].fillna(0, inplace=True)\n",
" elif col == \"mths_since_last_delinq\":\n",
" df[col].fillna(999, inplace=True)\n",
" elif col == \"mths_since_last_record\":\n",
" df[col].fillna(999, inplace=True)\n",
" elif col == \"collections_12_mths_ex_med\":\n",
" df[col].fillna(0, inplace=True)\n",
" elif col == \"mths_since_last_major_derog\":\n",
" df[col].fillna(999, inplace=True)\n",
" elif col == \"mths_since_rcnt_il\":\n",
" df[col].fillna(999, inplace=True)\n",
" elif col == \"acc_open_past_24mths\":\n",
" df[col].fillna(0, inplace=True)\n",
" elif col == \"chargeoff_within_12_mths\":\n",
" df[col].fillna(0, inplace=True)\n",
" elif col == \"mths_since_recent_bc\":\n",
" df[col].fillna(999, inplace=True)\n",
" elif col == \"mths_since_recent_bc_dlq\":\n",
" df[col].fillna(999, inplace=True)\n",
" elif col == \"mths_since_recent_inq\":\n",
" df[col].fillna(999, inplace=True)\n",
" elif col == \"mths_since_recent_revol_delinq\":\n",
" df[col].fillna(999, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"top_35 = [\"int_rate\", \n",
" \"dti\", \n",
" \"term_ 60 months\",\n",
" \"bc_open_to_buy\",\n",
" \"revol_util\",\n",
" \"installment\",\n",
" \"avg_cur_bal\",\n",
" \"tot_hi_cred_lim\",\n",
" \"revol_bal\",\n",
" \"funded_amnt_inv\",\n",
" \"bc_util\",\n",
" \"tot_cur_bal\",\n",
" \"total_bc_limit\",\n",
" \"total_rev_hi_lim\",\n",
" \"funded_amnt\",\n",
" \"loan_amnt\",\n",
" \"mo_sin_old_rev_tl_op\",\n",
" \"total_bal_ex_mort\",\n",
" \"issue_d_Dec-2016\",\n",
" \"total_acc\",\n",
" \"mo_sin_old_il_acct\",\n",
" \"mths_since_recent_bc\",\n",
" \"total_il_high_credit_limit\",\n",
" \"inq_last_6mths\",\n",
" \"acc_open_past_24mths\",\n",
" \"mo_sin_rcnt_tl\",\n",
" \"mo_sin_rcnt_rev_tl_op\",\n",
" \"percent_bc_gt_75\",\n",
" \"num_rev_accts\",\n",
" \"mths_since_last_delinq\",\n",
" \"open_acc\",\n",
" \"mths_since_recent_inq\",\n",
" \"grade_B\",\n",
" \"num_bc_tl\",\n",
" \"loan_status_Late\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df_reduced_features = df.loc[:, top_35]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_reduced_features.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"scaler = StandardScaler()\n",
"matrix_df = df_reduced_features.as_matrix()\n",
"matrix = scaler.fit_transform(matrix_df)\n",
"scaled_df = pd.DataFrame(matrix, columns=df_reduced_features.columns)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"scaler = StandardScaler()\n",
"matrix_df = df_reduced_features.as_matrix()\n",
"scalar_object_35 = scaler.fit(matrix_df)\n",
"matrix = scalar_object_35.transform(matrix_df)\n",
"scaled_df_35 = pd.DataFrame(matrix, columns=df_reduced_features.columns)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"check = scaled_df_35 == scaled_df # lets pickle the scaler"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"check.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pickle_object(scalar_object_35, \"scaler_35_features\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"pickle_object(scaled_df, \"rf_df_35\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"upload_to_bucket('rf_df_35.pkl', \"rf_df_35.pkl\",\"gabr-project-3\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"upload_to_bucket(\"scaler_35_features.pkl\", \"scaler_35_features.pkl\", \"gabr-project-3\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = unpickle_object(\"rf_df_35.pkl\")"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"engine = create_engine(os.environ[\"PSQL_CONN\"])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df.to_sql(\"dummied_dataset\", con=engine)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"BELOW WE DIRECTLY QUERY THE DATABASE BELOW: Nothing has to be held in memory again!"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>index</th>\n",
" <th>int_rate</th>\n",
" <th>dti</th>\n",
" <th>term_ 60 months</th>\n",
" <th>bc_open_to_buy</th>\n",
" <th>revol_util</th>\n",
" <th>installment</th>\n",
" <th>avg_cur_bal</th>\n",
" <th>tot_hi_cred_lim</th>\n",
" <th>revol_bal</th>\n",
" <th>funded_amnt_inv</th>\n",
" <th>bc_util</th>\n",
" <th>tot_cur_bal</th>\n",
" <th>total_bc_limit</th>\n",
" <th>total_rev_hi_lim</th>\n",
" <th>funded_amnt</th>\n",
" <th>loan_amnt</th>\n",
" <th>mo_sin_old_rev_tl_op</th>\n",
" <th>total_bal_ex_mort</th>\n",
" <th>issue_d_Dec-2016</th>\n",
" <th>total_acc</th>\n",
" <th>mo_sin_old_il_acct</th>\n",
" <th>mths_since_recent_bc</th>\n",
" <th>total_il_high_credit_limit</th>\n",
" <th>inq_last_6mths</th>\n",
" <th>acc_open_past_24mths</th>\n",
" <th>mo_sin_rcnt_tl</th>\n",
" <th>mo_sin_rcnt_rev_tl_op</th>\n",
" <th>percent_bc_gt_75</th>\n",
" <th>num_rev_accts</th>\n",
" <th>mths_since_last_delinq</th>\n",
" <th>open_acc</th>\n",
" <th>mths_since_recent_inq</th>\n",
" <th>grade_B</th>\n",
" <th>num_bc_tl</th>\n",
" <th>loan_status_Late</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>-0.691239</td>\n",
" <td>0.316533</td>\n",
" <td>-0.572832</td>\n",
" <td>-0.336618</td>\n",
" <td>1.217037</td>\n",
" <td>-1.076647</td>\n",
" <td>-0.344728</td>\n",
" <td>-0.323194</td>\n",
" <td>-0.107344</td>\n",
" <td>-1.088801</td>\n",
" <td>0.149284</td>\n",
" <td>-0.354923</td>\n",
" <td>-0.290113</td>\n",
" <td>-0.215951</td>\n",
" <td>-1.095558</td>\n",
" <td>-1.096804</td>\n",
" <td>-0.173567</td>\n",
" <td>-0.242634</td>\n",
" <td>-0.113402</td>\n",
" <td>-1.373327</td>\n",
" <td>0.046177</td>\n",
" <td>2.9681</td>\n",
" <td>-0.221848</td>\n",
" <td>0.195522</td>\n",
" <td>-1.305392</td>\n",
" <td>-0.292118</td>\n",
" <td>-0.277218</td>\n",
" <td>0.028544</td>\n",
" <td>-0.135032</td>\n",
" <td>0.951804</td>\n",
" <td>-1.611450</td>\n",
" <td>2.180953</td>\n",
" <td>1.584982</td>\n",
" <td>-0.114309</td>\n",
" <td>-0.598253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>0.332064</td>\n",
" <td>-0.526679</td>\n",
" <td>1.745714</td>\n",
" <td>-0.336618</td>\n",
" <td>-1.824751</td>\n",
" <td>-1.484993</td>\n",
" <td>-0.344728</td>\n",
" <td>-0.323194</td>\n",
" <td>-0.669430</td>\n",
" <td>-1.382859</td>\n",
" <td>0.149284</td>\n",
" <td>-0.354923</td>\n",
" <td>-0.290113</td>\n",
" <td>-0.215951</td>\n",
" <td>-1.393251</td>\n",
" <td>-1.394124</td>\n",
" <td>-0.173567</td>\n",
" <td>-0.242634</td>\n",
" <td>-0.113402</td>\n",
" <td>-1.792454</td>\n",
" <td>0.046177</td>\n",
" <td>2.9681</td>\n",
" <td>-0.221848</td>\n",
" <td>3.933241</td>\n",
" <td>-1.305392</td>\n",
" <td>-0.292118</td>\n",
" <td>-0.277218</td>\n",
" <td>0.028544</td>\n",
" <td>-0.135032</td>\n",
" <td>0.951804</td>\n",
" <td>-1.611450</td>\n",
" <td>2.180953</td>\n",
" <td>-0.630922</td>\n",
" <td>-0.114309</td>\n",
" <td>1.671534</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>0.484895</td>\n",
" <td>-0.282416</td>\n",
" <td>-0.572832</td>\n",
" <td>-0.336618</td>\n",
" <td>1.822939</td>\n",
" <td>-1.387900</td>\n",
" <td>-0.344728</td>\n",
" <td>-0.323194</td>\n",
" <td>-0.609796</td>\n",
" <td>-1.394741</td>\n",
" <td>0.149284</td>\n",
" <td>-0.354923</td>\n",
" <td>-0.290113</td>\n",
" <td>-0.215951</td>\n",
" <td>-1.405159</td>\n",
" <td>-1.406017</td>\n",
" <td>-0.173567</td>\n",
" <td>-0.242634</td>\n",
" <td>-0.113402</td>\n",
" <td>-1.289501</td>\n",
" <td>0.046177</td>\n",
" <td>2.9681</td>\n",
" <td>-0.221848</td>\n",
" <td>1.129952</td>\n",
" <td>-1.305392</td>\n",
" <td>-0.292118</td>\n",
" <td>-0.277218</td>\n",
" <td>0.028544</td>\n",
" <td>-0.135032</td>\n",
" <td>0.951804</td>\n",
" <td>-1.804259</td>\n",
" <td>2.180953</td>\n",
" <td>-0.630922</td>\n",
" <td>-0.114309</td>\n",
" <td>-0.598253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>-0.062196</td>\n",
" <td>0.074485</td>\n",
" <td>-0.572832</td>\n",
" <td>-0.336618</td>\n",
" <td>-1.349855</td>\n",
" <td>-0.377418</td>\n",
" <td>-0.344728</td>\n",
" <td>-0.323194</td>\n",
" <td>-0.485640</td>\n",
" <td>-0.491774</td>\n",
" <td>0.149284</td>\n",
" <td>-0.354923</td>\n",
" <td>-0.290113</td>\n",
" <td>-0.215951</td>\n",
" <td>-0.500173</td>\n",
" <td>-0.502163</td>\n",
" <td>-0.173567</td>\n",
" <td>-0.242634</td>\n",
" <td>-0.113402</td>\n",
" <td>0.973787</td>\n",
" <td>0.046177</td>\n",
" <td>2.9681</td>\n",
" <td>-0.221848</td>\n",
" <td>0.195522</td>\n",
" <td>-1.305392</td>\n",
" <td>-0.292118</td>\n",
" <td>-0.277218</td>\n",
" <td>0.028544</td>\n",
" <td>-0.135032</td>\n",
" <td>-1.048563</td>\n",
" <td>-0.261787</td>\n",
" <td>2.180953</td>\n",
" <td>-0.630922</td>\n",
" <td>-0.114309</td>\n",
" <td>-0.598253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>-0.239391</td>\n",
" <td>0.009306</td>\n",
" <td>1.745714</td>\n",
" <td>-0.336618</td>\n",
" <td>-0.002953</td>\n",
" <td>-1.453448</td>\n",
" <td>-0.344728</td>\n",
" <td>-0.323194</td>\n",
" <td>0.556906</td>\n",
" <td>-1.323454</td>\n",
" <td>0.149284</td>\n",
" <td>-0.354923</td>\n",
" <td>-0.290113</td>\n",
" <td>-0.215951</td>\n",
" <td>-1.333712</td>\n",
" <td>-1.334660</td>\n",
" <td>-0.173567</td>\n",
" <td>-0.242634</td>\n",
" <td>-0.113402</td>\n",
" <td>1.057612</td>\n",
" <td>0.046177</td>\n",
" <td>2.9681</td>\n",
" <td>-0.221848</td>\n",
" <td>-0.738908</td>\n",
" <td>-1.305392</td>\n",
" <td>-0.292118</td>\n",
" <td>-0.277218</td>\n",
" <td>0.028544</td>\n",
" <td>-0.135032</td>\n",
" <td>-1.042337</td>\n",
" <td>0.702258</td>\n",
" <td>2.180953</td>\n",
" <td>1.584982</td>\n",
" <td>-0.114309</td>\n",
" <td>-0.598253</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" index int_rate dti term_ 60 months bc_open_to_buy revol_util \\\n",
"0 0 -0.691239 0.316533 -0.572832 -0.336618 1.217037 \n",
"1 1 0.332064 -0.526679 1.745714 -0.336618 -1.824751 \n",
"2 2 0.484895 -0.282416 -0.572832 -0.336618 1.822939 \n",
"3 3 -0.062196 0.074485 -0.572832 -0.336618 -1.349855 \n",
"4 4 -0.239391 0.009306 1.745714 -0.336618 -0.002953 \n",
"\n",
" installment avg_cur_bal tot_hi_cred_lim revol_bal funded_amnt_inv \\\n",
"0 -1.076647 -0.344728 -0.323194 -0.107344 -1.088801 \n",
"1 -1.484993 -0.344728 -0.323194 -0.669430 -1.382859 \n",
"2 -1.387900 -0.344728 -0.323194 -0.609796 -1.394741 \n",
"3 -0.377418 -0.344728 -0.323194 -0.485640 -0.491774 \n",
"4 -1.453448 -0.344728 -0.323194 0.556906 -1.323454 \n",
"\n",
" bc_util tot_cur_bal total_bc_limit total_rev_hi_lim funded_amnt \\\n",
"0 0.149284 -0.354923 -0.290113 -0.215951 -1.095558 \n",
"1 0.149284 -0.354923 -0.290113 -0.215951 -1.393251 \n",
"2 0.149284 -0.354923 -0.290113 -0.215951 -1.405159 \n",
"3 0.149284 -0.354923 -0.290113 -0.215951 -0.500173 \n",
"4 0.149284 -0.354923 -0.290113 -0.215951 -1.333712 \n",
"\n",
" loan_amnt mo_sin_old_rev_tl_op total_bal_ex_mort issue_d_Dec-2016 \\\n",
"0 -1.096804 -0.173567 -0.242634 -0.113402 \n",
"1 -1.394124 -0.173567 -0.242634 -0.113402 \n",
"2 -1.406017 -0.173567 -0.242634 -0.113402 \n",
"3 -0.502163 -0.173567 -0.242634 -0.113402 \n",
"4 -1.334660 -0.173567 -0.242634 -0.113402 \n",
"\n",
" total_acc mo_sin_old_il_acct mths_since_recent_bc \\\n",
"0 -1.373327 0.046177 2.9681 \n",
"1 -1.792454 0.046177 2.9681 \n",
"2 -1.289501 0.046177 2.9681 \n",
"3 0.973787 0.046177 2.9681 \n",
"4 1.057612 0.046177 2.9681 \n",
"\n",
" total_il_high_credit_limit inq_last_6mths acc_open_past_24mths \\\n",
"0 -0.221848 0.195522 -1.305392 \n",
"1 -0.221848 3.933241 -1.305392 \n",
"2 -0.221848 1.129952 -1.305392 \n",
"3 -0.221848 0.195522 -1.305392 \n",
"4 -0.221848 -0.738908 -1.305392 \n",
"\n",
" mo_sin_rcnt_tl mo_sin_rcnt_rev_tl_op percent_bc_gt_75 num_rev_accts \\\n",
"0 -0.292118 -0.277218 0.028544 -0.135032 \n",
"1 -0.292118 -0.277218 0.028544 -0.135032 \n",
"2 -0.292118 -0.277218 0.028544 -0.135032 \n",
"3 -0.292118 -0.277218 0.028544 -0.135032 \n",
"4 -0.292118 -0.277218 0.028544 -0.135032 \n",
"\n",
" mths_since_last_delinq open_acc mths_since_recent_inq grade_B \\\n",
"0 0.951804 -1.611450 2.180953 1.584982 \n",
"1 0.951804 -1.611450 2.180953 -0.630922 \n",
"2 0.951804 -1.804259 2.180953 -0.630922 \n",
"3 -1.048563 -0.261787 2.180953 -0.630922 \n",
"4 -1.042337 0.702258 2.180953 1.584982 \n",
"\n",
" num_bc_tl loan_status_Late \n",
"0 -0.114309 -0.598253 \n",
"1 -0.114309 1.671534 \n",
"2 -0.114309 -0.598253 \n",
"3 -0.114309 -0.598253 \n",
"4 -0.114309 -0.598253 "
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_sql_query('''SELECT * FROM dummied_dataset LIMIT 5''', engine)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
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| mit |
pedritomelenas/LMD | Relaciones y Algebras de Boole/Tutorial relaciones.ipynb | 1 | 110176 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# La clase `relacion`\n",
"\n",
"Para utilizar la clase, primero importamos el fichero que la contiene"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from relaciones import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Para definir una relación, podemos o bien dar un conjunto de parejas, o bien un conjunto de parejas y el universo donde están los elementos de los pares. En caso de no proporcionar el universo, se tomará como universo el conjunto de todos los valores que aparecen en las parejas."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Por ejemplo, definamos en el conjunto $\\{0,1,\\ldots,11\\}$ la relación de ser congruentes módulo 5"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"u =set(range(12))\n",
"rl = set((a,b) for a in u for b in u if (a-b)%5 ==0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r =relacion(rl,u)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Sabemos que esta relación es de equivalencia"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_equivalencia()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Y podemos calcular sus clases de equivalencia (hemos congelado las clases para poder crear un conjunto de conjuntos)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{frozenset({1, 6, 11}),\n",
" frozenset({4, 9}),\n",
" frozenset({3, 8}),\n",
" frozenset({2, 7}),\n",
" frozenset({0, 5, 10})}"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.clases_equivalencia()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"El universo es un atributo de la clase, y se puede acceder a él de la siguiente forma"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.universo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"También podemos determinar si dos elementos están relacionados, bien usando el método `rel` o llamando a la relación con los dos elementos"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.rel(1,1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r(1,6)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(1,1) in r.rels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Si dibujamos las relaciones entre los elementos, aparecerán claramente las clases de equivalencia"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
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"data": {
"text/plain": [
"'Digraph.gv.svg'"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.pinta()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Esta relación no es de orden"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_orden()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Pues no es antisimétrica"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_antisimetrica()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Diagramas de Hasse"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Veamos ahora como ejemplo el conjunto de los divisores de 12 con la relación de divisibilidad"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"d = set(a for a in range(1,13) if 12%a ==0)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"rd =relacion(set((a,b) for a in d for b in d if b%a ==0))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podemos dibujar el diagrama de Hasse como sigue"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
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"Relación binaria"
]
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"source": [
"rd.hasse()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"O bien pintar todas las relaciones"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
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"output_type": "display_data"
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"data": {
"text/plain": [
"'Digraph.gv.svg'"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.pinta()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Claramente esta relación es de orden, y no es un orden total"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.es_orden()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.es_orden_total()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Los elementos notables se calculan con los métodos que tienen nombres acordes a ellos"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{6}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.maximales(set({2,3,6}))"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{2, 3}"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.maximales(set({2,3}))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"O incluso podemos destacar esos elementos en el dibujo de relaciones"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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]
},
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"output_type": "display_data"
},
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"data": {
"text/plain": [
"'Digraph.gv.svg'"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.pinta(rd.mayorantes(set({2,3})))"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{2, 3}"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.minimales(set({2,3}))"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.minimo(set({2,4}))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Retículos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Continuamos con los divisores de 12"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.es_reticulo_inferior()\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.es_reticulo_superior()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.complemento(4)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.cero"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"12"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.uno"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rd.es_complementado()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Veamos qué elementos no tienen complemento"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[2, 6]"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[a for a in rd.universo if rd.complemento(a)==None]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ahora damos un ejemplo que sí es complementado, de hecho un álgebra de Boole"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"u = set(a for a in range(1,31) if 30%a==0)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{1, 2, 3, 5, 6, 10, 15, 30}"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"u"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p = set((a,b) for a in u for b in u if b%a ==0)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r = relacion(p)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_distributivo()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{2, 3, 5}"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.atomos()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_algebra_Boole()"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
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"data": {
"text/plain": [
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]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.pinta(r.atomos())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Retículos de divisores "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Podemos definir retículos con los divisores de un entero o bien dar un conjunto ordenado por la relación de divisibilidad. Para ellos utilizaremos `divisores`"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r = divisores(24)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"collapsed": false
},
"outputs": [
{
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{
"data": {
"text/plain": [
"Relación binaria"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.hasse()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{(1, 2),\n",
" (1, 3),\n",
" (2, 4),\n",
" (2, 6),\n",
" (3, 6),\n",
" (4, 8),\n",
" (4, 12),\n",
" (6, 12),\n",
" (8, 24),\n",
" (12, 24)}"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"_.rels"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### El diamante"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r = divisores({0,1,2,3,5})"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
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"</g>\n",
"<!-- 1->0 -->\n",
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"<path d=\"M63.0978,-27.1688C43.79,-35.2497 17.7777,-49.6782 5.70771,-72 -1.90257,-86.0742 -1.90257,-93.9258 5.70771,-108 15.8917,-126.834 36.0015,-140.049 53.635,-148.572\" fill=\"none\" stroke=\"black\"/>\n",
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"</g>\n",
"<!-- 1->2 -->\n",
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"</g>\n",
"<!-- 1->3 -->\n",
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"</g>\n",
"<!-- 1->5 -->\n",
"<g class=\"edge\" id=\"edge7\"><title>1->5</title>\n",
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"</g>\n",
"<!-- 2->0 -->\n",
"<g class=\"edge\" id=\"edge8\"><title>2->0</title>\n",
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"</g>\n",
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"<g class=\"edge\" id=\"edge6\"><title>3->0</title>\n",
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"</g>\n",
"<!-- 5->0 -->\n",
"<g class=\"edge\" id=\"edge9\"><title>5->0</title>\n",
"<path d=\"M200.91,-101.784C178.25,-113.534 141.816,-132.425 116.207,-145.704\" fill=\"none\" stroke=\"black\"/>\n",
"<polygon fill=\"black\" points=\"114.574,-142.608 107.308,-150.319 117.796,-148.822 114.574,-142.608\" stroke=\"black\"/>\n",
"</g>\n",
"<!-- 5->5 -->\n",
"<g class=\"edge\" id=\"edge5\"><title>5->5</title>\n",
"<path d=\"M241.603,-77.5679C254.396,-74.3247 266.708,-78.4688 266.708,-90 266.708,-98.3782 260.208,-102.857 251.747,-103.436\" fill=\"none\" stroke=\"black\"/>\n",
"<polygon fill=\"black\" points=\"251.899,-99.9335 241.603,-102.432 251.21,-106.9 251.899,-99.9335\" stroke=\"black\"/>\n",
"</g>\n",
"</g>\n",
"</svg>"
],
"text/plain": [
"<IPython.core.display.SVG object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"'Digraph.gv.svg'"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.pinta()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg height=\"188pt\" viewBox=\"0.00 0.00 206.00 188.00\" width=\"206pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
"<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 184)\">\n",
"<title>%3</title>\n",
"<polygon fill=\"white\" points=\"-4,4 -4,-184 202,-184 202,4 -4,4\" stroke=\"none\"/>\n",
"<!-- 0 -->\n",
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]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/plain": [
"Relación binaria"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.hasse()"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_orden()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_orden_total()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_reticulo()"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_subreticulo({0,1,2,3})"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_distributivo()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_complementado()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### El pentágono"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r = divisores({1,0,2,4,3})"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"<!-- 2--1 -->\n",
"<g class=\"edge\" id=\"edge1\"><title>2--1</title>\n",
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"</g>\n",
"<!-- 3--1 -->\n",
"<g class=\"edge\" id=\"edge3\"><title>3--1</title>\n",
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"</g>\n",
"<!-- 4--2 -->\n",
"<g class=\"edge\" id=\"edge4\"><title>4--2</title>\n",
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"</g>\n",
"</g>\n",
"</svg>"
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"<IPython.core.display.SVG object>"
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},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/plain": [
"Relación binaria"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.hasse()"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_distributivo()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_complementado()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Otros ejemplos"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Retículo que no es distributivo ni complementado"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r=divisores({1,0,2,18,9,3})"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {
"collapsed": false
},
"outputs": [
{
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"</g>\n",
"<!-- 2--1 -->\n",
"<g class=\"edge\" id=\"edge1\"><title>2--1</title>\n",
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"<!-- 18--2 -->\n",
"<g class=\"edge\" id=\"edge4\"><title>18--2</title>\n",
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"<g class=\"node\" id=\"node6\"><title>9</title>\n",
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"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"94\" y=\"-158.3\">9</text>\n",
"</g>\n",
"<!-- 18--9 -->\n",
"<g class=\"edge\" id=\"edge6\"><title>18--9</title>\n",
"<path d=\"M69.5836,-216.411C74.6846,-205.252 81.3812,-190.604 86.4709,-179.47\" fill=\"none\" stroke=\"black\"/>\n",
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"<!-- 3 -->\n",
"<g class=\"node\" id=\"node5\"><title>3</title>\n",
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"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"99\" y=\"-86.3\">3</text>\n",
"</g>\n",
"<!-- 3--1 -->\n",
"<g class=\"edge\" id=\"edge3\"><title>3--1</title>\n",
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"</g>\n",
"<!-- 9--3 -->\n",
"<g class=\"edge\" id=\"edge5\"><title>9--3</title>\n",
"<path d=\"M95.236,-143.697C96.011,-132.846 97.0059,-118.917 97.7783,-108.104\" fill=\"none\" stroke=\"black\"/>\n",
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"</g>\n",
"</svg>"
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"<IPython.core.display.SVG object>"
]
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},
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"data": {
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"Relación binaria"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.hasse()"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_complementado()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_distributivo()"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[2, 18, 3, 9]"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"[s for s in r.universo if r.complemento(s)==None]"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_reticulo()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Retículo distributivo, no complementado"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"r = divisores({1,2,4})"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg height=\"188pt\" viewBox=\"0.00 0.00 62.00 188.00\" width=\"62pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
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"<!-- 1 -->\n",
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"</svg>"
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"<IPython.core.display.SVG object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Relación binaria"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.hasse()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_distributivo()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_complementado()"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"r=divisores({0,1,2,3,4,6})"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/svg+xml": [
"<svg height=\"260pt\" viewBox=\"0.00 0.00 134.00 260.00\" width=\"134pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
"<g class=\"graph\" id=\"graph0\" transform=\"scale(1 1) rotate(0) translate(4 256)\">\n",
"<title>%3</title>\n",
"<polygon fill=\"white\" points=\"-4,4 -4,-256 130,-256 130,4 -4,4\" stroke=\"none\"/>\n",
"<!-- 0 -->\n",
"<g class=\"node\" id=\"node1\"><title>0</title>\n",
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"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"27\" y=\"-158.3\">4</text>\n",
"</g>\n",
"<!-- 0--4 -->\n",
"<g class=\"edge\" id=\"edge7\"><title>0--4</title>\n",
"<path d=\"M54.6504,-216.765C48.8345,-205.456 41.1103,-190.437 35.3043,-179.147\" fill=\"none\" stroke=\"black\"/>\n",
"</g>\n",
"<!-- 6 -->\n",
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"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"99\" y=\"-158.3\">6</text>\n",
"</g>\n",
"<!-- 0--6 -->\n",
"<g class=\"edge\" id=\"edge4\"><title>0--6</title>\n",
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"</g>\n",
"<!-- 1 -->\n",
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"</g>\n",
"<!-- 2 -->\n",
"<g class=\"node\" id=\"node3\"><title>2</title>\n",
"<ellipse cx=\"27\" cy=\"-90\" fill=\"none\" rx=\"27\" ry=\"18\" stroke=\"black\"/>\n",
"<text font-family=\"Times,serif\" font-size=\"14.00\" text-anchor=\"middle\" x=\"27\" y=\"-86.3\">2</text>\n",
"</g>\n",
"<!-- 2--1 -->\n",
"<g class=\"edge\" id=\"edge1\"><title>2--1</title>\n",
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"</g>\n",
"<!-- 3 -->\n",
"<g class=\"node\" id=\"node4\"><title>3</title>\n",
"<ellipse cx=\"99\" cy=\"-90\" fill=\"none\" rx=\"27\" ry=\"18\" stroke=\"black\"/>\n",
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"</g>\n",
"<!-- 3--1 -->\n",
"<g class=\"edge\" id=\"edge3\"><title>3--1</title>\n",
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"</g>\n",
"<!-- 4--2 -->\n",
"<g class=\"edge\" id=\"edge6\"><title>4--2</title>\n",
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"</g>\n",
"<!-- 6--2 -->\n",
"<g class=\"edge\" id=\"edge2\"><title>6--2</title>\n",
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"</g>\n",
"<!-- 6--3 -->\n",
"<g class=\"edge\" id=\"edge5\"><title>6--3</title>\n",
"<path d=\"M99,-143.697C99,-132.846 99,-118.917 99,-108.104\" fill=\"none\" stroke=\"black\"/>\n",
"</g>\n",
"</g>\n",
"</svg>"
],
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"<IPython.core.display.SVG object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"Relación binaria"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.hasse()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_distributivo()"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"False"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.es_complementado()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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| mit |
cggh/scikit-allel | notebooks/profiling/nsl.ipynb | 1 | 1808311 | null | mit |
felixcheung/spark-notebook-examples | IPython_notebook/Seaborn and Bokeh.ipynb | 1 | 2237426 | null | apache-2.0 |
HHSIDEAlab/DDOD-HealthData.gov | get_changes_in_data.json.ipynb | 1 | 73165 | {
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1\n"
]
},
{
"data": {
"text/plain": [
"\"\\n------------------------------------------------\\n--- A note about comparison techniques used ---\\n------------------------------------------------\\njson_delta is best for serializing/deserializing structures and\\nminimizing comm overhead. It's may not be ideal for specialized\\ncomparison of existing JSON\\n\""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import json\n",
"import requests\n",
"import json_delta\n",
"\n",
"\n",
"'''\n",
"------------------------------------------------\n",
"--- A note about comparison techniques used ---\n",
"------------------------------------------------\n",
"json_delta is best for serializing/deserializing structures and\n",
"minimizing comm overhead. It's may not be ideal for specialized\n",
"comparison of existing JSON\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"import glob # Wildcard search\n",
"\n",
"file_pattern = \"snapshots/\"\n",
"file_pattern += \"HealthData.gov[_][0-9][0-9][0-9][0-9][-][0-9][0-9][-][0-9][0-9][_]data.json\"\n",
"\n",
"#print \"glob.glob(\"+file_pattern+\")\"\n",
"#print glob.glob(file_pattern)\n"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<generator object generate_udiff_lines at 0x1087d62d0>\n",
" {\n",
" \"format\": \"text/csv\",\n",
" ...,\n",
"+ \"_is_federal_dataset\": false,\n",
" \n",
"+ \"keyword\": [\n",
"+ \"health\"\n",
"+ ],\n",
" \n",
" \"accessURL\":\n",
"- \"https://data.kcmo.org/api/views/ks2s-yguy/rows.csv?accessType=DOWNLOAD\",\n",
"+ \"https://data.mo.gov/api/views/ks2s-yguy/rows.csv?accessType=DOWNLOAD\",\n",
" \n",
" \"webService\":\n",
"- \"https://data.kcmo.org/resource/ks2s-yguy\",\n",
"+ \"https://data.mo.gov/resource/ks2s-yguy\",\n",
" \n",
" \"distribution\":\n",
"- [\n",
"- {\n",
"- \"accessURL\": \"https://data.kcmo.org/api/views/ks2s-yguy/rows.csv?accessType=DOWNLOAD\", \n",
"- \"identifier\": \"fb610ec4-d39e-497e-8a07-ca4e68a312bd\", \n",
"- \"format\": \"text/csv\"\n",
"- }, \n",
"- {\n",
"- \"accessURL\": \"https://data.kcmo.org/api/views/ks2s-yguy/rows.json?accessType=DOWNLOAD\", \n",
"- \"identifier\": \"69034009-efed-4ee2-bef3-65822b6bd9d4\", \n",
"- \"format\": \"application/json\"\n",
"- }, \n",
"- {\n",
"- \"accessURL\": \"https://data.kcmo.org/api/views/ks2s-yguy/rows.xml?accessType=DOWNLOAD\", \n",
"- \"identifier\": \"580bcfa2-f082-422d-abb0-57e08a31ad2e\", \n",
"- \"format\": \"application/xml\"\n",
"- }, \n",
"- {\n",
"- \"accessURL\": \"https://data.kcmo.org/api/views/ks2s-yguy/rows.rdf?accessType=DOWNLOAD\", \n",
"- \"identifier\": \"b36faca1-6937-4c8a-805c-7b85de83ec2f\", \n",
"- \"format\": \"application/xml+rdf\"\n",
"- }, \n",
"- {\n",
"- \"accessURL\": \"https://data.kcmo.org/api/views/ks2s-yguy/rows.xls?accessType=DOWNLOAD\", \n",
"- \"identifier\": \"fc255b13-7e30-490d-a40f-8bbc3f430ccd\", \n",
"- \"format\": \"application/excel\"\n",
"- }, \n",
"- {\n",
"- \"accessURL\": \"https://data.kcmo.org/api/views/ks2s-yguy/rows.xlsx?accessType=DOWNLOAD\", \n",
"- \"identifier\": \"445bf997-6b8f-454b-ad85-82559df12bd0\", \n",
"- \"format\": \"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet\"\n",
"- }\n",
"- ],\n",
"+ [\n",
"+ {\n",
"+ \"accessURL\": \"https://data.mo.gov/api/views/ks2s-yguy/rows.csv?accessType=DOWNLOAD\", \n",
"+ \"identifier\": \"46654d34-e080-44a3-b66a-a72b9d4b6dad\", \n",
"+ \"format\": \"text/csv\"\n",
"+ }, \n",
"+ {\n",
"+ \"accessURL\": \"https://data.mo.gov/api/views/ks2s-yguy/rows.json?accessType=DOWNLOAD\", \n",
"+ \"identifier\": \"d8e385dd-5b05-492b-be05-afd8d4147fba\", \n",
"+ \"format\": \"application/json\"\n",
"+ }, \n",
"+ {\n",
"+ \"accessURL\": \"https://data.mo.gov/api/views/ks2s-yguy/rows.xml?accessType=DOWNLOAD\", \n",
"+ \"identifier\": \"b1213343-6dfb-45f7-927d-22c17b58095c\", \n",
"+ \"format\": \"application/xml\"\n",
"+ }, \n",
"+ {\n",
"+ \"accessURL\": \"https://data.mo.gov/api/views/ks2s-yguy/rows.rdf?accessType=DOWNLOAD\", \n",
"+ \"identifier\": \"9e78289f-c0ad-447b-a05c-e80691bada68\", \n",
"+ \"format\": \"application/xml+rdf\"\n",
"+ }, \n",
"+ {\n",
"+ \"accessURL\": \"https://data.mo.gov/api/views/ks2s-yguy/rows.xls?accessType=DOWNLOAD\", \n",
"+ \"identifier\": \"1e53ea62-6461-4467-95c7-13279c6fab13\", \n",
"+ \"format\": \"application/excel\"\n",
"+ }, \n",
"+ {\n",
"+ \"accessURL\": \"https://data.mo.gov/api/views/ks2s-yguy/rows.xlsx?accessType=DOWNLOAD\", \n",
"+ \"identifier\": \"c2ef2a97-570a-4e95-bb15-eb5314b66b55\", \n",
"+ \"format\": \"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet\"\n",
"+ }\n",
"+ ],\n",
" \n",
" \"landingPage\":\n",
"- \"https://data.kcmo.org/resource/ks2s-yguy\"\n",
"+ \"https://data.mo.gov/resource/ks2s-yguy\"\n",
" }\n"
]
}
],
"source": [
"def load_file(json_file_name):\n",
" with open(json_file_name) as json_file:\n",
" json_data_struct = json.load(json_file)\n",
" return json_data_struct\n",
"\n",
"file_pattern = \"snapshots/\"\n",
"file_pattern += \"HealthData.gov[_][0-9][0-9][0-9][0-9][-][0-9][0-9][-][0-9][0-9][_]data.json\"\n",
"file_list = glob.glob(file_pattern)\n",
"\n",
"\n",
"json_data_list = [dict() for x in range(len(file_list))]\n",
"\n",
"\n",
"'''\n",
"#--- Test comparison ---\n",
"json_data_list[0] = load_file(file_list[0])\n",
"json_data_list[1] = load_file(file_list[1])\n",
"totals = compare_datasets(json_data_list[0],json_data_list[1])\n",
"print totals\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 278,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<generator object generate_udiff_lines at 0x107b1f0f0>\n",
" {\n",
" \"foo\":\n",
"- \"bar\"\n",
"+ \"baz\"\n",
" }\n"
]
}
],
"source": [
"# json_delta.load_and_diff('{\"foo\":\"bar\"}', '{\"foo\":\"baz\"}', verbose=False)\n",
"# print \"\\n\\n----------------------------\\n\\n\"\n",
"test = json_delta.load_and_udiff('{\"foo\":\"bar\"}', '{\"foo\":\"baz\"}')\n",
"print test\n",
"print '\\n'.join(test)"
]
},
{
"cell_type": "code",
"execution_count": 269,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<generator object generate_udiff_lines at 0x10b069d70>\n",
"-\"{\\\"foo\\\":\\\"bar\\\"}\"\n",
"+\"{\\\"foo\\\":\\\"baz\\\"}\"\n"
]
}
],
"source": [
"test = json_delta.udiff(left='{\"foo\":\"bar\"}', right='{\"foo\":\"baz\"}')\n",
"print test\n",
"print '\\n'.join(test)"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# recursively sort any lists it finds (and convert dictionaries\n",
"# to lists of (key, value) pairs so that they're orderable):\n",
"\n",
"def ordered_json(obj):\n",
" if isinstance(obj, dict):\n",
" return sorted((k, ordered_json(v)) for k, v in obj.items())\n",
" if isinstance(obj, list):\n",
" return sorted(ordered_json(x) for x in obj)\n",
" else:\n",
" return obj\n",
" \n",
"# print json_data[1]['dataset'][0]\n",
"#print\n",
"# print ordered_json(json_data[1]['dataset'][0])"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Instance: list -- Type <type 'list'>\n",
"Instance: dictionary -- Type <type 'dict'>\n",
"Instance: list -- Type <type 'list'>\n",
"Instance: dictionary -- Type <type 'dict'>\n",
"Instance: dictionary -- Type <type 'dict'>\n",
"Instance: list -- Type <type 'list'>\n",
"Instance: dictionary -- Type <type 'dict'>\n",
"[[('def0', 'xyz'), ('def1', [[('lab0', 'b(max depth reached)'), ('lab1', 'a(max depth reached)')], 'abc1', 'abc2'])], [('def0', 'xyz'), ('def1', [[('lab0', 'c(max depth reached)'), ('lab1', 'a(max depth reached)')], 'abc1', 'abc2'])]]\n",
"\n"
]
},
{
"data": {
"text/plain": [
"\"\\nConvert tuple to dictionary: But this isn't sufficient\\n\\n\""
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# recursively sort any lists it finds (and convert dictionaries\n",
"# to lists of (key, value) pairs so that they're orderable):\n",
"import collections\n",
"\n",
"def ordered_json2(obj,max_depth):\n",
"\n",
" if max_depth == 1: return obj + \"(max depth reached)\"\n",
" \n",
" if isinstance(obj, dict):\n",
" print \"Instance: dictionary -- Type \" + str(type(obj))\n",
" return sorted((k, ordered_json2(v, max_depth-1)) for k, v in obj.items())\n",
" #return collections.OrderedDict(sorted(obj.items()))\n",
" ## return sorted(ordered_json2({k:v}, max_depth-1) for k, v in obj.items())\n",
" if isinstance(obj, list):\n",
" print \"Instance: list -- Type \" + str(type(obj))\n",
" return sorted(ordered_json2(x,max_depth-1) for x in obj)\n",
" else:\n",
" return obj\n",
"\n",
"test = {}\n",
"#print ordered_json2(,5)\n",
"#print ordered_json2({'def1':['abc2','abc1',{'lab1':\"c\",'lab0':\"b\"}],'def0':'xyz'},5)\n",
"before_test = {'def1':['abc2','abc1',{'lab1':\"a\",'lab0':\"b\"}],'def0':'xyz'}\n",
"after_test = {'def1':['abc2','abc1',{'lab1':\"a\",'lab0':\"c\"}],'def0':'xyz'}\n",
"print ordered_json2([before_test,after_test],5)\n",
"\n",
"#print json_data[1]['dataset'][0]\n",
"print\n",
"#print ordered_json(json_data[1]['dataset'][0])\n",
"'''\n",
"Convert tuple to dictionary: But this isn't sufficient\n",
"\n",
"'''"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"1633"
]
},
"execution_count": 218,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(json_data[0]['dataset'])"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "SyntaxError",
"evalue": "Missing parentheses in call to 'print' (<ipython-input-1-a02407370c03>, line 62)",
"output_type": "error",
"traceback": [
"\u001b[0;36m File \u001b[0;32m\"<ipython-input-1-a02407370c03>\"\u001b[0;36m, line \u001b[0;32m62\u001b[0m\n\u001b[0;31m print \"\\n\\n==dumps_before==========\\n\\n\"\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'\n"
]
}
],
"source": [
"# Create a dictionary of values for comparison\n",
"import json_delta\n",
"\n",
"def compare_datasets(dataset_list_before, dataset_list_after):\n",
" json_compare_dict = {}\n",
" dataset_list_diff = {\"Counts\":{\"Added\":0, \"Deleted\":0, \"Changed\":0, \"No Change\":0},\n",
" \"Diff\":\"\"}\n",
"\n",
" ''' \n",
" #=== Sort entire datasets recursively for easier comparison later ===\n",
" print(\"\\n\\n===dataset_before===\")\n",
" print(dataset_before)\n",
" print(\"\\n\\n===dataset_after===\")\n",
" print(dataset_after)\n",
" #dataset_before = ordered_json(dataset_before)\n",
" #dataset_after = ordered_json(dataset_after )\n",
" print(\"\\n\\n===ordered_json(dataset_before===\")\n",
" print(dataset_before)\n",
" print(\"\\n\\n===ordered_json(dataset_after===\")\n",
" print(dataset_after)\n",
" \n",
" '''\n",
"\n",
" #=== First load the \"after\" values ===\n",
" for index, dataset_after in enumerate(dataset_list_after):\n",
"\n",
" check_key = dataset_after['identifier']\n",
"\n",
" json_compare_dict[check_key] = {'Status' :\"Added\",\n",
" 'Before' :None,\n",
" 'After' :dataset_after,\n",
" 'Difference':None\n",
" }\n",
" dataset_list_diff[\"Counts\"][\"Added\"] += 1\n",
"\n",
"\n",
" #=== Second load the \"before\" values ===\n",
" for index, dataset_before in enumerate(dataset_list_before):\n",
"\n",
" check_key = dataset_before['identifier']\n",
"\n",
" if check_key in json_compare_dict:\n",
" # Not deleted, so check for differences\n",
" \n",
" dataset_after = json_compare_dict[check_key]['After']\n",
"\n",
" # Must compare sorted versions of json struct\n",
" if ordered_json(dataset_after) == ordered_json(dataset_before):\n",
" diff_status = \"No Change\"\n",
" else:\n",
" diff_status = \"Changed\"\n",
" \n",
" # Analyze difference only if changed\n",
" udiff_list = json_delta.udiff(\n",
" dataset_before, \n",
" dataset_after\n",
" )\n",
" udiff_output = '\\n'.join(udiff_list)\n",
" dataset_list_diff[\"Diff\"] = udiff_output\n",
" \n",
" \n",
" print \"\\n\\n==dumps_before==========\\n\\n\"\n",
" print 'type='+str(type(dataset_before))\n",
" print json.dumps(dataset_before)\n",
" print \"\\n\\n==dumps_after==========\\n\\n\"\n",
" print 'type='+str(type(dataset_after))\n",
" print json.dumps(dataset_after)\n",
" print \"\\n\\n==load udiff list==========\\n\\n\"\n",
" print 'type='+str(type(udiff_list))\n",
" print udiff_list\n",
" print \"\\n\\n==load udiff output==========\\n\\n\"\n",
" print 'type='+str(type(udiff_output))\n",
" print udiff_output\n",
"\n",
" \n",
" return dataset_list_diff\n",
"\n",
" else:\n",
" # Deleted\n",
" diff_status = \"Deleted\"\n",
" \n",
" \n",
" json_compare_dict[check_key] = {'Status':diff_status,\n",
" 'Before':dataset_before\n",
" }\n",
" dataset_list_diff[\"Counts\"][diff_status] += 1\n",
" dataset_list_diff[\"Counts\"][\"Added\" ] -= 1\n",
" \n",
" dataset_list_diff[\"Counts\"][\"Added\"] = max(0,dataset_list_diff[\"Counts\"][\"Added\"])\n",
" return dataset_list_diff\n",
" \n",
" \n",
" \n",
"#--- Test it ---\n",
"#totals = compare_datasets(json_data[0]['dataset'][:2],json_data[1]['dataset'][:2])\n",
"#totals = compare_datasets(json_data[0]['dataset'],json_data[1]['dataset'])\n",
"#print totals\n",
"totals = compare_datasets(json_data_list[0],json_data_list[1])\n",
"print totals[\"Counts\"]\n",
"print totals[\"Diff\"]"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"https://data.kcmo.org/resource/ks2s-yguy\n",
"State of Missouri\n",
"{\n",
" \"accessURL\": \"https://data.kcmo.org/api/views/ks2s-yguy/rows.csv?accessType=DOWNLOAD\", \n",
" \"publisher\": \"State of Missouri\", \n",
" \"accessLevel\": \"Public\", \n",
" \"description\": \"Cooling Centers Sites in Missouri\", \n",
" \"title\": \"Missouri Cooling Centers Sites\", \n",
" \"format\": \"text/csv\", \n",
" \"landingPage\": \"https://data.kcmo.org/resource/ks2s-yguy\", \n",
" \"modified\": \"2013-03-19\", \n",
" \"theme\": [\n",
" \"Health\"\n",
" ], \n",
" \"dataQuality\": true, \n",
" \"contactPoint\": \"Health Data Initiative\", \n",
" \"identifier\": \"a2bded07-837d-4671-89be-748deb455f36\", \n",
" \"mbox\": \"[email protected]\", \n",
" \"distribution\": [\n",
" {\n",
" \"accessURL\": \"https://data.kcmo.org/api/views/ks2s-yguy/rows.csv?accessType=DOWNLOAD\", \n",
" \"identifier\": \"fb610ec4-d39e-497e-8a07-ca4e68a312bd\", \n",
" \"format\": \"text/csv\"\n",
" }, \n",
" {\n",
" \"accessURL\": \"https://data.kcmo.org/api/views/ks2s-yguy/rows.json?accessType=DOWNLOAD\", \n",
" \"identifier\": \"69034009-efed-4ee2-bef3-65822b6bd9d4\", \n",
" \"format\": \"application/json\"\n",
" }, \n",
" {\n",
" \"accessURL\": \"https://data.kcmo.org/api/views/ks2s-yguy/rows.xml?accessType=DOWNLOAD\", \n",
" \"identifier\": \"580bcfa2-f082-422d-abb0-57e08a31ad2e\", \n",
" \"format\": \"application/xml\"\n",
" }, \n",
" {\n",
" \"accessURL\": \"https://data.kcmo.org/api/views/ks2s-yguy/rows.rdf?accessType=DOWNLOAD\", \n",
" \"identifier\": \"b36faca1-6937-4c8a-805c-7b85de83ec2f\", \n",
" \"format\": \"application/xml+rdf\"\n",
" }, \n",
" {\n",
" \"accessURL\": \"https://data.kcmo.org/api/views/ks2s-yguy/rows.xls?accessType=DOWNLOAD\", \n",
" \"identifier\": \"fc255b13-7e30-490d-a40f-8bbc3f430ccd\", \n",
" \"format\": \"application/excel\"\n",
" }, \n",
" {\n",
" \"accessURL\": \"https://data.kcmo.org/api/views/ks2s-yguy/rows.xlsx?accessType=DOWNLOAD\", \n",
" \"identifier\": \"445bf997-6b8f-454b-ad85-82559df12bd0\", \n",
" \"format\": \"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet\"\n",
" }\n",
" ], \n",
" \"webService\": \"https://data.kcmo.org/resource/ks2s-yguy\"\n",
"}\n"
]
}
],
"source": [
"if \"landingPage\" in json_data_list[0][0]: print json_data_list[0][0][\"landingPage\"]\n",
"if \"publisher\" in json_data_list[0][0]: print json_data_list[0][0][\"publisher\"]\n",
"if \"bureauCode\" in json_data_list[0][0]: print json_data_list[0][0][\"bureauCode\"]\n",
"print json.dumps(json_data_list[0][0], sort_keys=False, indent=4)\n"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{'Added': 33, 'Not New': 1633}"
]
},
"execution_count": 211,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#=== Check status counts between consecutive days ===\n",
"\n",
"totals = {}\n",
"for key, value in json_compare_dict.iteritems():\n",
" #print dataset['identifier']\n",
" check_status = value['Status']\n",
" if check_status in totals:\n",
" totals[check_status] += 1\n",
" else:\n",
" totals[check_status] = 1\n",
" #break\n",
"\n",
"totals"
]
},
{
"cell_type": "code",
"execution_count": 244,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#json_data[0]['dataset'][0,1,3]\n",
"for dataset in json_data[0]['dataset'][:2]:\n",
"# print dataset\n",
" check_key = dataset['identifier']\n",
"#json_data[0]['dataset'][:2]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import requests\n",
"# find list of files\n",
"directory_url = 'http://data-staging.civicagency.org/archive/datajson'\n",
"directory_response = requests.get(directory_url)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#directory_response.status_code\n",
"#directory_response.text\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Get HTML page\n",
"from lxml import html\n",
"tree = html.fromstring(directory_response.content)\n",
"tree.xpath"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from bs4 import BeautifulSoup\n",
"directory_soup = BeautifulSoup(directory_response.text)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://data-staging.civicagency.org/archive/datajson/2014-02-24/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-03-27/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-04-14/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-04-25/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-04-27/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-05-04/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-05-11/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-05-18/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-05-25/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-01/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-03/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-08/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-15/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-20/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-21/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-22/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-23/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-24/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-25/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-26/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-27/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-28/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-29/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-06-30/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-07-01/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-07-02/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-07-03/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-07-04/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-07-05/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-07-06/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-14/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-15/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-16/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-17/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-18/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-19/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-20/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-21/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-22/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-23/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-24/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-25/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-26/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-27/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-28/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-29/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-30/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-08-31/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-01/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-02/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-03/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-04/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-05/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-06/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-07/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-08/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-09/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-10/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-11/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-12/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-13/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-14/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-15/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-16/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-17/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-18/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-19/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-20/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-21/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-22/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-23/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-24/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-25/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-26/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-27/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-28/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-29/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-09-30/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-01/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-02/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-03/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-04/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-05/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-11/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-12/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-13/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-14/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-15/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-16/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-17/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-18/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-19/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-20/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-21/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-22/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-23/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-24/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-25/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-26/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-27/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-28/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2014-10-29/49021.json\n",
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"http://data-staging.civicagency.org/archive/datajson/2015-06-23/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-06-24/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-06-25/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-06-26/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-06-27/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-06-28/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-06-29/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-06-30/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-01/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-02/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-03/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-04/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-05/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-06/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-07/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-08/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-09/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-10/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-12/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-14/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-16/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-18/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-20/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-22/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-23/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-24/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-29/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-07-30/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-01/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-03/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-04/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-05/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-06/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-07/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-08/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-09/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-10/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-11/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-12/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-13/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-14/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-15/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-16/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-17/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-18/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-19/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-20/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-21/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-22/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-23/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-24/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-25/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-26/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-27/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-28/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-29/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-30/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-08-31/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-01/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-02/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-03/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-04/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-05/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-06/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-07/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-08/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-09/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-10/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-11/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-12/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-13/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-14/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-15/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-16/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-17/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-18/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-19/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-20/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-21/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-22/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-23/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-24/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-25/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-26/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-27/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-28/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-29/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-09-30/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-01/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-02/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-03/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-04/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-05/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-06/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-07/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-08/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-09/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-10/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-11/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-12/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-13/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-14/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-15/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-16/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-17/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-18/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-19/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-20/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-21/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-22/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-23/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-24/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-25/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-26/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-27/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-28/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-29/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-30/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-10-31/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-01/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-02/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-03/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-04/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-05/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-06/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-07/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-08/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-09/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-10/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-11/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-12/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-13/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-14/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-15/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-16/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-17/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-18/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-19/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-20/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-21/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-22/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-23/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-24/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-25/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-26/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-27/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-28/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-29/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-11-30/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-01/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-02/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-03/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-04/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-05/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-06/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-07/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-08/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-09/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-10/49021.json\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-11/49021.json\n"
]
}
],
"source": [
"suffix_url = '/49021.json'\n",
"datajson_url_list = []\n",
"\n",
"for a_tag in directory_soup.find_all('a', href=True):\n",
" a_text = a_tag.text.replace(\"/\", \"\")\n",
" if valid_date(a_text): \n",
" print(directory_url+\"/\"+a_text+suffix_url)\n",
" datajson_url_list.append(directory_url+\"/\"+a_text+suffix_url)\n",
" #print(\"Found the URL:\"+ a_tag['href'] + \" Text: \"+ a_tag.text, valid_date(a_tag.text))\n",
" #print re.sub('[/]', '', a_tag.text)\n",
"\n",
" \n",
"# Sorts list to start with most recent\n",
"datajson_url_list.sort(reverse=True) \n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://data-staging.civicagency.org/archive/datajson/2015-12-11/49021.json\n",
"404\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-10/49021.json\n",
"404\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-09/49021.json\n",
"404\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-08/49021.json\n",
"404\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-07/49021.json\n",
"404\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-06/49021.json\n",
"404\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-05/49021.json\n",
"404\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-04/49021.json\n",
"404\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-03/49021.json\n",
"404\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-02/49021.json\n",
"404\n"
]
}
],
"source": [
"read_limit = 10\n",
"\n",
"for index,url in enumerate(datajson_url_list):\n",
" print url\n",
" head = requests.head(url,headers={'Accept-Encoding': 'identity'})\n",
" # print (head.headers['content-length'])\n",
"\n",
" if head.status_code == 200:\n",
" break \n",
" else:\n",
" print head.status_code\n",
"\n",
" if read_limit:\n",
" if index+1 >= read_limit:\n",
" break \n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"http://data-staging.civicagency.org/archive/datajson/2015-09-02/49021.json: 1840306\n",
"http://data-staging.civicagency.org/archive/datajson/2015-12-11/49021.json: 0\n"
]
}
],
"source": [
"# Function to obtain size of page from headers\n",
"def get_page_size(url):\n",
" head = requests.head(url,headers={'Accept-Encoding': 'identity'})\n",
" # print (head.headers['content-length'])\n",
"\n",
" if head.status_code == 200:\n",
" if 'Content-Length' in head.headers:\n",
" size = head.headers['Content-Length']\n",
" return size\n",
" return 0 # When no size is avail\n",
"\n",
"\n",
"#--- Test function ---\n",
"print datajson_url_list[100]+\": \"+str(get_page_size(datajson_url_list[100]))\n",
"print datajson_url_list[0] +\": \"+str(get_page_size(datajson_url_list[0 ]))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"False\n"
]
}
],
"source": [
"# Function to help find relevant links \n",
"import datetime\n",
"def valid_date(date_text):\n",
" try:\n",
" datetime.datetime.strptime(date_text, '%Y-%m-%d')\n",
" return True\n",
" except ValueError:\n",
" # raise ValueError(\"Incorrect data format, should be YYYY-MM-DD\")\n",
" return False\n",
"\n",
"print(valid_date('2003-12-223'))"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Applications/anaconda/anaconda/lib/python2.7/site-packages/IPython/parallel.py:13: ShimWarning: The `IPython.parallel` package has been deprecated. You should import from ipyparallel instead.\n",
" \"You should import from ipyparallel instead.\", ShimWarning)\n"
]
}
],
"source": [
"Accept\n",
"requests.head(url,headers={'Accept-Encoding': 'identity'})\n",
"\n",
"r = requests.head('http://pymotw.com/2/urllib/index.html',headers={'Accept-Encoding': 'identity'})\n",
">>> r.headers['content-length']"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"{u'@type': u'dcat:Dataset',\n",
" u'accessLevel': u'public',\n",
" u'bureauCode': [u'009:00'],\n",
" u'contactPoint': {u'fn': u'Jackie Haven',\n",
" u'hasEmail': u'mailto:[email protected]'},\n",
" u'description': u'<p>MyPyramid Food Data provides information on the total calories; calories from solid fats, added sugars, and alcohol (extras); MyPyramid food group and subgroup amounts; and saturated fat content of over 1,000 commonly eaten foods with corresponding commonly used portion amounts. This information is key to help consumers meet the recommendations of the Dietary Guidelines for Americans and manage their weight by understanding how many calories are consumed from \"extras.\" CNPP has created an interactive tool from this data set available on the web at MyFood-a-pedia.gov. A mobile version is coming soon to provide consumers with assistance on-the-go.</p>\\n',\n",
" u'identifier': u'USDA-FNS-00001',\n",
" u'keyword': [u'health'],\n",
" u'language': [u'en'],\n",
" u'modified': u'2015-08-29',\n",
" u'programCode': [u'009:000'],\n",
" u'publisher': {u'@type': u'org:Organization',\n",
" u'name': u'US Department of Agriculture'},\n",
" u'title': u'MyPyramid Food Raw Data'}"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"#json_data[0] #['Dataset']\n",
"json_data[0]['dataset'][0]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
},
"toc": {
"toc_cell": false,
"toc_number_sections": true,
"toc_threshold": 4,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| gpl-2.0 |
birdsarah/bokeh-miscellany | old/geo/Bokeh map.ipynb | 1 | 2033142 | null | gpl-2.0 |
KartikPadmanabhan/2013_fall_ASTR599 | notebooks/13_OOP.ipynb | 4 | 134926 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<small><i>This notebook was put together by Morgan Fouesneau for UW's [Astro 599](http://www.astro.washington.edu/users/vanderplas/Astr599/) course. Source and license info is on [GitHub](https://github.com/jakevdp/2013_fall_ASTR599/).</i></small>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Object-oriented programming: Principles in 1h by Examples\n",
"-- **M. Fouesneau** \n",
"[ [email protected] ]\n",
"\n",
"Astro599 ( http://www.astro.washington.edu/users/vanderplas/Astr599/schedule )\n",
"\n",
"Prezi presentation is available at: http://bit.ly/18FylbC"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# make sure pylab runs inline when using the notebook\n",
"%pylab inline \n",
"import numpy as np\n",
"import pylab as plt"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def ezrc(fontSize=22., lineWidth=2., labelSize=None, tickmajorsize=10, tickminorsize=5):\n",
" \"\"\"\n",
" slides - Define params to make pretty fig for slides\n",
" \"\"\"\n",
" from pylab import rc, rcParams\n",
" if labelSize is None:\n",
" labelSize = fontSize + 5\n",
" rc('figure', figsize=(9, 7))\n",
" #rc('figure', figsize=(8, 6))\n",
" rc('lines', linewidth=lineWidth)\n",
" rcParams['grid.linewidth'] = lineWidth\n",
" rc('font', size=fontSize, family='serif', weight='small')\n",
" rc('axes', linewidth=lineWidth, labelsize=labelSize)\n",
" rc('legend', borderpad=0.1, markerscale=1., fancybox=False)\n",
" #rc('text', usetex=True)\n",
" rc('image', aspect='auto')\n",
" #rc('ps', useafm=True, fonttype=3)\n",
" rcParams['xtick.major.size'] = tickmajorsize\n",
" rcParams['xtick.minor.size'] = tickminorsize\n",
" rcParams['ytick.major.size'] = tickmajorsize\n",
" rcParams['ytick.minor.size'] = tickminorsize\n",
" \n",
"ezrc(20) # making figures cleaner for slides"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## In python All is objects"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Text is object**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"s = 'some text'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"s.upper()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"'SOME TEXT'"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Modules are objects**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"np"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"<module 'numpy' from '/Users/jakevdp/anaconda/python.app/Contents/lib/python2.7/site-packages/numpy/__init__.pyc'>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"type(np)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"module"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.__name__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"'numpy'"
]
}
],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.__class__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
"module"
]
}
],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print np.__dict__.keys() # we'll see __dict__ later"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"['disp', 'union1d', 'all', 'issubsctype', 'savez', 'atleast_2d', 'restoredot', 'ptp', 'unicode_', 'ix_', 'mirr', 'blackman', 'FLOATING_POINT_SUPPORT', 'busdaycalendar', 'pkgload', 'void', 'unicode0', 'ERR_RAISE', 'void0', 'tri', 'diag_indices', 'array_equal', 'fmod', 'True_', 'indices', 'loads', 'round', 'set_numeric_ops', 'pmt', '_mat', 'cosh', 'object0', 'rate', 'FPE_OVERFLOW', 'index_exp', 'append', 'compat', 'nanargmax', 'hstack', 'typename', 'diag', 'rollaxis', 'ERR_WARN', 'polyfit', 'version', 'memmap', 'nan_to_num', 'complex64', 'fmax', 'spacing', 'sinh', '__git_revision__', 'PackageLoader', 'sinc', 'trunc', 'vstack', 'ERR_PRINT', 'asscalar', 'less_equal', 'BUFSIZE', 'object_', 'divide', 'csingle', 'dtype', 'unsignedinteger', 'fastCopyAndTranspose', 'bitwise_and', 'uintc', 'select', 'deg2rad', 'bytes_', 'eye', 'kron', 'newbuffer', 'negative', 'busday_offset', 'mintypecode', 'MAXDIMS', 'sort', 'einsum', 'uint0', 'zeros_like', 'int_asbuffer', 'uint8', 'chararray', 'linspace', 'resize', 'uint64', 'ma', 'true_divide', 'Inf', 'finfo', 'triu_indices', 'infty', 'add_newdoc', 'seterrcall', 'logical_or', 'minimum', 'WRAP', 'tan', 'absolute', 'array_repr', 'get_array_wrap', 'polymul', 'tile', 'array_str', 'setdiff1d', 'sin', 'longlong', 'product', 'int16', 'str_', 'mat', 'fv', 'max', 'asanyarray', 'uint', 'npv', 'logaddexp', 'flatnonzero', 'short', 'correlate', 'fromstring', 'left_shift', 'searchsorted', 'int64', 'may_share_memory', 'dsplit', 'intersect1d', 'can_cast', 'ppmt', 'show_config', 'cumsum', 'roots', 'outer', 'CLIP', 'fix', 'busday_count', 'timedelta64', 'degrees', 'choose', 'FPE_INVALID', 'recfromcsv', 'fill_diagonal', 'empty_like', 'logaddexp2', 'greater', 'histogram2d', 'polyint', 'rank', 'datetime64', 'complexfloating', 'ndindex', 'ctypeslib', 'PZERO', 'isfortran', 'asfarray', 'radians', 'fliplr', 'alen', 'recarray', 'modf', 'mean', 'square', 'ogrid', 'nanargmin', 'r_', 'diag_indices_from', 'hanning', 's_', 'allclose', 'extract', 'float16', 'ulonglong', 'matrix', 'asarray', 'poly1d', 'promote_types', 'rec', 'datetime_as_string', 'uint32', 'math', 'log2', '__builtins__', 'cumproduct', 'diagonal', 'atleast_1d', 'meshgrid', 'transpose', 'column_stack', 'put', 'byte', 'remainder', 'row_stack', 'expm1', 'nper', 'ndfromtxt', 'place', 'DataSource', 'newaxis', 'arccos', 'signedinteger', 'ndim', 'rint', 'number', 'arctan2', 'little_endian', 'ldexp', 'lookfor', 'array', 'vsplit', 'common_type', 'size', 'logical_xor', 'geterrcall', 'sometrue', 'exp2', 'bool8', 'msort', 'alltrue', 'zeros', 'False_', '__NUMPY_SETUP__', 'nansum', 'bool_', 'inexact', 'broadcast', 'copyto', 'amin', 'arctanh', 'typecodes', 'rot90', 'savetxt', 'sign', 'sctypes', 'std', 'not_equal', 'fromfunction', 'tril_indices_from', '__config__', 'double', 'require', 'typeNA', 'str', 'getbuffer', 'abs', 'clip', 'savez_compressed', 'frompyfunc', 'triu_indices_from', 'conjugate', 'alterdot', 'asfortranarray', 'binary_repr', 'angle', 'lib', 'min', 'unwrap', 'apply_over_axes', 'ERR_LOG', 'right_shift', 'take', 'get_numarray_include', 'trace', 'warnings', 'any', 'who', 'compress', 'histogramdd', 'issubclass_', 'multiply', 'mask_indices', 'amax', 'logical_not', 'average', 'nan', 'nbytes', 'exp', 'result_type', 'dot', 'maximum_sctype', 'longfloat', 'random', 'setxor1d', 'copy', 'FPE_UNDERFLOW', 'frexp', 'errstate', 'nanmin', 'swapaxes', 'SHIFT_OVERFLOW', 'complex256', 'fft', 'digitize', '__file__', 'NZERO', 'ceil', 'ones', 'add_newdoc_ufunc', 'deprecate', 'median', 'geterr', 'convolve', 'isreal', 'where', 'isfinite', 'SHIFT_UNDERFLOW', 'MachAr', 'argmax', 'testing', 'deprecate_with_doc', 'polyder', 'rad2deg', 'isnan', '__all__', 'irr', 'sctypeDict', 'NINF', 'min_scalar_type', 'count_nonzero', 'sort_complex', 'nested_iters', 'concatenate', 'ERR_DEFAULT2', 'vdot', 'bincount', 'copysign', 'array2string', 'corrcoef', 'fromregex', 'vectorize', 'set_printoptions', 'trim_zeros', 'unravel_index', 'cos', 'float64', 'arccosh', 'ushort', 'equal', 'cumprod', 'float_', 'vander', 'geterrobj', 'load', 'fromiter', 'poly', 'bitwise_or', 'polynomial', 'diff', 'iterable', 'array_split', 'get_include', 'pv', 'tensordot', 'piecewise', 'invert', 'UFUNC_PYVALS_NAME', 'SHIFT_INVALID', 'ubyte', 'c_', 'flexible', 'pi', '__doc__', 'empty', 'find_common_type', 'isposinf', 'arcsin', 'sctypeNA', 'imag', 'sctype2char', 'singlecomplex', 'SHIFT_DIVIDEBYZERO', 'matrixlib', 'apply_along_axis', 'reciprocal', 'tanh', 'dstack', 'cov', 'cast', 'logspace', 'packbits', 'issctype', 'mgrid', 'longdouble', 'signbit', 'conj', 'asmatrix', 'inf', 'flatiter', 'bitwise_xor', 'fabs', 'generic', 'reshape', 'NaN', 'cross', 'sqrt', '__package__', 'longcomplex', 'complex', 'pad', 'split', 'floor_divide', '__version__', 'format_parser', 'nextafter', 'polyval', 'flipud', 'i0', 'iscomplexobj', 'mafromtxt', 'bartlett', 'polydiv', 'identity', 'safe_eval', 'greater_equal', 'floor', 'trapz', 'PINF', 'recfromtxt', 'add_newdocs', 'RankWarning', 'ascontiguousarray', 'less', 'putmask', 'UFUNC_BUFSIZE_DEFAULT', 'unicode', 'half', 'NAN', 'typeDict', '__path__', 'shape', 'setbufsize', 'cfloat', 'RAISE', 'isscalar', 'character', 'bench', 'source', 'add', 'uint16', 'bool', 'ufunc', 'save', 'ravel', 'float32', 'real', 'int32', 'tril_indices', 'around', 'lexsort', 'complex_', 'ComplexWarning', 'ipmt', '_import_tools', 'atleast_3d', 'isneginf', 'integer', 'unique', 'mod', 'insert', 'bitwise_not', 'getbufsize', 'array_equiv', 'get_printoptions', 'asarray_chkfinite', 'in1d', 'interp', 'hypot', 'logical_and', 'arange', 'diagflat', 'float128', 'byte_bounds', 'nonzero', 'kaiser', 'polysub', 'fromfile', 'prod', 'nanmax', 'core', 'object', 'seterrobj', 'power', 'nditer', 'percentile', 'FPE_DIVIDEBYZERO', '__name__', 'subtract', 'frombuffer', 'iscomplex', 'add_docstring', 'argsort', 'fmin', 'ones_like', 'is_busday', 'arcsinh', 'intc', 'float', 'ndenumerate', 'intp', 'unpackbits', 'Infinity', 'log', 'cdouble', 'complex128', 'long', 'round_', 'broadcast_arrays', 'inner', 'var', 'int_', 'log10', 'uintp', 'linalg', 'histogram', 'issubdtype', 'int0', 'squeeze', 'int8', 'info', 'seterr', 'argmin', 'genfromtxt', 'maximum', 'record', 'obj2sctype', 'clongdouble', 'sum', 'isrealobj', 'log1p', 'delete', 'tril', 'int', 'ediff1d', 'char', 'single', 'loadtxt', 'ScalarType', 'triu', 'floating', 'expand_dims', 'Tester', 'polyadd', 'ERR_IGNORE', 'emath', 'arctan', 'bmat', 'isclose', 'ERR_DEFAULT', 'test', 'roll', 'string0', 'compare_chararrays', 'iinfo', 'real_if_close', 'repeat', 'hamming', 'ALLOW_THREADS', 'ravel_multi_index', 'string_', 'isinf', 'ndarray', 'e', 'ERR_CALL', 'datetime_data', 'clongfloat', 'hsplit', 'gradient', 'base_repr', 'argwhere', 'set_string_function']\n"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Matplotlib of course**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pylab as plt\n",
"plt.plot(range(10))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"[<matplotlib.lines.Line2D at 0x105ee1d10>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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o3769Ro4cWam5kKRGjRrpjjvukCRt3LgxmDrhEuvWST16+JuLq6+WcnNpLgAA\ntQtok+ejjz5a41iTJk0kMVvhNWVl/mTItGmSMf4I6htvSMnJTlcGAHCDkFMke/bskSTdeuutIReD\nyEAEFQAQqoD2YNSksLBQbdu2VWpqqnbu3PnDRdmD4VpEUAEA1Qn2vT2kGYxJkyapQYMGWrJkSbXj\nWVlZtV4jLS1NaWlpoZQBmyxcKI0f70+J9OwpZWeTEgGAaODz+eTz+Wy9Zp1nMJYtW6bMzEz98Y9/\n1JAhQypflBkMVykpkR5/XHrtNf/XjzwizZwpxcY6WxcAIHLUywzG+vXrNXbsWM2fP79KcwF3IYIK\nAAiHoBuMDRs2aNiwYZozZ44yeSdyNe6CCgAIl6ByARs3btTQoUM1a9asSs3FZ599plWrVtldG8KE\nu6ACAMIt4AZj06ZNGjJkiGbOnKlRo0ZVGtuxY4fmzp1re3GwX2GhlJEhle+/nT5dysnhfAsAgL0C\n2uT5/vvva/DgwUpKSlK/fv2qbPDYt2+fEhIS9P777/svyibPiEQEFQBQV2HZ5LlkyRKdPXtW3333\nnVauXFnxS8p/kWVZ6tevXx3KRX1ZsEB67DEiqACA+hHSQVs1XpQZjIhRUiJNmOBvMCQiqACAuqnX\ng7YQ2YigAgCcQoPhURdHULt0kd56i5QIAKD+cPsqj7k0gpqRIe3cSXMBAKhfzGB4CHdBBQBEChoM\njyCCCgCIJHy29YAFC6Q+ffzNRc+e/lM5aS4AAE6iwXCxkhJpzBj/4+xZfwR1yxbOtwAAOI8lEpci\nggoAiGQ0GC5EBBUAEOlYInERIqgAALdgBsMliKACANyEBsMFiKACANyGz78RjggqAMCNaDAiFBFU\nAICbsUQSgYigAgDcjgYjwhBBBQB4AUskEYIIKgDAS5jBiABEUAEAXkOD4TAiqAAAL+IzsoOIoAIA\nvIoGwwFEUAEAXscSST27NIL6yivSyJFOVwUAgL1oMOoREVQAQLRgiaQeEEEFAEQbZjDCjAgqACAa\n0WCEERFUAEC04nN0mFwcQf3JT4igAgCiS50ajKNHj+q+++5TTEyMXn/9dbtrcrXqIqibNxNBBQBE\nl6CXSFatWqXHH39c586dkyRZlmV7UW5FBBUAAL+gZjBefvll/du//Ztef/113XPPPeGqyZXWrZN6\n9PA3F126SLm5NBcAgOgVVIPRvXt35eXlaeDAgTLGhKsmVyGCCgBAVUEtkdxyyy3hqsOVCgv9B2et\nW0cEFQBOEfLpAAARTUlEQVSAixFTrSMiqAAA1IzP2nWwcCERVAAALiesMxhZWVm1fk9aWprS0tLC\nWYZtSkqkCRP8Z1xI/gjqzJlSbKyzdQEAEAqfzyefz2frNR1vMNzi0gjq3LlSZqbTVQEAELpAPuxP\nmzYtqGuyByMA3AUVAIDgsAfjMoigAgBQN8xg1IAIKgAAdUeDUQ0iqAAAhCaoBsMYo+LiYklSaWmp\nJOnUqVM6fvy4YmJi1LRpU/srrGcLFkiPPea/UdlPfiL98Y/cqAwAgGBZJogzvwsKCtSlS5cfftiy\nKo4M79y5s/Lz8yuel+Sq48SJoAIAULNg39uDajDCVYTTuAsqAACXF+x7e9TvwSCCCgCA/aI2E0EE\nFQCA8InKGQwiqAAAhFfUNRhEUAEACL+o+sy+YAF3QQUAoD5ERYNRUiKNGeN/nD3rj6Bu3sz5FgAA\nhIvnl0iIoAIAUP883WAQQQUAwBmeXCIhggoAgLM8N4NBBBUAAOd5qsEgggoAQGTwzOf6iyOoPXsS\nQQUAwEmubzCqi6Bu2UIEFQAAJ7l6iYQIKgAAkcm1DQYRVAAAIpfrlkiIoAIAEPlcNYNBBBUAAHdw\nTYNBBBUAAPdwxWd/IqgAALhLRDcYRFABAHCniF0iIYIKAIB7RWSDQQQVAAB3i6glEiKoAAB4Q8TM\nYBBBBQDAOyKiwSCCCgCAtzg+P+DWCKrP53O6hLDi9bmbl1+fl1+bxOtzO6+/vmA41mC4PYLq9T9E\nvD538/Lr8/Jrk3h9buf11xcMR5ZILo2gzp0rZWY6UQkAAAiHgGcwTpw4oSeffFKdOnVSfHy8unXr\npueee07nz58P6heuWyf16OFvLrp0kXJzaS4AAPCagGYwTpw4ob59+6q4uFgrVqxQjx49tHbtWj30\n0EPatm2b1qxZo5ha4h5lZf5kyLRpkjH+COqSJVJysi2vAwAARJCAZjCeffZZ5eXlad68eerTp49i\nY2M1ZMgQZWVlae3atXr11Vcv+/OFhdLgwVJWlv/r6dOlP/2J5gIAAK+qtcH4/vvv9dprr6lt27Ya\nOHBgpbHMzExZlqXf//73Nf78rl3+JZF16/wR1HXrpClTON8CAAAvq/VtftOmTTp79qx69+5dZax5\n8+a69tprtXfvXn355ZdVxt0aQQUAAKGptcH4+9//Lknq3LlztePlz3/66adVxtwaQQUAACEytXjs\nsceMZVlm+vTp1Y7ff//9xrIsM3fu3IrnJPHgwYMHDx48PPgIVK0zGGfOnJEkNWrUqNrxK664QpJ0\n+vTp2i4FAACiRK0x1fj4eEnSuXPnqh0vLS2VJCUkJFQ855/EAAAA0arWGYyrrrpKklRUVFTt+PHj\nxyVJrVu3trEsAADgZrU2GP/0T/8kSdq3b1+14wUFBbIsSzfddJO9lQEAANeqtcHo37+/YmNjtWPH\njipj//jHP7Rnzx5dc8016tq1q23HiUeyo0eP6r777lNMTIxef/11p8uxzZo1azR8+HB16tRJsbGx\nSk5OVr9+/bR06VKnSwuZMUbr16/X448/ru7du6tFixZq1qyZUlNTNWnSJH377bdOl2i78tN1azth\n1y0yMzMrXk91j0OHDjldYsg2bNigu+++W1dddZXi4uLUsWNHZWRkaMWKFU6XVmeLFy++7L+38seH\nH37odKkh2bBhgwYNGqROnTopISFB11xzje677z7t3LnT6dJCtnz5cvXr109JSUlKSEhQamqqXnzx\nxcDe1wPZCVqeJHnvvfcqPf/f//3fxrIs8/LLL5vi4mKTmppqOnToYLZu3WpKSkrM6tWrTWJiohk0\naJC5cOFCwDtPI9XKlSvNlVdeaZKTk41lWeb11193uiRbTJ8+3ViWZQYMGGA+/vhjc+bMGfP555+b\ne+65x1iWZX71q185XWJIjh49aizLMtdff73ZtGmTOXXqlDl27JiZP3++iY2NNa1btzbffPON02Xa\npri42LRv395YlmViYmKcLscWmZmZpk2bNuaGG26o9nHkyBGnSwzJ1KlTTWJiopk3b54pLCw0p0+f\nNjk5OaZZs2Zm4MCBTpdXZ4sWLTIJCQk1/ntr2bKladSokTl48KDTpdZZ+fvg7bffbvLy8syZM2fM\njh07zD//8z+bBg0amOzsbKdLrLNf/epXxrIsM3HiRLN//35TVFRkFi1aZBo3bmzuvPNOc/78+cv+\nfEANRnFxsbnxxhtN+/btzZYtW8zp06fN22+/bRITE83AgQPNhQsXzIQJE4xlWWbt2rWVfvall14y\nlmWZOXPm1P1VRoA//OEPpn379mbt2rUmMzPTUw3Gs88+a9q0aWNOnTpV6fnS0lJzzTXXGMuyzKZN\nmxyqLnTlDcb27durjP361782lmWZqVOn1n9hYTJu3Dhzyy23eK7B8Mp/b5davXq1sSzL/PGPf6wy\n9tJLL5mHH37YgarssWjRInPbbbfVOH7bbbeZYcOG1WNF9jp79qxJTEw0DRo0MEePHq009re//a3i\ng40b/elPfzKWZZmf/vSnVcaef/55Y1mWmT179mWvEdD8adOmTbVt2zbde++9GjFihJKTk/Wb3/xG\nv/nNb7RmzRqdOnUqpOPE3aB79+7Ky8vTwIEDPZeSad++vUaOHFkpCST5o8l33HGHJGnjxo1OlGaL\npKQk+Xw+9erVq8pY165dJUnFxcX1XVZYbN26VYsWLdJrr73mdCm289p/d+UmT56sLl266N57760y\n9q//+q+13uspknXp0kX9+/evduzzzz+Xz+fTuHHj6rkq+xQVFenkyZNq2bKlWrZsWWksJSVFkvT1\n1187UVrIsrOzJUn33HNPlbHyP6uzZ8++7DUCupuq5G8yfv/731fbKARynPiePXv05Zdf6tprrw30\nV0aUW265xekSwubRRx+tcaxJkyaS3P2Xe8OGDXXrrbdWO7Z9+3ZJ0u23316fJYVFaWmpxo4dq0mT\nJlX85YbI9vHHH2v37t0aOXKk06WExa233lrjf3tz5szRddddp/T09Hquyj6tW7dW27Zt9e233+ro\n0aNq1apVxVheXp4k6cc//rFT5YWkfG9adQnRNm3aSJL27t2rAwcOqGPHjtVew5YdYKEcJ47ItmfP\nHkmq8S8JNyopKdEXX3yhSZMmadWqVcrKylJGRobTZYVsxowZkqQpU6Y4XEl4vP/+++rfv79atWql\nhIQEpaSkaPLkyRVReTcqb3A7dOiglStXqlevXmrcuLGSkpI0YMAA+Xw+ZwsMk5MnT2rJkiWX/XDj\nFosXL1ZSUpKGDx+uvLw8nTlzRjt27NCYMWPUsWNHzZ071+kS66S8WTp8+HCVsSNHjlT88+7du2u8\nhi0NRnkByTXcfz0pKUmS9N1339nx61BPCgsL9Ze//EXdu3fXnXfe6XQ5tli3bp0SEhJ0ww03aPny\n5VqyZIn+/d//3emyQpaXl6ff/e53mj9/fo2n7rrdhx9+qCeeeEIHDhzQ4cOH9fTTT2vWrFnq2bNn\ntX8JusFXX30lSVq2bJmeeuopPf/88zp27Ji2bNmi4uJipaenuzpFUpOlS5fq/PnzGjVqlNOlhCw9\nPV25ubmSpJtuukmNGzfWzTffrOuvv17bt29XamqqwxXWzeDBgyVJOTk5VcbeffddSf6Z7cs1+LY0\nGBwn7k2TJk1SgwYNtGTJEqdLsc3AgQNVVlam/Px8PfHEExo9erQGDhyowsJCp0urs7KyMo0dO1aj\nRo1S3759nS4nLJ588knl5ubq7rvvVnx8vJo2bapRo0bpueeeU35+vsaPH+90iXVy4sQJSf5zhhYu\nXKj09HTFx8crNTVVb775piRp/PjxOnnypJNl2m7OnDkaMWKEmjVr5nQpIcvOzlaPHj3UsGFDffLJ\nJzp58qS2bNmi3bt3q0ePHhXNh9uMGDFC6enp2rp1qyZOnKgDBw7o+PHjWr58uWbMmFExw3HZ5XM7\ndpvW5YZobjZy5EhPpUiqs3TpUtOwYUOzevVqp0sJq9mzZxvLssyDDz7odCl1NmvWLNO+fXtz4sSJ\nSs97KUVSk1OnThnLskzDhg3N8ePHnS4naA8//LCxLMs0b9682vE+ffoYy7LM22+/Xc+Vhc+HH35o\nLMsyH330kdOlhCw/P9/ExcWZdu3amTNnzlQaKygoMLGxsaZ9+/ZVEnpuUVpaap5//nlz4403mri4\nONOsWTPz85//3Gzfvt387Gc/qzY5ejFbZjA4Ttxb1q9fr7Fjx2r+/PkaMmSI0+WE1ejRoyVJb775\npitn2L7++mtNnjxZs2fPVmJiotPl1LuEhAS1bt1aZWVl2rt3r9PlBK18WblDhw7Vjnfq1EnSD0sp\nXjBnzhz16tVL3bt3d7qUkK1cuVJnz57VXXfdpbi4uEpjnTp1Uu/evXXw4EGtX7/eoQpD06hRIz3z\nzDP69NNPdebMGR0/flzvvfeeevfurcLCQlmWVZHEq44tDQbHiXvHhg0bNGzYMM2ZM0eZmZlOlxN2\n8fHxatmypYwxys/Pd7qcoG3cuFGnTp3SsGHDqpyOKPmnL8u/riku6HbGxQmn8rRPTTeTLGdZVn2U\nE3bfffedVq9e7dolrUsVFBRI+iFVcany5/fv319fJdWLc+fOKT8/Xy1atAh/gxHMceKIXBs3btTQ\noUM1a9asSs3FZ599plWrVjlXWIhmzJihX/ziF9WOlZaWVuy/aNq0aX2WZYvMzEyVlZVV+5D8b0zl\nX2/atMnhautm27Ztuu6666odO3nypI4cOaKYmBhX/v1SHo/+5ptvKv6dXaz8jen666+v17rCZf78\n+UpMTNTw4cOdLsUW5Wdf1HRUffnzl56R4RarV6+u2Cd0sa1bt6qkpKTWf4+2NBhNmjTR6NGjdejQ\nIa1du7bS2OLFiyVJTzzxhB2/CmGyadMmDRkyRDNnzqyys3vHjh2ujVpJ0vnz57V58+ZqD9Navny5\nysrKlJKSUmOWG84qLS3V3r17q72vwyuvvCLJv+PdjRsG27Vrp6FDh+r777+vsls/Pz9f27dvV7t2\n7SoOvHOzCxcuaN68eRo1alTFxn+3K4+3v/vuuyopKak0tn//fv31r39VXFyca8/6GDFihN55551K\nzxlj9MILL6hp06Z69tlnL38BuzaDBHKcuJuVlZWZoqIiU1RUZEaMGFFx/HlRUZEpLi52uryQbNq0\nycTHx5s2bdqY4cOHm/vvv7/So1evXiYtLc3pMuts2rRpFUfebt682Zw4ccIcOnTIzJkzxyQmJprE\nxESzbds2p8u0RWlpacWf0/JNnsePHzdFRUW13jcgUn3wwQfGsixzzTXXmD//+c/m+PHj5vjx4+a1\n114zCQkJpnPnzq6+l8XBgwdNx44dTYcOHYzP5zNnz541n376qendu7dp3Lixq4/pv9jbb79tYmJi\nTH5+vtOl2Gr8+PHGsiwzcOBA8/e//92cPHnSbNu2zfzoRz8yMTExZtasWU6XWGflG1jff/99c/r0\nabNnzx7zy1/+0jRu3NisX7++1p+3rcEwxt9kPPHEE6ZDhw4mNjbWXHvttWbGjBnm3Llzdv4aR+zb\nt89YllXxiImJqfjnq6++2unyQpKZmWliYmIqXlP5P1/89eXuJxDpzpw5Y1auXGmGDh1qOnToYK64\n4goTHx9vrrvuOjN+/Hizb98+p0u0zaJFiyr9Gb343+EHH3zgdHl15vP5zNixY821115r4uLiTHx8\nvLnxxhvNM888Y4qKipwuL2RHjhwx48aNq/jzedVVV5kHHnjAfPbZZ06XZps77rjD/PznP3e6jLBY\ntmyZSUtLM0lJSaZhw4amVatW5q677jIbNmxwurSQZGVlmb59+5orr7zSxMbGmo4dO5rRo0ebr776\nKqCft4xx8Q4pAAAQkWzZgwEAAHAxGgwAAGA7GgwAAGA7GgwAAGA7GgwAAGA7GgwAAGC7/wP/Oql+\nggjPywAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x105eb9b90>"
]
}
],
"prompt_number": 10
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`[<matplotlib.lines.Line2D at 0x30c3e90>]`\n",
"\n",
"plot returns an instance of a **Line2D object**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pylab as plt\n",
"r = []\n",
"r.append( plt.plot(range(10)) )\n",
"r.append( plt.xlabel('measured') )\n",
"r.append( plt.ylabel('calculated') )\n",
"r.append( plt.title('Measured vs. calculated') )\n",
"r.append( plt.grid(True) )\n",
"print r"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[[<matplotlib.lines.Line2D object at 0x10615e750>], <matplotlib.text.Text object at 0x102302910>, <matplotlib.text.Text object at 0x106148e50>, <matplotlib.text.Text object at 0x106151810>, None]\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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p06YoUaIEDh8+jNTUVOzevRt//vknmjZtaunY5Cf3G1bmbXtjtkZqX1G3mU/u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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x105ee98d0>"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pylab as plt\n",
"# ------- make a figure ----------------------------\n",
"plt.plot(range(10))\n",
"plt.grid(True)\n",
"\n",
"# ------- access objects and change them ------------\n",
"ax = plt.gca() # gca == get current axis\n",
"line = ax.lines[0] \n",
"line.set_marker('o')\n",
"line.set_markersize(20)\n",
"line.set_linewidth(2)\n",
"plt.setp(line, color='g') # set property\n",
"\n",
"#change the figure style\n",
"plt.tick_params(which='both', width=2)\n",
"ax.xaxis.set_ticks_position('bottom')\n",
"ax.yaxis.set_ticks_position('left')\n",
"for k, sk in ax.spines.items():\n",
" if k in ('top', 'right'):\n",
" sk.set_color('None') \n",
" else:\n",
" sk.set_position(('outward', 20))\n",
" \n",
"# add labels\n",
"plt.xlabel('measured') \n",
"plt.ylabel('calculated') \n",
"plt.title('Measured vs. calculated')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.text.Text at 0x106191390>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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7/qya6v3111/Izc2Ft7e3XiIEAP7+/gCAq1evWrQNQgjFXuymYHzKxviUy55j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\nxmKIiIiIbNr/AyumNlhtKhW1AAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x10614b490>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Let's make a class Vector"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The simplest form of class definition looks like this:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class DummyClass:\n",
" pass"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Class objects support two kinds of operations: **attribute references** and **methods**.\n",
"\n",
"Attribute references use the standard syntax used for all attribute references in Python: `obj.name`. \n",
"\n",
" \n",
"So, a class definition looked like this:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class DummyClass:\n",
" \"\"\"A simple example class\"\"\"\n",
" i = 12345\n",
" def f(self):\n",
" return 'hello world'"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"then `DummyClass.i` and `DummyClass.f` are valid attribute references, returning an integer and a function object, respectively."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### First exercise, make a vector class:\n",
"\n",
"**attributes**\n",
"\n",
"* x, y, z: 3d coordinates\n",
"\n",
"**methods**\n",
"\n",
"* sum\n",
"* scalar product\n",
"* etc"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When a class defines an `__init__()` method, class instantiation automatically invokes `__init__()` for the newly-created class instance. So in this example, a new, initialized instance can be obtained by:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Vector(object):\n",
" def __init__(self):\n",
" pass"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Note**: Often, the first argument of a method is called `self`. \n",
"This is nothing more than a convention: the name self has absolutely no special meaning to Python. \n",
" \n",
"Note, however, that by not following the convention your code may be less readable to other Python programmers, and it is also conceivable that a class browser program might be written that relies upon such a convention."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Vector(object): \n",
" def __init__(self, x,y,z): \n",
" \"\"\" constructor \"\"\"\n",
" pass\n",
" \n",
" def sum(self, p): \n",
" \"\"\" addition: p1+p2 \"\"\"\n",
" pass\n",
" \n",
" def scalarProd(self,p):\n",
" \"\"\" A.B = sum(a[k].b[k]) \"\"\"\n",
" pass"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A solution:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# your solution here"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = Vector(1, 0, 0)\n",
"y = Vector(0, 1, 0)\n",
"z = Vector(0, 0, 1)\n",
"\n",
"print \"x.y = \", x.scalarProd(y)\n",
"\n",
"print x"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"x.y = None\n",
"<__main__.Vector object at 0x106165ad0>\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"`object.__repr__(self)`\n",
"\n",
"Called by the `repr()` built-in function and by string conversions (reverse quotes) to compute the \"official\" string representation of an object. \n",
"\n",
"`object.__str__(self)`\n",
"\n",
"Called by the `str()` built-in function and by the print statement to compute the \"informal\" string representation of an object. \n",
"\n",
"\n",
"http://docs.python.org/2/reference/datamodel.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Vector(object): \n",
" \n",
" # your previous solution\n",
" \n",
" def __str__(self):\n",
" # your solution here"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = Vector(1, 0, 0)\n",
"y = Vector(0, 1, 0)\n",
"z = Vector(0, 0, 1)\n",
"\n",
"print \"x.y = \", x.scalarProd(y)\n",
"print x"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"What about checking that $x^2 + y^2 + 2\\cdot x \\cdot y = (x + y) ^ 2$"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x.scalarProd(x) + y.scalarProd(y)\n",
"x.scalarProd(x) + y.scalarProd(y) + 2 * x.scalarProd(y) == x.sum(y).scalarProd(x.sum(y))"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x ** 2 + y ** 2 + 2 * x * y == (x + y) ** 2"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Implement common operators `__add__`, `__sub__`, `__mul__`, ...\n",
"see http://docs.python.org/2/reference/datamodel.html"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"# put the new version of your class here"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = Vector(1, 0, 0)\n",
"y = Vector(0, 1, 0)\n",
"z = Vector(0, 0, 1)\n",
"\n",
"xy = x * y\n",
"print \"x.y = \", xy\n",
"print x\n",
"x * x + y * y + 2 * x * y == (x + y) ** 2"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"###Bonus exercise\n",
"\n",
"Generalize the class vector and all operations to N-dimensions\n",
"(tip: you can use numpy arrays)\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#code here"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## A Teaser about Decorators\n",
"\n",
"This section is a very quick introduction to a very pythonic coding process: **decorations**\n",
" \n",
"A decorator is just a **callable** object that takes a function as an argument and returns a replacement function. We\u2019ll start simply and work our way up to useful decorators. In other words: **a function that writes a function of a function**\n",
" \n",
"Look carefully through our decorator example below. We defined a function named `outer` that has a single parameter `some_func`. Inside `outer` we define an nested function named `inner`. \n",
"\n",
"Since Python 2.4, you can wrap a function in a decorator by pre-pending the function definition with a decorator name and the `@` symbol. In the code samples below above we decorated our function using this syntax, however it\u2019s important to recognize that this is no different than simply replacing the original variable with the return from the wrapper function- **Python just adds some syntactic sugar to make what is going on very explicit.**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def outer(some_func):\n",
" def inner():\n",
" print \"before some_func\"\n",
" ret = some_func() # 1\n",
" return ret + 1\n",
" return inner\n",
"\n",
"@outer\n",
"def foo():\n",
" return 1\n",
"\n",
"# transparently equivalent to\n",
"# foo = outer(foo)\n",
"\n",
"foo()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"before some_func\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"2"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Examples of 2 decorators: \n",
" \n",
"* timing a function (which also implements the context manager usage)\n",
"* memoization feature: caching output of a function according to its call arguments."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import collections\n",
"import functools\n",
"from functools import wraps\n",
"import time, math, sys\n",
"\n",
"class timeit(object):\n",
"\t\"\"\" Time a block of your code.\n",
" Decorator and context manager\n",
"\t\"\"\"\n",
"\tdef __init__(self, f=None, verbose=True, text=None):\n",
"\t\tself.f = f\n",
"\t\tif not self.f is None:\n",
"\t\t\tif type(self.f) != str:\n",
"\t\t\t\tfunctools.update_wrapper(self, f)\n",
"\t\t\t\tself.text = self.__name__\n",
"\t\t\telse:\n",
"\t\t\t\tself.text = f\n",
"\t\telse:\n",
"\t\t\tself.text = text or ''\n",
"\t\tself.verbose = verbose\n",
"\n",
"\tdef __enter__(self):\n",
"\t\tprint \"Timing %s\" % (self.text)\n",
"\t\tself.start = time.time()\n",
"\n",
"\tdef __exit__(self, exc_type, exc_val, exc_tb):\n",
"\t\tself.stop = time.time()\n",
"\t\tprint self.time\n",
"\n",
"\tdef __pretty_print(self, t):\n",
"\t\tunits = [u\"s\", u\"ms\",u'us',\"ns\"]\n",
"\t\tscaling = [1, 1e3, 1e6, 1e9]\n",
"\t\tif t > 0.0 and t < 1000.0:\n",
"\t\t\torder = min(-int(math.floor(math.log10(t)) // 3), 3)\n",
"\t\telif best >= 1000.0:\n",
"\t\t\torder = 0\n",
"\t\telse:\n",
"\t\t\torder = 3\n",
"\t\treturn \"%s Execution time: %.3g %s\" % (self.text, t * scaling[order], units[order])\n",
"\n",
"\t@property\n",
"\tdef time(self):\n",
"\t\treturn self.__pretty_print(self.stop-self.start)\n",
"\n",
"\tdef __call__(self, *args, **kwargs):\n",
"\t\tself.start = time.time()\n",
"\t\tr = self.f(*args, **kwargs)\n",
"\t\tself.stop = time.time()\n",
"\t\tif self.verbose:\n",
"\t\t\tprint self.time\n",
"\t\treturn r\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class memoize(dict):\n",
"\t'''Decorator that caches a function's return\n",
" value each time it is called. If called \n",
" later with the same arguments, the cached \n",
" value is returned (not reevaluated).\n",
"\t'''\n",
"\tdef __init__(self, func):\n",
"\t\tself.func = func\n",
"\t\tfunctools.update_wrapper(self, func)\n",
"\n",
"\tdef __getitem__(self, *key):\n",
"\t\treturn dict.__getitem__(self, key)\n",
"\n",
"\tdef __missing__(self, key):\n",
"\t\tret = self[key] = self.func(*key)\n",
"\t\treturn ret\n",
"\n",
"\t__call__ = __getitem__\n",
"\n",
"\tdef __repr__(self):\n",
"\t\t'''Return the function's docstring.'''\n",
"\t\treturn self.func.__doc__\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 20
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"@timeit\n",
"@memoize\n",
"def f(*args, **kwargs):\n",
" import time\n",
" time.sleep(5)\n",
" return (args, kwargs)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 21
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f(10)\n",
"f(10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"f Execution time: 5 s\n",
"f Execution time: 5.01 us\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 22,
"text": [
"((10,), {})"
]
}
],
"prompt_number": 22
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Implementing the IMF as an object"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Inheritance\n",
"Of course, a language feature would not be worthy of the name \u201cclass\u201d without supporting inheritance. The syntax for a derived class definition looks like this:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class IMF(object):\n",
" mass = [0.1, 120.]\n",
"\n",
" \n",
"class Salpeter(IMF):\n",
" pass"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"f = Salpeter()\n",
"print f.mass"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Execution of a derived class definition proceeds the same as for a base class. When the class object is constructed, the base class is remembered. This is used for resolving attribute references: if a requested attribute is not found in the class, the search proceeds to look in the base class. This rule is applied recursively if the base class itself is derived from some other class.\n",
"\n",
"There\u2019s nothing special about instantiation of derived classes: `Salpeter()` creates a new instance of the class. Method references are resolved as follows: the corresponding class attribute is searched, ascending up the chain of parent classes if necessary, and the method reference is valid if this yields a function object.\n",
"\n",
"Derived classes may override methods of their base classes.\n",
"\n",
"An overriding method in a derived class may in fact want to extend rather than simply replace the base class method of the same name. There is a simple way to call the base class method directly: just call `IMF.methodname(self, *args, **kwargs)`. It is common to call the parent constructor in the derived class constructor:\n",
"\n",
"```\n",
"def Salpeter(IMF):\n",
" def __init__(self, *args, **kwargs):\n",
" IMF.__init__(self, *args, **kwargs)\n",
"```\n",
"\n",
"Note that the above example is equivalent to doing nothing special and thus can be omitted (it is the implicit definition from inheritance)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Homework: fully implement an IMF concept.\n",
"\n",
"\n",
"**due Monday, October 14th**\n",
" \n",
"\n",
"\n",
"__Similarly to last week assignment__\n",
"\n",
"* Create a new notebook or a copy of the current one, and call it `HW2.ipynb`,\n",
"\n",
"* complete this part, embed resulting figures in the notebook. Comment your code: each function/method must have at least one line of description.\n",
"* The assignment should be turned in by submitting a pull request to the github repository: `jakevdp/ASTR599_homework`. (see Astro599 website for help)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**The requirements are:**\n",
"\n",
"* Computing the expected number of stars per mass bin\n",
"* Computing the mass enclosed in a given mass range\n",
"* Being able to draw random masses from an IMF\n",
"* What is the average mass predicted by an IMF?\n",
"\n",
"Below is a template of IMF class. **Feel free to adapt.**"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class IMF(object):\n",
" \"\"\"\n",
" IMF object class\n",
" let you define an IMF as multiple power-laws.\n",
"\n",
" attributes:\n",
" norm: norm of the function\n",
" \"\"\"\n",
"\n",
" def __init__(self, *args, **kwargs):\n",
" \"\"\"\n",
" \"\"\"\n",
" pass\n",
"\n",
" def get_enclosed_mass(self, Mmin, Mmax):\n",
" \"\"\"Get the enclosed mass over a given mass range.\n",
" \"\"\"\n",
" pass\n",
"\n",
" def get_enclosed_Nstar(self, Mmin, Mmax):\n",
" \"\"\"Get the enclosed dN over a given mass range\n",
" \"\"\"\n",
" pass\n",
"\n",
" def get_avg_mass(self, Mmin, Mmax):\n",
" \"\"\" get the avg mass over a given range \"\"\"\n",
" pass\n",
"\n",
" def getValue(self, m):\n",
" \"\"\" returns the value of the normalized IMF at a given mass m:\n",
" note: m can be an iterable\n",
" \"\"\"\n",
" pass\n",
"\n",
" def random(self, N, massMin=None, massMax=None):\n",
" \"\"\" Draw samples from this distribution\n",
" Samples are distributed over the interval [massMin, massMax]\n",
" Interval is truncated to the IMF range definition if it extents beyond it. \n",
" (taken as is otherwise)\n",
" \"\"\"\n",
" pass\n",
" \n",
" def __call__(self, m=None):\n",
" return self.getValue(m)"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Plot at least a couple of mass functions on the same figure"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#your code here"
],
"language": "python",
"metadata": {},
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Draw a random sample of N masses from one mass function and show that the sample follows the desired distribution"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#code here"
],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | apache-2.0 |
keras-team/keras-io | examples/audio/ipynb/speaker_recognition_using_cnn.ipynb | 1 | 21367 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"# Speaker Recognition\n",
"\n",
"**Author:** [Fadi Badine](https://twitter.com/fadibadine)<br>\n",
"**Date created:** 14/06/2020<br>\n",
"**Last modified:** 03/07/2020<br>\n",
"**Description:** Classify speakers using Fast Fourier Transform (FFT) and a 1D Convnet."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Introduction\n",
"\n",
"This example demonstrates how to create a model to classify speakers from the\n",
"frequency domain representation of speech recordings, obtained via Fast Fourier\n",
"Transform (FFT).\n",
"\n",
"It shows the following:\n",
"\n",
"- How to use `tf.data` to load, preprocess and feed audio streams into a model\n",
"- How to create a 1D convolutional network with residual\n",
"connections for audio classification.\n",
"\n",
"Our process:\n",
"\n",
"- We prepare a dataset of speech samples from different speakers, with the speaker as label.\n",
"- We add background noise to these samples to augment our data.\n",
"- We take the FFT of these samples.\n",
"- We train a 1D convnet to predict the correct speaker given a noisy FFT speech sample.\n",
"\n",
"Note:\n",
"\n",
"- This example should be run with TensorFlow 2.3 or higher, or `tf-nightly`.\n",
"- The noise samples in the dataset need to be resampled to a sampling rate of 16000 Hz\n",
"before using the code in this example. In order to do this, you will need to have\n",
"installed `ffmpg`."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Setup"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"import os\n",
"import shutil\n",
"import numpy as np\n",
"\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"\n",
"from pathlib import Path\n",
"from IPython.display import display, Audio\n",
"\n",
"# Get the data from https://www.kaggle.com/kongaevans/speaker-recognition-dataset/download\n",
"# and save it to the 'Downloads' folder in your HOME directory\n",
"DATASET_ROOT = os.path.join(os.path.expanduser(\"~\"), \"Downloads/16000_pcm_speeches\")\n",
"\n",
"# The folders in which we will put the audio samples and the noise samples\n",
"AUDIO_SUBFOLDER = \"audio\"\n",
"NOISE_SUBFOLDER = \"noise\"\n",
"\n",
"DATASET_AUDIO_PATH = os.path.join(DATASET_ROOT, AUDIO_SUBFOLDER)\n",
"DATASET_NOISE_PATH = os.path.join(DATASET_ROOT, NOISE_SUBFOLDER)\n",
"\n",
"# Percentage of samples to use for validation\n",
"VALID_SPLIT = 0.1\n",
"\n",
"# Seed to use when shuffling the dataset and the noise\n",
"SHUFFLE_SEED = 43\n",
"\n",
"# The sampling rate to use.\n",
"# This is the one used in all of the audio samples.\n",
"# We will resample all of the noise to this sampling rate.\n",
"# This will also be the output size of the audio wave samples\n",
"# (since all samples are of 1 second long)\n",
"SAMPLING_RATE = 16000\n",
"\n",
"# The factor to multiply the noise with according to:\n",
"# noisy_sample = sample + noise * prop * scale\n",
"# where prop = sample_amplitude / noise_amplitude\n",
"SCALE = 0.5\n",
"\n",
"BATCH_SIZE = 128\n",
"EPOCHS = 100\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Data preparation\n",
"\n",
"The dataset is composed of 7 folders, divided into 2 groups:\n",
"\n",
"- Speech samples, with 5 folders for 5 different speakers. Each folder contains\n",
"1500 audio files, each 1 second long and sampled at 16000 Hz.\n",
"- Background noise samples, with 2 folders and a total of 6 files. These files\n",
"are longer than 1 second (and originally not sampled at 16000 Hz, but we will resample them to 16000 Hz).\n",
"We will use those 6 files to create 354 1-second-long noise samples to be used for training.\n",
"\n",
"Let's sort these 2 categories into 2 folders:\n",
"\n",
"- An `audio` folder which will contain all the per-speaker speech sample folders\n",
"- A `noise` folder which will contain all the noise samples"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"Before sorting the audio and noise categories into 2 folders,\n",
"we have the following directory structure:\n",
"\n",
"```\n",
"main_directory/\n",
"...speaker_a/\n",
"...speaker_b/\n",
"...speaker_c/\n",
"...speaker_d/\n",
"...speaker_e/\n",
"...other/\n",
"..._background_noise_/\n",
"```\n",
"\n",
"After sorting, we end up with the following structure:\n",
"\n",
"```\n",
"main_directory/\n",
"...audio/\n",
"......speaker_a/\n",
"......speaker_b/\n",
"......speaker_c/\n",
"......speaker_d/\n",
"......speaker_e/\n",
"...noise/\n",
"......other/\n",
"......_background_noise_/\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"# If folder `audio`, does not exist, create it, otherwise do nothing\n",
"if os.path.exists(DATASET_AUDIO_PATH) is False:\n",
" os.makedirs(DATASET_AUDIO_PATH)\n",
"\n",
"# If folder `noise`, does not exist, create it, otherwise do nothing\n",
"if os.path.exists(DATASET_NOISE_PATH) is False:\n",
" os.makedirs(DATASET_NOISE_PATH)\n",
"\n",
"for folder in os.listdir(DATASET_ROOT):\n",
" if os.path.isdir(os.path.join(DATASET_ROOT, folder)):\n",
" if folder in [AUDIO_SUBFOLDER, NOISE_SUBFOLDER]:\n",
" # If folder is `audio` or `noise`, do nothing\n",
" continue\n",
" elif folder in [\"other\", \"_background_noise_\"]:\n",
" # If folder is one of the folders that contains noise samples,\n",
" # move it to the `noise` folder\n",
" shutil.move(\n",
" os.path.join(DATASET_ROOT, folder),\n",
" os.path.join(DATASET_NOISE_PATH, folder),\n",
" )\n",
" else:\n",
" # Otherwise, it should be a speaker folder, then move it to\n",
" # `audio` folder\n",
" shutil.move(\n",
" os.path.join(DATASET_ROOT, folder),\n",
" os.path.join(DATASET_AUDIO_PATH, folder),\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Noise preparation\n",
"\n",
"In this section:\n",
"\n",
"- We load all noise samples (which should have been resampled to 16000)\n",
"- We split those noise samples to chuncks of 16000 samples which\n",
"correspond to 1 second duration each"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"# Get the list of all noise files\n",
"noise_paths = []\n",
"for subdir in os.listdir(DATASET_NOISE_PATH):\n",
" subdir_path = Path(DATASET_NOISE_PATH) / subdir\n",
" if os.path.isdir(subdir_path):\n",
" noise_paths += [\n",
" os.path.join(subdir_path, filepath)\n",
" for filepath in os.listdir(subdir_path)\n",
" if filepath.endswith(\".wav\")\n",
" ]\n",
"\n",
"print(\n",
" \"Found {} files belonging to {} directories\".format(\n",
" len(noise_paths), len(os.listdir(DATASET_NOISE_PATH))\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"Resample all noise samples to 16000 Hz"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"command = (\n",
" \"for dir in `ls -1 \" + DATASET_NOISE_PATH + \"`; do \"\n",
" \"for file in `ls -1 \" + DATASET_NOISE_PATH + \"/$dir/*.wav`; do \"\n",
" \"sample_rate=`ffprobe -hide_banner -loglevel panic -show_streams \"\n",
" \"$file | grep sample_rate | cut -f2 -d=`; \"\n",
" \"if [ $sample_rate -ne 16000 ]; then \"\n",
" \"ffmpeg -hide_banner -loglevel panic -y \"\n",
" \"-i $file -ar 16000 temp.wav; \"\n",
" \"mv temp.wav $file; \"\n",
" \"fi; done; done\"\n",
")\n",
"os.system(command)\n",
"\n",
"# Split noise into chunks of 16,000 steps each\n",
"def load_noise_sample(path):\n",
" sample, sampling_rate = tf.audio.decode_wav(\n",
" tf.io.read_file(path), desired_channels=1\n",
" )\n",
" if sampling_rate == SAMPLING_RATE:\n",
" # Number of slices of 16000 each that can be generated from the noise sample\n",
" slices = int(sample.shape[0] / SAMPLING_RATE)\n",
" sample = tf.split(sample[: slices * SAMPLING_RATE], slices)\n",
" return sample\n",
" else:\n",
" print(\"Sampling rate for {} is incorrect. Ignoring it\".format(path))\n",
" return None\n",
"\n",
"\n",
"noises = []\n",
"for path in noise_paths:\n",
" sample = load_noise_sample(path)\n",
" if sample:\n",
" noises.extend(sample)\n",
"noises = tf.stack(noises)\n",
"\n",
"print(\n",
" \"{} noise files were split into {} noise samples where each is {} sec. long\".format(\n",
" len(noise_paths), noises.shape[0], noises.shape[1] // SAMPLING_RATE\n",
" )\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Dataset generation"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"def paths_and_labels_to_dataset(audio_paths, labels):\n",
" \"\"\"Constructs a dataset of audios and labels.\"\"\"\n",
" path_ds = tf.data.Dataset.from_tensor_slices(audio_paths)\n",
" audio_ds = path_ds.map(lambda x: path_to_audio(x))\n",
" label_ds = tf.data.Dataset.from_tensor_slices(labels)\n",
" return tf.data.Dataset.zip((audio_ds, label_ds))\n",
"\n",
"\n",
"def path_to_audio(path):\n",
" \"\"\"Reads and decodes an audio file.\"\"\"\n",
" audio = tf.io.read_file(path)\n",
" audio, _ = tf.audio.decode_wav(audio, 1, SAMPLING_RATE)\n",
" return audio\n",
"\n",
"\n",
"def add_noise(audio, noises=None, scale=0.5):\n",
" if noises is not None:\n",
" # Create a random tensor of the same size as audio ranging from\n",
" # 0 to the number of noise stream samples that we have.\n",
" tf_rnd = tf.random.uniform(\n",
" (tf.shape(audio)[0],), 0, noises.shape[0], dtype=tf.int32\n",
" )\n",
" noise = tf.gather(noises, tf_rnd, axis=0)\n",
"\n",
" # Get the amplitude proportion between the audio and the noise\n",
" prop = tf.math.reduce_max(audio, axis=1) / tf.math.reduce_max(noise, axis=1)\n",
" prop = tf.repeat(tf.expand_dims(prop, axis=1), tf.shape(audio)[1], axis=1)\n",
"\n",
" # Adding the rescaled noise to audio\n",
" audio = audio + noise * prop * scale\n",
"\n",
" return audio\n",
"\n",
"\n",
"def audio_to_fft(audio):\n",
" # Since tf.signal.fft applies FFT on the innermost dimension,\n",
" # we need to squeeze the dimensions and then expand them again\n",
" # after FFT\n",
" audio = tf.squeeze(audio, axis=-1)\n",
" fft = tf.signal.fft(\n",
" tf.cast(tf.complex(real=audio, imag=tf.zeros_like(audio)), tf.complex64)\n",
" )\n",
" fft = tf.expand_dims(fft, axis=-1)\n",
"\n",
" # Return the absolute value of the first half of the FFT\n",
" # which represents the positive frequencies\n",
" return tf.math.abs(fft[:, : (audio.shape[1] // 2), :])\n",
"\n",
"\n",
"# Get the list of audio file paths along with their corresponding labels\n",
"\n",
"class_names = os.listdir(DATASET_AUDIO_PATH)\n",
"print(\"Our class names: {}\".format(class_names,))\n",
"\n",
"audio_paths = []\n",
"labels = []\n",
"for label, name in enumerate(class_names):\n",
" print(\"Processing speaker {}\".format(name,))\n",
" dir_path = Path(DATASET_AUDIO_PATH) / name\n",
" speaker_sample_paths = [\n",
" os.path.join(dir_path, filepath)\n",
" for filepath in os.listdir(dir_path)\n",
" if filepath.endswith(\".wav\")\n",
" ]\n",
" audio_paths += speaker_sample_paths\n",
" labels += [label] * len(speaker_sample_paths)\n",
"\n",
"print(\n",
" \"Found {} files belonging to {} classes.\".format(len(audio_paths), len(class_names))\n",
")\n",
"\n",
"# Shuffle\n",
"rng = np.random.RandomState(SHUFFLE_SEED)\n",
"rng.shuffle(audio_paths)\n",
"rng = np.random.RandomState(SHUFFLE_SEED)\n",
"rng.shuffle(labels)\n",
"\n",
"# Split into training and validation\n",
"num_val_samples = int(VALID_SPLIT * len(audio_paths))\n",
"print(\"Using {} files for training.\".format(len(audio_paths) - num_val_samples))\n",
"train_audio_paths = audio_paths[:-num_val_samples]\n",
"train_labels = labels[:-num_val_samples]\n",
"\n",
"print(\"Using {} files for validation.\".format(num_val_samples))\n",
"valid_audio_paths = audio_paths[-num_val_samples:]\n",
"valid_labels = labels[-num_val_samples:]\n",
"\n",
"# Create 2 datasets, one for training and the other for validation\n",
"train_ds = paths_and_labels_to_dataset(train_audio_paths, train_labels)\n",
"train_ds = train_ds.shuffle(buffer_size=BATCH_SIZE * 8, seed=SHUFFLE_SEED).batch(\n",
" BATCH_SIZE\n",
")\n",
"\n",
"valid_ds = paths_and_labels_to_dataset(valid_audio_paths, valid_labels)\n",
"valid_ds = valid_ds.shuffle(buffer_size=32 * 8, seed=SHUFFLE_SEED).batch(32)\n",
"\n",
"\n",
"# Add noise to the training set\n",
"train_ds = train_ds.map(\n",
" lambda x, y: (add_noise(x, noises, scale=SCALE), y),\n",
" num_parallel_calls=tf.data.AUTOTUNE,\n",
")\n",
"\n",
"# Transform audio wave to the frequency domain using `audio_to_fft`\n",
"train_ds = train_ds.map(\n",
" lambda x, y: (audio_to_fft(x), y), num_parallel_calls=tf.data.AUTOTUNE\n",
")\n",
"train_ds = train_ds.prefetch(tf.data.AUTOTUNE)\n",
"\n",
"valid_ds = valid_ds.map(\n",
" lambda x, y: (audio_to_fft(x), y), num_parallel_calls=tf.data.AUTOTUNE\n",
")\n",
"valid_ds = valid_ds.prefetch(tf.data.AUTOTUNE)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Model Definition"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"\n",
"def residual_block(x, filters, conv_num=3, activation=\"relu\"):\n",
" # Shortcut\n",
" s = keras.layers.Conv1D(filters, 1, padding=\"same\")(x)\n",
" for i in range(conv_num - 1):\n",
" x = keras.layers.Conv1D(filters, 3, padding=\"same\")(x)\n",
" x = keras.layers.Activation(activation)(x)\n",
" x = keras.layers.Conv1D(filters, 3, padding=\"same\")(x)\n",
" x = keras.layers.Add()([x, s])\n",
" x = keras.layers.Activation(activation)(x)\n",
" return keras.layers.MaxPool1D(pool_size=2, strides=2)(x)\n",
"\n",
"\n",
"def build_model(input_shape, num_classes):\n",
" inputs = keras.layers.Input(shape=input_shape, name=\"input\")\n",
"\n",
" x = residual_block(inputs, 16, 2)\n",
" x = residual_block(x, 32, 2)\n",
" x = residual_block(x, 64, 3)\n",
" x = residual_block(x, 128, 3)\n",
" x = residual_block(x, 128, 3)\n",
"\n",
" x = keras.layers.AveragePooling1D(pool_size=3, strides=3)(x)\n",
" x = keras.layers.Flatten()(x)\n",
" x = keras.layers.Dense(256, activation=\"relu\")(x)\n",
" x = keras.layers.Dense(128, activation=\"relu\")(x)\n",
"\n",
" outputs = keras.layers.Dense(num_classes, activation=\"softmax\", name=\"output\")(x)\n",
"\n",
" return keras.models.Model(inputs=inputs, outputs=outputs)\n",
"\n",
"\n",
"model = build_model((SAMPLING_RATE // 2, 1), len(class_names))\n",
"\n",
"model.summary()\n",
"\n",
"# Compile the model using Adam's default learning rate\n",
"model.compile(\n",
" optimizer=\"Adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"]\n",
")\n",
"\n",
"# Add callbacks:\n",
"# 'EarlyStopping' to stop training when the model is not enhancing anymore\n",
"# 'ModelCheckPoint' to always keep the model that has the best val_accuracy\n",
"model_save_filename = \"model.h5\"\n",
"\n",
"earlystopping_cb = keras.callbacks.EarlyStopping(patience=10, restore_best_weights=True)\n",
"mdlcheckpoint_cb = keras.callbacks.ModelCheckpoint(\n",
" model_save_filename, monitor=\"val_accuracy\", save_best_only=True\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"history = model.fit(\n",
" train_ds,\n",
" epochs=EPOCHS,\n",
" validation_data=valid_ds,\n",
" callbacks=[earlystopping_cb, mdlcheckpoint_cb],\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Evaluation"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"print(model.evaluate(valid_ds))"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"We get ~ 98% validation accuracy."
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text"
},
"source": [
"## Demonstration\n",
"\n",
"Let's take some samples and:\n",
"\n",
"- Predict the speaker\n",
"- Compare the prediction with the real speaker\n",
"- Listen to the audio to see that despite the samples being noisy,\n",
"the model is still pretty accurate"
]
},
{
"cell_type": "code",
"execution_count": 0,
"metadata": {
"colab_type": "code"
},
"outputs": [],
"source": [
"SAMPLES_TO_DISPLAY = 10\n",
"\n",
"test_ds = paths_and_labels_to_dataset(valid_audio_paths, valid_labels)\n",
"test_ds = test_ds.shuffle(buffer_size=BATCH_SIZE * 8, seed=SHUFFLE_SEED).batch(\n",
" BATCH_SIZE\n",
")\n",
"\n",
"test_ds = test_ds.map(lambda x, y: (add_noise(x, noises, scale=SCALE), y))\n",
"\n",
"for audios, labels in test_ds.take(1):\n",
" # Get the signal FFT\n",
" ffts = audio_to_fft(audios)\n",
" # Predict\n",
" y_pred = model.predict(ffts)\n",
" # Take random samples\n",
" rnd = np.random.randint(0, BATCH_SIZE, SAMPLES_TO_DISPLAY)\n",
" audios = audios.numpy()[rnd, :, :]\n",
" labels = labels.numpy()[rnd]\n",
" y_pred = np.argmax(y_pred, axis=-1)[rnd]\n",
"\n",
" for index in range(SAMPLES_TO_DISPLAY):\n",
" # For every sample, print the true and predicted label\n",
" # as well as run the voice with the noise\n",
" print(\n",
" \"Speaker:\\33{} {}\\33[0m\\tPredicted:\\33{} {}\\33[0m\".format(\n",
" \"[92m\" if labels[index] == y_pred[index] else \"[91m\",\n",
" class_names[labels[index]],\n",
" \"[92m\" if labels[index] == y_pred[index] else \"[91m\",\n",
" class_names[y_pred[index]],\n",
" )\n",
" )\n",
" display(Audio(audios[index, :, :].squeeze(), rate=SAMPLING_RATE))"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "speaker_recognition_using_cnn",
"private_outputs": false,
"provenance": [],
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | apache-2.0 |
rumcajs21/cpp-example-projects | gradient-descent/plot_predictions.ipynb | 1 | 21772 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fb8531fad30>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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atZljS/XEhnpVlBTqzHmTCG0AEDAzW+7u1YOt459dASDDWF5e0rDWuyafsJbD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\nACCk/j9ngxvJazJ7wgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fb855549f28>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import csv\n",
"\n",
"x1 = []\n",
"x2 = []\n",
"pred = []\n",
"\n",
"with open('../tests/data/test.mat', encoding='utf-8') as csvfile:\n",
" reader = csv.reader(csvfile, delimiter=',')\n",
" for row in reader:\n",
" x1.append(float(row[0]))\n",
" x2.append(float(row[1]))\n",
"with open('../tests/data/prediction.mat', encoding='utf-8') as csvfile:\n",
" reader = csv.reader(csvfile, delimiter=',')\n",
" for row in reader:\n",
" pred.append(float(row[0]))\n",
" \n",
"plt.figure(figsize=(15,6))\n",
"plt.title(\"Predictions: red=1, blue=-1\")\n",
"plt.scatter(x1, x2, s=100, cmap='Paired', marker='x', c=pred)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
jArumugam/python-notes | libraries/ML08 SNAPpy basic.ipynb | 1 | 1020058 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import snap"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import itertools"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"G = snap.LoadEdgeList(snap.PNGraph, \"www15-data/qa.txt\", 1, 5)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"snap.PlotInDegDistr(G, \"Stack-Java\", \"Stack-Java In Degree\")"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"snap.PrintInfo(G, \"QA Stats\", \"qa-info.txt\", False)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"PRankH = snap.TIntFltH()\n",
"snap.GetPageRank(G, PRankH)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"78 1.87043978704e-06\n",
"86 8.50750367088e-06\n",
"58 2.1289669943e-06\n",
"35 1.5029251112e-05\n",
"122 3.49681107757e-05\n"
]
}
],
"source": [
"for item in itertools.islice(PRankH, 5):\n",
" print item, PRankH[item]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Core3 = snap.GetKCore(G, 3)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"snap.PNGraph"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(Core3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Snap.py Tests\n",
"[Tutorial codes](http://snap.stanford.edu/snappy/index.html)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"SUCCESS, your version of Snap.py is 3.0.2\n"
]
}
],
"source": [
"status = False\n",
"try:\n",
" import snap\n",
" version = snap.Version\n",
" i = snap.TInt(5)\n",
" if i == 5:\n",
" status = True\n",
"except:\n",
" pass\n",
"\n",
"if status:\n",
" print \"SUCCESS, your version of Snap.py is %s\" % (version)\n",
"else:\n",
" print \"*** ERROR, no working Snap.py was found on your computer\"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Graph creation"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from snap import *\n",
"\n",
"def intro():\n",
"\n",
" # create a graph PNGraph\n",
" G1 = TNGraph.New()\n",
" G1.AddNode(1)\n",
" G1.AddNode(5)\n",
" G1.AddNode(32)\n",
" G1.AddEdge(1,5)\n",
" G1.AddEdge(5,1)\n",
" G1.AddEdge(5,32)\n",
" print \"G1: Nodes %d, Edges %d\" % (G1.GetNodes(), G1.GetEdges())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"G1: Nodes 3, Edges 3\n"
]
}
],
"source": [
"intro()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Iterators\n",
"```\n",
" GetId(): return node id\n",
" GetOutDeg(): return out-degree of a node\n",
" GetInDeg(): return in-degree of a node\n",
" GetOutNId(e): return node id of the endpoint of e-th out-edge\n",
" GetInNId(e): return node id of the endpoint of e-th in-edge\n",
" IsOutNId(int NId): do we point to node id n\n",
" IsInNId(n): does node id n point to us\n",
" IsNbrNId(n): is node n our neighbor\n",
" ```"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"G2: Nodes 100, Edges 1000\n",
"node id 0 with out-degree 7 and in-degree 9\n",
"node id 1 with out-degree 5 and in-degree 10\n",
"node id 2 with out-degree 12 and in-degree 12\n",
"node id 3 with out-degree 12 and in-degree 14\n",
"node id 4 with out-degree 11 and in-degree 9\n",
"node id 5 with out-degree 12 and in-degree 12\n",
"node id 6 with out-degree 10 and in-degree 7\n",
"node id 7 with out-degree 4 and in-degree 4\n",
"node id 8 with out-degree 10 and in-degree 7\n",
"node id 9 with out-degree 11 and in-degree 10\n",
"node id 10 with out-degree 6 and in-degree 15\n",
"node id 11 with out-degree 7 and in-degree 10\n",
"node id 12 with out-degree 13 and in-degree 9\n",
"node id 13 with out-degree 13 and in-degree 9\n",
"node id 14 with out-degree 14 and in-degree 13\n",
"node id 15 with out-degree 12 and in-degree 14\n",
"node id 16 with out-degree 11 and in-degree 5\n",
"node id 17 with out-degree 12 and in-degree 8\n",
"node id 18 with out-degree 11 and in-degree 10\n",
"node id 19 with out-degree 10 and in-degree 13\n",
"node id 20 with out-degree 16 and in-degree 7\n",
"node id 21 with out-degree 15 and in-degree 8\n",
"node id 22 with out-degree 9 and in-degree 8\n",
"node id 23 with out-degree 12 and in-degree 7\n",
"node id 24 with out-degree 10 and in-degree 10\n",
"node id 25 with out-degree 6 and in-degree 5\n",
"node id 26 with out-degree 12 and in-degree 6\n",
"node id 27 with out-degree 8 and in-degree 11\n",
"node id 28 with out-degree 11 and in-degree 10\n",
"node id 29 with out-degree 15 and in-degree 4\n",
"node id 30 with out-degree 11 and in-degree 8\n",
"node id 31 with out-degree 7 and in-degree 12\n",
"node id 32 with out-degree 4 and in-degree 15\n",
"node id 33 with out-degree 8 and in-degree 8\n",
"node id 34 with out-degree 7 and in-degree 9\n",
"node id 35 with out-degree 16 and in-degree 10\n",
"node id 36 with out-degree 8 and in-degree 8\n",
"node id 37 with out-degree 11 and in-degree 11\n",
"node id 38 with out-degree 10 and in-degree 7\n",
"node id 39 with out-degree 8 and in-degree 10\n",
"node id 40 with out-degree 9 and in-degree 5\n",
"node id 41 with out-degree 12 and in-degree 15\n",
"node id 42 with out-degree 5 and in-degree 16\n",
"node id 43 with out-degree 11 and in-degree 10\n",
"node id 44 with out-degree 14 and in-degree 10\n",
"node id 45 with out-degree 8 and in-degree 14\n",
"node id 46 with out-degree 9 and in-degree 8\n",
"node id 47 with out-degree 11 and in-degree 11\n",
"node id 48 with out-degree 6 and in-degree 7\n",
"node id 49 with out-degree 8 and in-degree 3\n",
"node id 50 with out-degree 12 and in-degree 10\n",
"node id 51 with out-degree 9 and in-degree 7\n",
"node id 52 with out-degree 21 and in-degree 14\n",
"node id 53 with out-degree 9 and in-degree 11\n",
"node id 54 with out-degree 11 and in-degree 6\n",
"node id 55 with out-degree 12 and in-degree 8\n",
"node id 56 with out-degree 9 and in-degree 6\n",
"node id 57 with out-degree 6 and in-degree 12\n",
"node id 58 with out-degree 16 and in-degree 11\n",
"node id 59 with out-degree 8 and in-degree 11\n",
"node id 60 with out-degree 6 and in-degree 9\n",
"node id 61 with out-degree 13 and in-degree 9\n",
"node id 62 with out-degree 8 and in-degree 14\n",
"node id 63 with out-degree 15 and in-degree 13\n",
"node id 64 with out-degree 7 and in-degree 12\n",
"node id 65 with out-degree 10 and in-degree 7\n",
"node id 66 with out-degree 3 and in-degree 13\n",
"node id 67 with out-degree 5 and in-degree 8\n",
"node id 68 with out-degree 10 and in-degree 10\n",
"node id 69 with out-degree 11 and in-degree 16\n",
"node id 70 with out-degree 11 and in-degree 10\n",
"node id 71 with out-degree 11 and in-degree 11\n",
"node id 72 with out-degree 9 and in-degree 12\n",
"node id 73 with out-degree 15 and in-degree 15\n",
"node id 74 with out-degree 10 and in-degree 18\n",
"node id 75 with out-degree 11 and in-degree 14\n",
"node id 76 with out-degree 14 and in-degree 8\n",
"node id 77 with out-degree 10 and in-degree 7\n",
"node id 78 with out-degree 8 and in-degree 13\n",
"node id 79 with out-degree 7 and in-degree 11\n",
"node id 80 with out-degree 8 and in-degree 10\n",
"node id 81 with out-degree 9 and in-degree 11\n",
"node id 82 with out-degree 12 and in-degree 14\n",
"node id 83 with out-degree 12 and in-degree 8\n",
"node id 84 with out-degree 8 and in-degree 9\n",
"node id 85 with out-degree 9 and in-degree 11\n",
"node id 86 with out-degree 9 and in-degree 13\n",
"node id 87 with out-degree 12 and in-degree 7\n",
"node id 88 with out-degree 10 and in-degree 9\n",
"node id 89 with out-degree 20 and in-degree 9\n",
"node id 90 with out-degree 9 and in-degree 5\n",
"node id 91 with out-degree 11 and in-degree 12\n",
"node id 92 with out-degree 8 and in-degree 16\n",
"node id 93 with out-degree 8 and in-degree 14\n",
"node id 94 with out-degree 8 and in-degree 7\n",
"node id 95 with out-degree 6 and in-degree 7\n",
"node id 96 with out-degree 9 and in-degree 12\n",
"node id 97 with out-degree 9 and in-degree 11\n",
"node id 98 with out-degree 10 and in-degree 8\n",
"node id 99 with out-degree 6 and in-degree 8\n",
"edge (0, 10)\n",
"edge (0, 20)\n",
"edge (0, 36)\n",
"edge (0, 47)\n",
"edge (0, 48)\n",
"edge (0, 50)\n",
"edge (0, 84)\n",
"edge (1, 12)\n",
"edge (1, 15)\n",
"edge (1, 42)\n",
"edge (1, 63)\n",
"edge (1, 64)\n",
"edge (2, 11)\n",
"edge (2, 19)\n",
"edge (2, 28)\n",
"edge (2, 29)\n",
"edge (2, 31)\n",
"edge (2, 34)\n",
"edge (2, 39)\n",
"edge (2, 52)\n",
"edge (2, 62)\n",
"edge (2, 66)\n",
"edge (2, 79)\n",
"edge (2, 92)\n",
"edge (3, 15)\n",
"edge (3, 19)\n",
"edge (3, 31)\n",
"edge (3, 36)\n",
"edge (3, 41)\n",
"edge (3, 57)\n",
"edge (3, 64)\n",
"edge (3, 72)\n",
"edge (3, 73)\n",
"edge (3, 78)\n",
"edge (3, 80)\n",
"edge (3, 98)\n",
"edge (4, 0)\n",
"edge (4, 3)\n",
"edge (4, 11)\n",
"edge (4, 23)\n",
"edge (4, 31)\n",
"edge (4, 46)\n",
"edge (4, 48)\n",
"edge (4, 69)\n",
"edge (4, 72)\n",
"edge (4, 85)\n",
"edge (4, 91)\n",
"edge (5, 0)\n",
"edge (5, 3)\n",
"edge (5, 15)\n",
"edge (5, 18)\n",
"edge (5, 32)\n",
"edge (5, 37)\n",
"edge (5, 66)\n",
"edge (5, 75)\n",
"edge (5, 82)\n",
"edge (5, 86)\n",
"edge (5, 90)\n",
"edge (5, 94)\n",
"edge (6, 14)\n",
"edge (6, 41)\n",
"edge (6, 42)\n",
"edge (6, 61)\n",
"edge (6, 69)\n",
"edge (6, 70)\n",
"edge (6, 83)\n",
"edge (6, 88)\n",
"edge (6, 96)\n",
"edge (6, 98)\n",
"edge (7, 5)\n",
"edge (7, 32)\n",
"edge (7, 61)\n",
"edge (7, 90)\n",
"edge (8, 2)\n",
"edge (8, 11)\n",
"edge (8, 16)\n",
"edge (8, 25)\n",
"edge (8, 38)\n",
"edge (8, 47)\n",
"edge (8, 52)\n",
"edge (8, 86)\n",
"edge (8, 91)\n",
"edge (8, 98)\n",
"edge (9, 0)\n",
"edge (9, 23)\n",
"edge (9, 32)\n",
"edge (9, 33)\n",
"edge (9, 35)\n",
"edge (9, 63)\n",
"edge (9, 70)\n",
"edge (9, 71)\n",
"edge (9, 74)\n",
"edge (9, 86)\n",
"edge (9, 97)\n",
"edge (10, 4)\n",
"edge (10, 9)\n",
"edge (10, 36)\n",
"edge (10, 82)\n",
"edge (10, 93)\n",
"edge (10, 95)\n",
"edge (11, 1)\n",
"edge (11, 12)\n",
"edge (11, 39)\n",
"edge (11, 45)\n",
"edge (11, 47)\n",
"edge (11, 73)\n",
"edge (11, 96)\n",
"edge (12, 1)\n",
"edge (12, 4)\n",
"edge (12, 8)\n",
"edge (12, 17)\n",
"edge (12, 22)\n",
"edge (12, 31)\n",
"edge (12, 51)\n",
"edge (12, 60)\n",
"edge (12, 67)\n",
"edge (12, 69)\n",
"edge (12, 72)\n",
"edge (12, 80)\n",
"edge (12, 92)\n",
"edge (13, 2)\n",
"edge (13, 3)\n",
"edge (13, 9)\n",
"edge (13, 15)\n",
"edge (13, 24)\n",
"edge (13, 27)\n",
"edge (13, 35)\n",
"edge (13, 45)\n",
"edge (13, 62)\n",
"edge (13, 69)\n",
"edge (13, 74)\n",
"edge (13, 76)\n",
"edge (13, 79)\n",
"edge (14, 7)\n",
"edge (14, 43)\n",
"edge (14, 52)\n",
"edge (14, 56)\n",
"edge (14, 60)\n",
"edge (14, 66)\n",
"edge (14, 69)\n",
"edge (14, 70)\n",
"edge (14, 76)\n",
"edge (14, 81)\n",
"edge (14, 84)\n",
"edge (14, 89)\n",
"edge (14, 92)\n",
"edge (14, 98)\n",
"edge (15, 12)\n",
"edge (15, 19)\n",
"edge (15, 35)\n",
"edge (15, 48)\n",
"edge (15, 51)\n",
"edge (15, 53)\n",
"edge (15, 68)\n",
"edge (15, 71)\n",
"edge (15, 72)\n",
"edge (15, 83)\n",
"edge (15, 93)\n",
"edge (15, 98)\n",
"edge (16, 3)\n",
"edge (16, 10)\n",
"edge (16, 19)\n",
"edge (16, 26)\n",
"edge (16, 38)\n",
"edge (16, 47)\n",
"edge (16, 57)\n",
"edge (16, 64)\n",
"edge (16, 68)\n",
"edge (16, 73)\n",
"edge (16, 88)\n",
"edge (17, 9)\n",
"edge (17, 14)\n",
"edge (17, 24)\n",
"edge (17, 32)\n",
"edge (17, 41)\n",
"edge (17, 44)\n",
"edge (17, 45)\n",
"edge (17, 50)\n",
"edge (17, 55)\n",
"edge (17, 79)\n",
"edge (17, 87)\n",
"edge (17, 91)\n",
"edge (18, 5)\n",
"edge (18, 32)\n",
"edge (18, 38)\n",
"edge (18, 39)\n",
"edge (18, 45)\n",
"edge (18, 51)\n",
"edge (18, 58)\n",
"edge (18, 63)\n",
"edge (18, 76)\n",
"edge (18, 82)\n",
"edge (18, 97)\n",
"edge (19, 14)\n",
"edge (19, 18)\n",
"edge (19, 22)\n",
"edge (19, 35)\n",
"edge (19, 42)\n",
"edge (19, 62)\n",
"edge (19, 67)\n",
"edge (19, 68)\n",
"edge (19, 88)\n",
"edge (19, 97)\n",
"edge (20, 5)\n",
"edge (20, 14)\n",
"edge (20, 17)\n",
"edge (20, 19)\n",
"edge (20, 24)\n",
"edge (20, 34)\n",
"edge (20, 37)\n",
"edge (20, 40)\n",
"edge (20, 48)\n",
"edge (20, 60)\n",
"edge (20, 62)\n",
"edge (20, 71)\n",
"edge (20, 76)\n",
"edge (20, 84)\n",
"edge (20, 86)\n",
"edge (20, 98)\n",
"edge (21, 10)\n",
"edge (21, 14)\n",
"edge (21, 22)\n",
"edge (21, 41)\n",
"edge (21, 50)\n",
"edge (21, 62)\n",
"edge (21, 67)\n",
"edge (21, 68)\n",
"edge (21, 72)\n",
"edge (21, 74)\n",
"edge (21, 80)\n",
"edge (21, 81)\n",
"edge (21, 92)\n",
"edge (21, 94)\n",
"edge (21, 95)\n",
"edge (22, 7)\n",
"edge (22, 18)\n",
"edge (22, 23)\n",
"edge (22, 26)\n",
"edge (22, 33)\n",
"edge (22, 37)\n",
"edge (22, 50)\n",
"edge (22, 56)\n",
"edge (22, 84)\n",
"edge (23, 3)\n",
"edge (23, 18)\n",
"edge (23, 30)\n",
"edge (23, 54)\n",
"edge (23, 61)\n",
"edge (23, 64)\n",
"edge (23, 69)\n",
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"edge (23, 85)\n",
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"edge (23, 91)\n",
"edge (24, 13)\n",
"edge (24, 14)\n",
"edge (24, 16)\n",
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"edge (24, 55)\n",
"edge (24, 63)\n",
"edge (24, 73)\n",
"edge (24, 78)\n",
"edge (24, 87)\n",
"edge (25, 23)\n",
"edge (25, 24)\n",
"edge (25, 52)\n",
"edge (25, 81)\n",
"edge (25, 89)\n",
"edge (25, 93)\n",
"edge (26, 25)\n",
"edge (26, 35)\n",
"edge (26, 39)\n",
"edge (26, 43)\n",
"edge (26, 45)\n",
"edge (26, 60)\n",
"edge (26, 62)\n",
"edge (26, 67)\n",
"edge (26, 74)\n",
"edge (26, 81)\n",
"edge (26, 86)\n",
"edge (26, 99)\n",
"edge (27, 33)\n",
"edge (27, 39)\n",
"edge (27, 44)\n",
"edge (27, 59)\n",
"edge (27, 69)\n",
"edge (27, 71)\n",
"edge (27, 77)\n",
"edge (27, 78)\n",
"edge (28, 12)\n",
"edge (28, 20)\n",
"edge (28, 31)\n",
"edge (28, 34)\n",
"edge (28, 38)\n",
"edge (28, 55)\n",
"edge (28, 58)\n",
"edge (28, 66)\n",
"edge (28, 68)\n",
"edge (28, 73)\n",
"edge (28, 90)\n",
"edge (29, 0)\n",
"edge (29, 4)\n",
"edge (29, 13)\n",
"edge (29, 33)\n",
"edge (29, 40)\n",
"edge (29, 43)\n",
"edge (29, 50)\n",
"edge (29, 55)\n",
"edge (29, 64)\n",
"edge (29, 67)\n",
"edge (29, 69)\n",
"edge (29, 72)\n",
"edge (29, 86)\n",
"edge (29, 89)\n",
"edge (29, 96)\n",
"edge (30, 5)\n",
"edge (30, 11)\n",
"edge (30, 17)\n",
"edge (30, 20)\n",
"edge (30, 42)\n",
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"edge (30, 44)\n",
"edge (30, 49)\n",
"edge (30, 72)\n",
"edge (30, 83)\n",
"edge (30, 86)\n",
"edge (31, 15)\n",
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"edge (31, 58)\n",
"edge (31, 68)\n",
"edge (31, 73)\n",
"edge (31, 82)\n",
"edge (31, 99)\n",
"edge (32, 33)\n",
"edge (32, 45)\n",
"edge (32, 50)\n",
"edge (32, 78)\n",
"edge (33, 21)\n",
"edge (33, 32)\n",
"edge (33, 51)\n",
"edge (33, 54)\n",
"edge (33, 65)\n",
"edge (33, 85)\n",
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"edge (33, 96)\n",
"edge (34, 4)\n",
"edge (34, 14)\n",
"edge (34, 16)\n",
"edge (34, 21)\n",
"edge (34, 53)\n",
"edge (34, 58)\n",
"edge (34, 81)\n",
"edge (35, 10)\n",
"edge (35, 26)\n",
"edge (35, 29)\n",
"edge (35, 41)\n",
"edge (35, 45)\n",
"edge (35, 47)\n",
"edge (35, 52)\n",
"edge (35, 56)\n",
"edge (35, 57)\n",
"edge (35, 58)\n",
"edge (35, 72)\n",
"edge (35, 75)\n",
"edge (35, 76)\n",
"edge (35, 84)\n",
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"edge (35, 96)\n",
"edge (36, 9)\n",
"edge (36, 12)\n",
"edge (36, 17)\n",
"edge (36, 18)\n",
"edge (36, 40)\n",
"edge (36, 41)\n",
"edge (36, 45)\n",
"edge (36, 93)\n",
"edge (37, 10)\n",
"edge (37, 18)\n",
"edge (37, 22)\n",
"edge (37, 27)\n",
"edge (37, 36)\n",
"edge (37, 62)\n",
"edge (37, 70)\n",
"edge (37, 72)\n",
"edge (37, 92)\n",
"edge (37, 96)\n",
"edge (37, 97)\n",
"edge (38, 5)\n",
"edge (38, 10)\n",
"edge (38, 13)\n",
"edge (38, 26)\n",
"edge (38, 47)\n",
"edge (38, 52)\n",
"edge (38, 63)\n",
"edge (38, 64)\n",
"edge (38, 85)\n",
"edge (38, 93)\n",
"edge (39, 11)\n",
"edge (39, 19)\n",
"edge (39, 41)\n",
"edge (39, 46)\n",
"edge (39, 57)\n",
"edge (39, 60)\n",
"edge (39, 87)\n",
"edge (39, 91)\n",
"edge (40, 1)\n",
"edge (40, 17)\n",
"edge (40, 39)\n",
"edge (40, 42)\n",
"edge (40, 59)\n",
"edge (40, 66)\n",
"edge (40, 75)\n",
"edge (40, 81)\n",
"edge (40, 96)\n",
"edge (41, 8)\n",
"edge (41, 28)\n",
"edge (41, 50)\n",
"edge (41, 52)\n",
"edge (41, 64)\n",
"edge (41, 65)\n",
"edge (41, 73)\n",
"edge (41, 79)\n",
"edge (41, 83)\n",
"edge (41, 88)\n",
"edge (41, 93)\n",
"edge (41, 97)\n",
"edge (42, 3)\n",
"edge (42, 16)\n",
"edge (42, 25)\n",
"edge (42, 28)\n",
"edge (42, 70)\n",
"edge (43, 13)\n",
"edge (43, 24)\n",
"edge (43, 30)\n",
"edge (43, 31)\n",
"edge (43, 41)\n",
"edge (43, 42)\n",
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"edge (43, 65)\n",
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"edge (44, 19)\n",
"edge (44, 27)\n",
"edge (44, 32)\n",
"edge (44, 53)\n",
"edge (44, 57)\n",
"edge (44, 59)\n",
"edge (44, 63)\n",
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]
}
],
"source": [
" # create a directed random graph on 100 nodes and 1k edges\n",
" G2 = GenRndGnm(PNGraph, 100, 1000)\n",
" print \"G2: Nodes %d, Edges %d\" % (G2.GetNodes(), G2.GetEdges())\n",
"\n",
" # traverse the nodes\n",
" for NI in G2.Nodes():\n",
" print \"node id %d with out-degree %d and in-degree %d\" % (\n",
" NI.GetId(), NI.GetOutDeg(), NI.GetInDeg())\n",
" # traverse the edges\n",
" for EI in G2.Edges():\n",
" print \"edge (%d, %d)\" % (EI.GetSrcNId(), EI.GetDstNId())\n",
"\n",
" # traverse the edges by nodes\n",
" for NI in G2.Nodes():\n",
" for Id in NI.GetOutEdges():\n",
" print \"edge (%d %d)\" % (NI.GetId(), Id)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Input and Output"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"G3: Nodes 1000, Edges 4682\n",
"G4: Nodes 1000, Edges 4682\n"
]
}
],
"source": [
" # generate a network using Forest Fire model\n",
" G3 = GenForestFire(1000, 0.35, 0.35)\n",
" print \"G3: Nodes %d, Edges %d\" % (G3.GetNodes(), G3.GetEdges())\n",
"\n",
" # save and load binary\n",
" FOut = TFOut(\"test.graph\")\n",
" G3.Save(FOut)\n",
" FOut.Flush()\n",
" FIn = TFIn(\"test.graph\")\n",
" G4 = TNGraph.Load(FIn)\n",
" print \"G4: Nodes %d, Edges %d\" % (G4.GetNodes(), G4.GetEdges())"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"G5: Nodes 1000, Edges 4682\n"
]
}
],
"source": [
" # save and load from a text file\n",
" SaveEdgeList(G4, \"test.txt\", \"Save as tab-separated list of edges\")\n",
" G5 = LoadEdgeList(PNGraph, \"test.txt\", 0, 1)\n",
" print \"G5: Nodes %d, Edges %d\" % (G5.GetNodes(), G5.GetEdges())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Manipulating"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"G6: Nodes 1000, Edges 3258\n",
"G7: Nodes 1000, Edges 3258\n",
"G6a: Nodes 957, Edges 2860\n"
]
}
],
"source": [
" # generate a network using Forest Fire model\n",
" G6 = GenForestFire(1000, 0.35, 0.35)\n",
" print \"G6: Nodes %d, Edges %d\" % (G6.GetNodes(), G6.GetEdges())\n",
" # convert to undirected graph\n",
" G7 = ConvertGraph(PUNGraph,G6)\n",
" print \"G7: Nodes %d, Edges %d\" % (G7.GetNodes(), G7.GetEdges())\n",
" # get largest weakly connected component of G\n",
" WccG = GetMxWcc(G6)\n",
" # get a subgraph induced on nodes {0,1,2,3,4,5}\n",
" SubG = GetSubGraph(G6, TIntV.GetV(0,1,2,3,4))\n",
" # get 3-core of G\n",
" Core3 = GetKCore(G6, 3)\n",
" # delete nodes of out degree 10 and in degree 5\n",
" DelDegKNodes(G6, 10, 5)\n",
" print \"G6a: Nodes %d, Edges %d\" % (G6.GetNodes(), G6.GetEdges())"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"G8: Nodes 1000, Edges 2994\n"
]
}
],
"source": [
" # generate a Preferential Attachment graph on 1000 nodes and node out degree of 3\n",
" G8 = GenPrefAttach(1000, 3)\n",
" print \"G8: Nodes %d, Edges %d\" % (G8.GetNodes(), G8.GetEdges())"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.02996243139860919"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
" # vector of pairs of integers (size, count)\n",
" CntV = TIntPrV()\n",
" # get distribution of connected components (component size, count)\n",
" GetWccSzCnt(G8, CntV)\n",
" # get degree distribution pairs (degree, count)\n",
" GetOutDegCnt(G8, CntV)\n",
" # vector of floats\n",
" EigV = TFltV()\n",
" # get first eigenvector of graph adjacency matrix\n",
" GetEigVec(G8, EigV)\n",
" # get diameter of G8\n",
" GetBfsFullDiam(G8, 100)\n",
" # count the number of triads in G8, get the clustering coefficient of G8\n",
" GetTriads(G8)\n",
" GetClustCf(G8)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" distribution of connected compnents None\n",
"degree distribution None\n",
"Eigen vector\n",
"Full diameter 6\n",
"triads 244\n",
"clustering coefficient 0.0299624313986\n"
]
}
],
"source": [
" # vector of pairs of integers (size, count)\n",
" CntV = TIntPrV()\n",
" # get distribution of connected components (component size, count)\n",
" print 'distribution of connected compnents', GetWccSzCnt(G8, CntV)\n",
" # get degree distribution pairs (degree, count)\n",
" print 'degree distribution', GetOutDegCnt(G8, CntV)\n",
" # vector of floats\n",
" EigV = TFltV()\n",
" # get first eigenvector of graph adjacency matrix\n",
" print 'Eigen vector'\n",
" # get diameter of G8\n",
" print 'Full diameter', GetBfsFullDiam(G8, 100)\n",
" # count the number of triads in G8, get the clustering coefficient of G8\n",
" print 'triads', GetTriads(G8)\n",
" print 'clustering coefficient', GetClustCf(G8)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----- vector ----- \n",
"5\n",
"3\n",
"6\n",
"item 1\n",
"item 2\n",
"item 6\n",
"item 4\n",
"item 5\n",
"0 1\n",
"1 2\n",
"2 6\n",
"3 4\n",
"4 5\n",
"----- hash table ----- \n"
]
}
],
"source": [
"import snap\n",
"\n",
"print \"----- vector ----- \"\n",
"\n",
"v = snap.TIntV()\n",
"\n",
"v.Add(1)\n",
"v.Add(2)\n",
"v.Add(3)\n",
"v.Add(4)\n",
"v.Add(5)\n",
"\n",
"print v.Len()\n",
"print v[2]\n",
"\n",
"v.SetVal(2, 2*v[2])\n",
"print v[2]\n",
"\n",
"for item in v:\n",
" print 'item', item\n",
"\n",
"for i in range(0, v.Len()):\n",
" print i, v[i]\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----- vector ----- \n",
"5\n",
"3\n",
"6\n",
"1\n",
"2\n",
"6\n",
"4\n",
"5\n",
"0 1\n",
"1 2\n",
"2 6\n",
"3 4\n",
"4 5\n",
"----- hash table ----- \n",
"5\n",
"h[3] = three\n",
"h[3] = four\n",
"5 five\n",
"3 four\n",
"9 nine\n",
"6 six\n",
"1 one\n",
"----- pair ----- \n",
"1\n",
"one\n",
"----- graphs ----- \n",
"G2: Nodes 100, Edges 1000\n",
"node id 0 with out-degree 12 and in-degree 10\n",
"node id 1 with out-degree 13 and in-degree 9\n",
"node id 2 with out-degree 13 and in-degree 13\n",
"node id 3 with out-degree 10 and in-degree 8\n",
"node id 4 with out-degree 5 and in-degree 11\n",
"node id 5 with out-degree 13 and in-degree 15\n",
"node id 6 with out-degree 13 and in-degree 6\n",
"node id 7 with out-degree 12 and in-degree 11\n",
"node id 8 with out-degree 9 and in-degree 5\n",
"node id 9 with out-degree 13 and in-degree 9\n",
"node id 10 with out-degree 5 and in-degree 9\n",
"node id 11 with out-degree 10 and in-degree 9\n",
"node id 12 with out-degree 9 and in-degree 8\n",
"node id 13 with out-degree 12 and in-degree 8\n",
"node id 14 with out-degree 6 and in-degree 10\n",
"node id 15 with out-degree 6 and in-degree 11\n",
"node id 16 with out-degree 15 and in-degree 10\n",
"node id 17 with out-degree 6 and in-degree 10\n",
"node id 18 with out-degree 10 and in-degree 8\n",
"node id 19 with out-degree 7 and in-degree 11\n",
"node id 20 with out-degree 14 and in-degree 8\n",
"node id 21 with out-degree 11 and in-degree 7\n",
"node id 22 with out-degree 7 and in-degree 9\n",
"node id 23 with out-degree 10 and in-degree 11\n",
"node id 24 with out-degree 11 and in-degree 10\n",
"node id 25 with out-degree 12 and in-degree 7\n",
"node id 26 with out-degree 8 and in-degree 11\n",
"node id 27 with out-degree 7 and in-degree 10\n",
"node id 28 with out-degree 10 and in-degree 9\n",
"node id 29 with out-degree 10 and in-degree 8\n",
"node id 30 with out-degree 7 and in-degree 6\n",
"node id 31 with out-degree 6 and in-degree 13\n",
"node id 32 with out-degree 9 and in-degree 10\n",
"node id 33 with out-degree 10 and in-degree 9\n",
"node id 34 with out-degree 12 and in-degree 12\n",
"node id 35 with out-degree 10 and in-degree 11\n",
"node id 36 with out-degree 6 and in-degree 11\n",
"node id 37 with out-degree 10 and in-degree 9\n",
"node id 38 with out-degree 11 and in-degree 10\n",
"node id 39 with out-degree 5 and in-degree 11\n",
"node id 40 with out-degree 8 and in-degree 11\n",
"node id 41 with out-degree 8 and in-degree 9\n",
"node id 42 with out-degree 6 and in-degree 8\n",
"node id 43 with out-degree 6 and in-degree 9\n",
"node id 44 with out-degree 14 and in-degree 13\n",
"node id 45 with out-degree 12 and in-degree 9\n",
"node id 46 with out-degree 10 and in-degree 8\n",
"node id 47 with out-degree 14 and in-degree 9\n",
"node id 48 with out-degree 11 and in-degree 13\n",
"node id 49 with out-degree 9 and in-degree 12\n",
"node id 50 with out-degree 10 and in-degree 8\n",
"node id 51 with out-degree 5 and in-degree 15\n",
"node id 52 with out-degree 7 and in-degree 12\n",
"node id 53 with out-degree 11 and in-degree 7\n",
"node id 54 with out-degree 14 and in-degree 13\n",
"node id 55 with out-degree 10 and in-degree 12\n",
"node id 56 with out-degree 12 and in-degree 12\n",
"node id 57 with out-degree 9 and in-degree 8\n",
"node id 58 with out-degree 8 and in-degree 17\n",
"node id 59 with out-degree 10 and in-degree 10\n",
"node id 60 with out-degree 12 and in-degree 6\n",
"node id 61 with out-degree 14 and in-degree 7\n",
"node id 62 with out-degree 10 and in-degree 12\n",
"node id 63 with out-degree 10 and in-degree 14\n",
"node id 64 with out-degree 9 and in-degree 12\n",
"node id 65 with out-degree 6 and in-degree 9\n",
"node id 66 with out-degree 8 and in-degree 11\n",
"node id 67 with out-degree 11 and in-degree 6\n",
"node id 68 with out-degree 14 and in-degree 9\n",
"node id 69 with out-degree 14 and in-degree 13\n",
"node id 70 with out-degree 17 and in-degree 9\n",
"node id 71 with out-degree 12 and in-degree 13\n",
"node id 72 with out-degree 10 and in-degree 10\n",
"node id 73 with out-degree 12 and in-degree 12\n",
"node id 74 with out-degree 12 and in-degree 13\n",
"node id 75 with out-degree 16 and in-degree 7\n",
"node id 76 with out-degree 8 and in-degree 4\n",
"node id 77 with out-degree 8 and in-degree 11\n",
"node id 78 with out-degree 7 and in-degree 13\n",
"node id 79 with out-degree 12 and in-degree 11\n",
"node id 80 with out-degree 7 and in-degree 11\n",
"node id 81 with out-degree 9 and in-degree 14\n",
"node id 82 with out-degree 6 and in-degree 10\n",
"node id 83 with out-degree 7 and in-degree 12\n",
"node id 84 with out-degree 9 and in-degree 11\n",
"node id 85 with out-degree 12 and in-degree 9\n",
"node id 86 with out-degree 11 and in-degree 10\n",
"node id 87 with out-degree 20 and in-degree 11\n",
"node id 88 with out-degree 16 and in-degree 4\n",
"node id 89 with out-degree 14 and in-degree 16\n",
"node id 90 with out-degree 8 and in-degree 8\n",
"node id 91 with out-degree 7 and in-degree 5\n",
"node id 92 with out-degree 7 and in-degree 16\n",
"node id 93 with out-degree 14 and in-degree 8\n",
"node id 94 with out-degree 10 and in-degree 10\n",
"node id 95 with out-degree 11 and in-degree 7\n",
"node id 96 with out-degree 8 and in-degree 6\n",
"node id 97 with out-degree 10 and in-degree 9\n",
"node id 98 with out-degree 6 and in-degree 8\n",
"node id 99 with out-degree 7 and in-degree 15\n",
"edge (0, 4)\n",
"edge (0, 13)\n",
"edge (0, 15)\n",
"edge (0, 19)\n",
"edge (0, 20)\n",
"edge (0, 28)\n",
"edge (0, 30)\n",
"edge (0, 47)\n",
"edge (0, 55)\n",
"edge (0, 56)\n",
"edge (0, 82)\n",
"edge (0, 89)\n",
"edge (1, 26)\n",
"edge (1, 44)\n",
"edge (1, 63)\n",
"edge (1, 66)\n",
"edge (1, 70)\n",
"edge (1, 74)\n",
"edge (1, 80)\n",
"edge (1, 81)\n",
"edge (1, 85)\n",
"edge (1, 86)\n",
"edge (1, 89)\n",
"edge (1, 90)\n",
"edge (1, 92)\n",
"edge (2, 7)\n",
"edge (2, 9)\n",
"edge (2, 23)\n",
"edge (2, 24)\n",
"edge (2, 26)\n",
"edge (2, 28)\n",
"edge (2, 31)\n",
"edge (2, 36)\n",
"edge (2, 42)\n",
"edge (2, 49)\n",
"edge (2, 53)\n",
"edge (2, 58)\n",
"edge (2, 80)\n",
"edge (3, 0)\n",
"edge (3, 11)\n",
"edge (3, 24)\n",
"edge (3, 54)\n",
"edge (3, 55)\n",
"edge (3, 58)\n",
"edge (3, 62)\n",
"edge (3, 66)\n",
"edge (3, 72)\n",
"edge (3, 82)\n",
"edge (4, 7)\n",
"edge (4, 52)\n",
"edge (4, 59)\n",
"edge (4, 66)\n",
"edge (4, 75)\n",
"edge (5, 10)\n",
"edge (5, 26)\n",
"edge (5, 27)\n",
"edge (5, 31)\n",
"edge (5, 38)\n",
"edge (5, 41)\n",
"edge (5, 61)\n",
"edge (5, 70)\n",
"edge (5, 82)\n",
"edge (5, 85)\n",
"edge (5, 92)\n",
"edge (5, 94)\n",
"edge (5, 95)\n",
"edge (6, 14)\n",
"edge (6, 35)\n",
"edge (6, 47)\n",
"edge (6, 48)\n",
"edge (6, 51)\n",
"edge (6, 53)\n",
"edge (6, 65)\n",
"edge (6, 68)\n",
"edge (6, 70)\n",
"edge (6, 71)\n",
"edge (6, 78)\n",
"edge (6, 79)\n",
"edge (6, 89)\n",
"edge (7, 5)\n",
"edge (7, 11)\n",
"edge (7, 37)\n",
"edge (7, 43)\n",
"edge (7, 51)\n",
"edge (7, 57)\n",
"edge (7, 67)\n",
"edge (7, 78)\n",
"edge (7, 79)\n",
"edge (7, 80)\n",
"edge (7, 98)\n",
"edge (7, 99)\n",
"edge (8, 2)\n",
"edge (8, 38)\n",
"edge (8, 48)\n",
"edge (8, 49)\n",
"edge (8, 50)\n",
"edge (8, 53)\n",
"edge (8, 69)\n",
"edge (8, 81)\n",
"edge (8, 96)\n",
"edge (9, 19)\n",
"edge (9, 24)\n",
"edge (9, 32)\n",
"edge (9, 38)\n",
"edge (9, 44)\n",
"edge (9, 52)\n",
"edge (9, 59)\n",
"edge (9, 68)\n",
"edge (9, 71)\n",
"edge (9, 81)\n",
"edge (9, 84)\n",
"edge (9, 87)\n",
"edge (9, 95)\n",
"edge (10, 7)\n",
"edge (10, 58)\n",
"edge (10, 87)\n",
"edge (10, 88)\n",
"edge (10, 94)\n",
"edge (11, 8)\n",
"edge (11, 13)\n",
"edge (11, 23)\n",
"edge (11, 31)\n",
"edge (11, 72)\n",
"edge (11, 78)\n",
"edge (11, 82)\n",
"edge (11, 92)\n",
"edge (11, 93)\n",
"edge (11, 97)\n",
"edge (12, 21)\n",
"edge (12, 23)\n",
"edge (12, 31)\n",
"edge (12, 55)\n",
"edge (12, 57)\n",
"edge (12, 58)\n",
"edge (12, 74)\n",
"edge (12, 94)\n",
"edge (12, 99)\n",
"edge (13, 4)\n",
"edge (13, 10)\n",
"edge (13, 18)\n",
"edge (13, 29)\n",
"edge (13, 30)\n",
"edge (13, 34)\n",
"edge (13, 48)\n",
"edge (13, 51)\n",
"edge (13, 64)\n",
"edge (13, 71)\n",
"edge (13, 81)\n",
"edge (13, 98)\n",
"edge (14, 7)\n",
"edge (14, 21)\n",
"edge (14, 34)\n",
"edge (14, 38)\n",
"edge (14, 46)\n",
"edge (14, 76)\n",
"edge (15, 28)\n",
"edge (15, 48)\n",
"edge (15, 51)\n",
"edge (15, 67)\n",
"edge (15, 69)\n",
"edge (15, 92)\n",
"edge (16, 18)\n",
"edge (16, 19)\n",
"edge (16, 23)\n",
"edge (16, 24)\n",
"edge (16, 34)\n",
"edge (16, 38)\n",
"edge (16, 40)\n",
"edge (16, 41)\n",
"edge (16, 46)\n",
"edge (16, 49)\n",
"edge (16, 54)\n",
"edge (16, 55)\n",
"edge (16, 67)\n",
"edge (16, 79)\n",
"edge (16, 99)\n",
"edge (17, 5)\n",
"edge (17, 54)\n",
"edge (17, 55)\n",
"edge (17, 56)\n",
"edge (17, 64)\n",
"edge (17, 70)\n",
"edge (18, 0)\n",
"edge (18, 5)\n",
"edge (18, 43)\n",
"edge (18, 45)\n",
"edge (18, 47)\n",
"edge (18, 49)\n",
"edge (18, 58)\n",
"edge (18, 73)\n",
"edge (18, 94)\n",
"edge (18, 97)\n",
"edge (19, 16)\n",
"edge (19, 39)\n",
"edge (19, 55)\n",
"edge (19, 81)\n",
"edge (19, 85)\n",
"edge (19, 87)\n",
"edge (19, 96)\n",
"edge (20, 5)\n",
"edge (20, 25)\n",
"edge (20, 31)\n",
"edge (20, 32)\n",
"edge (20, 38)\n",
"edge (20, 40)\n",
"edge (20, 46)\n",
"edge (20, 58)\n",
"edge (20, 62)\n",
"edge (20, 71)\n",
"edge (20, 81)\n",
"edge (20, 86)\n",
"edge (20, 92)\n",
"edge (20, 93)\n",
"edge (21, 2)\n",
"edge (21, 12)\n",
"edge (21, 16)\n",
"edge (21, 22)\n",
"edge (21, 26)\n",
"edge (21, 35)\n",
"edge (21, 36)\n",
"edge (21, 58)\n",
"edge (21, 62)\n",
"edge (21, 71)\n",
"edge (21, 74)\n",
"edge (22, 4)\n",
"edge (22, 8)\n",
"edge (22, 12)\n",
"edge (22, 59)\n",
"edge (22, 72)\n",
"edge (22, 74)\n",
"edge (22, 89)\n",
"edge (23, 0)\n",
"edge (23, 1)\n",
"edge (23, 9)\n",
"edge (23, 22)\n",
"edge (23, 39)\n",
"edge (23, 47)\n",
"edge (23, 51)\n",
"edge (23, 68)\n",
"edge (23, 72)\n",
"edge (23, 89)\n",
"edge (24, 13)\n",
"edge (24, 14)\n",
"edge (24, 34)\n",
"edge (24, 39)\n",
"edge (24, 40)\n",
"edge (24, 43)\n",
"edge (24, 66)\n",
"edge (24, 72)\n",
"edge (24, 77)\n",
"edge (24, 83)\n",
"edge (24, 91)\n",
"edge (25, 1)\n",
"edge (25, 3)\n",
"edge (25, 11)\n",
"edge (25, 21)\n",
"edge (25, 29)\n",
"edge (25, 35)\n",
"edge (25, 59)\n",
"edge (25, 63)\n",
"edge (25, 66)\n",
"edge (25, 67)\n",
"edge (25, 83)\n",
"edge (25, 95)\n",
"edge (26, 14)\n",
"edge (26, 27)\n",
"edge (26, 42)\n",
"edge (26, 63)\n",
"edge (26, 65)\n",
"edge (26, 69)\n",
"edge (26, 73)\n",
"edge (26, 81)\n",
"edge (27, 7)\n",
"edge (27, 17)\n",
"edge (27, 18)\n",
"edge (27, 29)\n",
"edge (27, 44)\n",
"edge (27, 56)\n",
"edge (27, 74)\n",
"edge (28, 19)\n",
"edge (28, 31)\n",
"edge (28, 55)\n",
"edge (28, 63)\n",
"edge (28, 73)\n",
"edge (28, 78)\n",
"edge (28, 80)\n",
"edge (28, 87)\n",
"edge (28, 89)\n",
"edge (28, 92)\n",
"edge (29, 22)\n",
"edge (29, 49)\n",
"edge (29, 52)\n",
"edge (29, 63)\n",
"edge (29, 65)\n",
"edge (29, 69)\n",
"edge (29, 80)\n",
"edge (29, 84)\n",
"edge (29, 92)\n",
"edge (29, 99)\n",
"edge (30, 4)\n",
"edge (30, 31)\n",
"edge (30, 32)\n",
"edge (30, 33)\n",
"edge (30, 45)\n",
"edge (30, 59)\n",
"edge (30, 83)\n",
"edge (31, 6)\n",
"edge (31, 25)\n",
"edge (31, 48)\n",
"edge (31, 57)\n",
"edge (31, 61)\n",
"edge (31, 83)\n",
"edge (32, 1)\n",
"edge (32, 12)\n",
"edge (32, 40)\n",
"edge (32, 42)\n",
"edge (32, 48)\n",
"edge (32, 77)\n",
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"edge (87 95)\n",
"edge (88 12)\n",
"edge (88 23)\n",
"edge (88 27)\n",
"edge (88 30)\n",
"edge (88 32)\n",
"edge (88 48)\n",
"edge (88 54)\n",
"edge (88 65)\n",
"edge (88 67)\n",
"edge (88 74)\n",
"edge (88 79)\n",
"edge (88 82)\n",
"edge (88 87)\n",
"edge (88 91)\n",
"edge (88 97)\n",
"edge (88 99)\n",
"edge (89 1)\n",
"edge (89 5)\n",
"edge (89 7)\n",
"edge (89 20)\n",
"edge (89 21)\n",
"edge (89 27)\n",
"edge (89 32)\n",
"edge (89 39)\n",
"edge (89 47)\n",
"edge (89 52)\n",
"edge (89 65)\n",
"edge (89 69)\n",
"edge (89 81)\n",
"edge (89 85)\n",
"edge (90 34)\n",
"edge (90 43)\n",
"edge (90 45)\n",
"edge (90 49)\n",
"edge (90 55)\n",
"edge (90 57)\n",
"edge (90 58)\n",
"edge (90 69)\n",
"edge (91 5)\n",
"edge (91 8)\n",
"edge (91 20)\n",
"edge (91 32)\n",
"edge (91 37)\n",
"edge (91 81)\n",
"edge (91 93)\n",
"edge (92 8)\n",
"edge (92 26)\n",
"edge (92 30)\n",
"edge (92 51)\n",
"edge (92 64)\n",
"edge (92 68)\n",
"edge (92 74)\n",
"edge (93 2)\n",
"edge (93 3)\n",
"edge (93 15)\n",
"edge (93 16)\n",
"edge (93 23)\n",
"edge (93 32)\n",
"edge (93 34)\n",
"edge (93 42)\n",
"edge (93 47)\n",
"edge (93 54)\n",
"edge (93 70)\n",
"edge (93 73)\n",
"edge (93 75)\n",
"edge (93 86)\n",
"edge (94 2)\n",
"edge (94 6)\n",
"edge (94 7)\n",
"edge (94 10)\n",
"edge (94 16)\n",
"edge (94 23)\n",
"edge (94 32)\n",
"edge (94 65)\n",
"edge (94 69)\n",
"edge (94 89)\n",
"edge (95 2)\n",
"edge (95 12)\n",
"edge (95 23)\n",
"edge (95 43)\n",
"edge (95 49)\n",
"edge (95 52)\n",
"edge (95 60)\n",
"edge (95 64)\n",
"edge (95 74)\n",
"edge (95 79)\n",
"edge (95 86)\n",
"edge (96 9)\n",
"edge (96 16)\n",
"edge (96 31)\n",
"edge (96 42)\n",
"edge (96 49)\n",
"edge (96 50)\n",
"edge (96 86)\n",
"edge (96 97)\n",
"edge (97 22)\n",
"edge (97 29)\n",
"edge (97 34)\n",
"edge (97 35)\n",
"edge (97 53)\n",
"edge (97 55)\n",
"edge (97 63)\n",
"edge (97 78)\n",
"edge (97 89)\n",
"edge (97 98)\n",
"edge (98 2)\n",
"edge (98 3)\n",
"edge (98 5)\n",
"edge (98 25)\n",
"edge (98 54)\n",
"edge (98 95)\n",
"edge (99 10)\n",
"edge (99 14)\n",
"edge (99 44)\n",
"edge (99 56)\n",
"edge (99 63)\n",
"edge (99 79)\n",
"edge (99 95)\n",
"G4: Nodes 100, Edges 1000\n",
"G5: Nodes 100, Edges 1000\n",
"G6: Nodes 10000, Edges 5000\n",
"G7: Nodes 10000, Edges 5000\n",
"G8: Nodes 1000, Edges 6911\n",
"Core3: Nodes 571, Edges 6317\n",
"G9: Nodes 10000, Edges 1000\n",
"size 1: count 8198\n",
"size 2: count 659\n",
"size 3: count 104\n",
"size 4: count 27\n",
"size 5: count 9\n",
"size 6: count 2\n",
"size 7: count 1\n",
"degree 0: count 9058\n",
"degree 1: count 890\n",
"degree 2: count 46\n",
"degree 3: count 6\n",
"G10: Nodes 100, Edges 294\n",
"1: 0.412284\n",
"2: 0.249042\n",
"3: 0.204939\n",
"4: 0.292043\n",
"5: 0.230486\n",
"6: 0.179806\n",
"7: 0.175166\n",
"8: 0.086497\n",
"9: 0.159791\n",
"10: 0.125670\n",
"11: 0.153737\n",
"12: 0.203394\n",
"13: 0.070715\n",
"14: 0.156287\n",
"15: 0.104129\n",
"16: 0.079011\n",
"17: 0.058322\n",
"18: 0.109895\n",
"19: 0.132164\n",
"20: 0.044924\n",
"21: 0.107281\n",
"22: 0.148690\n",
"23: 0.072702\n",
"24: 0.057669\n",
"25: 0.054973\n",
"26: 0.085493\n",
"27: 0.096643\n",
"28: 0.084191\n",
"29: 0.067523\n",
"30: 0.083652\n",
"31: 0.059848\n",
"32: 0.081064\n",
"33: 0.066406\n",
"34: 0.098870\n",
"35: 0.103119\n",
"36: 0.054745\n",
"37: 0.085031\n",
"38: 0.147556\n",
"39: 0.068366\n",
"40: 0.053767\n",
"41: 0.051797\n",
"42: 0.053870\n",
"43: 0.060753\n",
"44: 0.090406\n",
"45: 0.034270\n",
"46: 0.074116\n",
"47: 0.028060\n",
"48: 0.062607\n",
"49: 0.033952\n",
"50: 0.043391\n",
"51: 0.033681\n",
"52: 0.076643\n",
"53: 0.019117\n",
"54: 0.044120\n",
"55: 0.087339\n",
"56: 0.049939\n",
"57: 0.067003\n",
"58: 0.083287\n",
"59: 0.040221\n",
"60: 0.084412\n",
"61: 0.033576\n",
"62: 0.086401\n",
"63: 0.069511\n",
"64: 0.055328\n",
"65: 0.036767\n",
"66: 0.033352\n",
"67: 0.054785\n",
"68: 0.032970\n",
"69: 0.032811\n",
"70: 0.043979\n",
"71: 0.038438\n",
"72: 0.042018\n",
"73: 0.041227\n",
"74: 0.064247\n",
"75: 0.039632\n",
"76: 0.030317\n",
"77: 0.034797\n",
"78: 0.040095\n",
"79: 0.084124\n",
"80: 0.075434\n",
"81: 0.041775\n",
"82: 0.082191\n",
"83: 0.051066\n",
"84: 0.041768\n",
"85: 0.055637\n",
"86: 0.060663\n",
"87: 0.040872\n",
"88: 0.048535\n",
"89: 0.040351\n",
"90: 0.043741\n",
"91: 0.027480\n",
"92: 0.034840\n",
"93: 0.033573\n",
"94: 0.042865\n",
"95: 0.028547\n",
"96: 0.084600\n",
"97: 0.061809\n",
"98: 0.019191\n",
"99: 0.035224\n",
"100: 0.063304\n",
"diam 4\n",
"triads 85\n",
"cf 0.150121004958\n"
]
}
],
"source": [
"print \"----- hash table ----- \"\n",
"\n",
"h = snap.TIntStrH()\n",
"\n",
"h[5] = \"five\"\n",
"h[3] = \"three\"\n",
"h[9] = \"nine\"\n",
"h[6] = \"six\"\n",
"h[1] = \"one\"\n",
"\n",
"print h.Len()\n",
"\n",
"print \"h[3] =\", h[3]\n",
"\n",
"h[3] = \"four\"\n",
"print \"h[3] =\", h[3]\n",
"\n",
"for key in h:\n",
" print key, h[key]\n",
"\n",
"print \"----- pair ----- \"\n",
"\n",
"p = snap.TIntStrPr(1, \"one\");\n",
"\n",
"print p.GetVal1()\n",
"print p.GetVal2()\n",
"\n",
"print \"----- graphs ----- \"\n",
"\n",
"G1 = snap.TUNGraph.New()\n",
"G2 = snap.TNGraph.New()\n",
"N1 = snap.TNEANet.New()\n",
"\n",
"G1.AddNode(1)\n",
"G1.AddNode(5)\n",
"G1.AddNode(32)\n",
"\n",
"G1.AddEdge(1,5)\n",
"G1.AddEdge(5,1)\n",
"G1.AddEdge(5,32)\n",
"\n",
"# create a directed random graph on 100 nodes and 1k edges\n",
"G2 = snap.GenRndGnm(snap.PNGraph, 100, 1000)\n",
"print \"G2: Nodes %d, Edges %d\" % (G2.GetNodes(), G2.GetEdges())\n",
"\n",
"# traverse the nodes\n",
"for NI in G2.Nodes():\n",
" print \"node id %d with out-degree %d and in-degree %d\" % (\n",
" NI.GetId(), NI.GetOutDeg(), NI.GetInDeg())\n",
"# traverse the edges\n",
"for EI in G2.Edges():\n",
" print \"edge (%d, %d)\" % (EI.GetSrcNId(), EI.GetDstNId())\n",
"\n",
"# traverse the edges by nodes\n",
"for NI in G2.Nodes():\n",
" for Id in NI.GetOutEdges():\n",
" print \"edge (%d %d)\" % (NI.GetId(), Id)\n",
"\n",
"# save and load binary\n",
"FOut = snap.TFOut(\"test.graph\")\n",
"G2.Save(FOut)\n",
"FOut.Flush()\n",
"FIn = snap.TFIn(\"test.graph\")\n",
"G4 = snap.TNGraph.Load(FIn)\n",
"print \"G4: Nodes %d, Edges %d\" % (G4.GetNodes(), G4.GetEdges())\n",
"\n",
"# save and load from a text file\n",
"snap.SaveEdgeList(G4, \"test.txt\", \"Save as tab-separated list of edges\")\n",
"G5 = snap.LoadEdgeList(snap.PNGraph, \"test.txt\", 0, 1)\n",
"print \"G5: Nodes %d, Edges %d\" % (G5.GetNodes(), G5.GetEdges())\n",
"\n",
"# create a directed random graph on 10k nodes and 5k edges\n",
"G6 = snap.GenRndGnm(snap.PNGraph, 10000, 5000)\n",
"print \"G6: Nodes %d, Edges %d\" % (G6.GetNodes(), G6.GetEdges())\n",
"# convert to undirected graph\n",
"G7 = snap.ConvertGraph(snap.PUNGraph, G6)\n",
"print \"G7: Nodes %d, Edges %d\" % (G7.GetNodes(), G7.GetEdges())\n",
"# get largest weakly connected component\n",
"WccG = snap.GetMxWcc(G6)\n",
"\n",
"# generate a network using Forest Fire model\n",
"G8 = snap.GenForestFire(1000, 0.35, 0.35)\n",
"print \"G8: Nodes %d, Edges %d\" % (G8.GetNodes(), G8.GetEdges())\n",
"\n",
"# get a subgraph induced on nodes {0,1,2,3,4}\n",
"SubG = snap.GetSubGraph(G8, snap.TIntV.GetV(0,1,2,3,4))\n",
"\n",
"# get 3-core of G8\n",
"Core3 = snap.GetKCore(G8, 3)\n",
"print \"Core3: Nodes %d, Edges %d\" % (Core3.GetNodes(), Core3.GetEdges())\n",
"\n",
"# delete nodes of out degree 3 and in degree 2\n",
"snap.DelDegKNodes(G8, 3, 2)\n",
"\n",
"# create a directed random graph on 10k nodes and 1k edges\n",
"G9 = snap.GenRndGnm(snap.PNGraph, 10000, 1000)\n",
"print \"G9: Nodes %d, Edges %d\" % (G9.GetNodes(), G9.GetEdges())\n",
"\n",
"# define a vector of pairs of integers (size, count) and\n",
"# get a distribution of connected components (component size, count)\n",
"CntV = snap.TIntPrV()\n",
"snap.GetWccSzCnt(G9, CntV)\n",
"for p in CntV:\n",
" print \"size %d: count %d\" % (p.GetVal1(), p.GetVal2())\n",
"\n",
"# get degree distribution pairs (out-degree, count):\n",
"snap.GetOutDegCnt(G9, CntV)\n",
"for p in CntV:\n",
" print \"degree %d: count %d\" % (p.GetVal1(), p.GetVal2())\n",
"\n",
"# generate a Preferential Attachment graph on 100 nodes and out-degree of 3\n",
"G10 = snap.GenPrefAttach(100, 3)\n",
"print \"G10: Nodes %d, Edges %d\" % (G10.GetNodes(), G10.GetEdges())\n",
"\n",
"# define a vector of floats and get first eigenvector of graph adjacency matrix\n",
"EigV = snap.TFltV()\n",
"snap.GetEigVec(G10, EigV)\n",
"nr = 0\n",
"for f in EigV:\n",
" nr += 1\n",
" print \"%d: %.6f\" % (nr, f)\n",
"\n",
"# get an approximation of graph diameter\n",
"diam = snap.GetBfsFullDiam(G10, 10)\n",
"print \"diam\", diam\n",
"\n",
"# count the number of triads:\n",
"triads = snap.GetTriads(G10)\n",
"print \"triads\", triads\n",
"\n",
"# get the clustering coefficient\n",
"cf = snap.GetClustCf(G10)\n",
"print \"cf\", cf"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"----- DefaultConstructor -----\n",
"graph DefaultConstructor:Graph, nodes 0, edges 0, empty yes\n",
"----- ManipulateNodesEdges -----\n",
"graph ManipulateNodesEdges:Graph1, nodes 10000, edges 100000, empty no\n",
"graph ManipulateNodesEdges:Graph2, nodes 10000, edges1 50000, edges2 100000\n",
"graph ManipulateNodesEdges:Graph3, nodes 10000, edges 100000, empty no\n",
"graph type = <class 'snap.PNEANet'>\n",
"graph ManipulateNodesEdges:Graph4, nodes 10000, edges 100000, empty no\n",
"graph ManipulateNodesEdges:Graph5, nodes 0, edges 0, empty yes\n",
"graph ManipulateNodesEdges:Graph6, nodes 0, edges 0, empty yes\n",
"----- ManipulateNodesEdgesAttributes -----\n",
"Added nodes\n",
"Added attributes\n",
"Attribute: int, Node: 3, Val: 6\n",
"Attribute: int, Node: 50, Val: 100\n",
"Attribute: int, Node: 700, Val: 1400\n",
"Attribute: int, Node: 900, Val: 1800\n",
"Attribute: float, Node: 5, Val: 3.410000\n",
"Attribute: float, Node: 50, Val: 2.718000\n",
"Attribute: float, Node: 300, Val: 150.000000\n",
"Attribute: float, Node: 653, Val: 653.000000\n",
"Attribute: str, Node: 10, Val: abc\n",
"Attribute: str, Node: 20, Val: def\n",
"Attribute: str, Node: 400, Val: ghi\n",
"Vertical Node: 55, Attr: int\n",
"Vertical Node: 55, Attr: float\n",
"Vertical Node: 55, Attr: str\n",
"Vertical Node (no int) : 55, Attr: float\n",
"Vertical Node (no int) : 55, Attr: str\n",
"Vertical Node (no str) : 55, Attr: int\n",
"Vertical Node (no str) : 55, Attr: float\n",
"Vertical Node (no str) : 55, Attr_Val: int\n",
"Vertical Node (no str) : 55, Attr_Val: float\n",
"Average: 70 (should be 70)\n",
"E Attribute: int, Edge: 3, Val: 6\n",
"E Attribute: int, Edge: 55, Val: 110\n",
"E Attribute: int, Edge: 705, Val: 1410\n",
"E Attribute: int, Edge: 905, Val: 1810\n",
"E Attribute: float, Edge: 5, Val: 4.410000\n",
"E Attribute: float, Edge: 50, Val: 3.718000\n",
"E Attribute: float, Edge: 300, Val: 151.000000\n",
"E Attribute: float, Edge: 653, Val: 654.000000\n",
"E Attribute: str, Edge: 10, Val: abc\n",
"E Attribute: str, Edge: 20, Val: def\n",
"E Attribute: str, Edge: 400, Val: ghi\n",
"Vertical Edge (no str) : 55, Attr_Val: 6\n",
"Vertical Edge (no str) : 55, Attr_Val: 3.41\n",
"Average: 70 (should be 70)\n"
]
}
],
"source": [
"import random\n",
"import sys\n",
"from snap import *\n",
"\n",
"def PrintGStats(s, Graph):\n",
" '''\n",
" Print graph statistics\n",
" '''\n",
"\n",
" print \"graph %s, nodes %d, edges %d, empty %s\" % (\n",
" s, Graph.GetNodes(), Graph.GetEdges(),\n",
" \"yes\" if Graph.Empty() else \"no\")\n",
"\n",
"def DefaultConstructor():\n",
" '''\n",
" Test the default constructor\n",
" '''\n",
"\n",
" Graph = TNEANet.New()\n",
" PrintGStats(\"DefaultConstructor:Graph\", Graph)\n",
"\n",
"def ManipulateNodesEdges():\n",
" '''\n",
" Test node, edge creation\n",
" '''\n",
"\n",
" NNodes = 10000\n",
" NEdges = 100000\n",
" FName = \"test.graph\"\n",
"\n",
" Graph = TNEANet.New()\n",
" t = Graph.Empty()\n",
"\n",
" # create the nodes\n",
" for i in range(0, NNodes):\n",
" Graph.AddNode(i)\n",
"\n",
" t = Graph.Empty()\n",
" n = Graph.GetNodes()\n",
" \n",
" # create random edges\n",
" NCount = NEdges\n",
" while NCount > 0:\n",
" x = int(random.random() * NNodes)\n",
" y = int(random.random() * NNodes)\n",
" # skip the loops in this test\n",
" if x != y and not Graph.IsEdge(x,y):\n",
" n = Graph.AddEdge(x, y)\n",
" NCount -= 1\n",
"\n",
" PrintGStats(\"ManipulateNodesEdges:Graph1\", Graph)\n",
"\n",
" # get all the nodes\n",
" NCount = 0\n",
" NI = Graph.BegNI()\n",
" while NI < Graph.EndNI():\n",
" NCount += 1\n",
" NI.Next()\n",
"\n",
" # get all the edges for all the nodes\n",
" ECount1 = 0\n",
" NI = Graph.BegNI()\n",
" while NI < Graph.EndNI():\n",
" ECount1 += NI.GetOutDeg()\n",
" NI.Next()\n",
"\n",
" ECount1 = ECount1 / 2\n",
"\n",
" # get all the edges directly\n",
" ECount2 = 0\n",
" EI = Graph.BegEI()\n",
" while EI < Graph.EndEI():\n",
" ECount2 += 1\n",
" EI.Next()\n",
"\n",
" print \"graph ManipulateNodesEdges:Graph2, nodes %d, edges1 %d, edges2 %d\"\\\n",
" % (NCount, ECount1, ECount2)\n",
"\n",
" # assignment\n",
" Graph1 = Graph\n",
" PrintGStats(\"ManipulateNodesEdges:Graph3\", Graph1)\n",
"\n",
" # save the graph\n",
" print \"graph type = \", type(Graph)\n",
" #FOut = TFOut(TStr(FName))\n",
" FOut = TFOut(FName)\n",
" Graph.Save(FOut)\n",
" FOut.Flush()\n",
"\n",
" # load the graph\n",
" #FIn = TFIn(TStr(FName))\n",
" FIn = TFIn(FName)\n",
" Graph2 = TNEANet(FIn)\n",
" PrintGStats(\"ManipulateNodesEdges:Graph4\" , Graph2)\n",
"\n",
" # remove all the nodes and edges\n",
" for i in range(0, NNodes):\n",
" n = Graph.GetRndNId()\n",
" Graph.DelNode(n)\n",
"\n",
" PrintGStats(\"ManipulateNodesEdges:Graph5\" , Graph)\n",
" \n",
" Graph1.Clr()\n",
" PrintGStats(\"ManipulateNodesEdges:Graph6\" , Graph1)\n",
"\n",
"def ManipulateNodeEdgeAttributes():\n",
" '''\n",
" Test node attribute functionality\n",
" '''\n",
"\n",
" NNodes = 1000\n",
" NEdges = 1000\n",
" \n",
" Graph = TNEANet.New()\n",
" t = Graph.Empty()\n",
"\n",
" # create the nodes\n",
" for i in range(0, NNodes):\n",
" Graph.AddNode(i)\n",
"\n",
" t = Graph.Empty()\n",
" n = Graph.GetNodes()\n",
"\n",
" # create random edges\n",
" NCount = NEdges\n",
" while NCount > 0:\n",
" x = int(random.random() * NNodes)\n",
" y = int(random.random() * NNodes)\n",
" # skip the loops in this test\n",
" if x != y and not Graph.IsEdge(x,y):\n",
" n = Graph.AddEdge(x, y)\n",
" NCount -= 1\n",
"\n",
" print \"Added nodes\"\n",
"\n",
" # create attributes and fill all nodes\n",
" #attr1 = TStr(\"str\")\n",
" #attr2 = TStr(\"int\")\n",
" #attr3 = TStr(\"float\")\n",
" #attr4 = TStr(\"default\")\n",
" attr1 = \"str\"\n",
" attr2 = \"int\"\n",
" attr3 = \"float\"\n",
" attr4 = \"default\"\n",
" \n",
" # Test verticaliterator for node 3, 50, 700, 900\n",
" # Check if we can set defaults to 0 fordata.\n",
" Graph.AddIntAttrN(attr2, 0)\n",
" Graph.AddIntAttrDatN(3, 3*2, attr2)\n",
" Graph.AddIntAttrDatN(50, 50*2, attr2)\n",
" Graph.AddIntAttrDatN(700, 700*2, attr2)\n",
" Graph.AddIntAttrDatN(900, 900*2, attr2)\n",
" \n",
" print \"Added attributes\"\n",
" \n",
" NodeId = 0\n",
" NI = Graph.BegNAIntI(attr2)\n",
" while NI < Graph.EndNAIntI(attr2):\n",
" if NI.GetDat() != 0:\n",
" print \"Attribute: %s, Node: %i, Val: %d\" % (attr2, NodeId, NI.GetDat())\n",
" #print \"Attribute: %s, Node: %i, Val: %d\" % (attr2(), NodeId, NI.GetDat())\n",
" NodeId += 1\n",
" NI.Next()\n",
"\n",
" # Test vertical flt iterator for node 3, 50, 700, 900\n",
" Graph.AddFltAttrDatN(5, 3.41, attr3)\n",
" Graph.AddFltAttrDatN(50, 2.718, attr3)\n",
" Graph.AddFltAttrDatN(300, 150.0, attr3)\n",
" \n",
" Graph.AddFltAttrDatN(653, 653, attr3)\n",
" NodeId = 0\n",
" NCount = 0\n",
" NI = Graph.BegNI()\n",
" while NI < Graph.EndNI():\n",
" NCount += 1\n",
" NI.Next()\n",
"\n",
" NI = Graph.BegNAFltI(attr3)\n",
" NodeId = 0\n",
" while NI < Graph.EndNAFltI(attr3):\n",
" if NI.GetDat() != TFlt.Mn:\n",
" print \"Attribute: %s, Node: %i, Val: %f\" % (attr3, NodeId, NI.GetDat())\n",
" #print \"Attribute: %s, Node: %i, Val: %f\" % (attr3(), NodeId, NI.GetDat())\n",
" NodeId += 1\n",
" NI.Next()\n",
"\n",
" # Test vertical str iterator for node 3, 50, 700, 900\n",
" #Graph.AddStrAttrDatN(10, TStr(\"abc\"), attr1)\n",
" #Graph.AddStrAttrDatN(20, TStr(\"def\"), attr1)\n",
" #Graph.AddStrAttrDatN(400, TStr(\"ghi\"), attr1)\n",
" Graph.AddStrAttrDatN(10, \"abc\", attr1)\n",
" Graph.AddStrAttrDatN(20, \"def\", attr1)\n",
" Graph.AddStrAttrDatN(400, \"ghi\", attr1)\n",
" # this does not show since \"\"=null\n",
" #Graph.AddStrAttrDatN(455, TStr(\"\"), attr1)\n",
" # TODO Graph.AddStrAttrDatN(455, \"\", attr1)\n",
" NodeId = 0\n",
"\n",
" NI = Graph.BegNAStrI(attr1)\n",
" NodeId = 0\n",
" while NI < Graph.EndNAStrI(attr1):\n",
" if NI.GetDat() != TStr.GetNullStr():\n",
" print \"Attribute: %s, Node: %i, Val: %s\" % (attr1, NodeId, NI.GetDat())\n",
" #print \"Attribute: %s, Node: %i, Val: %s\" % (attr1(), NodeId, NI.GetDat())\n",
" NodeId += 1\n",
" NI.Next()\n",
"\n",
" # Test vertical iterator over many types (must skip default/deleted attr)\n",
" NId = 55\n",
" #Graph.AddStrAttrDatN(NId, TStr(\"aaa\"), attr1)\n",
" Graph.AddStrAttrDatN(NId, \"aaa\", attr1)\n",
" Graph.AddIntAttrDatN(NId, 3*2, attr2)\n",
" Graph.AddFltAttrDatN(NId, 3.41, attr3)\n",
" #Graph.AddStrAttrDatN(80, TStr(\"dont appear\"), attr4) # should not show up\n",
" Graph.AddStrAttrDatN(80, \"dont appear\", attr4) # should not show up\n",
" NIdAttrName = TStrV()\n",
" Graph.AttrNameNI(NId, NIdAttrName)\n",
" AttrLen = NIdAttrName.Len()\n",
" for i in range(AttrLen):\n",
" print \"Vertical Node: %i, Attr: %s\" % (NId, NIdAttrName.GetI(i)())\n",
"\n",
" Graph.DelAttrDatN(NId, attr2)\n",
" Graph.AttrNameNI(NId, NIdAttrName)\n",
" AttrLen = NIdAttrName.Len()\n",
" for i in range(AttrLen):\n",
" print \"Vertical Node (no int) : %i, Attr: %s\" % (NId, NIdAttrName.GetI(i)())\n",
"\n",
" Graph.AddIntAttrDatN(NId, 3*2, attr2)\n",
" Graph.DelAttrN(attr1)\n",
" Graph.AttrNameNI(NId, NIdAttrName)\n",
" AttrLen = NIdAttrName.Len()\n",
" for i in range(AttrLen):\n",
" print \"Vertical Node (no str) : %i, Attr: %s\" % (NId, NIdAttrName.GetI(i)())\n",
"\n",
" NIdAttrValue = TStrV()\n",
" Graph.AttrValueNI(NId, NIdAttrValue)\n",
" AttrLen = NIdAttrValue.Len()\n",
" for i in range(AttrLen):\n",
" print \"Vertical Node (no str) : %i, Attr_Val: %s\" % (NId, NIdAttrName.GetI(i)())\n",
"\n",
" for i in range(NNodes):\n",
" Graph.AddIntAttrDatN(i, 70, attr2)\n",
"\n",
" total = 0\n",
" NI = Graph.BegNAIntI(attr2)\n",
" while NI < Graph.EndNAIntI(attr2):\n",
" total += NI.GetDat()\n",
" NI.Next()\n",
"\n",
" print \"Average: %i (should be 70)\" % (total/NNodes)\n",
"\n",
" # Test verticaliterator for edge\n",
" Graph.AddIntAttrDatE(3, 3*2, attr2)\n",
" Graph.AddIntAttrDatE(55, 55*2, attr2)\n",
" Graph.AddIntAttrDatE(705, 705*2, attr2)\n",
" Graph.AddIntAttrDatE(905, 905*2, attr2)\n",
" EdgeId = 0\n",
" EI = Graph.BegEAIntI(attr2)\n",
" while EI < Graph.EndEAIntI(attr2):\n",
" if EI.GetDat() != TInt.Mn:\n",
" print \"E Attribute: %s, Edge: %i, Val: %i\"\\\n",
" % (attr2, EdgeId, EI.GetDat())\n",
" #% (attr2(), EdgeId, EI.GetDat())\n",
" EdgeId += 1\n",
" EI.Next()\n",
"\n",
" # Test vertical flt iterator for edge\n",
" Graph.AddFltAttrE(attr3, 0.00)\n",
" Graph.AddFltAttrDatE(5, 4.41, attr3)\n",
" Graph.AddFltAttrDatE(50, 3.718, attr3)\n",
" Graph.AddFltAttrDatE(300, 151.0, attr3)\n",
" Graph.AddFltAttrDatE(653, 654, attr3)\n",
" EdgeId = 0\n",
" EI = Graph.BegEAFltI(attr3)\n",
" while EI < Graph.EndEAFltI(attr3):\n",
" # Check if defaults are set to 0.\n",
" if EI.GetDat() != 0:\n",
" print \"E Attribute: %s, Edge: %i, Val: %f\" % \\\n",
" (attr3, EdgeId, EI.GetDat())\n",
" #(attr3(), EdgeId, EI.GetDat())\n",
" EdgeId += 1\n",
" EI.Next()\n",
"\n",
" # Test vertical str iterator for edge\n",
" #Graph.AddStrAttrDatE(10, TStr(\"abc\"), attr1)\n",
" #Graph.AddStrAttrDatE(20, TStr(\"def\"), attr1)\n",
" #Graph.AddStrAttrDatE(400, TStr(\"ghi\"), attr1)\n",
" Graph.AddStrAttrDatE(10, \"abc\", attr1)\n",
" Graph.AddStrAttrDatE(20, \"def\", attr1)\n",
" Graph.AddStrAttrDatE(400, \"ghi\", attr1)\n",
" # this does not show since \"\"=null\n",
" #Graph.AddStrAttrDatE(455, TStr(\"\"), attr1)\n",
" # TODO Graph.AddStrAttrDatE(455, \"\", attr1)\n",
" EdgeId = 0\n",
" EI = Graph.BegEAStrI(attr1)\n",
" while EI < Graph.EndEAStrI(attr1):\n",
" if EI.GetDat() != TStr.GetNullStr():\n",
" print \"E Attribute: %s, Edge: %i, Val: %s\" %\\\n",
" (attr1, EdgeId, EI.GetDat())\n",
" #(attr1(), EdgeId, EI.GetDat())\n",
" EdgeId += 1\n",
" EI.Next()\n",
"\n",
" # Test vertical iterator over many types (must skip default/deleted attr)\n",
" EId = 55\n",
" #Graph.AddStrAttrDatE(EId, TStr(\"aaa\"), attr1)\n",
" Graph.AddStrAttrDatE(EId, \"aaa\", attr1)\n",
" Graph.AddIntAttrDatE(EId, 3*2, attr2)\n",
" Graph.AddFltAttrDatE(EId, 3.41, attr3)\n",
" #Graph.AddStrAttrDatE(80, TStr(\"dont appear\"), attr4) # should not show up\n",
" Graph.AddStrAttrDatE(80, \"dont appear\", attr4) # should not show up\n",
" EIdAttrName = TStrV()\n",
"# Graph.AttrNameEI(EId, EIdAttrName)\n",
" AttrLen = EIdAttrName.Len()\n",
" for i in range(AttrLen):\n",
" print \"Vertical Edge: %i, Attr: %s\" % (EId, EIdAttrName.GetI(i))\n",
"\n",
" Graph.DelAttrDatE(EId, attr2)\n",
"# Graph.AttrNameEI(EId, EIdAttrName)\n",
" AttrLen = EIdAttrName.Len()\n",
" for i in range(AttrLen):\n",
" print \"Vertical Edge (no int) : %i, Attr: %s\" % (EId, EIdAttrName.GetI(i))\n",
"\n",
" Graph.AddIntAttrDatE(EId, 3*2, attr2)\n",
" Graph.DelAttrE(attr1)\n",
"# Graph.AttrNameEI(EId, EIdAttrName)\n",
" AttrLen = EIdAttrName.Len()\n",
" for i in range(AttrLen):\n",
" print \"Vertical Edge (no str) : %i, Attr: %s\" % (EId, EIdAttrName.GetI(i)())\n",
"\n",
" EIdAttrValue = TStrV()\n",
" #Graph.AttrValueEI(TInt(EId), EIdAttrValue)\n",
" Graph.AttrValueEI(EId, EIdAttrValue)\n",
" AttrLen = EIdAttrValue.Len()\n",
" for i in range(AttrLen):\n",
" print \"Vertical Edge (no str) : %i, Attr_Val: %s\" % (EId, EIdAttrValue.GetI(i)())\n",
"\n",
" for i in range(NEdges):\n",
" Graph.AddIntAttrDatE(i, 70, attr2)\n",
"\n",
" total = 0\n",
" EI = Graph.BegNAIntI(attr2)\n",
" while EI < Graph.EndNAIntI(attr2):\n",
" total += EI.GetDat()\n",
" EI.Next()\n",
"\n",
" print \"Average: %i (should be 70)\" % (total/NEdges)\n",
" \n",
" Graph.Clr()\n",
"\n",
"if __name__ == '__main__':\n",
" print \"----- DefaultConstructor -----\"\n",
" DefaultConstructor()\n",
" print \"----- ManipulateNodesEdges -----\"\n",
" ManipulateNodesEdges()\n",
" print \"----- ManipulateNodesEdgesAttributes -----\"\n",
" ManipulateNodeEdgeAttributes()\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"IsConnected(G) = False\n",
"IsWeaklyConnected(G) = False\n",
"WccSzCnt[0] = (1, 3615)\n",
"WccSzCnt[1] = (2, 738)\n",
"WccSzCnt[2] = (3, 256)\n",
"WccSzCnt[3] = (4, 104)\n",
"WccSzCnt[4] = (5, 61)\n",
"WccSzCnt[5] = (6, 53)\n",
"WccSzCnt[6] = (7, 33)\n",
"WccSzCnt[7] = (8, 22)\n",
"WccSzCnt[8] = (9, 22)\n",
"WccSzCnt[9] = (10, 14)\n",
"WccSzCnt[10] = (11, 10)\n",
"WccSzCnt[11] = (12, 8)\n",
"WccSzCnt[12] = (13, 9)\n",
"WccSzCnt[13] = (14, 5)\n",
"WccSzCnt[14] = (15, 5)\n",
"WccSzCnt[15] = (16, 6)\n",
"WccSzCnt[16] = (17, 5)\n",
"WccSzCnt[17] = (18, 4)\n",
"WccSzCnt[18] = (19, 1)\n",
"WccSzCnt[19] = (20, 4)\n",
"WccSzCnt[20] = (21, 2)\n",
"WccSzCnt[21] = (23, 2)\n",
"WccSzCnt[22] = (24, 1)\n",
"WccSzCnt[23] = (27, 1)\n",
"WccSzCnt[24] = (28, 2)\n",
"WccSzCnt[25] = (29, 1)\n",
"WccSzCnt[26] = (30, 1)\n",
"WccSzCnt[27] = (31, 2)\n",
"WccSzCnt[28] = (33, 1)\n",
"WccSzCnt[29] = (34, 2)\n",
"WccSzCnt[30] = (35, 1)\n",
"WccSzCnt[31] = (40, 1)\n",
"WccSzCnt[32] = (41, 1)\n",
"WccSzCnt[33] = (44, 1)\n",
"WccSzCnt[34] = (46, 1)\n",
"WccSzCnt[35] = (48, 1)\n",
"WccSzCnt[36] = (53, 1)\n",
"WccSzCnt[37] = (63, 1)\n",
"WccSzCnt[38] = (186, 1)\n",
"WccSzCnt[39] = (272, 1)\n",
"WccSzCnt[40] = (292, 1)\n",
"CnCom.Len() = 1\n",
"WCnComV[0].Len() = 292\n",
"WCnComV[0][0] = 2\n",
"WCnComV[0][1] = 128\n",
"WCnComV[0][2] = 157\n",
"WCnComV[0][3] = 168\n",
"WCnComV[0][4] = 213\n",
"WCnComV[0][5] = 226\n",
"WCnComV[0][6] = 233\n",
"WCnComV[0][7] = 262\n",
"WCnComV[0][8] = 322\n",
"WCnComV[0][9] = 357\n",
"WCnComV[0][10] = 489\n",
"WCnComV[0][11] = 500\n",
"WCnComV[0][12] = 552\n",
"WCnComV[0][13] = 560\n",
"WCnComV[0][14] = 565\n",
"WCnComV[0][15] = 611\n",
"WCnComV[0][16] = 809\n",
"WCnComV[0][17] = 813\n",
"WCnComV[0][18] = 867\n",
"WCnComV[0][19] = 878\n",
"WCnComV[0][20] = 894\n",
"WCnComV[0][21] = 902\n",
"WCnComV[0][22] = 968\n",
"WCnComV[0][23] = 1029\n",
"WCnComV[0][24] = 1032\n",
"WCnComV[0][25] = 1034\n",
"WCnComV[0][26] = 1098\n",
"WCnComV[0][27] = 1133\n",
"WCnComV[0][28] = 1143\n",
"WCnComV[0][29] = 1179\n",
"WCnComV[0][30] = 1195\n",
"WCnComV[0][31] = 1265\n",
"WCnComV[0][32] = 1300\n",
"WCnComV[0][33] = 1307\n",
"WCnComV[0][34] = 1328\n",
"WCnComV[0][35] = 1341\n",
"WCnComV[0][36] = 1345\n",
"WCnComV[0][37] = 1346\n",
"WCnComV[0][38] = 1350\n",
"WCnComV[0][39] = 1458\n",
"WCnComV[0][40] = 1511\n",
"WCnComV[0][41] = 1585\n",
"WCnComV[0][42] = 1592\n",
"WCnComV[0][43] = 1619\n",
"WCnComV[0][44] = 1631\n",
"WCnComV[0][45] = 1728\n",
"WCnComV[0][46] = 1888\n",
"WCnComV[0][47] = 1954\n",
"WCnComV[0][48] = 1959\n",
"WCnComV[0][49] = 1963\n",
"WCnComV[0][50] = 1975\n",
"WCnComV[0][51] = 1995\n",
"WCnComV[0][52] = 2069\n",
"WCnComV[0][53] = 2136\n",
"WCnComV[0][54] = 2143\n",
"WCnComV[0][55] = 2156\n",
"WCnComV[0][56] = 2188\n",
"WCnComV[0][57] = 2292\n",
"WCnComV[0][58] = 2312\n",
"WCnComV[0][59] = 2382\n",
"WCnComV[0][60] = 2387\n",
"WCnComV[0][61] = 2435\n",
"WCnComV[0][62] = 2486\n",
"WCnComV[0][63] = 2553\n",
"WCnComV[0][64] = 2579\n",
"WCnComV[0][65] = 2597\n",
"WCnComV[0][66] = 2607\n",
"WCnComV[0][67] = 2626\n",
"WCnComV[0][68] = 2629\n",
"WCnComV[0][69] = 2656\n",
"WCnComV[0][70] = 2673\n",
"WCnComV[0][71] = 2719\n",
"WCnComV[0][72] = 2785\n",
"WCnComV[0][73] = 2796\n",
"WCnComV[0][74] = 2822\n",
"WCnComV[0][75] = 2838\n",
"WCnComV[0][76] = 2899\n",
"WCnComV[0][77] = 2935\n",
"WCnComV[0][78] = 2979\n",
"WCnComV[0][79] = 3000\n",
"WCnComV[0][80] = 3093\n",
"WCnComV[0][81] = 3111\n",
"WCnComV[0][82] = 3115\n",
"WCnComV[0][83] = 3206\n",
"WCnComV[0][84] = 3207\n",
"WCnComV[0][85] = 3271\n",
"WCnComV[0][86] = 3280\n",
"WCnComV[0][87] = 3284\n",
"WCnComV[0][88] = 3286\n",
"WCnComV[0][89] = 3299\n",
"WCnComV[0][90] = 3300\n",
"WCnComV[0][91] = 3301\n",
"WCnComV[0][92] = 3302\n",
"WCnComV[0][93] = 3315\n",
"WCnComV[0][94] = 3327\n",
"WCnComV[0][95] = 3422\n",
"WCnComV[0][96] = 3444\n",
"WCnComV[0][97] = 3467\n",
"WCnComV[0][98] = 3503\n",
"WCnComV[0][99] = 3517\n",
"WCnComV[0][100] = 3528\n",
"WCnComV[0][101] = 3534\n",
"WCnComV[0][102] = 3712\n",
"WCnComV[0][103] = 3719\n",
"WCnComV[0][104] = 3735\n",
"WCnComV[0][105] = 3786\n",
"WCnComV[0][106] = 3790\n",
"WCnComV[0][107] = 3849\n",
"WCnComV[0][108] = 3890\n",
"WCnComV[0][109] = 3895\n",
"WCnComV[0][110] = 3949\n",
"WCnComV[0][111] = 3951\n",
"WCnComV[0][112] = 4057\n",
"WCnComV[0][113] = 4083\n",
"WCnComV[0][114] = 4205\n",
"WCnComV[0][115] = 4226\n",
"WCnComV[0][116] = 4227\n",
"WCnComV[0][117] = 4303\n",
"WCnComV[0][118] = 4343\n",
"WCnComV[0][119] = 4389\n",
"WCnComV[0][120] = 4413\n",
"WCnComV[0][121] = 4420\n",
"WCnComV[0][122] = 4458\n",
"WCnComV[0][123] = 4460\n",
"WCnComV[0][124] = 4478\n",
"WCnComV[0][125] = 4505\n",
"WCnComV[0][126] = 4543\n",
"WCnComV[0][127] = 4570\n",
"WCnComV[0][128] = 4647\n",
"WCnComV[0][129] = 4655\n",
"WCnComV[0][130] = 4664\n",
"WCnComV[0][131] = 4726\n",
"WCnComV[0][132] = 4807\n",
"WCnComV[0][133] = 4816\n",
"WCnComV[0][134] = 4821\n",
"WCnComV[0][135] = 4822\n",
"WCnComV[0][136] = 4951\n",
"WCnComV[0][137] = 4959\n",
"WCnComV[0][138] = 4972\n",
"WCnComV[0][139] = 4975\n",
"WCnComV[0][140] = 5086\n",
"WCnComV[0][141] = 5095\n",
"WCnComV[0][142] = 5133\n",
"WCnComV[0][143] = 5142\n",
"WCnComV[0][144] = 5182\n",
"WCnComV[0][145] = 5186\n",
"WCnComV[0][146] = 5230\n",
"WCnComV[0][147] = 5249\n",
"WCnComV[0][148] = 5254\n",
"WCnComV[0][149] = 5325\n",
"WCnComV[0][150] = 5387\n",
"WCnComV[0][151] = 5388\n",
"WCnComV[0][152] = 5420\n",
"WCnComV[0][153] = 5528\n",
"WCnComV[0][154] = 5547\n",
"WCnComV[0][155] = 5576\n",
"WCnComV[0][156] = 5590\n",
"WCnComV[0][157] = 5591\n",
"WCnComV[0][158] = 5623\n",
"WCnComV[0][159] = 5670\n",
"WCnComV[0][160] = 5677\n",
"WCnComV[0][161] = 5708\n",
"WCnComV[0][162] = 5743\n",
"WCnComV[0][163] = 5767\n",
"WCnComV[0][164] = 5828\n",
"WCnComV[0][165] = 5834\n",
"WCnComV[0][166] = 5841\n",
"WCnComV[0][167] = 5916\n",
"WCnComV[0][168] = 5991\n",
"WCnComV[0][169] = 6071\n",
"WCnComV[0][170] = 6074\n",
"WCnComV[0][171] = 6098\n",
"WCnComV[0][172] = 6133\n",
"WCnComV[0][173] = 6229\n",
"WCnComV[0][174] = 6279\n",
"WCnComV[0][175] = 6286\n",
"WCnComV[0][176] = 6297\n",
"WCnComV[0][177] = 6320\n",
"WCnComV[0][178] = 6392\n",
"WCnComV[0][179] = 6395\n",
"WCnComV[0][180] = 6474\n",
"WCnComV[0][181] = 6502\n",
"WCnComV[0][182] = 6509\n",
"WCnComV[0][183] = 6540\n",
"WCnComV[0][184] = 6559\n",
"WCnComV[0][185] = 6612\n",
"WCnComV[0][186] = 6618\n",
"WCnComV[0][187] = 6670\n",
"WCnComV[0][188] = 6693\n",
"WCnComV[0][189] = 6709\n",
"WCnComV[0][190] = 6748\n",
"WCnComV[0][191] = 6759\n",
"WCnComV[0][192] = 6778\n",
"WCnComV[0][193] = 6791\n",
"WCnComV[0][194] = 6814\n",
"WCnComV[0][195] = 6818\n",
"WCnComV[0][196] = 6911\n",
"WCnComV[0][197] = 6956\n",
"WCnComV[0][198] = 7126\n",
"WCnComV[0][199] = 7174\n",
"WCnComV[0][200] = 7180\n",
"WCnComV[0][201] = 7190\n",
"WCnComV[0][202] = 7215\n",
"WCnComV[0][203] = 7238\n",
"WCnComV[0][204] = 7280\n",
"WCnComV[0][205] = 7377\n",
"WCnComV[0][206] = 7378\n",
"WCnComV[0][207] = 7413\n",
"WCnComV[0][208] = 7508\n",
"WCnComV[0][209] = 7521\n",
"WCnComV[0][210] = 7550\n",
"WCnComV[0][211] = 7571\n",
"WCnComV[0][212] = 7572\n",
"WCnComV[0][213] = 7589\n",
"WCnComV[0][214] = 7659\n",
"WCnComV[0][215] = 7664\n",
"WCnComV[0][216] = 7734\n",
"WCnComV[0][217] = 7740\n",
"WCnComV[0][218] = 7766\n",
"WCnComV[0][219] = 7807\n",
"WCnComV[0][220] = 7879\n",
"WCnComV[0][221] = 7893\n",
"WCnComV[0][222] = 7909\n",
"WCnComV[0][223] = 8001\n",
"WCnComV[0][224] = 8007\n",
"WCnComV[0][225] = 8022\n",
"WCnComV[0][226] = 8160\n",
"WCnComV[0][227] = 8161\n",
"WCnComV[0][228] = 8188\n",
"WCnComV[0][229] = 8224\n",
"WCnComV[0][230] = 8228\n",
"WCnComV[0][231] = 8252\n",
"WCnComV[0][232] = 8451\n",
"WCnComV[0][233] = 8455\n",
"WCnComV[0][234] = 8463\n",
"WCnComV[0][235] = 8588\n",
"WCnComV[0][236] = 8620\n",
"WCnComV[0][237] = 8651\n",
"WCnComV[0][238] = 8694\n",
"WCnComV[0][239] = 8700\n",
"WCnComV[0][240] = 8703\n",
"WCnComV[0][241] = 8721\n",
"WCnComV[0][242] = 8725\n",
"WCnComV[0][243] = 8729\n",
"WCnComV[0][244] = 8742\n",
"WCnComV[0][245] = 8748\n",
"WCnComV[0][246] = 8751\n",
"WCnComV[0][247] = 8799\n",
"WCnComV[0][248] = 8806\n",
"WCnComV[0][249] = 8815\n",
"WCnComV[0][250] = 8832\n",
"WCnComV[0][251] = 8863\n",
"WCnComV[0][252] = 8920\n",
"WCnComV[0][253] = 9000\n",
"WCnComV[0][254] = 9003\n",
"WCnComV[0][255] = 9045\n",
"WCnComV[0][256] = 9101\n",
"WCnComV[0][257] = 9173\n",
"WCnComV[0][258] = 9178\n",
"WCnComV[0][259] = 9215\n",
"WCnComV[0][260] = 9221\n",
"WCnComV[0][261] = 9222\n",
"WCnComV[0][262] = 9235\n",
"WCnComV[0][263] = 9268\n",
"WCnComV[0][264] = 9289\n",
"WCnComV[0][265] = 9307\n",
"WCnComV[0][266] = 9312\n",
"WCnComV[0][267] = 9358\n",
"WCnComV[0][268] = 9368\n",
"WCnComV[0][269] = 9412\n",
"WCnComV[0][270] = 9426\n",
"WCnComV[0][271] = 9430\n",
"WCnComV[0][272] = 9476\n",
"WCnComV[0][273] = 9478\n",
"WCnComV[0][274] = 9492\n",
"WCnComV[0][275] = 9502\n",
"WCnComV[0][276] = 9521\n",
"WCnComV[0][277] = 9549\n",
"WCnComV[0][278] = 9554\n",
"WCnComV[0][279] = 9629\n",
"WCnComV[0][280] = 9635\n",
"WCnComV[0][281] = 9694\n",
"WCnComV[0][282] = 9747\n",
"WCnComV[0][283] = 9756\n",
"WCnComV[0][284] = 9790\n",
"WCnComV[0][285] = 9808\n",
"WCnComV[0][286] = 9820\n",
"WCnComV[0][287] = 9823\n",
"WCnComV[0][288] = 9857\n",
"WCnComV[0][289] = 9965\n",
"WCnComV[0][290] = 9978\n",
"WCnComV[0][291] = 9983\n",
"WCnComV[1].Len() = 272\n",
"WCnComV[1][0] = 52\n",
"WCnComV[1][1] = 57\n",
"WCnComV[1][2] = 66\n",
"WCnComV[1][3] = 74\n",
"WCnComV[1][4] = 88\n",
"WCnComV[1][5] = 274\n",
"WCnComV[1][6] = 278\n",
"WCnComV[1][7] = 336\n",
"WCnComV[1][8] = 388\n",
"WCnComV[1][9] = 490\n",
"WCnComV[1][10] = 617\n",
"WCnComV[1][11] = 618\n",
"WCnComV[1][12] = 652\n",
"WCnComV[1][13] = 659\n",
"WCnComV[1][14] = 687\n",
"WCnComV[1][15] = 695\n",
"WCnComV[1][16] = 699\n",
"WCnComV[1][17] = 721\n",
"WCnComV[1][18] = 728\n",
"WCnComV[1][19] = 736\n",
"WCnComV[1][20] = 776\n",
"WCnComV[1][21] = 814\n",
"WCnComV[1][22] = 822\n",
"WCnComV[1][23] = 910\n",
"WCnComV[1][24] = 925\n",
"WCnComV[1][25] = 973\n",
"WCnComV[1][26] = 1003\n",
"WCnComV[1][27] = 1059\n",
"WCnComV[1][28] = 1079\n",
"WCnComV[1][29] = 1106\n",
"WCnComV[1][30] = 1136\n",
"WCnComV[1][31] = 1252\n",
"WCnComV[1][32] = 1266\n",
"WCnComV[1][33] = 1318\n",
"WCnComV[1][34] = 1371\n",
"WCnComV[1][35] = 1530\n",
"WCnComV[1][36] = 1540\n",
"WCnComV[1][37] = 1626\n",
"WCnComV[1][38] = 1655\n",
"WCnComV[1][39] = 1658\n",
"WCnComV[1][40] = 1685\n",
"WCnComV[1][41] = 1719\n",
"WCnComV[1][42] = 1723\n",
"WCnComV[1][43] = 1790\n",
"WCnComV[1][44] = 1795\n",
"WCnComV[1][45] = 1817\n",
"WCnComV[1][46] = 1839\n",
"WCnComV[1][47] = 1883\n",
"WCnComV[1][48] = 1931\n",
"WCnComV[1][49] = 1956\n",
"WCnComV[1][50] = 2007\n",
"WCnComV[1][51] = 2068\n",
"WCnComV[1][52] = 2101\n",
"WCnComV[1][53] = 2115\n",
"WCnComV[1][54] = 2116\n",
"WCnComV[1][55] = 2158\n",
"WCnComV[1][56] = 2192\n",
"WCnComV[1][57] = 2229\n",
"WCnComV[1][58] = 2257\n",
"WCnComV[1][59] = 2267\n",
"WCnComV[1][60] = 2271\n",
"WCnComV[1][61] = 2288\n",
"WCnComV[1][62] = 2297\n",
"WCnComV[1][63] = 2299\n",
"WCnComV[1][64] = 2364\n",
"WCnComV[1][65] = 2394\n",
"WCnComV[1][66] = 2410\n",
"WCnComV[1][67] = 2418\n",
"WCnComV[1][68] = 2472\n",
"WCnComV[1][69] = 2489\n",
"WCnComV[1][70] = 2492\n",
"WCnComV[1][71] = 2535\n",
"WCnComV[1][72] = 2536\n",
"WCnComV[1][73] = 2540\n",
"WCnComV[1][74] = 2547\n",
"WCnComV[1][75] = 2551\n",
"WCnComV[1][76] = 2561\n",
"WCnComV[1][77] = 2654\n",
"WCnComV[1][78] = 2741\n",
"WCnComV[1][79] = 2790\n",
"WCnComV[1][80] = 2808\n",
"WCnComV[1][81] = 2836\n",
"WCnComV[1][82] = 2839\n",
"WCnComV[1][83] = 2856\n",
"WCnComV[1][84] = 2953\n",
"WCnComV[1][85] = 3066\n",
"WCnComV[1][86] = 3125\n",
"WCnComV[1][87] = 3163\n",
"WCnComV[1][88] = 3166\n",
"WCnComV[1][89] = 3182\n",
"WCnComV[1][90] = 3272\n",
"WCnComV[1][91] = 3295\n",
"WCnComV[1][92] = 3313\n",
"WCnComV[1][93] = 3332\n",
"WCnComV[1][94] = 3457\n",
"WCnComV[1][95] = 3506\n",
"WCnComV[1][96] = 3533\n",
"WCnComV[1][97] = 3590\n",
"WCnComV[1][98] = 3599\n",
"WCnComV[1][99] = 3612\n",
"WCnComV[1][100] = 3616\n",
"WCnComV[1][101] = 3620\n",
"WCnComV[1][102] = 3658\n",
"WCnComV[1][103] = 3729\n",
"WCnComV[1][104] = 3745\n",
"WCnComV[1][105] = 3778\n",
"WCnComV[1][106] = 3822\n",
"WCnComV[1][107] = 3889\n",
"WCnComV[1][108] = 3893\n",
"WCnComV[1][109] = 3903\n",
"WCnComV[1][110] = 3908\n",
"WCnComV[1][111] = 3915\n",
"WCnComV[1][112] = 3933\n",
"WCnComV[1][113] = 3946\n",
"WCnComV[1][114] = 3975\n",
"WCnComV[1][115] = 4033\n",
"WCnComV[1][116] = 4073\n",
"WCnComV[1][117] = 4141\n",
"WCnComV[1][118] = 4217\n",
"WCnComV[1][119] = 4252\n",
"WCnComV[1][120] = 4264\n",
"WCnComV[1][121] = 4301\n",
"WCnComV[1][122] = 4327\n",
"WCnComV[1][123] = 4331\n",
"WCnComV[1][124] = 4382\n",
"WCnComV[1][125] = 4395\n",
"WCnComV[1][126] = 4400\n",
"WCnComV[1][127] = 4441\n",
"WCnComV[1][128] = 4446\n",
"WCnComV[1][129] = 4447\n",
"WCnComV[1][130] = 4557\n",
"WCnComV[1][131] = 4672\n",
"WCnComV[1][132] = 4760\n",
"WCnComV[1][133] = 4844\n",
"WCnComV[1][134] = 4967\n",
"WCnComV[1][135] = 5047\n",
"WCnComV[1][136] = 5184\n",
"WCnComV[1][137] = 5240\n",
"WCnComV[1][138] = 5261\n",
"WCnComV[1][139] = 5342\n",
"WCnComV[1][140] = 5349\n",
"WCnComV[1][141] = 5350\n",
"WCnComV[1][142] = 5380\n",
"WCnComV[1][143] = 5465\n",
"WCnComV[1][144] = 5466\n",
"WCnComV[1][145] = 5484\n",
"WCnComV[1][146] = 5538\n",
"WCnComV[1][147] = 5566\n",
"WCnComV[1][148] = 5604\n",
"WCnComV[1][149] = 5614\n",
"WCnComV[1][150] = 5627\n",
"WCnComV[1][151] = 5680\n",
"WCnComV[1][152] = 5683\n",
"WCnComV[1][153] = 5691\n",
"WCnComV[1][154] = 5697\n",
"WCnComV[1][155] = 5712\n",
"WCnComV[1][156] = 5716\n",
"WCnComV[1][157] = 5762\n",
"WCnComV[1][158] = 5766\n",
"WCnComV[1][159] = 5790\n",
"WCnComV[1][160] = 5792\n",
"WCnComV[1][161] = 5800\n",
"WCnComV[1][162] = 5801\n",
"WCnComV[1][163] = 5820\n",
"WCnComV[1][164] = 5855\n",
"WCnComV[1][165] = 5908\n",
"WCnComV[1][166] = 5992\n",
"WCnComV[1][167] = 6068\n",
"WCnComV[1][168] = 6110\n",
"WCnComV[1][169] = 6112\n",
"WCnComV[1][170] = 6125\n",
"WCnComV[1][171] = 6191\n",
"WCnComV[1][172] = 6240\n",
"WCnComV[1][173] = 6386\n",
"WCnComV[1][174] = 6389\n",
"WCnComV[1][175] = 6406\n",
"WCnComV[1][176] = 6456\n",
"WCnComV[1][177] = 6555\n",
"WCnComV[1][178] = 6684\n",
"WCnComV[1][179] = 6731\n",
"WCnComV[1][180] = 6734\n",
"WCnComV[1][181] = 6761\n",
"WCnComV[1][182] = 6807\n",
"WCnComV[1][183] = 6918\n",
"WCnComV[1][184] = 7092\n",
"WCnComV[1][185] = 7121\n",
"WCnComV[1][186] = 7155\n",
"WCnComV[1][187] = 7163\n",
"WCnComV[1][188] = 7179\n",
"WCnComV[1][189] = 7186\n",
"WCnComV[1][190] = 7329\n",
"WCnComV[1][191] = 7343\n",
"WCnComV[1][192] = 7386\n",
"WCnComV[1][193] = 7411\n",
"WCnComV[1][194] = 7466\n",
"WCnComV[1][195] = 7490\n",
"WCnComV[1][196] = 7544\n",
"WCnComV[1][197] = 7562\n",
"WCnComV[1][198] = 7604\n",
"WCnComV[1][199] = 7761\n",
"WCnComV[1][200] = 7786\n",
"WCnComV[1][201] = 7790\n",
"WCnComV[1][202] = 7795\n",
"WCnComV[1][203] = 7796\n",
"WCnComV[1][204] = 7873\n",
"WCnComV[1][205] = 7892\n",
"WCnComV[1][206] = 7894\n",
"WCnComV[1][207] = 7927\n",
"WCnComV[1][208] = 7935\n",
"WCnComV[1][209] = 7936\n",
"WCnComV[1][210] = 7955\n",
"WCnComV[1][211] = 7987\n",
"WCnComV[1][212] = 8046\n",
"WCnComV[1][213] = 8094\n",
"WCnComV[1][214] = 8112\n",
"WCnComV[1][215] = 8119\n",
"WCnComV[1][216] = 8129\n",
"WCnComV[1][217] = 8186\n",
"WCnComV[1][218] = 8237\n",
"WCnComV[1][219] = 8289\n",
"WCnComV[1][220] = 8315\n",
"WCnComV[1][221] = 8333\n",
"WCnComV[1][222] = 8372\n",
"WCnComV[1][223] = 8381\n",
"WCnComV[1][224] = 8386\n",
"WCnComV[1][225] = 8388\n",
"WCnComV[1][226] = 8389\n",
"WCnComV[1][227] = 8400\n",
"WCnComV[1][228] = 8466\n",
"WCnComV[1][229] = 8498\n",
"WCnComV[1][230] = 8508\n",
"WCnComV[1][231] = 8515\n",
"WCnComV[1][232] = 8532\n",
"WCnComV[1][233] = 8685\n",
"WCnComV[1][234] = 8691\n",
"WCnComV[1][235] = 8738\n",
"WCnComV[1][236] = 8784\n",
"WCnComV[1][237] = 8846\n",
"WCnComV[1][238] = 8847\n",
"WCnComV[1][239] = 8991\n",
"WCnComV[1][240] = 9057\n",
"WCnComV[1][241] = 9077\n",
"WCnComV[1][242] = 9100\n",
"WCnComV[1][243] = 9102\n",
"WCnComV[1][244] = 9138\n",
"WCnComV[1][245] = 9140\n",
"WCnComV[1][246] = 9211\n",
"WCnComV[1][247] = 9291\n",
"WCnComV[1][248] = 9318\n",
"WCnComV[1][249] = 9347\n",
"WCnComV[1][250] = 9359\n",
"WCnComV[1][251] = 9419\n",
"WCnComV[1][252] = 9505\n",
"WCnComV[1][253] = 9541\n",
"WCnComV[1][254] = 9558\n",
"WCnComV[1][255] = 9559\n",
"WCnComV[1][256] = 9571\n",
"WCnComV[1][257] = 9573\n",
"WCnComV[1][258] = 9647\n",
"WCnComV[1][259] = 9662\n",
"WCnComV[1][260] = 9679\n",
"WCnComV[1][261] = 9706\n",
"WCnComV[1][262] = 9732\n",
"WCnComV[1][263] = 9806\n",
"WCnComV[1][264] = 9826\n",
"WCnComV[1][265] = 9859\n",
"WCnComV[1][266] = 9872\n",
"WCnComV[1][267] = 9910\n",
"WCnComV[1][268] = 9916\n",
"WCnComV[1][269] = 9917\n",
"WCnComV[1][270] = 9921\n",
"WCnComV[1][271] = 9943\n",
"WCnComV[2].Len() = 186\n",
"WCnComV[2][0] = 20\n",
"WCnComV[2][1] = 77\n",
"WCnComV[2][2] = 94\n",
"WCnComV[2][3] = 115\n",
"WCnComV[2][4] = 130\n",
"WCnComV[2][5] = 246\n",
"WCnComV[2][6] = 254\n",
"WCnComV[2][7] = 337\n",
"WCnComV[2][8] = 344\n",
"WCnComV[2][9] = 405\n",
"WCnComV[2][10] = 412\n",
"WCnComV[2][11] = 463\n",
"WCnComV[2][12] = 499\n",
"WCnComV[2][13] = 556\n",
"WCnComV[2][14] = 629\n",
"WCnComV[2][15] = 662\n",
"WCnComV[2][16] = 682\n",
"WCnComV[2][17] = 748\n",
"WCnComV[2][18] = 821\n",
"WCnComV[2][19] = 844\n",
"WCnComV[2][20] = 972\n",
"WCnComV[2][21] = 1063\n",
"WCnComV[2][22] = 1070\n",
"WCnComV[2][23] = 1099\n",
"WCnComV[2][24] = 1121\n",
"WCnComV[2][25] = 1123\n",
"WCnComV[2][26] = 1205\n",
"WCnComV[2][27] = 1242\n",
"WCnComV[2][28] = 1246\n",
"WCnComV[2][29] = 1248\n",
"WCnComV[2][30] = 1275\n",
"WCnComV[2][31] = 1409\n",
"WCnComV[2][32] = 1549\n",
"WCnComV[2][33] = 1633\n",
"WCnComV[2][34] = 1649\n",
"WCnComV[2][35] = 1704\n",
"WCnComV[2][36] = 1725\n",
"WCnComV[2][37] = 1732\n",
"WCnComV[2][38] = 1734\n",
"WCnComV[2][39] = 1878\n",
"WCnComV[2][40] = 1892\n",
"WCnComV[2][41] = 2187\n",
"WCnComV[2][42] = 2206\n",
"WCnComV[2][43] = 2232\n",
"WCnComV[2][44] = 2236\n",
"WCnComV[2][45] = 2281\n",
"WCnComV[2][46] = 2287\n",
"WCnComV[2][47] = 2302\n",
"WCnComV[2][48] = 2497\n",
"WCnComV[2][49] = 2558\n",
"WCnComV[2][50] = 2572\n",
"WCnComV[2][51] = 2578\n",
"WCnComV[2][52] = 2740\n",
"WCnComV[2][53] = 2797\n",
"WCnComV[2][54] = 2940\n",
"WCnComV[2][55] = 2963\n",
"WCnComV[2][56] = 3017\n",
"WCnComV[2][57] = 3024\n",
"WCnComV[2][58] = 3034\n",
"WCnComV[2][59] = 3036\n",
"WCnComV[2][60] = 3151\n",
"WCnComV[2][61] = 3264\n",
"WCnComV[2][62] = 3375\n",
"WCnComV[2][63] = 3406\n",
"WCnComV[2][64] = 3410\n",
"WCnComV[2][65] = 3419\n",
"WCnComV[2][66] = 3442\n",
"WCnComV[2][67] = 3569\n",
"WCnComV[2][68] = 3796\n",
"WCnComV[2][69] = 3866\n",
"WCnComV[2][70] = 3939\n",
"WCnComV[2][71] = 3950\n",
"WCnComV[2][72] = 3986\n",
"WCnComV[2][73] = 4021\n",
"WCnComV[2][74] = 4082\n",
"WCnComV[2][75] = 4154\n",
"WCnComV[2][76] = 4231\n",
"WCnComV[2][77] = 4294\n",
"WCnComV[2][78] = 4314\n",
"WCnComV[2][79] = 4316\n",
"WCnComV[2][80] = 4370\n",
"WCnComV[2][81] = 4417\n",
"WCnComV[2][82] = 4528\n",
"WCnComV[2][83] = 4538\n",
"WCnComV[2][84] = 4666\n",
"WCnComV[2][85] = 4673\n",
"WCnComV[2][86] = 4768\n",
"WCnComV[2][87] = 4801\n",
"WCnComV[2][88] = 4910\n",
"WCnComV[2][89] = 5003\n",
"WCnComV[2][90] = 5016\n",
"WCnComV[2][91] = 5037\n",
"WCnComV[2][92] = 5051\n",
"WCnComV[2][93] = 5061\n",
"WCnComV[2][94] = 5072\n",
"WCnComV[2][95] = 5105\n",
"WCnComV[2][96] = 5122\n",
"WCnComV[2][97] = 5147\n",
"WCnComV[2][98] = 5220\n",
"WCnComV[2][99] = 5228\n",
"WCnComV[2][100] = 5235\n",
"WCnComV[2][101] = 5292\n",
"WCnComV[2][102] = 5307\n",
"WCnComV[2][103] = 5335\n",
"WCnComV[2][104] = 5385\n",
"WCnComV[2][105] = 5416\n",
"WCnComV[2][106] = 5427\n",
"WCnComV[2][107] = 5470\n",
"WCnComV[2][108] = 5513\n",
"WCnComV[2][109] = 5529\n",
"WCnComV[2][110] = 5656\n",
"WCnComV[2][111] = 5673\n",
"WCnComV[2][112] = 5779\n",
"WCnComV[2][113] = 5867\n",
"WCnComV[2][114] = 5892\n",
"WCnComV[2][115] = 5943\n",
"WCnComV[2][116] = 5986\n",
"WCnComV[2][117] = 6150\n",
"WCnComV[2][118] = 6179\n",
"WCnComV[2][119] = 6196\n",
"WCnComV[2][120] = 6207\n",
"WCnComV[2][121] = 6272\n",
"WCnComV[2][122] = 6354\n",
"WCnComV[2][123] = 6375\n",
"WCnComV[2][124] = 6499\n",
"WCnComV[2][125] = 6510\n",
"WCnComV[2][126] = 6534\n",
"WCnComV[2][127] = 6611\n",
"WCnComV[2][128] = 6786\n",
"WCnComV[2][129] = 6822\n",
"WCnComV[2][130] = 6862\n",
"WCnComV[2][131] = 6890\n",
"WCnComV[2][132] = 6929\n",
"WCnComV[2][133] = 6984\n",
"WCnComV[2][134] = 7023\n",
"WCnComV[2][135] = 7102\n",
"WCnComV[2][136] = 7169\n",
"WCnComV[2][137] = 7248\n",
"WCnComV[2][138] = 7253\n",
"WCnComV[2][139] = 7297\n",
"WCnComV[2][140] = 7304\n",
"WCnComV[2][141] = 7305\n",
"WCnComV[2][142] = 7324\n",
"WCnComV[2][143] = 7327\n",
"WCnComV[2][144] = 7360\n",
"WCnComV[2][145] = 7406\n",
"WCnComV[2][146] = 7425\n",
"WCnComV[2][147] = 7441\n",
"WCnComV[2][148] = 7517\n",
"WCnComV[2][149] = 7600\n",
"WCnComV[2][150] = 7644\n",
"WCnComV[2][151] = 7736\n",
"WCnComV[2][152] = 7852\n",
"WCnComV[2][153] = 7890\n",
"WCnComV[2][154] = 7891\n",
"WCnComV[2][155] = 8024\n",
"WCnComV[2][156] = 8093\n",
"WCnComV[2][157] = 8124\n",
"WCnComV[2][158] = 8150\n",
"WCnComV[2][159] = 8414\n",
"WCnComV[2][160] = 8573\n",
"WCnComV[2][161] = 8609\n",
"WCnComV[2][162] = 8618\n",
"WCnComV[2][163] = 8634\n",
"WCnComV[2][164] = 8643\n",
"WCnComV[2][165] = 8646\n",
"WCnComV[2][166] = 8686\n",
"WCnComV[2][167] = 8760\n",
"WCnComV[2][168] = 8801\n",
"WCnComV[2][169] = 8905\n",
"WCnComV[2][170] = 9014\n",
"WCnComV[2][171] = 9093\n",
"WCnComV[2][172] = 9128\n",
"WCnComV[2][173] = 9165\n",
"WCnComV[2][174] = 9189\n",
"WCnComV[2][175] = 9353\n",
"WCnComV[2][176] = 9441\n",
"WCnComV[2][177] = 9456\n",
"WCnComV[2][178] = 9574\n",
"WCnComV[2][179] = 9646\n",
"WCnComV[2][180] = 9782\n",
"WCnComV[2][181] = 9856\n",
"WCnComV[2][182] = 9886\n",
"WCnComV[2][183] = 9914\n",
"WCnComV[2][184] = 9937\n",
"WCnComV[2][185] = 9955\n",
"WCnComV[3].Len() = 63\n",
"WCnComV[3][0] = 877\n",
"WCnComV[3][1] = 896\n",
"WCnComV[3][2] = 1241\n",
"WCnComV[3][3] = 1351\n",
"WCnComV[3][4] = 1394\n",
"WCnComV[3][5] = 1553\n",
"WCnComV[3][6] = 1741\n",
"WCnComV[3][7] = 1803\n",
"WCnComV[3][8] = 1845\n",
"WCnComV[3][9] = 1945\n",
"WCnComV[3][10] = 2167\n",
"WCnComV[3][11] = 2270\n",
"WCnComV[3][12] = 2355\n",
"WCnComV[3][13] = 2415\n",
"WCnComV[3][14] = 2490\n",
"WCnComV[3][15] = 2652\n",
"WCnComV[3][16] = 2745\n",
"WCnComV[3][17] = 2788\n",
"WCnComV[3][18] = 3056\n",
"WCnComV[3][19] = 3243\n",
"WCnComV[3][20] = 3353\n",
"WCnComV[3][21] = 3551\n",
"WCnComV[3][22] = 3568\n",
"WCnComV[3][23] = 3859\n",
"WCnComV[3][24] = 3868\n",
"WCnComV[3][25] = 3897\n",
"WCnComV[3][26] = 3910\n",
"WCnComV[3][27] = 4201\n",
"WCnComV[3][28] = 4438\n",
"WCnComV[3][29] = 4573\n",
"WCnComV[3][30] = 4598\n",
"WCnComV[3][31] = 4628\n",
"WCnComV[3][32] = 4803\n",
"WCnComV[3][33] = 4811\n",
"WCnComV[3][34] = 5148\n",
"WCnComV[3][35] = 5412\n",
"WCnComV[3][36] = 5442\n",
"WCnComV[3][37] = 5450\n",
"WCnComV[3][38] = 5477\n",
"WCnComV[3][39] = 5497\n",
"WCnComV[3][40] = 6160\n",
"WCnComV[3][41] = 6293\n",
"WCnComV[3][42] = 6557\n",
"WCnComV[3][43] = 7035\n",
"WCnComV[3][44] = 7046\n",
"WCnComV[3][45] = 7216\n",
"WCnComV[3][46] = 7445\n",
"WCnComV[3][47] = 7537\n",
"WCnComV[3][48] = 7681\n",
"WCnComV[3][49] = 7911\n",
"WCnComV[3][50] = 8183\n",
"WCnComV[3][51] = 8272\n",
"WCnComV[3][52] = 8627\n",
"WCnComV[3][53] = 8762\n",
"WCnComV[3][54] = 8824\n",
"WCnComV[3][55] = 8892\n",
"WCnComV[3][56] = 9137\n",
"WCnComV[3][57] = 9181\n",
"WCnComV[3][58] = 9301\n",
"WCnComV[3][59] = 9508\n",
"WCnComV[3][60] = 9525\n",
"WCnComV[3][61] = 9566\n",
"WCnComV[3][62] = 9700\n",
"WCnComV[4].Len() = 53\n",
"WCnComV[4][0] = 291\n",
"WCnComV[4][1] = 779\n",
"WCnComV[4][2] = 1037\n",
"WCnComV[4][3] = 1085\n",
"WCnComV[4][4] = 1213\n",
"WCnComV[4][5] = 1475\n",
"WCnComV[4][6] = 1493\n",
"WCnComV[4][7] = 1584\n",
"WCnComV[4][8] = 1587\n",
"WCnComV[4][9] = 2022\n",
"WCnComV[4][10] = 2290\n",
"WCnComV[4][11] = 2534\n",
"WCnComV[4][12] = 2556\n",
"WCnComV[4][13] = 2591\n",
"WCnComV[4][14] = 2846\n",
"WCnComV[4][15] = 3296\n",
"WCnComV[4][16] = 3421\n",
"WCnComV[4][17] = 3436\n",
"WCnComV[4][18] = 3593\n",
"WCnComV[4][19] = 4457\n",
"WCnComV[4][20] = 4488\n",
"WCnComV[4][21] = 4893\n",
"WCnComV[4][22] = 4916\n",
"WCnComV[4][23] = 5188\n",
"WCnComV[4][24] = 5362\n",
"WCnComV[4][25] = 5622\n",
"WCnComV[4][26] = 5655\n",
"WCnComV[4][27] = 5679\n",
"WCnComV[4][28] = 5699\n",
"WCnComV[4][29] = 6065\n",
"WCnComV[4][30] = 6174\n",
"WCnComV[4][31] = 6529\n",
"WCnComV[4][32] = 6593\n",
"WCnComV[4][33] = 6641\n",
"WCnComV[4][34] = 6949\n",
"WCnComV[4][35] = 7040\n",
"WCnComV[4][36] = 7060\n",
"WCnComV[4][37] = 7127\n",
"WCnComV[4][38] = 7222\n",
"WCnComV[4][39] = 7314\n",
"WCnComV[4][40] = 7427\n",
"WCnComV[4][41] = 7478\n",
"WCnComV[4][42] = 8235\n",
"WCnComV[4][43] = 8355\n",
"WCnComV[4][44] = 8415\n",
"WCnComV[4][45] = 8514\n",
"WCnComV[4][46] = 8518\n",
"WCnComV[4][47] = 8539\n",
"WCnComV[4][48] = 8677\n",
"WCnComV[4][49] = 8787\n",
"WCnComV[4][50] = 9247\n",
"WCnComV[4][51] = 9506\n",
"WCnComV[4][52] = 9655\n",
"WCnComV[5].Len() = 48\n",
"WCnComV[5][0] = 782\n",
"WCnComV[5][1] = 1232\n",
"WCnComV[5][2] = 1456\n",
"WCnComV[5][3] = 1805\n",
"WCnComV[5][4] = 1925\n",
"WCnComV[5][5] = 2092\n",
"WCnComV[5][6] = 2124\n",
"WCnComV[5][7] = 2155\n",
"WCnComV[5][8] = 2454\n",
"WCnComV[5][9] = 2601\n",
"WCnComV[5][10] = 2640\n",
"WCnComV[5][11] = 3331\n",
"WCnComV[5][12] = 3379\n",
"WCnComV[5][13] = 3388\n",
"WCnComV[5][14] = 3500\n",
"WCnComV[5][15] = 3660\n",
"WCnComV[5][16] = 3699\n",
"WCnComV[5][17] = 4207\n",
"WCnComV[5][18] = 4398\n",
"WCnComV[5][19] = 4495\n",
"WCnComV[5][20] = 4627\n",
"WCnComV[5][21] = 5462\n",
"WCnComV[5][22] = 5807\n",
"WCnComV[5][23] = 6077\n",
"WCnComV[5][24] = 6604\n",
"WCnComV[5][25] = 7112\n",
"WCnComV[5][26] = 7279\n",
"WCnComV[5][27] = 7325\n",
"WCnComV[5][28] = 7448\n",
"WCnComV[5][29] = 7870\n",
"WCnComV[5][30] = 7989\n",
"WCnComV[5][31] = 8227\n",
"WCnComV[5][32] = 8369\n",
"WCnComV[5][33] = 8374\n",
"WCnComV[5][34] = 8441\n",
"WCnComV[5][35] = 8519\n",
"WCnComV[5][36] = 8577\n",
"WCnComV[5][37] = 8611\n",
"WCnComV[5][38] = 8733\n",
"WCnComV[5][39] = 8794\n",
"WCnComV[5][40] = 9008\n",
"WCnComV[5][41] = 9132\n",
"WCnComV[5][42] = 9206\n",
"WCnComV[5][43] = 9285\n",
"WCnComV[5][44] = 9308\n",
"WCnComV[5][45] = 9321\n",
"WCnComV[5][46] = 9327\n",
"WCnComV[5][47] = 9599\n",
"WCnComV[6].Len() = 46\n",
"WCnComV[6][0] = 92\n",
"WCnComV[6][1] = 225\n",
"WCnComV[6][2] = 612\n",
"WCnComV[6][3] = 1046\n",
"WCnComV[6][4] = 2483\n",
"WCnComV[6][5] = 2620\n",
"WCnComV[6][6] = 2761\n",
"WCnComV[6][7] = 2941\n",
"WCnComV[6][8] = 3231\n",
"WCnComV[6][9] = 3349\n",
"WCnComV[6][10] = 3627\n",
"WCnComV[6][11] = 3865\n",
"WCnComV[6][12] = 3926\n",
"WCnComV[6][13] = 3947\n",
"WCnComV[6][14] = 3954\n",
"WCnComV[6][15] = 3998\n",
"WCnComV[6][16] = 4263\n",
"WCnComV[6][17] = 4277\n",
"WCnComV[6][18] = 4604\n",
"WCnComV[6][19] = 4690\n",
"WCnComV[6][20] = 5011\n",
"WCnComV[6][21] = 5504\n",
"WCnComV[6][22] = 5979\n",
"WCnComV[6][23] = 6067\n",
"WCnComV[6][24] = 6322\n",
"WCnComV[6][25] = 6344\n",
"WCnComV[6][26] = 7124\n",
"WCnComV[6][27] = 7241\n",
"WCnComV[6][28] = 7291\n",
"WCnComV[6][29] = 7567\n",
"WCnComV[6][30] = 7616\n",
"WCnComV[6][31] = 7628\n",
"WCnComV[6][32] = 7665\n",
"WCnComV[6][33] = 7744\n",
"WCnComV[6][34] = 7835\n",
"WCnComV[6][35] = 7979\n",
"WCnComV[6][36] = 8316\n",
"WCnComV[6][37] = 8321\n",
"WCnComV[6][38] = 8348\n",
"WCnComV[6][39] = 8506\n",
"WCnComV[6][40] = 8763\n",
"WCnComV[6][41] = 9091\n",
"WCnComV[6][42] = 9261\n",
"WCnComV[6][43] = 9451\n",
"WCnComV[6][44] = 9772\n",
"WCnComV[6][45] = 9922\n",
"WCnComV[7].Len() = 44\n",
"WCnComV[7][0] = 230\n",
"WCnComV[7][1] = 542\n",
"WCnComV[7][2] = 579\n",
"WCnComV[7][3] = 1377\n",
"WCnComV[7][4] = 1820\n",
"WCnComV[7][5] = 2094\n",
"WCnComV[7][6] = 2632\n",
"WCnComV[7][7] = 3053\n",
"WCnComV[7][8] = 3141\n",
"WCnComV[7][9] = 3382\n",
"WCnComV[7][10] = 3752\n",
"WCnComV[7][11] = 4244\n",
"WCnComV[7][12] = 4426\n",
"WCnComV[7][13] = 4753\n",
"WCnComV[7][14] = 4778\n",
"WCnComV[7][15] = 4883\n",
"WCnComV[7][16] = 5255\n",
"WCnComV[7][17] = 5289\n",
"WCnComV[7][18] = 5358\n",
"WCnComV[7][19] = 5938\n",
"WCnComV[7][20] = 6198\n",
"WCnComV[7][21] = 6450\n",
"WCnComV[7][22] = 6490\n",
"WCnComV[7][23] = 6544\n",
"WCnComV[7][24] = 6622\n",
"WCnComV[7][25] = 6664\n",
"WCnComV[7][26] = 6736\n",
"WCnComV[7][27] = 7030\n",
"WCnComV[7][28] = 7054\n",
"WCnComV[7][29] = 7091\n",
"WCnComV[7][30] = 7354\n",
"WCnComV[7][31] = 7625\n",
"WCnComV[7][32] = 7999\n",
"WCnComV[7][33] = 8118\n",
"WCnComV[7][34] = 8442\n",
"WCnComV[7][35] = 8641\n",
"WCnComV[7][36] = 8982\n",
"WCnComV[7][37] = 9072\n",
"WCnComV[7][38] = 9169\n",
"WCnComV[7][39] = 9298\n",
"WCnComV[7][40] = 9645\n",
"WCnComV[7][41] = 9836\n",
"WCnComV[7][42] = 9948\n",
"WCnComV[7][43] = 9988\n",
"WCnComV[8].Len() = 41\n",
"WCnComV[8][0] = 49\n",
"WCnComV[8][1] = 146\n",
"WCnComV[8][2] = 177\n",
"WCnComV[8][3] = 296\n",
"WCnComV[8][4] = 360\n",
"WCnComV[8][5] = 1089\n",
"WCnComV[8][6] = 1659\n",
"WCnComV[8][7] = 1881\n",
"WCnComV[8][8] = 1904\n",
"WCnComV[8][9] = 2406\n",
"WCnComV[8][10] = 2818\n",
"WCnComV[8][11] = 2958\n",
"WCnComV[8][12] = 3258\n",
"WCnComV[8][13] = 3592\n",
"WCnComV[8][14] = 3731\n",
"WCnComV[8][15] = 3733\n",
"WCnComV[8][16] = 4089\n",
"WCnComV[8][17] = 4255\n",
"WCnComV[8][18] = 4601\n",
"WCnComV[8][19] = 5172\n",
"WCnComV[8][20] = 5309\n",
"WCnComV[8][21] = 5344\n",
"WCnComV[8][22] = 5471\n",
"WCnComV[8][23] = 5613\n",
"WCnComV[8][24] = 5935\n",
"WCnComV[8][25] = 6194\n",
"WCnComV[8][26] = 6511\n",
"WCnComV[8][27] = 6558\n",
"WCnComV[8][28] = 6770\n",
"WCnComV[8][29] = 6869\n",
"WCnComV[8][30] = 7189\n",
"WCnComV[8][31] = 7342\n",
"WCnComV[8][32] = 7493\n",
"WCnComV[8][33] = 7623\n",
"WCnComV[8][34] = 8136\n",
"WCnComV[8][35] = 8198\n",
"WCnComV[8][36] = 8428\n",
"WCnComV[8][37] = 8911\n",
"WCnComV[8][38] = 8952\n",
"WCnComV[8][39] = 9054\n",
"WCnComV[8][40] = 9708\n",
"WCnComV[9].Len() = 40\n",
"WCnComV[9][0] = 42\n",
"WCnComV[9][1] = 555\n",
"WCnComV[9][2] = 570\n",
"WCnComV[9][3] = 609\n",
"WCnComV[9][4] = 668\n",
"WCnComV[9][5] = 787\n",
"WCnComV[9][6] = 1851\n",
"WCnComV[9][7] = 1886\n",
"WCnComV[9][8] = 2137\n",
"WCnComV[9][9] = 2216\n",
"WCnComV[9][10] = 2311\n",
"WCnComV[9][11] = 2865\n",
"WCnComV[9][12] = 3026\n",
"WCnComV[9][13] = 3561\n",
"WCnComV[9][14] = 3935\n",
"WCnComV[9][15] = 3944\n",
"WCnComV[9][16] = 3958\n",
"WCnComV[9][17] = 4276\n",
"WCnComV[9][18] = 4396\n",
"WCnComV[9][19] = 4752\n",
"WCnComV[9][20] = 4766\n",
"WCnComV[9][21] = 5346\n",
"WCnComV[9][22] = 5399\n",
"WCnComV[9][23] = 5551\n",
"WCnComV[9][24] = 5681\n",
"WCnComV[9][25] = 5840\n",
"WCnComV[9][26] = 6796\n",
"WCnComV[9][27] = 7053\n",
"WCnComV[9][28] = 7470\n",
"WCnComV[9][29] = 7558\n",
"WCnComV[9][30] = 8062\n",
"WCnComV[9][31] = 8213\n",
"WCnComV[9][32] = 8570\n",
"WCnComV[9][33] = 8701\n",
"WCnComV[9][34] = 8943\n",
"WCnComV[9][35] = 9018\n",
"WCnComV[9][36] = 9590\n",
"WCnComV[9][37] = 9623\n",
"WCnComV[9][38] = 9723\n",
"WCnComV[9][39] = 9896\n",
"WCnComV[10].Len() = 35\n",
"WCnComV[10][0] = 29\n",
"WCnComV[10][1] = 98\n",
"WCnComV[10][2] = 312\n",
"WCnComV[10][3] = 459\n",
"WCnComV[10][4] = 516\n",
"WCnComV[10][5] = 549\n",
"WCnComV[10][6] = 664\n",
"WCnComV[10][7] = 1053\n",
"WCnComV[10][8] = 1748\n",
"WCnComV[10][9] = 1993\n",
"WCnComV[10][10] = 2715\n",
"WCnComV[10][11] = 3297\n",
"WCnComV[10][12] = 3563\n",
"WCnComV[10][13] = 3677\n",
"WCnComV[10][14] = 3753\n",
"WCnComV[10][15] = 5214\n",
"WCnComV[10][16] = 5276\n",
"WCnComV[10][17] = 5632\n",
"WCnComV[10][18] = 5756\n",
"WCnComV[10][19] = 5870\n",
"WCnComV[10][20] = 6020\n",
"WCnComV[10][21] = 6047\n",
"WCnComV[10][22] = 6190\n",
"WCnComV[10][23] = 6261\n",
"WCnComV[10][24] = 6422\n",
"WCnComV[10][25] = 6835\n",
"WCnComV[10][26] = 7006\n",
"WCnComV[10][27] = 7511\n",
"WCnComV[10][28] = 8145\n",
"WCnComV[10][29] = 8163\n",
"WCnComV[10][30] = 8225\n",
"WCnComV[10][31] = 8258\n",
"WCnComV[10][32] = 8336\n",
"WCnComV[10][33] = 8902\n",
"WCnComV[10][34] = 9659\n",
"WCnComV[11].Len() = 34\n",
"WCnComV[11][0] = 171\n",
"WCnComV[11][1] = 608\n",
"WCnComV[11][2] = 1537\n",
"WCnComV[11][3] = 1753\n",
"WCnComV[11][4] = 1832\n",
"WCnComV[11][5] = 1916\n",
"WCnComV[11][6] = 2059\n",
"WCnComV[11][7] = 2138\n",
"WCnComV[11][8] = 2166\n",
"WCnComV[11][9] = 2712\n",
"WCnComV[11][10] = 2714\n",
"WCnComV[11][11] = 3050\n",
"WCnComV[11][12] = 3361\n",
"WCnComV[11][13] = 3611\n",
"WCnComV[11][14] = 4010\n",
"WCnComV[11][15] = 4539\n",
"WCnComV[11][16] = 4641\n",
"WCnComV[11][17] = 4892\n",
"WCnComV[11][18] = 5141\n",
"WCnComV[11][19] = 5290\n",
"WCnComV[11][20] = 5489\n",
"WCnComV[11][21] = 5745\n",
"WCnComV[11][22] = 6103\n",
"WCnComV[11][23] = 6448\n",
"WCnComV[11][24] = 6688\n",
"WCnComV[11][25] = 7065\n",
"WCnComV[11][26] = 7145\n",
"WCnComV[11][27] = 7235\n",
"WCnComV[11][28] = 7403\n",
"WCnComV[11][29] = 7513\n",
"WCnComV[11][30] = 7678\n",
"WCnComV[11][31] = 7967\n",
"WCnComV[11][32] = 8562\n",
"WCnComV[11][33] = 9486\n",
"WCnComV[12].Len() = 34\n",
"WCnComV[12][0] = 143\n",
"WCnComV[12][1] = 202\n",
"WCnComV[12][2] = 320\n",
"WCnComV[12][3] = 702\n",
"WCnComV[12][4] = 1030\n",
"WCnComV[12][5] = 1405\n",
"WCnComV[12][6] = 2366\n",
"WCnComV[12][7] = 2467\n",
"WCnComV[12][8] = 2590\n",
"WCnComV[12][9] = 2831\n",
"WCnComV[12][10] = 2853\n",
"WCnComV[12][11] = 2886\n",
"WCnComV[12][12] = 3180\n",
"WCnComV[12][13] = 3609\n",
"WCnComV[12][14] = 3647\n",
"WCnComV[12][15] = 4005\n",
"WCnComV[12][16] = 4830\n",
"WCnComV[12][17] = 4869\n",
"WCnComV[12][18] = 4948\n",
"WCnComV[12][19] = 5206\n",
"WCnComV[12][20] = 5533\n",
"WCnComV[12][21] = 5883\n",
"WCnComV[12][22] = 6221\n",
"WCnComV[12][23] = 6237\n",
"WCnComV[12][24] = 6294\n",
"WCnComV[12][25] = 6313\n",
"WCnComV[12][26] = 6428\n",
"WCnComV[12][27] = 6626\n",
"WCnComV[12][28] = 7706\n",
"WCnComV[12][29] = 7725\n",
"WCnComV[12][30] = 7985\n",
"WCnComV[12][31] = 8180\n",
"WCnComV[12][32] = 8791\n",
"WCnComV[12][33] = 8857\n",
"WCnComV[13].Len() = 33\n",
"WCnComV[13][0] = 370\n",
"WCnComV[13][1] = 383\n",
"WCnComV[13][2] = 1013\n",
"WCnComV[13][3] = 1024\n",
"WCnComV[13][4] = 1176\n",
"WCnComV[13][5] = 1353\n",
"WCnComV[13][6] = 1600\n",
"WCnComV[13][7] = 1869\n",
"WCnComV[13][8] = 2641\n",
"WCnComV[13][9] = 2981\n",
"WCnComV[13][10] = 3198\n",
"WCnComV[13][11] = 3211\n",
"WCnComV[13][12] = 3221\n",
"WCnComV[13][13] = 3471\n",
"WCnComV[13][14] = 3578\n",
"WCnComV[13][15] = 3891\n",
"WCnComV[13][16] = 4042\n",
"WCnComV[13][17] = 5339\n",
"WCnComV[13][18] = 6704\n",
"WCnComV[13][19] = 6965\n",
"WCnComV[13][20] = 7208\n",
"WCnComV[13][21] = 7530\n",
"WCnComV[13][22] = 7667\n",
"WCnComV[13][23] = 7881\n",
"WCnComV[13][24] = 8197\n",
"WCnComV[13][25] = 8465\n",
"WCnComV[13][26] = 8753\n",
"WCnComV[13][27] = 8759\n",
"WCnComV[13][28] = 8843\n",
"WCnComV[13][29] = 9371\n",
"WCnComV[13][30] = 9575\n",
"WCnComV[13][31] = 9616\n",
"WCnComV[13][32] = 9873\n",
"WCnComV[14].Len() = 31\n",
"WCnComV[14][0] = 533\n",
"WCnComV[14][1] = 755\n",
"WCnComV[14][2] = 835\n",
"WCnComV[14][3] = 1105\n",
"WCnComV[14][4] = 1131\n",
"WCnComV[14][5] = 1356\n",
"WCnComV[14][6] = 1411\n",
"WCnComV[14][7] = 1505\n",
"WCnComV[14][8] = 1785\n",
"WCnComV[14][9] = 2006\n",
"WCnComV[14][10] = 2304\n",
"WCnComV[14][11] = 2686\n",
"WCnComV[14][12] = 2750\n",
"WCnComV[14][13] = 2773\n",
"WCnComV[14][14] = 3020\n",
"WCnComV[14][15] = 3450\n",
"WCnComV[14][16] = 4870\n",
"WCnComV[14][17] = 5279\n",
"WCnComV[14][18] = 5421\n",
"WCnComV[14][19] = 5553\n",
"WCnComV[14][20] = 5874\n",
"WCnComV[14][21] = 6794\n",
"WCnComV[14][22] = 7346\n",
"WCnComV[14][23] = 7368\n",
"WCnComV[14][24] = 7839\n",
"WCnComV[14][25] = 8356\n",
"WCnComV[14][26] = 8648\n",
"WCnComV[14][27] = 8870\n",
"WCnComV[14][28] = 9364\n",
"WCnComV[14][29] = 9512\n",
"WCnComV[14][30] = 9572\n",
"WCnComV[15].Len() = 31\n",
"WCnComV[15][0] = 152\n",
"WCnComV[15][1] = 433\n",
"WCnComV[15][2] = 460\n",
"WCnComV[15][3] = 954\n",
"WCnComV[15][4] = 1007\n",
"WCnComV[15][5] = 1370\n",
"WCnComV[15][6] = 1707\n",
"WCnComV[15][7] = 1842\n",
"WCnComV[15][8] = 1846\n",
"WCnComV[15][9] = 2465\n",
"WCnComV[15][10] = 2541\n",
"WCnComV[15][11] = 3044\n",
"WCnComV[15][12] = 3573\n",
"WCnComV[15][13] = 4747\n",
"WCnComV[15][14] = 4865\n",
"WCnComV[15][15] = 5149\n",
"WCnComV[15][16] = 5581\n",
"WCnComV[15][17] = 5947\n",
"WCnComV[15][18] = 6073\n",
"WCnComV[15][19] = 6624\n",
"WCnComV[15][20] = 6816\n",
"WCnComV[15][21] = 7013\n",
"WCnComV[15][22] = 7384\n",
"WCnComV[15][23] = 7565\n",
"WCnComV[15][24] = 7646\n",
"WCnComV[15][25] = 8262\n",
"WCnComV[15][26] = 8881\n",
"WCnComV[15][27] = 8891\n",
"WCnComV[15][28] = 9159\n",
"WCnComV[15][29] = 9162\n",
"WCnComV[15][30] = 9913\n",
"WCnComV[16].Len() = 30\n",
"WCnComV[16][0] = 584\n",
"WCnComV[16][1] = 969\n",
"WCnComV[16][2] = 1472\n",
"WCnComV[16][3] = 1611\n",
"WCnComV[16][4] = 1951\n",
"WCnComV[16][5] = 2072\n",
"WCnComV[16][6] = 2403\n",
"WCnComV[16][7] = 2577\n",
"WCnComV[16][8] = 2710\n",
"WCnComV[16][9] = 2995\n",
"WCnComV[16][10] = 3290\n",
"WCnComV[16][11] = 4002\n",
"WCnComV[16][12] = 4305\n",
"WCnComV[16][13] = 4387\n",
"WCnComV[16][14] = 4456\n",
"WCnComV[16][15] = 4518\n",
"WCnComV[16][16] = 4531\n",
"WCnComV[16][17] = 4710\n",
"WCnComV[16][18] = 4942\n",
"WCnComV[16][19] = 5407\n",
"WCnComV[16][20] = 5443\n",
"WCnComV[16][21] = 5467\n",
"WCnComV[16][22] = 5640\n",
"WCnComV[16][23] = 6230\n",
"WCnComV[16][24] = 6851\n",
"WCnComV[16][25] = 7049\n",
"WCnComV[16][26] = 8031\n",
"WCnComV[16][27] = 8695\n",
"WCnComV[16][28] = 9544\n",
"WCnComV[16][29] = 9696\n",
"WCnComV[17].Len() = 29\n",
"WCnComV[17][0] = 413\n",
"WCnComV[17][1] = 1219\n",
"WCnComV[17][2] = 1396\n",
"WCnComV[17][3] = 1807\n",
"WCnComV[17][4] = 1941\n",
"WCnComV[17][5] = 2176\n",
"WCnComV[17][6] = 2251\n",
"WCnComV[17][7] = 2825\n",
"WCnComV[17][8] = 2932\n",
"WCnComV[17][9] = 3127\n",
"WCnComV[17][10] = 3137\n",
"WCnComV[17][11] = 3244\n",
"WCnComV[17][12] = 3338\n",
"WCnComV[17][13] = 3651\n",
"WCnComV[17][14] = 4646\n",
"WCnComV[17][15] = 4758\n",
"WCnComV[17][16] = 5250\n",
"WCnComV[17][17] = 5911\n",
"WCnComV[17][18] = 7022\n",
"WCnComV[17][19] = 7318\n",
"WCnComV[17][20] = 7540\n",
"WCnComV[17][21] = 7601\n",
"WCnComV[17][22] = 7669\n",
"WCnComV[17][23] = 7850\n",
"WCnComV[17][24] = 8070\n",
"WCnComV[17][25] = 8708\n",
"WCnComV[17][26] = 8934\n",
"WCnComV[17][27] = 9075\n",
"WCnComV[17][28] = 9589\n",
"WCnComV[18].Len() = 28\n",
"WCnComV[18][0] = 960\n",
"WCnComV[18][1] = 1373\n",
"WCnComV[18][2] = 1968\n",
"WCnComV[18][3] = 2585\n",
"WCnComV[18][4] = 3008\n",
"WCnComV[18][5] = 3077\n",
"WCnComV[18][6] = 3179\n",
"WCnComV[18][7] = 3381\n",
"WCnComV[18][8] = 3766\n",
"WCnComV[18][9] = 4193\n",
"WCnComV[18][10] = 4657\n",
"WCnComV[18][11] = 4773\n",
"WCnComV[18][12] = 4813\n",
"WCnComV[18][13] = 5888\n",
"WCnComV[18][14] = 6063\n",
"WCnComV[18][15] = 6091\n",
"WCnComV[18][16] = 6168\n",
"WCnComV[18][17] = 6594\n",
"WCnComV[18][18] = 6678\n",
"WCnComV[18][19] = 7070\n",
"WCnComV[18][20] = 7322\n",
"WCnComV[18][21] = 7439\n",
"WCnComV[18][22] = 7707\n",
"WCnComV[18][23] = 8216\n",
"WCnComV[18][24] = 8233\n",
"WCnComV[18][25] = 8842\n",
"WCnComV[18][26] = 8858\n",
"WCnComV[18][27] = 8899\n",
"WCnComV[19].Len() = 28\n",
"WCnComV[19][0] = 126\n",
"WCnComV[19][1] = 218\n",
"WCnComV[19][2] = 456\n",
"WCnComV[19][3] = 2058\n",
"WCnComV[19][4] = 2743\n",
"WCnComV[19][5] = 3097\n",
"WCnComV[19][6] = 3570\n",
"WCnComV[19][7] = 4348\n",
"WCnComV[19][8] = 4421\n",
"WCnComV[19][9] = 4607\n",
"WCnComV[19][10] = 5306\n",
"WCnComV[19][11] = 5944\n",
"WCnComV[19][12] = 5962\n",
"WCnComV[19][13] = 6348\n",
"WCnComV[19][14] = 6936\n",
"WCnComV[19][15] = 7214\n",
"WCnComV[19][16] = 7257\n",
"WCnComV[19][17] = 7355\n",
"WCnComV[19][18] = 7479\n",
"WCnComV[19][19] = 7783\n",
"WCnComV[19][20] = 7793\n",
"WCnComV[19][21] = 7800\n",
"WCnComV[19][22] = 8164\n",
"WCnComV[19][23] = 8204\n",
"WCnComV[19][24] = 8649\n",
"WCnComV[19][25] = 9265\n",
"WCnComV[19][26] = 9274\n",
"WCnComV[19][27] = 9827\n",
"WCnComV[20].Len() = 27\n",
"WCnComV[20][0] = 293\n",
"WCnComV[20][1] = 543\n",
"WCnComV[20][2] = 765\n",
"WCnComV[20][3] = 999\n",
"WCnComV[20][4] = 1188\n",
"WCnComV[20][5] = 1675\n",
"WCnComV[20][6] = 1885\n",
"WCnComV[20][7] = 2178\n",
"WCnComV[20][8] = 2753\n",
"WCnComV[20][9] = 3203\n",
"WCnComV[20][10] = 3322\n",
"WCnComV[20][11] = 3371\n",
"WCnComV[20][12] = 3907\n",
"WCnComV[20][13] = 3962\n",
"WCnComV[20][14] = 4081\n",
"WCnComV[20][15] = 4394\n",
"WCnComV[20][16] = 4590\n",
"WCnComV[20][17] = 4724\n",
"WCnComV[20][18] = 5355\n",
"WCnComV[20][19] = 6388\n",
"WCnComV[20][20] = 7717\n",
"WCnComV[20][21] = 7844\n",
"WCnComV[20][22] = 8121\n",
"WCnComV[20][23] = 8574\n",
"WCnComV[20][24] = 8992\n",
"WCnComV[20][25] = 9224\n",
"WCnComV[20][26] = 9957\n",
"WCnComV[21].Len() = 24\n",
"WCnComV[21][0] = 186\n",
"WCnComV[21][1] = 1271\n",
"WCnComV[21][2] = 1433\n",
"WCnComV[21][3] = 1699\n",
"WCnComV[21][4] = 1733\n",
"WCnComV[21][5] = 2368\n",
"WCnComV[21][6] = 2397\n",
"WCnComV[21][7] = 2867\n",
"WCnComV[21][8] = 3091\n",
"WCnComV[21][9] = 3311\n",
"WCnComV[21][10] = 3820\n",
"WCnComV[21][11] = 4075\n",
"WCnComV[21][12] = 4374\n",
"WCnComV[21][13] = 4462\n",
"WCnComV[21][14] = 4879\n",
"WCnComV[21][15] = 4989\n",
"WCnComV[21][16] = 5163\n",
"WCnComV[21][17] = 5375\n",
"WCnComV[21][18] = 5836\n",
"WCnComV[21][19] = 7098\n",
"WCnComV[21][20] = 7768\n",
"WCnComV[21][21] = 7951\n",
"WCnComV[21][22] = 8559\n",
"WCnComV[21][23] = 9425\n",
"WCnComV[22].Len() = 23\n",
"WCnComV[22][0] = 1683\n",
"WCnComV[22][1] = 1695\n",
"WCnComV[22][2] = 2175\n",
"WCnComV[22][3] = 2459\n",
"WCnComV[22][4] = 2814\n",
"WCnComV[22][5] = 3150\n",
"WCnComV[22][6] = 3531\n",
"WCnComV[22][7] = 3732\n",
"WCnComV[22][8] = 4586\n",
"WCnComV[22][9] = 4631\n",
"WCnComV[22][10] = 4800\n",
"WCnComV[22][11] = 5596\n",
"WCnComV[22][12] = 6012\n",
"WCnComV[22][13] = 6266\n",
"WCnComV[22][14] = 6903\n",
"WCnComV[22][15] = 6945\n",
"WCnComV[22][16] = 7080\n",
"WCnComV[22][17] = 7732\n",
"WCnComV[22][18] = 8556\n",
"WCnComV[22][19] = 9076\n",
"WCnComV[22][20] = 9269\n",
"WCnComV[22][21] = 9341\n",
"WCnComV[22][22] = 9727\n",
"WCnComV[23].Len() = 23\n",
"WCnComV[23][0] = 841\n",
"WCnComV[23][1] = 879\n",
"WCnComV[23][2] = 888\n",
"WCnComV[23][3] = 1021\n",
"WCnComV[23][4] = 1158\n",
"WCnComV[23][5] = 1651\n",
"WCnComV[23][6] = 2388\n",
"WCnComV[23][7] = 2704\n",
"WCnComV[23][8] = 2879\n",
"WCnComV[23][9] = 3562\n",
"WCnComV[23][10] = 3589\n",
"WCnComV[23][11] = 5065\n",
"WCnComV[23][12] = 5417\n",
"WCnComV[23][13] = 5788\n",
"WCnComV[23][14] = 6117\n",
"WCnComV[23][15] = 6377\n",
"WCnComV[23][16] = 6677\n",
"WCnComV[23][17] = 7547\n",
"WCnComV[23][18] = 8221\n",
"WCnComV[23][19] = 8727\n",
"WCnComV[23][20] = 9310\n",
"WCnComV[23][21] = 9561\n",
"WCnComV[23][22] = 9617\n",
"WCnComV[24].Len() = 21\n",
"WCnComV[24][0] = 277\n",
"WCnComV[24][1] = 486\n",
"WCnComV[24][2] = 820\n",
"WCnComV[24][3] = 940\n",
"WCnComV[24][4] = 1775\n",
"WCnComV[24][5] = 1824\n",
"WCnComV[24][6] = 2400\n",
"WCnComV[24][7] = 3051\n",
"WCnComV[24][8] = 3649\n",
"WCnComV[24][9] = 4220\n",
"WCnComV[24][10] = 4381\n",
"WCnComV[24][11] = 4635\n",
"WCnComV[24][12] = 5438\n",
"WCnComV[24][13] = 5802\n",
"WCnComV[24][14] = 6008\n",
"WCnComV[24][15] = 6437\n",
"WCnComV[24][16] = 7716\n",
"WCnComV[24][17] = 8083\n",
"WCnComV[24][18] = 8820\n",
"WCnComV[24][19] = 9802\n",
"WCnComV[24][20] = 9894\n",
"WCnComV[25].Len() = 21\n",
"WCnComV[25][0] = 39\n",
"WCnComV[25][1] = 116\n",
"WCnComV[25][2] = 193\n",
"WCnComV[25][3] = 2134\n",
"WCnComV[25][4] = 2511\n",
"WCnComV[25][5] = 3392\n",
"WCnComV[25][6] = 3940\n",
"WCnComV[25][7] = 4224\n",
"WCnComV[25][8] = 4503\n",
"WCnComV[25][9] = 5176\n",
"WCnComV[25][10] = 5312\n",
"WCnComV[25][11] = 6281\n",
"WCnComV[25][12] = 6295\n",
"WCnComV[25][13] = 6424\n",
"WCnComV[25][14] = 7287\n",
"WCnComV[25][15] = 7923\n",
"WCnComV[25][16] = 8375\n",
"WCnComV[25][17] = 8758\n",
"WCnComV[25][18] = 9133\n",
"WCnComV[25][19] = 9431\n",
"WCnComV[25][20] = 9928\n",
"WCnComV[26].Len() = 20\n",
"WCnComV[26][0] = 354\n",
"WCnComV[26][1] = 572\n",
"WCnComV[26][2] = 3184\n",
"WCnComV[26][3] = 3378\n",
"WCnComV[26][4] = 3610\n",
"WCnComV[26][5] = 4434\n",
"WCnComV[26][6] = 4740\n",
"WCnComV[26][7] = 4990\n",
"WCnComV[26][8] = 5323\n",
"WCnComV[26][9] = 5731\n",
"WCnComV[26][10] = 5737\n",
"WCnComV[26][11] = 6143\n",
"WCnComV[26][12] = 6726\n",
"WCnComV[26][13] = 6838\n",
"WCnComV[26][14] = 7221\n",
"WCnComV[26][15] = 7454\n",
"WCnComV[26][16] = 8497\n",
"WCnComV[26][17] = 9005\n",
"WCnComV[26][18] = 9035\n",
"WCnComV[26][19] = 9885\n",
"WCnComV[27].Len() = 20\n",
"WCnComV[27][0] = 304\n",
"WCnComV[27][1] = 726\n",
"WCnComV[27][2] = 1288\n",
"WCnComV[27][3] = 1562\n",
"WCnComV[27][4] = 2509\n",
"WCnComV[27][5] = 2983\n",
"WCnComV[27][6] = 3023\n",
"WCnComV[27][7] = 3394\n",
"WCnComV[27][8] = 3777\n",
"WCnComV[27][9] = 3964\n",
"WCnComV[27][10] = 3984\n",
"WCnComV[27][11] = 4755\n",
"WCnComV[27][12] = 4857\n",
"WCnComV[27][13] = 5430\n",
"WCnComV[27][14] = 6078\n",
"WCnComV[27][15] = 7119\n",
"WCnComV[27][16] = 8432\n",
"WCnComV[27][17] = 9254\n",
"WCnComV[27][18] = 9376\n",
"WCnComV[27][19] = 9664\n",
"WCnComV[28].Len() = 20\n",
"WCnComV[28][0] = 178\n",
"WCnComV[28][1] = 745\n",
"WCnComV[28][2] = 1155\n",
"WCnComV[28][3] = 1508\n",
"WCnComV[28][4] = 1708\n",
"WCnComV[28][5] = 1848\n",
"WCnComV[28][6] = 2427\n",
"WCnComV[28][7] = 3484\n",
"WCnComV[28][8] = 3553\n",
"WCnComV[28][9] = 3808\n",
"WCnComV[28][10] = 4004\n",
"WCnComV[28][11] = 4065\n",
"WCnComV[28][12] = 4815\n",
"WCnComV[28][13] = 5124\n",
"WCnComV[28][14] = 5410\n",
"WCnComV[28][15] = 6475\n",
"WCnComV[28][16] = 7137\n",
"WCnComV[28][17] = 9252\n",
"WCnComV[28][18] = 9398\n",
"WCnComV[28][19] = 9577\n",
"WCnComV[29].Len() = 20\n",
"WCnComV[29][0] = 5\n",
"WCnComV[29][1] = 414\n",
"WCnComV[29][2] = 1044\n",
"WCnComV[29][3] = 1239\n",
"WCnComV[29][4] = 1563\n",
"WCnComV[29][5] = 1588\n",
"WCnComV[29][6] = 2283\n",
"WCnComV[29][7] = 4704\n",
"WCnComV[29][8] = 4732\n",
"WCnComV[29][9] = 4790\n",
"WCnComV[29][10] = 5201\n",
"WCnComV[29][11] = 5671\n",
"WCnComV[29][12] = 5842\n",
"WCnComV[29][13] = 6176\n",
"WCnComV[29][14] = 6239\n",
"WCnComV[29][15] = 6438\n",
"WCnComV[29][16] = 7564\n",
"WCnComV[29][17] = 8006\n",
"WCnComV[29][18] = 8880\n",
"WCnComV[29][19] = 9678\n",
"WCnComV[30].Len() = 19\n",
"WCnComV[30][0] = 101\n",
"WCnComV[30][1] = 140\n",
"WCnComV[30][2] = 464\n",
"WCnComV[30][3] = 992\n",
"WCnComV[30][4] = 1182\n",
"WCnComV[30][5] = 2546\n",
"WCnComV[30][6] = 3270\n",
"WCnComV[30][7] = 3504\n",
"WCnComV[30][8] = 4466\n",
"WCnComV[30][9] = 5847\n",
"WCnComV[30][10] = 6005\n",
"WCnComV[30][11] = 6155\n",
"WCnComV[30][12] = 6290\n",
"WCnComV[30][13] = 6868\n",
"WCnComV[30][14] = 7570\n",
"WCnComV[30][15] = 7637\n",
"WCnComV[30][16] = 8177\n",
"WCnComV[30][17] = 9673\n",
"WCnComV[30][18] = 9918\n",
"WCnComV[31].Len() = 18\n",
"WCnComV[31][0] = 342\n",
"WCnComV[31][1] = 919\n",
"WCnComV[31][2] = 3435\n",
"WCnComV[31][3] = 3911\n",
"WCnComV[31][4] = 4356\n",
"WCnComV[31][5] = 4616\n",
"WCnComV[31][6] = 4928\n",
"WCnComV[31][7] = 6113\n",
"WCnComV[31][8] = 6118\n",
"WCnComV[31][9] = 6222\n",
"WCnComV[31][10] = 6518\n",
"WCnComV[31][11] = 6823\n",
"WCnComV[31][12] = 7250\n",
"WCnComV[31][13] = 7939\n",
"WCnComV[31][14] = 7969\n",
"WCnComV[31][15] = 8084\n",
"WCnComV[31][16] = 8730\n",
"WCnComV[31][17] = 9123\n",
"WCnComV[32].Len() = 18\n",
"WCnComV[32][0] = 67\n",
"WCnComV[32][1] = 315\n",
"WCnComV[32][2] = 504\n",
"WCnComV[32][3] = 1026\n",
"WCnComV[32][4] = 1251\n",
"WCnComV[32][5] = 1706\n",
"WCnComV[32][6] = 1718\n",
"WCnComV[32][7] = 1847\n",
"WCnComV[32][8] = 2320\n",
"WCnComV[32][9] = 2769\n",
"WCnComV[32][10] = 3995\n",
"WCnComV[32][11] = 4143\n",
"WCnComV[32][12] = 6241\n",
"WCnComV[32][13] = 6352\n",
"WCnComV[32][14] = 7874\n",
"WCnComV[32][15] = 9277\n",
"WCnComV[32][16] = 9580\n",
"WCnComV[32][17] = 9729\n",
"WCnComV[33].Len() = 18\n",
"WCnComV[33][0] = 32\n",
"WCnComV[33][1] = 325\n",
"WCnComV[33][2] = 346\n",
"WCnComV[33][3] = 993\n",
"WCnComV[33][4] = 1192\n",
"WCnComV[33][5] = 1939\n",
"WCnComV[33][6] = 2707\n",
"WCnComV[33][7] = 3303\n",
"WCnComV[33][8] = 3614\n",
"WCnComV[33][9] = 4595\n",
"WCnComV[33][10] = 4600\n",
"WCnComV[33][11] = 5000\n",
"WCnComV[33][12] = 5097\n",
"WCnComV[33][13] = 5605\n",
"WCnComV[33][14] = 6466\n",
"WCnComV[33][15] = 7482\n",
"WCnComV[33][16] = 7864\n",
"WCnComV[33][17] = 8604\n",
"WCnComV[34].Len() = 18\n",
"WCnComV[34][0] = 8\n",
"WCnComV[34][1] = 318\n",
"WCnComV[34][2] = 733\n",
"WCnComV[34][3] = 1074\n",
"WCnComV[34][4] = 1146\n",
"WCnComV[34][5] = 1661\n",
"WCnComV[34][6] = 1970\n",
"WCnComV[34][7] = 3173\n",
"WCnComV[34][8] = 5282\n",
"WCnComV[34][9] = 5511\n",
"WCnComV[34][10] = 5678\n",
"WCnComV[34][11] = 6527\n",
"WCnComV[34][12] = 7395\n",
"WCnComV[34][13] = 7509\n",
"WCnComV[34][14] = 7745\n",
"WCnComV[34][15] = 8030\n",
"WCnComV[34][16] = 8049\n",
"WCnComV[34][17] = 8236\n",
"WCnComV[35].Len() = 17\n",
"WCnComV[35][0] = 1830\n",
"WCnComV[35][1] = 2242\n",
"WCnComV[35][2] = 2385\n",
"WCnComV[35][3] = 2900\n",
"WCnComV[35][4] = 3239\n",
"WCnComV[35][5] = 4108\n",
"WCnComV[35][6] = 4961\n",
"WCnComV[35][7] = 5912\n",
"WCnComV[35][8] = 7452\n",
"WCnComV[35][9] = 7619\n",
"WCnComV[35][10] = 8100\n",
"WCnComV[35][11] = 8200\n",
"WCnComV[35][12] = 8282\n",
"WCnComV[35][13] = 8320\n",
"WCnComV[35][14] = 8563\n",
"WCnComV[35][15] = 9527\n",
"WCnComV[35][16] = 9833\n",
"WCnComV[36].Len() = 17\n",
"WCnComV[36][0] = 269\n",
"WCnComV[36][1] = 371\n",
"WCnComV[36][2] = 823\n",
"WCnComV[36][3] = 895\n",
"WCnComV[36][4] = 1108\n",
"WCnComV[36][5] = 1822\n",
"WCnComV[36][6] = 2960\n",
"WCnComV[36][7] = 3469\n",
"WCnComV[36][8] = 4455\n",
"WCnComV[36][9] = 4679\n",
"WCnComV[36][10] = 4901\n",
"WCnComV[36][11] = 5451\n",
"WCnComV[36][12] = 5608\n",
"WCnComV[36][13] = 6152\n",
"WCnComV[36][14] = 8283\n",
"WCnComV[36][15] = 9878\n",
"WCnComV[36][16] = 9946\n",
"WCnComV[37].Len() = 17\n",
"WCnComV[37][0] = 196\n",
"WCnComV[37][1] = 285\n",
"WCnComV[37][2] = 1599\n",
"WCnComV[37][3] = 2942\n",
"WCnComV[37][4] = 3709\n",
"WCnComV[37][5] = 3923\n",
"WCnComV[37][6] = 5565\n",
"WCnComV[37][7] = 6089\n",
"WCnComV[37][8] = 6423\n",
"WCnComV[37][9] = 7882\n",
"WCnComV[37][10] = 9033\n",
"WCnComV[37][11] = 9195\n",
"WCnComV[37][12] = 9612\n",
"WCnComV[37][13] = 9644\n",
"WCnComV[37][14] = 9660\n",
"WCnComV[37][15] = 9924\n",
"WCnComV[37][16] = 9958\n",
"WCnComV[38].Len() = 17\n",
"WCnComV[38][0] = 173\n",
"WCnComV[38][1] = 654\n",
"WCnComV[38][2] = 955\n",
"WCnComV[38][3] = 1614\n",
"WCnComV[38][4] = 1711\n",
"WCnComV[38][5] = 2026\n",
"WCnComV[38][6] = 2539\n",
"WCnComV[38][7] = 2832\n",
"WCnComV[38][8] = 3101\n",
"WCnComV[38][9] = 5662\n",
"WCnComV[38][10] = 6915\n",
"WCnComV[38][11] = 7298\n",
"WCnComV[38][12] = 8474\n",
"WCnComV[38][13] = 9034\n",
"WCnComV[38][14] = 9489\n",
"WCnComV[38][15] = 9855\n",
"WCnComV[38][16] = 9987\n",
"WCnComV[39].Len() = 17\n",
"WCnComV[39][0] = 28\n",
"WCnComV[39][1] = 1304\n",
"WCnComV[39][2] = 1372\n",
"WCnComV[39][3] = 1477\n",
"WCnComV[39][4] = 2861\n",
"WCnComV[39][5] = 3178\n",
"WCnComV[39][6] = 4040\n",
"WCnComV[39][7] = 4158\n",
"WCnComV[39][8] = 4734\n",
"WCnComV[39][9] = 5428\n",
"WCnComV[39][10] = 5877\n",
"WCnComV[39][11] = 6053\n",
"WCnComV[39][12] = 8203\n",
"WCnComV[39][13] = 8440\n",
"WCnComV[39][14] = 8479\n",
"WCnComV[39][15] = 9467\n",
"WCnComV[39][16] = 9731\n",
"WCnComV[40].Len() = 16\n",
"WCnComV[40][0] = 703\n",
"WCnComV[40][1] = 2131\n",
"WCnComV[40][2] = 3396\n",
"WCnComV[40][3] = 3548\n",
"WCnComV[40][4] = 3758\n",
"WCnComV[40][5] = 3983\n",
"WCnComV[40][6] = 4024\n",
"WCnComV[40][7] = 5769\n",
"WCnComV[40][8] = 5794\n",
"WCnComV[40][9] = 6025\n",
"WCnComV[40][10] = 6826\n",
"WCnComV[40][11] = 6919\n",
"WCnComV[40][12] = 7703\n",
"WCnComV[40][13] = 8182\n",
"WCnComV[40][14] = 8645\n",
"WCnComV[40][15] = 9180\n",
"WCnComV[41].Len() = 16\n",
"WCnComV[41][0] = 676\n",
"WCnComV[41][1] = 1196\n",
"WCnComV[41][2] = 2426\n",
"WCnComV[41][3] = 2948\n",
"WCnComV[41][4] = 3367\n",
"WCnComV[41][5] = 5248\n",
"WCnComV[41][6] = 5474\n",
"WCnComV[41][7] = 5501\n",
"WCnComV[41][8] = 5525\n",
"WCnComV[41][9] = 6090\n",
"WCnComV[41][10] = 6128\n",
"WCnComV[41][11] = 6246\n",
"WCnComV[41][12] = 6284\n",
"WCnComV[41][13] = 6321\n",
"WCnComV[41][14] = 7687\n",
"WCnComV[41][15] = 9323\n",
"WCnComV[42].Len() = 16\n",
"WCnComV[42][0] = 583\n",
"WCnComV[42][1] = 1329\n",
"WCnComV[42][2] = 2118\n",
"WCnComV[42][3] = 3041\n",
"WCnComV[42][4] = 3688\n",
"WCnComV[42][5] = 3878\n",
"WCnComV[42][6] = 3979\n",
"WCnComV[42][7] = 4388\n",
"WCnComV[42][8] = 6288\n",
"WCnComV[42][9] = 6880\n",
"WCnComV[42][10] = 7959\n",
"WCnComV[42][11] = 8365\n",
"WCnComV[42][12] = 8888\n",
"WCnComV[42][13] = 9605\n",
"WCnComV[42][14] = 9609\n",
"WCnComV[42][15] = 9815\n",
"WCnComV[43].Len() = 16\n",
"WCnComV[43][0] = 409\n",
"WCnComV[43][1] = 410\n",
"WCnComV[43][2] = 627\n",
"WCnComV[43][3] = 1913\n",
"WCnComV[43][4] = 3898\n",
"WCnComV[43][5] = 3953\n",
"WCnComV[43][6] = 4190\n",
"WCnComV[43][7] = 4232\n",
"WCnComV[43][8] = 6359\n",
"WCnComV[43][9] = 6934\n",
"WCnComV[43][10] = 7888\n",
"WCnComV[43][11] = 9141\n",
"WCnComV[43][12] = 9349\n",
"WCnComV[43][13] = 9555\n",
"WCnComV[43][14] = 9828\n",
"WCnComV[43][15] = 9969\n",
"WCnComV[44].Len() = 16\n",
"WCnComV[44][0] = 290\n",
"WCnComV[44][1] = 1330\n",
"WCnComV[44][2] = 1558\n",
"WCnComV[44][3] = 3730\n",
"WCnComV[44][4] = 4721\n",
"WCnComV[44][5] = 4797\n",
"WCnComV[44][6] = 5429\n",
"WCnComV[44][7] = 6311\n",
"WCnComV[44][8] = 6513\n",
"WCnComV[44][9] = 6957\n",
"WCnComV[44][10] = 7024\n",
"WCnComV[44][11] = 7176\n",
"WCnComV[44][12] = 8265\n",
"WCnComV[44][13] = 8273\n",
"WCnComV[44][14] = 8383\n",
"WCnComV[44][15] = 9919\n",
"WCnComV[45].Len() = 16\n",
"WCnComV[45][0] = 110\n",
"WCnComV[45][1] = 860\n",
"WCnComV[45][2] = 1035\n",
"WCnComV[45][3] = 1597\n",
"WCnComV[45][4] = 3495\n",
"WCnComV[45][5] = 3725\n",
"WCnComV[45][6] = 3882\n",
"WCnComV[45][7] = 3920\n",
"WCnComV[45][8] = 3996\n",
"WCnComV[45][9] = 4804\n",
"WCnComV[45][10] = 5909\n",
"WCnComV[45][11] = 6899\n",
"WCnComV[45][12] = 7421\n",
"WCnComV[45][13] = 7826\n",
"WCnComV[45][14] = 8082\n",
"WCnComV[45][15] = 9010\n",
"WCnComV[46].Len() = 15\n",
"WCnComV[46][0] = 1946\n",
"WCnComV[46][1] = 3071\n",
"WCnComV[46][2] = 3195\n",
"WCnComV[46][3] = 3913\n",
"WCnComV[46][4] = 4049\n",
"WCnComV[46][5] = 5057\n",
"WCnComV[46][6] = 6199\n",
"WCnComV[46][7] = 6483\n",
"WCnComV[46][8] = 6909\n",
"WCnComV[46][9] = 6963\n",
"WCnComV[46][10] = 7096\n",
"WCnComV[46][11] = 8768\n",
"WCnComV[46][12] = 9279\n",
"WCnComV[46][13] = 9280\n",
"WCnComV[46][14] = 9383\n",
"WCnComV[47].Len() = 15\n",
"WCnComV[47][0] = 812\n",
"WCnComV[47][1] = 1875\n",
"WCnComV[47][2] = 1933\n",
"WCnComV[47][3] = 2254\n",
"WCnComV[47][4] = 2775\n",
"WCnComV[47][5] = 3278\n",
"WCnComV[47][6] = 4257\n",
"WCnComV[47][7] = 4639\n",
"WCnComV[47][8] = 5239\n",
"WCnComV[47][9] = 7087\n",
"WCnComV[47][10] = 7218\n",
"WCnComV[47][11] = 7546\n",
"WCnComV[47][12] = 7788\n",
"WCnComV[47][13] = 7954\n",
"WCnComV[47][14] = 8747\n",
"WCnComV[48].Len() = 15\n",
"WCnComV[48][0] = 644\n",
"WCnComV[48][1] = 1602\n",
"WCnComV[48][2] = 3015\n",
"WCnComV[48][3] = 5784\n",
"WCnComV[48][4] = 6405\n",
"WCnComV[48][5] = 6596\n",
"WCnComV[48][6] = 6616\n",
"WCnComV[48][7] = 6721\n",
"WCnComV[48][8] = 7568\n",
"WCnComV[48][9] = 7815\n",
"WCnComV[48][10] = 8055\n",
"WCnComV[48][11] = 8137\n",
"WCnComV[48][12] = 8288\n",
"WCnComV[48][13] = 8534\n",
"WCnComV[48][14] = 9479\n",
"WCnComV[49].Len() = 15\n",
"WCnComV[49][0] = 61\n",
"WCnComV[49][1] = 700\n",
"WCnComV[49][2] = 1011\n",
"WCnComV[49][3] = 1769\n",
"WCnComV[49][4] = 1798\n",
"WCnComV[49][5] = 2425\n",
"WCnComV[49][6] = 2824\n",
"WCnComV[49][7] = 2876\n",
"WCnComV[49][8] = 2908\n",
"WCnComV[49][9] = 3390\n",
"WCnComV[49][10] = 4966\n",
"WCnComV[49][11] = 5823\n",
"WCnComV[49][12] = 5829\n",
"WCnComV[49][13] = 6977\n",
"WCnComV[49][14] = 8917\n",
"WCnComV[50].Len() = 15\n",
"WCnComV[50][0] = 17\n",
"WCnComV[50][1] = 154\n",
"WCnComV[50][2] = 1169\n",
"WCnComV[50][3] = 1183\n",
"WCnComV[50][4] = 1440\n",
"WCnComV[50][5] = 1710\n",
"WCnComV[50][6] = 1938\n",
"WCnComV[50][7] = 2915\n",
"WCnComV[50][8] = 3493\n",
"WCnComV[50][9] = 3919\n",
"WCnComV[50][10] = 4196\n",
"WCnComV[50][11] = 4592\n",
"WCnComV[50][12] = 5942\n",
"WCnComV[50][13] = 6017\n",
"WCnComV[50][14] = 8919\n",
"WCnComV[51].Len() = 14\n",
"WCnComV[51][0] = 1518\n",
"WCnComV[51][1] = 1920\n",
"WCnComV[51][2] = 2333\n",
"WCnComV[51][3] = 2349\n",
"WCnComV[51][4] = 2481\n",
"WCnComV[51][5] = 4097\n",
"WCnComV[51][6] = 4299\n",
"WCnComV[51][7] = 4484\n",
"WCnComV[51][8] = 4911\n",
"WCnComV[51][9] = 5023\n",
"WCnComV[51][10] = 5517\n",
"WCnComV[51][11] = 6115\n",
"WCnComV[51][12] = 6829\n",
"WCnComV[51][13] = 8511\n",
"WCnComV[52].Len() = 14\n",
"WCnComV[52][0] = 1065\n",
"WCnComV[52][1] = 1902\n",
"WCnComV[52][2] = 2639\n",
"WCnComV[52][3] = 3863\n",
"WCnComV[52][4] = 4735\n",
"WCnComV[52][5] = 4897\n",
"WCnComV[52][6] = 6022\n",
"WCnComV[52][7] = 6669\n",
"WCnComV[52][8] = 6898\n",
"WCnComV[52][9] = 7741\n",
"WCnComV[52][10] = 8304\n",
"WCnComV[52][11] = 8421\n",
"WCnComV[52][12] = 8504\n",
"WCnComV[52][13] = 9735\n",
"WCnComV[53].Len() = 14\n",
"WCnComV[53][0] = 953\n",
"WCnComV[53][1] = 1113\n",
"WCnComV[53][2] = 1740\n",
"WCnComV[53][3] = 1797\n",
"WCnComV[53][4] = 4266\n",
"WCnComV[53][5] = 4744\n",
"WCnComV[53][6] = 6274\n",
"WCnComV[53][7] = 6342\n",
"WCnComV[53][8] = 8499\n",
"WCnComV[53][9] = 9042\n",
"WCnComV[53][10] = 9440\n",
"WCnComV[53][11] = 9627\n",
"WCnComV[53][12] = 9781\n",
"WCnComV[53][13] = 9834\n",
"WCnComV[54].Len() = 14\n",
"WCnComV[54][0] = 794\n",
"WCnComV[54][1] = 1386\n",
"WCnComV[54][2] = 1486\n",
"WCnComV[54][3] = 1548\n",
"WCnComV[54][4] = 1953\n",
"WCnComV[54][5] = 2324\n",
"WCnComV[54][6] = 3779\n",
"WCnComV[54][7] = 4184\n",
"WCnComV[54][8] = 5492\n",
"WCnComV[54][9] = 5586\n",
"WCnComV[54][10] = 6908\n",
"WCnComV[54][11] = 7399\n",
"WCnComV[54][12] = 8967\n",
"WCnComV[54][13] = 9239\n",
"WCnComV[55].Len() = 14\n",
"WCnComV[55][0] = 407\n",
"WCnComV[55][1] = 1987\n",
"WCnComV[55][2] = 2456\n",
"WCnComV[55][3] = 2477\n",
"WCnComV[55][4] = 2920\n",
"WCnComV[55][5] = 2944\n",
"WCnComV[55][6] = 5356\n",
"WCnComV[55][7] = 6296\n",
"WCnComV[55][8] = 6889\n",
"WCnComV[55][9] = 6905\n",
"WCnComV[55][10] = 7932\n",
"WCnComV[55][11] = 9223\n",
"WCnComV[55][12] = 9704\n",
"WCnComV[55][13] = 9893\n",
"WCnComV[56].Len() = 13\n",
"WCnComV[56][0] = 2061\n",
"WCnComV[56][1] = 2737\n",
"WCnComV[56][2] = 3623\n",
"WCnComV[56][3] = 3771\n",
"WCnComV[56][4] = 3982\n",
"WCnComV[56][5] = 5069\n",
"WCnComV[56][6] = 5151\n",
"WCnComV[56][7] = 5396\n",
"WCnComV[56][8] = 6132\n",
"WCnComV[56][9] = 6766\n",
"WCnComV[56][10] = 8944\n",
"WCnComV[56][11] = 9457\n",
"WCnComV[56][12] = 9981\n",
"WCnComV[57].Len() = 13\n",
"WCnComV[57][0] = 1226\n",
"WCnComV[57][1] = 1841\n",
"WCnComV[57][2] = 2392\n",
"WCnComV[57][3] = 3476\n",
"WCnComV[57][4] = 4661\n",
"WCnComV[57][5] = 4880\n",
"WCnComV[57][6] = 5159\n",
"WCnComV[57][7] = 6663\n",
"WCnComV[57][8] = 6998\n",
"WCnComV[57][9] = 8526\n",
"WCnComV[57][10] = 8654\n",
"WCnComV[57][11] = 9182\n",
"WCnComV[57][12] = 9187\n",
"WCnComV[58].Len() = 13\n",
"WCnComV[58][0] = 674\n",
"WCnComV[58][1] = 917\n",
"WCnComV[58][2] = 1020\n",
"WCnComV[58][3] = 1430\n",
"WCnComV[58][4] = 1833\n",
"WCnComV[58][5] = 2097\n",
"WCnComV[58][6] = 2732\n",
"WCnComV[58][7] = 3087\n",
"WCnComV[58][8] = 4336\n",
"WCnComV[58][9] = 5543\n",
"WCnComV[58][10] = 6058\n",
"WCnComV[58][11] = 6121\n",
"WCnComV[58][12] = 8187\n",
"WCnComV[59].Len() = 13\n",
"WCnComV[59][0] = 669\n",
"WCnComV[59][1] = 985\n",
"WCnComV[59][2] = 1788\n",
"WCnComV[59][3] = 2025\n",
"WCnComV[59][4] = 2173\n",
"WCnComV[59][5] = 2576\n",
"WCnComV[59][6] = 4808\n",
"WCnComV[59][7] = 5950\n",
"WCnComV[59][8] = 6364\n",
"WCnComV[59][9] = 6771\n",
"WCnComV[59][10] = 7666\n",
"WCnComV[59][11] = 8051\n",
"WCnComV[59][12] = 9409\n",
"WCnComV[60].Len() = 13\n",
"WCnComV[60][0] = 448\n",
"WCnComV[60][1] = 483\n",
"WCnComV[60][2] = 984\n",
"WCnComV[60][3] = 1674\n",
"WCnComV[60][4] = 2657\n",
"WCnComV[60][5] = 3006\n",
"WCnComV[60][6] = 3132\n",
"WCnComV[60][7] = 5922\n",
"WCnComV[60][8] = 6380\n",
"WCnComV[60][9] = 6991\n",
"WCnComV[60][10] = 7194\n",
"WCnComV[60][11] = 8613\n",
"WCnComV[60][12] = 9768\n",
"WCnComV[61].Len() = 13\n",
"WCnComV[61][0] = 430\n",
"WCnComV[61][1] = 704\n",
"WCnComV[61][2] = 1320\n",
"WCnComV[61][3] = 2087\n",
"WCnComV[61][4] = 3154\n",
"WCnComV[61][5] = 5711\n",
"WCnComV[61][6] = 6329\n",
"WCnComV[61][7] = 6953\n",
"WCnComV[61][8] = 7047\n",
"WCnComV[61][9] = 7243\n",
"WCnComV[61][10] = 7504\n",
"WCnComV[61][11] = 8153\n",
"WCnComV[61][12] = 8366\n",
"WCnComV[62].Len() = 13\n",
"WCnComV[62][0] = 356\n",
"WCnComV[62][1] = 1917\n",
"WCnComV[62][2] = 2360\n",
"WCnComV[62][3] = 3468\n",
"WCnComV[62][4] = 5026\n",
"WCnComV[62][5] = 5203\n",
"WCnComV[62][6] = 5688\n",
"WCnComV[62][7] = 6018\n",
"WCnComV[62][8] = 6587\n",
"WCnComV[62][9] = 7672\n",
"WCnComV[62][10] = 8080\n",
"WCnComV[62][11] = 8362\n",
"WCnComV[62][12] = 8724\n",
"WCnComV[63].Len() = 13\n",
"WCnComV[63][0] = 328\n",
"WCnComV[63][1] = 707\n",
"WCnComV[63][2] = 934\n",
"WCnComV[63][3] = 1145\n",
"WCnComV[63][4] = 2010\n",
"WCnComV[63][5] = 2994\n",
"WCnComV[63][6] = 5007\n",
"WCnComV[63][7] = 6543\n",
"WCnComV[63][8] = 6572\n",
"WCnComV[63][9] = 7648\n",
"WCnComV[63][10] = 8958\n",
"WCnComV[63][11] = 9032\n",
"WCnComV[63][12] = 9601\n",
"WCnComV[64].Len() = 13\n",
"WCnComV[64][0] = 50\n",
"WCnComV[64][1] = 1236\n",
"WCnComV[64][2] = 2434\n",
"WCnComV[64][3] = 3131\n",
"WCnComV[64][4] = 4085\n",
"WCnComV[64][5] = 4286\n",
"WCnComV[64][6] = 4315\n",
"WCnComV[64][7] = 4605\n",
"WCnComV[64][8] = 5130\n",
"WCnComV[64][9] = 5876\n",
"WCnComV[64][10] = 6563\n",
"WCnComV[64][11] = 8635\n",
"WCnComV[64][12] = 9428\n",
"WCnComV[65].Len() = 12\n",
"WCnComV[65][0] = 2563\n",
"WCnComV[65][1] = 2782\n",
"WCnComV[65][2] = 2978\n",
"WCnComV[65][3] = 3781\n",
"WCnComV[65][4] = 4589\n",
"WCnComV[65][5] = 5751\n",
"WCnComV[65][6] = 5896\n",
"WCnComV[65][7] = 6795\n",
"WCnComV[65][8] = 7085\n",
"WCnComV[65][9] = 7414\n",
"WCnComV[65][10] = 7966\n",
"WCnComV[65][11] = 8201\n",
"WCnComV[66].Len() = 12\n",
"WCnComV[66][0] = 1156\n",
"WCnComV[66][1] = 2171\n",
"WCnComV[66][2] = 2213\n",
"WCnComV[66][3] = 2390\n",
"WCnComV[66][4] = 3804\n",
"WCnComV[66][5] = 4278\n",
"WCnComV[66][6] = 4340\n",
"WCnComV[66][7] = 5510\n",
"WCnComV[66][8] = 6877\n",
"WCnComV[66][9] = 8300\n",
"WCnComV[66][10] = 9330\n",
"WCnComV[66][11] = 9345\n",
"WCnComV[67].Len() = 12\n",
"WCnComV[67][0] = 783\n",
"WCnComV[67][1] = 965\n",
"WCnComV[67][2] = 1055\n",
"WCnComV[67][3] = 2774\n",
"WCnComV[67][4] = 4215\n",
"WCnComV[67][5] = 4312\n",
"WCnComV[67][6] = 4643\n",
"WCnComV[67][7] = 5542\n",
"WCnComV[67][8] = 6695\n",
"WCnComV[67][9] = 7760\n",
"WCnComV[67][10] = 8211\n",
"WCnComV[67][11] = 9485\n",
"WCnComV[68].Len() = 12\n",
"WCnComV[68][0] = 566\n",
"WCnComV[68][1] = 2566\n",
"WCnComV[68][2] = 4229\n",
"WCnComV[68][3] = 4625\n",
"WCnComV[68][4] = 5747\n",
"WCnComV[68][5] = 6057\n",
"WCnComV[68][6] = 7861\n",
"WCnComV[68][7] = 7995\n",
"WCnComV[68][8] = 8054\n",
"WCnComV[68][9] = 8435\n",
"WCnComV[68][10] = 9311\n",
"WCnComV[68][11] = 9876\n",
"WCnComV[69].Len() = 12\n",
"WCnComV[69][0] = 339\n",
"WCnComV[69][1] = 1962\n",
"WCnComV[69][2] = 3145\n",
"WCnComV[69][3] = 3312\n",
"WCnComV[69][4] = 4621\n",
"WCnComV[69][5] = 5625\n",
"WCnComV[69][6] = 5849\n",
"WCnComV[69][7] = 6301\n",
"WCnComV[69][8] = 6489\n",
"WCnComV[69][9] = 6564\n",
"WCnComV[69][10] = 7497\n",
"WCnComV[69][11] = 9287\n",
"WCnComV[70].Len() = 12\n",
"WCnComV[70][0] = 198\n",
"WCnComV[70][1] = 1857\n",
"WCnComV[70][2] = 2073\n",
"WCnComV[70][3] = 3004\n",
"WCnComV[70][4] = 5354\n",
"WCnComV[70][5] = 5512\n",
"WCnComV[70][6] = 6407\n",
"WCnComV[70][7] = 6659\n",
"WCnComV[70][8] = 6832\n",
"WCnComV[70][9] = 7709\n",
"WCnComV[70][10] = 8970\n",
"WCnComV[70][11] = 9482\n",
"WCnComV[71].Len() = 12\n",
"WCnComV[71][0] = 113\n",
"WCnComV[71][1] = 2417\n",
"WCnComV[71][2] = 3579\n",
"WCnComV[71][3] = 3665\n",
"WCnComV[71][4] = 3881\n",
"WCnComV[71][5] = 4153\n",
"WCnComV[71][6] = 5660\n",
"WCnComV[71][7] = 6384\n",
"WCnComV[71][8] = 7498\n",
"WCnComV[71][9] = 8328\n",
"WCnComV[71][10] = 8767\n",
"WCnComV[71][11] = 8969\n",
"WCnComV[72].Len() = 12\n",
"WCnComV[72][0] = 47\n",
"WCnComV[72][1] = 265\n",
"WCnComV[72][2] = 720\n",
"WCnComV[72][3] = 760\n",
"WCnComV[72][4] = 1190\n",
"WCnComV[72][5] = 2259\n",
"WCnComV[72][6] = 2735\n",
"WCnComV[72][7] = 2847\n",
"WCnComV[72][8] = 3800\n",
"WCnComV[72][9] = 5330\n",
"WCnComV[72][10] = 6328\n",
"WCnComV[72][11] = 7938\n",
"WCnComV[73].Len() = 11\n",
"WCnComV[73][0] = 2625\n",
"WCnComV[73][1] = 3439\n",
"WCnComV[73][2] = 4485\n",
"WCnComV[73][3] = 4837\n",
"WCnComV[73][4] = 4935\n",
"WCnComV[73][5] = 4946\n",
"WCnComV[73][6] = 5314\n",
"WCnComV[73][7] = 6369\n",
"WCnComV[73][8] = 7052\n",
"WCnComV[73][9] = 7654\n",
"WCnComV[73][10] = 9529\n",
"WCnComV[74].Len() = 11\n",
"WCnComV[74][0] = 1636\n",
"WCnComV[74][1] = 1789\n",
"WCnComV[74][2] = 1934\n",
"WCnComV[74][3] = 6271\n",
"WCnComV[74][4] = 7026\n",
"WCnComV[74][5] = 7254\n",
"WCnComV[74][6] = 7317\n",
"WCnComV[74][7] = 7855\n",
"WCnComV[74][8] = 8035\n",
"WCnComV[74][9] = 9209\n",
"WCnComV[74][10] = 9539\n",
"WCnComV[75].Len() = 11\n",
"WCnComV[75][0] = 689\n",
"WCnComV[75][1] = 1267\n",
"WCnComV[75][2] = 2681\n",
"WCnComV[75][3] = 3653\n",
"WCnComV[75][4] = 4848\n",
"WCnComV[75][5] = 5602\n",
"WCnComV[75][6] = 6443\n",
"WCnComV[75][7] = 7649\n",
"WCnComV[75][8] = 7897\n",
"WCnComV[75][9] = 8818\n",
"WCnComV[75][10] = 9379\n",
"WCnComV[76].Len() = 11\n",
"WCnComV[76][0] = 673\n",
"WCnComV[76][1] = 1154\n",
"WCnComV[76][2] = 4146\n",
"WCnComV[76][3] = 4202\n",
"WCnComV[76][4] = 4324\n",
"WCnComV[76][5] = 5687\n",
"WCnComV[76][6] = 6349\n",
"WCnComV[76][7] = 6954\n",
"WCnComV[76][8] = 7227\n",
"WCnComV[76][9] = 7588\n",
"WCnComV[76][10] = 9553\n",
"WCnComV[77].Len() = 11\n",
"WCnComV[77][0] = 586\n",
"WCnComV[77][1] = 1289\n",
"WCnComV[77][2] = 1335\n",
"WCnComV[77][3] = 1879\n",
"WCnComV[77][4] = 2611\n",
"WCnComV[77][5] = 3847\n",
"WCnComV[77][6] = 4859\n",
"WCnComV[77][7] = 6051\n",
"WCnComV[77][8] = 6061\n",
"WCnComV[77][9] = 6715\n",
"WCnComV[77][10] = 8545\n",
"WCnComV[78].Len() = 11\n",
"WCnComV[78][0] = 508\n",
"WCnComV[78][1] = 616\n",
"WCnComV[78][2] = 1416\n",
"WCnComV[78][3] = 1624\n",
"WCnComV[78][4] = 3140\n",
"WCnComV[78][5] = 4173\n",
"WCnComV[78][6] = 4415\n",
"WCnComV[78][7] = 4427\n",
"WCnComV[78][8] = 5548\n",
"WCnComV[78][9] = 6120\n",
"WCnComV[78][10] = 8683\n",
"WCnComV[79].Len() = 11\n",
"WCnComV[79][0] = 238\n",
"WCnComV[79][1] = 635\n",
"WCnComV[79][2] = 768\n",
"WCnComV[79][3] = 1948\n",
"WCnComV[79][4] = 2112\n",
"WCnComV[79][5] = 3227\n",
"WCnComV[79][6] = 3522\n",
"WCnComV[79][7] = 3597\n",
"WCnComV[79][8] = 6597\n",
"WCnComV[79][9] = 7992\n",
"WCnComV[79][10] = 9313\n",
"WCnComV[80].Len() = 11\n",
"WCnComV[80][0] = 137\n",
"WCnComV[80][1] = 731\n",
"WCnComV[80][2] = 1443\n",
"WCnComV[80][3] = 2883\n",
"WCnComV[80][4] = 2936\n",
"WCnComV[80][5] = 3412\n",
"WCnComV[80][6] = 3690\n",
"WCnComV[80][7] = 4418\n",
"WCnComV[80][8] = 4712\n",
"WCnComV[80][9] = 6075\n",
"WCnComV[80][10] = 8254\n",
"WCnComV[81].Len() = 11\n",
"WCnComV[81][0] = 60\n",
"WCnComV[81][1] = 493\n",
"WCnComV[81][2] = 863\n",
"WCnComV[81][3] = 961\n",
"WCnComV[81][4] = 1054\n",
"WCnComV[81][5] = 1407\n",
"WCnComV[81][6] = 2369\n",
"WCnComV[81][7] = 3134\n",
"WCnComV[81][8] = 7114\n",
"WCnComV[81][9] = 7273\n",
"WCnComV[81][10] = 7483\n",
"WCnComV[82].Len() = 11\n",
"WCnComV[82][0] = 38\n",
"WCnComV[82][1] = 661\n",
"WCnComV[82][2] = 1501\n",
"WCnComV[82][3] = 1507\n",
"WCnComV[82][4] = 3105\n",
"WCnComV[82][5] = 4699\n",
"WCnComV[82][6] = 6805\n",
"WCnComV[82][7] = 7591\n",
"WCnComV[82][8] = 7872\n",
"WCnComV[82][9] = 9427\n",
"WCnComV[82][10] = 9843\n",
"WCnComV[83].Len() = 10\n",
"WCnComV[83][0] = 5160\n",
"WCnComV[83][1] = 5822\n",
"WCnComV[83][2] = 5879\n",
"WCnComV[83][3] = 6566\n",
"WCnComV[83][4] = 6916\n",
"WCnComV[83][5] = 7164\n",
"WCnComV[83][6] = 8277\n",
"WCnComV[83][7] = 8445\n",
"WCnComV[83][8] = 8833\n",
"WCnComV[83][9] = 9610\n",
"WCnComV[84].Len() = 10\n",
"WCnComV[84][0] = 2525\n",
"WCnComV[84][1] = 3054\n",
"WCnComV[84][2] = 3478\n",
"WCnComV[84][3] = 4027\n",
"WCnComV[84][4] = 4772\n",
"WCnComV[84][5] = 5760\n",
"WCnComV[84][6] = 6643\n",
"WCnComV[84][7] = 6964\n",
"WCnComV[84][8] = 7245\n",
"WCnComV[84][9] = 8092\n",
"WCnComV[85].Len() = 10\n",
"WCnComV[85][0] = 2245\n",
"WCnComV[85][1] = 3354\n",
"WCnComV[85][2] = 3359\n",
"WCnComV[85][3] = 4847\n",
"WCnComV[85][4] = 6341\n",
"WCnComV[85][5] = 7469\n",
"WCnComV[85][6] = 8740\n",
"WCnComV[85][7] = 8916\n",
"WCnComV[85][8] = 9198\n",
"WCnComV[85][9] = 9692\n",
"WCnComV[86].Len() = 10\n",
"WCnComV[86][0] = 1514\n",
"WCnComV[86][1] = 1568\n",
"WCnComV[86][2] = 1681\n",
"WCnComV[86][3] = 1768\n",
"WCnComV[86][4] = 3183\n",
"WCnComV[86][5] = 4898\n",
"WCnComV[86][6] = 6002\n",
"WCnComV[86][7] = 6282\n",
"WCnComV[86][8] = 9058\n",
"WCnComV[86][9] = 9114\n",
"WCnComV[87].Len() = 10\n",
"WCnComV[87][0] = 595\n",
"WCnComV[87][1] = 628\n",
"WCnComV[87][2] = 931\n",
"WCnComV[87][3] = 1259\n",
"WCnComV[87][4] = 2928\n",
"WCnComV[87][5] = 4128\n",
"WCnComV[87][6] = 5395\n",
"WCnComV[87][7] = 5577\n",
"WCnComV[87][8] = 7748\n",
"WCnComV[87][9] = 8281\n",
"WCnComV[88].Len() = 10\n",
"WCnComV[88][0] = 591\n",
"WCnComV[88][1] = 5246\n",
"WCnComV[88][2] = 5856\n",
"WCnComV[88][3] = 6273\n",
"WCnComV[88][4] = 6493\n",
"WCnComV[88][5] = 6545\n",
"WCnComV[88][6] = 9304\n",
"WCnComV[88][7] = 9535\n",
"WCnComV[88][8] = 9792\n",
"WCnComV[88][9] = 9809\n",
"WCnComV[89].Len() = 10\n",
"WCnComV[89][0] = 506\n",
"WCnComV[89][1] = 559\n",
"WCnComV[89][2] = 834\n",
"WCnComV[89][3] = 911\n",
"WCnComV[89][4] = 1017\n",
"WCnComV[89][5] = 2133\n",
"WCnComV[89][6] = 4923\n",
"WCnComV[89][7] = 6338\n",
"WCnComV[89][8] = 9675\n",
"WCnComV[89][9] = 9898\n",
"WCnComV[90].Len() = 10\n",
"WCnComV[90][0] = 231\n",
"WCnComV[90][1] = 373\n",
"WCnComV[90][2] = 1693\n",
"WCnComV[90][3] = 1903\n",
"WCnComV[90][4] = 5078\n",
"WCnComV[90][5] = 6202\n",
"WCnComV[90][6] = 6327\n",
"WCnComV[90][7] = 6459\n",
"WCnComV[90][8] = 7931\n",
"WCnComV[90][9] = 8402\n",
"WCnComV[91].Len() = 10\n",
"WCnComV[91][0] = 199\n",
"WCnComV[91][1] = 780\n",
"WCnComV[91][2] = 2099\n",
"WCnComV[91][3] = 2691\n",
"WCnComV[91][4] = 3404\n",
"WCnComV[91][5] = 4104\n",
"WCnComV[91][6] = 4206\n",
"WCnComV[91][7] = 5115\n",
"WCnComV[91][8] = 9540\n",
"WCnComV[91][9] = 9758\n",
"WCnComV[92].Len() = 10\n",
"WCnComV[92][0] = 172\n",
"WCnComV[92][1] = 234\n",
"WCnComV[92][2] = 1043\n",
"WCnComV[92][3] = 1445\n",
"WCnComV[92][4] = 3600\n",
"WCnComV[92][5] = 4536\n",
"WCnComV[92][6] = 6107\n",
"WCnComV[92][7] = 7088\n",
"WCnComV[92][8] = 9546\n",
"WCnComV[92][9] = 9638\n",
"WCnComV[93].Len() = 10\n",
"WCnComV[93][0] = 156\n",
"WCnComV[93][1] = 756\n",
"WCnComV[93][2] = 3558\n",
"WCnComV[93][3] = 4007\n",
"WCnComV[93][4] = 5361\n",
"WCnComV[93][5] = 7002\n",
"WCnComV[93][6] = 8367\n",
"WCnComV[93][7] = 8716\n",
"WCnComV[93][8] = 9090\n",
"WCnComV[93][9] = 9096\n",
"WCnComV[94].Len() = 10\n",
"WCnComV[94][0] = 149\n",
"WCnComV[94][1] = 240\n",
"WCnComV[94][2] = 678\n",
"WCnComV[94][3] = 2922\n",
"WCnComV[94][4] = 4247\n",
"WCnComV[94][5] = 5084\n",
"WCnComV[94][6] = 7523\n",
"WCnComV[94][7] = 8107\n",
"WCnComV[94][8] = 8132\n",
"WCnComV[94][9] = 8924\n",
"WCnComV[95].Len() = 10\n",
"WCnComV[95][0] = 132\n",
"WCnComV[95][1] = 528\n",
"WCnComV[95][2] = 4551\n",
"WCnComV[95][3] = 5304\n",
"WCnComV[95][4] = 5746\n",
"WCnComV[95][5] = 5781\n",
"WCnComV[95][6] = 7018\n",
"WCnComV[95][7] = 7084\n",
"WCnComV[95][8] = 8430\n",
"WCnComV[95][9] = 9220\n",
"WCnComV[96].Len() = 10\n",
"WCnComV[96][0] = 114\n",
"WCnComV[96][1] = 986\n",
"WCnComV[96][2] = 1354\n",
"WCnComV[96][3] = 1983\n",
"WCnComV[96][4] = 2014\n",
"WCnComV[96][5] = 4159\n",
"WCnComV[96][6] = 5988\n",
"WCnComV[96][7] = 6774\n",
"WCnComV[96][8] = 6836\n",
"WCnComV[96][9] = 9797\n",
"WCnComV[97].Len() = 9\n",
"WCnComV[97][0] = 3461\n",
"WCnComV[97][1] = 4110\n",
"WCnComV[97][2] = 4756\n",
"WCnComV[97][3] = 5066\n",
"WCnComV[97][4] = 5469\n",
"WCnComV[97][5] = 8010\n",
"WCnComV[97][6] = 8781\n",
"WCnComV[97][7] = 9151\n",
"WCnComV[97][8] = 9908\n",
"WCnComV[98].Len() = 9\n",
"WCnComV[98][0] = 2757\n",
"WCnComV[98][1] = 3156\n",
"WCnComV[98][2] = 3543\n",
"WCnComV[98][3] = 3839\n",
"WCnComV[98][4] = 3852\n",
"WCnComV[98][5] = 4476\n",
"WCnComV[98][6] = 6156\n",
"WCnComV[98][7] = 6855\n",
"WCnComV[98][8] = 7997\n",
"WCnComV[99].Len() = 9\n",
"WCnComV[99][0] = 1929\n",
"WCnComV[99][1] = 4076\n",
"WCnComV[99][2] = 4178\n",
"WCnComV[99][3] = 6441\n",
"WCnComV[99][4] = 6591\n",
"WCnComV[99][5] = 6955\n",
"WCnComV[99][6] = 8033\n",
"WCnComV[99][7] = 8043\n",
"WCnComV[99][8] = 8340\n",
"WCnComV[100].Len() = 9\n",
"WCnComV[100][0] = 1821\n",
"WCnComV[100][1] = 1843\n",
"WCnComV[100][2] = 2152\n",
"WCnComV[100][3] = 2756\n",
"WCnComV[100][4] = 4298\n",
"WCnComV[100][5] = 4317\n",
"WCnComV[100][6] = 5833\n",
"WCnComV[100][7] = 7846\n",
"WCnComV[100][8] = 9761\n",
"WCnComV[101].Len() = 9\n",
"WCnComV[101][0] = 1746\n",
"WCnComV[101][1] = 2011\n",
"WCnComV[101][2] = 3446\n",
"WCnComV[101][3] = 5976\n",
"WCnComV[101][4] = 6218\n",
"WCnComV[101][5] = 6951\n",
"WCnComV[101][6] = 7061\n",
"WCnComV[101][7] = 8298\n",
"WCnComV[101][8] = 9011\n",
"WCnComV[102].Len() = 9\n",
"WCnComV[102][0] = 1551\n",
"WCnComV[102][1] = 5059\n",
"WCnComV[102][2] = 5838\n",
"WCnComV[102][3] = 7086\n",
"WCnComV[102][4] = 7613\n",
"WCnComV[102][5] = 8127\n",
"WCnComV[102][6] = 8841\n",
"WCnComV[102][7] = 9079\n",
"WCnComV[102][8] = 9342\n",
"WCnComV[103].Len() = 9\n",
"WCnComV[103][0] = 1420\n",
"WCnComV[103][1] = 2296\n",
"WCnComV[103][2] = 2463\n",
"WCnComV[103][3] = 3572\n",
"WCnComV[103][4] = 4537\n",
"WCnComV[103][5] = 5776\n",
"WCnComV[103][6] = 7139\n",
"WCnComV[103][7] = 9466\n",
"WCnComV[103][8] = 9864\n",
"WCnComV[104].Len() = 9\n",
"WCnComV[104][0] = 1309\n",
"WCnComV[104][1] = 2478\n",
"WCnComV[104][2] = 2605\n",
"WCnComV[104][3] = 3175\n",
"WCnComV[104][4] = 3901\n",
"WCnComV[104][5] = 5270\n",
"WCnComV[104][6] = 6922\n",
"WCnComV[104][7] = 7804\n",
"WCnComV[104][8] = 7866\n",
"WCnComV[105].Len() = 9\n",
"WCnComV[105][0] = 1083\n",
"WCnComV[105][1] = 2586\n",
"WCnComV[105][2] = 3287\n",
"WCnComV[105][3] = 3755\n",
"WCnComV[105][4] = 5202\n",
"WCnComV[105][5] = 5296\n",
"WCnComV[105][6] = 7251\n",
"WCnComV[105][7] = 7787\n",
"WCnComV[105][8] = 9149\n",
"WCnComV[106].Len() = 9\n",
"WCnComV[106][0] = 1002\n",
"WCnComV[106][1] = 1783\n",
"WCnComV[106][2] = 3659\n",
"WCnComV[106][3] = 5638\n",
"WCnComV[106][4] = 5659\n",
"WCnComV[106][5] = 5927\n",
"WCnComV[106][6] = 7064\n",
"WCnComV[106][7] = 7751\n",
"WCnComV[106][8] = 9382\n",
"WCnComV[107].Len() = 9\n",
"WCnComV[107][0] = 943\n",
"WCnComV[107][1] = 3170\n",
"WCnComV[107][2] = 4361\n",
"WCnComV[107][3] = 5197\n",
"WCnComV[107][4] = 7036\n",
"WCnComV[107][5] = 7387\n",
"WCnComV[107][6] = 8016\n",
"WCnComV[107][7] = 8800\n",
"WCnComV[107][8] = 9356\n",
"WCnComV[108].Len() = 9\n",
"WCnComV[108][0] = 929\n",
"WCnComV[108][1] = 1202\n",
"WCnComV[108][2] = 3447\n",
"WCnComV[108][3] = 4258\n",
"WCnComV[108][4] = 4405\n",
"WCnComV[108][5] = 4958\n",
"WCnComV[108][6] = 5738\n",
"WCnComV[108][7] = 7614\n",
"WCnComV[108][8] = 9944\n",
"WCnComV[109].Len() = 9\n",
"WCnComV[109][0] = 764\n",
"WCnComV[109][1] = 2191\n",
"WCnComV[109][2] = 3510\n",
"WCnComV[109][3] = 3524\n",
"WCnComV[109][4] = 4157\n",
"WCnComV[109][5] = 5370\n",
"WCnComV[109][6] = 6420\n",
"WCnComV[109][7] = 7857\n",
"WCnComV[109][8] = 8517\n",
"WCnComV[110].Len() = 9\n",
"WCnComV[110][0] = 677\n",
"WCnComV[110][1] = 2792\n",
"WCnComV[110][2] = 2882\n",
"WCnComV[110][3] = 4372\n",
"WCnComV[110][4] = 4969\n",
"WCnComV[110][5] = 5311\n",
"WCnComV[110][6] = 6432\n",
"WCnComV[110][7] = 8492\n",
"WCnComV[110][8] = 8825\n",
"WCnComV[111].Len() = 9\n",
"WCnComV[111][0] = 478\n",
"WCnComV[111][1] = 2046\n",
"WCnComV[111][2] = 2249\n",
"WCnComV[111][3] = 3873\n",
"WCnComV[111][4] = 4676\n",
"WCnComV[111][5] = 5983\n",
"WCnComV[111][6] = 6498\n",
"WCnComV[111][7] = 8782\n",
"WCnComV[111][8] = 8797\n",
"WCnComV[112].Len() = 9\n",
"WCnComV[112][0] = 446\n",
"WCnComV[112][1] = 650\n",
"WCnComV[112][2] = 1181\n",
"WCnComV[112][3] = 2552\n",
"WCnComV[112][4] = 5519\n",
"WCnComV[112][5] = 6682\n",
"WCnComV[112][6] = 6886\n",
"WCnComV[112][7] = 7000\n",
"WCnComV[112][8] = 7583\n",
"WCnComV[113].Len() = 9\n",
"WCnComV[113][0] = 385\n",
"WCnComV[113][1] = 1200\n",
"WCnComV[113][2] = 2276\n",
"WCnComV[113][3] = 2514\n",
"WCnComV[113][4] = 5665\n",
"WCnComV[113][5] = 6276\n",
"WCnComV[113][6] = 7965\n",
"WCnComV[113][7] = 8750\n",
"WCnComV[113][8] = 8922\n",
"WCnComV[114].Len() = 9\n",
"WCnComV[114][0] = 365\n",
"WCnComV[114][1] = 422\n",
"WCnComV[114][2] = 857\n",
"WCnComV[114][3] = 1780\n",
"WCnComV[114][4] = 2739\n",
"WCnComV[114][5] = 3948\n",
"WCnComV[114][6] = 4162\n",
"WCnComV[114][7] = 4225\n",
"WCnComV[114][8] = 7296\n",
"WCnComV[115].Len() = 9\n",
"WCnComV[115][0] = 229\n",
"WCnComV[115][1] = 828\n",
"WCnComV[115][2] = 1523\n",
"WCnComV[115][3] = 3837\n",
"WCnComV[115][4] = 4899\n",
"WCnComV[115][5] = 5035\n",
"WCnComV[115][6] = 5083\n",
"WCnComV[115][7] = 6531\n",
"WCnComV[115][8] = 9407\n",
"WCnComV[116].Len() = 9\n",
"WCnComV[116][0] = 136\n",
"WCnComV[116][1] = 457\n",
"WCnComV[116][2] = 527\n",
"WCnComV[116][3] = 1184\n",
"WCnComV[116][4] = 1463\n",
"WCnComV[116][5] = 8018\n",
"WCnComV[116][6] = 8915\n",
"WCnComV[116][7] = 9230\n",
"WCnComV[116][8] = 9951\n",
"WCnComV[117].Len() = 9\n",
"WCnComV[117][0] = 46\n",
"WCnComV[117][1] = 509\n",
"WCnComV[117][2] = 802\n",
"WCnComV[117][3] = 1487\n",
"WCnComV[117][4] = 3081\n",
"WCnComV[117][5] = 7050\n",
"WCnComV[117][6] = 7863\n",
"WCnComV[117][7] = 8111\n",
"WCnComV[117][8] = 8471\n",
"WCnComV[118].Len() = 9\n",
"WCnComV[118][0] = 21\n",
"WCnComV[118][1] = 679\n",
"WCnComV[118][2] = 1224\n",
"WCnComV[118][3] = 2030\n",
"WCnComV[118][4] = 2651\n",
"WCnComV[118][5] = 3900\n",
"WCnComV[118][6] = 4195\n",
"WCnComV[118][7] = 8788\n",
"WCnComV[118][8] = 8896\n",
"WCnComV[119].Len() = 8\n",
"WCnComV[119][0] = 3846\n",
"WCnComV[119][1] = 5987\n",
"WCnComV[119][2] = 6845\n",
"WCnComV[119][3] = 7172\n",
"WCnComV[119][4] = 7443\n",
"WCnComV[119][5] = 9174\n",
"WCnComV[119][6] = 9399\n",
"WCnComV[119][7] = 9728\n",
"WCnComV[120].Len() = 8\n",
"WCnComV[120][0] = 3757\n",
"WCnComV[120][1] = 4465\n",
"WCnComV[120][2] = 5400\n",
"WCnComV[120][3] = 5526\n",
"WCnComV[120][4] = 5869\n",
"WCnComV[120][5] = 6412\n",
"WCnComV[120][6] = 6848\n",
"WCnComV[120][7] = 8103\n",
"WCnComV[121].Len() = 8\n",
"WCnComV[121][0] = 3048\n",
"WCnComV[121][1] = 3096\n",
"WCnComV[121][2] = 4619\n",
"WCnComV[121][3] = 5425\n",
"WCnComV[121][4] = 5454\n",
"WCnComV[121][5] = 7682\n",
"WCnComV[121][6] = 9241\n",
"WCnComV[121][7] = 9666\n",
"WCnComV[122].Len() = 8\n",
"WCnComV[122][0] = 2667\n",
"WCnComV[122][1] = 3012\n",
"WCnComV[122][2] = 5441\n",
"WCnComV[122][3] = 6739\n",
"WCnComV[122][4] = 7267\n",
"WCnComV[122][5] = 8346\n",
"WCnComV[122][6] = 9136\n",
"WCnComV[122][7] = 9494\n",
"WCnComV[123].Len() = 8\n",
"WCnComV[123][0] = 2185\n",
"WCnComV[123][1] = 2199\n",
"WCnComV[123][2] = 2300\n",
"WCnComV[123][3] = 2612\n",
"WCnComV[123][4] = 3454\n",
"WCnComV[123][5] = 4831\n",
"WCnComV[123][6] = 6471\n",
"WCnComV[123][7] = 8148\n",
"WCnComV[124].Len() = 8\n",
"WCnComV[124][0] = 1871\n",
"WCnComV[124][1] = 2522\n",
"WCnComV[124][2] = 2727\n",
"WCnComV[124][3] = 3520\n",
"WCnComV[124][4] = 3902\n",
"WCnComV[124][5] = 4099\n",
"WCnComV[124][6] = 9786\n",
"WCnComV[124][7] = 9787\n",
"WCnComV[125].Len() = 8\n",
"WCnComV[125][0] = 1786\n",
"WCnComV[125][1] = 5572\n",
"WCnComV[125][2] = 7025\n",
"WCnComV[125][3] = 7921\n",
"WCnComV[125][4] = 7952\n",
"WCnComV[125][5] = 8406\n",
"WCnComV[125][6] = 8640\n",
"WCnComV[125][7] = 8828\n",
"WCnComV[126].Len() = 8\n",
"WCnComV[126][0] = 1720\n",
"WCnComV[126][1] = 1907\n",
"WCnComV[126][2] = 3370\n",
"WCnComV[126][3] = 7840\n",
"WCnComV[126][4] = 8059\n",
"WCnComV[126][5] = 8217\n",
"WCnComV[126][6] = 8368\n",
"WCnComV[126][7] = 9622\n",
"WCnComV[127].Len() = 8\n",
"WCnComV[127][0] = 1646\n",
"WCnComV[127][1] = 1864\n",
"WCnComV[127][2] = 2431\n",
"WCnComV[127][3] = 6952\n",
"WCnComV[127][4] = 7062\n",
"WCnComV[127][5] = 9654\n",
"WCnComV[127][6] = 9805\n",
"WCnComV[127][7] = 9989\n",
"WCnComV[128].Len() = 8\n",
"WCnComV[128][0] = 1276\n",
"WCnComV[128][1] = 2823\n",
"WCnComV[128][2] = 3807\n",
"WCnComV[128][3] = 4084\n",
"WCnComV[128][4] = 4796\n",
"WCnComV[128][5] = 5050\n",
"WCnComV[128][6] = 5091\n",
"WCnComV[128][7] = 7067\n",
"WCnComV[129].Len() = 8\n",
"WCnComV[129][0] = 1270\n",
"WCnComV[129][1] = 2442\n",
"WCnComV[129][2] = 4326\n",
"WCnComV[129][3] = 4685\n",
"WCnComV[129][4] = 6140\n",
"WCnComV[129][5] = 7078\n",
"WCnComV[129][6] = 7574\n",
"WCnComV[129][7] = 8039\n",
"WCnComV[130].Len() = 8\n",
"WCnComV[130][0] = 713\n",
"WCnComV[130][1] = 1966\n",
"WCnComV[130][2] = 3276\n",
"WCnComV[130][3] = 3588\n",
"WCnComV[130][4] = 7756\n",
"WCnComV[130][5] = 8242\n",
"WCnComV[130][6] = 9976\n",
"WCnComV[130][7] = 9996\n",
"WCnComV[131].Len() = 8\n",
"WCnComV[131][0] = 657\n",
"WCnComV[131][1] = 1468\n",
"WCnComV[131][2] = 3566\n",
"WCnComV[131][3] = 4658\n",
"WCnComV[131][4] = 5666\n",
"WCnComV[131][5] = 8339\n",
"WCnComV[131][6] = 9442\n",
"WCnComV[131][7] = 9501\n",
"WCnComV[132].Len() = 8\n",
"WCnComV[132][0] = 458\n",
"WCnComV[132][1] = 518\n",
"WCnComV[132][2] = 577\n",
"WCnComV[132][3] = 950\n",
"WCnComV[132][4] = 1287\n",
"WCnComV[132][5] = 1552\n",
"WCnComV[132][6] = 4223\n",
"WCnComV[132][7] = 7165\n",
"WCnComV[133].Len() = 8\n",
"WCnComV[133][0] = 455\n",
"WCnComV[133][1] = 2449\n",
"WCnComV[133][2] = 3537\n",
"WCnComV[133][3] = 4262\n",
"WCnComV[133][4] = 6530\n",
"WCnComV[133][5] = 6842\n",
"WCnComV[133][6] = 7563\n",
"WCnComV[133][7] = 7587\n",
"WCnComV[134].Len() = 8\n",
"WCnComV[134][0] = 175\n",
"WCnComV[134][1] = 1273\n",
"WCnComV[134][2] = 1595\n",
"WCnComV[134][3] = 2057\n",
"WCnComV[134][4] = 2313\n",
"WCnComV[134][5] = 2965\n",
"WCnComV[134][6] = 3363\n",
"WCnComV[134][7] = 8190\n",
"WCnComV[135].Len() = 8\n",
"WCnComV[135][0] = 118\n",
"WCnComV[135][1] = 2145\n",
"WCnComV[135][2] = 2989\n",
"WCnComV[135][3] = 3904\n",
"WCnComV[135][4] = 6585\n",
"WCnComV[135][5] = 6745\n",
"WCnComV[135][6] = 7782\n",
"WCnComV[135][7] = 8502\n",
"WCnComV[136].Len() = 8\n",
"WCnComV[136][0] = 105\n",
"WCnComV[136][1] = 364\n",
"WCnComV[136][2] = 2550\n",
"WCnComV[136][3] = 5965\n",
"WCnComV[136][4] = 6000\n",
"WCnComV[136][5] = 7914\n",
"WCnComV[136][6] = 9350\n",
"WCnComV[136][7] = 9972\n",
"WCnComV[137].Len() = 8\n",
"WCnComV[137][0] = 102\n",
"WCnComV[137][1] = 3317\n",
"WCnComV[137][2] = 3654\n",
"WCnComV[137][3] = 4741\n",
"WCnComV[137][4] = 6633\n",
"WCnComV[137][5] = 7028\n",
"WCnComV[137][6] = 7940\n",
"WCnComV[137][7] = 9299\n",
"WCnComV[138].Len() = 8\n",
"WCnComV[138][0] = 96\n",
"WCnComV[138][1] = 1992\n",
"WCnComV[138][2] = 2852\n",
"WCnComV[138][3] = 3853\n",
"WCnComV[138][4] = 4709\n",
"WCnComV[138][5] = 6298\n",
"WCnComV[138][6] = 6595\n",
"WCnComV[138][7] = 6729\n",
"WCnComV[139].Len() = 8\n",
"WCnComV[139][0] = 71\n",
"WCnComV[139][1] = 179\n",
"WCnComV[139][2] = 4006\n",
"WCnComV[139][3] = 4051\n",
"WCnComV[139][4] = 4489\n",
"WCnComV[139][5] = 5889\n",
"WCnComV[139][6] = 7884\n",
"WCnComV[139][7] = 9331\n",
"WCnComV[140].Len() = 8\n",
"WCnComV[140][0] = 4\n",
"WCnComV[140][1] = 615\n",
"WCnComV[140][2] = 5554\n",
"WCnComV[140][3] = 5752\n",
"WCnComV[140][4] = 8503\n",
"WCnComV[140][5] = 8953\n",
"WCnComV[140][6] = 9125\n",
"WCnComV[140][7] = 9536\n",
"WCnComV[141].Len() = 7\n",
"WCnComV[141][0] = 5419\n",
"WCnComV[141][1] = 6532\n",
"WCnComV[141][2] = 6724\n",
"WCnComV[141][3] = 7370\n",
"WCnComV[141][4] = 7836\n",
"WCnComV[141][5] = 9710\n",
"WCnComV[141][6] = 9714\n",
"WCnComV[142].Len() = 7\n",
"WCnComV[142][0] = 5045\n",
"WCnComV[142][1] = 5495\n",
"WCnComV[142][2] = 5999\n",
"WCnComV[142][3] = 6737\n",
"WCnComV[142][4] = 8358\n",
"WCnComV[142][5] = 8595\n",
"WCnComV[142][6] = 9932\n",
"WCnComV[143].Len() = 7\n",
"WCnComV[143][0] = 3251\n",
"WCnComV[143][1] = 3793\n",
"WCnComV[143][2] = 3997\n",
"WCnComV[143][3] = 7372\n",
"WCnComV[143][4] = 7390\n",
"WCnComV[143][5] = 9170\n",
"WCnComV[143][6] = 9186\n",
"WCnComV[144].Len() = 7\n",
"WCnComV[144][0] = 2990\n",
"WCnComV[144][1] = 3273\n",
"WCnComV[144][2] = 4428\n",
"WCnComV[144][3] = 4921\n",
"WCnComV[144][4] = 6165\n",
"WCnComV[144][5] = 7777\n",
"WCnComV[144][6] = 8746\n",
"WCnComV[145].Len() = 7\n",
"WCnComV[145][0] = 2584\n",
"WCnComV[145][1] = 3212\n",
"WCnComV[145][2] = 3857\n",
"WCnComV[145][3] = 6408\n",
"WCnComV[145][4] = 7276\n",
"WCnComV[145][5] = 9434\n",
"WCnComV[145][6] = 9670\n",
"WCnComV[146].Len() = 7\n",
"WCnComV[146][0] = 2291\n",
"WCnComV[146][1] = 2812\n",
"WCnComV[146][2] = 4786\n",
"WCnComV[146][3] = 6142\n",
"WCnComV[146][4] = 7705\n",
"WCnComV[146][5] = 8937\n",
"WCnComV[146][6] = 9899\n",
"WCnComV[147].Len() = 7\n",
"WCnComV[147][0] = 2253\n",
"WCnComV[147][1] = 5535\n",
"WCnComV[147][2] = 8195\n",
"WCnComV[147][3] = 8208\n",
"WCnComV[147][4] = 8286\n",
"WCnComV[147][5] = 8470\n",
"WCnComV[147][6] = 9718\n",
"WCnComV[148].Len() = 7\n",
"WCnComV[148][0] = 2252\n",
"WCnComV[148][1] = 2314\n",
"WCnComV[148][2] = 3773\n",
"WCnComV[148][3] = 5458\n",
"WCnComV[148][4] = 7673\n",
"WCnComV[148][5] = 7841\n",
"WCnComV[148][6] = 8598\n",
"WCnComV[149].Len() = 7\n",
"WCnComV[149][0] = 2000\n",
"WCnComV[149][1] = 4090\n",
"WCnComV[149][2] = 4603\n",
"WCnComV[149][3] = 6094\n",
"WCnComV[149][4] = 6324\n",
"WCnComV[149][5] = 8086\n",
"WCnComV[149][6] = 8095\n",
"WCnComV[150].Len() = 7\n",
"WCnComV[150][0] = 1853\n",
"WCnComV[150][1] = 6501\n",
"WCnComV[150][2] = 6551\n",
"WCnComV[150][3] = 7196\n",
"WCnComV[150][4] = 8202\n",
"WCnComV[150][5] = 8947\n",
"WCnComV[150][6] = 9360\n",
"WCnComV[151].Len() = 7\n",
"WCnComV[151][0] = 1744\n",
"WCnComV[151][1] = 2319\n",
"WCnComV[151][2] = 2904\n",
"WCnComV[151][3] = 3475\n",
"WCnComV[151][4] = 5741\n",
"WCnComV[151][5] = 7886\n",
"WCnComV[151][6] = 8699\n",
"WCnComV[152].Len() = 7\n",
"WCnComV[152][0] = 1629\n",
"WCnComV[152][1] = 1840\n",
"WCnComV[152][2] = 7527\n",
"WCnComV[152][3] = 7907\n",
"WCnComV[152][4] = 8072\n",
"WCnComV[152][5] = 8599\n",
"WCnComV[152][6] = 9822\n",
"WCnComV[153].Len() = 7\n",
"WCnComV[153][0] = 1559\n",
"WCnComV[153][1] = 1770\n",
"WCnComV[153][2] = 3529\n",
"WCnComV[153][3] = 4000\n",
"WCnComV[153][4] = 5735\n",
"WCnComV[153][5] = 6487\n",
"WCnComV[153][6] = 7150\n",
"WCnComV[154].Len() = 7\n",
"WCnComV[154][0] = 1465\n",
"WCnComV[154][1] = 2842\n",
"WCnComV[154][2] = 4561\n",
"WCnComV[154][3] = 6095\n",
"WCnComV[154][4] = 6460\n",
"WCnComV[154][5] = 6820\n",
"WCnComV[154][6] = 9564\n",
"WCnComV[155].Len() = 7\n",
"WCnComV[155][0] = 1087\n",
"WCnComV[155][1] = 2923\n",
"WCnComV[155][2] = 3710\n",
"WCnComV[155][3] = 3740\n",
"WCnComV[155][4] = 6250\n",
"WCnComV[155][5] = 7693\n",
"WCnComV[155][6] = 9975\n",
"WCnComV[156].Len() = 7\n",
"WCnComV[156][0] = 734\n",
"WCnComV[156][1] = 2237\n",
"WCnComV[156][2] = 4593\n",
"WCnComV[156][3] = 8987\n",
"WCnComV[156][4] = 9161\n",
"WCnComV[156][5] = 9640\n",
"WCnComV[156][6] = 9880\n",
"WCnComV[157].Len() = 7\n",
"WCnComV[157][0] = 718\n",
"WCnComV[157][1] = 1277\n",
"WCnComV[157][2] = 2728\n",
"WCnComV[157][3] = 4861\n",
"WCnComV[157][4] = 5684\n",
"WCnComV[157][5] = 5774\n",
"WCnComV[157][6] = 5835\n",
"WCnComV[158].Len() = 7\n",
"WCnComV[158][0] = 705\n",
"WCnComV[158][1] = 1627\n",
"WCnComV[158][2] = 4009\n",
"WCnComV[158][3] = 4328\n",
"WCnComV[158][4] = 6728\n",
"WCnComV[158][5] = 7631\n",
"WCnComV[158][6] = 9677\n",
"WCnComV[159].Len() = 7\n",
"WCnComV[159][0] = 697\n",
"WCnComV[159][1] = 4522\n",
"WCnComV[159][2] = 5297\n",
"WCnComV[159][3] = 7001\n",
"WCnComV[159][4] = 7650\n",
"WCnComV[159][5] = 7683\n",
"WCnComV[159][6] = 9422\n",
"WCnComV[160].Len() = 7\n",
"WCnComV[160][0] = 599\n",
"WCnComV[160][1] = 3395\n",
"WCnComV[160][2] = 4377\n",
"WCnComV[160][3] = 6692\n",
"WCnComV[160][4] = 7105\n",
"WCnComV[160][5] = 9019\n",
"WCnComV[160][6] = 9776\n",
"WCnComV[161].Len() = 7\n",
"WCnComV[161][0] = 546\n",
"WCnComV[161][1] = 5389\n",
"WCnComV[161][2] = 6488\n",
"WCnComV[161][3] = 7842\n",
"WCnComV[161][4] = 8056\n",
"WCnComV[161][5] = 8813\n",
"WCnComV[161][6] = 9245\n",
"WCnComV[162].Len() = 7\n",
"WCnComV[162][0] = 476\n",
"WCnComV[162][1] = 1076\n",
"WCnComV[162][2] = 1170\n",
"WCnComV[162][3] = 2100\n",
"WCnComV[162][4] = 2855\n",
"WCnComV[162][5] = 3860\n",
"WCnComV[162][6] = 7288\n",
"WCnComV[163].Len() = 7\n",
"WCnComV[163][0] = 400\n",
"WCnComV[163][1] = 2642\n",
"WCnComV[163][2] = 3967\n",
"WCnComV[163][3] = 4025\n",
"WCnComV[163][4] = 4842\n",
"WCnComV[163][5] = 8357\n",
"WCnComV[163][6] = 9762\n",
"WCnComV[164].Len() = 7\n",
"WCnComV[164][0] = 335\n",
"WCnComV[164][1] = 1319\n",
"WCnComV[164][2] = 2889\n",
"WCnComV[164][3] = 5076\n",
"WCnComV[164][4] = 7323\n",
"WCnComV[164][5] = 8675\n",
"WCnComV[164][6] = 9068\n",
"WCnComV[165].Len() = 7\n",
"WCnComV[165][0] = 331\n",
"WCnComV[165][1] = 450\n",
"WCnComV[165][2] = 3976\n",
"WCnComV[165][3] = 5897\n",
"WCnComV[165][4] = 6085\n",
"WCnComV[165][5] = 7799\n",
"WCnComV[165][6] = 8496\n",
"WCnComV[166].Len() = 7\n",
"WCnComV[166][0] = 263\n",
"WCnComV[166][1] = 1616\n",
"WCnComV[166][2] = 5791\n",
"WCnComV[166][3] = 6210\n",
"WCnComV[166][4] = 6245\n",
"WCnComV[166][5] = 6650\n",
"WCnComV[166][6] = 7141\n",
"WCnComV[167].Len() = 7\n",
"WCnComV[167][0] = 237\n",
"WCnComV[167][1] = 426\n",
"WCnComV[167][2] = 1369\n",
"WCnComV[167][3] = 1727\n",
"WCnComV[167][4] = 3177\n",
"WCnComV[167][5] = 5303\n",
"WCnComV[167][6] = 6507\n",
"WCnComV[168].Len() = 7\n",
"WCnComV[168][0] = 190\n",
"WCnComV[168][1] = 330\n",
"WCnComV[168][2] = 3399\n",
"WCnComV[168][3] = 6788\n",
"WCnComV[168][4] = 7206\n",
"WCnComV[168][5] = 8710\n",
"WCnComV[168][6] = 9939\n",
"WCnComV[169].Len() = 7\n",
"WCnComV[169][0] = 159\n",
"WCnComV[169][1] = 387\n",
"WCnComV[169][2] = 898\n",
"WCnComV[169][3] = 1687\n",
"WCnComV[169][4] = 5472\n",
"WCnComV[169][5] = 6698\n",
"WCnComV[169][6] = 7095\n",
"WCnComV[170].Len() = 7\n",
"WCnComV[170][0] = 134\n",
"WCnComV[170][1] = 716\n",
"WCnComV[170][2] = 2038\n",
"WCnComV[170][3] = 4016\n",
"WCnComV[170][4] = 4559\n",
"WCnComV[170][5] = 7743\n",
"WCnComV[170][6] = 8722\n",
"WCnComV[171].Len() = 7\n",
"WCnComV[171][0] = 75\n",
"WCnComV[171][1] = 2662\n",
"WCnComV[171][2] = 3464\n",
"WCnComV[171][3] = 5121\n",
"WCnComV[171][4] = 6129\n",
"WCnComV[171][5] = 6753\n",
"WCnComV[171][6] = 9556\n",
"WCnComV[172].Len() = 7\n",
"WCnComV[172][0] = 56\n",
"WCnComV[172][1] = 771\n",
"WCnComV[172][2] = 2148\n",
"WCnComV[172][3] = 3720\n",
"WCnComV[172][4] = 7295\n",
"WCnComV[172][5] = 7692\n",
"WCnComV[172][6] = 9413\n",
"WCnComV[173].Len() = 7\n",
"WCnComV[173][0] = 40\n",
"WCnComV[173][1] = 2347\n",
"WCnComV[173][2] = 3391\n",
"WCnComV[173][3] = 7205\n",
"WCnComV[173][4] = 9103\n",
"WCnComV[173][5] = 9464\n",
"WCnComV[173][6] = 9906\n",
"WCnComV[174].Len() = 6\n",
"WCnComV[174][0] = 4017\n",
"WCnComV[174][1] = 5446\n",
"WCnComV[174][2] = 6347\n",
"WCnComV[174][3] = 6765\n",
"WCnComV[174][4] = 8505\n",
"WCnComV[174][5] = 9172\n",
"WCnComV[175].Len() = 6\n",
"WCnComV[175][0] = 3921\n",
"WCnComV[175][1] = 4344\n",
"WCnComV[175][2] = 4527\n",
"WCnComV[175][3] = 5367\n",
"WCnComV[175][4] = 6914\n",
"WCnComV[175][5] = 7729\n",
"WCnComV[176].Len() = 6\n",
"WCnComV[176][0] = 3917\n",
"WCnComV[176][1] = 4743\n",
"WCnComV[176][2] = 6122\n",
"WCnComV[176][3] = 7133\n",
"WCnComV[176][4] = 9192\n",
"WCnComV[176][5] = 9550\n",
"WCnComV[177].Len() = 6\n",
"WCnComV[177][0] = 3717\n",
"WCnComV[177][1] = 4056\n",
"WCnComV[177][2] = 4519\n",
"WCnComV[177][3] = 4533\n",
"WCnComV[177][4] = 6740\n",
"WCnComV[177][5] = 7579\n",
"WCnComV[178].Len() = 6\n",
"WCnComV[178][0] = 3697\n",
"WCnComV[178][1] = 4851\n",
"WCnComV[178][2] = 4852\n",
"WCnComV[178][3] = 6458\n",
"WCnComV[178][4] = 8113\n",
"WCnComV[178][5] = 8299\n",
"WCnComV[179].Len() = 6\n",
"WCnComV[179][0] = 3582\n",
"WCnComV[179][1] = 5138\n",
"WCnComV[179][2] = 5447\n",
"WCnComV[179][3] = 7444\n",
"WCnComV[179][4] = 8126\n",
"WCnComV[179][5] = 9778\n",
"WCnComV[180].Len() = 6\n",
"WCnComV[180][0] = 3025\n",
"WCnComV[180][1] = 4096\n",
"WCnComV[180][2] = 5251\n",
"WCnComV[180][3] = 6336\n",
"WCnComV[180][4] = 8110\n",
"WCnComV[180][5] = 8996\n",
"WCnComV[181].Len() = 6\n",
"WCnComV[181][0] = 2755\n",
"WCnComV[181][1] = 4512\n",
"WCnComV[181][2] = 4805\n",
"WCnComV[181][3] = 6712\n",
"WCnComV[181][4] = 6758\n",
"WCnComV[181][5] = 8848\n",
"WCnComV[182].Len() = 6\n",
"WCnComV[182][0] = 2613\n",
"WCnComV[182][1] = 3133\n",
"WCnComV[182][2] = 3887\n",
"WCnComV[182][3] = 4774\n",
"WCnComV[182][4] = 8159\n",
"WCnComV[182][5] = 9764\n",
"WCnComV[183].Len() = 6\n",
"WCnComV[183][0] = 2593\n",
"WCnComV[183][1] = 2747\n",
"WCnComV[183][2] = 3226\n",
"WCnComV[183][3] = 3242\n",
"WCnComV[183][4] = 3681\n",
"WCnComV[183][5] = 9884\n",
"WCnComV[184].Len() = 6\n",
"WCnComV[184][0] = 2530\n",
"WCnComV[184][1] = 6300\n",
"WCnComV[184][2] = 6457\n",
"WCnComV[184][3] = 7278\n",
"WCnComV[184][4] = 8042\n",
"WCnComV[184][5] = 9156\n",
"WCnComV[185].Len() = 6\n",
"WCnComV[185][0] = 2416\n",
"WCnComV[185][1] = 4429\n",
"WCnComV[185][2] = 4611\n",
"WCnComV[185][3] = 5357\n",
"WCnComV[185][4] = 7181\n",
"WCnComV[185][5] = 7299\n",
"WCnComV[186].Len() = 6\n",
"WCnComV[186][0] = 2278\n",
"WCnComV[186][1] = 4763\n",
"WCnComV[186][2] = 4950\n",
"WCnComV[186][3] = 6011\n",
"WCnComV[186][4] = 6390\n",
"WCnComV[186][5] = 6416\n",
"WCnComV[187].Len() = 6\n",
"WCnComV[187][0] = 2215\n",
"WCnComV[187][1] = 2443\n",
"WCnComV[187][2] = 5009\n",
"WCnComV[187][3] = 6481\n",
"WCnComV[187][4] = 7188\n",
"WCnComV[187][5] = 8583\n",
"WCnComV[188].Len() = 6\n",
"WCnComV[188][0] = 2186\n",
"WCnComV[188][1] = 5436\n",
"WCnComV[188][2] = 6131\n",
"WCnComV[188][3] = 7162\n",
"WCnComV[188][4] = 8179\n",
"WCnComV[188][5] = 8921\n",
"WCnComV[189].Len() = 6\n",
"WCnComV[189][0] = 1991\n",
"WCnComV[189][1] = 2085\n",
"WCnComV[189][2] = 2140\n",
"WCnComV[189][3] = 3075\n",
"WCnComV[189][4] = 6573\n",
"WCnComV[189][5] = 7418\n",
"WCnComV[190].Len() = 6\n",
"WCnComV[190][0] = 1809\n",
"WCnComV[190][1] = 3010\n",
"WCnComV[190][2] = 5540\n",
"WCnComV[190][3] = 5994\n",
"WCnComV[190][4] = 8946\n",
"WCnComV[190][5] = 9086\n",
"WCnComV[191].Len() = 6\n",
"WCnComV[191][0] = 1605\n",
"WCnComV[191][1] = 4239\n",
"WCnComV[191][2] = 4351\n",
"WCnComV[191][3] = 5639\n",
"WCnComV[191][4] = 6035\n",
"WCnComV[191][5] = 6287\n",
"WCnComV[192].Len() = 6\n",
"WCnComV[192][0] = 1547\n",
"WCnComV[192][1] = 1858\n",
"WCnComV[192][2] = 1986\n",
"WCnComV[192][3] = 3670\n",
"WCnComV[192][4] = 4736\n",
"WCnComV[192][5] = 7069\n",
"WCnComV[193].Len() = 6\n",
"WCnComV[193][0] = 1497\n",
"WCnComV[193][1] = 1500\n",
"WCnComV[193][2] = 2110\n",
"WCnComV[193][3] = 4788\n",
"WCnComV[193][4] = 8312\n",
"WCnComV[193][5] = 9766\n",
"WCnComV[194].Len() = 6\n",
"WCnComV[194][0] = 1404\n",
"WCnComV[194][1] = 5619\n",
"WCnComV[194][2] = 6548\n",
"WCnComV[194][3] = 6948\n",
"WCnComV[194][4] = 7612\n",
"WCnComV[194][5] = 8052\n",
"WCnComV[195].Len() = 6\n",
"WCnComV[195][0] = 1349\n",
"WCnComV[195][1] = 1761\n",
"WCnComV[195][2] = 6453\n",
"WCnComV[195][3] = 7896\n",
"WCnComV[195][4] = 9472\n",
"WCnComV[195][5] = 9730\n",
"WCnComV[196].Len() = 6\n",
"WCnComV[196][0] = 1348\n",
"WCnComV[196][1] = 1861\n",
"WCnComV[196][2] = 4094\n",
"WCnComV[196][3] = 4364\n",
"WCnComV[196][4] = 9737\n",
"WCnComV[196][5] = 9994\n",
"WCnComV[197].Len() = 6\n",
"WCnComV[197][0] = 1315\n",
"WCnComV[197][1] = 1731\n",
"WCnComV[197][2] = 1994\n",
"WCnComV[197][3] = 3845\n",
"WCnComV[197][4] = 8585\n",
"WCnComV[197][5] = 9591\n",
"WCnComV[198].Len() = 6\n",
"WCnComV[198][0] = 1261\n",
"WCnComV[198][1] = 1449\n",
"WCnComV[198][2] = 1812\n",
"WCnComV[198][3] = 2149\n",
"WCnComV[198][4] = 8231\n",
"WCnComV[198][5] = 9406\n",
"WCnComV[199].Len() = 6\n",
"WCnComV[199][0] = 1161\n",
"WCnComV[199][1] = 2407\n",
"WCnComV[199][2] = 3802\n",
"WCnComV[199][3] = 7249\n",
"WCnComV[199][4] = 7367\n",
"WCnComV[199][5] = 7750\n",
"WCnComV[200].Len() = 6\n",
"WCnComV[200][0] = 1157\n",
"WCnComV[200][1] = 3587\n",
"WCnComV[200][2] = 6642\n",
"WCnComV[200][3] = 9722\n",
"WCnComV[200][4] = 9811\n",
"WCnComV[200][5] = 9986\n",
"WCnComV[201].Len() = 6\n",
"WCnComV[201][0] = 1116\n",
"WCnComV[201][1] = 2875\n",
"WCnComV[201][2] = 3877\n",
"WCnComV[201][3] = 4700\n",
"WCnComV[201][4] = 4761\n",
"WCnComV[201][5] = 5957\n",
"WCnComV[202].Len() = 6\n",
"WCnComV[202][0] = 1023\n",
"WCnComV[202][1] = 3722\n",
"WCnComV[202][2] = 3811\n",
"WCnComV[202][3] = 7917\n",
"WCnComV[202][4] = 9134\n",
"WCnComV[202][5] = 9533\n",
"WCnComV[203].Len() = 6\n",
"WCnComV[203][0] = 979\n",
"WCnComV[203][1] = 2331\n",
"WCnComV[203][2] = 3147\n",
"WCnComV[203][3] = 5207\n",
"WCnComV[203][4] = 8334\n",
"WCnComV[203][5] = 8624\n",
"WCnComV[204].Len() = 6\n",
"WCnComV[204][0] = 976\n",
"WCnComV[204][1] = 4219\n",
"WCnComV[204][2] = 6581\n",
"WCnComV[204][3] = 6629\n",
"WCnComV[204][4] = 7328\n",
"WCnComV[204][5] = 8032\n",
"WCnComV[205].Len() = 6\n",
"WCnComV[205][0] = 889\n",
"WCnComV[205][1] = 3076\n",
"WCnComV[205][2] = 4251\n",
"WCnComV[205][3] = 6307\n",
"WCnComV[205][4] = 7310\n",
"WCnComV[205][5] = 8392\n",
"WCnComV[206].Len() = 6\n",
"WCnComV[206][0] = 811\n",
"WCnComV[206][1] = 1828\n",
"WCnComV[206][2] = 1898\n",
"WCnComV[206][3] = 3598\n",
"WCnComV[206][4] = 5146\n",
"WCnComV[206][5] = 9078\n",
"WCnComV[207].Len() = 6\n",
"WCnComV[207][0] = 667\n",
"WCnComV[207][1] = 3715\n",
"WCnComV[207][2] = 4444\n",
"WCnComV[207][3] = 6435\n",
"WCnComV[207][4] = 6887\n",
"WCnComV[207][5] = 9992\n",
"WCnComV[208].Len() = 6\n",
"WCnComV[208][0] = 642\n",
"WCnComV[208][1] = 826\n",
"WCnComV[208][2] = 2243\n",
"WCnComV[208][3] = 2760\n",
"WCnComV[208][4] = 4176\n",
"WCnComV[208][5] = 9839\n",
"WCnComV[209].Len() = 6\n",
"WCnComV[209][0] = 624\n",
"WCnComV[209][1] = 684\n",
"WCnComV[209][2] = 3596\n",
"WCnComV[209][3] = 5233\n",
"WCnComV[209][4] = 5271\n",
"WCnComV[209][5] = 7668\n",
"WCnComV[210].Len() = 6\n",
"WCnComV[210][0] = 610\n",
"WCnComV[210][1] = 2421\n",
"WCnComV[210][2] = 4576\n",
"WCnComV[210][3] = 5109\n",
"WCnComV[210][4] = 5381\n",
"WCnComV[210][5] = 5906\n",
"WCnComV[211].Len() = 6\n",
"WCnComV[211][0] = 553\n",
"WCnComV[211][1] = 6371\n",
"WCnComV[211][2] = 6925\n",
"WCnComV[211][3] = 7895\n",
"WCnComV[211][4] = 8173\n",
"WCnComV[211][5] = 8720\n",
"WCnComV[212].Len() = 6\n",
"WCnComV[212][0] = 428\n",
"WCnComV[212][1] = 831\n",
"WCnComV[212][2] = 5880\n",
"WCnComV[212][3] = 7994\n",
"WCnComV[212][4] = 8170\n",
"WCnComV[212][5] = 9196\n",
"WCnComV[213].Len() = 6\n",
"WCnComV[213][0] = 423\n",
"WCnComV[213][1] = 690\n",
"WCnComV[213][2] = 817\n",
"WCnComV[213][3] = 3340\n",
"WCnComV[213][4] = 8232\n",
"WCnComV[213][5] = 8850\n",
"WCnComV[214].Len() = 6\n",
"WCnComV[214][0] = 398\n",
"WCnComV[214][1] = 1125\n",
"WCnComV[214][2] = 1827\n",
"WCnComV[214][3] = 4384\n",
"WCnComV[214][4] = 9338\n",
"WCnComV[214][5] = 9974\n",
"WCnComV[215].Len() = 6\n",
"WCnComV[215][0] = 392\n",
"WCnComV[215][1] = 1274\n",
"WCnComV[215][2] = 1325\n",
"WCnComV[215][3] = 4523\n",
"WCnComV[215][4] = 5520\n",
"WCnComV[215][5] = 9633\n",
"WCnComV[216].Len() = 6\n",
"WCnComV[216][0] = 340\n",
"WCnComV[216][1] = 1623\n",
"WCnComV[216][2] = 4835\n",
"WCnComV[216][3] = 5265\n",
"WCnComV[216][4] = 7821\n",
"WCnComV[216][5] = 8909\n",
"WCnComV[217].Len() = 6\n",
"WCnComV[217][0] = 275\n",
"WCnComV[217][1] = 723\n",
"WCnComV[217][2] = 1971\n",
"WCnComV[217][3] = 4968\n",
"WCnComV[217][4] = 7438\n",
"WCnComV[217][5] = 7525\n",
"WCnComV[218].Len() = 6\n",
"WCnComV[218][0] = 270\n",
"WCnComV[218][1] = 1849\n",
"WCnComV[218][2] = 4509\n",
"WCnComV[218][3] = 7473\n",
"WCnComV[218][4] = 7735\n",
"WCnComV[218][5] = 8337\n",
"WCnComV[219].Len() = 6\n",
"WCnComV[219][0] = 258\n",
"WCnComV[219][1] = 1144\n",
"WCnComV[219][2] = 6134\n",
"WCnComV[219][3] = 6519\n",
"WCnComV[219][4] = 6937\n",
"WCnComV[219][5] = 9232\n",
"WCnComV[220].Len() = 6\n",
"WCnComV[220][0] = 244\n",
"WCnComV[220][1] = 729\n",
"WCnComV[220][2] = 2644\n",
"WCnComV[220][3] = 3966\n",
"WCnComV[220][4] = 4106\n",
"WCnComV[220][5] = 8985\n",
"WCnComV[221].Len() = 6\n",
"WCnComV[221][0] = 158\n",
"WCnComV[221][1] = 2098\n",
"WCnComV[221][2] = 3539\n",
"WCnComV[221][3] = 4585\n",
"WCnComV[221][4] = 4912\n",
"WCnComV[221][5] = 5225\n",
"WCnComV[222].Len() = 6\n",
"WCnComV[222][0] = 151\n",
"WCnComV[222][1] = 2169\n",
"WCnComV[222][2] = 4014\n",
"WCnComV[222][3] = 7103\n",
"WCnComV[222][4] = 8680\n",
"WCnComV[222][5] = 8865\n",
"WCnComV[223].Len() = 6\n",
"WCnComV[223][0] = 119\n",
"WCnComV[223][1] = 691\n",
"WCnComV[223][2] = 2212\n",
"WCnComV[223][3] = 3552\n",
"WCnComV[223][4] = 3787\n",
"WCnComV[223][5] = 5433\n",
"WCnComV[224].Len() = 6\n",
"WCnComV[224][0] = 91\n",
"WCnComV[224][1] = 2432\n",
"WCnComV[224][2] = 2659\n",
"WCnComV[224][3] = 4867\n",
"WCnComV[224][4] = 7905\n",
"WCnComV[224][5] = 8332\n",
"WCnComV[225].Len() = 6\n",
"WCnComV[225][0] = 84\n",
"WCnComV[225][1] = 85\n",
"WCnComV[225][2] = 529\n",
"WCnComV[225][3] = 6452\n",
"WCnComV[225][4] = 6923\n",
"WCnComV[225][5] = 7609\n",
"WCnComV[226].Len() = 6\n",
"WCnComV[226][0] = 11\n",
"WCnComV[226][1] = 2147\n",
"WCnComV[226][2] = 4260\n",
"WCnComV[226][3] = 4555\n",
"WCnComV[226][4] = 6542\n",
"WCnComV[226][5] = 7203\n",
"WCnComV[227].Len() = 5\n",
"WCnComV[227][0] = 5882\n",
"WCnComV[227][1] = 7361\n",
"WCnComV[227][2] = 7943\n",
"WCnComV[227][3] = 8732\n",
"WCnComV[227][4] = 9746\n",
"WCnComV[228].Len() = 5\n",
"WCnComV[228][0] = 5574\n",
"WCnComV[228][1] = 6708\n",
"WCnComV[228][2] = 6790\n",
"WCnComV[228][3] = 7252\n",
"WCnComV[228][4] = 9680\n",
"WCnComV[229].Len() = 5\n",
"WCnComV[229][0] = 5507\n",
"WCnComV[229][1] = 6277\n",
"WCnComV[229][2] = 7641\n",
"WCnComV[229][3] = 9154\n",
"WCnComV[229][4] = 9352\n",
"WCnComV[230].Len() = 5\n",
"WCnComV[230][0] = 5473\n",
"WCnComV[230][1] = 6213\n",
"WCnComV[230][2] = 6425\n",
"WCnComV[230][3] = 8219\n",
"WCnComV[230][4] = 9807\n",
"WCnComV[231].Len() = 5\n",
"WCnComV[231][0] = 5028\n",
"WCnComV[231][1] = 7077\n",
"WCnComV[231][2] = 7167\n",
"WCnComV[231][3] = 9063\n",
"WCnComV[231][4] = 9111\n",
"WCnComV[232].Len() = 5\n",
"WCnComV[232][0] = 4854\n",
"WCnComV[232][1] = 6943\n",
"WCnComV[232][2] = 6985\n",
"WCnComV[232][3] = 8908\n",
"WCnComV[232][4] = 9639\n",
"WCnComV[233].Len() = 5\n",
"WCnComV[233][0] = 4468\n",
"WCnComV[233][1] = 5502\n",
"WCnComV[233][2] = 7528\n",
"WCnComV[233][3] = 7974\n",
"WCnComV[233][4] = 9438\n",
"WCnComV[234].Len() = 5\n",
"WCnComV[234][0] = 3782\n",
"WCnComV[234][1] = 4211\n",
"WCnComV[234][2] = 4906\n",
"WCnComV[234][3] = 5837\n",
"WCnComV[234][4] = 7998\n",
"WCnComV[235].Len() = 5\n",
"WCnComV[235][0] = 3763\n",
"WCnComV[235][1] = 4888\n",
"WCnComV[235][2] = 5759\n",
"WCnComV[235][3] = 8246\n",
"WCnComV[235][4] = 8308\n",
"WCnComV[236].Len() = 5\n",
"WCnComV[236][0] = 3749\n",
"WCnComV[236][1] = 5832\n",
"WCnComV[236][2] = 7043\n",
"WCnComV[236][3] = 8373\n",
"WCnComV[236][4] = 9463\n",
"WCnComV[237].Len() = 5\n",
"WCnComV[237][0] = 3702\n",
"WCnComV[237][1] = 4459\n",
"WCnComV[237][2] = 5498\n",
"WCnComV[237][3] = 5974\n",
"WCnComV[237][4] = 9770\n",
"WCnComV[238].Len() = 5\n",
"WCnComV[238][0] = 3542\n",
"WCnComV[238][1] = 3931\n",
"WCnComV[238][2] = 5977\n",
"WCnComV[238][3] = 6638\n",
"WCnComV[238][4] = 9838\n",
"WCnComV[239].Len() = 5\n",
"WCnComV[239][0] = 3256\n",
"WCnComV[239][1] = 5859\n",
"WCnComV[239][2] = 7548\n",
"WCnComV[239][3] = 8617\n",
"WCnComV[239][4] = 9821\n",
"WCnComV[240].Len() = 5\n",
"WCnComV[240][0] = 3153\n",
"WCnComV[240][1] = 5634\n",
"WCnComV[240][2] = 6318\n",
"WCnComV[240][3] = 6561\n",
"WCnComV[240][4] = 9977\n",
"WCnComV[241].Len() = 5\n",
"WCnComV[241][0] = 3130\n",
"WCnComV[241][1] = 3193\n",
"WCnComV[241][2] = 5384\n",
"WCnComV[241][3] = 7691\n",
"WCnComV[241][4] = 9649\n",
"WCnComV[242].Len() = 5\n",
"WCnComV[242][0] = 2991\n",
"WCnComV[242][1] = 4245\n",
"WCnComV[242][2] = 5205\n",
"WCnComV[242][3] = 5595\n",
"WCnComV[242][4] = 7718\n",
"WCnComV[243].Len() = 5\n",
"WCnComV[243][0] = 2959\n",
"WCnComV[243][1] = 2969\n",
"WCnComV[243][2] = 8398\n",
"WCnComV[243][3] = 9146\n",
"WCnComV[243][4] = 9336\n",
"WCnComV[244].Len() = 5\n",
"WCnComV[244][0] = 2943\n",
"WCnComV[244][1] = 5043\n",
"WCnComV[244][2] = 6627\n",
"WCnComV[244][3] = 7828\n",
"WCnComV[244][4] = 7982\n",
"WCnComV[245].Len() = 5\n",
"WCnComV[245][0] = 2850\n",
"WCnComV[245][1] = 2946\n",
"WCnComV[245][2] = 3347\n",
"WCnComV[245][3] = 7187\n",
"WCnComV[245][4] = 7674\n",
"WCnComV[246].Len() = 5\n",
"WCnComV[246][0] = 2557\n",
"WCnComV[246][1] = 3557\n",
"WCnComV[246][2] = 4046\n",
"WCnComV[246][3] = 9152\n",
"WCnComV[246][4] = 9207\n",
"WCnComV[247].Len() = 5\n",
"WCnComV[247][0] = 2537\n",
"WCnComV[247][1] = 2930\n",
"WCnComV[247][2] = 3767\n",
"WCnComV[247][3] = 4973\n",
"WCnComV[247][4] = 9139\n",
"WCnComV[248].Len() = 5\n",
"WCnComV[248][0] = 2317\n",
"WCnComV[248][1] = 2918\n",
"WCnComV[248][2] = 7341\n",
"WCnComV[248][3] = 9036\n",
"WCnComV[248][4] = 9337\n",
"WCnComV[249].Len() = 5\n",
"WCnComV[249][0] = 2141\n",
"WCnComV[249][1] = 4242\n",
"WCnComV[249][2] = 6787\n",
"WCnComV[249][3] = 8527\n",
"WCnComV[249][4] = 9800\n",
"WCnComV[250].Len() = 5\n",
"WCnComV[250][0] = 1702\n",
"WCnComV[250][1] = 3477\n",
"WCnComV[250][2] = 3744\n",
"WCnComV[250][3] = 4198\n",
"WCnComV[250][4] = 8420\n",
"WCnComV[251].Len() = 5\n",
"WCnComV[251][0] = 1641\n",
"WCnComV[251][1] = 5921\n",
"WCnComV[251][2] = 8347\n",
"WCnComV[251][3] = 9319\n",
"WCnComV[251][4] = 9733\n",
"WCnComV[252].Len() = 5\n",
"WCnComV[252][0] = 1609\n",
"WCnComV[252][1] = 3695\n",
"WCnComV[252][2] = 7362\n",
"WCnComV[252][3] = 7942\n",
"WCnComV[252][4] = 9586\n",
"WCnComV[253].Len() = 5\n",
"WCnComV[253][0] = 1580\n",
"WCnComV[253][1] = 4638\n",
"WCnComV[253][2] = 6577\n",
"WCnComV[253][3] = 6858\n",
"WCnComV[253][4] = 9386\n",
"WCnComV[254].Len() = 5\n",
"WCnComV[254][0] = 1436\n",
"WCnComV[254][1] = 2921\n",
"WCnComV[254][2] = 5171\n",
"WCnComV[254][3] = 8155\n",
"WCnComV[254][4] = 8457\n",
"WCnComV[255].Len() = 5\n",
"WCnComV[255][0] = 1408\n",
"WCnComV[255][1] = 2294\n",
"WCnComV[255][2] = 2762\n",
"WCnComV[255][3] = 8199\n",
"WCnComV[255][4] = 9037\n",
"WCnComV[256].Len() = 5\n",
"WCnComV[256][0] = 1406\n",
"WCnComV[256][1] = 1784\n",
"WCnComV[256][2] = 4591\n",
"WCnComV[256][3] = 5749\n",
"WCnComV[256][4] = 9309\n",
"WCnComV[257].Len() = 5\n",
"WCnComV[257][0] = 1398\n",
"WCnComV[257][1] = 3293\n",
"WCnComV[257][2] = 4031\n",
"WCnComV[257][3] = 4430\n",
"WCnComV[257][4] = 8523\n",
"WCnComV[258].Len() = 5\n",
"WCnComV[258][0] = 1286\n",
"WCnComV[258][1] = 3219\n",
"WCnComV[258][2] = 4358\n",
"WCnComV[258][3] = 4750\n",
"WCnComV[258][4] = 6209\n",
"WCnComV[259].Len() = 5\n",
"WCnComV[259][0] = 989\n",
"WCnComV[259][1] = 5071\n",
"WCnComV[259][2] = 5773\n",
"WCnComV[259][3] = 5845\n",
"WCnComV[259][4] = 6326\n",
"WCnComV[260].Len() = 5\n",
"WCnComV[260][0] = 947\n",
"WCnComV[260][1] = 1038\n",
"WCnComV[260][2] = 5727\n",
"WCnComV[260][3] = 7396\n",
"WCnComV[260][4] = 9570\n",
"WCnComV[261].Len() = 5\n",
"WCnComV[261][0] = 936\n",
"WCnComV[261][1] = 4079\n",
"WCnComV[261][2] = 6599\n",
"WCnComV[261][3] = 8607\n",
"WCnComV[261][4] = 9759\n",
"WCnComV[262].Len() = 5\n",
"WCnComV[262][0] = 926\n",
"WCnComV[262][1] = 1231\n",
"WCnComV[262][2] = 2089\n",
"WCnComV[262][3] = 2910\n",
"WCnComV[262][4] = 6635\n",
"WCnComV[263].Len() = 5\n",
"WCnComV[263][0] = 914\n",
"WCnComV[263][1] = 3364\n",
"WCnComV[263][2] = 4936\n",
"WCnComV[263][3] = 6894\n",
"WCnComV[263][4] = 8012\n",
"WCnComV[264].Len() = 5\n",
"WCnComV[264][0] = 907\n",
"WCnComV[264][1] = 1905\n",
"WCnComV[264][2] = 2663\n",
"WCnComV[264][3] = 3521\n",
"WCnComV[264][4] = 9720\n",
"WCnComV[265].Len() = 5\n",
"WCnComV[265][0] = 886\n",
"WCnComV[265][1] = 1990\n",
"WCnComV[265][2] = 4135\n",
"WCnComV[265][3] = 6340\n",
"WCnComV[265][4] = 6385\n",
"WCnComV[266].Len() = 5\n",
"WCnComV[266][0] = 872\n",
"WCnComV[266][1] = 1729\n",
"WCnComV[266][2] = 6607\n",
"WCnComV[266][3] = 7449\n",
"WCnComV[266][4] = 9275\n",
"WCnComV[267].Len() = 5\n",
"WCnComV[267][0] = 819\n",
"WCnComV[267][1] = 2500\n",
"WCnComV[267][2] = 3069\n",
"WCnComV[267][3] = 5455\n",
"WCnComV[267][4] = 6989\n",
"WCnComV[268].Len() = 5\n",
"WCnComV[268][0] = 686\n",
"WCnComV[268][1] = 807\n",
"WCnComV[268][2] = 3144\n",
"WCnComV[268][3] = 5285\n",
"WCnComV[268][4] = 5926\n",
"WCnComV[269].Len() = 5\n",
"WCnComV[269][0] = 645\n",
"WCnComV[269][1] = 1510\n",
"WCnComV[269][2] = 2731\n",
"WCnComV[269][3] = 9686\n",
"WCnComV[269][4] = 9835\n",
"WCnComV[270].Len() = 5\n",
"WCnComV[270][0] = 632\n",
"WCnComV[270][1] = 4502\n",
"WCnComV[270][2] = 5505\n",
"WCnComV[270][3] = 5815\n",
"WCnComV[270][4] = 7489\n",
"WCnComV[271].Len() = 5\n",
"WCnComV[271][0] = 575\n",
"WCnComV[271][1] = 1323\n",
"WCnComV[271][2] = 4167\n",
"WCnComV[271][3] = 6870\n",
"WCnComV[271][4] = 8011\n",
"WCnComV[272].Len() = 5\n",
"WCnComV[272][0] = 487\n",
"WCnComV[272][1] = 8114\n",
"WCnComV[272][2] = 8293\n",
"WCnComV[272][3] = 9150\n",
"WCnComV[272][4] = 9473\n",
"WCnComV[273].Len() = 5\n",
"WCnComV[273][0] = 474\n",
"WCnComV[273][1] = 3405\n",
"WCnComV[273][2] = 5027\n",
"WCnComV[273][3] = 6967\n",
"WCnComV[273][4] = 7104\n",
"WCnComV[274].Len() = 5\n",
"WCnComV[274][0] = 473\n",
"WCnComV[274][1] = 773\n",
"WCnComV[274][2] = 3664\n",
"WCnComV[274][3] = 6995\n",
"WCnComV[274][4] = 8105\n",
"WCnComV[275].Len() = 5\n",
"WCnComV[275][0] = 402\n",
"WCnComV[275][1] = 3165\n",
"WCnComV[275][2] = 4680\n",
"WCnComV[275][3] = 5928\n",
"WCnComV[275][4] = 8757\n",
"WCnComV[276].Len() = 5\n",
"WCnComV[276][0] = 378\n",
"WCnComV[276][1] = 2457\n",
"WCnComV[276][2] = 5848\n",
"WCnComV[276][3] = 9468\n",
"WCnComV[276][4] = 9703\n",
"WCnComV[277].Len() = 5\n",
"WCnComV[277][0] = 377\n",
"WCnComV[277][1] = 6148\n",
"WCnComV[277][2] = 6762\n",
"WCnComV[277][3] = 8345\n",
"WCnComV[277][4] = 9504\n",
"WCnComV[278].Len() = 5\n",
"WCnComV[278][0] = 314\n",
"WCnComV[278][1] = 920\n",
"WCnComV[278][2] = 1751\n",
"WCnComV[278][3] = 8019\n",
"WCnComV[278][4] = 8872\n",
"WCnComV[279].Len() = 5\n",
"WCnComV[279][0] = 166\n",
"WCnComV[279][1] = 957\n",
"WCnComV[279][2] = 1765\n",
"WCnComV[279][3] = 3308\n",
"WCnComV[279][4] = 7577\n",
"WCnComV[280].Len() = 5\n",
"WCnComV[280][0] = 129\n",
"WCnComV[280][1] = 762\n",
"WCnComV[280][2] = 1317\n",
"WCnComV[280][3] = 6512\n",
"WCnComV[280][4] = 7972\n",
"WCnComV[281].Len() = 5\n",
"WCnComV[281][0] = 125\n",
"WCnComV[281][1] = 5514\n",
"WCnComV[281][2] = 7199\n",
"WCnComV[281][3] = 8296\n",
"WCnComV[281][4] = 9576\n",
"WCnComV[282].Len() = 5\n",
"WCnComV[282][0] = 83\n",
"WCnComV[282][1] = 6116\n",
"WCnComV[282][2] = 7560\n",
"WCnComV[282][3] = 8015\n",
"WCnComV[282][4] = 9726\n",
"WCnComV[283].Len() = 5\n",
"WCnComV[283][0] = 79\n",
"WCnComV[283][1] = 966\n",
"WCnComV[283][2] = 1575\n",
"WCnComV[283][3] = 1794\n",
"WCnComV[283][4] = 8524\n",
"WCnComV[284].Len() = 5\n",
"WCnComV[284][0] = 62\n",
"WCnComV[284][1] = 1036\n",
"WCnComV[284][2] = 3366\n",
"WCnComV[284][3] = 6738\n",
"WCnComV[284][4] = 8972\n",
"WCnComV[285].Len() = 5\n",
"WCnComV[285][0] = 24\n",
"WCnComV[285][1] = 484\n",
"WCnComV[285][2] = 978\n",
"WCnComV[285][3] = 4694\n",
"WCnComV[285][4] = 6895\n",
"WCnComV[286].Len() = 5\n",
"WCnComV[286][0] = 15\n",
"WCnComV[286][1] = 1924\n",
"WCnComV[286][2] = 2573\n",
"WCnComV[286][3] = 4890\n",
"WCnComV[286][4] = 7316\n",
"WCnComV[287].Len() = 5\n",
"WCnComV[287][0] = 13\n",
"WCnComV[287][1] = 384\n",
"WCnComV[287][2] = 850\n",
"WCnComV[287][3] = 1363\n",
"WCnComV[287][4] = 3304\n",
"WCnComV[288].Len() = 4\n",
"WCnComV[288][0] = 4999\n",
"WCnComV[288][1] = 6751\n",
"WCnComV[288][2] = 7234\n",
"WCnComV[288][3] = 9874\n",
"WCnComV[289].Len() = 4\n",
"WCnComV[289][0] = 4703\n",
"WCnComV[289][1] = 5509\n",
"WCnComV[289][2] = 8184\n",
"WCnComV[289][3] = 9621\n",
"WCnComV[290].Len() = 4\n",
"WCnComV[290][0] = 4684\n",
"WCnComV[290][1] = 6671\n",
"WCnComV[290][2] = 8359\n",
"WCnComV[290][3] = 9328\n",
"WCnComV[291].Len() = 4\n",
"WCnComV[291][0] = 4486\n",
"WCnComV[291][1] = 4728\n",
"WCnComV[291][2] = 5192\n",
"WCnComV[291][3] = 8151\n",
"WCnComV[292].Len() = 4\n",
"WCnComV[292][0] = 4256\n",
"WCnComV[292][1] = 5089\n",
"WCnComV[292][2] = 5295\n",
"WCnComV[292][3] = 9381\n",
"WCnComV[293].Len() = 4\n",
"WCnComV[293][0] = 4187\n",
"WCnComV[293][1] = 5049\n",
"WCnComV[293][2] = 6981\n",
"WCnComV[293][3] = 8448\n",
"WCnComV[294].Len() = 4\n",
"WCnComV[294][0] = 4008\n",
"WCnComV[294][1] = 4779\n",
"WCnComV[294][2] = 4918\n",
"WCnComV[294][3] = 5244\n",
"WCnComV[295].Len() = 4\n",
"WCnComV[295][0] = 3880\n",
"WCnComV[295][1] = 4780\n",
"WCnComV[295][2] = 6520\n",
"WCnComV[295][3] = 8026\n",
"WCnComV[296].Len() = 4\n",
"WCnComV[296][0] = 3855\n",
"WCnComV[296][1] = 4092\n",
"WCnComV[296][2] = 4945\n",
"WCnComV[296][3] = 9560\n",
"WCnComV[297].Len() = 4\n",
"WCnComV[297][0] = 3825\n",
"WCnComV[297][1] = 4169\n",
"WCnComV[297][2] = 4862\n",
"WCnComV[297][3] = 6009\n",
"WCnComV[298].Len() = 4\n",
"WCnComV[298][0] = 3465\n",
"WCnComV[298][1] = 5729\n",
"WCnComV[298][2] = 8579\n",
"WCnComV[298][3] = 9112\n",
"WCnComV[299].Len() = 4\n",
"WCnComV[299][0] = 3369\n",
"WCnComV[299][1] = 5584\n",
"WCnComV[299][2] = 7671\n",
"WCnComV[299][3] = 9814\n",
"WCnComV[300].Len() = 4\n",
"WCnComV[300][0] = 3289\n",
"WCnComV[300][1] = 5260\n",
"WCnComV[300][2] = 7463\n",
"WCnComV[300][3] = 9448\n",
"WCnComV[301].Len() = 4\n",
"WCnComV[301][0] = 3259\n",
"WCnComV[301][1] = 3337\n",
"WCnComV[301][2] = 5329\n",
"WCnComV[301][3] = 8226\n",
"WCnComV[302].Len() = 4\n",
"WCnComV[302][0] = 3225\n",
"WCnComV[302][1] = 4659\n",
"WCnComV[302][2] = 9002\n",
"WCnComV[302][3] = 9160\n",
"WCnComV[303].Len() = 4\n",
"WCnComV[303][0] = 3188\n",
"WCnComV[303][1] = 3545\n",
"WCnComV[303][2] = 4102\n",
"WCnComV[303][3] = 8382\n",
"WCnComV[304].Len() = 4\n",
"WCnComV[304][0] = 3082\n",
"WCnComV[304][1] = 6305\n",
"WCnComV[304][2] = 6372\n",
"WCnComV[304][3] = 6410\n",
"WCnComV[305].Len() = 4\n",
"WCnComV[305][0] = 3001\n",
"WCnComV[305][1] = 4111\n",
"WCnComV[305][2] = 4878\n",
"WCnComV[305][3] = 8557\n",
"WCnComV[306].Len() = 4\n",
"WCnComV[306][0] = 2843\n",
"WCnComV[306][1] = 5664\n",
"WCnComV[306][2] = 5730\n",
"WCnComV[306][3] = 9719\n",
"WCnComV[307].Len() = 4\n",
"WCnComV[307][0] = 2837\n",
"WCnComV[307][1] = 5053\n",
"WCnComV[307][2] = 7901\n",
"WCnComV[307][3] = 8633\n",
"WCnComV[308].Len() = 4\n",
"WCnComV[308][0] = 2804\n",
"WCnComV[308][1] = 5589\n",
"WCnComV[308][2] = 8873\n",
"WCnComV[308][3] = 9585\n",
"WCnComV[309].Len() = 4\n",
"WCnComV[309][0] = 2778\n",
"WCnComV[309][1] = 4473\n",
"WCnComV[309][2] = 7282\n",
"WCnComV[309][3] = 9909\n",
"WCnComV[310].Len() = 4\n",
"WCnComV[310][0] = 2679\n",
"WCnComV[310][1] = 5167\n",
"WCnComV[310][2] = 6444\n",
"WCnComV[310][3] = 6986\n",
"WCnComV[311].Len() = 4\n",
"WCnComV[311][0] = 2636\n",
"WCnComV[311][1] = 3576\n",
"WCnComV[311][2] = 5113\n",
"WCnComV[311][3] = 9098\n",
"WCnComV[312].Len() = 4\n",
"WCnComV[312][0] = 2617\n",
"WCnComV[312][1] = 4521\n",
"WCnComV[312][2] = 6646\n",
"WCnComV[312][3] = 8861\n",
"WCnComV[313].Len() = 4\n",
"WCnComV[313][0] = 2513\n",
"WCnComV[313][1] = 3485\n",
"WCnComV[313][2] = 5162\n",
"WCnComV[313][3] = 7347\n",
"WCnComV[314].Len() = 4\n",
"WCnComV[314][0] = 2493\n",
"WCnComV[314][1] = 4687\n",
"WCnComV[314][2] = 9092\n",
"WCnComV[314][3] = 9852\n",
"WCnComV[315].Len() = 4\n",
"WCnComV[315][0] = 2461\n",
"WCnComV[315][1] = 3146\n",
"WCnComV[315][2] = 4058\n",
"WCnComV[315][3] = 7226\n",
"WCnComV[316].Len() = 4\n",
"WCnComV[316][0] = 2336\n",
"WCnComV[316][1] = 4834\n",
"WCnComV[316][2] = 7397\n",
"WCnComV[316][3] = 8488\n",
"WCnComV[317].Len() = 4\n",
"WCnComV[317][0] = 2269\n",
"WCnComV[317][1] = 3737\n",
"WCnComV[317][2] = 5552\n",
"WCnComV[317][3] = 8698\n",
"WCnComV[318].Len() = 4\n",
"WCnComV[318][0] = 2248\n",
"WCnComV[318][1] = 4827\n",
"WCnComV[318][2] = 8486\n",
"WCnComV[318][3] = 8594\n",
"WCnComV[319].Len() = 4\n",
"WCnComV[319][0] = 2239\n",
"WCnComV[319][1] = 2471\n",
"WCnComV[319][2] = 4170\n",
"WCnComV[319][3] = 8942\n",
"WCnComV[320].Len() = 4\n",
"WCnComV[320][0] = 2197\n",
"WCnComV[320][1] = 2767\n",
"WCnComV[320][2] = 8397\n",
"WCnComV[320][3] = 9643\n",
"WCnComV[321].Len() = 4\n",
"WCnComV[321][0] = 2120\n",
"WCnComV[321][1] = 4825\n",
"WCnComV[321][2] = 6440\n",
"WCnComV[321][3] = 9557\n",
"WCnComV[322].Len() = 4\n",
"WCnComV[322][0] = 2064\n",
"WCnComV[322][1] = 5341\n",
"WCnComV[322][2] = 5783\n",
"WCnComV[322][3] = 7392\n",
"WCnComV[323].Len() = 4\n",
"WCnComV[323][0] = 2063\n",
"WCnComV[323][1] = 3673\n",
"WCnComV[323][2] = 4063\n",
"WCnComV[323][3] = 8555\n",
"WCnComV[324].Len() = 4\n",
"WCnComV[324][0] = 2054\n",
"WCnComV[324][1] = 3021\n",
"WCnComV[324][2] = 5092\n",
"WCnComV[324][3] = 8656\n",
"WCnComV[325].Len() = 4\n",
"WCnComV[325][0] = 2050\n",
"WCnComV[325][1] = 5593\n",
"WCnComV[325][2] = 7599\n",
"WCnComV[325][3] = 8890\n",
"WCnComV[326].Len() = 4\n",
"WCnComV[326][0] = 2031\n",
"WCnComV[326][1] = 2851\n",
"WCnComV[326][2] = 4665\n",
"WCnComV[326][3] = 4896\n",
"WCnComV[327].Len() = 4\n",
"WCnComV[327][0] = 2012\n",
"WCnComV[327][1] = 4363\n",
"WCnComV[327][2] = 4497\n",
"WCnComV[327][3] = 8544\n",
"WCnComV[328].Len() = 4\n",
"WCnComV[328][0] = 1912\n",
"WCnComV[328][1] = 7209\n",
"WCnComV[328][2] = 7573\n",
"WCnComV[328][3] = 9392\n",
"WCnComV[329].Len() = 4\n",
"WCnComV[329][0] = 1872\n",
"WCnComV[329][1] = 3739\n",
"WCnComV[329][2] = 4445\n",
"WCnComV[329][3] = 5826\n",
"WCnComV[330].Len() = 4\n",
"WCnComV[330][0] = 1754\n",
"WCnComV[330][1] = 2582\n",
"WCnComV[330][2] = 6037\n",
"WCnComV[330][3] = 6101\n",
"WCnComV[331].Len() = 4\n",
"WCnComV[331][0] = 1701\n",
"WCnComV[331][1] = 4739\n",
"WCnComV[331][2] = 6701\n",
"WCnComV[331][3] = 8117\n",
"WCnComV[332].Len() = 4\n",
"WCnComV[332][0] = 1589\n",
"WCnComV[332][1] = 4127\n",
"WCnComV[332][2] = 7173\n",
"WCnComV[332][3] = 8175\n",
"WCnComV[333].Len() = 4\n",
"WCnComV[333][0] = 1535\n",
"WCnComV[333][1] = 4708\n",
"WCnComV[333][2] = 5189\n",
"WCnComV[333][3] = 6782\n",
"WCnComV[334].Len() = 4\n",
"WCnComV[334][0] = 1531\n",
"WCnComV[334][1] = 1989\n",
"WCnComV[334][2] = 4962\n",
"WCnComV[334][3] = 9999\n",
"WCnComV[335].Len() = 4\n",
"WCnComV[335][0] = 1522\n",
"WCnComV[335][1] = 7356\n",
"WCnComV[335][2] = 8756\n",
"WCnComV[335][3] = 9606\n",
"WCnComV[336].Len() = 4\n",
"WCnComV[336][0] = 1483\n",
"WCnComV[336][1] = 6285\n",
"WCnComV[336][2] = 7108\n",
"WCnComV[336][3] = 7627\n",
"WCnComV[337].Len() = 4\n",
"WCnComV[337][0] = 1469\n",
"WCnComV[337][1] = 1895\n",
"WCnComV[337][2] = 3937\n",
"WCnComV[337][3] = 8537\n",
"WCnComV[338].Len() = 4\n",
"WCnComV[338][0] = 1441\n",
"WCnComV[338][1] = 4552\n",
"WCnComV[338][2] = 5643\n",
"WCnComV[338][3] = 9562\n",
"WCnComV[339].Len() = 4\n",
"WCnComV[339][0] = 1422\n",
"WCnComV[339][1] = 5108\n",
"WCnComV[339][2] = 6927\n",
"WCnComV[339][3] = 9656\n",
"WCnComV[340].Len() = 4\n",
"WCnComV[340][0] = 1414\n",
"WCnComV[340][1] = 6839\n",
"WCnComV[340][2] = 8602\n",
"WCnComV[340][3] = 8879\n",
"WCnComV[341].Len() = 4\n",
"WCnComV[341][0] = 1413\n",
"WCnComV[341][1] = 2422\n",
"WCnComV[341][2] = 4038\n",
"WCnComV[341][3] = 5326\n",
"WCnComV[342].Len() = 4\n",
"WCnComV[342][0] = 1401\n",
"WCnComV[342][1] = 4560\n",
"WCnComV[342][2] = 4840\n",
"WCnComV[342][3] = 7731\n",
"WCnComV[343].Len() = 4\n",
"WCnComV[343][0] = 1390\n",
"WCnComV[343][1] = 6613\n",
"WCnComV[343][2] = 9085\n",
"WCnComV[343][3] = 9661\n",
"WCnComV[344].Len() = 4\n",
"WCnComV[344][0] = 1364\n",
"WCnComV[344][1] = 1760\n",
"WCnComV[344][2] = 4707\n",
"WCnComV[344][3] = 8142\n",
"WCnComV[345].Len() = 4\n",
"WCnComV[345][0] = 1343\n",
"WCnComV[345][1] = 1491\n",
"WCnComV[345][2] = 4994\n",
"WCnComV[345][3] = 5740\n",
"WCnComV[346].Len() = 4\n",
"WCnComV[346][0] = 1303\n",
"WCnComV[346][1] = 3525\n",
"WCnComV[346][2] = 5550\n",
"WCnComV[346][3] = 6227\n",
"WCnComV[347].Len() = 4\n",
"WCnComV[347][0] = 1298\n",
"WCnComV[347][1] = 3185\n",
"WCnComV[347][2] = 4003\n",
"WCnComV[347][3] = 6421\n",
"WCnComV[348].Len() = 4\n",
"WCnComV[348][0] = 1263\n",
"WCnComV[348][1] = 7964\n",
"WCnComV[348][2] = 8622\n",
"WCnComV[348][3] = 9844\n",
"WCnComV[349].Len() = 4\n",
"WCnComV[349][0] = 1254\n",
"WCnComV[349][1] = 1415\n",
"WCnComV[349][2] = 7755\n",
"WCnComV[349][3] = 9528\n",
"WCnComV[350].Len() = 4\n",
"WCnComV[350][0] = 1247\n",
"WCnComV[350][1] = 1302\n",
"WCnComV[350][2] = 4930\n",
"WCnComV[350][3] = 7153\n",
"WCnComV[351].Len() = 4\n",
"WCnComV[351][0] = 1204\n",
"WCnComV[351][1] = 1485\n",
"WCnComV[351][2] = 5409\n",
"WCnComV[351][3] = 5516\n",
"WCnComV[352].Len() = 4\n",
"WCnComV[352][0] = 1198\n",
"WCnComV[352][1] = 3357\n",
"WCnComV[352][2] = 4876\n",
"WCnComV[352][3] = 6837\n",
"WCnComV[353].Len() = 4\n",
"WCnComV[353][0] = 1149\n",
"WCnComV[353][1] = 3684\n",
"WCnComV[353][2] = 5958\n",
"WCnComV[353][3] = 8605\n",
"WCnComV[354].Len() = 4\n",
"WCnComV[354][0] = 1140\n",
"WCnComV[354][1] = 3633\n",
"WCnComV[354][2] = 5650\n",
"WCnComV[354][3] = 9493\n",
"WCnComV[355].Len() = 4\n",
"WCnComV[355][0] = 1056\n",
"WCnComV[355][1] = 4087\n",
"WCnComV[355][2] = 6667\n",
"WCnComV[355][3] = 8469\n",
"WCnComV[356].Len() = 4\n",
"WCnComV[356][0] = 951\n",
"WCnComV[356][1] = 5216\n",
"WCnComV[356][2] = 8096\n",
"WCnComV[356][3] = 8461\n",
"WCnComV[357].Len() = 4\n",
"WCnComV[357][0] = 948\n",
"WCnComV[357][1] = 1844\n",
"WCnComV[357][2] = 2018\n",
"WCnComV[357][3] = 9411\n",
"WCnComV[358].Len() = 4\n",
"WCnComV[358][0] = 927\n",
"WCnComV[358][1] = 1464\n",
"WCnComV[358][2] = 3776\n",
"WCnComV[358][3] = 7039\n",
"WCnComV[359].Len() = 4\n",
"WCnComV[359][0] = 906\n",
"WCnComV[359][1] = 7476\n",
"WCnComV[359][2] = 8582\n",
"WCnComV[359][3] = 9618\n",
"WCnComV[360].Len() = 4\n",
"WCnComV[360][0] = 797\n",
"WCnComV[360][1] = 3508\n",
"WCnComV[360][2] = 3815\n",
"WCnComV[360][3] = 6676\n",
"WCnComV[361].Len() = 4\n",
"WCnComV[361][0] = 791\n",
"WCnComV[361][1] = 1172\n",
"WCnComV[361][2] = 8571\n",
"WCnComV[361][3] = 9377\n",
"WCnComV[362].Len() = 4\n",
"WCnComV[362][0] = 754\n",
"WCnComV[362][1] = 2196\n",
"WCnComV[362][2] = 3871\n",
"WCnComV[362][3] = 5005\n",
"WCnComV[363].Len() = 4\n",
"WCnComV[363][0] = 647\n",
"WCnComV[363][1] = 1235\n",
"WCnComV[363][2] = 3973\n",
"WCnComV[363][3] = 6258\n",
"WCnComV[364].Len() = 4\n",
"WCnComV[364][0] = 641\n",
"WCnComV[364][1] = 6244\n",
"WCnComV[364][2] = 7617\n",
"WCnComV[364][3] = 8817\n",
"WCnComV[365].Len() = 4\n",
"WCnComV[365][0] = 636\n",
"WCnComV[365][1] = 1536\n",
"WCnComV[365][2] = 5718\n",
"WCnComV[365][3] = 6461\n",
"WCnComV[366].Len() = 4\n",
"WCnComV[366][0] = 623\n",
"WCnComV[366][1] = 1360\n",
"WCnComV[366][2] = 3245\n",
"WCnComV[366][3] = 5569\n",
"WCnComV[367].Len() = 4\n",
"WCnComV[367][0] = 614\n",
"WCnComV[367][1] = 4189\n",
"WCnComV[367][2] = 7557\n",
"WCnComV[367][3] = 8067\n",
"WCnComV[368].Len() = 4\n",
"WCnComV[368][0] = 606\n",
"WCnComV[368][1] = 5899\n",
"WCnComV[368][2] = 6553\n",
"WCnComV[368][3] = 8403\n",
"WCnComV[369].Len() = 4\n",
"WCnComV[369][0] = 569\n",
"WCnComV[369][1] = 3492\n",
"WCnComV[369][2] = 3789\n",
"WCnComV[369][3] = 7264\n",
"WCnComV[370].Len() = 4\n",
"WCnComV[370][0] = 544\n",
"WCnComV[370][1] = 851\n",
"WCnComV[370][2] = 3104\n",
"WCnComV[370][3] = 4109\n",
"WCnComV[371].Len() = 4\n",
"WCnComV[371][0] = 519\n",
"WCnComV[371][1] = 1250\n",
"WCnComV[371][2] = 7407\n",
"WCnComV[371][3] = 8264\n",
"WCnComV[372].Len() = 4\n",
"WCnComV[372][0] = 461\n",
"WCnComV[372][1] = 1617\n",
"WCnComV[372][2] = 4015\n",
"WCnComV[372][3] = 5036\n",
"WCnComV[373].Len() = 4\n",
"WCnComV[373][0] = 438\n",
"WCnComV[373][1] = 1431\n",
"WCnComV[373][2] = 1923\n",
"WCnComV[373][3] = 3783\n",
"WCnComV[374].Len() = 4\n",
"WCnComV[374][0] = 427\n",
"WCnComV[374][1] = 6700\n",
"WCnComV[374][2] = 6861\n",
"WCnComV[374][3] = 9387\n",
"WCnComV[375].Len() = 4\n",
"WCnComV[375][0] = 403\n",
"WCnComV[375][1] = 7202\n",
"WCnComV[375][2] = 7286\n",
"WCnComV[375][3] = 8827\n",
"WCnComV[376].Len() = 4\n",
"WCnComV[376][0] = 394\n",
"WCnComV[376][1] = 4126\n",
"WCnComV[376][2] = 4192\n",
"WCnComV[376][3] = 7865\n",
"WCnComV[377].Len() = 4\n",
"WCnComV[377][0] = 393\n",
"WCnComV[377][1] = 6308\n",
"WCnComV[377][2] = 6491\n",
"WCnComV[377][3] = 8302\n",
"WCnComV[378].Len() = 4\n",
"WCnComV[378][0] = 382\n",
"WCnComV[378][1] = 2694\n",
"WCnComV[378][2] = 5081\n",
"WCnComV[378][3] = 5218\n",
"WCnComV[379].Len() = 4\n",
"WCnComV[379][0] = 363\n",
"WCnComV[379][1] = 1358\n",
"WCnComV[379][2] = 6026\n",
"WCnComV[379][3] = 9956\n",
"WCnComV[380].Len() = 4\n",
"WCnComV[380][0] = 361\n",
"WCnComV[380][1] = 5607\n",
"WCnComV[380][2] = 5739\n",
"WCnComV[380][3] = 8135\n",
"WCnComV[381].Len() = 4\n",
"WCnComV[381][0] = 298\n",
"WCnComV[381][1] = 1525\n",
"WCnComV[381][2] = 3089\n",
"WCnComV[381][3] = 8249\n",
"WCnComV[382].Len() = 4\n",
"WCnComV[382][0] = 267\n",
"WCnComV[382][1] = 1818\n",
"WCnComV[382][2] = 8329\n",
"WCnComV[382][3] = 8692\n",
"WCnComV[383].Len() = 4\n",
"WCnComV[383][0] = 235\n",
"WCnComV[383][1] = 2700\n",
"WCnComV[383][2] = 6801\n",
"WCnComV[383][3] = 9691\n",
"WCnComV[384].Len() = 4\n",
"WCnComV[384][0] = 217\n",
"WCnComV[384][1] = 2379\n",
"WCnComV[384][2] = 7635\n",
"WCnComV[384][3] = 7847\n",
"WCnComV[385].Len() = 4\n",
"WCnComV[385][0] = 205\n",
"WCnComV[385][1] = 1395\n",
"WCnComV[385][2] = 2261\n",
"WCnComV[385][3] = 4675\n",
"WCnComV[386].Len() = 4\n",
"WCnComV[386][0] = 197\n",
"WCnComV[386][1] = 352\n",
"WCnComV[386][2] = 1773\n",
"WCnComV[386][3] = 4701\n",
"WCnComV[387].Len() = 4\n",
"WCnComV[387][0] = 183\n",
"WCnComV[387][1] = 3426\n",
"WCnComV[387][2] = 8529\n",
"WCnComV[387][3] = 9417\n",
"WCnComV[388].Len() = 4\n",
"WCnComV[388][0] = 181\n",
"WCnComV[388][1] = 1715\n",
"WCnComV[388][2] = 3742\n",
"WCnComV[388][3] = 9709\n",
"WCnComV[389].Len() = 4\n",
"WCnComV[389][0] = 148\n",
"WCnComV[389][1] = 4253\n",
"WCnComV[389][2] = 9193\n",
"WCnComV[389][3] = 9685\n",
"WCnComV[390].Len() = 4\n",
"WCnComV[390][0] = 106\n",
"WCnComV[390][1] = 2622\n",
"WCnComV[390][2] = 3403\n",
"WCnComV[390][3] = 3810\n",
"WCnComV[391].Len() = 4\n",
"WCnComV[391][0] = 35\n",
"WCnComV[391][1] = 2200\n",
"WCnComV[391][2] = 2610\n",
"WCnComV[391][3] = 9022\n",
"WCnComV[392].Len() = 3\n",
"WCnComV[392][0] = 8671\n",
"WCnComV[392][1] = 8821\n",
"WCnComV[392][2] = 9418\n",
"WCnComV[393].Len() = 3\n",
"WCnComV[393][0] = 8172\n",
"WCnComV[393][1] = 8705\n",
"WCnComV[393][2] = 9810\n",
"WCnComV[394].Len() = 3\n",
"WCnComV[394][0] = 7853\n",
"WCnComV[394][1] = 8589\n",
"WCnComV[394][2] = 9604\n",
"WCnComV[395].Len() = 3\n",
"WCnComV[395][0] = 7814\n",
"WCnComV[395][1] = 9104\n",
"WCnComV[395][2] = 9985\n",
"WCnComV[396].Len() = 3\n",
"WCnComV[396][0] = 7538\n",
"WCnComV[396][1] = 8087\n",
"WCnComV[396][2] = 8481\n",
"WCnComV[397].Len() = 3\n",
"WCnComV[397][0] = 7436\n",
"WCnComV[397][1] = 7714\n",
"WCnComV[397][2] = 8552\n",
"WCnComV[398].Len() = 3\n",
"WCnComV[398][0] = 7118\n",
"WCnComV[398][1] = 7838\n",
"WCnComV[398][2] = 8422\n",
"WCnComV[399].Len() = 3\n",
"WCnComV[399][0] = 6885\n",
"WCnComV[399][1] = 7551\n",
"WCnComV[399][2] = 7662\n",
"WCnComV[400].Len() = 3\n",
"WCnComV[400][0] = 6833\n",
"WCnComV[400][1] = 7485\n",
"WCnComV[400][2] = 8775\n",
"WCnComV[401].Len() = 3\n",
"WCnComV[401][0] = 6768\n",
"WCnComV[401][1] = 7434\n",
"WCnComV[401][2] = 9824\n",
"WCnComV[402].Len() = 3\n",
"WCnComV[402][0] = 6679\n",
"WCnComV[402][1] = 7902\n",
"WCnComV[402][2] = 9297\n",
"WCnComV[403].Len() = 3\n",
"WCnComV[403][0] = 6644\n",
"WCnComV[403][1] = 8603\n",
"WCnComV[403][2] = 8955\n",
"WCnComV[404].Len() = 3\n",
"WCnComV[404][0] = 6592\n",
"WCnComV[404][1] = 9095\n",
"WCnComV[404][2] = 9423\n",
"WCnComV[405].Len() = 3\n",
"WCnComV[405][0] = 6494\n",
"WCnComV[405][1] = 6571\n",
"WCnComV[405][2] = 7111\n",
"WCnComV[406].Len() = 3\n",
"WCnComV[406][0] = 6337\n",
"WCnComV[406][1] = 7462\n",
"WCnComV[406][2] = 8993\n",
"WCnComV[407].Len() = 3\n",
"WCnComV[407][0] = 6004\n",
"WCnComV[407][1] = 6256\n",
"WCnComV[407][2] = 8079\n",
"WCnComV[408].Len() = 3\n",
"WCnComV[408][0] = 5963\n",
"WCnComV[408][1] = 7779\n",
"WCnComV[408][2] = 8353\n",
"WCnComV[409].Len() = 3\n",
"WCnComV[409][0] = 5862\n",
"WCnComV[409][1] = 6180\n",
"WCnComV[409][2] = 6831\n",
"WCnComV[410].Len() = 3\n",
"WCnComV[410][0] = 5809\n",
"WCnComV[410][1] = 6066\n",
"WCnComV[410][2] = 8311\n",
"WCnComV[411].Len() = 3\n",
"WCnComV[411][0] = 5675\n",
"WCnComV[411][1] = 7803\n",
"WCnComV[411][2] = 9389\n",
"WCnComV[412].Len() = 3\n",
"WCnComV[412][0] = 5657\n",
"WCnComV[412][1] = 6547\n",
"WCnComV[412][2] = 7708\n",
"WCnComV[413].Len() = 3\n",
"WCnComV[413][0] = 5636\n",
"WCnComV[413][1] = 6516\n",
"WCnComV[413][2] = 8267\n",
"WCnComV[414].Len() = 3\n",
"WCnComV[414][0] = 5453\n",
"WCnComV[414][1] = 8352\n",
"WCnComV[414][2] = 9088\n",
"WCnComV[415].Len() = 3\n",
"WCnComV[415][0] = 5406\n",
"WCnComV[415][1] = 6226\n",
"WCnComV[415][2] = 7724\n",
"WCnComV[416].Len() = 3\n",
"WCnComV[416][0] = 5377\n",
"WCnComV[416][1] = 5959\n",
"WCnComV[416][2] = 7055\n",
"WCnComV[417].Len() = 3\n",
"WCnComV[417][0] = 5317\n",
"WCnComV[417][1] = 7512\n",
"WCnComV[417][2] = 8977\n",
"WCnComV[418].Len() = 3\n",
"WCnComV[418][0] = 5241\n",
"WCnComV[418][1] = 5424\n",
"WCnComV[418][2] = 5864\n",
"WCnComV[419].Len() = 3\n",
"WCnComV[419][0] = 5082\n",
"WCnComV[419][1] = 7293\n",
"WCnComV[419][2] = 8739\n",
"WCnComV[420].Len() = 3\n",
"WCnComV[420][0] = 5080\n",
"WCnComV[420][1] = 7375\n",
"WCnComV[420][2] = 9373\n",
"WCnComV[421].Len() = 3\n",
"WCnComV[421][0] = 5034\n",
"WCnComV[421][1] = 5104\n",
"WCnComV[421][2] = 6151\n",
"WCnComV[422].Len() = 3\n",
"WCnComV[422][0] = 5021\n",
"WCnComV[422][1] = 6206\n",
"WCnComV[422][2] = 6717\n",
"WCnComV[423].Len() = 3\n",
"WCnComV[423][0] = 5012\n",
"WCnComV[423][1] = 5948\n",
"WCnComV[423][2] = 7968\n",
"WCnComV[424].Len() = 3\n",
"WCnComV[424][0] = 4988\n",
"WCnComV[424][1] = 5811\n",
"WCnComV[424][2] = 8303\n",
"WCnComV[425].Len() = 3\n",
"WCnComV[425][0] = 4940\n",
"WCnComV[425][1] = 7695\n",
"WCnComV[425][2] = 9588\n",
"WCnComV[426].Len() = 3\n",
"WCnComV[426][0] = 4909\n",
"WCnComV[426][1] = 4939\n",
"WCnComV[426][2] = 8276\n",
"WCnComV[427].Len() = 3\n",
"WCnComV[427][0] = 4795\n",
"WCnComV[427][1] = 7146\n",
"WCnComV[427][2] = 7503\n",
"WCnComV[428].Len() = 3\n",
"WCnComV[428][0] = 4648\n",
"WCnComV[428][1] = 5630\n",
"WCnComV[428][2] = 7608\n",
"WCnComV[429].Len() = 3\n",
"WCnComV[429][0] = 4645\n",
"WCnComV[429][1] = 5275\n",
"WCnComV[429][2] = 5724\n",
"WCnComV[430].Len() = 3\n",
"WCnComV[430][0] = 4545\n",
"WCnComV[430][1] = 9519\n",
"WCnComV[430][2] = 9982\n",
"WCnComV[431].Len() = 3\n",
"WCnComV[431][0] = 4514\n",
"WCnComV[431][1] = 7933\n",
"WCnComV[431][2] = 8004\n",
"WCnComV[432].Len() = 3\n",
"WCnComV[432][0] = 4464\n",
"WCnComV[432][1] = 7412\n",
"WCnComV[432][2] = 8965\n",
"WCnComV[433].Len() = 3\n",
"WCnComV[433][0] = 4399\n",
"WCnComV[433][1] = 5758\n",
"WCnComV[433][2] = 9563\n",
"WCnComV[434].Len() = 3\n",
"WCnComV[434][0] = 4362\n",
"WCnComV[434][1] = 5117\n",
"WCnComV[434][2] = 5193\n",
"WCnComV[435].Len() = 3\n",
"WCnComV[435][0] = 4345\n",
"WCnComV[435][1] = 7880\n",
"WCnComV[435][2] = 7980\n",
"WCnComV[436].Len() = 3\n",
"WCnComV[436][0] = 4291\n",
"WCnComV[436][1] = 8253\n",
"WCnComV[436][2] = 9853\n",
"WCnComV[437].Len() = 3\n",
"WCnComV[437][0] = 4274\n",
"WCnComV[437][1] = 4855\n",
"WCnComV[437][2] = 9108\n",
"WCnComV[438].Len() = 3\n",
"WCnComV[438][0] = 4145\n",
"WCnComV[438][1] = 4350\n",
"WCnComV[438][2] = 7037\n",
"WCnComV[439].Len() = 3\n",
"WCnComV[439][0] = 4050\n",
"WCnComV[439][1] = 8935\n",
"WCnComV[439][2] = 9862\n",
"WCnComV[440].Len() = 3\n",
"WCnComV[440][0] = 4028\n",
"WCnComV[440][1] = 8912\n",
"WCnComV[440][2] = 9687\n",
"WCnComV[441].Len() = 3\n",
"WCnComV[441][0] = 3980\n",
"WCnComV[441][1] = 4895\n",
"WCnComV[441][2] = 4992\n",
"WCnComV[442].Len() = 3\n",
"WCnComV[442][0] = 3978\n",
"WCnComV[442][1] = 7300\n",
"WCnComV[442][2] = 7934\n",
"WCnComV[443].Len() = 3\n",
"WCnComV[443][0] = 3977\n",
"WCnComV[443][1] = 4620\n",
"WCnComV[443][2] = 5924\n",
"WCnComV[444].Len() = 3\n",
"WCnComV[444][0] = 3960\n",
"WCnComV[444][1] = 5482\n",
"WCnComV[444][2] = 5853\n",
"WCnComV[445].Len() = 3\n",
"WCnComV[445][0] = 3927\n",
"WCnComV[445][1] = 5019\n",
"WCnComV[445][2] = 8459\n",
"WCnComV[446].Len() = 3\n",
"WCnComV[446][0] = 3912\n",
"WCnComV[446][1] = 4112\n",
"WCnComV[446][2] = 7240\n",
"WCnComV[447].Len() = 3\n",
"WCnComV[447][0] = 3888\n",
"WCnComV[447][1] = 4933\n",
"WCnComV[447][2] = 9444\n",
"WCnComV[448].Len() = 3\n",
"WCnComV[448][0] = 3803\n",
"WCnComV[448][1] = 7376\n",
"WCnComV[448][2] = 9858\n",
"WCnComV[449].Len() = 3\n",
"WCnComV[449][0] = 3775\n",
"WCnComV[449][1] = 5925\n",
"WCnComV[449][2] = 9027\n",
"WCnComV[450].Len() = 3\n",
"WCnComV[450][0] = 3774\n",
"WCnComV[450][1] = 5778\n",
"WCnComV[450][2] = 9663\n",
"WCnComV[451].Len() = 3\n",
"WCnComV[451][0] = 3716\n",
"WCnComV[451][1] = 5444\n",
"WCnComV[451][2] = 7715\n",
"WCnComV[452].Len() = 3\n",
"WCnComV[452][0] = 3685\n",
"WCnComV[452][1] = 4954\n",
"WCnComV[452][2] = 5227\n",
"WCnComV[453].Len() = 3\n",
"WCnComV[453][0] = 3655\n",
"WCnComV[453][1] = 5415\n",
"WCnComV[453][2] = 7618\n",
"WCnComV[454].Len() = 3\n",
"WCnComV[454][0] = 3604\n",
"WCnComV[454][1] = 7033\n",
"WCnComV[454][2] = 9281\n",
"WCnComV[455].Len() = 3\n",
"WCnComV[455][0] = 3602\n",
"WCnComV[455][1] = 4922\n",
"WCnComV[455][2] = 5798\n",
"WCnComV[456].Len() = 3\n",
"WCnComV[456][0] = 3541\n",
"WCnComV[456][1] = 8621\n",
"WCnComV[456][2] = 8669\n",
"WCnComV[457].Len() = 3\n",
"WCnComV[457][0] = 3456\n",
"WCnComV[457][1] = 5539\n",
"WCnComV[457][2] = 8021\n",
"WCnComV[458].Len() = 3\n",
"WCnComV[458][0] = 3408\n",
"WCnComV[458][1] = 4295\n",
"WCnComV[458][2] = 4378\n",
"WCnComV[459].Len() = 3\n",
"WCnComV[459][0] = 3281\n",
"WCnComV[459][1] = 3326\n",
"WCnComV[459][2] = 6093\n",
"WCnComV[460].Len() = 3\n",
"WCnComV[460][0] = 3275\n",
"WCnComV[460][1] = 3645\n",
"WCnComV[460][2] = 5532\n",
"WCnComV[461].Len() = 3\n",
"WCnComV[461][0] = 3274\n",
"WCnComV[461][1] = 7961\n",
"WCnComV[461][2] = 8409\n",
"WCnComV[462].Len() = 3\n",
"WCnComV[462][0] = 3241\n",
"WCnComV[462][1] = 5413\n",
"WCnComV[462][2] = 8986\n",
"WCnComV[463].Len() = 3\n",
"WCnComV[463][0] = 3230\n",
"WCnComV[463][1] = 3707\n",
"WCnComV[463][2] = 4071\n",
"WCnComV[464].Len() = 3\n",
"WCnComV[464][0] = 3223\n",
"WCnComV[464][1] = 4254\n",
"WCnComV[464][2] = 7151\n",
"WCnComV[465].Len() = 3\n",
"WCnComV[465][0] = 3202\n",
"WCnComV[465][1] = 8769\n",
"WCnComV[465][2] = 9613\n",
"WCnComV[466].Len() = 3\n",
"WCnComV[466][0] = 3142\n",
"WCnComV[466][1] = 6060\n",
"WCnComV[466][2] = 6316\n",
"WCnComV[467].Len() = 3\n",
"WCnComV[467][0] = 3124\n",
"WCnComV[467][1] = 6772\n",
"WCnComV[467][2] = 7193\n",
"WCnComV[468].Len() = 3\n",
"WCnComV[468][0] = 3116\n",
"WCnComV[468][1] = 4183\n",
"WCnComV[468][2] = 7350\n",
"WCnComV[469].Len() = 3\n",
"WCnComV[469][0] = 3039\n",
"WCnComV[469][1] = 7275\n",
"WCnComV[469][2] = 8405\n",
"WCnComV[470].Len() = 3\n",
"WCnComV[470][0] = 3027\n",
"WCnComV[470][1] = 4904\n",
"WCnComV[470][2] = 6970\n",
"WCnComV[471].Len() = 3\n",
"WCnComV[471][0] = 2970\n",
"WCnComV[471][1] = 5479\n",
"WCnComV[471][2] = 8313\n",
"WCnComV[472].Len() = 3\n",
"WCnComV[472][0] = 2924\n",
"WCnComV[472][1] = 3238\n",
"WCnComV[472][2] = 8462\n",
"WCnComV[473].Len() = 3\n",
"WCnComV[473][0] = 2903\n",
"WCnComV[473][1] = 6997\n",
"WCnComV[473][2] = 9651\n",
"WCnComV[474].Len() = 3\n",
"WCnComV[474][0] = 2896\n",
"WCnComV[474][1] = 4737\n",
"WCnComV[474][2] = 6506\n",
"WCnComV[475].Len() = 3\n",
"WCnComV[475][0] = 2893\n",
"WCnComV[475][1] = 3433\n",
"WCnComV[475][2] = 9026\n",
"WCnComV[476].Len() = 3\n",
"WCnComV[476][0] = 2887\n",
"WCnComV[476][1] = 5562\n",
"WCnComV[476][2] = 6283\n",
"WCnComV[477].Len() = 3\n",
"WCnComV[477][0] = 2868\n",
"WCnComV[477][1] = 4970\n",
"WCnComV[477][2] = 5806\n",
"WCnComV[478].Len() = 3\n",
"WCnComV[478][0] = 2821\n",
"WCnComV[478][1] = 3892\n",
"WCnComV[478][2] = 5714\n",
"WCnComV[479].Len() = 3\n",
"WCnComV[479][0] = 2787\n",
"WCnComV[479][1] = 4282\n",
"WCnComV[479][2] = 5063\n",
"WCnComV[480].Len() = 3\n",
"WCnComV[480][0] = 2726\n",
"WCnComV[480][1] = 8005\n",
"WCnComV[480][2] = 8713\n",
"WCnComV[481].Len() = 3\n",
"WCnComV[481][0] = 2720\n",
"WCnComV[481][1] = 2878\n",
"WCnComV[481][2] = 7711\n",
"WCnComV[482].Len() = 3\n",
"WCnComV[482][0] = 2702\n",
"WCnComV[482][1] = 2765\n",
"WCnComV[482][2] = 8661\n",
"WCnComV[483].Len() = 3\n",
"WCnComV[483][0] = 2682\n",
"WCnComV[483][1] = 2811\n",
"WCnComV[483][2] = 5404\n",
"WCnComV[484].Len() = 3\n",
"WCnComV[484][0] = 2680\n",
"WCnComV[484][1] = 3498\n",
"WCnComV[484][2] = 4355\n",
"WCnComV[485].Len() = 3\n",
"WCnComV[485][0] = 2655\n",
"WCnComV[485][1] = 5181\n",
"WCnComV[485][2] = 6706\n",
"WCnComV[486].Len() = 3\n",
"WCnComV[486][0] = 2634\n",
"WCnComV[486][1] = 3813\n",
"WCnComV[486][2] = 5715\n",
"WCnComV[487].Len() = 3\n",
"WCnComV[487][0] = 2618\n",
"WCnComV[487][1] = 6468\n",
"WCnComV[487][2] = 7348\n",
"WCnComV[488].Len() = 3\n",
"WCnComV[488][0] = 2604\n",
"WCnComV[488][1] = 6365\n",
"WCnComV[488][2] = 9751\n",
"WCnComV[489].Len() = 3\n",
"WCnComV[489][0] = 2516\n",
"WCnComV[489][1] = 8125\n",
"WCnComV[489][2] = 9238\n",
"WCnComV[490].Len() = 3\n",
"WCnComV[490][0] = 2494\n",
"WCnComV[490][1] = 2630\n",
"WCnComV[490][2] = 7871\n",
"WCnComV[491].Len() = 3\n",
"WCnComV[491][0] = 2476\n",
"WCnComV[491][1] = 4023\n",
"WCnComV[491][2] = 7491\n",
"WCnComV[492].Len() = 3\n",
"WCnComV[492][0] = 2452\n",
"WCnComV[492][1] = 4731\n",
"WCnComV[492][2] = 7472\n",
"WCnComV[493].Len() = 3\n",
"WCnComV[493][0] = 2414\n",
"WCnComV[493][1] = 6930\n",
"WCnComV[493][2] = 8512\n",
"WCnComV[494].Len() = 3\n",
"WCnComV[494][0] = 2411\n",
"WCnComV[494][1] = 4293\n",
"WCnComV[494][2] = 8453\n",
"WCnComV[495].Len() = 3\n",
"WCnComV[495][0] = 2358\n",
"WCnComV[495][1] = 4674\n",
"WCnComV[495][2] = 6023\n",
"WCnComV[496].Len() = 3\n",
"WCnComV[496][0] = 2342\n",
"WCnComV[496][1] = 3139\n",
"WCnComV[496][2] = 9044\n",
"WCnComV[497].Len() = 3\n",
"WCnComV[497][0] = 2341\n",
"WCnComV[497][1] = 2721\n",
"WCnComV[497][2] = 7645\n",
"WCnComV[498].Len() = 3\n",
"WCnComV[498][0] = 2322\n",
"WCnComV[498][1] = 3032\n",
"WCnComV[498][2] = 6912\n",
"WCnComV[499].Len() = 3\n",
"WCnComV[499][0] = 2310\n",
"WCnComV[499][1] = 4130\n",
"WCnComV[499][2] = 8670\n",
"WCnComV[500].Len() = 3\n",
"WCnComV[500][0] = 2309\n",
"WCnComV[500][1] = 3055\n",
"WCnComV[500][2] = 8779\n",
"WCnComV[501].Len() = 3\n",
"WCnComV[501][0] = 2285\n",
"WCnComV[501][1] = 6173\n",
"WCnComV[501][2] = 6242\n",
"WCnComV[502].Len() = 3\n",
"WCnComV[502][0] = 2274\n",
"WCnComV[502][1] = 6711\n",
"WCnComV[502][2] = 7389\n",
"WCnComV[503].Len() = 3\n",
"WCnComV[503][0] = 2272\n",
"WCnComV[503][1] = 7640\n",
"WCnComV[503][2] = 9551\n",
"WCnComV[504].Len() = 3\n",
"WCnComV[504][0] = 2227\n",
"WCnComV[504][1] = 6050\n",
"WCnComV[504][2] = 6863\n",
"WCnComV[505].Len() = 3\n",
"WCnComV[505][0] = 2222\n",
"WCnComV[505][1] = 5789\n",
"WCnComV[505][2] = 9363\n",
"WCnComV[506].Len() = 3\n",
"WCnComV[506][0] = 2203\n",
"WCnComV[506][1] = 2672\n",
"WCnComV[506][2] = 9295\n",
"WCnComV[507].Len() = 3\n",
"WCnComV[507][0] = 2174\n",
"WCnComV[507][1] = 5259\n",
"WCnComV[507][2] = 5499\n",
"WCnComV[508].Len() = 3\n",
"WCnComV[508][0] = 2163\n",
"WCnComV[508][1] = 6720\n",
"WCnComV[508][2] = 8619\n",
"WCnComV[509].Len() = 3\n",
"WCnComV[509][0] = 2153\n",
"WCnComV[509][1] = 4471\n",
"WCnComV[509][2] = 7502\n",
"WCnComV[510].Len() = 3\n",
"WCnComV[510][0] = 2151\n",
"WCnComV[510][1] = 3269\n",
"WCnComV[510][2] = 6961\n",
"WCnComV[511].Len() = 3\n",
"WCnComV[511][0] = 2102\n",
"WCnComV[511][1] = 6334\n",
"WCnComV[511][2] = 8063\n",
"WCnComV[512].Len() = 3\n",
"WCnComV[512][0] = 2053\n",
"WCnComV[512][1] = 4026\n",
"WCnComV[512][2] = 8090\n",
"WCnComV[513].Len() = 3\n",
"WCnComV[513][0] = 2051\n",
"WCnComV[513][1] = 2209\n",
"WCnComV[513][2] = 5575\n",
"WCnComV[514].Len() = 3\n",
"WCnComV[514][0] = 2027\n",
"WCnComV[514][1] = 6649\n",
"WCnComV[514][2] = 9315\n",
"WCnComV[515].Len() = 3\n",
"WCnComV[515][0] = 2019\n",
"WCnComV[515][1] = 8343\n",
"WCnComV[515][2] = 9926\n",
"WCnComV[516].Len() = 3\n",
"WCnComV[516][0] = 2003\n",
"WCnComV[516][1] = 8477\n",
"WCnComV[516][2] = 9648\n",
"WCnComV[517].Len() = 3\n",
"WCnComV[517][0] = 1997\n",
"WCnComV[517][1] = 3318\n",
"WCnComV[517][2] = 9166\n",
"WCnComV[518].Len() = 3\n",
"WCnComV[518][0] = 1978\n",
"WCnComV[518][1] = 3022\n",
"WCnComV[518][2] = 7845\n",
"WCnComV[519].Len() = 3\n",
"WCnComV[519][0] = 1921\n",
"WCnComV[519][1] = 3899\n",
"WCnComV[519][2] = 4425\n",
"WCnComV[520].Len() = 3\n",
"WCnComV[520][0] = 1900\n",
"WCnComV[520][1] = 5119\n",
"WCnComV[520][2] = 7435\n",
"WCnComV[521].Len() = 3\n",
"WCnComV[521][0] = 1896\n",
"WCnComV[521][1] = 4903\n",
"WCnComV[521][2] = 8065\n",
"WCnComV[522].Len() = 3\n",
"WCnComV[522][0] = 1887\n",
"WCnComV[522][1] = 1967\n",
"WCnComV[522][2] = 5914\n",
"WCnComV[523].Len() = 3\n",
"WCnComV[523][0] = 1877\n",
"WCnComV[523][1] = 5902\n",
"WCnComV[523][2] = 8761\n",
"WCnComV[524].Len() = 3\n",
"WCnComV[524][0] = 1856\n",
"WCnComV[524][1] = 6623\n",
"WCnComV[524][2] = 7698\n",
"WCnComV[525].Len() = 3\n",
"WCnComV[525][0] = 1831\n",
"WCnComV[525][1] = 7889\n",
"WCnComV[525][2] = 8139\n",
"WCnComV[526].Len() = 3\n",
"WCnComV[526][0] = 1825\n",
"WCnComV[526][1] = 5612\n",
"WCnComV[526][2] = 7576\n",
"WCnComV[527].Len() = 3\n",
"WCnComV[527][0] = 1823\n",
"WCnComV[527][1] = 6539\n",
"WCnComV[527][2] = 7450\n",
"WCnComV[528].Len() = 3\n",
"WCnComV[528][0] = 1808\n",
"WCnComV[528][1] = 4850\n",
"WCnComV[528][2] = 5620\n",
"WCnComV[529].Len() = 3\n",
"WCnComV[529][0] = 1806\n",
"WCnComV[529][1] = 6269\n",
"WCnComV[529][2] = 8194\n",
"WCnComV[530].Len() = 3\n",
"WCnComV[530][0] = 1796\n",
"WCnComV[530][1] = 5039\n",
"WCnComV[530][2] = 8860\n",
"WCnComV[531].Len() = 3\n",
"WCnComV[531][0] = 1766\n",
"WCnComV[531][1] = 2386\n",
"WCnComV[531][2] = 6049\n",
"WCnComV[532].Len() = 3\n",
"WCnComV[532][0] = 1762\n",
"WCnComV[532][1] = 5618\n",
"WCnComV[532][2] = 9879\n",
"WCnComV[533].Len() = 3\n",
"WCnComV[533][0] = 1758\n",
"WCnComV[533][1] = 9246\n",
"WCnComV[533][2] = 9848\n",
"WCnComV[534].Len() = 3\n",
"WCnComV[534][0] = 1743\n",
"WCnComV[534][1] = 9016\n",
"WCnComV[534][2] = 9471\n",
"WCnComV[535].Len() = 3\n",
"WCnComV[535][0] = 1660\n",
"WCnComV[535][1] = 6959\n",
"WCnComV[535][2] = 9046\n",
"WCnComV[536].Len() = 3\n",
"WCnComV[536][0] = 1598\n",
"WCnComV[536][1] = 2898\n",
"WCnComV[536][2] = 6921\n",
"WCnComV[537].Len() = 3\n",
"WCnComV[537][0] = 1573\n",
"WCnComV[537][1] = 4296\n",
"WCnComV[537][2] = 6185\n",
"WCnComV[538].Len() = 3\n",
"WCnComV[538][0] = 1570\n",
"WCnComV[538][1] = 5621\n",
"WCnComV[538][2] = 6840\n",
"WCnComV[539].Len() = 3\n",
"WCnComV[539][0] = 1555\n",
"WCnComV[539][1] = 2445\n",
"WCnComV[539][2] = 8593\n",
"WCnComV[540].Len() = 3\n",
"WCnComV[540][0] = 1533\n",
"WCnComV[540][1] = 8256\n",
"WCnComV[540][2] = 9702\n",
"WCnComV[541].Len() = 3\n",
"WCnComV[541][0] = 1517\n",
"WCnComV[541][1] = 3538\n",
"WCnComV[541][2] = 4569\n",
"WCnComV[542].Len() = 3\n",
"WCnComV[542][0] = 1513\n",
"WCnComV[542][1] = 4689\n",
"WCnComV[542][2] = 5046\n",
"WCnComV[543].Len() = 3\n",
"WCnComV[543][0] = 1498\n",
"WCnComV[543][1] = 2301\n",
"WCnComV[543][2] = 3052\n",
"WCnComV[544].Len() = 3\n",
"WCnComV[544][0] = 1476\n",
"WCnComV[544][1] = 8547\n",
"WCnComV[544][2] = 8809\n",
"WCnComV[545].Len() = 3\n",
"WCnComV[545][0] = 1452\n",
"WCnComV[545][1] = 6560\n",
"WCnComV[545][2] = 8997\n",
"WCnComV[546].Len() = 3\n",
"WCnComV[546][0] = 1439\n",
"WCnComV[546][1] = 1506\n",
"WCnComV[546][2] = 1969\n",
"WCnComV[547].Len() = 3\n",
"WCnComV[547][0] = 1393\n",
"WCnComV[547][1] = 4357\n",
"WCnComV[547][2] = 8165\n",
"WCnComV[548].Len() = 3\n",
"WCnComV[548][0] = 1374\n",
"WCnComV[548][1] = 4938\n",
"WCnComV[548][2] = 8317\n",
"WCnComV[549].Len() = 3\n",
"WCnComV[549][0] = 1285\n",
"WCnComV[549][1] = 3098\n",
"WCnComV[549][2] = 6668\n",
"WCnComV[550].Len() = 3\n",
"WCnComV[550][0] = 1258\n",
"WCnComV[550][1] = 4036\n",
"WCnComV[550][2] = 4334\n",
"WCnComV[551].Len() = 3\n",
"WCnComV[551][0] = 1256\n",
"WCnComV[551][1] = 2881\n",
"WCnComV[551][2] = 9748\n",
"WCnComV[552].Len() = 3\n",
"WCnComV[552][0] = 1233\n",
"WCnComV[552][1] = 7332\n",
"WCnComV[552][2] = 7595\n",
"WCnComV[553].Len() = 3\n",
"WCnComV[553][0] = 1208\n",
"WCnComV[553][1] = 2401\n",
"WCnComV[553][2] = 5709\n",
"WCnComV[554].Len() = 3\n",
"WCnComV[554][0] = 1207\n",
"WCnComV[554][1] = 6947\n",
"WCnComV[554][2] = 7549\n",
"WCnComV[555].Len() = 3\n",
"WCnComV[555][0] = 1130\n",
"WCnComV[555][1] = 5281\n",
"WCnComV[555][2] = 7089\n",
"WCnComV[556].Len() = 3\n",
"WCnComV[556][0] = 1118\n",
"WCnComV[556][1] = 8143\n",
"WCnComV[556][2] = 9030\n",
"WCnComV[557].Len() = 3\n",
"WCnComV[557][0] = 1115\n",
"WCnComV[557][1] = 1454\n",
"WCnComV[557][2] = 1668\n",
"WCnComV[558].Len() = 3\n",
"WCnComV[558][0] = 1111\n",
"WCnComV[558][1] = 5401\n",
"WCnComV[558][2] = 8630\n",
"WCnComV[559].Len() = 3\n",
"WCnComV[559][0] = 1110\n",
"WCnComV[559][1] = 2937\n",
"WCnComV[559][2] = 6062\n",
"WCnComV[560].Len() = 3\n",
"WCnComV[560][0] = 1109\n",
"WCnComV[560][1] = 2962\n",
"WCnComV[560][2] = 9984\n",
"WCnComV[561].Len() = 3\n",
"WCnComV[561][0] = 1100\n",
"WCnComV[561][1] = 2526\n",
"WCnComV[561][2] = 6705\n",
"WCnComV[562].Len() = 3\n",
"WCnComV[562][0] = 1092\n",
"WCnComV[562][1] = 5431\n",
"WCnComV[562][2] = 8027\n",
"WCnComV[563].Len() = 3\n",
"WCnComV[563][0] = 1084\n",
"WCnComV[563][1] = 1618\n",
"WCnComV[563][2] = 5973\n",
"WCnComV[564].Len() = 3\n",
"WCnComV[564][0] = 1080\n",
"WCnComV[564][1] = 5808\n",
"WCnComV[564][2] = 9420\n",
"WCnComV[565].Len() = 3\n",
"WCnComV[565][0] = 1072\n",
"WCnComV[565][1] = 1378\n",
"WCnComV[565][2] = 3535\n",
"WCnComV[566].Len() = 3\n",
"WCnComV[566][0] = 1069\n",
"WCnComV[566][1] = 2013\n",
"WCnComV[566][2] = 6299\n",
"WCnComV[567].Len() = 3\n",
"WCnComV[567][0] = 1064\n",
"WCnComV[567][1] = 2771\n",
"WCnComV[567][2] = 4978\n",
"WCnComV[568].Len() = 3\n",
"WCnComV[568][0] = 1062\n",
"WCnComV[568][1] = 6588\n",
"WCnComV[568][2] = 9116\n",
"WCnComV[569].Len() = 3\n",
"WCnComV[569][0] = 1057\n",
"WCnComV[569][1] = 8764\n",
"WCnComV[569][2] = 9907\n",
"WCnComV[570].Len() = 3\n",
"WCnComV[570][0] = 1042\n",
"WCnComV[570][1] = 6893\n",
"WCnComV[570][2] = 9890\n",
"WCnComV[571].Len() = 3\n",
"WCnComV[571][0] = 1022\n",
"WCnComV[571][1] = 1197\n",
"WCnComV[571][2] = 8115\n",
"WCnComV[572].Len() = 3\n",
"WCnComV[572][0] = 1016\n",
"WCnComV[572][1] = 1680\n",
"WCnComV[572][2] = 8718\n",
"WCnComV[573].Len() = 3\n",
"WCnComV[573][0] = 1015\n",
"WCnComV[573][1] = 2332\n",
"WCnComV[573][2] = 5191\n",
"WCnComV[574].Len() = 3\n",
"WCnComV[574][0] = 1008\n",
"WCnComV[574][1] = 1253\n",
"WCnComV[574][2] = 3925\n",
"WCnComV[575].Len() = 3\n",
"WCnComV[575][0] = 938\n",
"WCnComV[575][1] = 2631\n",
"WCnComV[575][2] = 2675\n",
"WCnComV[576].Len() = 3\n",
"WCnComV[576][0] = 913\n",
"WCnComV[576][1] = 1478\n",
"WCnComV[576][2] = 7510\n",
"WCnComV[577].Len() = 3\n",
"WCnComV[577][0] = 884\n",
"WCnComV[577][1] = 1834\n",
"WCnComV[577][2] = 2295\n",
"WCnComV[578].Len() = 3\n",
"WCnComV[578][0] = 876\n",
"WCnComV[578][1] = 8564\n",
"WCnComV[578][2] = 9243\n",
"WCnComV[579].Len() = 3\n",
"WCnComV[579][0] = 871\n",
"WCnComV[579][1] = 6537\n",
"WCnComV[579][2] = 9799\n",
"WCnComV[580].Len() = 3\n",
"WCnComV[580][0] = 869\n",
"WCnComV[580][1] = 3816\n",
"WCnComV[580][2] = 8207\n",
"WCnComV[581].Len() = 3\n",
"WCnComV[581][0] = 846\n",
"WCnComV[581][1] = 3772\n",
"WCnComV[581][2] = 6486\n",
"WCnComV[582].Len() = 3\n",
"WCnComV[582][0] = 800\n",
"WCnComV[582][1] = 2870\n",
"WCnComV[582][2] = 9980\n",
"WCnComV[583].Len() = 3\n",
"WCnComV[583][0] = 798\n",
"WCnComV[583][1] = 1117\n",
"WCnComV[583][2] = 8780\n",
"WCnComV[584].Len() = 3\n",
"WCnComV[584][0] = 788\n",
"WCnComV[584][1] = 3514\n",
"WCnComV[584][2] = 5754\n",
"WCnComV[585].Len() = 3\n",
"WCnComV[585][0] = 785\n",
"WCnComV[585][1] = 1290\n",
"WCnComV[585][2] = 7809\n",
"WCnComV[586].Len() = 3\n",
"WCnComV[586][0] = 751\n",
"WCnComV[586][1] = 2961\n",
"WCnComV[586][2] = 7722\n",
"WCnComV[587].Len() = 3\n",
"WCnComV[587][0] = 727\n",
"WCnComV[587][1] = 7522\n",
"WCnComV[587][2] = 7700\n",
"WCnComV[588].Len() = 3\n",
"WCnComV[588][0] = 724\n",
"WCnComV[588][1] = 2524\n",
"WCnComV[588][2] = 7496\n",
"WCnComV[589].Len() = 3\n",
"WCnComV[589][0] = 722\n",
"WCnComV[589][1] = 4622\n",
"WCnComV[589][2] = 7019\n",
"WCnComV[590].Len() = 3\n",
"WCnComV[590][0] = 719\n",
"WCnComV[590][1] = 3064\n",
"WCnComV[590][2] = 6135\n",
"WCnComV[591].Len() = 3\n",
"WCnComV[591][0] = 694\n",
"WCnComV[591][1] = 1321\n",
"WCnComV[591][2] = 3502\n",
"WCnComV[592].Len() = 3\n",
"WCnComV[592][0] = 613\n",
"WCnComV[592][1] = 7575\n",
"WCnComV[592][2] = 9202\n",
"WCnComV[593].Len() = 3\n",
"WCnComV[593][0] = 605\n",
"WCnComV[593][1] = 747\n",
"WCnComV[593][2] = 1717\n",
"WCnComV[594].Len() = 3\n",
"WCnComV[594][0] = 597\n",
"WCnComV[594][1] = 8736\n",
"WCnComV[594][2] = 8874\n",
"WCnComV[595].Len() = 3\n",
"WCnComV[595][0] = 593\n",
"WCnComV[595][1] = 2742\n",
"WCnComV[595][2] = 3355\n",
"WCnComV[596].Len() = 3\n",
"WCnComV[596][0] = 587\n",
"WCnComV[596][1] = 4436\n",
"WCnComV[596][2] = 9603\n",
"WCnComV[597].Len() = 3\n",
"WCnComV[597][0] = 576\n",
"WCnComV[597][1] = 7474\n",
"WCnComV[597][2] = 9831\n",
"WCnComV[598].Len() = 3\n",
"WCnComV[598][0] = 574\n",
"WCnComV[598][1] = 883\n",
"WCnComV[598][2] = 2580\n",
"WCnComV[599].Len() = 3\n",
"WCnComV[599][0] = 573\n",
"WCnComV[599][1] = 2925\n",
"WCnComV[599][2] = 8284\n",
"WCnComV[600].Len() = 3\n",
"WCnComV[600][0] = 568\n",
"WCnComV[600][1] = 5014\n",
"WCnComV[600][2] = 9950\n",
"WCnComV[601].Len() = 3\n",
"WCnComV[601][0] = 536\n",
"WCnComV[601][1] = 8154\n",
"WCnComV[601][2] = 8974\n",
"WCnComV[602].Len() = 3\n",
"WCnComV[602][0] = 513\n",
"WCnComV[602][1] = 2113\n",
"WCnComV[602][2] = 9025\n",
"WCnComV[603].Len() = 3\n",
"WCnComV[603][0] = 494\n",
"WCnComV[603][1] = 660\n",
"WCnComV[603][2] = 8426\n",
"WCnComV[604].Len() = 3\n",
"WCnComV[604][0] = 472\n",
"WCnComV[604][1] = 1944\n",
"WCnComV[604][2] = 5609\n",
"WCnComV[605].Len() = 3\n",
"WCnComV[605][0] = 471\n",
"WCnComV[605][1] = 2844\n",
"WCnComV[605][2] = 4558\n",
"WCnComV[606].Len() = 3\n",
"WCnComV[606][0] = 447\n",
"WCnComV[606][1] = 4197\n",
"WCnComV[606][2] = 4971\n",
"WCnComV[607].Len() = 3\n",
"WCnComV[607][0] = 415\n",
"WCnComV[607][1] = 2037\n",
"WCnComV[607][2] = 4787\n",
"WCnComV[608].Len() = 3\n",
"WCnComV[608][0] = 406\n",
"WCnComV[608][1] = 847\n",
"WCnComV[608][2] = 8933\n",
"WCnComV[609].Len() = 3\n",
"WCnComV[609][0] = 399\n",
"WCnComV[609][1] = 7159\n",
"WCnComV[609][2] = 9094\n",
"WCnComV[610].Len() = 3\n",
"WCnComV[610][0] = 379\n",
"WCnComV[610][1] = 1308\n",
"WCnComV[610][2] = 5644\n",
"WCnComV[611].Len() = 3\n",
"WCnComV[611][0] = 374\n",
"WCnComV[611][1] = 3262\n",
"WCnComV[611][2] = 4322\n",
"WCnComV[612].Len() = 3\n",
"WCnComV[612][0] = 368\n",
"WCnComV[612][1] = 738\n",
"WCnComV[612][2] = 6317\n",
"WCnComV[613].Len() = 3\n",
"WCnComV[613][0] = 351\n",
"WCnComV[613][1] = 3218\n",
"WCnComV[613][2] = 7247\n",
"WCnComV[614].Len() = 3\n",
"WCnComV[614][0] = 338\n",
"WCnComV[614][1] = 4302\n",
"WCnComV[614][2] = 8008\n",
"WCnComV[615].Len() = 3\n",
"WCnComV[615][0] = 324\n",
"WCnComV[615][1] = 6235\n",
"WCnComV[615][2] = 6854\n",
"WCnComV[616].Len() = 3\n",
"WCnComV[616][0] = 319\n",
"WCnComV[616][1] = 853\n",
"WCnComV[616][2] = 2279\n",
"WCnComV[617].Len() = 3\n",
"WCnComV[617][0] = 316\n",
"WCnComV[617][1] = 7128\n",
"WCnComV[617][2] = 7451\n",
"WCnComV[618].Len() = 3\n",
"WCnComV[618][0] = 307\n",
"WCnComV[618][1] = 2784\n",
"WCnComV[618][2] = 4240\n",
"WCnComV[619].Len() = 3\n",
"WCnComV[619][0] = 303\n",
"WCnComV[619][1] = 2515\n",
"WCnComV[619][2] = 5982\n",
"WCnComV[620].Len() = 3\n",
"WCnComV[620][0] = 301\n",
"WCnComV[620][1] = 3836\n",
"WCnComV[620][2] = 5825\n",
"WCnComV[621].Len() = 3\n",
"WCnComV[621][0] = 264\n",
"WCnComV[621][1] = 1462\n",
"WCnComV[621][2] = 6169\n",
"WCnComV[622].Len() = 3\n",
"WCnComV[622][0] = 257\n",
"WCnComV[622][1] = 7851\n",
"WCnComV[622][2] = 8961\n",
"WCnComV[623].Len() = 3\n",
"WCnComV[623][0] = 253\n",
"WCnComV[623][1] = 2323\n",
"WCnComV[623][2] = 5929\n",
"WCnComV[624].Len() = 3\n",
"WCnComV[624][0] = 250\n",
"WCnComV[624][1] = 2521\n",
"WCnComV[624][2] = 4637\n",
"WCnComV[625].Len() = 3\n",
"WCnComV[625][0] = 245\n",
"WCnComV[625][1] = 1691\n",
"WCnComV[625][2] = 5393\n",
"WCnComV[626].Len() = 3\n",
"WCnComV[626][0] = 243\n",
"WCnComV[626][1] = 8301\n",
"WCnComV[626][2] = 9973\n",
"WCnComV[627].Len() = 3\n",
"WCnComV[627][0] = 241\n",
"WCnComV[627][1] = 2103\n",
"WCnComV[627][2] = 7231\n",
"WCnComV[628].Len() = 3\n",
"WCnComV[628][0] = 222\n",
"WCnComV[628][1] = 3657\n",
"WCnComV[628][2] = 5852\n",
"WCnComV[629].Len() = 3\n",
"WCnComV[629][0] = 212\n",
"WCnComV[629][1] = 1867\n",
"WCnComV[629][2] = 2479\n",
"WCnComV[630].Len() = 3\n",
"WCnComV[630][0] = 208\n",
"WCnComV[630][1] = 5813\n",
"WCnComV[630][2] = 5850\n",
"WCnComV[631].Len() = 3\n",
"WCnComV[631][0] = 204\n",
"WCnComV[631][1] = 1690\n",
"WCnComV[631][2] = 5090\n",
"WCnComV[632].Len() = 3\n",
"WCnComV[632][0] = 203\n",
"WCnComV[632][1] = 3801\n",
"WCnComV[632][2] = 5253\n",
"WCnComV[633].Len() = 3\n",
"WCnComV[633][0] = 182\n",
"WCnComV[633][1] = 3794\n",
"WCnComV[633][2] = 4124\n",
"WCnComV[634].Len() = 3\n",
"WCnComV[634][0] = 170\n",
"WCnComV[634][1] = 8569\n",
"WCnComV[634][2] = 9385\n",
"WCnComV[635].Len() = 3\n",
"WCnComV[635][0] = 165\n",
"WCnComV[635][1] = 6086\n",
"WCnComV[635][2] = 6651\n",
"WCnComV[636].Len() = 3\n",
"WCnComV[636][0] = 163\n",
"WCnComV[636][1] = 2017\n",
"WCnComV[636][2] = 6451\n",
"WCnComV[637].Len() = 3\n",
"WCnComV[637][0] = 144\n",
"WCnComV[637][1] = 2528\n",
"WCnComV[637][2] = 8930\n",
"WCnComV[638].Len() = 3\n",
"WCnComV[638][0] = 142\n",
"WCnComV[638][1] = 2395\n",
"WCnComV[638][2] = 6931\n",
"WCnComV[639].Len() = 3\n",
"WCnComV[639][0] = 127\n",
"WCnComV[639][1] = 1049\n",
"WCnComV[639][2] = 8950\n",
"WCnComV[640].Len() = 3\n",
"WCnComV[640][0] = 123\n",
"WCnComV[640][1] = 1742\n",
"WCnComV[640][2] = 5209\n",
"WCnComV[641].Len() = 3\n",
"WCnComV[641][0] = 107\n",
"WCnComV[641][1] = 1778\n",
"WCnComV[641][2] = 6871\n",
"WCnComV[642].Len() = 3\n",
"WCnComV[642][0] = 95\n",
"WCnComV[642][1] = 1582\n",
"WCnComV[642][2] = 4149\n",
"WCnComV[643].Len() = 3\n",
"WCnComV[643][0] = 89\n",
"WCnComV[643][1] = 396\n",
"WCnComV[643][2] = 5015\n",
"WCnComV[644].Len() = 3\n",
"WCnComV[644][0] = 80\n",
"WCnComV[644][1] = 4718\n",
"WCnComV[644][2] = 7334\n",
"WCnComV[645].Len() = 3\n",
"WCnComV[645][0] = 69\n",
"WCnComV[645][1] = 4181\n",
"WCnComV[645][2] = 7487\n",
"WCnComV[646].Len() = 3\n",
"WCnComV[646][0] = 16\n",
"WCnComV[646][1] = 3474\n",
"WCnComV[646][2] = 6944\n",
"WCnComV[647].Len() = 3\n",
"WCnComV[647][0] = 14\n",
"WCnComV[647][1] = 4150\n",
"WCnComV[647][2] = 5629\n",
"WCnComV[648].Len() = 2\n",
"WCnComV[648][0] = 9168\n",
"WCnComV[648][1] = 9674\n",
"WCnComV[649].Len() = 2\n",
"WCnComV[649][0] = 9084\n",
"WCnComV[649][1] = 9630\n",
"WCnComV[650].Len() = 2\n",
"WCnComV[650][0] = 9021\n",
"WCnComV[650][1] = 9283\n",
"WCnComV[651].Len() = 2\n",
"WCnComV[651][0] = 9015\n",
"WCnComV[651][1] = 9750\n",
"WCnComV[652].Len() = 2\n",
"WCnComV[652][0] = 8976\n",
"WCnComV[652][1] = 9388\n",
"WCnComV[653].Len() = 2\n",
"WCnComV[653][0] = 8819\n",
"WCnComV[653][1] = 8876\n",
"WCnComV[654].Len() = 2\n",
"WCnComV[654][0] = 8803\n",
"WCnComV[654][1] = 9682\n",
"WCnComV[655].Len() = 2\n",
"WCnComV[655][0] = 8681\n",
"WCnComV[655][1] = 9757\n",
"WCnComV[656].Len() = 2\n",
"WCnComV[656][0] = 8659\n",
"WCnComV[656][1] = 9242\n",
"WCnComV[657].Len() = 2\n",
"WCnComV[657][0] = 8612\n",
"WCnComV[657][1] = 9393\n",
"WCnComV[658].Len() = 2\n",
"WCnComV[658][0] = 8606\n",
"WCnComV[658][1] = 9964\n",
"WCnComV[659].Len() = 2\n",
"WCnComV[659][0] = 8576\n",
"WCnComV[659][1] = 8734\n",
"WCnComV[660].Len() = 2\n",
"WCnComV[660][0] = 8489\n",
"WCnComV[660][1] = 9259\n",
"WCnComV[661].Len() = 2\n",
"WCnComV[661][0] = 8475\n",
"WCnComV[661][1] = 9518\n",
"WCnComV[662].Len() = 2\n",
"WCnComV[662][0] = 8460\n",
"WCnComV[662][1] = 8565\n",
"WCnComV[663].Len() = 2\n",
"WCnComV[663][0] = 8449\n",
"WCnComV[663][1] = 9225\n",
"WCnComV[664].Len() = 2\n",
"WCnComV[664][0] = 8396\n",
"WCnComV[664][1] = 9130\n",
"WCnComV[665].Len() = 2\n",
"WCnComV[665][0] = 8376\n",
"WCnComV[665][1] = 8581\n",
"WCnComV[666].Len() = 2\n",
"WCnComV[666][0] = 8318\n",
"WCnComV[666][1] = 9049\n",
"WCnComV[667].Len() = 2\n",
"WCnComV[667][0] = 8294\n",
"WCnComV[667][1] = 9200\n",
"WCnComV[668].Len() = 2\n",
"WCnComV[668][0] = 8259\n",
"WCnComV[668][1] = 9278\n",
"WCnComV[669].Len() = 2\n",
"WCnComV[669][0] = 8181\n",
"WCnComV[669][1] = 8494\n",
"WCnComV[670].Len() = 2\n",
"WCnComV[670][0] = 8073\n",
"WCnComV[670][1] = 9365\n",
"WCnComV[671].Len() = 2\n",
"WCnComV[671][0] = 8071\n",
"WCnComV[671][1] = 9499\n",
"WCnComV[672].Len() = 2\n",
"WCnComV[672][0] = 8047\n",
"WCnComV[672][1] = 9012\n",
"WCnComV[673].Len() = 2\n",
"WCnComV[673][0] = 8037\n",
"WCnComV[673][1] = 9263\n",
"WCnComV[674].Len() = 2\n",
"WCnComV[674][0] = 7977\n",
"WCnComV[674][1] = 9752\n",
"WCnComV[675].Len() = 2\n",
"WCnComV[675][0] = 7875\n",
"WCnComV[675][1] = 7953\n",
"WCnComV[676].Len() = 2\n",
"WCnComV[676][0] = 7856\n",
"WCnComV[676][1] = 9495\n",
"WCnComV[677].Len() = 2\n",
"WCnComV[677][0] = 7827\n",
"WCnComV[677][1] = 8580\n",
"WCnComV[678].Len() = 2\n",
"WCnComV[678][0] = 7825\n",
"WCnComV[678][1] = 9883\n",
"WCnComV[679].Len() = 2\n",
"WCnComV[679][0] = 7727\n",
"WCnComV[679][1] = 9596\n",
"WCnComV[680].Len() = 2\n",
"WCnComV[680][0] = 7704\n",
"WCnComV[680][1] = 8331\n",
"WCnComV[681].Len() = 2\n",
"WCnComV[681][0] = 7632\n",
"WCnComV[681][1] = 7946\n",
"WCnComV[682].Len() = 2\n",
"WCnComV[682][0] = 7539\n",
"WCnComV[682][1] = 9739\n",
"WCnComV[683].Len() = 2\n",
"WCnComV[683][0] = 7535\n",
"WCnComV[683][1] = 8443\n",
"WCnComV[684].Len() = 2\n",
"WCnComV[684][0] = 7458\n",
"WCnComV[684][1] = 9126\n",
"WCnComV[685].Len() = 2\n",
"WCnComV[685][0] = 7430\n",
"WCnComV[685][1] = 9226\n",
"WCnComV[686].Len() = 2\n",
"WCnComV[686][0] = 7426\n",
"WCnComV[686][1] = 9701\n",
"WCnComV[687].Len() = 2\n",
"WCnComV[687][0] = 7416\n",
"WCnComV[687][1] = 9578\n",
"WCnComV[688].Len() = 2\n",
"WCnComV[688][0] = 7391\n",
"WCnComV[688][1] = 7542\n",
"WCnComV[689].Len() = 2\n",
"WCnComV[689][0] = 7366\n",
"WCnComV[689][1] = 9197\n",
"WCnComV[690].Len() = 2\n",
"WCnComV[690][0] = 7337\n",
"WCnComV[690][1] = 8926\n",
"WCnComV[691].Len() = 2\n",
"WCnComV[691][0] = 7319\n",
"WCnComV[691][1] = 8956\n",
"WCnComV[692].Len() = 2\n",
"WCnComV[692][0] = 7302\n",
"WCnComV[692][1] = 8989\n",
"WCnComV[693].Len() = 2\n",
"WCnComV[693][0] = 7283\n",
"WCnComV[693][1] = 8341\n",
"WCnComV[694].Len() = 2\n",
"WCnComV[694][0] = 7256\n",
"WCnComV[694][1] = 8741\n",
"WCnComV[695].Len() = 2\n",
"WCnComV[695][0] = 7177\n",
"WCnComV[695][1] = 8220\n",
"WCnComV[696].Len() = 2\n",
"WCnComV[696][0] = 7117\n",
"WCnComV[696][1] = 9779\n",
"WCnComV[697].Len() = 2\n",
"WCnComV[697][0] = 7082\n",
"WCnComV[697][1] = 9915\n",
"WCnComV[698].Len() = 2\n",
"WCnComV[698][0] = 7063\n",
"WCnComV[698][1] = 8490\n",
"WCnComV[699].Len() = 2\n",
"WCnComV[699][0] = 7031\n",
"WCnComV[699][1] = 8979\n",
"WCnComV[700].Len() = 2\n",
"WCnComV[700][0] = 6974\n",
"WCnComV[700][1] = 7963\n",
"WCnComV[701].Len() = 2\n",
"WCnComV[701][0] = 6881\n",
"WCnComV[701][1] = 7603\n",
"WCnComV[702].Len() = 2\n",
"WCnComV[702][0] = 6873\n",
"WCnComV[702][1] = 8495\n",
"WCnComV[703].Len() = 2\n",
"WCnComV[703][0] = 6847\n",
"WCnComV[703][1] = 7352\n",
"WCnComV[704].Len() = 2\n",
"WCnComV[704][0] = 6806\n",
"WCnComV[704][1] = 7610\n",
"WCnComV[705].Len() = 2\n",
"WCnComV[705][0] = 6784\n",
"WCnComV[705][1] = 8324\n",
"WCnComV[706].Len() = 2\n",
"WCnComV[706][0] = 6754\n",
"WCnComV[706][1] = 9410\n",
"WCnComV[707].Len() = 2\n",
"WCnComV[707][0] = 6752\n",
"WCnComV[707][1] = 7244\n",
"WCnComV[708].Len() = 2\n",
"WCnComV[708][0] = 6674\n",
"WCnComV[708][1] = 7611\n",
"WCnComV[709].Len() = 2\n",
"WCnComV[709][0] = 6639\n",
"WCnComV[709][1] = 8808\n",
"WCnComV[710].Len() = 2\n",
"WCnComV[710][0] = 6625\n",
"WCnComV[710][1] = 8549\n",
"WCnComV[711].Len() = 2\n",
"WCnComV[711][0] = 6619\n",
"WCnComV[711][1] = 6940\n",
"WCnComV[712].Len() = 2\n",
"WCnComV[712][0] = 6603\n",
"WCnComV[712][1] = 7764\n",
"WCnComV[713].Len() = 2\n",
"WCnComV[713][0] = 6567\n",
"WCnComV[713][1] = 9683\n",
"WCnComV[714].Len() = 2\n",
"WCnComV[714][0] = 6536\n",
"WCnComV[714][1] = 6655\n",
"WCnComV[715].Len() = 2\n",
"WCnComV[715][0] = 6535\n",
"WCnComV[715][1] = 8601\n",
"WCnComV[716].Len() = 2\n",
"WCnComV[716][0] = 6525\n",
"WCnComV[716][1] = 8468\n",
"WCnComV[717].Len() = 2\n",
"WCnComV[717][0] = 6492\n",
"WCnComV[717][1] = 7122\n",
"WCnComV[718].Len() = 2\n",
"WCnComV[718][0] = 6447\n",
"WCnComV[718][1] = 6950\n",
"WCnComV[719].Len() = 2\n",
"WCnComV[719][0] = 6445\n",
"WCnComV[719][1] = 9712\n",
"WCnComV[720].Len() = 2\n",
"WCnComV[720][0] = 6413\n",
"WCnComV[720][1] = 7676\n",
"WCnComV[721].Len() = 2\n",
"WCnComV[721][0] = 6393\n",
"WCnComV[721][1] = 9749\n",
"WCnComV[722].Len() = 2\n",
"WCnComV[722][0] = 6355\n",
"WCnComV[722][1] = 8456\n",
"WCnComV[723].Len() = 2\n",
"WCnComV[723][0] = 6339\n",
"WCnComV[723][1] = 6813\n",
"WCnComV[724].Len() = 2\n",
"WCnComV[724][0] = 6330\n",
"WCnComV[724][1] = 8053\n",
"WCnComV[725].Len() = 2\n",
"WCnComV[725][0] = 6325\n",
"WCnComV[725][1] = 6760\n",
"WCnComV[726].Len() = 2\n",
"WCnComV[726][0] = 6323\n",
"WCnComV[726][1] = 9369\n",
"WCnComV[727].Len() = 2\n",
"WCnComV[727][0] = 6278\n",
"WCnComV[727][1] = 6637\n",
"WCnComV[728].Len() = 2\n",
"WCnComV[728][0] = 6225\n",
"WCnComV[728][1] = 6902\n",
"WCnComV[729].Len() = 2\n",
"WCnComV[729][0] = 6178\n",
"WCnComV[729][1] = 8266\n",
"WCnComV[730].Len() = 2\n",
"WCnComV[730][0] = 6175\n",
"WCnComV[730][1] = 7820\n",
"WCnComV[731].Len() = 2\n",
"WCnComV[731][0] = 6162\n",
"WCnComV[731][1] = 8862\n",
"WCnComV[732].Len() = 2\n",
"WCnComV[732][0] = 6159\n",
"WCnComV[732][1] = 7928\n",
"WCnComV[733].Len() = 2\n",
"WCnComV[733][0] = 6104\n",
"WCnComV[733][1] = 7602\n",
"WCnComV[734].Len() = 2\n",
"WCnComV[734][0] = 6079\n",
"WCnComV[734][1] = 8829\n",
"WCnComV[735].Len() = 2\n",
"WCnComV[735][0] = 6070\n",
"WCnComV[735][1] = 8673\n",
"WCnComV[736].Len() = 2\n",
"WCnComV[736][0] = 6052\n",
"WCnComV[736][1] = 6582\n",
"WCnComV[737].Len() = 2\n",
"WCnComV[737][0] = 6016\n",
"WCnComV[737][1] = 7833\n",
"WCnComV[738].Len() = 2\n",
"WCnComV[738][0] = 6010\n",
"WCnComV[738][1] = 9524\n",
"WCnComV[739].Len() = 2\n",
"WCnComV[739][0] = 6003\n",
"WCnComV[739][1] = 6589\n",
"WCnComV[740].Len() = 2\n",
"WCnComV[740][0] = 5990\n",
"WCnComV[740][1] = 7545\n",
"WCnComV[741].Len() = 2\n",
"WCnComV[741][0] = 5984\n",
"WCnComV[741][1] = 6332\n",
"WCnComV[742].Len() = 2\n",
"WCnComV[742][0] = 5975\n",
"WCnComV[742][1] = 7488\n",
"WCnComV[743].Len() = 2\n",
"WCnComV[743][0] = 5949\n",
"WCnComV[743][1] = 7904\n",
"WCnComV[744].Len() = 2\n",
"WCnComV[744][0] = 5918\n",
"WCnComV[744][1] = 9829\n",
"WCnComV[745].Len() = 2\n",
"WCnComV[745][0] = 5904\n",
"WCnComV[745][1] = 9317\n",
"WCnComV[746].Len() = 2\n",
"WCnComV[746][0] = 5901\n",
"WCnComV[746][1] = 7311\n",
"WCnComV[747].Len() = 2\n",
"WCnComV[747][0] = 5894\n",
"WCnComV[747][1] = 8923\n",
"WCnComV[748].Len() = 2\n",
"WCnComV[748][0] = 5884\n",
"WCnComV[748][1] = 9851\n",
"WCnComV[749].Len() = 2\n",
"WCnComV[749][0] = 5858\n",
"WCnComV[749][1] = 7867\n",
"WCnComV[750].Len() = 2\n",
"WCnComV[750][0] = 5843\n",
"WCnComV[750][1] = 9475\n",
"WCnComV[751].Len() = 2\n",
"WCnComV[751][0] = 5839\n",
"WCnComV[751][1] = 9272\n",
"WCnComV[752].Len() = 2\n",
"WCnComV[752][0] = 5827\n",
"WCnComV[752][1] = 6714\n",
"WCnComV[753].Len() = 2\n",
"WCnComV[753][0] = 5786\n",
"WCnComV[753][1] = 6036\n",
"WCnComV[754].Len() = 2\n",
"WCnComV[754][0] = 5782\n",
"WCnComV[754][1] = 6043\n",
"WCnComV[755].Len() = 2\n",
"WCnComV[755][0] = 5768\n",
"WCnComV[755][1] = 7749\n",
"WCnComV[756].Len() = 2\n",
"WCnComV[756][0] = 5764\n",
"WCnComV[756][1] = 9113\n",
"WCnComV[757].Len() = 2\n",
"WCnComV[757][0] = 5763\n",
"WCnComV[757][1] = 8918\n",
"WCnComV[758].Len() = 2\n",
"WCnComV[758][0] = 5723\n",
"WCnComV[758][1] = 8501\n",
"WCnComV[759].Len() = 2\n",
"WCnComV[759][0] = 5693\n",
"WCnComV[759][1] = 8413\n",
"WCnComV[760].Len() = 2\n",
"WCnComV[760][0] = 5676\n",
"WCnComV[760][1] = 8360\n",
"WCnComV[761].Len() = 2\n",
"WCnComV[761][0] = 5646\n",
"WCnComV[761][1] = 6141\n",
"WCnComV[762].Len() = 2\n",
"WCnComV[762][0] = 5633\n",
"WCnComV[762][1] = 8644\n",
"WCnComV[763].Len() = 2\n",
"WCnComV[763][0] = 5626\n",
"WCnComV[763][1] = 6779\n",
"WCnComV[764].Len() = 2\n",
"WCnComV[764][0] = 5599\n",
"WCnComV[764][1] = 9378\n",
"WCnComV[765].Len() = 2\n",
"WCnComV[765][0] = 5597\n",
"WCnComV[765][1] = 6686\n",
"WCnComV[766].Len() = 2\n",
"WCnComV[766][0] = 5592\n",
"WCnComV[766][1] = 6757\n",
"WCnComV[767].Len() = 2\n",
"WCnComV[767][0] = 5579\n",
"WCnComV[767][1] = 6552\n",
"WCnComV[768].Len() = 2\n",
"WCnComV[768][0] = 5570\n",
"WCnComV[768][1] = 8088\n",
"WCnComV[769].Len() = 2\n",
"WCnComV[769][0] = 5560\n",
"WCnComV[769][1] = 8290\n",
"WCnComV[770].Len() = 2\n",
"WCnComV[770][0] = 5559\n",
"WCnComV[770][1] = 6764\n",
"WCnComV[771].Len() = 2\n",
"WCnComV[771][0] = 5558\n",
"WCnComV[771][1] = 6852\n",
"WCnComV[772].Len() = 2\n",
"WCnComV[772][0] = 5546\n",
"WCnComV[772][1] = 8427\n",
"WCnComV[773].Len() = 2\n",
"WCnComV[773][0] = 5491\n",
"WCnComV[773][1] = 8509\n",
"WCnComV[774].Len() = 2\n",
"WCnComV[774][0] = 5476\n",
"WCnComV[774][1] = 7182\n",
"WCnComV[775].Len() = 2\n",
"WCnComV[775][0] = 5461\n",
"WCnComV[775][1] = 8672\n",
"WCnComV[776].Len() = 2\n",
"WCnComV[776][0] = 5460\n",
"WCnComV[776][1] = 6856\n",
"WCnComV[777].Len() = 2\n",
"WCnComV[777][0] = 5457\n",
"WCnComV[777][1] = 6549\n",
"WCnComV[778].Len() = 2\n",
"WCnComV[778][0] = 5456\n",
"WCnComV[778][1] = 9144\n",
"WCnComV[779].Len() = 2\n",
"WCnComV[779][0] = 5448\n",
"WCnComV[779][1] = 6228\n",
"WCnComV[780].Len() = 2\n",
"WCnComV[780][0] = 5405\n",
"WCnComV[780][1] = 6254\n",
"WCnComV[781].Len() = 2\n",
"WCnComV[781][0] = 5403\n",
"WCnComV[781][1] = 8192\n",
"WCnComV[782].Len() = 2\n",
"WCnComV[782][0] = 5382\n",
"WCnComV[782][1] = 8684\n",
"WCnComV[783].Len() = 2\n",
"WCnComV[783][0] = 5372\n",
"WCnComV[783][1] = 8058\n",
"WCnComV[784].Len() = 2\n",
"WCnComV[784][0] = 5369\n",
"WCnComV[784][1] = 8826\n",
"WCnComV[785].Len() = 2\n",
"WCnComV[785][0] = 5365\n",
"WCnComV[785][1] = 5818\n",
"WCnComV[786].Len() = 2\n",
"WCnComV[786][0] = 5364\n",
"WCnComV[786][1] = 8238\n",
"WCnComV[787].Len() = 2\n",
"WCnComV[787][0] = 5345\n",
"WCnComV[787][1] = 5972\n",
"WCnComV[788].Len() = 2\n",
"WCnComV[788][0] = 5333\n",
"WCnComV[788][1] = 5969\n",
"WCnComV[789].Len() = 2\n",
"WCnComV[789][0] = 5299\n",
"WCnComV[789][1] = 5772\n",
"WCnComV[790].Len() = 2\n",
"WCnComV[790][0] = 5293\n",
"WCnComV[790][1] = 6353\n",
"WCnComV[791].Len() = 2\n",
"WCnComV[791][0] = 5284\n",
"WCnComV[791][1] = 8560\n",
"WCnComV[792].Len() = 2\n",
"WCnComV[792][0] = 5277\n",
"WCnComV[792][1] = 9816\n",
"WCnComV[793].Len() = 2\n",
"WCnComV[793][0] = 5274\n",
"WCnComV[793][1] = 9219\n",
"WCnComV[794].Len() = 2\n",
"WCnComV[794][0] = 5266\n",
"WCnComV[794][1] = 7410\n",
"WCnComV[795].Len() = 2\n",
"WCnComV[795][0] = 5256\n",
"WCnComV[795][1] = 6610\n",
"WCnComV[796].Len() = 2\n",
"WCnComV[796][0] = 5243\n",
"WCnComV[796][1] = 7232\n",
"WCnComV[797].Len() = 2\n",
"WCnComV[797][0] = 5234\n",
"WCnComV[797][1] = 9474\n",
"WCnComV[798].Len() = 2\n",
"WCnComV[798][0] = 5198\n",
"WCnComV[798][1] = 9048\n",
"WCnComV[799].Len() = 2\n",
"WCnComV[799][0] = 5180\n",
"WCnComV[799][1] = 9963\n",
"WCnComV[800].Len() = 2\n",
"WCnComV[800][0] = 5156\n",
"WCnComV[800][1] = 6741\n",
"WCnComV[801].Len() = 2\n",
"WCnComV[801][0] = 5143\n",
"WCnComV[801][1] = 7003\n",
"WCnComV[802].Len() = 2\n",
"WCnComV[802][0] = 5136\n",
"WCnComV[802][1] = 9783\n",
"WCnComV[803].Len() = 2\n",
"WCnComV[803][0] = 5110\n",
"WCnComV[803][1] = 9925\n",
"WCnComV[804].Len() = 2\n",
"WCnComV[804][0] = 5098\n",
"WCnComV[804][1] = 7581\n",
"WCnComV[805].Len() = 2\n",
"WCnComV[805][0] = 5094\n",
"WCnComV[805][1] = 7948\n",
"WCnComV[806].Len() = 2\n",
"WCnComV[806][0] = 5093\n",
"WCnComV[806][1] = 5682\n",
"WCnComV[807].Len() = 2\n",
"WCnComV[807][0] = 5087\n",
"WCnComV[807][1] = 8895\n",
"WCnComV[808].Len() = 2\n",
"WCnComV[808][0] = 5073\n",
"WCnComV[808][1] = 7344\n",
"WCnComV[809].Len() = 2\n",
"WCnComV[809][0] = 5040\n",
"WCnComV[809][1] = 8968\n",
"WCnComV[810].Len() = 2\n",
"WCnComV[810][0] = 5024\n",
"WCnComV[810][1] = 5748\n",
"WCnComV[811].Len() = 2\n",
"WCnComV[811][0] = 5018\n",
"WCnComV[811][1] = 6600\n",
"WCnComV[812].Len() = 2\n",
"WCnComV[812][0] = 5017\n",
"WCnComV[812][1] = 8637\n",
"WCnComV[813].Len() = 2\n",
"WCnComV[813][0] = 4976\n",
"WCnComV[813][1] = 6809\n",
"WCnComV[814].Len() = 2\n",
"WCnComV[814][0] = 4965\n",
"WCnComV[814][1] = 5391\n",
"WCnComV[815].Len() = 2\n",
"WCnComV[815][0] = 4955\n",
"WCnComV[815][1] = 7083\n",
"WCnComV[816].Len() = 2\n",
"WCnComV[816][0] = 4919\n",
"WCnComV[816][1] = 6080\n",
"WCnComV[817].Len() = 2\n",
"WCnComV[817][0] = 4914\n",
"WCnComV[817][1] = 9115\n",
"WCnComV[818].Len() = 2\n",
"WCnComV[818][0] = 4884\n",
"WCnComV[818][1] = 6231\n",
"WCnComV[819].Len() = 2\n",
"WCnComV[819][0] = 4875\n",
"WCnComV[819][1] = 7422\n",
"WCnComV[820].Len() = 2\n",
"WCnComV[820][0] = 4846\n",
"WCnComV[820][1] = 7263\n",
"WCnComV[821].Len() = 2\n",
"WCnComV[821][0] = 4841\n",
"WCnComV[821][1] = 5100\n",
"WCnComV[822].Len() = 2\n",
"WCnComV[822][0] = 4833\n",
"WCnComV[822][1] = 5588\n",
"WCnComV[823].Len() = 2\n",
"WCnComV[823][0] = 4820\n",
"WCnComV[823][1] = 6431\n",
"WCnComV[824].Len() = 2\n",
"WCnComV[824][0] = 4806\n",
"WCnComV[824][1] = 8000\n",
"WCnComV[825].Len() = 2\n",
"WCnComV[825][0] = 4802\n",
"WCnComV[825][1] = 8074\n",
"WCnComV[826].Len() = 2\n",
"WCnComV[826][0] = 4784\n",
"WCnComV[826][1] = 7100\n",
"WCnComV[827].Len() = 2\n",
"WCnComV[827][0] = 4782\n",
"WCnComV[827][1] = 4953\n",
"WCnComV[828].Len() = 2\n",
"WCnComV[828][0] = 4775\n",
"WCnComV[828][1] = 5946\n",
"WCnComV[829].Len() = 2\n",
"WCnComV[829][0] = 4754\n",
"WCnComV[829][1] = 8438\n",
"WCnComV[830].Len() = 2\n",
"WCnComV[830][0] = 4745\n",
"WCnComV[830][1] = 5144\n",
"WCnComV[831].Len() = 2\n",
"WCnComV[831][0] = 4729\n",
"WCnComV[831][1] = 7556\n",
"WCnComV[832].Len() = 2\n",
"WCnComV[832][0] = 4727\n",
"WCnComV[832][1] = 9658\n",
"WCnComV[833].Len() = 2\n",
"WCnComV[833][0] = 4722\n",
"WCnComV[833][1] = 9066\n",
"WCnComV[834].Len() = 2\n",
"WCnComV[834][0] = 4702\n",
"WCnComV[834][1] = 9340\n",
"WCnComV[835].Len() = 2\n",
"WCnComV[835][0] = 4688\n",
"WCnComV[835][1] = 9657\n",
"WCnComV[836].Len() = 2\n",
"WCnComV[836][0] = 4678\n",
"WCnComV[836][1] = 6528\n",
"WCnComV[837].Len() = 2\n",
"WCnComV[837][0] = 4663\n",
"WCnComV[837][1] = 8687\n",
"WCnComV[838].Len() = 2\n",
"WCnComV[838][0] = 4651\n",
"WCnComV[838][1] = 5278\n",
"WCnComV[839].Len() = 2\n",
"WCnComV[839][0] = 4649\n",
"WCnComV[839][1] = 5459\n",
"WCnComV[840].Len() = 2\n",
"WCnComV[840][0] = 4630\n",
"WCnComV[840][1] = 7331\n",
"WCnComV[841].Len() = 2\n",
"WCnComV[841][0] = 4623\n",
"WCnComV[841][1] = 5886\n",
"WCnComV[842].Len() = 2\n",
"WCnComV[842][0] = 4617\n",
"WCnComV[842][1] = 5736\n",
"WCnComV[843].Len() = 2\n",
"WCnComV[843][0] = 4609\n",
"WCnComV[843][1] = 9688\n",
"WCnComV[844].Len() = 2\n",
"WCnComV[844][0] = 4608\n",
"WCnComV[844][1] = 6689\n",
"WCnComV[845].Len() = 2\n",
"WCnComV[845][0] = 4594\n",
"WCnComV[845][1] = 4877\n",
"WCnComV[846].Len() = 2\n",
"WCnComV[846][0] = 4581\n",
"WCnComV[846][1] = 7339\n",
"WCnComV[847].Len() = 2\n",
"WCnComV[847][0] = 4579\n",
"WCnComV[847][1] = 8485\n",
"WCnComV[848].Len() = 2\n",
"WCnComV[848][0] = 4575\n",
"WCnComV[848][1] = 7739\n",
"WCnComV[849].Len() = 2\n",
"WCnComV[849][0] = 4571\n",
"WCnComV[849][1] = 7358\n",
"WCnComV[850].Len() = 2\n",
"WCnComV[850][0] = 4556\n",
"WCnComV[850][1] = 8101\n",
"WCnComV[851].Len() = 2\n",
"WCnComV[851][0] = 4550\n",
"WCnComV[851][1] = 7862\n",
"WCnComV[852].Len() = 2\n",
"WCnComV[852][0] = 4544\n",
"WCnComV[852][1] = 7405\n",
"WCnComV[853].Len() = 2\n",
"WCnComV[853][0] = 4501\n",
"WCnComV[853][1] = 7066\n",
"WCnComV[854].Len() = 2\n",
"WCnComV[854][0] = 4493\n",
"WCnComV[854][1] = 6969\n",
"WCnComV[855].Len() = 2\n",
"WCnComV[855][0] = 4487\n",
"WCnComV[855][1] = 6015\n",
"WCnComV[856].Len() = 2\n",
"WCnComV[856][0] = 4481\n",
"WCnComV[856][1] = 6331\n",
"WCnComV[857].Len() = 2\n",
"WCnComV[857][0] = 4454\n",
"WCnComV[857][1] = 8835\n",
"WCnComV[858].Len() = 2\n",
"WCnComV[858][0] = 4449\n",
"WCnComV[858][1] = 9074\n",
"WCnComV[859].Len() = 2\n",
"WCnComV[859][0] = 4424\n",
"WCnComV[859][1] = 9176\n",
"WCnComV[860].Len() = 2\n",
"WCnComV[860][0] = 4408\n",
"WCnComV[860][1] = 6707\n",
"WCnComV[861].Len() = 2\n",
"WCnComV[861][0] = 4404\n",
"WCnComV[861][1] = 6038\n",
"WCnComV[862].Len() = 2\n",
"WCnComV[862][0] = 4397\n",
"WCnComV[862][1] = 7465\n",
"WCnComV[863].Len() = 2\n",
"WCnComV[863][0] = 4354\n",
"WCnComV[863][1] = 5689\n",
"WCnComV[864].Len() = 2\n",
"WCnComV[864][0] = 4353\n",
"WCnComV[864][1] = 9266\n",
"WCnComV[865].Len() = 2\n",
"WCnComV[865][0] = 4335\n",
"WCnComV[865][1] = 6055\n",
"WCnComV[866].Len() = 2\n",
"WCnComV[866][0] = 4332\n",
"WCnComV[866][1] = 9001\n",
"WCnComV[867].Len() = 2\n",
"WCnComV[867][0] = 4329\n",
"WCnComV[867][1] = 8480\n",
"WCnComV[868].Len() = 2\n",
"WCnComV[868][0] = 4321\n",
"WCnComV[868][1] = 7913\n",
"WCnComV[869].Len() = 2\n",
"WCnComV[869][0] = 4313\n",
"WCnComV[869][1] = 7017\n",
"WCnComV[870].Len() = 2\n",
"WCnComV[870][0] = 4290\n",
"WCnComV[870][1] = 9470\n",
"WCnComV[871].Len() = 2\n",
"WCnComV[871][0] = 4279\n",
"WCnComV[871][1] = 5347\n",
"WCnComV[872].Len() = 2\n",
"WCnComV[872][0] = 4271\n",
"WCnComV[872][1] = 4986\n",
"WCnComV[873].Len() = 2\n",
"WCnComV[873][0] = 4246\n",
"WCnComV[873][1] = 5706\n",
"WCnComV[874].Len() = 2\n",
"WCnComV[874][0] = 4222\n",
"WCnComV[874][1] = 5968\n",
"WCnComV[875].Len() = 2\n",
"WCnComV[875][0] = 4179\n",
"WCnComV[875][1] = 4248\n",
"WCnComV[876].Len() = 2\n",
"WCnComV[876][0] = 4175\n",
"WCnComV[876][1] = 4738\n",
"WCnComV[877].Len() = 2\n",
"WCnComV[877][0] = 4172\n",
"WCnComV[877][1] = 8156\n",
"WCnComV[878].Len() = 2\n",
"WCnComV[878][0] = 4164\n",
"WCnComV[878][1] = 8507\n",
"WCnComV[879].Len() = 2\n",
"WCnComV[879][0] = 4151\n",
"WCnComV[879][1] = 8914\n",
"WCnComV[880].Len() = 2\n",
"WCnComV[880][0] = 4147\n",
"WCnComV[880][1] = 4152\n",
"WCnComV[881].Len() = 2\n",
"WCnComV[881][0] = 4138\n",
"WCnComV[881][1] = 6097\n",
"WCnComV[882].Len() = 2\n",
"WCnComV[882][0] = 4134\n",
"WCnComV[882][1] = 6722\n",
"WCnComV[883].Len() = 2\n",
"WCnComV[883][0] = 4133\n",
"WCnComV[883][1] = 5103\n",
"WCnComV[884].Len() = 2\n",
"WCnComV[884][0] = 4120\n",
"WCnComV[884][1] = 7699\n",
"WCnComV[885].Len() = 2\n",
"WCnComV[885][0] = 4117\n",
"WCnComV[885][1] = 6904\n",
"WCnComV[886].Len() = 2\n",
"WCnComV[886][0] = 4100\n",
"WCnComV[886][1] = 4511\n",
"WCnComV[887].Len() = 2\n",
"WCnComV[887][0] = 4093\n",
"WCnComV[887][1] = 5534\n",
"WCnComV[888].Len() = 2\n",
"WCnComV[888][0] = 4091\n",
"WCnComV[888][1] = 4520\n",
"WCnComV[889].Len() = 2\n",
"WCnComV[889][0] = 4088\n",
"WCnComV[889][1] = 7976\n",
"WCnComV[890].Len() = 2\n",
"WCnComV[890][0] = 4070\n",
"WCnComV[890][1] = 6941\n",
"WCnComV[891].Len() = 2\n",
"WCnComV[891][0] = 4069\n",
"WCnComV[891][1] = 8484\n",
"WCnComV[892].Len() = 2\n",
"WCnComV[892][0] = 4067\n",
"WCnComV[892][1] = 9447\n",
"WCnComV[893].Len() = 2\n",
"WCnComV[893][0] = 4048\n",
"WCnComV[893][1] = 9595\n",
"WCnComV[894].Len() = 2\n",
"WCnComV[894][0] = 4041\n",
"WCnComV[894][1] = 7272\n",
"WCnComV[895].Len() = 2\n",
"WCnComV[895][0] = 4037\n",
"WCnComV[895][1] = 6875\n",
"WCnComV[896].Len() = 2\n",
"WCnComV[896][0] = 4013\n",
"WCnComV[896][1] = 5022\n",
"WCnComV[897].Len() = 2\n",
"WCnComV[897][0] = 4012\n",
"WCnComV[897][1] = 5177\n",
"WCnComV[898].Len() = 2\n",
"WCnComV[898][0] = 4011\n",
"WCnComV[898][1] = 9452\n",
"WCnComV[899].Len() = 2\n",
"WCnComV[899][0] = 3989\n",
"WCnComV[899][1] = 7306\n",
"WCnComV[900].Len() = 2\n",
"WCnComV[900][0] = 3985\n",
"WCnComV[900][1] = 5398\n",
"WCnComV[901].Len() = 2\n",
"WCnComV[901][0] = 3961\n",
"WCnComV[901][1] = 8816\n",
"WCnComV[902].Len() = 2\n",
"WCnComV[902][0] = 3943\n",
"WCnComV[902][1] = 5536\n",
"WCnComV[903].Len() = 2\n",
"WCnComV[903][0] = 3942\n",
"WCnComV[903][1] = 8731\n",
"WCnComV[904].Len() = 2\n",
"WCnComV[904][0] = 3934\n",
"WCnComV[904][1] = 8636\n",
"WCnComV[905].Len() = 2\n",
"WCnComV[905][0] = 3929\n",
"WCnComV[905][1] = 5123\n",
"WCnComV[906].Len() = 2\n",
"WCnComV[906][0] = 3928\n",
"WCnComV[906][1] = 8322\n",
"WCnComV[907].Len() = 2\n",
"WCnComV[907][0] = 3909\n",
"WCnComV[907][1] = 6920\n",
"WCnComV[908].Len() = 2\n",
"WCnComV[908][0] = 3906\n",
"WCnComV[908][1] = 9949\n",
"WCnComV[909].Len() = 2\n",
"WCnComV[909][0] = 3851\n",
"WCnComV[909][1] = 7829\n",
"WCnComV[910].Len() = 2\n",
"WCnComV[910][0] = 3850\n",
"WCnComV[910][1] = 5280\n",
"WCnComV[911].Len() = 2\n",
"WCnComV[911][0] = 3840\n",
"WCnComV[911][1] = 6628\n",
"WCnComV[912].Len() = 2\n",
"WCnComV[912][0] = 3835\n",
"WCnComV[912][1] = 5435\n",
"WCnComV[913].Len() = 2\n",
"WCnComV[913][0] = 3833\n",
"WCnComV[913][1] = 9968\n",
"WCnComV[914].Len() = 2\n",
"WCnComV[914][0] = 3831\n",
"WCnComV[914][1] = 3841\n",
"WCnComV[915].Len() = 2\n",
"WCnComV[915][0] = 3819\n",
"WCnComV[915][1] = 9250\n",
"WCnComV[916].Len() = 2\n",
"WCnComV[916][0] = 3784\n",
"WCnComV[916][1] = 5224\n",
"WCnComV[917].Len() = 2\n",
"WCnComV[917][0] = 3780\n",
"WCnComV[917][1] = 8804\n",
"WCnComV[918].Len() = 2\n",
"WCnComV[918][0] = 3769\n",
"WCnComV[918][1] = 6083\n",
"WCnComV[919].Len() = 2\n",
"WCnComV[919][0] = 3768\n",
"WCnComV[919][1] = 8541\n",
"WCnComV[920].Len() = 2\n",
"WCnComV[920][0] = 3750\n",
"WCnComV[920][1] = 6522\n",
"WCnComV[921].Len() = 2\n",
"WCnComV[921][0] = 3748\n",
"WCnComV[921][1] = 5503\n",
"WCnComV[922].Len() = 2\n",
"WCnComV[922][0] = 3736\n",
"WCnComV[922][1] = 9415\n",
"WCnComV[923].Len() = 2\n",
"WCnComV[923][0] = 3727\n",
"WCnComV[923][1] = 5980\n",
"WCnComV[924].Len() = 2\n",
"WCnComV[924][0] = 3724\n",
"WCnComV[924][1] = 3971\n",
"WCnComV[925].Len() = 2\n",
"WCnComV[925][0] = 3714\n",
"WCnComV[925][1] = 8550\n",
"WCnComV[926].Len() = 2\n",
"WCnComV[926][0] = 3700\n",
"WCnComV[926][1] = 8034\n",
"WCnComV[927].Len() = 2\n",
"WCnComV[927][0] = 3696\n",
"WCnComV[927][1] = 5645\n",
"WCnComV[928].Len() = 2\n",
"WCnComV[928][0] = 3692\n",
"WCnComV[928][1] = 6755\n",
"WCnComV[929].Len() = 2\n",
"WCnComV[929][0] = 3682\n",
"WCnComV[929][1] = 4386\n",
"WCnComV[930].Len() = 2\n",
"WCnComV[930][0] = 3676\n",
"WCnComV[930][1] = 4881\n",
"WCnComV[931].Len() = 2\n",
"WCnComV[931][0] = 3668\n",
"WCnComV[931][1] = 7195\n",
"WCnComV[932].Len() = 2\n",
"WCnComV[932][0] = 3661\n",
"WCnComV[932][1] = 7908\n",
"WCnComV[933].Len() = 2\n",
"WCnComV[933][0] = 3642\n",
"WCnComV[933][1] = 6719\n",
"WCnComV[934].Len() = 2\n",
"WCnComV[934][0] = 3641\n",
"WCnComV[934][1] = 5694\n",
"WCnComV[935].Len() = 2\n",
"WCnComV[935][0] = 3637\n",
"WCnComV[935][1] = 7962\n",
"WCnComV[936].Len() = 2\n",
"WCnComV[936][0] = 3632\n",
"WCnComV[936][1] = 5313\n",
"WCnComV[937].Len() = 2\n",
"WCnComV[937][0] = 3626\n",
"WCnComV[937][1] = 9127\n",
"WCnComV[938].Len() = 2\n",
"WCnComV[938][0] = 3622\n",
"WCnComV[938][1] = 7440\n",
"WCnComV[939].Len() = 2\n",
"WCnComV[939][0] = 3605\n",
"WCnComV[939][1] = 6958\n",
"WCnComV[940].Len() = 2\n",
"WCnComV[940][0] = 3594\n",
"WCnComV[940][1] = 7620\n",
"WCnComV[941].Len() = 2\n",
"WCnComV[941][0] = 3591\n",
"WCnComV[941][1] = 5713\n",
"WCnComV[942].Len() = 2\n",
"WCnComV[942][0] = 3584\n",
"WCnComV[942][1] = 9628\n",
"WCnComV[943].Len() = 2\n",
"WCnComV[943][0] = 3555\n",
"WCnComV[943][1] = 8623\n",
"WCnComV[944].Len() = 2\n",
"WCnComV[944][0] = 3546\n",
"WCnComV[944][1] = 6303\n",
"WCnComV[945].Len() = 2\n",
"WCnComV[945][0] = 3516\n",
"WCnComV[945][1] = 7142\n",
"WCnComV[946].Len() = 2\n",
"WCnComV[946][0] = 3491\n",
"WCnComV[946][1] = 7495\n",
"WCnComV[947].Len() = 2\n",
"WCnComV[947][0] = 3480\n",
"WCnComV[947][1] = 6660\n",
"WCnComV[948].Len() = 2\n",
"WCnComV[948][0] = 3470\n",
"WCnComV[948][1] = 3618\n",
"WCnComV[949].Len() = 2\n",
"WCnComV[949][0] = 3458\n",
"WCnComV[949][1] = 9567\n",
"WCnComV[950].Len() = 2\n",
"WCnComV[950][0] = 3453\n",
"WCnComV[950][1] = 7237\n",
"WCnComV[951].Len() = 2\n",
"WCnComV[951][0] = 3451\n",
"WCnComV[951][1] = 6654\n",
"WCnComV[952].Len() = 2\n",
"WCnComV[952][0] = 3445\n",
"WCnComV[952][1] = 9961\n",
"WCnComV[953].Len() = 2\n",
"WCnComV[953][0] = 3441\n",
"WCnComV[953][1] = 8540\n",
"WCnComV[954].Len() = 2\n",
"WCnComV[954][0] = 3434\n",
"WCnComV[954][1] = 5658\n",
"WCnComV[955].Len() = 2\n",
"WCnComV[955][0] = 3431\n",
"WCnComV[955][1] = 5936\n",
"WCnComV[956].Len() = 2\n",
"WCnComV[956][0] = 3407\n",
"WCnComV[956][1] = 3922\n",
"WCnComV[957].Len() = 2\n",
"WCnComV[957][0] = 3402\n",
"WCnComV[957][1] = 4171\n",
"WCnComV[958].Len() = 2\n",
"WCnComV[958][0] = 3400\n",
"WCnComV[958][1] = 8940\n",
"WCnComV[959].Len() = 2\n",
"WCnComV[959][0] = 3397\n",
"WCnComV[959][1] = 3821\n",
"WCnComV[960].Len() = 2\n",
"WCnComV[960][0] = 3386\n",
"WCnComV[960][1] = 9689\n",
"WCnComV[961].Len() = 2\n",
"WCnComV[961][0] = 3365\n",
"WCnComV[961][1] = 4730\n",
"WCnComV[962].Len() = 2\n",
"WCnComV[962][0] = 3362\n",
"WCnComV[962][1] = 5397\n",
"WCnComV[963].Len() = 2\n",
"WCnComV[963][0] = 3339\n",
"WCnComV[963][1] = 9119\n",
"WCnComV[964].Len() = 2\n",
"WCnComV[964][0] = 3333\n",
"WCnComV[964][1] = 6710\n",
"WCnComV[965].Len() = 2\n",
"WCnComV[965][0] = 3324\n",
"WCnComV[965][1] = 9667\n",
"WCnComV[966].Len() = 2\n",
"WCnComV[966][0] = 3323\n",
"WCnComV[966][1] = 7883\n",
"WCnComV[967].Len() = 2\n",
"WCnComV[967][0] = 3292\n",
"WCnComV[967][1] = 7596\n",
"WCnComV[968].Len() = 2\n",
"WCnComV[968][0] = 3268\n",
"WCnComV[968][1] = 6938\n",
"WCnComV[969].Len() = 2\n",
"WCnComV[969][0] = 3263\n",
"WCnComV[969][1] = 6803\n",
"WCnComV[970].Len() = 2\n",
"WCnComV[970][0] = 3254\n",
"WCnComV[970][1] = 5905\n",
"WCnComV[971].Len() = 2\n",
"WCnComV[971][0] = 3246\n",
"WCnComV[971][1] = 9523\n",
"WCnComV[972].Len() = 2\n",
"WCnComV[972][0] = 3215\n",
"WCnComV[972][1] = 5154\n",
"WCnComV[973].Len() = 2\n",
"WCnComV[973][0] = 3205\n",
"WCnComV[973][1] = 8898\n",
"WCnComV[974].Len() = 2\n",
"WCnComV[974][0] = 3196\n",
"WCnComV[974][1] = 4439\n",
"WCnComV[975].Len() = 2\n",
"WCnComV[975][0] = 3155\n",
"WCnComV[975][1] = 4268\n",
"WCnComV[976].Len() = 2\n",
"WCnComV[976][0] = 3152\n",
"WCnComV[976][1] = 5753\n",
"WCnComV[977].Len() = 2\n",
"WCnComV[977][0] = 3149\n",
"WCnComV[977][1] = 6401\n",
"WCnComV[978].Len() = 2\n",
"WCnComV[978][0] = 3138\n",
"WCnComV[978][1] = 6205\n",
"WCnComV[979].Len() = 2\n",
"WCnComV[979][0] = 3128\n",
"WCnComV[979][1] = 4044\n",
"WCnComV[980].Len() = 2\n",
"WCnComV[980][0] = 3126\n",
"WCnComV[980][1] = 8268\n",
"WCnComV[981].Len() = 2\n",
"WCnComV[981][0] = 3123\n",
"WCnComV[981][1] = 3424\n",
"WCnComV[982].Len() = 2\n",
"WCnComV[982][0] = 3120\n",
"WCnComV[982][1] = 5175\n",
"WCnComV[983].Len() = 2\n",
"WCnComV[983][0] = 3118\n",
"WCnComV[983][1] = 6399\n",
"WCnComV[984].Len() = 2\n",
"WCnComV[984][0] = 3114\n",
"WCnComV[984][1] = 4480\n",
"WCnComV[985].Len() = 2\n",
"WCnComV[985][0] = 3103\n",
"WCnComV[985][1] = 3507\n",
"WCnComV[986].Len() = 2\n",
"WCnComV[986][0] = 3092\n",
"WCnComV[986][1] = 7651\n",
"WCnComV[987].Len() = 2\n",
"WCnComV[987][0] = 3085\n",
"WCnComV[987][1] = 6449\n",
"WCnComV[988].Len() = 2\n",
"WCnComV[988][0] = 3074\n",
"WCnComV[988][1] = 9510\n",
"WCnComV[989].Len() = 2\n",
"WCnComV[989][0] = 3072\n",
"WCnComV[989][1] = 8521\n",
"WCnComV[990].Len() = 2\n",
"WCnComV[990][0] = 3063\n",
"WCnComV[990][1] = 9593\n",
"WCnComV[991].Len() = 2\n",
"WCnComV[991][0] = 3046\n",
"WCnComV[991][1] = 3428\n",
"WCnComV[992].Len() = 2\n",
"WCnComV[992][0] = 3042\n",
"WCnComV[992][1] = 3635\n",
"WCnComV[993].Len() = 2\n",
"WCnComV[993][0] = 3033\n",
"WCnComV[993][1] = 3667\n",
"WCnComV[994].Len() = 2\n",
"WCnComV[994][0] = 3013\n",
"WCnComV[994][1] = 3393\n",
"WCnComV[995].Len() = 2\n",
"WCnComV[995][0] = 3009\n",
"WCnComV[995][1] = 7099\n",
"WCnComV[996].Len() = 2\n",
"WCnComV[996][0] = 3007\n",
"WCnComV[996][1] = 9698\n",
"WCnComV[997].Len() = 2\n",
"WCnComV[997][0] = 2997\n",
"WCnComV[997][1] = 9927\n",
"WCnComV[998].Len() = 2\n",
"WCnComV[998][0] = 2993\n",
"WCnComV[998][1] = 9408\n",
"WCnComV[999].Len() = 2\n",
"WCnComV[999][0] = 2988\n",
"WCnComV[999][1] = 3585\n",
"WCnComV[1000].Len() = 2\n",
"WCnComV[1000][0] = 2973\n",
"WCnComV[1000][1] = 9945\n",
"WCnComV[1001].Len() = 2\n",
"WCnComV[1001][0] = 2971\n",
"WCnComV[1001][1] = 8964\n",
"WCnComV[1002].Len() = 2\n",
"WCnComV[1002][0] = 2967\n",
"WCnComV[1002][1] = 4045\n",
"WCnComV[1003].Len() = 2\n",
"WCnComV[1003][0] = 2947\n",
"WCnComV[1003][1] = 3159\n",
"WCnComV[1004].Len() = 2\n",
"WCnComV[1004][0] = 2934\n",
"WCnComV[1004][1] = 3876\n",
"WCnComV[1005].Len() = 2\n",
"WCnComV[1005][0] = 2929\n",
"WCnComV[1005][1] = 8500\n",
"WCnComV[1006].Len() = 2\n",
"WCnComV[1006][0] = 2917\n",
"WCnComV[1006][1] = 5487\n",
"WCnComV[1007].Len() = 2\n",
"WCnComV[1007][0] = 2916\n",
"WCnComV[1007][1] = 6040\n",
"WCnComV[1008].Len() = 2\n",
"WCnComV[1008][0] = 2911\n",
"WCnComV[1008][1] = 5099\n",
"WCnComV[1009].Len() = 2\n",
"WCnComV[1009][0] = 2901\n",
"WCnComV[1009][1] = 7816\n",
"WCnComV[1010].Len() = 2\n",
"WCnComV[1010][0] = 2895\n",
"WCnComV[1010][1] = 6992\n",
"WCnComV[1011].Len() = 2\n",
"WCnComV[1011][0] = 2890\n",
"WCnComV[1011][1] = 3884\n",
"WCnComV[1012].Len() = 2\n",
"WCnComV[1012][0] = 2885\n",
"WCnComV[1012][1] = 7041\n",
"WCnComV[1013].Len() = 2\n",
"WCnComV[1013][0] = 2873\n",
"WCnComV[1013][1] = 6030\n",
"WCnComV[1014].Len() = 2\n",
"WCnComV[1014][0] = 2871\n",
"WCnComV[1014][1] = 7398\n",
"WCnComV[1015].Len() = 2\n",
"WCnComV[1015][0] = 2869\n",
"WCnComV[1015][1] = 4098\n",
"WCnComV[1016].Len() = 2\n",
"WCnComV[1016][0] = 2866\n",
"WCnComV[1016][1] = 5500\n",
"WCnComV[1017].Len() = 2\n",
"WCnComV[1017][0] = 2862\n",
"WCnComV[1017][1] = 7271\n",
"WCnComV[1018].Len() = 2\n",
"WCnComV[1018][0] = 2857\n",
"WCnComV[1018][1] = 9024\n",
"WCnComV[1019].Len() = 2\n",
"WCnComV[1019][0] = 2854\n",
"WCnComV[1019][1] = 9507\n",
"WCnComV[1020].Len() = 2\n",
"WCnComV[1020][0] = 2840\n",
"WCnComV[1020][1] = 9400\n",
"WCnComV[1021].Len() = 2\n",
"WCnComV[1021][0] = 2828\n",
"WCnComV[1021][1] = 3119\n",
"WCnComV[1022].Len() = 2\n",
"WCnComV[1022][0] = 2826\n",
"WCnComV[1022][1] = 2906\n",
"WCnComV[1023].Len() = 2\n",
"WCnComV[1023][0] = 2816\n",
"WCnComV[1023][1] = 9516\n",
"WCnComV[1024].Len() = 2\n",
"WCnComV[1024][0] = 2809\n",
"WCnComV[1024][1] = 9513\n",
"WCnComV[1025].Len() = 2\n",
"WCnComV[1025][0] = 2806\n",
"WCnComV[1025][1] = 5132\n",
"WCnComV[1026].Len() = 2\n",
"WCnComV[1026][0] = 2801\n",
"WCnComV[1026][1] = 7910\n",
"WCnComV[1027].Len() = 2\n",
"WCnComV[1027][0] = 2800\n",
"WCnComV[1027][1] = 3634\n",
"WCnComV[1028].Len() = 2\n",
"WCnComV[1028][0] = 2783\n",
"WCnComV[1028][1] = 3045\n",
"WCnComV[1029].Len() = 2\n",
"WCnComV[1029][0] = 2780\n",
"WCnComV[1029][1] = 7364\n",
"WCnComV[1030].Len() = 2\n",
"WCnComV[1030][0] = 2776\n",
"WCnComV[1030][1] = 7136\n",
"WCnComV[1031].Len() = 2\n",
"WCnComV[1031][0] = 2772\n",
"WCnComV[1031][1] = 8361\n",
"WCnComV[1032].Len() = 2\n",
"WCnComV[1032][0] = 2764\n",
"WCnComV[1032][1] = 9608\n",
"WCnComV[1033].Len() = 2\n",
"WCnComV[1033][0] = 2759\n",
"WCnComV[1033][1] = 9148\n",
"WCnComV[1034].Len() = 2\n",
"WCnComV[1034][0] = 2746\n",
"WCnComV[1034][1] = 7773\n",
"WCnComV[1035].Len() = 2\n",
"WCnComV[1035][0] = 2744\n",
"WCnComV[1035][1] = 5567\n",
"WCnComV[1036].Len() = 2\n",
"WCnComV[1036][0] = 2738\n",
"WCnComV[1036][1] = 4297\n",
"WCnComV[1037].Len() = 2\n",
"WCnComV[1037][0] = 2718\n",
"WCnComV[1037][1] = 4997\n",
"WCnComV[1038].Len() = 2\n",
"WCnComV[1038][0] = 2708\n",
"WCnComV[1038][1] = 5564\n",
"WCnComV[1039].Len() = 2\n",
"WCnComV[1039][0] = 2699\n",
"WCnComV[1039][1] = 3972\n",
"WCnComV[1040].Len() = 2\n",
"WCnComV[1040][0] = 2669\n",
"WCnComV[1040][1] = 5334\n",
"WCnComV[1041].Len() = 2\n",
"WCnComV[1041][0] = 2650\n",
"WCnComV[1041][1] = 6800\n",
"WCnComV[1042].Len() = 2\n",
"WCnComV[1042][0] = 2648\n",
"WCnComV[1042][1] = 2703\n",
"WCnComV[1043].Len() = 2\n",
"WCnComV[1043][0] = 2647\n",
"WCnComV[1043][1] = 2678\n",
"WCnComV[1044].Len() = 2\n",
"WCnComV[1044][0] = 2646\n",
"WCnComV[1044][1] = 3987\n",
"WCnComV[1045].Len() = 2\n",
"WCnComV[1045][0] = 2645\n",
"WCnComV[1045][1] = 7107\n",
"WCnComV[1046].Len() = 2\n",
"WCnComV[1046][0] = 2637\n",
"WCnComV[1046][1] = 6183\n",
"WCnComV[1047].Len() = 2\n",
"WCnComV[1047][0] = 2628\n",
"WCnComV[1047][1] = 7781\n",
"WCnComV[1048].Len() = 2\n",
"WCnComV[1048][0] = 2627\n",
"WCnComV[1048][1] = 2950\n",
"WCnComV[1049].Len() = 2\n",
"WCnComV[1049][0] = 2621\n",
"WCnComV[1049][1] = 3277\n",
"WCnComV[1050].Len() = 2\n",
"WCnComV[1050][0] = 2603\n",
"WCnComV[1050][1] = 5044\n",
"WCnComV[1051].Len() = 2\n",
"WCnComV[1051][0] = 2598\n",
"WCnComV[1051][1] = 3936\n",
"WCnComV[1052].Len() = 2\n",
"WCnComV[1052][0] = 2594\n",
"WCnComV[1052][1] = 3043\n",
"WCnComV[1053].Len() = 2\n",
"WCnComV[1053][0] = 2589\n",
"WCnComV[1053][1] = 3687\n",
"WCnComV[1054].Len() = 2\n",
"WCnComV[1054][0] = 2571\n",
"WCnComV[1054][1] = 8925\n",
"WCnComV[1055].Len() = 2\n",
"WCnComV[1055][0] = 2569\n",
"WCnComV[1055][1] = 4517\n",
"WCnComV[1056].Len() = 2\n",
"WCnComV[1056][0] = 2565\n",
"WCnComV[1056][1] = 4932\n",
"WCnComV[1057].Len() = 2\n",
"WCnComV[1057][0] = 2564\n",
"WCnComV[1057][1] = 9191\n",
"WCnComV[1058].Len() = 2\n",
"WCnComV[1058][0] = 2555\n",
"WCnComV[1058][1] = 8726\n",
"WCnComV[1059].Len() = 2\n",
"WCnComV[1059][0] = 2542\n",
"WCnComV[1059][1] = 4693\n",
"WCnComV[1060].Len() = 2\n",
"WCnComV[1060][0] = 2518\n",
"WCnComV[1060][1] = 2713\n",
"WCnComV[1061].Len() = 2\n",
"WCnComV[1061][0] = 2505\n",
"WCnComV[1061][1] = 6400\n",
"WCnComV[1062].Len() = 2\n",
"WCnComV[1062][0] = 2503\n",
"WCnComV[1062][1] = 5173\n",
"WCnComV[1063].Len() = 2\n",
"WCnComV[1063][0] = 2496\n",
"WCnComV[1063][1] = 9055\n",
"WCnComV[1064].Len() = 2\n",
"WCnComV[1064][0] = 2482\n",
"WCnComV[1064][1] = 8510\n",
"WCnComV[1065].Len() = 2\n",
"WCnComV[1065][0] = 2473\n",
"WCnComV[1065][1] = 4452\n",
"WCnComV[1066].Len() = 2\n",
"WCnComV[1066][0] = 2470\n",
"WCnComV[1066][1] = 6351\n",
"WCnComV[1067].Len() = 2\n",
"WCnComV[1067][0] = 2466\n",
"WCnComV[1067][1] = 5601\n",
"WCnComV[1068].Len() = 2\n",
"WCnComV[1068][0] = 2462\n",
"WCnComV[1068][1] = 3409\n",
"WCnComV[1069].Len() = 2\n",
"WCnComV[1069][0] = 2453\n",
"WCnComV[1069][1] = 3643\n",
"WCnComV[1070].Len() = 2\n",
"WCnComV[1070][0] = 2450\n",
"WCnComV[1070][1] = 4746\n",
"WCnComV[1071].Len() = 2\n",
"WCnComV[1071][0] = 2448\n",
"WCnComV[1071][1] = 9713\n",
"WCnComV[1072].Len() = 2\n",
"WCnComV[1072][0] = 2446\n",
"WCnComV[1072][1] = 5252\n",
"WCnComV[1073].Len() = 2\n",
"WCnComV[1073][0] = 2441\n",
"WCnComV[1073][1] = 2892\n",
"WCnComV[1074].Len() = 2\n",
"WCnComV[1074][0] = 2413\n",
"WCnComV[1074][1] = 3595\n",
"WCnComV[1075].Len() = 2\n",
"WCnComV[1075][0] = 2408\n",
"WCnComV[1075][1] = 6360\n",
"WCnComV[1076].Len() = 2\n",
"WCnComV[1076][0] = 2398\n",
"WCnComV[1076][1] = 6647\n",
"WCnComV[1077].Len() = 2\n",
"WCnComV[1077][0] = 2372\n",
"WCnComV[1077][1] = 4562\n",
"WCnComV[1078].Len() = 2\n",
"WCnComV[1078][0] = 2370\n",
"WCnComV[1078][1] = 9135\n",
"WCnComV[1079].Len() = 2\n",
"WCnComV[1079][0] = 2367\n",
"WCnComV[1079][1] = 3432\n",
"WCnComV[1080].Len() = 2\n",
"WCnComV[1080][0] = 2362\n",
"WCnComV[1080][1] = 6024\n",
"WCnComV[1081].Len() = 2\n",
"WCnComV[1081][0] = 2353\n",
"WCnComV[1081][1] = 9461\n",
"WCnComV[1082].Len() = 2\n",
"WCnComV[1082][0] = 2346\n",
"WCnComV[1082][1] = 7906\n",
"WCnComV[1083].Len() = 2\n",
"WCnComV[1083][0] = 2334\n",
"WCnComV[1083][1] = 5878\n",
"WCnComV[1084].Len() = 2\n",
"WCnComV[1084][0] = 2306\n",
"WCnComV[1084][1] = 6418\n",
"WCnComV[1085].Len() = 2\n",
"WCnComV[1085][0] = 2293\n",
"WCnComV[1085][1] = 5257\n",
"WCnComV[1086].Len() = 2\n",
"WCnComV[1086][0] = 2280\n",
"WCnComV[1086][1] = 8152\n",
"WCnComV[1087].Len() = 2\n",
"WCnComV[1087][0] = 2265\n",
"WCnComV[1087][1] = 9047\n",
"WCnComV[1088].Len() = 2\n",
"WCnComV[1088][0] = 2262\n",
"WCnComV[1088][1] = 3346\n",
"WCnComV[1089].Len() = 2\n",
"WCnComV[1089][0] = 2255\n",
"WCnComV[1089][1] = 3844\n",
"WCnComV[1090].Len() = 2\n",
"WCnComV[1090][0] = 2241\n",
"WCnComV[1090][1] = 2957\n",
"WCnComV[1091].Len() = 2\n",
"WCnComV[1091][0] = 2235\n",
"WCnComV[1091][1] = 6980\n",
"WCnComV[1092].Len() = 2\n",
"WCnComV[1092][0] = 2230\n",
"WCnComV[1092][1] = 2430\n",
"WCnComV[1093].Len() = 2\n",
"WCnComV[1093][0] = 2208\n",
"WCnComV[1093][1] = 5652\n",
"WCnComV[1094].Len() = 2\n",
"WCnComV[1094][0] = 2195\n",
"WCnComV[1094][1] = 5954\n",
"WCnComV[1095].Len() = 2\n",
"WCnComV[1095][0] = 2189\n",
"WCnComV[1095][1] = 3237\n",
"WCnComV[1096].Len() = 2\n",
"WCnComV[1096][0] = 2181\n",
"WCnComV[1096][1] = 4949\n",
"WCnComV[1097].Len() = 2\n",
"WCnComV[1097][0] = 2172\n",
"WCnComV[1097][1] = 4548\n",
"WCnComV[1098].Len() = 2\n",
"WCnComV[1098][0] = 2170\n",
"WCnComV[1098][1] = 7320\n",
"WCnComV[1099].Len() = 2\n",
"WCnComV[1099][0] = 2164\n",
"WCnComV[1099][1] = 8109\n",
"WCnComV[1100].Len() = 2\n",
"WCnComV[1100][0] = 2160\n",
"WCnComV[1100][1] = 3483\n",
"WCnComV[1101].Len() = 2\n",
"WCnComV[1101][0] = 2159\n",
"WCnComV[1101][1] = 5336\n",
"WCnComV[1102].Len() = 2\n",
"WCnComV[1102][0] = 2157\n",
"WCnComV[1102][1] = 6935\n",
"WCnComV[1103].Len() = 2\n",
"WCnComV[1103][0] = 2144\n",
"WCnComV[1103][1] = 3065\n",
"WCnComV[1104].Len() = 2\n",
"WCnComV[1104][0] = 2142\n",
"WCnComV[1104][1] = 7983\n",
"WCnComV[1105].Len() = 2\n",
"WCnComV[1105][0] = 2122\n",
"WCnComV[1105][1] = 4284\n",
"WCnComV[1106].Len() = 2\n",
"WCnComV[1106][0] = 2104\n",
"WCnComV[1106][1] = 7516\n",
"WCnComV[1107].Len() = 2\n",
"WCnComV[1107][0] = 2090\n",
"WCnComV[1107][1] = 4995\n",
"WCnComV[1108].Len() = 2\n",
"WCnComV[1108][0] = 2084\n",
"WCnComV[1108][1] = 4652\n",
"WCnComV[1109].Len() = 2\n",
"WCnComV[1109][0] = 2082\n",
"WCnComV[1109][1] = 5563\n",
"WCnComV[1110].Len() = 2\n",
"WCnComV[1110][0] = 2081\n",
"WCnComV[1110][1] = 5672\n",
"WCnComV[1111].Len() = 2\n",
"WCnComV[1111][0] = 2077\n",
"WCnComV[1111][1] = 3265\n",
"WCnComV[1112].Len() = 2\n",
"WCnComV[1112][0] = 2074\n",
"WCnComV[1112][1] = 5615\n",
"WCnComV[1113].Len() = 2\n",
"WCnComV[1113][0] = 2062\n",
"WCnComV[1113][1] = 9842\n",
"WCnComV[1114].Len() = 2\n",
"WCnComV[1114][0] = 2044\n",
"WCnComV[1114][1] = 6470\n",
"WCnComV[1115].Len() = 2\n",
"WCnComV[1115][0] = 2034\n",
"WCnComV[1115][1] = 8045\n",
"WCnComV[1116].Len() = 2\n",
"WCnComV[1116][0] = 2023\n",
"WCnComV[1116][1] = 5496\n",
"WCnComV[1117].Len() = 2\n",
"WCnComV[1117][0] = 2021\n",
"WCnComV[1117][1] = 6163\n",
"WCnComV[1118].Len() = 2\n",
"WCnComV[1118][0] = 2005\n",
"WCnComV[1118][1] = 3718\n",
"WCnComV[1119].Len() = 2\n",
"WCnComV[1119][0] = 1988\n",
"WCnComV[1119][1] = 9171\n",
"WCnComV[1120].Len() = 2\n",
"WCnComV[1120][0] = 1977\n",
"WCnComV[1120][1] = 2909\n",
"WCnComV[1121].Len() = 2\n",
"WCnComV[1121][0] = 1974\n",
"WCnComV[1121][1] = 2485\n",
"WCnComV[1122].Len() = 2\n",
"WCnComV[1122][0] = 1973\n",
"WCnComV[1122][1] = 3216\n",
"WCnComV[1123].Len() = 2\n",
"WCnComV[1123][0] = 1940\n",
"WCnComV[1123][1] = 7058\n",
"WCnComV[1124].Len() = 2\n",
"WCnComV[1124][0] = 1890\n",
"WCnComV[1124][1] = 6725\n",
"WCnComV[1125].Len() = 2\n",
"WCnComV[1125][0] = 1884\n",
"WCnComV[1125][1] = 4506\n",
"WCnComV[1126].Len() = 2\n",
"WCnComV[1126][0] = 1882\n",
"WCnComV[1126][1] = 8983\n",
"WCnComV[1127].Len() = 2\n",
"WCnComV[1127][0] = 1870\n",
"WCnComV[1127][1] = 9052\n",
"WCnComV[1128].Len() = 2\n",
"WCnComV[1128][0] = 1868\n",
"WCnComV[1128][1] = 9887\n",
"WCnComV[1129].Len() = 2\n",
"WCnComV[1129][0] = 1865\n",
"WCnComV[1129][1] = 5696\n",
"WCnComV[1130].Len() = 2\n",
"WCnComV[1130][0] = 1854\n",
"WCnComV[1130][1] = 7514\n",
"WCnComV[1131].Len() = 2\n",
"WCnComV[1131][0] = 1850\n",
"WCnComV[1131][1] = 9164\n",
"WCnComV[1132].Len() = 2\n",
"WCnComV[1132][0] = 1838\n",
"WCnComV[1132][1] = 5985\n",
"WCnComV[1133].Len() = 2\n",
"WCnComV[1133][0] = 1836\n",
"WCnComV[1133][1] = 5070\n",
"WCnComV[1134].Len() = 2\n",
"WCnComV[1134][0] = 1792\n",
"WCnComV[1134][1] = 9237\n",
"WCnComV[1135].Len() = 2\n",
"WCnComV[1135][0] = 1782\n",
"WCnComV[1135][1] = 6368\n",
"WCnComV[1136].Len() = 2\n",
"WCnComV[1136][0] = 1779\n",
"WCnComV[1136][1] = 6263\n",
"WCnComV[1137].Len() = 2\n",
"WCnComV[1137][0] = 1776\n",
"WCnComV[1137][1] = 3862\n",
"WCnComV[1138].Len() = 2\n",
"WCnComV[1138][0] = 1774\n",
"WCnComV[1138][1] = 7460\n",
"WCnComV[1139].Len() = 2\n",
"WCnComV[1139][0] = 1772\n",
"WCnComV[1139][1] = 4654\n",
"WCnComV[1140].Len() = 2\n",
"WCnComV[1140][0] = 1771\n",
"WCnComV[1140][1] = 3974\n",
"WCnComV[1141].Len() = 2\n",
"WCnComV[1141][0] = 1756\n",
"WCnComV[1141][1] = 6586\n",
"WCnComV[1142].Len() = 2\n",
"WCnComV[1142][0] = 1755\n",
"WCnComV[1142][1] = 4086\n",
"WCnComV[1143].Len() = 2\n",
"WCnComV[1143][0] = 1752\n",
"WCnComV[1143][1] = 6827\n",
"WCnComV[1144].Len() = 2\n",
"WCnComV[1144][0] = 1739\n",
"WCnComV[1144][1] = 3601\n",
"WCnComV[1145].Len() = 2\n",
"WCnComV[1145][0] = 1738\n",
"WCnComV[1145][1] = 8064\n",
"WCnComV[1146].Len() = 2\n",
"WCnComV[1146][0] = 1736\n",
"WCnComV[1146][1] = 8437\n",
"WCnComV[1147].Len() = 2\n",
"WCnComV[1147][0] = 1730\n",
"WCnComV[1147][1] = 5128\n",
"WCnComV[1148].Len() = 2\n",
"WCnComV[1148][0] = 1705\n",
"WCnComV[1148][1] = 1928\n",
"WCnComV[1149].Len() = 2\n",
"WCnComV[1149][0] = 1698\n",
"WCnComV[1149][1] = 2519\n",
"WCnComV[1150].Len() = 2\n",
"WCnComV[1150][0] = 1694\n",
"WCnComV[1150][1] = 2154\n",
"WCnComV[1151].Len() = 2\n",
"WCnComV[1151][0] = 1692\n",
"WCnComV[1151][1] = 3638\n",
"WCnComV[1152].Len() = 2\n",
"WCnComV[1152][0] = 1664\n",
"WCnComV[1152][1] = 8558\n",
"WCnComV[1153].Len() = 2\n",
"WCnComV[1153][0] = 1663\n",
"WCnComV[1153][1] = 2433\n",
"WCnComV[1154].Len() = 2\n",
"WCnComV[1154][0] = 1647\n",
"WCnComV[1154][1] = 8189\n",
"WCnComV[1155].Len() = 2\n",
"WCnComV[1155][0] = 1635\n",
"WCnComV[1155][1] = 6069\n",
"WCnComV[1156].Len() = 2\n",
"WCnComV[1156][0] = 1628\n",
"WCnComV[1156][1] = 3886\n",
"WCnComV[1157].Len() = 2\n",
"WCnComV[1157][0] = 1621\n",
"WCnComV[1157][1] = 6417\n",
"WCnComV[1158].Len() = 2\n",
"WCnComV[1158][0] = 1612\n",
"WCnComV[1158][1] = 7475\n",
"WCnComV[1159].Len() = 2\n",
"WCnComV[1159][0] = 1610\n",
"WCnComV[1159][1] = 2354\n",
"WCnComV[1160].Len() = 2\n",
"WCnComV[1160][0] = 1608\n",
"WCnComV[1160][1] = 7776\n",
"WCnComV[1161].Len() = 2\n",
"WCnComV[1161][0] = 1606\n",
"WCnComV[1161][1] = 5578\n",
"WCnComV[1162].Len() = 2\n",
"WCnComV[1162][0] = 1604\n",
"WCnComV[1162][1] = 1862\n",
"WCnComV[1163].Len() = 2\n",
"WCnComV[1163][0] = 1594\n",
"WCnComV[1163][1] = 4540\n",
"WCnComV[1164].Len() = 2\n",
"WCnComV[1164][0] = 1593\n",
"WCnComV[1164][1] = 6124\n",
"WCnComV[1165].Len() = 2\n",
"WCnComV[1165][0] = 1578\n",
"WCnComV[1165][1] = 2377\n",
"WCnComV[1166].Len() = 2\n",
"WCnComV[1166][0] = 1577\n",
"WCnComV[1166][1] = 1835\n",
"WCnComV[1167].Len() = 2\n",
"WCnComV[1167][0] = 1574\n",
"WCnComV[1167][1] = 3832\n",
"WCnComV[1168].Len() = 2\n",
"WCnComV[1168][0] = 1569\n",
"WCnComV[1168][1] = 8215\n",
"WCnComV[1169].Len() = 2\n",
"WCnComV[1169][0] = 1564\n",
"WCnComV[1169][1] = 9592\n",
"WCnComV[1170].Len() = 2\n",
"WCnComV[1170][0] = 1560\n",
"WCnComV[1170][1] = 2095\n",
"WCnComV[1171].Len() = 2\n",
"WCnComV[1171][0] = 1557\n",
"WCnComV[1171][1] = 6270\n",
"WCnComV[1172].Len() = 2\n",
"WCnComV[1172][0] = 1556\n",
"WCnComV[1172][1] = 3823\n",
"WCnComV[1173].Len() = 2\n",
"WCnComV[1173][0] = 1519\n",
"WCnComV[1173][1] = 4115\n",
"WCnComV[1174].Len() = 2\n",
"WCnComV[1174][0] = 1484\n",
"WCnComV[1174][1] = 5913\n",
"WCnComV[1175].Len() = 2\n",
"WCnComV[1175][0] = 1481\n",
"WCnComV[1175][1] = 2067\n",
"WCnComV[1176].Len() = 2\n",
"WCnComV[1176][0] = 1480\n",
"WCnComV[1176][1] = 3220\n",
"WCnComV[1177].Len() = 2\n",
"WCnComV[1177][0] = 1479\n",
"WCnComV[1177][1] = 7423\n",
"WCnComV[1178].Len() = 2\n",
"WCnComV[1178][0] = 1455\n",
"WCnComV[1178][1] = 5422\n",
"WCnComV[1179].Len() = 2\n",
"WCnComV[1179][0] = 1451\n",
"WCnComV[1179][1] = 5316\n",
"WCnComV[1180].Len() = 2\n",
"WCnComV[1180][0] = 1448\n",
"WCnComV[1180][1] = 6099\n",
"WCnComV[1181].Len() = 2\n",
"WCnComV[1181][0] = 1446\n",
"WCnComV[1181][1] = 9785\n",
"WCnComV[1182].Len() = 2\n",
"WCnComV[1182][0] = 1444\n",
"WCnComV[1182][1] = 2498\n",
"WCnComV[1183].Len() = 2\n",
"WCnComV[1183][0] = 1429\n",
"WCnComV[1183][1] = 5004\n",
"WCnComV[1184].Len() = 2\n",
"WCnComV[1184][0] = 1428\n",
"WCnComV[1184][1] = 7688\n",
"WCnComV[1185].Len() = 2\n",
"WCnComV[1185][0] = 1427\n",
"WCnComV[1185][1] = 8973\n",
"WCnComV[1186].Len() = 2\n",
"WCnComV[1186][0] = 1426\n",
"WCnComV[1186][1] = 6172\n",
"WCnComV[1187].Len() = 2\n",
"WCnComV[1187][0] = 1424\n",
"WCnComV[1187][1] = 5106\n",
"WCnComV[1188].Len() = 2\n",
"WCnComV[1188][0] = 1421\n",
"WCnComV[1188][1] = 7149\n",
"WCnComV[1189].Len() = 2\n",
"WCnComV[1189][0] = 1392\n",
"WCnComV[1189][1] = 3523\n",
"WCnComV[1190].Len() = 2\n",
"WCnComV[1190][0] = 1388\n",
"WCnComV[1190][1] = 6515\n",
"WCnComV[1191].Len() = 2\n",
"WCnComV[1191][0] = 1387\n",
"WCnComV[1191][1] = 7079\n",
"WCnComV[1192].Len() = 2\n",
"WCnComV[1192][0] = 1381\n",
"WCnComV[1192][1] = 9303\n",
"WCnComV[1193].Len() = 2\n",
"WCnComV[1193][0] = 1367\n",
"WCnComV[1193][1] = 5449\n",
"WCnComV[1194].Len() = 2\n",
"WCnComV[1194][0] = 1366\n",
"WCnComV[1194][1] = 3090\n",
"WCnComV[1195].Len() = 2\n",
"WCnComV[1195][0] = 1365\n",
"WCnComV[1195][1] = 4369\n",
"WCnComV[1196].Len() = 2\n",
"WCnComV[1196][0] = 1347\n",
"WCnComV[1196][1] = 7383\n",
"WCnComV[1197].Len() = 2\n",
"WCnComV[1197][0] = 1333\n",
"WCnComV[1197][1] = 7801\n",
"WCnComV[1198].Len() = 2\n",
"WCnComV[1198][0] = 1314\n",
"WCnComV[1198][1] = 2987\n",
"WCnComV[1199].Len() = 2\n",
"WCnComV[1199][0] = 1312\n",
"WCnComV[1199][1] = 7336\n",
"WCnComV[1200].Len() = 2\n",
"WCnComV[1200][0] = 1310\n",
"WCnComV[1200][1] = 5722\n",
"WCnComV[1201].Len() = 2\n",
"WCnComV[1201][0] = 1297\n",
"WCnComV[1201][1] = 9725\n",
"WCnComV[1202].Len() = 2\n",
"WCnComV[1202][0] = 1295\n",
"WCnComV[1202][1] = 2484\n",
"WCnComV[1203].Len() = 2\n",
"WCnComV[1203][0] = 1293\n",
"WCnComV[1203][1] = 8349\n",
"WCnComV[1204].Len() = 2\n",
"WCnComV[1204][0] = 1279\n",
"WCnComV[1204][1] = 3761\n",
"WCnComV[1205].Len() = 2\n",
"WCnComV[1205][0] = 1245\n",
"WCnComV[1205][1] = 7819\n",
"WCnComV[1206].Len() = 2\n",
"WCnComV[1206][0] = 1238\n",
"WCnComV[1206][1] = 2619\n",
"WCnComV[1207].Len() = 2\n",
"WCnComV[1207][0] = 1227\n",
"WCnComV[1207][1] = 1927\n",
"WCnComV[1208].Len() = 2\n",
"WCnComV[1208][0] = 1220\n",
"WCnComV[1208][1] = 8886\n",
"WCnComV[1209].Len() = 2\n",
"WCnComV[1209][0] = 1217\n",
"WCnComV[1209][1] = 4580\n",
"WCnComV[1210].Len() = 2\n",
"WCnComV[1210][0] = 1215\n",
"WCnComV[1210][1] = 7093\n",
"WCnComV[1211].Len() = 2\n",
"WCnComV[1211][0] = 1209\n",
"WCnComV[1211][1] = 8493\n",
"WCnComV[1212].Len() = 2\n",
"WCnComV[1212][0] = 1201\n",
"WCnComV[1212][1] = 4902\n",
"WCnComV[1213].Len() = 2\n",
"WCnComV[1213][0] = 1186\n",
"WCnComV[1213][1] = 7598\n",
"WCnComV[1214].Len() = 2\n",
"WCnComV[1214][0] = 1173\n",
"WCnComV[1214][1] = 9745\n",
"WCnComV[1215].Len() = 2\n",
"WCnComV[1215][0] = 1168\n",
"WCnComV[1215][1] = 8044\n",
"WCnComV[1216].Len() = 2\n",
"WCnComV[1216][0] = 1167\n",
"WCnComV[1216][1] = 1804\n",
"WCnComV[1217].Len() = 2\n",
"WCnComV[1217][0] = 1166\n",
"WCnComV[1217][1] = 3706\n",
"WCnComV[1218].Len() = 2\n",
"WCnComV[1218][0] = 1150\n",
"WCnComV[1218][1] = 3373\n",
"WCnComV[1219].Len() = 2\n",
"WCnComV[1219][0] = 1148\n",
"WCnComV[1219][1] = 8706\n",
"WCnComV[1220].Len() = 2\n",
"WCnComV[1220][0] = 1147\n",
"WCnComV[1220][1] = 5185\n",
"WCnComV[1221].Len() = 2\n",
"WCnComV[1221][0] = 1142\n",
"WCnComV[1221][1] = 6233\n",
"WCnComV[1222].Len() = 2\n",
"WCnComV[1222][0] = 1138\n",
"WCnComV[1222][1] = 2624\n",
"WCnComV[1223].Len() = 2\n",
"WCnComV[1223][0] = 1137\n",
"WCnComV[1223][1] = 5580\n",
"WCnComV[1224].Len() = 2\n",
"WCnComV[1224][0] = 1128\n",
"WCnComV[1224][1] = 2661\n",
"WCnComV[1225].Len() = 2\n",
"WCnComV[1225][0] = 1127\n",
"WCnComV[1225][1] = 7686\n",
"WCnComV[1226].Len() = 2\n",
"WCnComV[1226][0] = 1124\n",
"WCnComV[1226][1] = 4985\n",
"WCnComV[1227].Len() = 2\n",
"WCnComV[1227][0] = 1094\n",
"WCnComV[1227][1] = 4587\n",
"WCnComV[1228].Len() = 2\n",
"WCnComV[1228][0] = 1088\n",
"WCnComV[1228][1] = 1412\n",
"WCnComV[1229].Len() = 2\n",
"WCnComV[1229][0] = 1086\n",
"WCnComV[1229][1] = 5452\n",
"WCnComV[1230].Len() = 2\n",
"WCnComV[1230][0] = 1081\n",
"WCnComV[1230][1] = 8963\n",
"WCnComV[1231].Len() = 2\n",
"WCnComV[1231][0] = 1071\n",
"WCnComV[1231][1] = 5483\n",
"WCnComV[1232].Len() = 2\n",
"WCnComV[1232][0] = 1067\n",
"WCnComV[1232][1] = 9142\n",
"WCnComV[1233].Len() = 2\n",
"WCnComV[1233][0] = 1061\n",
"WCnComV[1233][1] = 6673\n",
"WCnComV[1234].Len() = 2\n",
"WCnComV[1234][0] = 1052\n",
"WCnComV[1234][1] = 8218\n",
"WCnComV[1235].Len() = 2\n",
"WCnComV[1235][0] = 1048\n",
"WCnComV[1235][1] = 5700\n",
"WCnComV[1236].Len() = 2\n",
"WCnComV[1236][0] = 1047\n",
"WCnComV[1236][1] = 7229\n",
"WCnComV[1237].Len() = 2\n",
"WCnComV[1237][0] = 1039\n",
"WCnComV[1237][1] = 2338\n",
"WCnComV[1238].Len() = 2\n",
"WCnComV[1238][0] = 1025\n",
"WCnComV[1238][1] = 7986\n",
"WCnComV[1239].Len() = 2\n",
"WCnComV[1239][0] = 1018\n",
"WCnComV[1239][1] = 9009\n",
"WCnComV[1240].Len() = 2\n",
"WCnComV[1240][0] = 1009\n",
"WCnComV[1240][1] = 1677\n",
"WCnComV[1241].Len() = 2\n",
"WCnComV[1241][0] = 1005\n",
"WCnComV[1241][1] = 4371\n",
"WCnComV[1242].Len() = 2\n",
"WCnComV[1242][0] = 998\n",
"WCnComV[1242][1] = 6391\n",
"WCnComV[1243].Len() = 2\n",
"WCnComV[1243][0] = 995\n",
"WCnComV[1243][1] = 5861\n",
"WCnComV[1244].Len() = 2\n",
"WCnComV[1244][0] = 991\n",
"WCnComV[1244][1] = 1815\n",
"WCnComV[1245].Len() = 2\n",
"WCnComV[1245][0] = 987\n",
"WCnComV[1245][1] = 2184\n",
"WCnComV[1246].Len() = 2\n",
"WCnComV[1246][0] = 982\n",
"WCnComV[1246][1] = 4783\n",
"WCnComV[1247].Len() = 2\n",
"WCnComV[1247][0] = 981\n",
"WCnComV[1247][1] = 8962\n",
"WCnComV[1248].Len() = 2\n",
"WCnComV[1248][0] = 980\n",
"WCnComV[1248][1] = 4432\n",
"WCnComV[1249].Len() = 2\n",
"WCnComV[1249][0] = 975\n",
"WCnComV[1249][1] = 1543\n",
"WCnComV[1250].Len() = 2\n",
"WCnComV[1250][0] = 964\n",
"WCnComV[1250][1] = 3234\n",
"WCnComV[1251].Len() = 2\n",
"WCnComV[1251][0] = 956\n",
"WCnComV[1251][1] = 8904\n",
"WCnComV[1252].Len() = 2\n",
"WCnComV[1252][0] = 942\n",
"WCnComV[1252][1] = 8884\n",
"WCnComV[1253].Len() = 2\n",
"WCnComV[1253][0] = 939\n",
"WCnComV[1253][1] = 2480\n",
"WCnComV[1254].Len() = 2\n",
"WCnComV[1254][0] = 937\n",
"WCnComV[1254][1] = 6496\n",
"WCnComV[1255].Len() = 2\n",
"WCnComV[1255][0] = 928\n",
"WCnComV[1255][1] = 3035\n",
"WCnComV[1256].Len() = 2\n",
"WCnComV[1256][0] = 921\n",
"WCnComV[1256][1] = 7051\n",
"WCnComV[1257].Len() = 2\n",
"WCnComV[1257][0] = 918\n",
"WCnComV[1257][1] = 4472\n",
"WCnComV[1258].Len() = 2\n",
"WCnComV[1258][0] = 905\n",
"WCnComV[1258][1] = 8704\n",
"WCnComV[1259].Len() = 2\n",
"WCnComV[1259][0] = 899\n",
"WCnComV[1259][1] = 8458\n",
"WCnComV[1260].Len() = 2\n",
"WCnComV[1260][0] = 893\n",
"WCnComV[1260][1] = 4213\n",
"WCnComV[1261].Len() = 2\n",
"WCnComV[1261][0] = 887\n",
"WCnComV[1261][1] = 4836\n",
"WCnComV[1262].Len() = 2\n",
"WCnComV[1262][0] = 882\n",
"WCnComV[1262][1] = 3171\n",
"WCnComV[1263].Len() = 2\n",
"WCnComV[1263][0] = 875\n",
"WCnComV[1263][1] = 9490\n",
"WCnComV[1264].Len() = 2\n",
"WCnComV[1264][0] = 873\n",
"WCnComV[1264][1] = 6462\n",
"WCnComV[1265].Len() = 2\n",
"WCnComV[1265][0] = 866\n",
"WCnComV[1265][1] = 8418\n",
"WCnComV[1266].Len() = 2\n",
"WCnComV[1266][0] = 861\n",
"WCnComV[1266][1] = 2849\n",
"WCnComV[1267].Len() = 2\n",
"WCnComV[1267][0] = 859\n",
"WCnComV[1267][1] = 5038\n",
"WCnComV[1268].Len() = 2\n",
"WCnComV[1268][0] = 845\n",
"WCnComV[1268][1] = 3414\n",
"WCnComV[1269].Len() = 2\n",
"WCnComV[1269][0] = 843\n",
"WCnComV[1269][1] = 7996\n",
"WCnComV[1270].Len() = 2\n",
"WCnComV[1270][0] = 842\n",
"WCnComV[1270][1] = 4368\n",
"WCnComV[1271].Len() = 2\n",
"WCnComV[1271][0] = 838\n",
"WCnComV[1271][1] = 2777\n",
"WCnComV[1272].Len() = 2\n",
"WCnComV[1272][0] = 837\n",
"WCnComV[1272][1] = 3430\n",
"WCnComV[1273].Len() = 2\n",
"WCnComV[1273][0] = 830\n",
"WCnComV[1273][1] = 4306\n",
"WCnComV[1274].Len() = 2\n",
"WCnComV[1274][0] = 827\n",
"WCnComV[1274][1] = 6652\n",
"WCnComV[1275].Len() = 2\n",
"WCnComV[1275][0] = 825\n",
"WCnComV[1275][1] = 5320\n",
"WCnComV[1276].Len() = 2\n",
"WCnComV[1276][0] = 824\n",
"WCnComV[1276][1] = 5360\n",
"WCnComV[1277].Len() = 2\n",
"WCnComV[1277][0] = 818\n",
"WCnComV[1277][1] = 6828\n",
"WCnComV[1278].Len() = 2\n",
"WCnComV[1278][0] = 816\n",
"WCnComV[1278][1] = 6966\n",
"WCnComV[1279].Len() = 2\n",
"WCnComV[1279][0] = 804\n",
"WCnComV[1279][1] = 5155\n",
"WCnComV[1280].Len() = 2\n",
"WCnComV[1280][0] = 803\n",
"WCnComV[1280][1] = 9832\n",
"WCnComV[1281].Len() = 2\n",
"WCnComV[1281][0] = 786\n",
"WCnComV[1281][1] = 7944\n",
"WCnComV[1282].Len() = 2\n",
"WCnComV[1282][0] = 781\n",
"WCnComV[1282][1] = 4644\n",
"WCnComV[1283].Len() = 2\n",
"WCnComV[1283][0] = 778\n",
"WCnComV[1283][1] = 6394\n",
"WCnComV[1284].Len() = 2\n",
"WCnComV[1284][0] = 775\n",
"WCnComV[1284][1] = 4900\n",
"WCnComV[1285].Len() = 2\n",
"WCnComV[1285][0] = 758\n",
"WCnComV[1285][1] = 3617\n",
"WCnComV[1286].Len() = 2\n",
"WCnComV[1286][0] = 757\n",
"WCnComV[1286][1] = 5373\n",
"WCnComV[1287].Len() = 2\n",
"WCnComV[1287][0] = 741\n",
"WCnComV[1287][1] = 9316\n",
"WCnComV[1288].Len() = 2\n",
"WCnComV[1288][0] = 740\n",
"WCnComV[1288][1] = 2635\n",
"WCnComV[1289].Len() = 2\n",
"WCnComV[1289][0] = 739\n",
"WCnComV[1289][1] = 3417\n",
"WCnComV[1290].Len() = 2\n",
"WCnComV[1290][0] = 737\n",
"WCnComV[1290][1] = 4390\n",
"WCnComV[1291].Len() = 2\n",
"WCnComV[1291][0] = 732\n",
"WCnComV[1291][1] = 4839\n",
"WCnComV[1292].Len() = 2\n",
"WCnComV[1292][0] = 730\n",
"WCnComV[1292][1] = 1512\n",
"WCnComV[1293].Len() = 2\n",
"WCnComV[1293][0] = 717\n",
"WCnComV[1293][1] = 8326\n",
"WCnComV[1294].Len() = 2\n",
"WCnComV[1294][0] = 710\n",
"WCnComV[1294][1] = 1122\n",
"WCnComV[1295].Len() = 2\n",
"WCnComV[1295][0] = 696\n",
"WCnComV[1295][1] = 8834\n",
"WCnComV[1296].Len() = 2\n",
"WCnComV[1296][0] = 685\n",
"WCnComV[1296][1] = 5545\n",
"WCnComV[1297].Len() = 2\n",
"WCnComV[1297][0] = 670\n",
"WCnComV[1297][1] = 9543\n",
"WCnComV[1298].Len() = 2\n",
"WCnComV[1298][0] = 666\n",
"WCnComV[1298][1] = 7408\n",
"WCnComV[1299].Len() = 2\n",
"WCnComV[1299][0] = 651\n",
"WCnComV[1299][1] = 9081\n",
"WCnComV[1300].Len() = 2\n",
"WCnComV[1300][0] = 625\n",
"WCnComV[1300][1] = 3674\n",
"WCnComV[1301].Len() = 2\n",
"WCnComV[1301][0] = 621\n",
"WCnComV[1301][1] = 1045\n",
"WCnComV[1302].Len() = 2\n",
"WCnComV[1302][0] = 601\n",
"WCnComV[1302][1] = 7515\n",
"WCnComV[1303].Len() = 2\n",
"WCnComV[1303][0] = 578\n",
"WCnComV[1303][1] = 4717\n",
"WCnComV[1304].Len() = 2\n",
"WCnComV[1304][0] = 562\n",
"WCnComV[1304][1] = 1979\n",
"WCnComV[1305].Len() = 2\n",
"WCnComV[1305][0] = 561\n",
"WCnComV[1305][1] = 2121\n",
"WCnComV[1306].Len() = 2\n",
"WCnComV[1306][0] = 551\n",
"WCnComV[1306][1] = 8319\n",
"WCnComV[1307].Len() = 2\n",
"WCnComV[1307][0] = 548\n",
"WCnComV[1307][1] = 1591\n",
"WCnComV[1308].Len() = 2\n",
"WCnComV[1308][0] = 545\n",
"WCnComV[1308][1] = 6620\n",
"WCnComV[1309].Len() = 2\n",
"WCnComV[1309][0] = 538\n",
"WCnComV[1309][1] = 9594\n",
"WCnComV[1310].Len() = 2\n",
"WCnComV[1310][0] = 537\n",
"WCnComV[1310][1] = 9455\n",
"WCnComV[1311].Len() = 2\n",
"WCnComV[1311][0] = 535\n",
"WCnComV[1311][1] = 9817\n",
"WCnComV[1312].Len() = 2\n",
"WCnComV[1312][0] = 532\n",
"WCnComV[1312][1] = 3157\n",
"WCnComV[1313].Len() = 2\n",
"WCnComV[1313][0] = 524\n",
"WCnComV[1313][1] = 6154\n",
"WCnComV[1314].Len() = 2\n",
"WCnComV[1314][0] = 523\n",
"WCnComV[1314][1] = 8433\n",
"WCnComV[1315].Len() = 2\n",
"WCnComV[1315][0] = 522\n",
"WCnComV[1315][1] = 1673\n",
"WCnComV[1316].Len() = 2\n",
"WCnComV[1316][0] = 521\n",
"WCnComV[1316][1] = 8017\n",
"WCnComV[1317].Len() = 2\n",
"WCnComV[1317][0] = 520\n",
"WCnComV[1317][1] = 6335\n",
"WCnComV[1318].Len() = 2\n",
"WCnComV[1318][0] = 514\n",
"WCnComV[1318][1] = 7505\n",
"WCnComV[1319].Len() = 2\n",
"WCnComV[1319][0] = 510\n",
"WCnComV[1319][1] = 4952\n",
"WCnComV[1320].Len() = 2\n",
"WCnComV[1320][0] = 505\n",
"WCnComV[1320][1] = 1949\n",
"WCnComV[1321].Len() = 2\n",
"WCnComV[1321][0] = 503\n",
"WCnComV[1321][1] = 2359\n",
"WCnComV[1322].Len() = 2\n",
"WCnComV[1322][0] = 491\n",
"WCnComV[1322][1] = 4280\n",
"WCnComV[1323].Len() = 2\n",
"WCnComV[1323][0] = 488\n",
"WCnComV[1323][1] = 2592\n",
"WCnComV[1324].Len() = 2\n",
"WCnComV[1324][0] = 479\n",
"WCnComV[1324][1] = 6993\n",
"WCnComV[1325].Len() = 2\n",
"WCnComV[1325][0] = 477\n",
"WCnComV[1325][1] = 6310\n",
"WCnComV[1326].Len() = 2\n",
"WCnComV[1326][0] = 475\n",
"WCnComV[1326][1] = 5475\n",
"WCnComV[1327].Len() = 2\n",
"WCnComV[1327][0] = 443\n",
"WCnComV[1327][1] = 4920\n",
"WCnComV[1328].Len() = 2\n",
"WCnComV[1328][0] = 425\n",
"WCnComV[1328][1] = 4541\n",
"WCnComV[1329].Len() = 2\n",
"WCnComV[1329][0] = 424\n",
"WCnComV[1329][1] = 4125\n",
"WCnComV[1330].Len() = 2\n",
"WCnComV[1330][0] = 419\n",
"WCnComV[1330][1] = 891\n",
"WCnComV[1331].Len() = 2\n",
"WCnComV[1331][0] = 408\n",
"WCnComV[1331][1] = 2284\n",
"WCnComV[1332].Len() = 2\n",
"WCnComV[1332][0] = 389\n",
"WCnComV[1332][1] = 9860\n",
"WCnComV[1333].Len() = 2\n",
"WCnComV[1333][0] = 376\n",
"WCnComV[1333][1] = 6812\n",
"WCnComV[1334].Len() = 2\n",
"WCnComV[1334][0] = 375\n",
"WCnComV[1334][1] = 9357\n",
"WCnComV[1335].Len() = 2\n",
"WCnComV[1335][0] = 369\n",
"WCnComV[1335][1] = 7220\n",
"WCnComV[1336].Len() = 2\n",
"WCnComV[1336][0] = 359\n",
"WCnComV[1336][1] = 4984\n",
"WCnComV[1337].Len() = 2\n",
"WCnComV[1337][0] = 343\n",
"WCnComV[1337][1] = 4401\n",
"WCnComV[1338].Len() = 2\n",
"WCnComV[1338][0] = 334\n",
"WCnComV[1338][1] = 1910\n",
"WCnComV[1339].Len() = 2\n",
"WCnComV[1339][0] = 333\n",
"WCnComV[1339][1] = 4982\n",
"WCnComV[1340].Len() = 2\n",
"WCnComV[1340][0] = 326\n",
"WCnComV[1340][1] = 6033\n",
"WCnComV[1341].Len() = 2\n",
"WCnComV[1341][0] = 310\n",
"WCnComV[1341][1] = 9699\n",
"WCnComV[1342].Len() = 2\n",
"WCnComV[1342][0] = 306\n",
"WCnComV[1342][1] = 1893\n",
"WCnComV[1343].Len() = 2\n",
"WCnComV[1343][0] = 305\n",
"WCnComV[1343][1] = 7268\n",
"WCnComV[1344].Len() = 2\n",
"WCnComV[1344][0] = 300\n",
"WCnComV[1344][1] = 1178\n",
"WCnComV[1345].Len() = 2\n",
"WCnComV[1345][0] = 297\n",
"WCnComV[1345][1] = 7765\n",
"WCnComV[1346].Len() = 2\n",
"WCnComV[1346][0] = 294\n",
"WCnComV[1346][1] = 6723\n",
"WCnComV[1347].Len() = 2\n",
"WCnComV[1347][0] = 287\n",
"WCnComV[1347][1] = 2130\n",
"WCnComV[1348].Len() = 2\n",
"WCnComV[1348][0] = 286\n",
"WCnComV[1348][1] = 4660\n",
"WCnComV[1349].Len() = 2\n",
"WCnComV[1349][0] = 283\n",
"WCnComV[1349][1] = 7309\n",
"WCnComV[1350].Len() = 2\n",
"WCnComV[1350][0] = 273\n",
"WCnComV[1350][1] = 3059\n",
"WCnComV[1351].Len() = 2\n",
"WCnComV[1351][0] = 272\n",
"WCnComV[1351][1] = 8859\n",
"WCnComV[1352].Len() = 2\n",
"WCnComV[1352][0] = 266\n",
"WCnComV[1352][1] = 8404\n",
"WCnComV[1353].Len() = 2\n",
"WCnComV[1353][0] = 261\n",
"WCnComV[1353][1] = 6876\n",
"WCnComV[1354].Len() = 2\n",
"WCnComV[1354][0] = 256\n",
"WCnComV[1354][1] = 6001\n",
"WCnComV[1355].Len() = 2\n",
"WCnComV[1355][0] = 252\n",
"WCnComV[1355][1] = 4200\n",
"WCnComV[1356].Len() = 2\n",
"WCnComV[1356][0] = 242\n",
"WCnComV[1356][1] = 1216\n",
"WCnComV[1357].Len() = 2\n",
"WCnComV[1357][0] = 236\n",
"WCnComV[1357][1] = 8584\n",
"WCnComV[1358].Len() = 2\n",
"WCnComV[1358][0] = 232\n",
"WCnComV[1358][1] = 2135\n",
"WCnComV[1359].Len() = 2\n",
"WCnComV[1359][0] = 221\n",
"WCnComV[1359][1] = 7499\n",
"WCnComV[1360].Len() = 2\n",
"WCnComV[1360][0] = 220\n",
"WCnComV[1360][1] = 3463\n",
"WCnComV[1361].Len() = 2\n",
"WCnComV[1361][0] = 209\n",
"WCnComV[1361][1] = 4798\n",
"WCnComV[1362].Len() = 2\n",
"WCnComV[1362][0] = 194\n",
"WCnComV[1362][1] = 3320\n",
"WCnComV[1363].Len() = 2\n",
"WCnComV[1363][0] = 185\n",
"WCnComV[1363][1] = 6034\n",
"WCnComV[1364].Len() = 2\n",
"WCnComV[1364][0] = 174\n",
"WCnComV[1364][1] = 7887\n",
"WCnComV[1365].Len() = 2\n",
"WCnComV[1365][0] = 169\n",
"WCnComV[1365][1] = 4696\n",
"WCnComV[1366].Len() = 2\n",
"WCnComV[1366][0] = 141\n",
"WCnComV[1366][1] = 3108\n",
"WCnComV[1367].Len() = 2\n",
"WCnComV[1367][0] = 120\n",
"WCnComV[1367][1] = 6763\n",
"WCnComV[1368].Len() = 2\n",
"WCnComV[1368][0] = 117\n",
"WCnComV[1368][1] = 3619\n",
"WCnComV[1369].Len() = 2\n",
"WCnComV[1369][0] = 108\n",
"WCnComV[1369][1] = 6197\n",
"WCnComV[1370].Len() = 2\n",
"WCnComV[1370][0] = 93\n",
"WCnComV[1370][1] = 4525\n",
"WCnComV[1371].Len() = 2\n",
"WCnComV[1371][0] = 87\n",
"WCnComV[1371][1] = 1763\n",
"WCnComV[1372].Len() = 2\n",
"WCnComV[1372][0] = 76\n",
"WCnComV[1372][1] = 7076\n",
"WCnComV[1373].Len() = 2\n",
"WCnComV[1373][0] = 70\n",
"WCnComV[1373][1] = 1667\n",
"WCnComV[1374].Len() = 2\n",
"WCnComV[1374][0] = 64\n",
"WCnComV[1374][1] = 4422\n",
"WCnComV[1375].Len() = 2\n",
"WCnComV[1375][0] = 58\n",
"WCnComV[1375][1] = 5310\n",
"WCnComV[1376].Len() = 2\n",
"WCnComV[1376][0] = 53\n",
"WCnComV[1376][1] = 2282\n",
"WCnComV[1377].Len() = 2\n",
"WCnComV[1377][0] = 48\n",
"WCnComV[1377][1] = 6204\n",
"WCnComV[1378].Len() = 2\n",
"WCnComV[1378][0] = 45\n",
"WCnComV[1378][1] = 9744\n",
"WCnComV[1379].Len() = 2\n",
"WCnComV[1379][0] = 44\n",
"WCnComV[1379][1] = 4614\n",
"WCnComV[1380].Len() = 2\n",
"WCnComV[1380][0] = 33\n",
"WCnComV[1380][1] = 4599\n",
"WCnComV[1381].Len() = 2\n",
"WCnComV[1381][0] = 30\n",
"WCnComV[1381][1] = 8314\n",
"WCnComV[1382].Len() = 2\n",
"WCnComV[1382][0] = 26\n",
"WCnComV[1382][1] = 9445\n",
"WCnComV[1383].Len() = 2\n",
"WCnComV[1383][0] = 23\n",
"WCnComV[1383][1] = 7652\n",
"WCnComV[1384].Len() = 2\n",
"WCnComV[1384][0] = 10\n",
"WCnComV[1384][1] = 65\n",
"WCnComV[1385].Len() = 2\n",
"WCnComV[1385][0] = 0\n",
"WCnComV[1385][1] = 1282\n",
"WCnComV[1386].Len() = 1\n",
"WCnComV[1386][0] = 9998\n",
"WCnComV[1387].Len() = 1\n",
"WCnComV[1387][0] = 9997\n",
"WCnComV[1388].Len() = 1\n",
"WCnComV[1388][0] = 9995\n",
"WCnComV[1389].Len() = 1\n",
"WCnComV[1389][0] = 9993\n",
"WCnComV[1390].Len() = 1\n",
"WCnComV[1390][0] = 9991\n",
"WCnComV[1391].Len() = 1\n",
"WCnComV[1391][0] = 9990\n",
"WCnComV[1392].Len() = 1\n",
"WCnComV[1392][0] = 9979\n",
"WCnComV[1393].Len() = 1\n",
"WCnComV[1393][0] = 9971\n",
"WCnComV[1394].Len() = 1\n",
"WCnComV[1394][0] = 9970\n",
"WCnComV[1395].Len() = 1\n",
"WCnComV[1395][0] = 9967\n",
"WCnComV[1396].Len() = 1\n",
"WCnComV[1396][0] = 9966\n",
"WCnComV[1397].Len() = 1\n",
"WCnComV[1397][0] = 9962\n",
"WCnComV[1398].Len() = 1\n",
"WCnComV[1398][0] = 9960\n",
"WCnComV[1399].Len() = 1\n",
"WCnComV[1399][0] = 9959\n",
"WCnComV[1400].Len() = 1\n",
"WCnComV[1400][0] = 9954\n",
"WCnComV[1401].Len() = 1\n",
"WCnComV[1401][0] = 9953\n",
"WCnComV[1402].Len() = 1\n",
"WCnComV[1402][0] = 9952\n",
"WCnComV[1403].Len() = 1\n",
"WCnComV[1403][0] = 9947\n",
"WCnComV[1404].Len() = 1\n",
"WCnComV[1404][0] = 9942\n",
"WCnComV[1405].Len() = 1\n",
"WCnComV[1405][0] = 9941\n",
"WCnComV[1406].Len() = 1\n",
"WCnComV[1406][0] = 9940\n",
"WCnComV[1407].Len() = 1\n",
"WCnComV[1407][0] = 9938\n",
"WCnComV[1408].Len() = 1\n",
"WCnComV[1408][0] = 9936\n",
"WCnComV[1409].Len() = 1\n",
"WCnComV[1409][0] = 9935\n",
"WCnComV[1410].Len() = 1\n",
"WCnComV[1410][0] = 9934\n",
"WCnComV[1411].Len() = 1\n",
"WCnComV[1411][0] = 9933\n",
"WCnComV[1412].Len() = 1\n",
"WCnComV[1412][0] = 9931\n",
"WCnComV[1413].Len() = 1\n",
"WCnComV[1413][0] = 9930\n",
"WCnComV[1414].Len() = 1\n",
"WCnComV[1414][0] = 9929\n",
"WCnComV[1415].Len() = 1\n",
"WCnComV[1415][0] = 9923\n",
"WCnComV[1416].Len() = 1\n",
"WCnComV[1416][0] = 9920\n",
"WCnComV[1417].Len() = 1\n",
"WCnComV[1417][0] = 9912\n",
"WCnComV[1418].Len() = 1\n",
"WCnComV[1418][0] = 9911\n",
"WCnComV[1419].Len() = 1\n",
"WCnComV[1419][0] = 9905\n",
"WCnComV[1420].Len() = 1\n",
"WCnComV[1420][0] = 9904\n",
"WCnComV[1421].Len() = 1\n",
"WCnComV[1421][0] = 9903\n",
"WCnComV[1422].Len() = 1\n",
"WCnComV[1422][0] = 9902\n",
"WCnComV[1423].Len() = 1\n",
"WCnComV[1423][0] = 9901\n",
"WCnComV[1424].Len() = 1\n",
"WCnComV[1424][0] = 9900\n",
"WCnComV[1425].Len() = 1\n",
"WCnComV[1425][0] = 9897\n",
"WCnComV[1426].Len() = 1\n",
"WCnComV[1426][0] = 9895\n",
"WCnComV[1427].Len() = 1\n",
"WCnComV[1427][0] = 9892\n",
"WCnComV[1428].Len() = 1\n",
"WCnComV[1428][0] = 9891\n",
"WCnComV[1429].Len() = 1\n",
"WCnComV[1429][0] = 9889\n",
"WCnComV[1430].Len() = 1\n",
"WCnComV[1430][0] = 9888\n",
"WCnComV[1431].Len() = 1\n",
"WCnComV[1431][0] = 9882\n",
"WCnComV[1432].Len() = 1\n",
"WCnComV[1432][0] = 9881\n",
"WCnComV[1433].Len() = 1\n",
"WCnComV[1433][0] = 9877\n",
"WCnComV[1434].Len() = 1\n",
"WCnComV[1434][0] = 9875\n",
"WCnComV[1435].Len() = 1\n",
"WCnComV[1435][0] = 9871\n",
"WCnComV[1436].Len() = 1\n",
"WCnComV[1436][0] = 9870\n",
"WCnComV[1437].Len() = 1\n",
"WCnComV[1437][0] = 9869\n",
"WCnComV[1438].Len() = 1\n",
"WCnComV[1438][0] = 9868\n",
"WCnComV[1439].Len() = 1\n",
"WCnComV[1439][0] = 9867\n",
"WCnComV[1440].Len() = 1\n",
"WCnComV[1440][0] = 9866\n",
"WCnComV[1441].Len() = 1\n",
"WCnComV[1441][0] = 9865\n",
"WCnComV[1442].Len() = 1\n",
"WCnComV[1442][0] = 9863\n",
"WCnComV[1443].Len() = 1\n",
"WCnComV[1443][0] = 9861\n",
"WCnComV[1444].Len() = 1\n",
"WCnComV[1444][0] = 9854\n",
"WCnComV[1445].Len() = 1\n",
"WCnComV[1445][0] = 9850\n",
"WCnComV[1446].Len() = 1\n",
"WCnComV[1446][0] = 9849\n",
"WCnComV[1447].Len() = 1\n",
"WCnComV[1447][0] = 9847\n",
"WCnComV[1448].Len() = 1\n",
"WCnComV[1448][0] = 9846\n",
"WCnComV[1449].Len() = 1\n",
"WCnComV[1449][0] = 9845\n",
"WCnComV[1450].Len() = 1\n",
"WCnComV[1450][0] = 9841\n",
"WCnComV[1451].Len() = 1\n",
"WCnComV[1451][0] = 9840\n",
"WCnComV[1452].Len() = 1\n",
"WCnComV[1452][0] = 9837\n",
"WCnComV[1453].Len() = 1\n",
"WCnComV[1453][0] = 9830\n",
"WCnComV[1454].Len() = 1\n",
"WCnComV[1454][0] = 9825\n",
"WCnComV[1455].Len() = 1\n",
"WCnComV[1455][0] = 9819\n",
"WCnComV[1456].Len() = 1\n",
"WCnComV[1456][0] = 9818\n",
"WCnComV[1457].Len() = 1\n",
"WCnComV[1457][0] = 9813\n",
"WCnComV[1458].Len() = 1\n",
"WCnComV[1458][0] = 9812\n",
"WCnComV[1459].Len() = 1\n",
"WCnComV[1459][0] = 9804\n",
"WCnComV[1460].Len() = 1\n",
"WCnComV[1460][0] = 9803\n",
"WCnComV[1461].Len() = 1\n",
"WCnComV[1461][0] = 9801\n",
"WCnComV[1462].Len() = 1\n",
"WCnComV[1462][0] = 9798\n",
"WCnComV[1463].Len() = 1\n",
"WCnComV[1463][0] = 9796\n",
"WCnComV[1464].Len() = 1\n",
"WCnComV[1464][0] = 9795\n",
"WCnComV[1465].Len() = 1\n",
"WCnComV[1465][0] = 9794\n",
"WCnComV[1466].Len() = 1\n",
"WCnComV[1466][0] = 9793\n",
"WCnComV[1467].Len() = 1\n",
"WCnComV[1467][0] = 9791\n",
"WCnComV[1468].Len() = 1\n",
"WCnComV[1468][0] = 9789\n",
"WCnComV[1469].Len() = 1\n",
"WCnComV[1469][0] = 9788\n",
"WCnComV[1470].Len() = 1\n",
"WCnComV[1470][0] = 9784\n",
"WCnComV[1471].Len() = 1\n",
"WCnComV[1471][0] = 9780\n",
"WCnComV[1472].Len() = 1\n",
"WCnComV[1472][0] = 9777\n",
"WCnComV[1473].Len() = 1\n",
"WCnComV[1473][0] = 9775\n",
"WCnComV[1474].Len() = 1\n",
"WCnComV[1474][0] = 9774\n",
"WCnComV[1475].Len() = 1\n",
"WCnComV[1475][0] = 9773\n",
"WCnComV[1476].Len() = 1\n",
"WCnComV[1476][0] = 9771\n",
"WCnComV[1477].Len() = 1\n",
"WCnComV[1477][0] = 9769\n",
"WCnComV[1478].Len() = 1\n",
"WCnComV[1478][0] = 9767\n",
"WCnComV[1479].Len() = 1\n",
"WCnComV[1479][0] = 9765\n",
"WCnComV[1480].Len() = 1\n",
"WCnComV[1480][0] = 9763\n",
"WCnComV[1481].Len() = 1\n",
"WCnComV[1481][0] = 9760\n",
"WCnComV[1482].Len() = 1\n",
"WCnComV[1482][0] = 9755\n",
"WCnComV[1483].Len() = 1\n",
"WCnComV[1483][0] = 9754\n",
"WCnComV[1484].Len() = 1\n",
"WCnComV[1484][0] = 9753\n",
"WCnComV[1485].Len() = 1\n",
"WCnComV[1485][0] = 9743\n",
"WCnComV[1486].Len() = 1\n",
"WCnComV[1486][0] = 9742\n",
"WCnComV[1487].Len() = 1\n",
"WCnComV[1487][0] = 9741\n",
"WCnComV[1488].Len() = 1\n",
"WCnComV[1488][0] = 9740\n",
"WCnComV[1489].Len() = 1\n",
"WCnComV[1489][0] = 9738\n",
"WCnComV[1490].Len() = 1\n",
"WCnComV[1490][0] = 9736\n",
"WCnComV[1491].Len() = 1\n",
"WCnComV[1491][0] = 9734\n",
"WCnComV[1492].Len() = 1\n",
"WCnComV[1492][0] = 9724\n",
"WCnComV[1493].Len() = 1\n",
"WCnComV[1493][0] = 9721\n",
"WCnComV[1494].Len() = 1\n",
"WCnComV[1494][0] = 9717\n",
"WCnComV[1495].Len() = 1\n",
"WCnComV[1495][0] = 9716\n",
"WCnComV[1496].Len() = 1\n",
"WCnComV[1496][0] = 9715\n",
"WCnComV[1497].Len() = 1\n",
"WCnComV[1497][0] = 9711\n",
"WCnComV[1498].Len() = 1\n",
"WCnComV[1498][0] = 9707\n",
"WCnComV[1499].Len() = 1\n",
"WCnComV[1499][0] = 9705\n",
"WCnComV[1500].Len() = 1\n",
"WCnComV[1500][0] = 9697\n",
"WCnComV[1501].Len() = 1\n",
"WCnComV[1501][0] = 9695\n",
"WCnComV[1502].Len() = 1\n",
"WCnComV[1502][0] = 9693\n",
"WCnComV[1503].Len() = 1\n",
"WCnComV[1503][0] = 9690\n",
"WCnComV[1504].Len() = 1\n",
"WCnComV[1504][0] = 9684\n",
"WCnComV[1505].Len() = 1\n",
"WCnComV[1505][0] = 9681\n",
"WCnComV[1506].Len() = 1\n",
"WCnComV[1506][0] = 9676\n",
"WCnComV[1507].Len() = 1\n",
"WCnComV[1507][0] = 9672\n",
"WCnComV[1508].Len() = 1\n",
"WCnComV[1508][0] = 9671\n",
"WCnComV[1509].Len() = 1\n",
"WCnComV[1509][0] = 9669\n",
"WCnComV[1510].Len() = 1\n",
"WCnComV[1510][0] = 9668\n",
"WCnComV[1511].Len() = 1\n",
"WCnComV[1511][0] = 9665\n",
"WCnComV[1512].Len() = 1\n",
"WCnComV[1512][0] = 9653\n",
"WCnComV[1513].Len() = 1\n",
"WCnComV[1513][0] = 9652\n",
"WCnComV[1514].Len() = 1\n",
"WCnComV[1514][0] = 9650\n",
"WCnComV[1515].Len() = 1\n",
"WCnComV[1515][0] = 9642\n",
"WCnComV[1516].Len() = 1\n",
"WCnComV[1516][0] = 9641\n",
"WCnComV[1517].Len() = 1\n",
"WCnComV[1517][0] = 9637\n",
"WCnComV[1518].Len() = 1\n",
"WCnComV[1518][0] = 9636\n",
"WCnComV[1519].Len() = 1\n",
"WCnComV[1519][0] = 9634\n",
"WCnComV[1520].Len() = 1\n",
"WCnComV[1520][0] = 9632\n",
"WCnComV[1521].Len() = 1\n",
"WCnComV[1521][0] = 9631\n",
"WCnComV[1522].Len() = 1\n",
"WCnComV[1522][0] = 9626\n",
"WCnComV[1523].Len() = 1\n",
"WCnComV[1523][0] = 9625\n",
"WCnComV[1524].Len() = 1\n",
"WCnComV[1524][0] = 9624\n",
"WCnComV[1525].Len() = 1\n",
"WCnComV[1525][0] = 9620\n",
"WCnComV[1526].Len() = 1\n",
"WCnComV[1526][0] = 9619\n",
"WCnComV[1527].Len() = 1\n",
"WCnComV[1527][0] = 9615\n",
"WCnComV[1528].Len() = 1\n",
"WCnComV[1528][0] = 9614\n",
"WCnComV[1529].Len() = 1\n",
"WCnComV[1529][0] = 9611\n",
"WCnComV[1530].Len() = 1\n",
"WCnComV[1530][0] = 9607\n",
"WCnComV[1531].Len() = 1\n",
"WCnComV[1531][0] = 9602\n",
"WCnComV[1532].Len() = 1\n",
"WCnComV[1532][0] = 9600\n",
"WCnComV[1533].Len() = 1\n",
"WCnComV[1533][0] = 9598\n",
"WCnComV[1534].Len() = 1\n",
"WCnComV[1534][0] = 9597\n",
"WCnComV[1535].Len() = 1\n",
"WCnComV[1535][0] = 9587\n",
"WCnComV[1536].Len() = 1\n",
"WCnComV[1536][0] = 9584\n",
"WCnComV[1537].Len() = 1\n",
"WCnComV[1537][0] = 9583\n",
"WCnComV[1538].Len() = 1\n",
"WCnComV[1538][0] = 9582\n",
"WCnComV[1539].Len() = 1\n",
"WCnComV[1539][0] = 9581\n",
"WCnComV[1540].Len() = 1\n",
"WCnComV[1540][0] = 9579\n",
"WCnComV[1541].Len() = 1\n",
"WCnComV[1541][0] = 9569\n",
"WCnComV[1542].Len() = 1\n",
"WCnComV[1542][0] = 9568\n",
"WCnComV[1543].Len() = 1\n",
"WCnComV[1543][0] = 9565\n",
"WCnComV[1544].Len() = 1\n",
"WCnComV[1544][0] = 9552\n",
"WCnComV[1545].Len() = 1\n",
"WCnComV[1545][0] = 9548\n",
"WCnComV[1546].Len() = 1\n",
"WCnComV[1546][0] = 9547\n",
"WCnComV[1547].Len() = 1\n",
"WCnComV[1547][0] = 9545\n",
"WCnComV[1548].Len() = 1\n",
"WCnComV[1548][0] = 9542\n",
"WCnComV[1549].Len() = 1\n",
"WCnComV[1549][0] = 9538\n",
"WCnComV[1550].Len() = 1\n",
"WCnComV[1550][0] = 9537\n",
"WCnComV[1551].Len() = 1\n",
"WCnComV[1551][0] = 9534\n",
"WCnComV[1552].Len() = 1\n",
"WCnComV[1552][0] = 9532\n",
"WCnComV[1553].Len() = 1\n",
"WCnComV[1553][0] = 9531\n",
"WCnComV[1554].Len() = 1\n",
"WCnComV[1554][0] = 9530\n",
"WCnComV[1555].Len() = 1\n",
"WCnComV[1555][0] = 9526\n",
"WCnComV[1556].Len() = 1\n",
"WCnComV[1556][0] = 9522\n",
"WCnComV[1557].Len() = 1\n",
"WCnComV[1557][0] = 9520\n",
"WCnComV[1558].Len() = 1\n",
"WCnComV[1558][0] = 9517\n",
"WCnComV[1559].Len() = 1\n",
"WCnComV[1559][0] = 9515\n",
"WCnComV[1560].Len() = 1\n",
"WCnComV[1560][0] = 9514\n",
"WCnComV[1561].Len() = 1\n",
"WCnComV[1561][0] = 9511\n",
"WCnComV[1562].Len() = 1\n",
"WCnComV[1562][0] = 9509\n",
"WCnComV[1563].Len() = 1\n",
"WCnComV[1563][0] = 9503\n",
"WCnComV[1564].Len() = 1\n",
"WCnComV[1564][0] = 9500\n",
"WCnComV[1565].Len() = 1\n",
"WCnComV[1565][0] = 9498\n",
"WCnComV[1566].Len() = 1\n",
"WCnComV[1566][0] = 9497\n",
"WCnComV[1567].Len() = 1\n",
"WCnComV[1567][0] = 9496\n",
"WCnComV[1568].Len() = 1\n",
"WCnComV[1568][0] = 9491\n",
"WCnComV[1569].Len() = 1\n",
"WCnComV[1569][0] = 9488\n",
"WCnComV[1570].Len() = 1\n",
"WCnComV[1570][0] = 9487\n",
"WCnComV[1571].Len() = 1\n",
"WCnComV[1571][0] = 9484\n",
"WCnComV[1572].Len() = 1\n",
"WCnComV[1572][0] = 9483\n",
"WCnComV[1573].Len() = 1\n",
"WCnComV[1573][0] = 9481\n",
"WCnComV[1574].Len() = 1\n",
"WCnComV[1574][0] = 9480\n",
"WCnComV[1575].Len() = 1\n",
"WCnComV[1575][0] = 9477\n",
"WCnComV[1576].Len() = 1\n",
"WCnComV[1576][0] = 9469\n",
"WCnComV[1577].Len() = 1\n",
"WCnComV[1577][0] = 9465\n",
"WCnComV[1578].Len() = 1\n",
"WCnComV[1578][0] = 9462\n",
"WCnComV[1579].Len() = 1\n",
"WCnComV[1579][0] = 9460\n",
"WCnComV[1580].Len() = 1\n",
"WCnComV[1580][0] = 9459\n",
"WCnComV[1581].Len() = 1\n",
"WCnComV[1581][0] = 9458\n",
"WCnComV[1582].Len() = 1\n",
"WCnComV[1582][0] = 9454\n",
"WCnComV[1583].Len() = 1\n",
"WCnComV[1583][0] = 9453\n",
"WCnComV[1584].Len() = 1\n",
"WCnComV[1584][0] = 9450\n",
"WCnComV[1585].Len() = 1\n",
"WCnComV[1585][0] = 9449\n",
"WCnComV[1586].Len() = 1\n",
"WCnComV[1586][0] = 9446\n",
"WCnComV[1587].Len() = 1\n",
"WCnComV[1587][0] = 9443\n",
"WCnComV[1588].Len() = 1\n",
"WCnComV[1588][0] = 9439\n",
"WCnComV[1589].Len() = 1\n",
"WCnComV[1589][0] = 9437\n",
"WCnComV[1590].Len() = 1\n",
"WCnComV[1590][0] = 9436\n",
"WCnComV[1591].Len() = 1\n",
"WCnComV[1591][0] = 9435\n",
"WCnComV[1592].Len() = 1\n",
"WCnComV[1592][0] = 9433\n",
"WCnComV[1593].Len() = 1\n",
"WCnComV[1593][0] = 9432\n",
"WCnComV[1594].Len() = 1\n",
"WCnComV[1594][0] = 9429\n",
"WCnComV[1595].Len() = 1\n",
"WCnComV[1595][0] = 9424\n",
"WCnComV[1596].Len() = 1\n",
"WCnComV[1596][0] = 9421\n",
"WCnComV[1597].Len() = 1\n",
"WCnComV[1597][0] = 9416\n",
"WCnComV[1598].Len() = 1\n",
"WCnComV[1598][0] = 9414\n",
"WCnComV[1599].Len() = 1\n",
"WCnComV[1599][0] = 9405\n",
"WCnComV[1600].Len() = 1\n",
"WCnComV[1600][0] = 9404\n",
"WCnComV[1601].Len() = 1\n",
"WCnComV[1601][0] = 9403\n",
"WCnComV[1602].Len() = 1\n",
"WCnComV[1602][0] = 9402\n",
"WCnComV[1603].Len() = 1\n",
"WCnComV[1603][0] = 9401\n",
"WCnComV[1604].Len() = 1\n",
"WCnComV[1604][0] = 9397\n",
"WCnComV[1605].Len() = 1\n",
"WCnComV[1605][0] = 9396\n",
"WCnComV[1606].Len() = 1\n",
"WCnComV[1606][0] = 9395\n",
"WCnComV[1607].Len() = 1\n",
"WCnComV[1607][0] = 9394\n",
"WCnComV[1608].Len() = 1\n",
"WCnComV[1608][0] = 9391\n",
"WCnComV[1609].Len() = 1\n",
"WCnComV[1609][0] = 9390\n",
"WCnComV[1610].Len() = 1\n",
"WCnComV[1610][0] = 9384\n",
"WCnComV[1611].Len() = 1\n",
"WCnComV[1611][0] = 9380\n",
"WCnComV[1612].Len() = 1\n",
"WCnComV[1612][0] = 9375\n",
"WCnComV[1613].Len() = 1\n",
"WCnComV[1613][0] = 9374\n",
"WCnComV[1614].Len() = 1\n",
"WCnComV[1614][0] = 9372\n",
"WCnComV[1615].Len() = 1\n",
"WCnComV[1615][0] = 9370\n",
"WCnComV[1616].Len() = 1\n",
"WCnComV[1616][0] = 9367\n",
"WCnComV[1617].Len() = 1\n",
"WCnComV[1617][0] = 9366\n",
"WCnComV[1618].Len() = 1\n",
"WCnComV[1618][0] = 9362\n",
"WCnComV[1619].Len() = 1\n",
"WCnComV[1619][0] = 9361\n",
"WCnComV[1620].Len() = 1\n",
"WCnComV[1620][0] = 9355\n",
"WCnComV[1621].Len() = 1\n",
"WCnComV[1621][0] = 9354\n",
"WCnComV[1622].Len() = 1\n",
"WCnComV[1622][0] = 9351\n",
"WCnComV[1623].Len() = 1\n",
"WCnComV[1623][0] = 9348\n",
"WCnComV[1624].Len() = 1\n",
"WCnComV[1624][0] = 9346\n",
"WCnComV[1625].Len() = 1\n",
"WCnComV[1625][0] = 9344\n",
"WCnComV[1626].Len() = 1\n",
"WCnComV[1626][0] = 9343\n",
"WCnComV[1627].Len() = 1\n",
"WCnComV[1627][0] = 9339\n",
"WCnComV[1628].Len() = 1\n",
"WCnComV[1628][0] = 9335\n",
"WCnComV[1629].Len() = 1\n",
"WCnComV[1629][0] = 9334\n",
"WCnComV[1630].Len() = 1\n",
"WCnComV[1630][0] = 9333\n",
"WCnComV[1631].Len() = 1\n",
"WCnComV[1631][0] = 9332\n",
"WCnComV[1632].Len() = 1\n",
"WCnComV[1632][0] = 9329\n",
"WCnComV[1633].Len() = 1\n",
"WCnComV[1633][0] = 9326\n",
"WCnComV[1634].Len() = 1\n",
"WCnComV[1634][0] = 9325\n",
"WCnComV[1635].Len() = 1\n",
"WCnComV[1635][0] = 9324\n",
"WCnComV[1636].Len() = 1\n",
"WCnComV[1636][0] = 9322\n",
"WCnComV[1637].Len() = 1\n",
"WCnComV[1637][0] = 9320\n",
"WCnComV[1638].Len() = 1\n",
"WCnComV[1638][0] = 9314\n",
"WCnComV[1639].Len() = 1\n",
"WCnComV[1639][0] = 9306\n",
"WCnComV[1640].Len() = 1\n",
"WCnComV[1640][0] = 9305\n",
"WCnComV[1641].Len() = 1\n",
"WCnComV[1641][0] = 9302\n",
"WCnComV[1642].Len() = 1\n",
"WCnComV[1642][0] = 9300\n",
"WCnComV[1643].Len() = 1\n",
"WCnComV[1643][0] = 9296\n",
"WCnComV[1644].Len() = 1\n",
"WCnComV[1644][0] = 9294\n",
"WCnComV[1645].Len() = 1\n",
"WCnComV[1645][0] = 9293\n",
"WCnComV[1646].Len() = 1\n",
"WCnComV[1646][0] = 9292\n",
"WCnComV[1647].Len() = 1\n",
"WCnComV[1647][0] = 9290\n",
"WCnComV[1648].Len() = 1\n",
"WCnComV[1648][0] = 9288\n",
"WCnComV[1649].Len() = 1\n",
"WCnComV[1649][0] = 9286\n",
"WCnComV[1650].Len() = 1\n",
"WCnComV[1650][0] = 9284\n",
"WCnComV[1651].Len() = 1\n",
"WCnComV[1651][0] = 9282\n",
"WCnComV[1652].Len() = 1\n",
"WCnComV[1652][0] = 9276\n",
"WCnComV[1653].Len() = 1\n",
"WCnComV[1653][0] = 9273\n",
"WCnComV[1654].Len() = 1\n",
"WCnComV[1654][0] = 9271\n",
"WCnComV[1655].Len() = 1\n",
"WCnComV[1655][0] = 9270\n",
"WCnComV[1656].Len() = 1\n",
"WCnComV[1656][0] = 9267\n",
"WCnComV[1657].Len() = 1\n",
"WCnComV[1657][0] = 9264\n",
"WCnComV[1658].Len() = 1\n",
"WCnComV[1658][0] = 9262\n",
"WCnComV[1659].Len() = 1\n",
"WCnComV[1659][0] = 9260\n",
"WCnComV[1660].Len() = 1\n",
"WCnComV[1660][0] = 9258\n",
"WCnComV[1661].Len() = 1\n",
"WCnComV[1661][0] = 9257\n",
"WCnComV[1662].Len() = 1\n",
"WCnComV[1662][0] = 9256\n",
"WCnComV[1663].Len() = 1\n",
"WCnComV[1663][0] = 9255\n",
"WCnComV[1664].Len() = 1\n",
"WCnComV[1664][0] = 9253\n",
"WCnComV[1665].Len() = 1\n",
"WCnComV[1665][0] = 9251\n",
"WCnComV[1666].Len() = 1\n",
"WCnComV[1666][0] = 9249\n",
"WCnComV[1667].Len() = 1\n",
"WCnComV[1667][0] = 9248\n",
"WCnComV[1668].Len() = 1\n",
"WCnComV[1668][0] = 9244\n",
"WCnComV[1669].Len() = 1\n",
"WCnComV[1669][0] = 9240\n",
"WCnComV[1670].Len() = 1\n",
"WCnComV[1670][0] = 9236\n",
"WCnComV[1671].Len() = 1\n",
"WCnComV[1671][0] = 9234\n",
"WCnComV[1672].Len() = 1\n",
"WCnComV[1672][0] = 9233\n",
"WCnComV[1673].Len() = 1\n",
"WCnComV[1673][0] = 9231\n",
"WCnComV[1674].Len() = 1\n",
"WCnComV[1674][0] = 9229\n",
"WCnComV[1675].Len() = 1\n",
"WCnComV[1675][0] = 9228\n",
"WCnComV[1676].Len() = 1\n",
"WCnComV[1676][0] = 9227\n",
"WCnComV[1677].Len() = 1\n",
"WCnComV[1677][0] = 9218\n",
"WCnComV[1678].Len() = 1\n",
"WCnComV[1678][0] = 9217\n",
"WCnComV[1679].Len() = 1\n",
"WCnComV[1679][0] = 9216\n",
"WCnComV[1680].Len() = 1\n",
"WCnComV[1680][0] = 9214\n",
"WCnComV[1681].Len() = 1\n",
"WCnComV[1681][0] = 9213\n",
"WCnComV[1682].Len() = 1\n",
"WCnComV[1682][0] = 9212\n",
"WCnComV[1683].Len() = 1\n",
"WCnComV[1683][0] = 9210\n",
"WCnComV[1684].Len() = 1\n",
"WCnComV[1684][0] = 9208\n",
"WCnComV[1685].Len() = 1\n",
"WCnComV[1685][0] = 9205\n",
"WCnComV[1686].Len() = 1\n",
"WCnComV[1686][0] = 9204\n",
"WCnComV[1687].Len() = 1\n",
"WCnComV[1687][0] = 9203\n",
"WCnComV[1688].Len() = 1\n",
"WCnComV[1688][0] = 9201\n",
"WCnComV[1689].Len() = 1\n",
"WCnComV[1689][0] = 9199\n",
"WCnComV[1690].Len() = 1\n",
"WCnComV[1690][0] = 9194\n",
"WCnComV[1691].Len() = 1\n",
"WCnComV[1691][0] = 9190\n",
"WCnComV[1692].Len() = 1\n",
"WCnComV[1692][0] = 9188\n",
"WCnComV[1693].Len() = 1\n",
"WCnComV[1693][0] = 9185\n",
"WCnComV[1694].Len() = 1\n",
"WCnComV[1694][0] = 9184\n",
"WCnComV[1695].Len() = 1\n",
"WCnComV[1695][0] = 9183\n",
"WCnComV[1696].Len() = 1\n",
"WCnComV[1696][0] = 9179\n",
"WCnComV[1697].Len() = 1\n",
"WCnComV[1697][0] = 9177\n",
"WCnComV[1698].Len() = 1\n",
"WCnComV[1698][0] = 9175\n",
"WCnComV[1699].Len() = 1\n",
"WCnComV[1699][0] = 9167\n",
"WCnComV[1700].Len() = 1\n",
"WCnComV[1700][0] = 9163\n",
"WCnComV[1701].Len() = 1\n",
"WCnComV[1701][0] = 9158\n",
"WCnComV[1702].Len() = 1\n",
"WCnComV[1702][0] = 9157\n",
"WCnComV[1703].Len() = 1\n",
"WCnComV[1703][0] = 9155\n",
"WCnComV[1704].Len() = 1\n",
"WCnComV[1704][0] = 9153\n",
"WCnComV[1705].Len() = 1\n",
"WCnComV[1705][0] = 9147\n",
"WCnComV[1706].Len() = 1\n",
"WCnComV[1706][0] = 9145\n",
"WCnComV[1707].Len() = 1\n",
"WCnComV[1707][0] = 9143\n",
"WCnComV[1708].Len() = 1\n",
"WCnComV[1708][0] = 9131\n",
"WCnComV[1709].Len() = 1\n",
"WCnComV[1709][0] = 9129\n",
"WCnComV[1710].Len() = 1\n",
"WCnComV[1710][0] = 9124\n",
"WCnComV[1711].Len() = 1\n",
"WCnComV[1711][0] = 9122\n",
"WCnComV[1712].Len() = 1\n",
"WCnComV[1712][0] = 9121\n",
"WCnComV[1713].Len() = 1\n",
"WCnComV[1713][0] = 9120\n",
"WCnComV[1714].Len() = 1\n",
"WCnComV[1714][0] = 9118\n",
"WCnComV[1715].Len() = 1\n",
"WCnComV[1715][0] = 9117\n",
"WCnComV[1716].Len() = 1\n",
"WCnComV[1716][0] = 9110\n",
"WCnComV[1717].Len() = 1\n",
"WCnComV[1717][0] = 9109\n",
"WCnComV[1718].Len() = 1\n",
"WCnComV[1718][0] = 9107\n",
"WCnComV[1719].Len() = 1\n",
"WCnComV[1719][0] = 9106\n",
"WCnComV[1720].Len() = 1\n",
"WCnComV[1720][0] = 9105\n",
"WCnComV[1721].Len() = 1\n",
"WCnComV[1721][0] = 9099\n",
"WCnComV[1722].Len() = 1\n",
"WCnComV[1722][0] = 9097\n",
"WCnComV[1723].Len() = 1\n",
"WCnComV[1723][0] = 9089\n",
"WCnComV[1724].Len() = 1\n",
"WCnComV[1724][0] = 9087\n",
"WCnComV[1725].Len() = 1\n",
"WCnComV[1725][0] = 9083\n",
"WCnComV[1726].Len() = 1\n",
"WCnComV[1726][0] = 9082\n",
"WCnComV[1727].Len() = 1\n",
"WCnComV[1727][0] = 9080\n",
"WCnComV[1728].Len() = 1\n",
"WCnComV[1728][0] = 9073\n",
"WCnComV[1729].Len() = 1\n",
"WCnComV[1729][0] = 9071\n",
"WCnComV[1730].Len() = 1\n",
"WCnComV[1730][0] = 9070\n",
"WCnComV[1731].Len() = 1\n",
"WCnComV[1731][0] = 9069\n",
"WCnComV[1732].Len() = 1\n",
"WCnComV[1732][0] = 9067\n",
"WCnComV[1733].Len() = 1\n",
"WCnComV[1733][0] = 9065\n",
"WCnComV[1734].Len() = 1\n",
"WCnComV[1734][0] = 9064\n",
"WCnComV[1735].Len() = 1\n",
"WCnComV[1735][0] = 9062\n",
"WCnComV[1736].Len() = 1\n",
"WCnComV[1736][0] = 9061\n",
"WCnComV[1737].Len() = 1\n",
"WCnComV[1737][0] = 9060\n",
"WCnComV[1738].Len() = 1\n",
"WCnComV[1738][0] = 9059\n",
"WCnComV[1739].Len() = 1\n",
"WCnComV[1739][0] = 9056\n",
"WCnComV[1740].Len() = 1\n",
"WCnComV[1740][0] = 9053\n",
"WCnComV[1741].Len() = 1\n",
"WCnComV[1741][0] = 9051\n",
"WCnComV[1742].Len() = 1\n",
"WCnComV[1742][0] = 9050\n",
"WCnComV[1743].Len() = 1\n",
"WCnComV[1743][0] = 9043\n",
"WCnComV[1744].Len() = 1\n",
"WCnComV[1744][0] = 9041\n",
"WCnComV[1745].Len() = 1\n",
"WCnComV[1745][0] = 9040\n",
"WCnComV[1746].Len() = 1\n",
"WCnComV[1746][0] = 9039\n",
"WCnComV[1747].Len() = 1\n",
"WCnComV[1747][0] = 9038\n",
"WCnComV[1748].Len() = 1\n",
"WCnComV[1748][0] = 9031\n",
"WCnComV[1749].Len() = 1\n",
"WCnComV[1749][0] = 9029\n",
"WCnComV[1750].Len() = 1\n",
"WCnComV[1750][0] = 9028\n",
"WCnComV[1751].Len() = 1\n",
"WCnComV[1751][0] = 9023\n",
"WCnComV[1752].Len() = 1\n",
"WCnComV[1752][0] = 9020\n",
"WCnComV[1753].Len() = 1\n",
"WCnComV[1753][0] = 9017\n",
"WCnComV[1754].Len() = 1\n",
"WCnComV[1754][0] = 9013\n",
"WCnComV[1755].Len() = 1\n",
"WCnComV[1755][0] = 9007\n",
"WCnComV[1756].Len() = 1\n",
"WCnComV[1756][0] = 9006\n",
"WCnComV[1757].Len() = 1\n",
"WCnComV[1757][0] = 9004\n",
"WCnComV[1758].Len() = 1\n",
"WCnComV[1758][0] = 8999\n",
"WCnComV[1759].Len() = 1\n",
"WCnComV[1759][0] = 8998\n",
"WCnComV[1760].Len() = 1\n",
"WCnComV[1760][0] = 8995\n",
"WCnComV[1761].Len() = 1\n",
"WCnComV[1761][0] = 8994\n",
"WCnComV[1762].Len() = 1\n",
"WCnComV[1762][0] = 8990\n",
"WCnComV[1763].Len() = 1\n",
"WCnComV[1763][0] = 8988\n",
"WCnComV[1764].Len() = 1\n",
"WCnComV[1764][0] = 8984\n",
"WCnComV[1765].Len() = 1\n",
"WCnComV[1765][0] = 8981\n",
"WCnComV[1766].Len() = 1\n",
"WCnComV[1766][0] = 8980\n",
"WCnComV[1767].Len() = 1\n",
"WCnComV[1767][0] = 8978\n",
"WCnComV[1768].Len() = 1\n",
"WCnComV[1768][0] = 8975\n",
"WCnComV[1769].Len() = 1\n",
"WCnComV[1769][0] = 8971\n",
"WCnComV[1770].Len() = 1\n",
"WCnComV[1770][0] = 8966\n",
"WCnComV[1771].Len() = 1\n",
"WCnComV[1771][0] = 8960\n",
"WCnComV[1772].Len() = 1\n",
"WCnComV[1772][0] = 8959\n",
"WCnComV[1773].Len() = 1\n",
"WCnComV[1773][0] = 8957\n",
"WCnComV[1774].Len() = 1\n",
"WCnComV[1774][0] = 8954\n",
"WCnComV[1775].Len() = 1\n",
"WCnComV[1775][0] = 8951\n",
"WCnComV[1776].Len() = 1\n",
"WCnComV[1776][0] = 8949\n",
"WCnComV[1777].Len() = 1\n",
"WCnComV[1777][0] = 8948\n",
"WCnComV[1778].Len() = 1\n",
"WCnComV[1778][0] = 8945\n",
"WCnComV[1779].Len() = 1\n",
"WCnComV[1779][0] = 8941\n",
"WCnComV[1780].Len() = 1\n",
"WCnComV[1780][0] = 8939\n",
"WCnComV[1781].Len() = 1\n",
"WCnComV[1781][0] = 8938\n",
"WCnComV[1782].Len() = 1\n",
"WCnComV[1782][0] = 8936\n",
"WCnComV[1783].Len() = 1\n",
"WCnComV[1783][0] = 8932\n",
"WCnComV[1784].Len() = 1\n",
"WCnComV[1784][0] = 8931\n",
"WCnComV[1785].Len() = 1\n",
"WCnComV[1785][0] = 8929\n",
"WCnComV[1786].Len() = 1\n",
"WCnComV[1786][0] = 8928\n",
"WCnComV[1787].Len() = 1\n",
"WCnComV[1787][0] = 8927\n",
"WCnComV[1788].Len() = 1\n",
"WCnComV[1788][0] = 8913\n",
"WCnComV[1789].Len() = 1\n",
"WCnComV[1789][0] = 8910\n",
"WCnComV[1790].Len() = 1\n",
"WCnComV[1790][0] = 8907\n",
"WCnComV[1791].Len() = 1\n",
"WCnComV[1791][0] = 8906\n",
"WCnComV[1792].Len() = 1\n",
"WCnComV[1792][0] = 8903\n",
"WCnComV[1793].Len() = 1\n",
"WCnComV[1793][0] = 8901\n",
"WCnComV[1794].Len() = 1\n",
"WCnComV[1794][0] = 8900\n",
"WCnComV[1795].Len() = 1\n",
"WCnComV[1795][0] = 8897\n",
"WCnComV[1796].Len() = 1\n",
"WCnComV[1796][0] = 8894\n",
"WCnComV[1797].Len() = 1\n",
"WCnComV[1797][0] = 8893\n",
"WCnComV[1798].Len() = 1\n",
"WCnComV[1798][0] = 8889\n",
"WCnComV[1799].Len() = 1\n",
"WCnComV[1799][0] = 8887\n",
"WCnComV[1800].Len() = 1\n",
"WCnComV[1800][0] = 8885\n",
"WCnComV[1801].Len() = 1\n",
"WCnComV[1801][0] = 8883\n",
"WCnComV[1802].Len() = 1\n",
"WCnComV[1802][0] = 8882\n",
"WCnComV[1803].Len() = 1\n",
"WCnComV[1803][0] = 8878\n",
"WCnComV[1804].Len() = 1\n",
"WCnComV[1804][0] = 8877\n",
"WCnComV[1805].Len() = 1\n",
"WCnComV[1805][0] = 8875\n",
"WCnComV[1806].Len() = 1\n",
"WCnComV[1806][0] = 8871\n",
"WCnComV[1807].Len() = 1\n",
"WCnComV[1807][0] = 8869\n",
"WCnComV[1808].Len() = 1\n",
"WCnComV[1808][0] = 8868\n",
"WCnComV[1809].Len() = 1\n",
"WCnComV[1809][0] = 8867\n",
"WCnComV[1810].Len() = 1\n",
"WCnComV[1810][0] = 8866\n",
"WCnComV[1811].Len() = 1\n",
"WCnComV[1811][0] = 8864\n",
"WCnComV[1812].Len() = 1\n",
"WCnComV[1812][0] = 8856\n",
"WCnComV[1813].Len() = 1\n",
"WCnComV[1813][0] = 8855\n",
"WCnComV[1814].Len() = 1\n",
"WCnComV[1814][0] = 8854\n",
"WCnComV[1815].Len() = 1\n",
"WCnComV[1815][0] = 8853\n",
"WCnComV[1816].Len() = 1\n",
"WCnComV[1816][0] = 8852\n",
"WCnComV[1817].Len() = 1\n",
"WCnComV[1817][0] = 8851\n",
"WCnComV[1818].Len() = 1\n",
"WCnComV[1818][0] = 8849\n",
"WCnComV[1819].Len() = 1\n",
"WCnComV[1819][0] = 8845\n",
"WCnComV[1820].Len() = 1\n",
"WCnComV[1820][0] = 8844\n",
"WCnComV[1821].Len() = 1\n",
"WCnComV[1821][0] = 8840\n",
"WCnComV[1822].Len() = 1\n",
"WCnComV[1822][0] = 8839\n",
"WCnComV[1823].Len() = 1\n",
"WCnComV[1823][0] = 8838\n",
"WCnComV[1824].Len() = 1\n",
"WCnComV[1824][0] = 8837\n",
"WCnComV[1825].Len() = 1\n",
"WCnComV[1825][0] = 8836\n",
"WCnComV[1826].Len() = 1\n",
"WCnComV[1826][0] = 8831\n",
"WCnComV[1827].Len() = 1\n",
"WCnComV[1827][0] = 8830\n",
"WCnComV[1828].Len() = 1\n",
"WCnComV[1828][0] = 8823\n",
"WCnComV[1829].Len() = 1\n",
"WCnComV[1829][0] = 8822\n",
"WCnComV[1830].Len() = 1\n",
"WCnComV[1830][0] = 8814\n",
"WCnComV[1831].Len() = 1\n",
"WCnComV[1831][0] = 8812\n",
"WCnComV[1832].Len() = 1\n",
"WCnComV[1832][0] = 8811\n",
"WCnComV[1833].Len() = 1\n",
"WCnComV[1833][0] = 8810\n",
"WCnComV[1834].Len() = 1\n",
"WCnComV[1834][0] = 8807\n",
"WCnComV[1835].Len() = 1\n",
"WCnComV[1835][0] = 8805\n",
"WCnComV[1836].Len() = 1\n",
"WCnComV[1836][0] = 8802\n",
"WCnComV[1837].Len() = 1\n",
"WCnComV[1837][0] = 8798\n",
"WCnComV[1838].Len() = 1\n",
"WCnComV[1838][0] = 8796\n",
"WCnComV[1839].Len() = 1\n",
"WCnComV[1839][0] = 8795\n",
"WCnComV[1840].Len() = 1\n",
"WCnComV[1840][0] = 8793\n",
"WCnComV[1841].Len() = 1\n",
"WCnComV[1841][0] = 8792\n",
"WCnComV[1842].Len() = 1\n",
"WCnComV[1842][0] = 8790\n",
"WCnComV[1843].Len() = 1\n",
"WCnComV[1843][0] = 8789\n",
"WCnComV[1844].Len() = 1\n",
"WCnComV[1844][0] = 8786\n",
"WCnComV[1845].Len() = 1\n",
"WCnComV[1845][0] = 8785\n",
"WCnComV[1846].Len() = 1\n",
"WCnComV[1846][0] = 8783\n",
"WCnComV[1847].Len() = 1\n",
"WCnComV[1847][0] = 8778\n",
"WCnComV[1848].Len() = 1\n",
"WCnComV[1848][0] = 8777\n",
"WCnComV[1849].Len() = 1\n",
"WCnComV[1849][0] = 8776\n",
"WCnComV[1850].Len() = 1\n",
"WCnComV[1850][0] = 8774\n",
"WCnComV[1851].Len() = 1\n",
"WCnComV[1851][0] = 8773\n",
"WCnComV[1852].Len() = 1\n",
"WCnComV[1852][0] = 8772\n",
"WCnComV[1853].Len() = 1\n",
"WCnComV[1853][0] = 8771\n",
"WCnComV[1854].Len() = 1\n",
"WCnComV[1854][0] = 8770\n",
"WCnComV[1855].Len() = 1\n",
"WCnComV[1855][0] = 8766\n",
"WCnComV[1856].Len() = 1\n",
"WCnComV[1856][0] = 8765\n",
"WCnComV[1857].Len() = 1\n",
"WCnComV[1857][0] = 8755\n",
"WCnComV[1858].Len() = 1\n",
"WCnComV[1858][0] = 8754\n",
"WCnComV[1859].Len() = 1\n",
"WCnComV[1859][0] = 8752\n",
"WCnComV[1860].Len() = 1\n",
"WCnComV[1860][0] = 8749\n",
"WCnComV[1861].Len() = 1\n",
"WCnComV[1861][0] = 8745\n",
"WCnComV[1862].Len() = 1\n",
"WCnComV[1862][0] = 8744\n",
"WCnComV[1863].Len() = 1\n",
"WCnComV[1863][0] = 8743\n",
"WCnComV[1864].Len() = 1\n",
"WCnComV[1864][0] = 8737\n",
"WCnComV[1865].Len() = 1\n",
"WCnComV[1865][0] = 8735\n",
"WCnComV[1866].Len() = 1\n",
"WCnComV[1866][0] = 8728\n",
"WCnComV[1867].Len() = 1\n",
"WCnComV[1867][0] = 8723\n",
"WCnComV[1868].Len() = 1\n",
"WCnComV[1868][0] = 8719\n",
"WCnComV[1869].Len() = 1\n",
"WCnComV[1869][0] = 8717\n",
"WCnComV[1870].Len() = 1\n",
"WCnComV[1870][0] = 8715\n",
"WCnComV[1871].Len() = 1\n",
"WCnComV[1871][0] = 8714\n",
"WCnComV[1872].Len() = 1\n",
"WCnComV[1872][0] = 8712\n",
"WCnComV[1873].Len() = 1\n",
"WCnComV[1873][0] = 8711\n",
"WCnComV[1874].Len() = 1\n",
"WCnComV[1874][0] = 8709\n",
"WCnComV[1875].Len() = 1\n",
"WCnComV[1875][0] = 8707\n",
"WCnComV[1876].Len() = 1\n",
"WCnComV[1876][0] = 8702\n",
"WCnComV[1877].Len() = 1\n",
"WCnComV[1877][0] = 8697\n",
"WCnComV[1878].Len() = 1\n",
"WCnComV[1878][0] = 8696\n",
"WCnComV[1879].Len() = 1\n",
"WCnComV[1879][0] = 8693\n",
"WCnComV[1880].Len() = 1\n",
"WCnComV[1880][0] = 8690\n",
"WCnComV[1881].Len() = 1\n",
"WCnComV[1881][0] = 8689\n",
"WCnComV[1882].Len() = 1\n",
"WCnComV[1882][0] = 8688\n",
"WCnComV[1883].Len() = 1\n",
"WCnComV[1883][0] = 8682\n",
"WCnComV[1884].Len() = 1\n",
"WCnComV[1884][0] = 8679\n",
"WCnComV[1885].Len() = 1\n",
"WCnComV[1885][0] = 8678\n",
"WCnComV[1886].Len() = 1\n",
"WCnComV[1886][0] = 8676\n",
"WCnComV[1887].Len() = 1\n",
"WCnComV[1887][0] = 8674\n",
"WCnComV[1888].Len() = 1\n",
"WCnComV[1888][0] = 8668\n",
"WCnComV[1889].Len() = 1\n",
"WCnComV[1889][0] = 8667\n",
"WCnComV[1890].Len() = 1\n",
"WCnComV[1890][0] = 8666\n",
"WCnComV[1891].Len() = 1\n",
"WCnComV[1891][0] = 8665\n",
"WCnComV[1892].Len() = 1\n",
"WCnComV[1892][0] = 8664\n",
"WCnComV[1893].Len() = 1\n",
"WCnComV[1893][0] = 8663\n",
"WCnComV[1894].Len() = 1\n",
"WCnComV[1894][0] = 8662\n",
"WCnComV[1895].Len() = 1\n",
"WCnComV[1895][0] = 8660\n",
"WCnComV[1896].Len() = 1\n",
"WCnComV[1896][0] = 8658\n",
"WCnComV[1897].Len() = 1\n",
"WCnComV[1897][0] = 8657\n",
"WCnComV[1898].Len() = 1\n",
"WCnComV[1898][0] = 8655\n",
"WCnComV[1899].Len() = 1\n",
"WCnComV[1899][0] = 8653\n",
"WCnComV[1900].Len() = 1\n",
"WCnComV[1900][0] = 8652\n",
"WCnComV[1901].Len() = 1\n",
"WCnComV[1901][0] = 8650\n",
"WCnComV[1902].Len() = 1\n",
"WCnComV[1902][0] = 8647\n",
"WCnComV[1903].Len() = 1\n",
"WCnComV[1903][0] = 8642\n",
"WCnComV[1904].Len() = 1\n",
"WCnComV[1904][0] = 8639\n",
"WCnComV[1905].Len() = 1\n",
"WCnComV[1905][0] = 8638\n",
"WCnComV[1906].Len() = 1\n",
"WCnComV[1906][0] = 8632\n",
"WCnComV[1907].Len() = 1\n",
"WCnComV[1907][0] = 8631\n",
"WCnComV[1908].Len() = 1\n",
"WCnComV[1908][0] = 8629\n",
"WCnComV[1909].Len() = 1\n",
"WCnComV[1909][0] = 8628\n",
"WCnComV[1910].Len() = 1\n",
"WCnComV[1910][0] = 8626\n",
"WCnComV[1911].Len() = 1\n",
"WCnComV[1911][0] = 8625\n",
"WCnComV[1912].Len() = 1\n",
"WCnComV[1912][0] = 8616\n",
"WCnComV[1913].Len() = 1\n",
"WCnComV[1913][0] = 8615\n",
"WCnComV[1914].Len() = 1\n",
"WCnComV[1914][0] = 8614\n",
"WCnComV[1915].Len() = 1\n",
"WCnComV[1915][0] = 8610\n",
"WCnComV[1916].Len() = 1\n",
"WCnComV[1916][0] = 8608\n",
"WCnComV[1917].Len() = 1\n",
"WCnComV[1917][0] = 8600\n",
"WCnComV[1918].Len() = 1\n",
"WCnComV[1918][0] = 8597\n",
"WCnComV[1919].Len() = 1\n",
"WCnComV[1919][0] = 8596\n",
"WCnComV[1920].Len() = 1\n",
"WCnComV[1920][0] = 8592\n",
"WCnComV[1921].Len() = 1\n",
"WCnComV[1921][0] = 8591\n",
"WCnComV[1922].Len() = 1\n",
"WCnComV[1922][0] = 8590\n",
"WCnComV[1923].Len() = 1\n",
"WCnComV[1923][0] = 8587\n",
"WCnComV[1924].Len() = 1\n",
"WCnComV[1924][0] = 8586\n",
"WCnComV[1925].Len() = 1\n",
"WCnComV[1925][0] = 8578\n",
"WCnComV[1926].Len() = 1\n",
"WCnComV[1926][0] = 8575\n",
"WCnComV[1927].Len() = 1\n",
"WCnComV[1927][0] = 8572\n",
"WCnComV[1928].Len() = 1\n",
"WCnComV[1928][0] = 8568\n",
"WCnComV[1929].Len() = 1\n",
"WCnComV[1929][0] = 8567\n",
"WCnComV[1930].Len() = 1\n",
"WCnComV[1930][0] = 8566\n",
"WCnComV[1931].Len() = 1\n",
"WCnComV[1931][0] = 8561\n",
"WCnComV[1932].Len() = 1\n",
"WCnComV[1932][0] = 8554\n",
"WCnComV[1933].Len() = 1\n",
"WCnComV[1933][0] = 8553\n",
"WCnComV[1934].Len() = 1\n",
"WCnComV[1934][0] = 8551\n",
"WCnComV[1935].Len() = 1\n",
"WCnComV[1935][0] = 8548\n",
"WCnComV[1936].Len() = 1\n",
"WCnComV[1936][0] = 8546\n",
"WCnComV[1937].Len() = 1\n",
"WCnComV[1937][0] = 8543\n",
"WCnComV[1938].Len() = 1\n",
"WCnComV[1938][0] = 8542\n",
"WCnComV[1939].Len() = 1\n",
"WCnComV[1939][0] = 8538\n",
"WCnComV[1940].Len() = 1\n",
"WCnComV[1940][0] = 8536\n",
"WCnComV[1941].Len() = 1\n",
"WCnComV[1941][0] = 8535\n",
"WCnComV[1942].Len() = 1\n",
"WCnComV[1942][0] = 8533\n",
"WCnComV[1943].Len() = 1\n",
"WCnComV[1943][0] = 8531\n",
"WCnComV[1944].Len() = 1\n",
"WCnComV[1944][0] = 8530\n",
"WCnComV[1945].Len() = 1\n",
"WCnComV[1945][0] = 8528\n",
"WCnComV[1946].Len() = 1\n",
"WCnComV[1946][0] = 8525\n",
"WCnComV[1947].Len() = 1\n",
"WCnComV[1947][0] = 8522\n",
"WCnComV[1948].Len() = 1\n",
"WCnComV[1948][0] = 8520\n",
"WCnComV[1949].Len() = 1\n",
"WCnComV[1949][0] = 8516\n",
"WCnComV[1950].Len() = 1\n",
"WCnComV[1950][0] = 8513\n",
"WCnComV[1951].Len() = 1\n",
"WCnComV[1951][0] = 8491\n",
"WCnComV[1952].Len() = 1\n",
"WCnComV[1952][0] = 8487\n",
"WCnComV[1953].Len() = 1\n",
"WCnComV[1953][0] = 8483\n",
"WCnComV[1954].Len() = 1\n",
"WCnComV[1954][0] = 8482\n",
"WCnComV[1955].Len() = 1\n",
"WCnComV[1955][0] = 8478\n",
"WCnComV[1956].Len() = 1\n",
"WCnComV[1956][0] = 8476\n",
"WCnComV[1957].Len() = 1\n",
"WCnComV[1957][0] = 8473\n",
"WCnComV[1958].Len() = 1\n",
"WCnComV[1958][0] = 8472\n",
"WCnComV[1959].Len() = 1\n",
"WCnComV[1959][0] = 8467\n",
"WCnComV[1960].Len() = 1\n",
"WCnComV[1960][0] = 8464\n",
"WCnComV[1961].Len() = 1\n",
"WCnComV[1961][0] = 8454\n",
"WCnComV[1962].Len() = 1\n",
"WCnComV[1962][0] = 8452\n",
"WCnComV[1963].Len() = 1\n",
"WCnComV[1963][0] = 8450\n",
"WCnComV[1964].Len() = 1\n",
"WCnComV[1964][0] = 8447\n",
"WCnComV[1965].Len() = 1\n",
"WCnComV[1965][0] = 8446\n",
"WCnComV[1966].Len() = 1\n",
"WCnComV[1966][0] = 8444\n",
"WCnComV[1967].Len() = 1\n",
"WCnComV[1967][0] = 8439\n",
"WCnComV[1968].Len() = 1\n",
"WCnComV[1968][0] = 8436\n",
"WCnComV[1969].Len() = 1\n",
"WCnComV[1969][0] = 8434\n",
"WCnComV[1970].Len() = 1\n",
"WCnComV[1970][0] = 8431\n",
"WCnComV[1971].Len() = 1\n",
"WCnComV[1971][0] = 8429\n",
"WCnComV[1972].Len() = 1\n",
"WCnComV[1972][0] = 8425\n",
"WCnComV[1973].Len() = 1\n",
"WCnComV[1973][0] = 8424\n",
"WCnComV[1974].Len() = 1\n",
"WCnComV[1974][0] = 8423\n",
"WCnComV[1975].Len() = 1\n",
"WCnComV[1975][0] = 8419\n",
"WCnComV[1976].Len() = 1\n",
"WCnComV[1976][0] = 8417\n",
"WCnComV[1977].Len() = 1\n",
"WCnComV[1977][0] = 8416\n",
"WCnComV[1978].Len() = 1\n",
"WCnComV[1978][0] = 8412\n",
"WCnComV[1979].Len() = 1\n",
"WCnComV[1979][0] = 8411\n",
"WCnComV[1980].Len() = 1\n",
"WCnComV[1980][0] = 8410\n",
"WCnComV[1981].Len() = 1\n",
"WCnComV[1981][0] = 8408\n",
"WCnComV[1982].Len() = 1\n",
"WCnComV[1982][0] = 8407\n",
"WCnComV[1983].Len() = 1\n",
"WCnComV[1983][0] = 8401\n",
"WCnComV[1984].Len() = 1\n",
"WCnComV[1984][0] = 8399\n",
"WCnComV[1985].Len() = 1\n",
"WCnComV[1985][0] = 8395\n",
"WCnComV[1986].Len() = 1\n",
"WCnComV[1986][0] = 8394\n",
"WCnComV[1987].Len() = 1\n",
"WCnComV[1987][0] = 8393\n",
"WCnComV[1988].Len() = 1\n",
"WCnComV[1988][0] = 8391\n",
"WCnComV[1989].Len() = 1\n",
"WCnComV[1989][0] = 8390\n",
"WCnComV[1990].Len() = 1\n",
"WCnComV[1990][0] = 8387\n",
"WCnComV[1991].Len() = 1\n",
"WCnComV[1991][0] = 8385\n",
"WCnComV[1992].Len() = 1\n",
"WCnComV[1992][0] = 8384\n",
"WCnComV[1993].Len() = 1\n",
"WCnComV[1993][0] = 8380\n",
"WCnComV[1994].Len() = 1\n",
"WCnComV[1994][0] = 8379\n",
"WCnComV[1995].Len() = 1\n",
"WCnComV[1995][0] = 8378\n",
"WCnComV[1996].Len() = 1\n",
"WCnComV[1996][0] = 8377\n",
"WCnComV[1997].Len() = 1\n",
"WCnComV[1997][0] = 8371\n",
"WCnComV[1998].Len() = 1\n",
"WCnComV[1998][0] = 8370\n",
"WCnComV[1999].Len() = 1\n",
"WCnComV[1999][0] = 8364\n",
"WCnComV[2000].Len() = 1\n",
"WCnComV[2000][0] = 8363\n",
"WCnComV[2001].Len() = 1\n",
"WCnComV[2001][0] = 8354\n",
"WCnComV[2002].Len() = 1\n",
"WCnComV[2002][0] = 8351\n",
"WCnComV[2003].Len() = 1\n",
"WCnComV[2003][0] = 8350\n",
"WCnComV[2004].Len() = 1\n",
"WCnComV[2004][0] = 8344\n",
"WCnComV[2005].Len() = 1\n",
"WCnComV[2005][0] = 8342\n",
"WCnComV[2006].Len() = 1\n",
"WCnComV[2006][0] = 8338\n",
"WCnComV[2007].Len() = 1\n",
"WCnComV[2007][0] = 8335\n",
"WCnComV[2008].Len() = 1\n",
"WCnComV[2008][0] = 8330\n",
"WCnComV[2009].Len() = 1\n",
"WCnComV[2009][0] = 8327\n",
"WCnComV[2010].Len() = 1\n",
"WCnComV[2010][0] = 8325\n",
"WCnComV[2011].Len() = 1\n",
"WCnComV[2011][0] = 8323\n",
"WCnComV[2012].Len() = 1\n",
"WCnComV[2012][0] = 8310\n",
"WCnComV[2013].Len() = 1\n",
"WCnComV[2013][0] = 8309\n",
"WCnComV[2014].Len() = 1\n",
"WCnComV[2014][0] = 8307\n",
"WCnComV[2015].Len() = 1\n",
"WCnComV[2015][0] = 8306\n",
"WCnComV[2016].Len() = 1\n",
"WCnComV[2016][0] = 8305\n",
"WCnComV[2017].Len() = 1\n",
"WCnComV[2017][0] = 8297\n",
"WCnComV[2018].Len() = 1\n",
"WCnComV[2018][0] = 8295\n",
"WCnComV[2019].Len() = 1\n",
"WCnComV[2019][0] = 8292\n",
"WCnComV[2020].Len() = 1\n",
"WCnComV[2020][0] = 8291\n",
"WCnComV[2021].Len() = 1\n",
"WCnComV[2021][0] = 8287\n",
"WCnComV[2022].Len() = 1\n",
"WCnComV[2022][0] = 8285\n",
"WCnComV[2023].Len() = 1\n",
"WCnComV[2023][0] = 8280\n",
"WCnComV[2024].Len() = 1\n",
"WCnComV[2024][0] = 8279\n",
"WCnComV[2025].Len() = 1\n",
"WCnComV[2025][0] = 8278\n",
"WCnComV[2026].Len() = 1\n",
"WCnComV[2026][0] = 8275\n",
"WCnComV[2027].Len() = 1\n",
"WCnComV[2027][0] = 8274\n",
"WCnComV[2028].Len() = 1\n",
"WCnComV[2028][0] = 8271\n",
"WCnComV[2029].Len() = 1\n",
"WCnComV[2029][0] = 8270\n",
"WCnComV[2030].Len() = 1\n",
"WCnComV[2030][0] = 8269\n",
"WCnComV[2031].Len() = 1\n",
"WCnComV[2031][0] = 8263\n",
"WCnComV[2032].Len() = 1\n",
"WCnComV[2032][0] = 8261\n",
"WCnComV[2033].Len() = 1\n",
"WCnComV[2033][0] = 8260\n",
"WCnComV[2034].Len() = 1\n",
"WCnComV[2034][0] = 8257\n",
"WCnComV[2035].Len() = 1\n",
"WCnComV[2035][0] = 8255\n",
"WCnComV[2036].Len() = 1\n",
"WCnComV[2036][0] = 8251\n",
"WCnComV[2037].Len() = 1\n",
"WCnComV[2037][0] = 8250\n",
"WCnComV[2038].Len() = 1\n",
"WCnComV[2038][0] = 8248\n",
"WCnComV[2039].Len() = 1\n",
"WCnComV[2039][0] = 8247\n",
"WCnComV[2040].Len() = 1\n",
"WCnComV[2040][0] = 8245\n",
"WCnComV[2041].Len() = 1\n",
"WCnComV[2041][0] = 8244\n",
"WCnComV[2042].Len() = 1\n",
"WCnComV[2042][0] = 8243\n",
"WCnComV[2043].Len() = 1\n",
"WCnComV[2043][0] = 8241\n",
"WCnComV[2044].Len() = 1\n",
"WCnComV[2044][0] = 8240\n",
"WCnComV[2045].Len() = 1\n",
"WCnComV[2045][0] = 8239\n",
"WCnComV[2046].Len() = 1\n",
"WCnComV[2046][0] = 8234\n",
"WCnComV[2047].Len() = 1\n",
"WCnComV[2047][0] = 8230\n",
"WCnComV[2048].Len() = 1\n",
"WCnComV[2048][0] = 8229\n",
"WCnComV[2049].Len() = 1\n",
"WCnComV[2049][0] = 8223\n",
"WCnComV[2050].Len() = 1\n",
"WCnComV[2050][0] = 8222\n",
"WCnComV[2051].Len() = 1\n",
"WCnComV[2051][0] = 8214\n",
"WCnComV[2052].Len() = 1\n",
"WCnComV[2052][0] = 8212\n",
"WCnComV[2053].Len() = 1\n",
"WCnComV[2053][0] = 8210\n",
"WCnComV[2054].Len() = 1\n",
"WCnComV[2054][0] = 8209\n",
"WCnComV[2055].Len() = 1\n",
"WCnComV[2055][0] = 8206\n",
"WCnComV[2056].Len() = 1\n",
"WCnComV[2056][0] = 8205\n",
"WCnComV[2057].Len() = 1\n",
"WCnComV[2057][0] = 8196\n",
"WCnComV[2058].Len() = 1\n",
"WCnComV[2058][0] = 8193\n",
"WCnComV[2059].Len() = 1\n",
"WCnComV[2059][0] = 8191\n",
"WCnComV[2060].Len() = 1\n",
"WCnComV[2060][0] = 8185\n",
"WCnComV[2061].Len() = 1\n",
"WCnComV[2061][0] = 8178\n",
"WCnComV[2062].Len() = 1\n",
"WCnComV[2062][0] = 8176\n",
"WCnComV[2063].Len() = 1\n",
"WCnComV[2063][0] = 8174\n",
"WCnComV[2064].Len() = 1\n",
"WCnComV[2064][0] = 8171\n",
"WCnComV[2065].Len() = 1\n",
"WCnComV[2065][0] = 8169\n",
"WCnComV[2066].Len() = 1\n",
"WCnComV[2066][0] = 8168\n",
"WCnComV[2067].Len() = 1\n",
"WCnComV[2067][0] = 8167\n",
"WCnComV[2068].Len() = 1\n",
"WCnComV[2068][0] = 8166\n",
"WCnComV[2069].Len() = 1\n",
"WCnComV[2069][0] = 8162\n",
"WCnComV[2070].Len() = 1\n",
"WCnComV[2070][0] = 8158\n",
"WCnComV[2071].Len() = 1\n",
"WCnComV[2071][0] = 8157\n",
"WCnComV[2072].Len() = 1\n",
"WCnComV[2072][0] = 8149\n",
"WCnComV[2073].Len() = 1\n",
"WCnComV[2073][0] = 8147\n",
"WCnComV[2074].Len() = 1\n",
"WCnComV[2074][0] = 8146\n",
"WCnComV[2075].Len() = 1\n",
"WCnComV[2075][0] = 8144\n",
"WCnComV[2076].Len() = 1\n",
"WCnComV[2076][0] = 8141\n",
"WCnComV[2077].Len() = 1\n",
"WCnComV[2077][0] = 8140\n",
"WCnComV[2078].Len() = 1\n",
"WCnComV[2078][0] = 8138\n",
"WCnComV[2079].Len() = 1\n",
"WCnComV[2079][0] = 8134\n",
"WCnComV[2080].Len() = 1\n",
"WCnComV[2080][0] = 8133\n",
"WCnComV[2081].Len() = 1\n",
"WCnComV[2081][0] = 8131\n",
"WCnComV[2082].Len() = 1\n",
"WCnComV[2082][0] = 8130\n",
"WCnComV[2083].Len() = 1\n",
"WCnComV[2083][0] = 8128\n",
"WCnComV[2084].Len() = 1\n",
"WCnComV[2084][0] = 8123\n",
"WCnComV[2085].Len() = 1\n",
"WCnComV[2085][0] = 8122\n",
"WCnComV[2086].Len() = 1\n",
"WCnComV[2086][0] = 8120\n",
"WCnComV[2087].Len() = 1\n",
"WCnComV[2087][0] = 8116\n",
"WCnComV[2088].Len() = 1\n",
"WCnComV[2088][0] = 8108\n",
"WCnComV[2089].Len() = 1\n",
"WCnComV[2089][0] = 8106\n",
"WCnComV[2090].Len() = 1\n",
"WCnComV[2090][0] = 8104\n",
"WCnComV[2091].Len() = 1\n",
"WCnComV[2091][0] = 8102\n",
"WCnComV[2092].Len() = 1\n",
"WCnComV[2092][0] = 8099\n",
"WCnComV[2093].Len() = 1\n",
"WCnComV[2093][0] = 8098\n",
"WCnComV[2094].Len() = 1\n",
"WCnComV[2094][0] = 8097\n",
"WCnComV[2095].Len() = 1\n",
"WCnComV[2095][0] = 8091\n",
"WCnComV[2096].Len() = 1\n",
"WCnComV[2096][0] = 8089\n",
"WCnComV[2097].Len() = 1\n",
"WCnComV[2097][0] = 8085\n",
"WCnComV[2098].Len() = 1\n",
"WCnComV[2098][0] = 8081\n",
"WCnComV[2099].Len() = 1\n",
"WCnComV[2099][0] = 8078\n",
"WCnComV[2100].Len() = 1\n",
"WCnComV[2100][0] = 8077\n",
"WCnComV[2101].Len() = 1\n",
"WCnComV[2101][0] = 8076\n",
"WCnComV[2102].Len() = 1\n",
"WCnComV[2102][0] = 8075\n",
"WCnComV[2103].Len() = 1\n",
"WCnComV[2103][0] = 8069\n",
"WCnComV[2104].Len() = 1\n",
"WCnComV[2104][0] = 8068\n",
"WCnComV[2105].Len() = 1\n",
"WCnComV[2105][0] = 8066\n",
"WCnComV[2106].Len() = 1\n",
"WCnComV[2106][0] = 8061\n",
"WCnComV[2107].Len() = 1\n",
"WCnComV[2107][0] = 8060\n",
"WCnComV[2108].Len() = 1\n",
"WCnComV[2108][0] = 8057\n",
"WCnComV[2109].Len() = 1\n",
"WCnComV[2109][0] = 8050\n",
"WCnComV[2110].Len() = 1\n",
"WCnComV[2110][0] = 8048\n",
"WCnComV[2111].Len() = 1\n",
"WCnComV[2111][0] = 8041\n",
"WCnComV[2112].Len() = 1\n",
"WCnComV[2112][0] = 8040\n",
"WCnComV[2113].Len() = 1\n",
"WCnComV[2113][0] = 8038\n",
"WCnComV[2114].Len() = 1\n",
"WCnComV[2114][0] = 8036\n",
"WCnComV[2115].Len() = 1\n",
"WCnComV[2115][0] = 8029\n",
"WCnComV[2116].Len() = 1\n",
"WCnComV[2116][0] = 8028\n",
"WCnComV[2117].Len() = 1\n",
"WCnComV[2117][0] = 8025\n",
"WCnComV[2118].Len() = 1\n",
"WCnComV[2118][0] = 8023\n",
"WCnComV[2119].Len() = 1\n",
"WCnComV[2119][0] = 8020\n",
"WCnComV[2120].Len() = 1\n",
"WCnComV[2120][0] = 8014\n",
"WCnComV[2121].Len() = 1\n",
"WCnComV[2121][0] = 8013\n",
"WCnComV[2122].Len() = 1\n",
"WCnComV[2122][0] = 8009\n",
"WCnComV[2123].Len() = 1\n",
"WCnComV[2123][0] = 8003\n",
"WCnComV[2124].Len() = 1\n",
"WCnComV[2124][0] = 8002\n",
"WCnComV[2125].Len() = 1\n",
"WCnComV[2125][0] = 7993\n",
"WCnComV[2126].Len() = 1\n",
"WCnComV[2126][0] = 7991\n",
"WCnComV[2127].Len() = 1\n",
"WCnComV[2127][0] = 7990\n",
"WCnComV[2128].Len() = 1\n",
"WCnComV[2128][0] = 7988\n",
"WCnComV[2129].Len() = 1\n",
"WCnComV[2129][0] = 7984\n",
"WCnComV[2130].Len() = 1\n",
"WCnComV[2130][0] = 7981\n",
"WCnComV[2131].Len() = 1\n",
"WCnComV[2131][0] = 7978\n",
"WCnComV[2132].Len() = 1\n",
"WCnComV[2132][0] = 7975\n",
"WCnComV[2133].Len() = 1\n",
"WCnComV[2133][0] = 7973\n",
"WCnComV[2134].Len() = 1\n",
"WCnComV[2134][0] = 7971\n",
"WCnComV[2135].Len() = 1\n",
"WCnComV[2135][0] = 7970\n",
"WCnComV[2136].Len() = 1\n",
"WCnComV[2136][0] = 7960\n",
"WCnComV[2137].Len() = 1\n",
"WCnComV[2137][0] = 7958\n",
"WCnComV[2138].Len() = 1\n",
"WCnComV[2138][0] = 7957\n",
"WCnComV[2139].Len() = 1\n",
"WCnComV[2139][0] = 7956\n",
"WCnComV[2140].Len() = 1\n",
"WCnComV[2140][0] = 7950\n",
"WCnComV[2141].Len() = 1\n",
"WCnComV[2141][0] = 7949\n",
"WCnComV[2142].Len() = 1\n",
"WCnComV[2142][0] = 7947\n",
"WCnComV[2143].Len() = 1\n",
"WCnComV[2143][0] = 7945\n",
"WCnComV[2144].Len() = 1\n",
"WCnComV[2144][0] = 7941\n",
"WCnComV[2145].Len() = 1\n",
"WCnComV[2145][0] = 7937\n",
"WCnComV[2146].Len() = 1\n",
"WCnComV[2146][0] = 7930\n",
"WCnComV[2147].Len() = 1\n",
"WCnComV[2147][0] = 7929\n",
"WCnComV[2148].Len() = 1\n",
"WCnComV[2148][0] = 7926\n",
"WCnComV[2149].Len() = 1\n",
"WCnComV[2149][0] = 7925\n",
"WCnComV[2150].Len() = 1\n",
"WCnComV[2150][0] = 7924\n",
"WCnComV[2151].Len() = 1\n",
"WCnComV[2151][0] = 7922\n",
"WCnComV[2152].Len() = 1\n",
"WCnComV[2152][0] = 7920\n",
"WCnComV[2153].Len() = 1\n",
"WCnComV[2153][0] = 7919\n",
"WCnComV[2154].Len() = 1\n",
"WCnComV[2154][0] = 7918\n",
"WCnComV[2155].Len() = 1\n",
"WCnComV[2155][0] = 7916\n",
"WCnComV[2156].Len() = 1\n",
"WCnComV[2156][0] = 7915\n",
"WCnComV[2157].Len() = 1\n",
"WCnComV[2157][0] = 7912\n",
"WCnComV[2158].Len() = 1\n",
"WCnComV[2158][0] = 7903\n",
"WCnComV[2159].Len() = 1\n",
"WCnComV[2159][0] = 7900\n",
"WCnComV[2160].Len() = 1\n",
"WCnComV[2160][0] = 7899\n",
"WCnComV[2161].Len() = 1\n",
"WCnComV[2161][0] = 7898\n",
"WCnComV[2162].Len() = 1\n",
"WCnComV[2162][0] = 7885\n",
"WCnComV[2163].Len() = 1\n",
"WCnComV[2163][0] = 7878\n",
"WCnComV[2164].Len() = 1\n",
"WCnComV[2164][0] = 7877\n",
"WCnComV[2165].Len() = 1\n",
"WCnComV[2165][0] = 7876\n",
"WCnComV[2166].Len() = 1\n",
"WCnComV[2166][0] = 7869\n",
"WCnComV[2167].Len() = 1\n",
"WCnComV[2167][0] = 7868\n",
"WCnComV[2168].Len() = 1\n",
"WCnComV[2168][0] = 7860\n",
"WCnComV[2169].Len() = 1\n",
"WCnComV[2169][0] = 7859\n",
"WCnComV[2170].Len() = 1\n",
"WCnComV[2170][0] = 7858\n",
"WCnComV[2171].Len() = 1\n",
"WCnComV[2171][0] = 7854\n",
"WCnComV[2172].Len() = 1\n",
"WCnComV[2172][0] = 7849\n",
"WCnComV[2173].Len() = 1\n",
"WCnComV[2173][0] = 7848\n",
"WCnComV[2174].Len() = 1\n",
"WCnComV[2174][0] = 7843\n",
"WCnComV[2175].Len() = 1\n",
"WCnComV[2175][0] = 7837\n",
"WCnComV[2176].Len() = 1\n",
"WCnComV[2176][0] = 7834\n",
"WCnComV[2177].Len() = 1\n",
"WCnComV[2177][0] = 7832\n",
"WCnComV[2178].Len() = 1\n",
"WCnComV[2178][0] = 7831\n",
"WCnComV[2179].Len() = 1\n",
"WCnComV[2179][0] = 7830\n",
"WCnComV[2180].Len() = 1\n",
"WCnComV[2180][0] = 7824\n",
"WCnComV[2181].Len() = 1\n",
"WCnComV[2181][0] = 7823\n",
"WCnComV[2182].Len() = 1\n",
"WCnComV[2182][0] = 7822\n",
"WCnComV[2183].Len() = 1\n",
"WCnComV[2183][0] = 7818\n",
"WCnComV[2184].Len() = 1\n",
"WCnComV[2184][0] = 7817\n",
"WCnComV[2185].Len() = 1\n",
"WCnComV[2185][0] = 7813\n",
"WCnComV[2186].Len() = 1\n",
"WCnComV[2186][0] = 7812\n",
"WCnComV[2187].Len() = 1\n",
"WCnComV[2187][0] = 7811\n",
"WCnComV[2188].Len() = 1\n",
"WCnComV[2188][0] = 7810\n",
"WCnComV[2189].Len() = 1\n",
"WCnComV[2189][0] = 7808\n",
"WCnComV[2190].Len() = 1\n",
"WCnComV[2190][0] = 7806\n",
"WCnComV[2191].Len() = 1\n",
"WCnComV[2191][0] = 7805\n",
"WCnComV[2192].Len() = 1\n",
"WCnComV[2192][0] = 7802\n",
"WCnComV[2193].Len() = 1\n",
"WCnComV[2193][0] = 7798\n",
"WCnComV[2194].Len() = 1\n",
"WCnComV[2194][0] = 7797\n",
"WCnComV[2195].Len() = 1\n",
"WCnComV[2195][0] = 7794\n",
"WCnComV[2196].Len() = 1\n",
"WCnComV[2196][0] = 7792\n",
"WCnComV[2197].Len() = 1\n",
"WCnComV[2197][0] = 7791\n",
"WCnComV[2198].Len() = 1\n",
"WCnComV[2198][0] = 7789\n",
"WCnComV[2199].Len() = 1\n",
"WCnComV[2199][0] = 7785\n",
"WCnComV[2200].Len() = 1\n",
"WCnComV[2200][0] = 7784\n",
"WCnComV[2201].Len() = 1\n",
"WCnComV[2201][0] = 7780\n",
"WCnComV[2202].Len() = 1\n",
"WCnComV[2202][0] = 7778\n",
"WCnComV[2203].Len() = 1\n",
"WCnComV[2203][0] = 7775\n",
"WCnComV[2204].Len() = 1\n",
"WCnComV[2204][0] = 7774\n",
"WCnComV[2205].Len() = 1\n",
"WCnComV[2205][0] = 7772\n",
"WCnComV[2206].Len() = 1\n",
"WCnComV[2206][0] = 7771\n",
"WCnComV[2207].Len() = 1\n",
"WCnComV[2207][0] = 7770\n",
"WCnComV[2208].Len() = 1\n",
"WCnComV[2208][0] = 7769\n",
"WCnComV[2209].Len() = 1\n",
"WCnComV[2209][0] = 7767\n",
"WCnComV[2210].Len() = 1\n",
"WCnComV[2210][0] = 7763\n",
"WCnComV[2211].Len() = 1\n",
"WCnComV[2211][0] = 7762\n",
"WCnComV[2212].Len() = 1\n",
"WCnComV[2212][0] = 7759\n",
"WCnComV[2213].Len() = 1\n",
"WCnComV[2213][0] = 7758\n",
"WCnComV[2214].Len() = 1\n",
"WCnComV[2214][0] = 7757\n",
"WCnComV[2215].Len() = 1\n",
"WCnComV[2215][0] = 7754\n",
"WCnComV[2216].Len() = 1\n",
"WCnComV[2216][0] = 7753\n",
"WCnComV[2217].Len() = 1\n",
"WCnComV[2217][0] = 7752\n",
"WCnComV[2218].Len() = 1\n",
"WCnComV[2218][0] = 7747\n",
"WCnComV[2219].Len() = 1\n",
"WCnComV[2219][0] = 7746\n",
"WCnComV[2220].Len() = 1\n",
"WCnComV[2220][0] = 7742\n",
"WCnComV[2221].Len() = 1\n",
"WCnComV[2221][0] = 7738\n",
"WCnComV[2222].Len() = 1\n",
"WCnComV[2222][0] = 7737\n",
"WCnComV[2223].Len() = 1\n",
"WCnComV[2223][0] = 7733\n",
"WCnComV[2224].Len() = 1\n",
"WCnComV[2224][0] = 7730\n",
"WCnComV[2225].Len() = 1\n",
"WCnComV[2225][0] = 7728\n",
"WCnComV[2226].Len() = 1\n",
"WCnComV[2226][0] = 7726\n",
"WCnComV[2227].Len() = 1\n",
"WCnComV[2227][0] = 7723\n",
"WCnComV[2228].Len() = 1\n",
"WCnComV[2228][0] = 7721\n",
"WCnComV[2229].Len() = 1\n",
"WCnComV[2229][0] = 7720\n",
"WCnComV[2230].Len() = 1\n",
"WCnComV[2230][0] = 7719\n",
"WCnComV[2231].Len() = 1\n",
"WCnComV[2231][0] = 7713\n",
"WCnComV[2232].Len() = 1\n",
"WCnComV[2232][0] = 7712\n",
"WCnComV[2233].Len() = 1\n",
"WCnComV[2233][0] = 7710\n",
"WCnComV[2234].Len() = 1\n",
"WCnComV[2234][0] = 7702\n",
"WCnComV[2235].Len() = 1\n",
"WCnComV[2235][0] = 7701\n",
"WCnComV[2236].Len() = 1\n",
"WCnComV[2236][0] = 7697\n",
"WCnComV[2237].Len() = 1\n",
"WCnComV[2237][0] = 7696\n",
"WCnComV[2238].Len() = 1\n",
"WCnComV[2238][0] = 7694\n",
"WCnComV[2239].Len() = 1\n",
"WCnComV[2239][0] = 7690\n",
"WCnComV[2240].Len() = 1\n",
"WCnComV[2240][0] = 7689\n",
"WCnComV[2241].Len() = 1\n",
"WCnComV[2241][0] = 7685\n",
"WCnComV[2242].Len() = 1\n",
"WCnComV[2242][0] = 7684\n",
"WCnComV[2243].Len() = 1\n",
"WCnComV[2243][0] = 7680\n",
"WCnComV[2244].Len() = 1\n",
"WCnComV[2244][0] = 7679\n",
"WCnComV[2245].Len() = 1\n",
"WCnComV[2245][0] = 7677\n",
"WCnComV[2246].Len() = 1\n",
"WCnComV[2246][0] = 7675\n",
"WCnComV[2247].Len() = 1\n",
"WCnComV[2247][0] = 7670\n",
"WCnComV[2248].Len() = 1\n",
"WCnComV[2248][0] = 7663\n",
"WCnComV[2249].Len() = 1\n",
"WCnComV[2249][0] = 7661\n",
"WCnComV[2250].Len() = 1\n",
"WCnComV[2250][0] = 7660\n",
"WCnComV[2251].Len() = 1\n",
"WCnComV[2251][0] = 7658\n",
"WCnComV[2252].Len() = 1\n",
"WCnComV[2252][0] = 7657\n",
"WCnComV[2253].Len() = 1\n",
"WCnComV[2253][0] = 7656\n",
"WCnComV[2254].Len() = 1\n",
"WCnComV[2254][0] = 7655\n",
"WCnComV[2255].Len() = 1\n",
"WCnComV[2255][0] = 7653\n",
"WCnComV[2256].Len() = 1\n",
"WCnComV[2256][0] = 7647\n",
"WCnComV[2257].Len() = 1\n",
"WCnComV[2257][0] = 7643\n",
"WCnComV[2258].Len() = 1\n",
"WCnComV[2258][0] = 7642\n",
"WCnComV[2259].Len() = 1\n",
"WCnComV[2259][0] = 7639\n",
"WCnComV[2260].Len() = 1\n",
"WCnComV[2260][0] = 7638\n",
"WCnComV[2261].Len() = 1\n",
"WCnComV[2261][0] = 7636\n",
"WCnComV[2262].Len() = 1\n",
"WCnComV[2262][0] = 7634\n",
"WCnComV[2263].Len() = 1\n",
"WCnComV[2263][0] = 7633\n",
"WCnComV[2264].Len() = 1\n",
"WCnComV[2264][0] = 7630\n",
"WCnComV[2265].Len() = 1\n",
"WCnComV[2265][0] = 7629\n",
"WCnComV[2266].Len() = 1\n",
"WCnComV[2266][0] = 7626\n",
"WCnComV[2267].Len() = 1\n",
"WCnComV[2267][0] = 7624\n",
"WCnComV[2268].Len() = 1\n",
"WCnComV[2268][0] = 7622\n",
"WCnComV[2269].Len() = 1\n",
"WCnComV[2269][0] = 7621\n",
"WCnComV[2270].Len() = 1\n",
"WCnComV[2270][0] = 7615\n",
"WCnComV[2271].Len() = 1\n",
"WCnComV[2271][0] = 7607\n",
"WCnComV[2272].Len() = 1\n",
"WCnComV[2272][0] = 7606\n",
"WCnComV[2273].Len() = 1\n",
"WCnComV[2273][0] = 7605\n",
"WCnComV[2274].Len() = 1\n",
"WCnComV[2274][0] = 7597\n",
"WCnComV[2275].Len() = 1\n",
"WCnComV[2275][0] = 7594\n",
"WCnComV[2276].Len() = 1\n",
"WCnComV[2276][0] = 7593\n",
"WCnComV[2277].Len() = 1\n",
"WCnComV[2277][0] = 7592\n",
"WCnComV[2278].Len() = 1\n",
"WCnComV[2278][0] = 7590\n",
"WCnComV[2279].Len() = 1\n",
"WCnComV[2279][0] = 7586\n",
"WCnComV[2280].Len() = 1\n",
"WCnComV[2280][0] = 7585\n",
"WCnComV[2281].Len() = 1\n",
"WCnComV[2281][0] = 7584\n",
"WCnComV[2282].Len() = 1\n",
"WCnComV[2282][0] = 7582\n",
"WCnComV[2283].Len() = 1\n",
"WCnComV[2283][0] = 7580\n",
"WCnComV[2284].Len() = 1\n",
"WCnComV[2284][0] = 7578\n",
"WCnComV[2285].Len() = 1\n",
"WCnComV[2285][0] = 7569\n",
"WCnComV[2286].Len() = 1\n",
"WCnComV[2286][0] = 7566\n",
"WCnComV[2287].Len() = 1\n",
"WCnComV[2287][0] = 7561\n",
"WCnComV[2288].Len() = 1\n",
"WCnComV[2288][0] = 7559\n",
"WCnComV[2289].Len() = 1\n",
"WCnComV[2289][0] = 7555\n",
"WCnComV[2290].Len() = 1\n",
"WCnComV[2290][0] = 7554\n",
"WCnComV[2291].Len() = 1\n",
"WCnComV[2291][0] = 7553\n",
"WCnComV[2292].Len() = 1\n",
"WCnComV[2292][0] = 7552\n",
"WCnComV[2293].Len() = 1\n",
"WCnComV[2293][0] = 7543\n",
"WCnComV[2294].Len() = 1\n",
"WCnComV[2294][0] = 7541\n",
"WCnComV[2295].Len() = 1\n",
"WCnComV[2295][0] = 7536\n",
"WCnComV[2296].Len() = 1\n",
"WCnComV[2296][0] = 7534\n",
"WCnComV[2297].Len() = 1\n",
"WCnComV[2297][0] = 7533\n",
"WCnComV[2298].Len() = 1\n",
"WCnComV[2298][0] = 7532\n",
"WCnComV[2299].Len() = 1\n",
"WCnComV[2299][0] = 7531\n",
"WCnComV[2300].Len() = 1\n",
"WCnComV[2300][0] = 7529\n",
"WCnComV[2301].Len() = 1\n",
"WCnComV[2301][0] = 7526\n",
"WCnComV[2302].Len() = 1\n",
"WCnComV[2302][0] = 7524\n",
"WCnComV[2303].Len() = 1\n",
"WCnComV[2303][0] = 7520\n",
"WCnComV[2304].Len() = 1\n",
"WCnComV[2304][0] = 7519\n",
"WCnComV[2305].Len() = 1\n",
"WCnComV[2305][0] = 7518\n",
"WCnComV[2306].Len() = 1\n",
"WCnComV[2306][0] = 7507\n",
"WCnComV[2307].Len() = 1\n",
"WCnComV[2307][0] = 7506\n",
"WCnComV[2308].Len() = 1\n",
"WCnComV[2308][0] = 7501\n",
"WCnComV[2309].Len() = 1\n",
"WCnComV[2309][0] = 7500\n",
"WCnComV[2310].Len() = 1\n",
"WCnComV[2310][0] = 7494\n",
"WCnComV[2311].Len() = 1\n",
"WCnComV[2311][0] = 7492\n",
"WCnComV[2312].Len() = 1\n",
"WCnComV[2312][0] = 7486\n",
"WCnComV[2313].Len() = 1\n",
"WCnComV[2313][0] = 7484\n",
"WCnComV[2314].Len() = 1\n",
"WCnComV[2314][0] = 7481\n",
"WCnComV[2315].Len() = 1\n",
"WCnComV[2315][0] = 7480\n",
"WCnComV[2316].Len() = 1\n",
"WCnComV[2316][0] = 7477\n",
"WCnComV[2317].Len() = 1\n",
"WCnComV[2317][0] = 7471\n",
"WCnComV[2318].Len() = 1\n",
"WCnComV[2318][0] = 7468\n",
"WCnComV[2319].Len() = 1\n",
"WCnComV[2319][0] = 7467\n",
"WCnComV[2320].Len() = 1\n",
"WCnComV[2320][0] = 7464\n",
"WCnComV[2321].Len() = 1\n",
"WCnComV[2321][0] = 7461\n",
"WCnComV[2322].Len() = 1\n",
"WCnComV[2322][0] = 7459\n",
"WCnComV[2323].Len() = 1\n",
"WCnComV[2323][0] = 7457\n",
"WCnComV[2324].Len() = 1\n",
"WCnComV[2324][0] = 7456\n",
"WCnComV[2325].Len() = 1\n",
"WCnComV[2325][0] = 7455\n",
"WCnComV[2326].Len() = 1\n",
"WCnComV[2326][0] = 7453\n",
"WCnComV[2327].Len() = 1\n",
"WCnComV[2327][0] = 7447\n",
"WCnComV[2328].Len() = 1\n",
"WCnComV[2328][0] = 7446\n",
"WCnComV[2329].Len() = 1\n",
"WCnComV[2329][0] = 7442\n",
"WCnComV[2330].Len() = 1\n",
"WCnComV[2330][0] = 7437\n",
"WCnComV[2331].Len() = 1\n",
"WCnComV[2331][0] = 7433\n",
"WCnComV[2332].Len() = 1\n",
"WCnComV[2332][0] = 7432\n",
"WCnComV[2333].Len() = 1\n",
"WCnComV[2333][0] = 7431\n",
"WCnComV[2334].Len() = 1\n",
"WCnComV[2334][0] = 7429\n",
"WCnComV[2335].Len() = 1\n",
"WCnComV[2335][0] = 7428\n",
"WCnComV[2336].Len() = 1\n",
"WCnComV[2336][0] = 7424\n",
"WCnComV[2337].Len() = 1\n",
"WCnComV[2337][0] = 7420\n",
"WCnComV[2338].Len() = 1\n",
"WCnComV[2338][0] = 7419\n",
"WCnComV[2339].Len() = 1\n",
"WCnComV[2339][0] = 7417\n",
"WCnComV[2340].Len() = 1\n",
"WCnComV[2340][0] = 7415\n",
"WCnComV[2341].Len() = 1\n",
"WCnComV[2341][0] = 7409\n",
"WCnComV[2342].Len() = 1\n",
"WCnComV[2342][0] = 7404\n",
"WCnComV[2343].Len() = 1\n",
"WCnComV[2343][0] = 7402\n",
"WCnComV[2344].Len() = 1\n",
"WCnComV[2344][0] = 7401\n",
"WCnComV[2345].Len() = 1\n",
"WCnComV[2345][0] = 7400\n",
"WCnComV[2346].Len() = 1\n",
"WCnComV[2346][0] = 7394\n",
"WCnComV[2347].Len() = 1\n",
"WCnComV[2347][0] = 7393\n",
"WCnComV[2348].Len() = 1\n",
"WCnComV[2348][0] = 7388\n",
"WCnComV[2349].Len() = 1\n",
"WCnComV[2349][0] = 7385\n",
"WCnComV[2350].Len() = 1\n",
"WCnComV[2350][0] = 7382\n",
"WCnComV[2351].Len() = 1\n",
"WCnComV[2351][0] = 7381\n",
"WCnComV[2352].Len() = 1\n",
"WCnComV[2352][0] = 7380\n",
"WCnComV[2353].Len() = 1\n",
"WCnComV[2353][0] = 7379\n",
"WCnComV[2354].Len() = 1\n",
"WCnComV[2354][0] = 7374\n",
"WCnComV[2355].Len() = 1\n",
"WCnComV[2355][0] = 7373\n",
"WCnComV[2356].Len() = 1\n",
"WCnComV[2356][0] = 7371\n",
"WCnComV[2357].Len() = 1\n",
"WCnComV[2357][0] = 7369\n",
"WCnComV[2358].Len() = 1\n",
"WCnComV[2358][0] = 7365\n",
"WCnComV[2359].Len() = 1\n",
"WCnComV[2359][0] = 7363\n",
"WCnComV[2360].Len() = 1\n",
"WCnComV[2360][0] = 7359\n",
"WCnComV[2361].Len() = 1\n",
"WCnComV[2361][0] = 7357\n",
"WCnComV[2362].Len() = 1\n",
"WCnComV[2362][0] = 7353\n",
"WCnComV[2363].Len() = 1\n",
"WCnComV[2363][0] = 7351\n",
"WCnComV[2364].Len() = 1\n",
"WCnComV[2364][0] = 7349\n",
"WCnComV[2365].Len() = 1\n",
"WCnComV[2365][0] = 7345\n",
"WCnComV[2366].Len() = 1\n",
"WCnComV[2366][0] = 7340\n",
"WCnComV[2367].Len() = 1\n",
"WCnComV[2367][0] = 7338\n",
"WCnComV[2368].Len() = 1\n",
"WCnComV[2368][0] = 7335\n",
"WCnComV[2369].Len() = 1\n",
"WCnComV[2369][0] = 7333\n",
"WCnComV[2370].Len() = 1\n",
"WCnComV[2370][0] = 7330\n",
"WCnComV[2371].Len() = 1\n",
"WCnComV[2371][0] = 7326\n",
"WCnComV[2372].Len() = 1\n",
"WCnComV[2372][0] = 7321\n",
"WCnComV[2373].Len() = 1\n",
"WCnComV[2373][0] = 7315\n",
"WCnComV[2374].Len() = 1\n",
"WCnComV[2374][0] = 7313\n",
"WCnComV[2375].Len() = 1\n",
"WCnComV[2375][0] = 7312\n",
"WCnComV[2376].Len() = 1\n",
"WCnComV[2376][0] = 7308\n",
"WCnComV[2377].Len() = 1\n",
"WCnComV[2377][0] = 7307\n",
"WCnComV[2378].Len() = 1\n",
"WCnComV[2378][0] = 7303\n",
"WCnComV[2379].Len() = 1\n",
"WCnComV[2379][0] = 7301\n",
"WCnComV[2380].Len() = 1\n",
"WCnComV[2380][0] = 7294\n",
"WCnComV[2381].Len() = 1\n",
"WCnComV[2381][0] = 7292\n",
"WCnComV[2382].Len() = 1\n",
"WCnComV[2382][0] = 7290\n",
"WCnComV[2383].Len() = 1\n",
"WCnComV[2383][0] = 7289\n",
"WCnComV[2384].Len() = 1\n",
"WCnComV[2384][0] = 7285\n",
"WCnComV[2385].Len() = 1\n",
"WCnComV[2385][0] = 7284\n",
"WCnComV[2386].Len() = 1\n",
"WCnComV[2386][0] = 7281\n",
"WCnComV[2387].Len() = 1\n",
"WCnComV[2387][0] = 7277\n",
"WCnComV[2388].Len() = 1\n",
"WCnComV[2388][0] = 7274\n",
"WCnComV[2389].Len() = 1\n",
"WCnComV[2389][0] = 7270\n",
"WCnComV[2390].Len() = 1\n",
"WCnComV[2390][0] = 7269\n",
"WCnComV[2391].Len() = 1\n",
"WCnComV[2391][0] = 7266\n",
"WCnComV[2392].Len() = 1\n",
"WCnComV[2392][0] = 7265\n",
"WCnComV[2393].Len() = 1\n",
"WCnComV[2393][0] = 7262\n",
"WCnComV[2394].Len() = 1\n",
"WCnComV[2394][0] = 7261\n",
"WCnComV[2395].Len() = 1\n",
"WCnComV[2395][0] = 7260\n",
"WCnComV[2396].Len() = 1\n",
"WCnComV[2396][0] = 7259\n",
"WCnComV[2397].Len() = 1\n",
"WCnComV[2397][0] = 7258\n",
"WCnComV[2398].Len() = 1\n",
"WCnComV[2398][0] = 7255\n",
"WCnComV[2399].Len() = 1\n",
"WCnComV[2399][0] = 7246\n",
"WCnComV[2400].Len() = 1\n",
"WCnComV[2400][0] = 7242\n",
"WCnComV[2401].Len() = 1\n",
"WCnComV[2401][0] = 7239\n",
"WCnComV[2402].Len() = 1\n",
"WCnComV[2402][0] = 7236\n",
"WCnComV[2403].Len() = 1\n",
"WCnComV[2403][0] = 7233\n",
"WCnComV[2404].Len() = 1\n",
"WCnComV[2404][0] = 7230\n",
"WCnComV[2405].Len() = 1\n",
"WCnComV[2405][0] = 7228\n",
"WCnComV[2406].Len() = 1\n",
"WCnComV[2406][0] = 7225\n",
"WCnComV[2407].Len() = 1\n",
"WCnComV[2407][0] = 7224\n",
"WCnComV[2408].Len() = 1\n",
"WCnComV[2408][0] = 7223\n",
"WCnComV[2409].Len() = 1\n",
"WCnComV[2409][0] = 7219\n",
"WCnComV[2410].Len() = 1\n",
"WCnComV[2410][0] = 7217\n",
"WCnComV[2411].Len() = 1\n",
"WCnComV[2411][0] = 7213\n",
"WCnComV[2412].Len() = 1\n",
"WCnComV[2412][0] = 7212\n",
"WCnComV[2413].Len() = 1\n",
"WCnComV[2413][0] = 7211\n",
"WCnComV[2414].Len() = 1\n",
"WCnComV[2414][0] = 7210\n",
"WCnComV[2415].Len() = 1\n",
"WCnComV[2415][0] = 7207\n",
"WCnComV[2416].Len() = 1\n",
"WCnComV[2416][0] = 7204\n",
"WCnComV[2417].Len() = 1\n",
"WCnComV[2417][0] = 7201\n",
"WCnComV[2418].Len() = 1\n",
"WCnComV[2418][0] = 7200\n",
"WCnComV[2419].Len() = 1\n",
"WCnComV[2419][0] = 7198\n",
"WCnComV[2420].Len() = 1\n",
"WCnComV[2420][0] = 7197\n",
"WCnComV[2421].Len() = 1\n",
"WCnComV[2421][0] = 7192\n",
"WCnComV[2422].Len() = 1\n",
"WCnComV[2422][0] = 7191\n",
"WCnComV[2423].Len() = 1\n",
"WCnComV[2423][0] = 7185\n",
"WCnComV[2424].Len() = 1\n",
"WCnComV[2424][0] = 7184\n",
"WCnComV[2425].Len() = 1\n",
"WCnComV[2425][0] = 7183\n",
"WCnComV[2426].Len() = 1\n",
"WCnComV[2426][0] = 7178\n",
"WCnComV[2427].Len() = 1\n",
"WCnComV[2427][0] = 7175\n",
"WCnComV[2428].Len() = 1\n",
"WCnComV[2428][0] = 7171\n",
"WCnComV[2429].Len() = 1\n",
"WCnComV[2429][0] = 7170\n",
"WCnComV[2430].Len() = 1\n",
"WCnComV[2430][0] = 7168\n",
"WCnComV[2431].Len() = 1\n",
"WCnComV[2431][0] = 7166\n",
"WCnComV[2432].Len() = 1\n",
"WCnComV[2432][0] = 7161\n",
"WCnComV[2433].Len() = 1\n",
"WCnComV[2433][0] = 7160\n",
"WCnComV[2434].Len() = 1\n",
"WCnComV[2434][0] = 7158\n",
"WCnComV[2435].Len() = 1\n",
"WCnComV[2435][0] = 7157\n",
"WCnComV[2436].Len() = 1\n",
"WCnComV[2436][0] = 7156\n",
"WCnComV[2437].Len() = 1\n",
"WCnComV[2437][0] = 7154\n",
"WCnComV[2438].Len() = 1\n",
"WCnComV[2438][0] = 7152\n",
"WCnComV[2439].Len() = 1\n",
"WCnComV[2439][0] = 7148\n",
"WCnComV[2440].Len() = 1\n",
"WCnComV[2440][0] = 7147\n",
"WCnComV[2441].Len() = 1\n",
"WCnComV[2441][0] = 7144\n",
"WCnComV[2442].Len() = 1\n",
"WCnComV[2442][0] = 7143\n",
"WCnComV[2443].Len() = 1\n",
"WCnComV[2443][0] = 7140\n",
"WCnComV[2444].Len() = 1\n",
"WCnComV[2444][0] = 7138\n",
"WCnComV[2445].Len() = 1\n",
"WCnComV[2445][0] = 7135\n",
"WCnComV[2446].Len() = 1\n",
"WCnComV[2446][0] = 7134\n",
"WCnComV[2447].Len() = 1\n",
"WCnComV[2447][0] = 7132\n",
"WCnComV[2448].Len() = 1\n",
"WCnComV[2448][0] = 7131\n",
"WCnComV[2449].Len() = 1\n",
"WCnComV[2449][0] = 7130\n",
"WCnComV[2450].Len() = 1\n",
"WCnComV[2450][0] = 7129\n",
"WCnComV[2451].Len() = 1\n",
"WCnComV[2451][0] = 7125\n",
"WCnComV[2452].Len() = 1\n",
"WCnComV[2452][0] = 7123\n",
"WCnComV[2453].Len() = 1\n",
"WCnComV[2453][0] = 7120\n",
"WCnComV[2454].Len() = 1\n",
"WCnComV[2454][0] = 7116\n",
"WCnComV[2455].Len() = 1\n",
"WCnComV[2455][0] = 7115\n",
"WCnComV[2456].Len() = 1\n",
"WCnComV[2456][0] = 7113\n",
"WCnComV[2457].Len() = 1\n",
"WCnComV[2457][0] = 7110\n",
"WCnComV[2458].Len() = 1\n",
"WCnComV[2458][0] = 7109\n",
"WCnComV[2459].Len() = 1\n",
"WCnComV[2459][0] = 7106\n",
"WCnComV[2460].Len() = 1\n",
"WCnComV[2460][0] = 7101\n",
"WCnComV[2461].Len() = 1\n",
"WCnComV[2461][0] = 7097\n",
"WCnComV[2462].Len() = 1\n",
"WCnComV[2462][0] = 7094\n",
"WCnComV[2463].Len() = 1\n",
"WCnComV[2463][0] = 7090\n",
"WCnComV[2464].Len() = 1\n",
"WCnComV[2464][0] = 7081\n",
"WCnComV[2465].Len() = 1\n",
"WCnComV[2465][0] = 7075\n",
"WCnComV[2466].Len() = 1\n",
"WCnComV[2466][0] = 7074\n",
"WCnComV[2467].Len() = 1\n",
"WCnComV[2467][0] = 7073\n",
"WCnComV[2468].Len() = 1\n",
"WCnComV[2468][0] = 7072\n",
"WCnComV[2469].Len() = 1\n",
"WCnComV[2469][0] = 7071\n",
"WCnComV[2470].Len() = 1\n",
"WCnComV[2470][0] = 7068\n",
"WCnComV[2471].Len() = 1\n",
"WCnComV[2471][0] = 7059\n",
"WCnComV[2472].Len() = 1\n",
"WCnComV[2472][0] = 7057\n",
"WCnComV[2473].Len() = 1\n",
"WCnComV[2473][0] = 7056\n",
"WCnComV[2474].Len() = 1\n",
"WCnComV[2474][0] = 7048\n",
"WCnComV[2475].Len() = 1\n",
"WCnComV[2475][0] = 7045\n",
"WCnComV[2476].Len() = 1\n",
"WCnComV[2476][0] = 7044\n",
"WCnComV[2477].Len() = 1\n",
"WCnComV[2477][0] = 7042\n",
"WCnComV[2478].Len() = 1\n",
"WCnComV[2478][0] = 7038\n",
"WCnComV[2479].Len() = 1\n",
"WCnComV[2479][0] = 7034\n",
"WCnComV[2480].Len() = 1\n",
"WCnComV[2480][0] = 7032\n",
"WCnComV[2481].Len() = 1\n",
"WCnComV[2481][0] = 7029\n",
"WCnComV[2482].Len() = 1\n",
"WCnComV[2482][0] = 7027\n",
"WCnComV[2483].Len() = 1\n",
"WCnComV[2483][0] = 7021\n",
"WCnComV[2484].Len() = 1\n",
"WCnComV[2484][0] = 7020\n",
"WCnComV[2485].Len() = 1\n",
"WCnComV[2485][0] = 7016\n",
"WCnComV[2486].Len() = 1\n",
"WCnComV[2486][0] = 7015\n",
"WCnComV[2487].Len() = 1\n",
"WCnComV[2487][0] = 7014\n",
"WCnComV[2488].Len() = 1\n",
"WCnComV[2488][0] = 7012\n",
"WCnComV[2489].Len() = 1\n",
"WCnComV[2489][0] = 7011\n",
"WCnComV[2490].Len() = 1\n",
"WCnComV[2490][0] = 7010\n",
"WCnComV[2491].Len() = 1\n",
"WCnComV[2491][0] = 7009\n",
"WCnComV[2492].Len() = 1\n",
"WCnComV[2492][0] = 7008\n",
"WCnComV[2493].Len() = 1\n",
"WCnComV[2493][0] = 7007\n",
"WCnComV[2494].Len() = 1\n",
"WCnComV[2494][0] = 7005\n",
"WCnComV[2495].Len() = 1\n",
"WCnComV[2495][0] = 7004\n",
"WCnComV[2496].Len() = 1\n",
"WCnComV[2496][0] = 6999\n",
"WCnComV[2497].Len() = 1\n",
"WCnComV[2497][0] = 6996\n",
"WCnComV[2498].Len() = 1\n",
"WCnComV[2498][0] = 6994\n",
"WCnComV[2499].Len() = 1\n",
"WCnComV[2499][0] = 6990\n",
"WCnComV[2500].Len() = 1\n",
"WCnComV[2500][0] = 6988\n",
"WCnComV[2501].Len() = 1\n",
"WCnComV[2501][0] = 6987\n",
"WCnComV[2502].Len() = 1\n",
"WCnComV[2502][0] = 6983\n",
"WCnComV[2503].Len() = 1\n",
"WCnComV[2503][0] = 6982\n",
"WCnComV[2504].Len() = 1\n",
"WCnComV[2504][0] = 6979\n",
"WCnComV[2505].Len() = 1\n",
"WCnComV[2505][0] = 6978\n",
"WCnComV[2506].Len() = 1\n",
"WCnComV[2506][0] = 6976\n",
"WCnComV[2507].Len() = 1\n",
"WCnComV[2507][0] = 6975\n",
"WCnComV[2508].Len() = 1\n",
"WCnComV[2508][0] = 6973\n",
"WCnComV[2509].Len() = 1\n",
"WCnComV[2509][0] = 6972\n",
"WCnComV[2510].Len() = 1\n",
"WCnComV[2510][0] = 6971\n",
"WCnComV[2511].Len() = 1\n",
"WCnComV[2511][0] = 6968\n",
"WCnComV[2512].Len() = 1\n",
"WCnComV[2512][0] = 6962\n",
"WCnComV[2513].Len() = 1\n",
"WCnComV[2513][0] = 6960\n",
"WCnComV[2514].Len() = 1\n",
"WCnComV[2514][0] = 6946\n",
"WCnComV[2515].Len() = 1\n",
"WCnComV[2515][0] = 6942\n",
"WCnComV[2516].Len() = 1\n",
"WCnComV[2516][0] = 6939\n",
"WCnComV[2517].Len() = 1\n",
"WCnComV[2517][0] = 6933\n",
"WCnComV[2518].Len() = 1\n",
"WCnComV[2518][0] = 6932\n",
"WCnComV[2519].Len() = 1\n",
"WCnComV[2519][0] = 6928\n",
"WCnComV[2520].Len() = 1\n",
"WCnComV[2520][0] = 6926\n",
"WCnComV[2521].Len() = 1\n",
"WCnComV[2521][0] = 6924\n",
"WCnComV[2522].Len() = 1\n",
"WCnComV[2522][0] = 6917\n",
"WCnComV[2523].Len() = 1\n",
"WCnComV[2523][0] = 6913\n",
"WCnComV[2524].Len() = 1\n",
"WCnComV[2524][0] = 6910\n",
"WCnComV[2525].Len() = 1\n",
"WCnComV[2525][0] = 6907\n",
"WCnComV[2526].Len() = 1\n",
"WCnComV[2526][0] = 6906\n",
"WCnComV[2527].Len() = 1\n",
"WCnComV[2527][0] = 6901\n",
"WCnComV[2528].Len() = 1\n",
"WCnComV[2528][0] = 6900\n",
"WCnComV[2529].Len() = 1\n",
"WCnComV[2529][0] = 6897\n",
"WCnComV[2530].Len() = 1\n",
"WCnComV[2530][0] = 6896\n",
"WCnComV[2531].Len() = 1\n",
"WCnComV[2531][0] = 6892\n",
"WCnComV[2532].Len() = 1\n",
"WCnComV[2532][0] = 6891\n",
"WCnComV[2533].Len() = 1\n",
"WCnComV[2533][0] = 6888\n",
"WCnComV[2534].Len() = 1\n",
"WCnComV[2534][0] = 6884\n",
"WCnComV[2535].Len() = 1\n",
"WCnComV[2535][0] = 6883\n",
"WCnComV[2536].Len() = 1\n",
"WCnComV[2536][0] = 6882\n",
"WCnComV[2537].Len() = 1\n",
"WCnComV[2537][0] = 6879\n",
"WCnComV[2538].Len() = 1\n",
"WCnComV[2538][0] = 6878\n",
"WCnComV[2539].Len() = 1\n",
"WCnComV[2539][0] = 6874\n",
"WCnComV[2540].Len() = 1\n",
"WCnComV[2540][0] = 6872\n",
"WCnComV[2541].Len() = 1\n",
"WCnComV[2541][0] = 6867\n",
"WCnComV[2542].Len() = 1\n",
"WCnComV[2542][0] = 6866\n",
"WCnComV[2543].Len() = 1\n",
"WCnComV[2543][0] = 6865\n",
"WCnComV[2544].Len() = 1\n",
"WCnComV[2544][0] = 6864\n",
"WCnComV[2545].Len() = 1\n",
"WCnComV[2545][0] = 6860\n",
"WCnComV[2546].Len() = 1\n",
"WCnComV[2546][0] = 6859\n",
"WCnComV[2547].Len() = 1\n",
"WCnComV[2547][0] = 6857\n",
"WCnComV[2548].Len() = 1\n",
"WCnComV[2548][0] = 6853\n",
"WCnComV[2549].Len() = 1\n",
"WCnComV[2549][0] = 6850\n",
"WCnComV[2550].Len() = 1\n",
"WCnComV[2550][0] = 6849\n",
"WCnComV[2551].Len() = 1\n",
"WCnComV[2551][0] = 6846\n",
"WCnComV[2552].Len() = 1\n",
"WCnComV[2552][0] = 6844\n",
"WCnComV[2553].Len() = 1\n",
"WCnComV[2553][0] = 6843\n",
"WCnComV[2554].Len() = 1\n",
"WCnComV[2554][0] = 6841\n",
"WCnComV[2555].Len() = 1\n",
"WCnComV[2555][0] = 6834\n",
"WCnComV[2556].Len() = 1\n",
"WCnComV[2556][0] = 6830\n",
"WCnComV[2557].Len() = 1\n",
"WCnComV[2557][0] = 6825\n",
"WCnComV[2558].Len() = 1\n",
"WCnComV[2558][0] = 6824\n",
"WCnComV[2559].Len() = 1\n",
"WCnComV[2559][0] = 6821\n",
"WCnComV[2560].Len() = 1\n",
"WCnComV[2560][0] = 6819\n",
"WCnComV[2561].Len() = 1\n",
"WCnComV[2561][0] = 6817\n",
"WCnComV[2562].Len() = 1\n",
"WCnComV[2562][0] = 6815\n",
"WCnComV[2563].Len() = 1\n",
"WCnComV[2563][0] = 6811\n",
"WCnComV[2564].Len() = 1\n",
"WCnComV[2564][0] = 6810\n",
"WCnComV[2565].Len() = 1\n",
"WCnComV[2565][0] = 6808\n",
"WCnComV[2566].Len() = 1\n",
"WCnComV[2566][0] = 6804\n",
"WCnComV[2567].Len() = 1\n",
"WCnComV[2567][0] = 6802\n",
"WCnComV[2568].Len() = 1\n",
"WCnComV[2568][0] = 6799\n",
"WCnComV[2569].Len() = 1\n",
"WCnComV[2569][0] = 6798\n",
"WCnComV[2570].Len() = 1\n",
"WCnComV[2570][0] = 6797\n",
"WCnComV[2571].Len() = 1\n",
"WCnComV[2571][0] = 6793\n",
"WCnComV[2572].Len() = 1\n",
"WCnComV[2572][0] = 6792\n",
"WCnComV[2573].Len() = 1\n",
"WCnComV[2573][0] = 6789\n",
"WCnComV[2574].Len() = 1\n",
"WCnComV[2574][0] = 6785\n",
"WCnComV[2575].Len() = 1\n",
"WCnComV[2575][0] = 6783\n",
"WCnComV[2576].Len() = 1\n",
"WCnComV[2576][0] = 6781\n",
"WCnComV[2577].Len() = 1\n",
"WCnComV[2577][0] = 6780\n",
"WCnComV[2578].Len() = 1\n",
"WCnComV[2578][0] = 6777\n",
"WCnComV[2579].Len() = 1\n",
"WCnComV[2579][0] = 6776\n",
"WCnComV[2580].Len() = 1\n",
"WCnComV[2580][0] = 6775\n",
"WCnComV[2581].Len() = 1\n",
"WCnComV[2581][0] = 6773\n",
"WCnComV[2582].Len() = 1\n",
"WCnComV[2582][0] = 6769\n",
"WCnComV[2583].Len() = 1\n",
"WCnComV[2583][0] = 6767\n",
"WCnComV[2584].Len() = 1\n",
"WCnComV[2584][0] = 6756\n",
"WCnComV[2585].Len() = 1\n",
"WCnComV[2585][0] = 6750\n",
"WCnComV[2586].Len() = 1\n",
"WCnComV[2586][0] = 6749\n",
"WCnComV[2587].Len() = 1\n",
"WCnComV[2587][0] = 6747\n",
"WCnComV[2588].Len() = 1\n",
"WCnComV[2588][0] = 6746\n",
"WCnComV[2589].Len() = 1\n",
"WCnComV[2589][0] = 6744\n",
"WCnComV[2590].Len() = 1\n",
"WCnComV[2590][0] = 6743\n",
"WCnComV[2591].Len() = 1\n",
"WCnComV[2591][0] = 6742\n",
"WCnComV[2592].Len() = 1\n",
"WCnComV[2592][0] = 6735\n",
"WCnComV[2593].Len() = 1\n",
"WCnComV[2593][0] = 6733\n",
"WCnComV[2594].Len() = 1\n",
"WCnComV[2594][0] = 6732\n",
"WCnComV[2595].Len() = 1\n",
"WCnComV[2595][0] = 6730\n",
"WCnComV[2596].Len() = 1\n",
"WCnComV[2596][0] = 6727\n",
"WCnComV[2597].Len() = 1\n",
"WCnComV[2597][0] = 6718\n",
"WCnComV[2598].Len() = 1\n",
"WCnComV[2598][0] = 6716\n",
"WCnComV[2599].Len() = 1\n",
"WCnComV[2599][0] = 6713\n",
"WCnComV[2600].Len() = 1\n",
"WCnComV[2600][0] = 6703\n",
"WCnComV[2601].Len() = 1\n",
"WCnComV[2601][0] = 6702\n",
"WCnComV[2602].Len() = 1\n",
"WCnComV[2602][0] = 6699\n",
"WCnComV[2603].Len() = 1\n",
"WCnComV[2603][0] = 6697\n",
"WCnComV[2604].Len() = 1\n",
"WCnComV[2604][0] = 6696\n",
"WCnComV[2605].Len() = 1\n",
"WCnComV[2605][0] = 6694\n",
"WCnComV[2606].Len() = 1\n",
"WCnComV[2606][0] = 6691\n",
"WCnComV[2607].Len() = 1\n",
"WCnComV[2607][0] = 6690\n",
"WCnComV[2608].Len() = 1\n",
"WCnComV[2608][0] = 6687\n",
"WCnComV[2609].Len() = 1\n",
"WCnComV[2609][0] = 6685\n",
"WCnComV[2610].Len() = 1\n",
"WCnComV[2610][0] = 6683\n",
"WCnComV[2611].Len() = 1\n",
"WCnComV[2611][0] = 6681\n",
"WCnComV[2612].Len() = 1\n",
"WCnComV[2612][0] = 6680\n",
"WCnComV[2613].Len() = 1\n",
"WCnComV[2613][0] = 6675\n",
"WCnComV[2614].Len() = 1\n",
"WCnComV[2614][0] = 6672\n",
"WCnComV[2615].Len() = 1\n",
"WCnComV[2615][0] = 6666\n",
"WCnComV[2616].Len() = 1\n",
"WCnComV[2616][0] = 6665\n",
"WCnComV[2617].Len() = 1\n",
"WCnComV[2617][0] = 6662\n",
"WCnComV[2618].Len() = 1\n",
"WCnComV[2618][0] = 6661\n",
"WCnComV[2619].Len() = 1\n",
"WCnComV[2619][0] = 6658\n",
"WCnComV[2620].Len() = 1\n",
"WCnComV[2620][0] = 6657\n",
"WCnComV[2621].Len() = 1\n",
"WCnComV[2621][0] = 6656\n",
"WCnComV[2622].Len() = 1\n",
"WCnComV[2622][0] = 6653\n",
"WCnComV[2623].Len() = 1\n",
"WCnComV[2623][0] = 6648\n",
"WCnComV[2624].Len() = 1\n",
"WCnComV[2624][0] = 6645\n",
"WCnComV[2625].Len() = 1\n",
"WCnComV[2625][0] = 6640\n",
"WCnComV[2626].Len() = 1\n",
"WCnComV[2626][0] = 6636\n",
"WCnComV[2627].Len() = 1\n",
"WCnComV[2627][0] = 6634\n",
"WCnComV[2628].Len() = 1\n",
"WCnComV[2628][0] = 6632\n",
"WCnComV[2629].Len() = 1\n",
"WCnComV[2629][0] = 6631\n",
"WCnComV[2630].Len() = 1\n",
"WCnComV[2630][0] = 6630\n",
"WCnComV[2631].Len() = 1\n",
"WCnComV[2631][0] = 6621\n",
"WCnComV[2632].Len() = 1\n",
"WCnComV[2632][0] = 6617\n",
"WCnComV[2633].Len() = 1\n",
"WCnComV[2633][0] = 6615\n",
"WCnComV[2634].Len() = 1\n",
"WCnComV[2634][0] = 6614\n",
"WCnComV[2635].Len() = 1\n",
"WCnComV[2635][0] = 6609\n",
"WCnComV[2636].Len() = 1\n",
"WCnComV[2636][0] = 6608\n",
"WCnComV[2637].Len() = 1\n",
"WCnComV[2637][0] = 6606\n",
"WCnComV[2638].Len() = 1\n",
"WCnComV[2638][0] = 6605\n",
"WCnComV[2639].Len() = 1\n",
"WCnComV[2639][0] = 6602\n",
"WCnComV[2640].Len() = 1\n",
"WCnComV[2640][0] = 6601\n",
"WCnComV[2641].Len() = 1\n",
"WCnComV[2641][0] = 6598\n",
"WCnComV[2642].Len() = 1\n",
"WCnComV[2642][0] = 6590\n",
"WCnComV[2643].Len() = 1\n",
"WCnComV[2643][0] = 6584\n",
"WCnComV[2644].Len() = 1\n",
"WCnComV[2644][0] = 6583\n",
"WCnComV[2645].Len() = 1\n",
"WCnComV[2645][0] = 6580\n",
"WCnComV[2646].Len() = 1\n",
"WCnComV[2646][0] = 6579\n",
"WCnComV[2647].Len() = 1\n",
"WCnComV[2647][0] = 6578\n",
"WCnComV[2648].Len() = 1\n",
"WCnComV[2648][0] = 6576\n",
"WCnComV[2649].Len() = 1\n",
"WCnComV[2649][0] = 6575\n",
"WCnComV[2650].Len() = 1\n",
"WCnComV[2650][0] = 6574\n",
"WCnComV[2651].Len() = 1\n",
"WCnComV[2651][0] = 6570\n",
"WCnComV[2652].Len() = 1\n",
"WCnComV[2652][0] = 6569\n",
"WCnComV[2653].Len() = 1\n",
"WCnComV[2653][0] = 6568\n",
"WCnComV[2654].Len() = 1\n",
"WCnComV[2654][0] = 6565\n",
"WCnComV[2655].Len() = 1\n",
"WCnComV[2655][0] = 6562\n",
"WCnComV[2656].Len() = 1\n",
"WCnComV[2656][0] = 6556\n",
"WCnComV[2657].Len() = 1\n",
"WCnComV[2657][0] = 6554\n",
"WCnComV[2658].Len() = 1\n",
"WCnComV[2658][0] = 6550\n",
"WCnComV[2659].Len() = 1\n",
"WCnComV[2659][0] = 6546\n",
"WCnComV[2660].Len() = 1\n",
"WCnComV[2660][0] = 6541\n",
"WCnComV[2661].Len() = 1\n",
"WCnComV[2661][0] = 6538\n",
"WCnComV[2662].Len() = 1\n",
"WCnComV[2662][0] = 6533\n",
"WCnComV[2663].Len() = 1\n",
"WCnComV[2663][0] = 6526\n",
"WCnComV[2664].Len() = 1\n",
"WCnComV[2664][0] = 6524\n",
"WCnComV[2665].Len() = 1\n",
"WCnComV[2665][0] = 6523\n",
"WCnComV[2666].Len() = 1\n",
"WCnComV[2666][0] = 6521\n",
"WCnComV[2667].Len() = 1\n",
"WCnComV[2667][0] = 6517\n",
"WCnComV[2668].Len() = 1\n",
"WCnComV[2668][0] = 6514\n",
"WCnComV[2669].Len() = 1\n",
"WCnComV[2669][0] = 6508\n",
"WCnComV[2670].Len() = 1\n",
"WCnComV[2670][0] = 6505\n",
"WCnComV[2671].Len() = 1\n",
"WCnComV[2671][0] = 6504\n",
"WCnComV[2672].Len() = 1\n",
"WCnComV[2672][0] = 6503\n",
"WCnComV[2673].Len() = 1\n",
"WCnComV[2673][0] = 6500\n",
"WCnComV[2674].Len() = 1\n",
"WCnComV[2674][0] = 6497\n",
"WCnComV[2675].Len() = 1\n",
"WCnComV[2675][0] = 6495\n",
"WCnComV[2676].Len() = 1\n",
"WCnComV[2676][0] = 6485\n",
"WCnComV[2677].Len() = 1\n",
"WCnComV[2677][0] = 6484\n",
"WCnComV[2678].Len() = 1\n",
"WCnComV[2678][0] = 6482\n",
"WCnComV[2679].Len() = 1\n",
"WCnComV[2679][0] = 6480\n",
"WCnComV[2680].Len() = 1\n",
"WCnComV[2680][0] = 6479\n",
"WCnComV[2681].Len() = 1\n",
"WCnComV[2681][0] = 6478\n",
"WCnComV[2682].Len() = 1\n",
"WCnComV[2682][0] = 6477\n",
"WCnComV[2683].Len() = 1\n",
"WCnComV[2683][0] = 6476\n",
"WCnComV[2684].Len() = 1\n",
"WCnComV[2684][0] = 6473\n",
"WCnComV[2685].Len() = 1\n",
"WCnComV[2685][0] = 6472\n",
"WCnComV[2686].Len() = 1\n",
"WCnComV[2686][0] = 6469\n",
"WCnComV[2687].Len() = 1\n",
"WCnComV[2687][0] = 6467\n",
"WCnComV[2688].Len() = 1\n",
"WCnComV[2688][0] = 6465\n",
"WCnComV[2689].Len() = 1\n",
"WCnComV[2689][0] = 6464\n",
"WCnComV[2690].Len() = 1\n",
"WCnComV[2690][0] = 6463\n",
"WCnComV[2691].Len() = 1\n",
"WCnComV[2691][0] = 6455\n",
"WCnComV[2692].Len() = 1\n",
"WCnComV[2692][0] = 6454\n",
"WCnComV[2693].Len() = 1\n",
"WCnComV[2693][0] = 6446\n",
"WCnComV[2694].Len() = 1\n",
"WCnComV[2694][0] = 6442\n",
"WCnComV[2695].Len() = 1\n",
"WCnComV[2695][0] = 6439\n",
"WCnComV[2696].Len() = 1\n",
"WCnComV[2696][0] = 6436\n",
"WCnComV[2697].Len() = 1\n",
"WCnComV[2697][0] = 6434\n",
"WCnComV[2698].Len() = 1\n",
"WCnComV[2698][0] = 6433\n",
"WCnComV[2699].Len() = 1\n",
"WCnComV[2699][0] = 6430\n",
"WCnComV[2700].Len() = 1\n",
"WCnComV[2700][0] = 6429\n",
"WCnComV[2701].Len() = 1\n",
"WCnComV[2701][0] = 6427\n",
"WCnComV[2702].Len() = 1\n",
"WCnComV[2702][0] = 6426\n",
"WCnComV[2703].Len() = 1\n",
"WCnComV[2703][0] = 6419\n",
"WCnComV[2704].Len() = 1\n",
"WCnComV[2704][0] = 6415\n",
"WCnComV[2705].Len() = 1\n",
"WCnComV[2705][0] = 6414\n",
"WCnComV[2706].Len() = 1\n",
"WCnComV[2706][0] = 6411\n",
"WCnComV[2707].Len() = 1\n",
"WCnComV[2707][0] = 6409\n",
"WCnComV[2708].Len() = 1\n",
"WCnComV[2708][0] = 6404\n",
"WCnComV[2709].Len() = 1\n",
"WCnComV[2709][0] = 6403\n",
"WCnComV[2710].Len() = 1\n",
"WCnComV[2710][0] = 6402\n",
"WCnComV[2711].Len() = 1\n",
"WCnComV[2711][0] = 6398\n",
"WCnComV[2712].Len() = 1\n",
"WCnComV[2712][0] = 6397\n",
"WCnComV[2713].Len() = 1\n",
"WCnComV[2713][0] = 6396\n",
"WCnComV[2714].Len() = 1\n",
"WCnComV[2714][0] = 6387\n",
"WCnComV[2715].Len() = 1\n",
"WCnComV[2715][0] = 6383\n",
"WCnComV[2716].Len() = 1\n",
"WCnComV[2716][0] = 6382\n",
"WCnComV[2717].Len() = 1\n",
"WCnComV[2717][0] = 6381\n",
"WCnComV[2718].Len() = 1\n",
"WCnComV[2718][0] = 6379\n",
"WCnComV[2719].Len() = 1\n",
"WCnComV[2719][0] = 6378\n",
"WCnComV[2720].Len() = 1\n",
"WCnComV[2720][0] = 6376\n",
"WCnComV[2721].Len() = 1\n",
"WCnComV[2721][0] = 6374\n",
"WCnComV[2722].Len() = 1\n",
"WCnComV[2722][0] = 6373\n",
"WCnComV[2723].Len() = 1\n",
"WCnComV[2723][0] = 6370\n",
"WCnComV[2724].Len() = 1\n",
"WCnComV[2724][0] = 6367\n",
"WCnComV[2725].Len() = 1\n",
"WCnComV[2725][0] = 6366\n",
"WCnComV[2726].Len() = 1\n",
"WCnComV[2726][0] = 6363\n",
"WCnComV[2727].Len() = 1\n",
"WCnComV[2727][0] = 6362\n",
"WCnComV[2728].Len() = 1\n",
"WCnComV[2728][0] = 6361\n",
"WCnComV[2729].Len() = 1\n",
"WCnComV[2729][0] = 6358\n",
"WCnComV[2730].Len() = 1\n",
"WCnComV[2730][0] = 6357\n",
"WCnComV[2731].Len() = 1\n",
"WCnComV[2731][0] = 6356\n",
"WCnComV[2732].Len() = 1\n",
"WCnComV[2732][0] = 6350\n",
"WCnComV[2733].Len() = 1\n",
"WCnComV[2733][0] = 6346\n",
"WCnComV[2734].Len() = 1\n",
"WCnComV[2734][0] = 6345\n",
"WCnComV[2735].Len() = 1\n",
"WCnComV[2735][0] = 6343\n",
"WCnComV[2736].Len() = 1\n",
"WCnComV[2736][0] = 6333\n",
"WCnComV[2737].Len() = 1\n",
"WCnComV[2737][0] = 6319\n",
"WCnComV[2738].Len() = 1\n",
"WCnComV[2738][0] = 6315\n",
"WCnComV[2739].Len() = 1\n",
"WCnComV[2739][0] = 6314\n",
"WCnComV[2740].Len() = 1\n",
"WCnComV[2740][0] = 6312\n",
"WCnComV[2741].Len() = 1\n",
"WCnComV[2741][0] = 6309\n",
"WCnComV[2742].Len() = 1\n",
"WCnComV[2742][0] = 6306\n",
"WCnComV[2743].Len() = 1\n",
"WCnComV[2743][0] = 6304\n",
"WCnComV[2744].Len() = 1\n",
"WCnComV[2744][0] = 6302\n",
"WCnComV[2745].Len() = 1\n",
"WCnComV[2745][0] = 6292\n",
"WCnComV[2746].Len() = 1\n",
"WCnComV[2746][0] = 6291\n",
"WCnComV[2747].Len() = 1\n",
"WCnComV[2747][0] = 6289\n",
"WCnComV[2748].Len() = 1\n",
"WCnComV[2748][0] = 6280\n",
"WCnComV[2749].Len() = 1\n",
"WCnComV[2749][0] = 6275\n",
"WCnComV[2750].Len() = 1\n",
"WCnComV[2750][0] = 6268\n",
"WCnComV[2751].Len() = 1\n",
"WCnComV[2751][0] = 6267\n",
"WCnComV[2752].Len() = 1\n",
"WCnComV[2752][0] = 6265\n",
"WCnComV[2753].Len() = 1\n",
"WCnComV[2753][0] = 6264\n",
"WCnComV[2754].Len() = 1\n",
"WCnComV[2754][0] = 6262\n",
"WCnComV[2755].Len() = 1\n",
"WCnComV[2755][0] = 6260\n",
"WCnComV[2756].Len() = 1\n",
"WCnComV[2756][0] = 6259\n",
"WCnComV[2757].Len() = 1\n",
"WCnComV[2757][0] = 6257\n",
"WCnComV[2758].Len() = 1\n",
"WCnComV[2758][0] = 6255\n",
"WCnComV[2759].Len() = 1\n",
"WCnComV[2759][0] = 6253\n",
"WCnComV[2760].Len() = 1\n",
"WCnComV[2760][0] = 6252\n",
"WCnComV[2761].Len() = 1\n",
"WCnComV[2761][0] = 6251\n",
"WCnComV[2762].Len() = 1\n",
"WCnComV[2762][0] = 6249\n",
"WCnComV[2763].Len() = 1\n",
"WCnComV[2763][0] = 6248\n",
"WCnComV[2764].Len() = 1\n",
"WCnComV[2764][0] = 6247\n",
"WCnComV[2765].Len() = 1\n",
"WCnComV[2765][0] = 6243\n",
"WCnComV[2766].Len() = 1\n",
"WCnComV[2766][0] = 6238\n",
"WCnComV[2767].Len() = 1\n",
"WCnComV[2767][0] = 6236\n",
"WCnComV[2768].Len() = 1\n",
"WCnComV[2768][0] = 6234\n",
"WCnComV[2769].Len() = 1\n",
"WCnComV[2769][0] = 6232\n",
"WCnComV[2770].Len() = 1\n",
"WCnComV[2770][0] = 6224\n",
"WCnComV[2771].Len() = 1\n",
"WCnComV[2771][0] = 6223\n",
"WCnComV[2772].Len() = 1\n",
"WCnComV[2772][0] = 6220\n",
"WCnComV[2773].Len() = 1\n",
"WCnComV[2773][0] = 6219\n",
"WCnComV[2774].Len() = 1\n",
"WCnComV[2774][0] = 6217\n",
"WCnComV[2775].Len() = 1\n",
"WCnComV[2775][0] = 6216\n",
"WCnComV[2776].Len() = 1\n",
"WCnComV[2776][0] = 6215\n",
"WCnComV[2777].Len() = 1\n",
"WCnComV[2777][0] = 6214\n",
"WCnComV[2778].Len() = 1\n",
"WCnComV[2778][0] = 6212\n",
"WCnComV[2779].Len() = 1\n",
"WCnComV[2779][0] = 6211\n",
"WCnComV[2780].Len() = 1\n",
"WCnComV[2780][0] = 6208\n",
"WCnComV[2781].Len() = 1\n",
"WCnComV[2781][0] = 6203\n",
"WCnComV[2782].Len() = 1\n",
"WCnComV[2782][0] = 6201\n",
"WCnComV[2783].Len() = 1\n",
"WCnComV[2783][0] = 6200\n",
"WCnComV[2784].Len() = 1\n",
"WCnComV[2784][0] = 6195\n",
"WCnComV[2785].Len() = 1\n",
"WCnComV[2785][0] = 6193\n",
"WCnComV[2786].Len() = 1\n",
"WCnComV[2786][0] = 6192\n",
"WCnComV[2787].Len() = 1\n",
"WCnComV[2787][0] = 6189\n",
"WCnComV[2788].Len() = 1\n",
"WCnComV[2788][0] = 6188\n",
"WCnComV[2789].Len() = 1\n",
"WCnComV[2789][0] = 6187\n",
"WCnComV[2790].Len() = 1\n",
"WCnComV[2790][0] = 6186\n",
"WCnComV[2791].Len() = 1\n",
"WCnComV[2791][0] = 6184\n",
"WCnComV[2792].Len() = 1\n",
"WCnComV[2792][0] = 6182\n",
"WCnComV[2793].Len() = 1\n",
"WCnComV[2793][0] = 6181\n",
"WCnComV[2794].Len() = 1\n",
"WCnComV[2794][0] = 6177\n",
"WCnComV[2795].Len() = 1\n",
"WCnComV[2795][0] = 6171\n",
"WCnComV[2796].Len() = 1\n",
"WCnComV[2796][0] = 6170\n",
"WCnComV[2797].Len() = 1\n",
"WCnComV[2797][0] = 6167\n",
"WCnComV[2798].Len() = 1\n",
"WCnComV[2798][0] = 6166\n",
"WCnComV[2799].Len() = 1\n",
"WCnComV[2799][0] = 6164\n",
"WCnComV[2800].Len() = 1\n",
"WCnComV[2800][0] = 6161\n",
"WCnComV[2801].Len() = 1\n",
"WCnComV[2801][0] = 6158\n",
"WCnComV[2802].Len() = 1\n",
"WCnComV[2802][0] = 6157\n",
"WCnComV[2803].Len() = 1\n",
"WCnComV[2803][0] = 6153\n",
"WCnComV[2804].Len() = 1\n",
"WCnComV[2804][0] = 6149\n",
"WCnComV[2805].Len() = 1\n",
"WCnComV[2805][0] = 6147\n",
"WCnComV[2806].Len() = 1\n",
"WCnComV[2806][0] = 6146\n",
"WCnComV[2807].Len() = 1\n",
"WCnComV[2807][0] = 6145\n",
"WCnComV[2808].Len() = 1\n",
"WCnComV[2808][0] = 6144\n",
"WCnComV[2809].Len() = 1\n",
"WCnComV[2809][0] = 6139\n",
"WCnComV[2810].Len() = 1\n",
"WCnComV[2810][0] = 6138\n",
"WCnComV[2811].Len() = 1\n",
"WCnComV[2811][0] = 6137\n",
"WCnComV[2812].Len() = 1\n",
"WCnComV[2812][0] = 6136\n",
"WCnComV[2813].Len() = 1\n",
"WCnComV[2813][0] = 6130\n",
"WCnComV[2814].Len() = 1\n",
"WCnComV[2814][0] = 6127\n",
"WCnComV[2815].Len() = 1\n",
"WCnComV[2815][0] = 6126\n",
"WCnComV[2816].Len() = 1\n",
"WCnComV[2816][0] = 6123\n",
"WCnComV[2817].Len() = 1\n",
"WCnComV[2817][0] = 6119\n",
"WCnComV[2818].Len() = 1\n",
"WCnComV[2818][0] = 6114\n",
"WCnComV[2819].Len() = 1\n",
"WCnComV[2819][0] = 6111\n",
"WCnComV[2820].Len() = 1\n",
"WCnComV[2820][0] = 6109\n",
"WCnComV[2821].Len() = 1\n",
"WCnComV[2821][0] = 6108\n",
"WCnComV[2822].Len() = 1\n",
"WCnComV[2822][0] = 6106\n",
"WCnComV[2823].Len() = 1\n",
"WCnComV[2823][0] = 6105\n",
"WCnComV[2824].Len() = 1\n",
"WCnComV[2824][0] = 6102\n",
"WCnComV[2825].Len() = 1\n",
"WCnComV[2825][0] = 6100\n",
"WCnComV[2826].Len() = 1\n",
"WCnComV[2826][0] = 6096\n",
"WCnComV[2827].Len() = 1\n",
"WCnComV[2827][0] = 6092\n",
"WCnComV[2828].Len() = 1\n",
"WCnComV[2828][0] = 6088\n",
"WCnComV[2829].Len() = 1\n",
"WCnComV[2829][0] = 6087\n",
"WCnComV[2830].Len() = 1\n",
"WCnComV[2830][0] = 6084\n",
"WCnComV[2831].Len() = 1\n",
"WCnComV[2831][0] = 6082\n",
"WCnComV[2832].Len() = 1\n",
"WCnComV[2832][0] = 6081\n",
"WCnComV[2833].Len() = 1\n",
"WCnComV[2833][0] = 6076\n",
"WCnComV[2834].Len() = 1\n",
"WCnComV[2834][0] = 6072\n",
"WCnComV[2835].Len() = 1\n",
"WCnComV[2835][0] = 6064\n",
"WCnComV[2836].Len() = 1\n",
"WCnComV[2836][0] = 6059\n",
"WCnComV[2837].Len() = 1\n",
"WCnComV[2837][0] = 6056\n",
"WCnComV[2838].Len() = 1\n",
"WCnComV[2838][0] = 6054\n",
"WCnComV[2839].Len() = 1\n",
"WCnComV[2839][0] = 6048\n",
"WCnComV[2840].Len() = 1\n",
"WCnComV[2840][0] = 6046\n",
"WCnComV[2841].Len() = 1\n",
"WCnComV[2841][0] = 6045\n",
"WCnComV[2842].Len() = 1\n",
"WCnComV[2842][0] = 6044\n",
"WCnComV[2843].Len() = 1\n",
"WCnComV[2843][0] = 6042\n",
"WCnComV[2844].Len() = 1\n",
"WCnComV[2844][0] = 6041\n",
"WCnComV[2845].Len() = 1\n",
"WCnComV[2845][0] = 6039\n",
"WCnComV[2846].Len() = 1\n",
"WCnComV[2846][0] = 6032\n",
"WCnComV[2847].Len() = 1\n",
"WCnComV[2847][0] = 6031\n",
"WCnComV[2848].Len() = 1\n",
"WCnComV[2848][0] = 6029\n",
"WCnComV[2849].Len() = 1\n",
"WCnComV[2849][0] = 6028\n",
"WCnComV[2850].Len() = 1\n",
"WCnComV[2850][0] = 6027\n",
"WCnComV[2851].Len() = 1\n",
"WCnComV[2851][0] = 6021\n",
"WCnComV[2852].Len() = 1\n",
"WCnComV[2852][0] = 6019\n",
"WCnComV[2853].Len() = 1\n",
"WCnComV[2853][0] = 6014\n",
"WCnComV[2854].Len() = 1\n",
"WCnComV[2854][0] = 6013\n",
"WCnComV[2855].Len() = 1\n",
"WCnComV[2855][0] = 6007\n",
"WCnComV[2856].Len() = 1\n",
"WCnComV[2856][0] = 6006\n",
"WCnComV[2857].Len() = 1\n",
"WCnComV[2857][0] = 5998\n",
"WCnComV[2858].Len() = 1\n",
"WCnComV[2858][0] = 5997\n",
"WCnComV[2859].Len() = 1\n",
"WCnComV[2859][0] = 5996\n",
"WCnComV[2860].Len() = 1\n",
"WCnComV[2860][0] = 5995\n",
"WCnComV[2861].Len() = 1\n",
"WCnComV[2861][0] = 5993\n",
"WCnComV[2862].Len() = 1\n",
"WCnComV[2862][0] = 5989\n",
"WCnComV[2863].Len() = 1\n",
"WCnComV[2863][0] = 5981\n",
"WCnComV[2864].Len() = 1\n",
"WCnComV[2864][0] = 5978\n",
"WCnComV[2865].Len() = 1\n",
"WCnComV[2865][0] = 5971\n",
"WCnComV[2866].Len() = 1\n",
"WCnComV[2866][0] = 5970\n",
"WCnComV[2867].Len() = 1\n",
"WCnComV[2867][0] = 5967\n",
"WCnComV[2868].Len() = 1\n",
"WCnComV[2868][0] = 5966\n",
"WCnComV[2869].Len() = 1\n",
"WCnComV[2869][0] = 5964\n",
"WCnComV[2870].Len() = 1\n",
"WCnComV[2870][0] = 5961\n",
"WCnComV[2871].Len() = 1\n",
"WCnComV[2871][0] = 5960\n",
"WCnComV[2872].Len() = 1\n",
"WCnComV[2872][0] = 5956\n",
"WCnComV[2873].Len() = 1\n",
"WCnComV[2873][0] = 5955\n",
"WCnComV[2874].Len() = 1\n",
"WCnComV[2874][0] = 5953\n",
"WCnComV[2875].Len() = 1\n",
"WCnComV[2875][0] = 5952\n",
"WCnComV[2876].Len() = 1\n",
"WCnComV[2876][0] = 5951\n",
"WCnComV[2877].Len() = 1\n",
"WCnComV[2877][0] = 5945\n",
"WCnComV[2878].Len() = 1\n",
"WCnComV[2878][0] = 5941\n",
"WCnComV[2879].Len() = 1\n",
"WCnComV[2879][0] = 5940\n",
"WCnComV[2880].Len() = 1\n",
"WCnComV[2880][0] = 5939\n",
"WCnComV[2881].Len() = 1\n",
"WCnComV[2881][0] = 5937\n",
"WCnComV[2882].Len() = 1\n",
"WCnComV[2882][0] = 5934\n",
"WCnComV[2883].Len() = 1\n",
"WCnComV[2883][0] = 5933\n",
"WCnComV[2884].Len() = 1\n",
"WCnComV[2884][0] = 5932\n",
"WCnComV[2885].Len() = 1\n",
"WCnComV[2885][0] = 5931\n",
"WCnComV[2886].Len() = 1\n",
"WCnComV[2886][0] = 5930\n",
"WCnComV[2887].Len() = 1\n",
"WCnComV[2887][0] = 5923\n",
"WCnComV[2888].Len() = 1\n",
"WCnComV[2888][0] = 5920\n",
"WCnComV[2889].Len() = 1\n",
"WCnComV[2889][0] = 5919\n",
"WCnComV[2890].Len() = 1\n",
"WCnComV[2890][0] = 5917\n",
"WCnComV[2891].Len() = 1\n",
"WCnComV[2891][0] = 5915\n",
"WCnComV[2892].Len() = 1\n",
"WCnComV[2892][0] = 5910\n",
"WCnComV[2893].Len() = 1\n",
"WCnComV[2893][0] = 5907\n",
"WCnComV[2894].Len() = 1\n",
"WCnComV[2894][0] = 5903\n",
"WCnComV[2895].Len() = 1\n",
"WCnComV[2895][0] = 5900\n",
"WCnComV[2896].Len() = 1\n",
"WCnComV[2896][0] = 5898\n",
"WCnComV[2897].Len() = 1\n",
"WCnComV[2897][0] = 5895\n",
"WCnComV[2898].Len() = 1\n",
"WCnComV[2898][0] = 5893\n",
"WCnComV[2899].Len() = 1\n",
"WCnComV[2899][0] = 5891\n",
"WCnComV[2900].Len() = 1\n",
"WCnComV[2900][0] = 5890\n",
"WCnComV[2901].Len() = 1\n",
"WCnComV[2901][0] = 5887\n",
"WCnComV[2902].Len() = 1\n",
"WCnComV[2902][0] = 5885\n",
"WCnComV[2903].Len() = 1\n",
"WCnComV[2903][0] = 5881\n",
"WCnComV[2904].Len() = 1\n",
"WCnComV[2904][0] = 5875\n",
"WCnComV[2905].Len() = 1\n",
"WCnComV[2905][0] = 5873\n",
"WCnComV[2906].Len() = 1\n",
"WCnComV[2906][0] = 5872\n",
"WCnComV[2907].Len() = 1\n",
"WCnComV[2907][0] = 5871\n",
"WCnComV[2908].Len() = 1\n",
"WCnComV[2908][0] = 5868\n",
"WCnComV[2909].Len() = 1\n",
"WCnComV[2909][0] = 5866\n",
"WCnComV[2910].Len() = 1\n",
"WCnComV[2910][0] = 5865\n",
"WCnComV[2911].Len() = 1\n",
"WCnComV[2911][0] = 5863\n",
"WCnComV[2912].Len() = 1\n",
"WCnComV[2912][0] = 5860\n",
"WCnComV[2913].Len() = 1\n",
"WCnComV[2913][0] = 5857\n",
"WCnComV[2914].Len() = 1\n",
"WCnComV[2914][0] = 5854\n",
"WCnComV[2915].Len() = 1\n",
"WCnComV[2915][0] = 5851\n",
"WCnComV[2916].Len() = 1\n",
"WCnComV[2916][0] = 5846\n",
"WCnComV[2917].Len() = 1\n",
"WCnComV[2917][0] = 5844\n",
"WCnComV[2918].Len() = 1\n",
"WCnComV[2918][0] = 5831\n",
"WCnComV[2919].Len() = 1\n",
"WCnComV[2919][0] = 5830\n",
"WCnComV[2920].Len() = 1\n",
"WCnComV[2920][0] = 5824\n",
"WCnComV[2921].Len() = 1\n",
"WCnComV[2921][0] = 5821\n",
"WCnComV[2922].Len() = 1\n",
"WCnComV[2922][0] = 5819\n",
"WCnComV[2923].Len() = 1\n",
"WCnComV[2923][0] = 5817\n",
"WCnComV[2924].Len() = 1\n",
"WCnComV[2924][0] = 5816\n",
"WCnComV[2925].Len() = 1\n",
"WCnComV[2925][0] = 5814\n",
"WCnComV[2926].Len() = 1\n",
"WCnComV[2926][0] = 5812\n",
"WCnComV[2927].Len() = 1\n",
"WCnComV[2927][0] = 5810\n",
"WCnComV[2928].Len() = 1\n",
"WCnComV[2928][0] = 5805\n",
"WCnComV[2929].Len() = 1\n",
"WCnComV[2929][0] = 5804\n",
"WCnComV[2930].Len() = 1\n",
"WCnComV[2930][0] = 5803\n",
"WCnComV[2931].Len() = 1\n",
"WCnComV[2931][0] = 5799\n",
"WCnComV[2932].Len() = 1\n",
"WCnComV[2932][0] = 5797\n",
"WCnComV[2933].Len() = 1\n",
"WCnComV[2933][0] = 5796\n",
"WCnComV[2934].Len() = 1\n",
"WCnComV[2934][0] = 5795\n",
"WCnComV[2935].Len() = 1\n",
"WCnComV[2935][0] = 5793\n",
"WCnComV[2936].Len() = 1\n",
"WCnComV[2936][0] = 5787\n",
"WCnComV[2937].Len() = 1\n",
"WCnComV[2937][0] = 5785\n",
"WCnComV[2938].Len() = 1\n",
"WCnComV[2938][0] = 5780\n",
"WCnComV[2939].Len() = 1\n",
"WCnComV[2939][0] = 5777\n",
"WCnComV[2940].Len() = 1\n",
"WCnComV[2940][0] = 5775\n",
"WCnComV[2941].Len() = 1\n",
"WCnComV[2941][0] = 5771\n",
"WCnComV[2942].Len() = 1\n",
"WCnComV[2942][0] = 5770\n",
"WCnComV[2943].Len() = 1\n",
"WCnComV[2943][0] = 5765\n",
"WCnComV[2944].Len() = 1\n",
"WCnComV[2944][0] = 5761\n",
"WCnComV[2945].Len() = 1\n",
"WCnComV[2945][0] = 5757\n",
"WCnComV[2946].Len() = 1\n",
"WCnComV[2946][0] = 5755\n",
"WCnComV[2947].Len() = 1\n",
"WCnComV[2947][0] = 5750\n",
"WCnComV[2948].Len() = 1\n",
"WCnComV[2948][0] = 5744\n",
"WCnComV[2949].Len() = 1\n",
"WCnComV[2949][0] = 5742\n",
"WCnComV[2950].Len() = 1\n",
"WCnComV[2950][0] = 5734\n",
"WCnComV[2951].Len() = 1\n",
"WCnComV[2951][0] = 5733\n",
"WCnComV[2952].Len() = 1\n",
"WCnComV[2952][0] = 5732\n",
"WCnComV[2953].Len() = 1\n",
"WCnComV[2953][0] = 5728\n",
"WCnComV[2954].Len() = 1\n",
"WCnComV[2954][0] = 5726\n",
"WCnComV[2955].Len() = 1\n",
"WCnComV[2955][0] = 5725\n",
"WCnComV[2956].Len() = 1\n",
"WCnComV[2956][0] = 5721\n",
"WCnComV[2957].Len() = 1\n",
"WCnComV[2957][0] = 5720\n",
"WCnComV[2958].Len() = 1\n",
"WCnComV[2958][0] = 5719\n",
"WCnComV[2959].Len() = 1\n",
"WCnComV[2959][0] = 5717\n",
"WCnComV[2960].Len() = 1\n",
"WCnComV[2960][0] = 5710\n",
"WCnComV[2961].Len() = 1\n",
"WCnComV[2961][0] = 5707\n",
"WCnComV[2962].Len() = 1\n",
"WCnComV[2962][0] = 5705\n",
"WCnComV[2963].Len() = 1\n",
"WCnComV[2963][0] = 5704\n",
"WCnComV[2964].Len() = 1\n",
"WCnComV[2964][0] = 5703\n",
"WCnComV[2965].Len() = 1\n",
"WCnComV[2965][0] = 5702\n",
"WCnComV[2966].Len() = 1\n",
"WCnComV[2966][0] = 5701\n",
"WCnComV[2967].Len() = 1\n",
"WCnComV[2967][0] = 5698\n",
"WCnComV[2968].Len() = 1\n",
"WCnComV[2968][0] = 5695\n",
"WCnComV[2969].Len() = 1\n",
"WCnComV[2969][0] = 5692\n",
"WCnComV[2970].Len() = 1\n",
"WCnComV[2970][0] = 5690\n",
"WCnComV[2971].Len() = 1\n",
"WCnComV[2971][0] = 5686\n",
"WCnComV[2972].Len() = 1\n",
"WCnComV[2972][0] = 5685\n",
"WCnComV[2973].Len() = 1\n",
"WCnComV[2973][0] = 5674\n",
"WCnComV[2974].Len() = 1\n",
"WCnComV[2974][0] = 5669\n",
"WCnComV[2975].Len() = 1\n",
"WCnComV[2975][0] = 5668\n",
"WCnComV[2976].Len() = 1\n",
"WCnComV[2976][0] = 5667\n",
"WCnComV[2977].Len() = 1\n",
"WCnComV[2977][0] = 5663\n",
"WCnComV[2978].Len() = 1\n",
"WCnComV[2978][0] = 5661\n",
"WCnComV[2979].Len() = 1\n",
"WCnComV[2979][0] = 5654\n",
"WCnComV[2980].Len() = 1\n",
"WCnComV[2980][0] = 5653\n",
"WCnComV[2981].Len() = 1\n",
"WCnComV[2981][0] = 5651\n",
"WCnComV[2982].Len() = 1\n",
"WCnComV[2982][0] = 5649\n",
"WCnComV[2983].Len() = 1\n",
"WCnComV[2983][0] = 5648\n",
"WCnComV[2984].Len() = 1\n",
"WCnComV[2984][0] = 5647\n",
"WCnComV[2985].Len() = 1\n",
"WCnComV[2985][0] = 5642\n",
"WCnComV[2986].Len() = 1\n",
"WCnComV[2986][0] = 5641\n",
"WCnComV[2987].Len() = 1\n",
"WCnComV[2987][0] = 5637\n",
"WCnComV[2988].Len() = 1\n",
"WCnComV[2988][0] = 5635\n",
"WCnComV[2989].Len() = 1\n",
"WCnComV[2989][0] = 5631\n",
"WCnComV[2990].Len() = 1\n",
"WCnComV[2990][0] = 5628\n",
"WCnComV[2991].Len() = 1\n",
"WCnComV[2991][0] = 5624\n",
"WCnComV[2992].Len() = 1\n",
"WCnComV[2992][0] = 5617\n",
"WCnComV[2993].Len() = 1\n",
"WCnComV[2993][0] = 5616\n",
"WCnComV[2994].Len() = 1\n",
"WCnComV[2994][0] = 5611\n",
"WCnComV[2995].Len() = 1\n",
"WCnComV[2995][0] = 5610\n",
"WCnComV[2996].Len() = 1\n",
"WCnComV[2996][0] = 5606\n",
"WCnComV[2997].Len() = 1\n",
"WCnComV[2997][0] = 5603\n",
"WCnComV[2998].Len() = 1\n",
"WCnComV[2998][0] = 5600\n",
"WCnComV[2999].Len() = 1\n",
"WCnComV[2999][0] = 5598\n",
"WCnComV[3000].Len() = 1\n",
"WCnComV[3000][0] = 5594\n",
"WCnComV[3001].Len() = 1\n",
"WCnComV[3001][0] = 5587\n",
"WCnComV[3002].Len() = 1\n",
"WCnComV[3002][0] = 5585\n",
"WCnComV[3003].Len() = 1\n",
"WCnComV[3003][0] = 5583\n",
"WCnComV[3004].Len() = 1\n",
"WCnComV[3004][0] = 5582\n",
"WCnComV[3005].Len() = 1\n",
"WCnComV[3005][0] = 5573\n",
"WCnComV[3006].Len() = 1\n",
"WCnComV[3006][0] = 5571\n",
"WCnComV[3007].Len() = 1\n",
"WCnComV[3007][0] = 5568\n",
"WCnComV[3008].Len() = 1\n",
"WCnComV[3008][0] = 5561\n",
"WCnComV[3009].Len() = 1\n",
"WCnComV[3009][0] = 5557\n",
"WCnComV[3010].Len() = 1\n",
"WCnComV[3010][0] = 5556\n",
"WCnComV[3011].Len() = 1\n",
"WCnComV[3011][0] = 5555\n",
"WCnComV[3012].Len() = 1\n",
"WCnComV[3012][0] = 5549\n",
"WCnComV[3013].Len() = 1\n",
"WCnComV[3013][0] = 5544\n",
"WCnComV[3014].Len() = 1\n",
"WCnComV[3014][0] = 5541\n",
"WCnComV[3015].Len() = 1\n",
"WCnComV[3015][0] = 5537\n",
"WCnComV[3016].Len() = 1\n",
"WCnComV[3016][0] = 5531\n",
"WCnComV[3017].Len() = 1\n",
"WCnComV[3017][0] = 5530\n",
"WCnComV[3018].Len() = 1\n",
"WCnComV[3018][0] = 5527\n",
"WCnComV[3019].Len() = 1\n",
"WCnComV[3019][0] = 5524\n",
"WCnComV[3020].Len() = 1\n",
"WCnComV[3020][0] = 5523\n",
"WCnComV[3021].Len() = 1\n",
"WCnComV[3021][0] = 5522\n",
"WCnComV[3022].Len() = 1\n",
"WCnComV[3022][0] = 5521\n",
"WCnComV[3023].Len() = 1\n",
"WCnComV[3023][0] = 5518\n",
"WCnComV[3024].Len() = 1\n",
"WCnComV[3024][0] = 5515\n",
"WCnComV[3025].Len() = 1\n",
"WCnComV[3025][0] = 5508\n",
"WCnComV[3026].Len() = 1\n",
"WCnComV[3026][0] = 5506\n",
"WCnComV[3027].Len() = 1\n",
"WCnComV[3027][0] = 5494\n",
"WCnComV[3028].Len() = 1\n",
"WCnComV[3028][0] = 5493\n",
"WCnComV[3029].Len() = 1\n",
"WCnComV[3029][0] = 5490\n",
"WCnComV[3030].Len() = 1\n",
"WCnComV[3030][0] = 5488\n",
"WCnComV[3031].Len() = 1\n",
"WCnComV[3031][0] = 5486\n",
"WCnComV[3032].Len() = 1\n",
"WCnComV[3032][0] = 5485\n",
"WCnComV[3033].Len() = 1\n",
"WCnComV[3033][0] = 5481\n",
"WCnComV[3034].Len() = 1\n",
"WCnComV[3034][0] = 5480\n",
"WCnComV[3035].Len() = 1\n",
"WCnComV[3035][0] = 5478\n",
"WCnComV[3036].Len() = 1\n",
"WCnComV[3036][0] = 5468\n",
"WCnComV[3037].Len() = 1\n",
"WCnComV[3037][0] = 5464\n",
"WCnComV[3038].Len() = 1\n",
"WCnComV[3038][0] = 5463\n",
"WCnComV[3039].Len() = 1\n",
"WCnComV[3039][0] = 5445\n",
"WCnComV[3040].Len() = 1\n",
"WCnComV[3040][0] = 5440\n",
"WCnComV[3041].Len() = 1\n",
"WCnComV[3041][0] = 5439\n",
"WCnComV[3042].Len() = 1\n",
"WCnComV[3042][0] = 5437\n",
"WCnComV[3043].Len() = 1\n",
"WCnComV[3043][0] = 5434\n",
"WCnComV[3044].Len() = 1\n",
"WCnComV[3044][0] = 5432\n",
"WCnComV[3045].Len() = 1\n",
"WCnComV[3045][0] = 5426\n",
"WCnComV[3046].Len() = 1\n",
"WCnComV[3046][0] = 5423\n",
"WCnComV[3047].Len() = 1\n",
"WCnComV[3047][0] = 5418\n",
"WCnComV[3048].Len() = 1\n",
"WCnComV[3048][0] = 5414\n",
"WCnComV[3049].Len() = 1\n",
"WCnComV[3049][0] = 5411\n",
"WCnComV[3050].Len() = 1\n",
"WCnComV[3050][0] = 5408\n",
"WCnComV[3051].Len() = 1\n",
"WCnComV[3051][0] = 5402\n",
"WCnComV[3052].Len() = 1\n",
"WCnComV[3052][0] = 5394\n",
"WCnComV[3053].Len() = 1\n",
"WCnComV[3053][0] = 5392\n",
"WCnComV[3054].Len() = 1\n",
"WCnComV[3054][0] = 5390\n",
"WCnComV[3055].Len() = 1\n",
"WCnComV[3055][0] = 5386\n",
"WCnComV[3056].Len() = 1\n",
"WCnComV[3056][0] = 5383\n",
"WCnComV[3057].Len() = 1\n",
"WCnComV[3057][0] = 5379\n",
"WCnComV[3058].Len() = 1\n",
"WCnComV[3058][0] = 5378\n",
"WCnComV[3059].Len() = 1\n",
"WCnComV[3059][0] = 5376\n",
"WCnComV[3060].Len() = 1\n",
"WCnComV[3060][0] = 5374\n",
"WCnComV[3061].Len() = 1\n",
"WCnComV[3061][0] = 5371\n",
"WCnComV[3062].Len() = 1\n",
"WCnComV[3062][0] = 5368\n",
"WCnComV[3063].Len() = 1\n",
"WCnComV[3063][0] = 5366\n",
"WCnComV[3064].Len() = 1\n",
"WCnComV[3064][0] = 5363\n",
"WCnComV[3065].Len() = 1\n",
"WCnComV[3065][0] = 5359\n",
"WCnComV[3066].Len() = 1\n",
"WCnComV[3066][0] = 5353\n",
"WCnComV[3067].Len() = 1\n",
"WCnComV[3067][0] = 5352\n",
"WCnComV[3068].Len() = 1\n",
"WCnComV[3068][0] = 5351\n",
"WCnComV[3069].Len() = 1\n",
"WCnComV[3069][0] = 5348\n",
"WCnComV[3070].Len() = 1\n",
"WCnComV[3070][0] = 5343\n",
"WCnComV[3071].Len() = 1\n",
"WCnComV[3071][0] = 5340\n",
"WCnComV[3072].Len() = 1\n",
"WCnComV[3072][0] = 5338\n",
"WCnComV[3073].Len() = 1\n",
"WCnComV[3073][0] = 5337\n",
"WCnComV[3074].Len() = 1\n",
"WCnComV[3074][0] = 5332\n",
"WCnComV[3075].Len() = 1\n",
"WCnComV[3075][0] = 5331\n",
"WCnComV[3076].Len() = 1\n",
"WCnComV[3076][0] = 5328\n",
"WCnComV[3077].Len() = 1\n",
"WCnComV[3077][0] = 5327\n",
"WCnComV[3078].Len() = 1\n",
"WCnComV[3078][0] = 5324\n",
"WCnComV[3079].Len() = 1\n",
"WCnComV[3079][0] = 5322\n",
"WCnComV[3080].Len() = 1\n",
"WCnComV[3080][0] = 5321\n",
"WCnComV[3081].Len() = 1\n",
"WCnComV[3081][0] = 5319\n",
"WCnComV[3082].Len() = 1\n",
"WCnComV[3082][0] = 5318\n",
"WCnComV[3083].Len() = 1\n",
"WCnComV[3083][0] = 5315\n",
"WCnComV[3084].Len() = 1\n",
"WCnComV[3084][0] = 5308\n",
"WCnComV[3085].Len() = 1\n",
"WCnComV[3085][0] = 5305\n",
"WCnComV[3086].Len() = 1\n",
"WCnComV[3086][0] = 5302\n",
"WCnComV[3087].Len() = 1\n",
"WCnComV[3087][0] = 5301\n",
"WCnComV[3088].Len() = 1\n",
"WCnComV[3088][0] = 5300\n",
"WCnComV[3089].Len() = 1\n",
"WCnComV[3089][0] = 5298\n",
"WCnComV[3090].Len() = 1\n",
"WCnComV[3090][0] = 5294\n",
"WCnComV[3091].Len() = 1\n",
"WCnComV[3091][0] = 5291\n",
"WCnComV[3092].Len() = 1\n",
"WCnComV[3092][0] = 5288\n",
"WCnComV[3093].Len() = 1\n",
"WCnComV[3093][0] = 5287\n",
"WCnComV[3094].Len() = 1\n",
"WCnComV[3094][0] = 5286\n",
"WCnComV[3095].Len() = 1\n",
"WCnComV[3095][0] = 5283\n",
"WCnComV[3096].Len() = 1\n",
"WCnComV[3096][0] = 5273\n",
"WCnComV[3097].Len() = 1\n",
"WCnComV[3097][0] = 5272\n",
"WCnComV[3098].Len() = 1\n",
"WCnComV[3098][0] = 5269\n",
"WCnComV[3099].Len() = 1\n",
"WCnComV[3099][0] = 5268\n",
"WCnComV[3100].Len() = 1\n",
"WCnComV[3100][0] = 5267\n",
"WCnComV[3101].Len() = 1\n",
"WCnComV[3101][0] = 5264\n",
"WCnComV[3102].Len() = 1\n",
"WCnComV[3102][0] = 5263\n",
"WCnComV[3103].Len() = 1\n",
"WCnComV[3103][0] = 5262\n",
"WCnComV[3104].Len() = 1\n",
"WCnComV[3104][0] = 5258\n",
"WCnComV[3105].Len() = 1\n",
"WCnComV[3105][0] = 5247\n",
"WCnComV[3106].Len() = 1\n",
"WCnComV[3106][0] = 5245\n",
"WCnComV[3107].Len() = 1\n",
"WCnComV[3107][0] = 5242\n",
"WCnComV[3108].Len() = 1\n",
"WCnComV[3108][0] = 5238\n",
"WCnComV[3109].Len() = 1\n",
"WCnComV[3109][0] = 5237\n",
"WCnComV[3110].Len() = 1\n",
"WCnComV[3110][0] = 5236\n",
"WCnComV[3111].Len() = 1\n",
"WCnComV[3111][0] = 5232\n",
"WCnComV[3112].Len() = 1\n",
"WCnComV[3112][0] = 5231\n",
"WCnComV[3113].Len() = 1\n",
"WCnComV[3113][0] = 5229\n",
"WCnComV[3114].Len() = 1\n",
"WCnComV[3114][0] = 5226\n",
"WCnComV[3115].Len() = 1\n",
"WCnComV[3115][0] = 5223\n",
"WCnComV[3116].Len() = 1\n",
"WCnComV[3116][0] = 5222\n",
"WCnComV[3117].Len() = 1\n",
"WCnComV[3117][0] = 5221\n",
"WCnComV[3118].Len() = 1\n",
"WCnComV[3118][0] = 5219\n",
"WCnComV[3119].Len() = 1\n",
"WCnComV[3119][0] = 5217\n",
"WCnComV[3120].Len() = 1\n",
"WCnComV[3120][0] = 5215\n",
"WCnComV[3121].Len() = 1\n",
"WCnComV[3121][0] = 5213\n",
"WCnComV[3122].Len() = 1\n",
"WCnComV[3122][0] = 5212\n",
"WCnComV[3123].Len() = 1\n",
"WCnComV[3123][0] = 5211\n",
"WCnComV[3124].Len() = 1\n",
"WCnComV[3124][0] = 5210\n",
"WCnComV[3125].Len() = 1\n",
"WCnComV[3125][0] = 5208\n",
"WCnComV[3126].Len() = 1\n",
"WCnComV[3126][0] = 5204\n",
"WCnComV[3127].Len() = 1\n",
"WCnComV[3127][0] = 5200\n",
"WCnComV[3128].Len() = 1\n",
"WCnComV[3128][0] = 5199\n",
"WCnComV[3129].Len() = 1\n",
"WCnComV[3129][0] = 5196\n",
"WCnComV[3130].Len() = 1\n",
"WCnComV[3130][0] = 5195\n",
"WCnComV[3131].Len() = 1\n",
"WCnComV[3131][0] = 5194\n",
"WCnComV[3132].Len() = 1\n",
"WCnComV[3132][0] = 5190\n",
"WCnComV[3133].Len() = 1\n",
"WCnComV[3133][0] = 5187\n",
"WCnComV[3134].Len() = 1\n",
"WCnComV[3134][0] = 5183\n",
"WCnComV[3135].Len() = 1\n",
"WCnComV[3135][0] = 5179\n",
"WCnComV[3136].Len() = 1\n",
"WCnComV[3136][0] = 5178\n",
"WCnComV[3137].Len() = 1\n",
"WCnComV[3137][0] = 5174\n",
"WCnComV[3138].Len() = 1\n",
"WCnComV[3138][0] = 5170\n",
"WCnComV[3139].Len() = 1\n",
"WCnComV[3139][0] = 5169\n",
"WCnComV[3140].Len() = 1\n",
"WCnComV[3140][0] = 5168\n",
"WCnComV[3141].Len() = 1\n",
"WCnComV[3141][0] = 5166\n",
"WCnComV[3142].Len() = 1\n",
"WCnComV[3142][0] = 5165\n",
"WCnComV[3143].Len() = 1\n",
"WCnComV[3143][0] = 5164\n",
"WCnComV[3144].Len() = 1\n",
"WCnComV[3144][0] = 5161\n",
"WCnComV[3145].Len() = 1\n",
"WCnComV[3145][0] = 5158\n",
"WCnComV[3146].Len() = 1\n",
"WCnComV[3146][0] = 5157\n",
"WCnComV[3147].Len() = 1\n",
"WCnComV[3147][0] = 5153\n",
"WCnComV[3148].Len() = 1\n",
"WCnComV[3148][0] = 5152\n",
"WCnComV[3149].Len() = 1\n",
"WCnComV[3149][0] = 5150\n",
"WCnComV[3150].Len() = 1\n",
"WCnComV[3150][0] = 5145\n",
"WCnComV[3151].Len() = 1\n",
"WCnComV[3151][0] = 5140\n",
"WCnComV[3152].Len() = 1\n",
"WCnComV[3152][0] = 5139\n",
"WCnComV[3153].Len() = 1\n",
"WCnComV[3153][0] = 5137\n",
"WCnComV[3154].Len() = 1\n",
"WCnComV[3154][0] = 5135\n",
"WCnComV[3155].Len() = 1\n",
"WCnComV[3155][0] = 5134\n",
"WCnComV[3156].Len() = 1\n",
"WCnComV[3156][0] = 5131\n",
"WCnComV[3157].Len() = 1\n",
"WCnComV[3157][0] = 5129\n",
"WCnComV[3158].Len() = 1\n",
"WCnComV[3158][0] = 5127\n",
"WCnComV[3159].Len() = 1\n",
"WCnComV[3159][0] = 5126\n",
"WCnComV[3160].Len() = 1\n",
"WCnComV[3160][0] = 5125\n",
"WCnComV[3161].Len() = 1\n",
"WCnComV[3161][0] = 5120\n",
"WCnComV[3162].Len() = 1\n",
"WCnComV[3162][0] = 5118\n",
"WCnComV[3163].Len() = 1\n",
"WCnComV[3163][0] = 5116\n",
"WCnComV[3164].Len() = 1\n",
"WCnComV[3164][0] = 5114\n",
"WCnComV[3165].Len() = 1\n",
"WCnComV[3165][0] = 5112\n",
"WCnComV[3166].Len() = 1\n",
"WCnComV[3166][0] = 5111\n",
"WCnComV[3167].Len() = 1\n",
"WCnComV[3167][0] = 5107\n",
"WCnComV[3168].Len() = 1\n",
"WCnComV[3168][0] = 5102\n",
"WCnComV[3169].Len() = 1\n",
"WCnComV[3169][0] = 5101\n",
"WCnComV[3170].Len() = 1\n",
"WCnComV[3170][0] = 5096\n",
"WCnComV[3171].Len() = 1\n",
"WCnComV[3171][0] = 5088\n",
"WCnComV[3172].Len() = 1\n",
"WCnComV[3172][0] = 5085\n",
"WCnComV[3173].Len() = 1\n",
"WCnComV[3173][0] = 5079\n",
"WCnComV[3174].Len() = 1\n",
"WCnComV[3174][0] = 5077\n",
"WCnComV[3175].Len() = 1\n",
"WCnComV[3175][0] = 5075\n",
"WCnComV[3176].Len() = 1\n",
"WCnComV[3176][0] = 5074\n",
"WCnComV[3177].Len() = 1\n",
"WCnComV[3177][0] = 5068\n",
"WCnComV[3178].Len() = 1\n",
"WCnComV[3178][0] = 5067\n",
"WCnComV[3179].Len() = 1\n",
"WCnComV[3179][0] = 5064\n",
"WCnComV[3180].Len() = 1\n",
"WCnComV[3180][0] = 5062\n",
"WCnComV[3181].Len() = 1\n",
"WCnComV[3181][0] = 5060\n",
"WCnComV[3182].Len() = 1\n",
"WCnComV[3182][0] = 5058\n",
"WCnComV[3183].Len() = 1\n",
"WCnComV[3183][0] = 5056\n",
"WCnComV[3184].Len() = 1\n",
"WCnComV[3184][0] = 5055\n",
"WCnComV[3185].Len() = 1\n",
"WCnComV[3185][0] = 5054\n",
"WCnComV[3186].Len() = 1\n",
"WCnComV[3186][0] = 5052\n",
"WCnComV[3187].Len() = 1\n",
"WCnComV[3187][0] = 5048\n",
"WCnComV[3188].Len() = 1\n",
"WCnComV[3188][0] = 5042\n",
"WCnComV[3189].Len() = 1\n",
"WCnComV[3189][0] = 5041\n",
"WCnComV[3190].Len() = 1\n",
"WCnComV[3190][0] = 5033\n",
"WCnComV[3191].Len() = 1\n",
"WCnComV[3191][0] = 5032\n",
"WCnComV[3192].Len() = 1\n",
"WCnComV[3192][0] = 5031\n",
"WCnComV[3193].Len() = 1\n",
"WCnComV[3193][0] = 5030\n",
"WCnComV[3194].Len() = 1\n",
"WCnComV[3194][0] = 5029\n",
"WCnComV[3195].Len() = 1\n",
"WCnComV[3195][0] = 5025\n",
"WCnComV[3196].Len() = 1\n",
"WCnComV[3196][0] = 5020\n",
"WCnComV[3197].Len() = 1\n",
"WCnComV[3197][0] = 5013\n",
"WCnComV[3198].Len() = 1\n",
"WCnComV[3198][0] = 5010\n",
"WCnComV[3199].Len() = 1\n",
"WCnComV[3199][0] = 5008\n",
"WCnComV[3200].Len() = 1\n",
"WCnComV[3200][0] = 5006\n",
"WCnComV[3201].Len() = 1\n",
"WCnComV[3201][0] = 5002\n",
"WCnComV[3202].Len() = 1\n",
"WCnComV[3202][0] = 5001\n",
"WCnComV[3203].Len() = 1\n",
"WCnComV[3203][0] = 4998\n",
"WCnComV[3204].Len() = 1\n",
"WCnComV[3204][0] = 4996\n",
"WCnComV[3205].Len() = 1\n",
"WCnComV[3205][0] = 4993\n",
"WCnComV[3206].Len() = 1\n",
"WCnComV[3206][0] = 4991\n",
"WCnComV[3207].Len() = 1\n",
"WCnComV[3207][0] = 4987\n",
"WCnComV[3208].Len() = 1\n",
"WCnComV[3208][0] = 4983\n",
"WCnComV[3209].Len() = 1\n",
"WCnComV[3209][0] = 4981\n",
"WCnComV[3210].Len() = 1\n",
"WCnComV[3210][0] = 4980\n",
"WCnComV[3211].Len() = 1\n",
"WCnComV[3211][0] = 4979\n",
"WCnComV[3212].Len() = 1\n",
"WCnComV[3212][0] = 4977\n",
"WCnComV[3213].Len() = 1\n",
"WCnComV[3213][0] = 4974\n",
"WCnComV[3214].Len() = 1\n",
"WCnComV[3214][0] = 4964\n",
"WCnComV[3215].Len() = 1\n",
"WCnComV[3215][0] = 4963\n",
"WCnComV[3216].Len() = 1\n",
"WCnComV[3216][0] = 4960\n",
"WCnComV[3217].Len() = 1\n",
"WCnComV[3217][0] = 4957\n",
"WCnComV[3218].Len() = 1\n",
"WCnComV[3218][0] = 4956\n",
"WCnComV[3219].Len() = 1\n",
"WCnComV[3219][0] = 4947\n",
"WCnComV[3220].Len() = 1\n",
"WCnComV[3220][0] = 4944\n",
"WCnComV[3221].Len() = 1\n",
"WCnComV[3221][0] = 4943\n",
"WCnComV[3222].Len() = 1\n",
"WCnComV[3222][0] = 4941\n",
"WCnComV[3223].Len() = 1\n",
"WCnComV[3223][0] = 4937\n",
"WCnComV[3224].Len() = 1\n",
"WCnComV[3224][0] = 4934\n",
"WCnComV[3225].Len() = 1\n",
"WCnComV[3225][0] = 4931\n",
"WCnComV[3226].Len() = 1\n",
"WCnComV[3226][0] = 4929\n",
"WCnComV[3227].Len() = 1\n",
"WCnComV[3227][0] = 4927\n",
"WCnComV[3228].Len() = 1\n",
"WCnComV[3228][0] = 4926\n",
"WCnComV[3229].Len() = 1\n",
"WCnComV[3229][0] = 4925\n",
"WCnComV[3230].Len() = 1\n",
"WCnComV[3230][0] = 4924\n",
"WCnComV[3231].Len() = 1\n",
"WCnComV[3231][0] = 4917\n",
"WCnComV[3232].Len() = 1\n",
"WCnComV[3232][0] = 4915\n",
"WCnComV[3233].Len() = 1\n",
"WCnComV[3233][0] = 4913\n",
"WCnComV[3234].Len() = 1\n",
"WCnComV[3234][0] = 4908\n",
"WCnComV[3235].Len() = 1\n",
"WCnComV[3235][0] = 4907\n",
"WCnComV[3236].Len() = 1\n",
"WCnComV[3236][0] = 4905\n",
"WCnComV[3237].Len() = 1\n",
"WCnComV[3237][0] = 4894\n",
"WCnComV[3238].Len() = 1\n",
"WCnComV[3238][0] = 4891\n",
"WCnComV[3239].Len() = 1\n",
"WCnComV[3239][0] = 4889\n",
"WCnComV[3240].Len() = 1\n",
"WCnComV[3240][0] = 4887\n",
"WCnComV[3241].Len() = 1\n",
"WCnComV[3241][0] = 4886\n",
"WCnComV[3242].Len() = 1\n",
"WCnComV[3242][0] = 4885\n",
"WCnComV[3243].Len() = 1\n",
"WCnComV[3243][0] = 4882\n",
"WCnComV[3244].Len() = 1\n",
"WCnComV[3244][0] = 4874\n",
"WCnComV[3245].Len() = 1\n",
"WCnComV[3245][0] = 4873\n",
"WCnComV[3246].Len() = 1\n",
"WCnComV[3246][0] = 4872\n",
"WCnComV[3247].Len() = 1\n",
"WCnComV[3247][0] = 4871\n",
"WCnComV[3248].Len() = 1\n",
"WCnComV[3248][0] = 4868\n",
"WCnComV[3249].Len() = 1\n",
"WCnComV[3249][0] = 4866\n",
"WCnComV[3250].Len() = 1\n",
"WCnComV[3250][0] = 4864\n",
"WCnComV[3251].Len() = 1\n",
"WCnComV[3251][0] = 4863\n",
"WCnComV[3252].Len() = 1\n",
"WCnComV[3252][0] = 4860\n",
"WCnComV[3253].Len() = 1\n",
"WCnComV[3253][0] = 4858\n",
"WCnComV[3254].Len() = 1\n",
"WCnComV[3254][0] = 4856\n",
"WCnComV[3255].Len() = 1\n",
"WCnComV[3255][0] = 4853\n",
"WCnComV[3256].Len() = 1\n",
"WCnComV[3256][0] = 4849\n",
"WCnComV[3257].Len() = 1\n",
"WCnComV[3257][0] = 4845\n",
"WCnComV[3258].Len() = 1\n",
"WCnComV[3258][0] = 4843\n",
"WCnComV[3259].Len() = 1\n",
"WCnComV[3259][0] = 4838\n",
"WCnComV[3260].Len() = 1\n",
"WCnComV[3260][0] = 4832\n",
"WCnComV[3261].Len() = 1\n",
"WCnComV[3261][0] = 4829\n",
"WCnComV[3262].Len() = 1\n",
"WCnComV[3262][0] = 4828\n",
"WCnComV[3263].Len() = 1\n",
"WCnComV[3263][0] = 4826\n",
"WCnComV[3264].Len() = 1\n",
"WCnComV[3264][0] = 4824\n",
"WCnComV[3265].Len() = 1\n",
"WCnComV[3265][0] = 4823\n",
"WCnComV[3266].Len() = 1\n",
"WCnComV[3266][0] = 4819\n",
"WCnComV[3267].Len() = 1\n",
"WCnComV[3267][0] = 4818\n",
"WCnComV[3268].Len() = 1\n",
"WCnComV[3268][0] = 4817\n",
"WCnComV[3269].Len() = 1\n",
"WCnComV[3269][0] = 4814\n",
"WCnComV[3270].Len() = 1\n",
"WCnComV[3270][0] = 4812\n",
"WCnComV[3271].Len() = 1\n",
"WCnComV[3271][0] = 4810\n",
"WCnComV[3272].Len() = 1\n",
"WCnComV[3272][0] = 4809\n",
"WCnComV[3273].Len() = 1\n",
"WCnComV[3273][0] = 4799\n",
"WCnComV[3274].Len() = 1\n",
"WCnComV[3274][0] = 4794\n",
"WCnComV[3275].Len() = 1\n",
"WCnComV[3275][0] = 4793\n",
"WCnComV[3276].Len() = 1\n",
"WCnComV[3276][0] = 4792\n",
"WCnComV[3277].Len() = 1\n",
"WCnComV[3277][0] = 4791\n",
"WCnComV[3278].Len() = 1\n",
"WCnComV[3278][0] = 4789\n",
"WCnComV[3279].Len() = 1\n",
"WCnComV[3279][0] = 4785\n",
"WCnComV[3280].Len() = 1\n",
"WCnComV[3280][0] = 4781\n",
"WCnComV[3281].Len() = 1\n",
"WCnComV[3281][0] = 4777\n",
"WCnComV[3282].Len() = 1\n",
"WCnComV[3282][0] = 4776\n",
"WCnComV[3283].Len() = 1\n",
"WCnComV[3283][0] = 4771\n",
"WCnComV[3284].Len() = 1\n",
"WCnComV[3284][0] = 4770\n",
"WCnComV[3285].Len() = 1\n",
"WCnComV[3285][0] = 4769\n",
"WCnComV[3286].Len() = 1\n",
"WCnComV[3286][0] = 4767\n",
"WCnComV[3287].Len() = 1\n",
"WCnComV[3287][0] = 4765\n",
"WCnComV[3288].Len() = 1\n",
"WCnComV[3288][0] = 4764\n",
"WCnComV[3289].Len() = 1\n",
"WCnComV[3289][0] = 4762\n",
"WCnComV[3290].Len() = 1\n",
"WCnComV[3290][0] = 4759\n",
"WCnComV[3291].Len() = 1\n",
"WCnComV[3291][0] = 4757\n",
"WCnComV[3292].Len() = 1\n",
"WCnComV[3292][0] = 4751\n",
"WCnComV[3293].Len() = 1\n",
"WCnComV[3293][0] = 4749\n",
"WCnComV[3294].Len() = 1\n",
"WCnComV[3294][0] = 4748\n",
"WCnComV[3295].Len() = 1\n",
"WCnComV[3295][0] = 4742\n",
"WCnComV[3296].Len() = 1\n",
"WCnComV[3296][0] = 4733\n",
"WCnComV[3297].Len() = 1\n",
"WCnComV[3297][0] = 4725\n",
"WCnComV[3298].Len() = 1\n",
"WCnComV[3298][0] = 4723\n",
"WCnComV[3299].Len() = 1\n",
"WCnComV[3299][0] = 4720\n",
"WCnComV[3300].Len() = 1\n",
"WCnComV[3300][0] = 4719\n",
"WCnComV[3301].Len() = 1\n",
"WCnComV[3301][0] = 4716\n",
"WCnComV[3302].Len() = 1\n",
"WCnComV[3302][0] = 4715\n",
"WCnComV[3303].Len() = 1\n",
"WCnComV[3303][0] = 4714\n",
"WCnComV[3304].Len() = 1\n",
"WCnComV[3304][0] = 4713\n",
"WCnComV[3305].Len() = 1\n",
"WCnComV[3305][0] = 4711\n",
"WCnComV[3306].Len() = 1\n",
"WCnComV[3306][0] = 4706\n",
"WCnComV[3307].Len() = 1\n",
"WCnComV[3307][0] = 4705\n",
"WCnComV[3308].Len() = 1\n",
"WCnComV[3308][0] = 4698\n",
"WCnComV[3309].Len() = 1\n",
"WCnComV[3309][0] = 4697\n",
"WCnComV[3310].Len() = 1\n",
"WCnComV[3310][0] = 4695\n",
"WCnComV[3311].Len() = 1\n",
"WCnComV[3311][0] = 4692\n",
"WCnComV[3312].Len() = 1\n",
"WCnComV[3312][0] = 4691\n",
"WCnComV[3313].Len() = 1\n",
"WCnComV[3313][0] = 4686\n",
"WCnComV[3314].Len() = 1\n",
"WCnComV[3314][0] = 4683\n",
"WCnComV[3315].Len() = 1\n",
"WCnComV[3315][0] = 4682\n",
"WCnComV[3316].Len() = 1\n",
"WCnComV[3316][0] = 4681\n",
"WCnComV[3317].Len() = 1\n",
"WCnComV[3317][0] = 4677\n",
"WCnComV[3318].Len() = 1\n",
"WCnComV[3318][0] = 4671\n",
"WCnComV[3319].Len() = 1\n",
"WCnComV[3319][0] = 4670\n",
"WCnComV[3320].Len() = 1\n",
"WCnComV[3320][0] = 4669\n",
"WCnComV[3321].Len() = 1\n",
"WCnComV[3321][0] = 4668\n",
"WCnComV[3322].Len() = 1\n",
"WCnComV[3322][0] = 4667\n",
"WCnComV[3323].Len() = 1\n",
"WCnComV[3323][0] = 4662\n",
"WCnComV[3324].Len() = 1\n",
"WCnComV[3324][0] = 4656\n",
"WCnComV[3325].Len() = 1\n",
"WCnComV[3325][0] = 4653\n",
"WCnComV[3326].Len() = 1\n",
"WCnComV[3326][0] = 4650\n",
"WCnComV[3327].Len() = 1\n",
"WCnComV[3327][0] = 4642\n",
"WCnComV[3328].Len() = 1\n",
"WCnComV[3328][0] = 4640\n",
"WCnComV[3329].Len() = 1\n",
"WCnComV[3329][0] = 4636\n",
"WCnComV[3330].Len() = 1\n",
"WCnComV[3330][0] = 4634\n",
"WCnComV[3331].Len() = 1\n",
"WCnComV[3331][0] = 4633\n",
"WCnComV[3332].Len() = 1\n",
"WCnComV[3332][0] = 4632\n",
"WCnComV[3333].Len() = 1\n",
"WCnComV[3333][0] = 4629\n",
"WCnComV[3334].Len() = 1\n",
"WCnComV[3334][0] = 4626\n",
"WCnComV[3335].Len() = 1\n",
"WCnComV[3335][0] = 4624\n",
"WCnComV[3336].Len() = 1\n",
"WCnComV[3336][0] = 4618\n",
"WCnComV[3337].Len() = 1\n",
"WCnComV[3337][0] = 4615\n",
"WCnComV[3338].Len() = 1\n",
"WCnComV[3338][0] = 4613\n",
"WCnComV[3339].Len() = 1\n",
"WCnComV[3339][0] = 4612\n",
"WCnComV[3340].Len() = 1\n",
"WCnComV[3340][0] = 4610\n",
"WCnComV[3341].Len() = 1\n",
"WCnComV[3341][0] = 4606\n",
"WCnComV[3342].Len() = 1\n",
"WCnComV[3342][0] = 4602\n",
"WCnComV[3343].Len() = 1\n",
"WCnComV[3343][0] = 4597\n",
"WCnComV[3344].Len() = 1\n",
"WCnComV[3344][0] = 4596\n",
"WCnComV[3345].Len() = 1\n",
"WCnComV[3345][0] = 4588\n",
"WCnComV[3346].Len() = 1\n",
"WCnComV[3346][0] = 4584\n",
"WCnComV[3347].Len() = 1\n",
"WCnComV[3347][0] = 4583\n",
"WCnComV[3348].Len() = 1\n",
"WCnComV[3348][0] = 4582\n",
"WCnComV[3349].Len() = 1\n",
"WCnComV[3349][0] = 4578\n",
"WCnComV[3350].Len() = 1\n",
"WCnComV[3350][0] = 4577\n",
"WCnComV[3351].Len() = 1\n",
"WCnComV[3351][0] = 4574\n",
"WCnComV[3352].Len() = 1\n",
"WCnComV[3352][0] = 4572\n",
"WCnComV[3353].Len() = 1\n",
"WCnComV[3353][0] = 4568\n",
"WCnComV[3354].Len() = 1\n",
"WCnComV[3354][0] = 4567\n",
"WCnComV[3355].Len() = 1\n",
"WCnComV[3355][0] = 4566\n",
"WCnComV[3356].Len() = 1\n",
"WCnComV[3356][0] = 4565\n",
"WCnComV[3357].Len() = 1\n",
"WCnComV[3357][0] = 4564\n",
"WCnComV[3358].Len() = 1\n",
"WCnComV[3358][0] = 4563\n",
"WCnComV[3359].Len() = 1\n",
"WCnComV[3359][0] = 4554\n",
"WCnComV[3360].Len() = 1\n",
"WCnComV[3360][0] = 4553\n",
"WCnComV[3361].Len() = 1\n",
"WCnComV[3361][0] = 4549\n",
"WCnComV[3362].Len() = 1\n",
"WCnComV[3362][0] = 4547\n",
"WCnComV[3363].Len() = 1\n",
"WCnComV[3363][0] = 4546\n",
"WCnComV[3364].Len() = 1\n",
"WCnComV[3364][0] = 4542\n",
"WCnComV[3365].Len() = 1\n",
"WCnComV[3365][0] = 4535\n",
"WCnComV[3366].Len() = 1\n",
"WCnComV[3366][0] = 4534\n",
"WCnComV[3367].Len() = 1\n",
"WCnComV[3367][0] = 4532\n",
"WCnComV[3368].Len() = 1\n",
"WCnComV[3368][0] = 4530\n",
"WCnComV[3369].Len() = 1\n",
"WCnComV[3369][0] = 4529\n",
"WCnComV[3370].Len() = 1\n",
"WCnComV[3370][0] = 4526\n",
"WCnComV[3371].Len() = 1\n",
"WCnComV[3371][0] = 4524\n",
"WCnComV[3372].Len() = 1\n",
"WCnComV[3372][0] = 4516\n",
"WCnComV[3373].Len() = 1\n",
"WCnComV[3373][0] = 4515\n",
"WCnComV[3374].Len() = 1\n",
"WCnComV[3374][0] = 4513\n",
"WCnComV[3375].Len() = 1\n",
"WCnComV[3375][0] = 4510\n",
"WCnComV[3376].Len() = 1\n",
"WCnComV[3376][0] = 4508\n",
"WCnComV[3377].Len() = 1\n",
"WCnComV[3377][0] = 4507\n",
"WCnComV[3378].Len() = 1\n",
"WCnComV[3378][0] = 4504\n",
"WCnComV[3379].Len() = 1\n",
"WCnComV[3379][0] = 4500\n",
"WCnComV[3380].Len() = 1\n",
"WCnComV[3380][0] = 4499\n",
"WCnComV[3381].Len() = 1\n",
"WCnComV[3381][0] = 4498\n",
"WCnComV[3382].Len() = 1\n",
"WCnComV[3382][0] = 4496\n",
"WCnComV[3383].Len() = 1\n",
"WCnComV[3383][0] = 4494\n",
"WCnComV[3384].Len() = 1\n",
"WCnComV[3384][0] = 4492\n",
"WCnComV[3385].Len() = 1\n",
"WCnComV[3385][0] = 4491\n",
"WCnComV[3386].Len() = 1\n",
"WCnComV[3386][0] = 4490\n",
"WCnComV[3387].Len() = 1\n",
"WCnComV[3387][0] = 4483\n",
"WCnComV[3388].Len() = 1\n",
"WCnComV[3388][0] = 4482\n",
"WCnComV[3389].Len() = 1\n",
"WCnComV[3389][0] = 4479\n",
"WCnComV[3390].Len() = 1\n",
"WCnComV[3390][0] = 4477\n",
"WCnComV[3391].Len() = 1\n",
"WCnComV[3391][0] = 4475\n",
"WCnComV[3392].Len() = 1\n",
"WCnComV[3392][0] = 4474\n",
"WCnComV[3393].Len() = 1\n",
"WCnComV[3393][0] = 4470\n",
"WCnComV[3394].Len() = 1\n",
"WCnComV[3394][0] = 4469\n",
"WCnComV[3395].Len() = 1\n",
"WCnComV[3395][0] = 4467\n",
"WCnComV[3396].Len() = 1\n",
"WCnComV[3396][0] = 4463\n",
"WCnComV[3397].Len() = 1\n",
"WCnComV[3397][0] = 4461\n",
"WCnComV[3398].Len() = 1\n",
"WCnComV[3398][0] = 4453\n",
"WCnComV[3399].Len() = 1\n",
"WCnComV[3399][0] = 4451\n",
"WCnComV[3400].Len() = 1\n",
"WCnComV[3400][0] = 4450\n",
"WCnComV[3401].Len() = 1\n",
"WCnComV[3401][0] = 4448\n",
"WCnComV[3402].Len() = 1\n",
"WCnComV[3402][0] = 4443\n",
"WCnComV[3403].Len() = 1\n",
"WCnComV[3403][0] = 4442\n",
"WCnComV[3404].Len() = 1\n",
"WCnComV[3404][0] = 4440\n",
"WCnComV[3405].Len() = 1\n",
"WCnComV[3405][0] = 4437\n",
"WCnComV[3406].Len() = 1\n",
"WCnComV[3406][0] = 4435\n",
"WCnComV[3407].Len() = 1\n",
"WCnComV[3407][0] = 4433\n",
"WCnComV[3408].Len() = 1\n",
"WCnComV[3408][0] = 4431\n",
"WCnComV[3409].Len() = 1\n",
"WCnComV[3409][0] = 4423\n",
"WCnComV[3410].Len() = 1\n",
"WCnComV[3410][0] = 4419\n",
"WCnComV[3411].Len() = 1\n",
"WCnComV[3411][0] = 4416\n",
"WCnComV[3412].Len() = 1\n",
"WCnComV[3412][0] = 4414\n",
"WCnComV[3413].Len() = 1\n",
"WCnComV[3413][0] = 4412\n",
"WCnComV[3414].Len() = 1\n",
"WCnComV[3414][0] = 4411\n",
"WCnComV[3415].Len() = 1\n",
"WCnComV[3415][0] = 4410\n",
"WCnComV[3416].Len() = 1\n",
"WCnComV[3416][0] = 4409\n",
"WCnComV[3417].Len() = 1\n",
"WCnComV[3417][0] = 4407\n",
"WCnComV[3418].Len() = 1\n",
"WCnComV[3418][0] = 4406\n",
"WCnComV[3419].Len() = 1\n",
"WCnComV[3419][0] = 4403\n",
"WCnComV[3420].Len() = 1\n",
"WCnComV[3420][0] = 4402\n",
"WCnComV[3421].Len() = 1\n",
"WCnComV[3421][0] = 4393\n",
"WCnComV[3422].Len() = 1\n",
"WCnComV[3422][0] = 4392\n",
"WCnComV[3423].Len() = 1\n",
"WCnComV[3423][0] = 4391\n",
"WCnComV[3424].Len() = 1\n",
"WCnComV[3424][0] = 4385\n",
"WCnComV[3425].Len() = 1\n",
"WCnComV[3425][0] = 4383\n",
"WCnComV[3426].Len() = 1\n",
"WCnComV[3426][0] = 4380\n",
"WCnComV[3427].Len() = 1\n",
"WCnComV[3427][0] = 4379\n",
"WCnComV[3428].Len() = 1\n",
"WCnComV[3428][0] = 4376\n",
"WCnComV[3429].Len() = 1\n",
"WCnComV[3429][0] = 4375\n",
"WCnComV[3430].Len() = 1\n",
"WCnComV[3430][0] = 4373\n",
"WCnComV[3431].Len() = 1\n",
"WCnComV[3431][0] = 4367\n",
"WCnComV[3432].Len() = 1\n",
"WCnComV[3432][0] = 4366\n",
"WCnComV[3433].Len() = 1\n",
"WCnComV[3433][0] = 4365\n",
"WCnComV[3434].Len() = 1\n",
"WCnComV[3434][0] = 4360\n",
"WCnComV[3435].Len() = 1\n",
"WCnComV[3435][0] = 4359\n",
"WCnComV[3436].Len() = 1\n",
"WCnComV[3436][0] = 4352\n",
"WCnComV[3437].Len() = 1\n",
"WCnComV[3437][0] = 4349\n",
"WCnComV[3438].Len() = 1\n",
"WCnComV[3438][0] = 4347\n",
"WCnComV[3439].Len() = 1\n",
"WCnComV[3439][0] = 4346\n",
"WCnComV[3440].Len() = 1\n",
"WCnComV[3440][0] = 4342\n",
"WCnComV[3441].Len() = 1\n",
"WCnComV[3441][0] = 4341\n",
"WCnComV[3442].Len() = 1\n",
"WCnComV[3442][0] = 4339\n",
"WCnComV[3443].Len() = 1\n",
"WCnComV[3443][0] = 4338\n",
"WCnComV[3444].Len() = 1\n",
"WCnComV[3444][0] = 4337\n",
"WCnComV[3445].Len() = 1\n",
"WCnComV[3445][0] = 4333\n",
"WCnComV[3446].Len() = 1\n",
"WCnComV[3446][0] = 4330\n",
"WCnComV[3447].Len() = 1\n",
"WCnComV[3447][0] = 4325\n",
"WCnComV[3448].Len() = 1\n",
"WCnComV[3448][0] = 4323\n",
"WCnComV[3449].Len() = 1\n",
"WCnComV[3449][0] = 4320\n",
"WCnComV[3450].Len() = 1\n",
"WCnComV[3450][0] = 4319\n",
"WCnComV[3451].Len() = 1\n",
"WCnComV[3451][0] = 4318\n",
"WCnComV[3452].Len() = 1\n",
"WCnComV[3452][0] = 4311\n",
"WCnComV[3453].Len() = 1\n",
"WCnComV[3453][0] = 4310\n",
"WCnComV[3454].Len() = 1\n",
"WCnComV[3454][0] = 4309\n",
"WCnComV[3455].Len() = 1\n",
"WCnComV[3455][0] = 4308\n",
"WCnComV[3456].Len() = 1\n",
"WCnComV[3456][0] = 4307\n",
"WCnComV[3457].Len() = 1\n",
"WCnComV[3457][0] = 4304\n",
"WCnComV[3458].Len() = 1\n",
"WCnComV[3458][0] = 4300\n",
"WCnComV[3459].Len() = 1\n",
"WCnComV[3459][0] = 4292\n",
"WCnComV[3460].Len() = 1\n",
"WCnComV[3460][0] = 4289\n",
"WCnComV[3461].Len() = 1\n",
"WCnComV[3461][0] = 4288\n",
"WCnComV[3462].Len() = 1\n",
"WCnComV[3462][0] = 4287\n",
"WCnComV[3463].Len() = 1\n",
"WCnComV[3463][0] = 4285\n",
"WCnComV[3464].Len() = 1\n",
"WCnComV[3464][0] = 4283\n",
"WCnComV[3465].Len() = 1\n",
"WCnComV[3465][0] = 4281\n",
"WCnComV[3466].Len() = 1\n",
"WCnComV[3466][0] = 4275\n",
"WCnComV[3467].Len() = 1\n",
"WCnComV[3467][0] = 4273\n",
"WCnComV[3468].Len() = 1\n",
"WCnComV[3468][0] = 4272\n",
"WCnComV[3469].Len() = 1\n",
"WCnComV[3469][0] = 4270\n",
"WCnComV[3470].Len() = 1\n",
"WCnComV[3470][0] = 4269\n",
"WCnComV[3471].Len() = 1\n",
"WCnComV[3471][0] = 4267\n",
"WCnComV[3472].Len() = 1\n",
"WCnComV[3472][0] = 4265\n",
"WCnComV[3473].Len() = 1\n",
"WCnComV[3473][0] = 4261\n",
"WCnComV[3474].Len() = 1\n",
"WCnComV[3474][0] = 4259\n",
"WCnComV[3475].Len() = 1\n",
"WCnComV[3475][0] = 4250\n",
"WCnComV[3476].Len() = 1\n",
"WCnComV[3476][0] = 4249\n",
"WCnComV[3477].Len() = 1\n",
"WCnComV[3477][0] = 4243\n",
"WCnComV[3478].Len() = 1\n",
"WCnComV[3478][0] = 4241\n",
"WCnComV[3479].Len() = 1\n",
"WCnComV[3479][0] = 4238\n",
"WCnComV[3480].Len() = 1\n",
"WCnComV[3480][0] = 4237\n",
"WCnComV[3481].Len() = 1\n",
"WCnComV[3481][0] = 4236\n",
"WCnComV[3482].Len() = 1\n",
"WCnComV[3482][0] = 4235\n",
"WCnComV[3483].Len() = 1\n",
"WCnComV[3483][0] = 4234\n",
"WCnComV[3484].Len() = 1\n",
"WCnComV[3484][0] = 4233\n",
"WCnComV[3485].Len() = 1\n",
"WCnComV[3485][0] = 4230\n",
"WCnComV[3486].Len() = 1\n",
"WCnComV[3486][0] = 4228\n",
"WCnComV[3487].Len() = 1\n",
"WCnComV[3487][0] = 4221\n",
"WCnComV[3488].Len() = 1\n",
"WCnComV[3488][0] = 4218\n",
"WCnComV[3489].Len() = 1\n",
"WCnComV[3489][0] = 4216\n",
"WCnComV[3490].Len() = 1\n",
"WCnComV[3490][0] = 4214\n",
"WCnComV[3491].Len() = 1\n",
"WCnComV[3491][0] = 4212\n",
"WCnComV[3492].Len() = 1\n",
"WCnComV[3492][0] = 4210\n",
"WCnComV[3493].Len() = 1\n",
"WCnComV[3493][0] = 4209\n",
"WCnComV[3494].Len() = 1\n",
"WCnComV[3494][0] = 4208\n",
"WCnComV[3495].Len() = 1\n",
"WCnComV[3495][0] = 4204\n",
"WCnComV[3496].Len() = 1\n",
"WCnComV[3496][0] = 4203\n",
"WCnComV[3497].Len() = 1\n",
"WCnComV[3497][0] = 4199\n",
"WCnComV[3498].Len() = 1\n",
"WCnComV[3498][0] = 4194\n",
"WCnComV[3499].Len() = 1\n",
"WCnComV[3499][0] = 4191\n",
"WCnComV[3500].Len() = 1\n",
"WCnComV[3500][0] = 4188\n",
"WCnComV[3501].Len() = 1\n",
"WCnComV[3501][0] = 4186\n",
"WCnComV[3502].Len() = 1\n",
"WCnComV[3502][0] = 4185\n",
"WCnComV[3503].Len() = 1\n",
"WCnComV[3503][0] = 4182\n",
"WCnComV[3504].Len() = 1\n",
"WCnComV[3504][0] = 4180\n",
"WCnComV[3505].Len() = 1\n",
"WCnComV[3505][0] = 4177\n",
"WCnComV[3506].Len() = 1\n",
"WCnComV[3506][0] = 4174\n",
"WCnComV[3507].Len() = 1\n",
"WCnComV[3507][0] = 4168\n",
"WCnComV[3508].Len() = 1\n",
"WCnComV[3508][0] = 4166\n",
"WCnComV[3509].Len() = 1\n",
"WCnComV[3509][0] = 4165\n",
"WCnComV[3510].Len() = 1\n",
"WCnComV[3510][0] = 4163\n",
"WCnComV[3511].Len() = 1\n",
"WCnComV[3511][0] = 4161\n",
"WCnComV[3512].Len() = 1\n",
"WCnComV[3512][0] = 4160\n",
"WCnComV[3513].Len() = 1\n",
"WCnComV[3513][0] = 4156\n",
"WCnComV[3514].Len() = 1\n",
"WCnComV[3514][0] = 4155\n",
"WCnComV[3515].Len() = 1\n",
"WCnComV[3515][0] = 4148\n",
"WCnComV[3516].Len() = 1\n",
"WCnComV[3516][0] = 4144\n",
"WCnComV[3517].Len() = 1\n",
"WCnComV[3517][0] = 4142\n",
"WCnComV[3518].Len() = 1\n",
"WCnComV[3518][0] = 4140\n",
"WCnComV[3519].Len() = 1\n",
"WCnComV[3519][0] = 4139\n",
"WCnComV[3520].Len() = 1\n",
"WCnComV[3520][0] = 4137\n",
"WCnComV[3521].Len() = 1\n",
"WCnComV[3521][0] = 4136\n",
"WCnComV[3522].Len() = 1\n",
"WCnComV[3522][0] = 4132\n",
"WCnComV[3523].Len() = 1\n",
"WCnComV[3523][0] = 4131\n",
"WCnComV[3524].Len() = 1\n",
"WCnComV[3524][0] = 4129\n",
"WCnComV[3525].Len() = 1\n",
"WCnComV[3525][0] = 4123\n",
"WCnComV[3526].Len() = 1\n",
"WCnComV[3526][0] = 4122\n",
"WCnComV[3527].Len() = 1\n",
"WCnComV[3527][0] = 4121\n",
"WCnComV[3528].Len() = 1\n",
"WCnComV[3528][0] = 4119\n",
"WCnComV[3529].Len() = 1\n",
"WCnComV[3529][0] = 4118\n",
"WCnComV[3530].Len() = 1\n",
"WCnComV[3530][0] = 4116\n",
"WCnComV[3531].Len() = 1\n",
"WCnComV[3531][0] = 4114\n",
"WCnComV[3532].Len() = 1\n",
"WCnComV[3532][0] = 4113\n",
"WCnComV[3533].Len() = 1\n",
"WCnComV[3533][0] = 4107\n",
"WCnComV[3534].Len() = 1\n",
"WCnComV[3534][0] = 4105\n",
"WCnComV[3535].Len() = 1\n",
"WCnComV[3535][0] = 4103\n",
"WCnComV[3536].Len() = 1\n",
"WCnComV[3536][0] = 4101\n",
"WCnComV[3537].Len() = 1\n",
"WCnComV[3537][0] = 4095\n",
"WCnComV[3538].Len() = 1\n",
"WCnComV[3538][0] = 4080\n",
"WCnComV[3539].Len() = 1\n",
"WCnComV[3539][0] = 4078\n",
"WCnComV[3540].Len() = 1\n",
"WCnComV[3540][0] = 4077\n",
"WCnComV[3541].Len() = 1\n",
"WCnComV[3541][0] = 4074\n",
"WCnComV[3542].Len() = 1\n",
"WCnComV[3542][0] = 4072\n",
"WCnComV[3543].Len() = 1\n",
"WCnComV[3543][0] = 4068\n",
"WCnComV[3544].Len() = 1\n",
"WCnComV[3544][0] = 4066\n",
"WCnComV[3545].Len() = 1\n",
"WCnComV[3545][0] = 4064\n",
"WCnComV[3546].Len() = 1\n",
"WCnComV[3546][0] = 4062\n",
"WCnComV[3547].Len() = 1\n",
"WCnComV[3547][0] = 4061\n",
"WCnComV[3548].Len() = 1\n",
"WCnComV[3548][0] = 4060\n",
"WCnComV[3549].Len() = 1\n",
"WCnComV[3549][0] = 4059\n",
"WCnComV[3550].Len() = 1\n",
"WCnComV[3550][0] = 4055\n",
"WCnComV[3551].Len() = 1\n",
"WCnComV[3551][0] = 4054\n",
"WCnComV[3552].Len() = 1\n",
"WCnComV[3552][0] = 4053\n",
"WCnComV[3553].Len() = 1\n",
"WCnComV[3553][0] = 4052\n",
"WCnComV[3554].Len() = 1\n",
"WCnComV[3554][0] = 4047\n",
"WCnComV[3555].Len() = 1\n",
"WCnComV[3555][0] = 4043\n",
"WCnComV[3556].Len() = 1\n",
"WCnComV[3556][0] = 4039\n",
"WCnComV[3557].Len() = 1\n",
"WCnComV[3557][0] = 4035\n",
"WCnComV[3558].Len() = 1\n",
"WCnComV[3558][0] = 4034\n",
"WCnComV[3559].Len() = 1\n",
"WCnComV[3559][0] = 4032\n",
"WCnComV[3560].Len() = 1\n",
"WCnComV[3560][0] = 4030\n",
"WCnComV[3561].Len() = 1\n",
"WCnComV[3561][0] = 4029\n",
"WCnComV[3562].Len() = 1\n",
"WCnComV[3562][0] = 4022\n",
"WCnComV[3563].Len() = 1\n",
"WCnComV[3563][0] = 4020\n",
"WCnComV[3564].Len() = 1\n",
"WCnComV[3564][0] = 4019\n",
"WCnComV[3565].Len() = 1\n",
"WCnComV[3565][0] = 4018\n",
"WCnComV[3566].Len() = 1\n",
"WCnComV[3566][0] = 4001\n",
"WCnComV[3567].Len() = 1\n",
"WCnComV[3567][0] = 3999\n",
"WCnComV[3568].Len() = 1\n",
"WCnComV[3568][0] = 3994\n",
"WCnComV[3569].Len() = 1\n",
"WCnComV[3569][0] = 3993\n",
"WCnComV[3570].Len() = 1\n",
"WCnComV[3570][0] = 3992\n",
"WCnComV[3571].Len() = 1\n",
"WCnComV[3571][0] = 3991\n",
"WCnComV[3572].Len() = 1\n",
"WCnComV[3572][0] = 3990\n",
"WCnComV[3573].Len() = 1\n",
"WCnComV[3573][0] = 3988\n",
"WCnComV[3574].Len() = 1\n",
"WCnComV[3574][0] = 3981\n",
"WCnComV[3575].Len() = 1\n",
"WCnComV[3575][0] = 3970\n",
"WCnComV[3576].Len() = 1\n",
"WCnComV[3576][0] = 3969\n",
"WCnComV[3577].Len() = 1\n",
"WCnComV[3577][0] = 3968\n",
"WCnComV[3578].Len() = 1\n",
"WCnComV[3578][0] = 3965\n",
"WCnComV[3579].Len() = 1\n",
"WCnComV[3579][0] = 3963\n",
"WCnComV[3580].Len() = 1\n",
"WCnComV[3580][0] = 3959\n",
"WCnComV[3581].Len() = 1\n",
"WCnComV[3581][0] = 3957\n",
"WCnComV[3582].Len() = 1\n",
"WCnComV[3582][0] = 3956\n",
"WCnComV[3583].Len() = 1\n",
"WCnComV[3583][0] = 3955\n",
"WCnComV[3584].Len() = 1\n",
"WCnComV[3584][0] = 3952\n",
"WCnComV[3585].Len() = 1\n",
"WCnComV[3585][0] = 3945\n",
"WCnComV[3586].Len() = 1\n",
"WCnComV[3586][0] = 3941\n",
"WCnComV[3587].Len() = 1\n",
"WCnComV[3587][0] = 3938\n",
"WCnComV[3588].Len() = 1\n",
"WCnComV[3588][0] = 3932\n",
"WCnComV[3589].Len() = 1\n",
"WCnComV[3589][0] = 3930\n",
"WCnComV[3590].Len() = 1\n",
"WCnComV[3590][0] = 3924\n",
"WCnComV[3591].Len() = 1\n",
"WCnComV[3591][0] = 3918\n",
"WCnComV[3592].Len() = 1\n",
"WCnComV[3592][0] = 3916\n",
"WCnComV[3593].Len() = 1\n",
"WCnComV[3593][0] = 3914\n",
"WCnComV[3594].Len() = 1\n",
"WCnComV[3594][0] = 3905\n",
"WCnComV[3595].Len() = 1\n",
"WCnComV[3595][0] = 3896\n",
"WCnComV[3596].Len() = 1\n",
"WCnComV[3596][0] = 3894\n",
"WCnComV[3597].Len() = 1\n",
"WCnComV[3597][0] = 3885\n",
"WCnComV[3598].Len() = 1\n",
"WCnComV[3598][0] = 3883\n",
"WCnComV[3599].Len() = 1\n",
"WCnComV[3599][0] = 3879\n",
"WCnComV[3600].Len() = 1\n",
"WCnComV[3600][0] = 3875\n",
"WCnComV[3601].Len() = 1\n",
"WCnComV[3601][0] = 3874\n",
"WCnComV[3602].Len() = 1\n",
"WCnComV[3602][0] = 3872\n",
"WCnComV[3603].Len() = 1\n",
"WCnComV[3603][0] = 3870\n",
"WCnComV[3604].Len() = 1\n",
"WCnComV[3604][0] = 3869\n",
"WCnComV[3605].Len() = 1\n",
"WCnComV[3605][0] = 3867\n",
"WCnComV[3606].Len() = 1\n",
"WCnComV[3606][0] = 3864\n",
"WCnComV[3607].Len() = 1\n",
"WCnComV[3607][0] = 3861\n",
"WCnComV[3608].Len() = 1\n",
"WCnComV[3608][0] = 3858\n",
"WCnComV[3609].Len() = 1\n",
"WCnComV[3609][0] = 3856\n",
"WCnComV[3610].Len() = 1\n",
"WCnComV[3610][0] = 3854\n",
"WCnComV[3611].Len() = 1\n",
"WCnComV[3611][0] = 3848\n",
"WCnComV[3612].Len() = 1\n",
"WCnComV[3612][0] = 3843\n",
"WCnComV[3613].Len() = 1\n",
"WCnComV[3613][0] = 3842\n",
"WCnComV[3614].Len() = 1\n",
"WCnComV[3614][0] = 3838\n",
"WCnComV[3615].Len() = 1\n",
"WCnComV[3615][0] = 3834\n",
"WCnComV[3616].Len() = 1\n",
"WCnComV[3616][0] = 3830\n",
"WCnComV[3617].Len() = 1\n",
"WCnComV[3617][0] = 3829\n",
"WCnComV[3618].Len() = 1\n",
"WCnComV[3618][0] = 3828\n",
"WCnComV[3619].Len() = 1\n",
"WCnComV[3619][0] = 3827\n",
"WCnComV[3620].Len() = 1\n",
"WCnComV[3620][0] = 3826\n",
"WCnComV[3621].Len() = 1\n",
"WCnComV[3621][0] = 3824\n",
"WCnComV[3622].Len() = 1\n",
"WCnComV[3622][0] = 3818\n",
"WCnComV[3623].Len() = 1\n",
"WCnComV[3623][0] = 3817\n",
"WCnComV[3624].Len() = 1\n",
"WCnComV[3624][0] = 3814\n",
"WCnComV[3625].Len() = 1\n",
"WCnComV[3625][0] = 3812\n",
"WCnComV[3626].Len() = 1\n",
"WCnComV[3626][0] = 3809\n",
"WCnComV[3627].Len() = 1\n",
"WCnComV[3627][0] = 3806\n",
"WCnComV[3628].Len() = 1\n",
"WCnComV[3628][0] = 3805\n",
"WCnComV[3629].Len() = 1\n",
"WCnComV[3629][0] = 3799\n",
"WCnComV[3630].Len() = 1\n",
"WCnComV[3630][0] = 3798\n",
"WCnComV[3631].Len() = 1\n",
"WCnComV[3631][0] = 3797\n",
"WCnComV[3632].Len() = 1\n",
"WCnComV[3632][0] = 3795\n",
"WCnComV[3633].Len() = 1\n",
"WCnComV[3633][0] = 3792\n",
"WCnComV[3634].Len() = 1\n",
"WCnComV[3634][0] = 3791\n",
"WCnComV[3635].Len() = 1\n",
"WCnComV[3635][0] = 3788\n",
"WCnComV[3636].Len() = 1\n",
"WCnComV[3636][0] = 3785\n",
"WCnComV[3637].Len() = 1\n",
"WCnComV[3637][0] = 3770\n",
"WCnComV[3638].Len() = 1\n",
"WCnComV[3638][0] = 3765\n",
"WCnComV[3639].Len() = 1\n",
"WCnComV[3639][0] = 3764\n",
"WCnComV[3640].Len() = 1\n",
"WCnComV[3640][0] = 3762\n",
"WCnComV[3641].Len() = 1\n",
"WCnComV[3641][0] = 3760\n",
"WCnComV[3642].Len() = 1\n",
"WCnComV[3642][0] = 3759\n",
"WCnComV[3643].Len() = 1\n",
"WCnComV[3643][0] = 3756\n",
"WCnComV[3644].Len() = 1\n",
"WCnComV[3644][0] = 3754\n",
"WCnComV[3645].Len() = 1\n",
"WCnComV[3645][0] = 3751\n",
"WCnComV[3646].Len() = 1\n",
"WCnComV[3646][0] = 3747\n",
"WCnComV[3647].Len() = 1\n",
"WCnComV[3647][0] = 3746\n",
"WCnComV[3648].Len() = 1\n",
"WCnComV[3648][0] = 3743\n",
"WCnComV[3649].Len() = 1\n",
"WCnComV[3649][0] = 3741\n",
"WCnComV[3650].Len() = 1\n",
"WCnComV[3650][0] = 3738\n",
"WCnComV[3651].Len() = 1\n",
"WCnComV[3651][0] = 3734\n",
"WCnComV[3652].Len() = 1\n",
"WCnComV[3652][0] = 3728\n",
"WCnComV[3653].Len() = 1\n",
"WCnComV[3653][0] = 3726\n",
"WCnComV[3654].Len() = 1\n",
"WCnComV[3654][0] = 3723\n",
"WCnComV[3655].Len() = 1\n",
"WCnComV[3655][0] = 3721\n",
"WCnComV[3656].Len() = 1\n",
"WCnComV[3656][0] = 3713\n",
"WCnComV[3657].Len() = 1\n",
"WCnComV[3657][0] = 3711\n",
"WCnComV[3658].Len() = 1\n",
"WCnComV[3658][0] = 3708\n",
"WCnComV[3659].Len() = 1\n",
"WCnComV[3659][0] = 3705\n",
"WCnComV[3660].Len() = 1\n",
"WCnComV[3660][0] = 3704\n",
"WCnComV[3661].Len() = 1\n",
"WCnComV[3661][0] = 3703\n",
"WCnComV[3662].Len() = 1\n",
"WCnComV[3662][0] = 3701\n",
"WCnComV[3663].Len() = 1\n",
"WCnComV[3663][0] = 3698\n",
"WCnComV[3664].Len() = 1\n",
"WCnComV[3664][0] = 3694\n",
"WCnComV[3665].Len() = 1\n",
"WCnComV[3665][0] = 3693\n",
"WCnComV[3666].Len() = 1\n",
"WCnComV[3666][0] = 3691\n",
"WCnComV[3667].Len() = 1\n",
"WCnComV[3667][0] = 3689\n",
"WCnComV[3668].Len() = 1\n",
"WCnComV[3668][0] = 3686\n",
"WCnComV[3669].Len() = 1\n",
"WCnComV[3669][0] = 3683\n",
"WCnComV[3670].Len() = 1\n",
"WCnComV[3670][0] = 3680\n",
"WCnComV[3671].Len() = 1\n",
"WCnComV[3671][0] = 3679\n",
"WCnComV[3672].Len() = 1\n",
"WCnComV[3672][0] = 3678\n",
"WCnComV[3673].Len() = 1\n",
"WCnComV[3673][0] = 3675\n",
"WCnComV[3674].Len() = 1\n",
"WCnComV[3674][0] = 3672\n",
"WCnComV[3675].Len() = 1\n",
"WCnComV[3675][0] = 3671\n",
"WCnComV[3676].Len() = 1\n",
"WCnComV[3676][0] = 3669\n",
"WCnComV[3677].Len() = 1\n",
"WCnComV[3677][0] = 3666\n",
"WCnComV[3678].Len() = 1\n",
"WCnComV[3678][0] = 3663\n",
"WCnComV[3679].Len() = 1\n",
"WCnComV[3679][0] = 3662\n",
"WCnComV[3680].Len() = 1\n",
"WCnComV[3680][0] = 3656\n",
"WCnComV[3681].Len() = 1\n",
"WCnComV[3681][0] = 3652\n",
"WCnComV[3682].Len() = 1\n",
"WCnComV[3682][0] = 3650\n",
"WCnComV[3683].Len() = 1\n",
"WCnComV[3683][0] = 3648\n",
"WCnComV[3684].Len() = 1\n",
"WCnComV[3684][0] = 3646\n",
"WCnComV[3685].Len() = 1\n",
"WCnComV[3685][0] = 3644\n",
"WCnComV[3686].Len() = 1\n",
"WCnComV[3686][0] = 3640\n",
"WCnComV[3687].Len() = 1\n",
"WCnComV[3687][0] = 3639\n",
"WCnComV[3688].Len() = 1\n",
"WCnComV[3688][0] = 3636\n",
"WCnComV[3689].Len() = 1\n",
"WCnComV[3689][0] = 3631\n",
"WCnComV[3690].Len() = 1\n",
"WCnComV[3690][0] = 3630\n",
"WCnComV[3691].Len() = 1\n",
"WCnComV[3691][0] = 3629\n",
"WCnComV[3692].Len() = 1\n",
"WCnComV[3692][0] = 3628\n",
"WCnComV[3693].Len() = 1\n",
"WCnComV[3693][0] = 3625\n",
"WCnComV[3694].Len() = 1\n",
"WCnComV[3694][0] = 3624\n",
"WCnComV[3695].Len() = 1\n",
"WCnComV[3695][0] = 3621\n",
"WCnComV[3696].Len() = 1\n",
"WCnComV[3696][0] = 3615\n",
"WCnComV[3697].Len() = 1\n",
"WCnComV[3697][0] = 3613\n",
"WCnComV[3698].Len() = 1\n",
"WCnComV[3698][0] = 3608\n",
"WCnComV[3699].Len() = 1\n",
"WCnComV[3699][0] = 3607\n",
"WCnComV[3700].Len() = 1\n",
"WCnComV[3700][0] = 3606\n",
"WCnComV[3701].Len() = 1\n",
"WCnComV[3701][0] = 3603\n",
"WCnComV[3702].Len() = 1\n",
"WCnComV[3702][0] = 3586\n",
"WCnComV[3703].Len() = 1\n",
"WCnComV[3703][0] = 3583\n",
"WCnComV[3704].Len() = 1\n",
"WCnComV[3704][0] = 3581\n",
"WCnComV[3705].Len() = 1\n",
"WCnComV[3705][0] = 3580\n",
"WCnComV[3706].Len() = 1\n",
"WCnComV[3706][0] = 3577\n",
"WCnComV[3707].Len() = 1\n",
"WCnComV[3707][0] = 3575\n",
"WCnComV[3708].Len() = 1\n",
"WCnComV[3708][0] = 3574\n",
"WCnComV[3709].Len() = 1\n",
"WCnComV[3709][0] = 3571\n",
"WCnComV[3710].Len() = 1\n",
"WCnComV[3710][0] = 3567\n",
"WCnComV[3711].Len() = 1\n",
"WCnComV[3711][0] = 3565\n",
"WCnComV[3712].Len() = 1\n",
"WCnComV[3712][0] = 3564\n",
"WCnComV[3713].Len() = 1\n",
"WCnComV[3713][0] = 3560\n",
"WCnComV[3714].Len() = 1\n",
"WCnComV[3714][0] = 3559\n",
"WCnComV[3715].Len() = 1\n",
"WCnComV[3715][0] = 3556\n",
"WCnComV[3716].Len() = 1\n",
"WCnComV[3716][0] = 3554\n",
"WCnComV[3717].Len() = 1\n",
"WCnComV[3717][0] = 3550\n",
"WCnComV[3718].Len() = 1\n",
"WCnComV[3718][0] = 3549\n",
"WCnComV[3719].Len() = 1\n",
"WCnComV[3719][0] = 3547\n",
"WCnComV[3720].Len() = 1\n",
"WCnComV[3720][0] = 3544\n",
"WCnComV[3721].Len() = 1\n",
"WCnComV[3721][0] = 3540\n",
"WCnComV[3722].Len() = 1\n",
"WCnComV[3722][0] = 3536\n",
"WCnComV[3723].Len() = 1\n",
"WCnComV[3723][0] = 3532\n",
"WCnComV[3724].Len() = 1\n",
"WCnComV[3724][0] = 3530\n",
"WCnComV[3725].Len() = 1\n",
"WCnComV[3725][0] = 3527\n",
"WCnComV[3726].Len() = 1\n",
"WCnComV[3726][0] = 3526\n",
"WCnComV[3727].Len() = 1\n",
"WCnComV[3727][0] = 3519\n",
"WCnComV[3728].Len() = 1\n",
"WCnComV[3728][0] = 3518\n",
"WCnComV[3729].Len() = 1\n",
"WCnComV[3729][0] = 3515\n",
"WCnComV[3730].Len() = 1\n",
"WCnComV[3730][0] = 3513\n",
"WCnComV[3731].Len() = 1\n",
"WCnComV[3731][0] = 3512\n",
"WCnComV[3732].Len() = 1\n",
"WCnComV[3732][0] = 3511\n",
"WCnComV[3733].Len() = 1\n",
"WCnComV[3733][0] = 3509\n",
"WCnComV[3734].Len() = 1\n",
"WCnComV[3734][0] = 3505\n",
"WCnComV[3735].Len() = 1\n",
"WCnComV[3735][0] = 3501\n",
"WCnComV[3736].Len() = 1\n",
"WCnComV[3736][0] = 3499\n",
"WCnComV[3737].Len() = 1\n",
"WCnComV[3737][0] = 3497\n",
"WCnComV[3738].Len() = 1\n",
"WCnComV[3738][0] = 3496\n",
"WCnComV[3739].Len() = 1\n",
"WCnComV[3739][0] = 3494\n",
"WCnComV[3740].Len() = 1\n",
"WCnComV[3740][0] = 3490\n",
"WCnComV[3741].Len() = 1\n",
"WCnComV[3741][0] = 3489\n",
"WCnComV[3742].Len() = 1\n",
"WCnComV[3742][0] = 3488\n",
"WCnComV[3743].Len() = 1\n",
"WCnComV[3743][0] = 3487\n",
"WCnComV[3744].Len() = 1\n",
"WCnComV[3744][0] = 3486\n",
"WCnComV[3745].Len() = 1\n",
"WCnComV[3745][0] = 3482\n",
"WCnComV[3746].Len() = 1\n",
"WCnComV[3746][0] = 3481\n",
"WCnComV[3747].Len() = 1\n",
"WCnComV[3747][0] = 3479\n",
"WCnComV[3748].Len() = 1\n",
"WCnComV[3748][0] = 3473\n",
"WCnComV[3749].Len() = 1\n",
"WCnComV[3749][0] = 3472\n",
"WCnComV[3750].Len() = 1\n",
"WCnComV[3750][0] = 3466\n",
"WCnComV[3751].Len() = 1\n",
"WCnComV[3751][0] = 3462\n",
"WCnComV[3752].Len() = 1\n",
"WCnComV[3752][0] = 3460\n",
"WCnComV[3753].Len() = 1\n",
"WCnComV[3753][0] = 3459\n",
"WCnComV[3754].Len() = 1\n",
"WCnComV[3754][0] = 3455\n",
"WCnComV[3755].Len() = 1\n",
"WCnComV[3755][0] = 3452\n",
"WCnComV[3756].Len() = 1\n",
"WCnComV[3756][0] = 3449\n",
"WCnComV[3757].Len() = 1\n",
"WCnComV[3757][0] = 3448\n",
"WCnComV[3758].Len() = 1\n",
"WCnComV[3758][0] = 3443\n",
"WCnComV[3759].Len() = 1\n",
"WCnComV[3759][0] = 3440\n",
"WCnComV[3760].Len() = 1\n",
"WCnComV[3760][0] = 3438\n",
"WCnComV[3761].Len() = 1\n",
"WCnComV[3761][0] = 3437\n",
"WCnComV[3762].Len() = 1\n",
"WCnComV[3762][0] = 3429\n",
"WCnComV[3763].Len() = 1\n",
"WCnComV[3763][0] = 3427\n",
"WCnComV[3764].Len() = 1\n",
"WCnComV[3764][0] = 3425\n",
"WCnComV[3765].Len() = 1\n",
"WCnComV[3765][0] = 3423\n",
"WCnComV[3766].Len() = 1\n",
"WCnComV[3766][0] = 3420\n",
"WCnComV[3767].Len() = 1\n",
"WCnComV[3767][0] = 3418\n",
"WCnComV[3768].Len() = 1\n",
"WCnComV[3768][0] = 3416\n",
"WCnComV[3769].Len() = 1\n",
"WCnComV[3769][0] = 3415\n",
"WCnComV[3770].Len() = 1\n",
"WCnComV[3770][0] = 3413\n",
"WCnComV[3771].Len() = 1\n",
"WCnComV[3771][0] = 3411\n",
"WCnComV[3772].Len() = 1\n",
"WCnComV[3772][0] = 3401\n",
"WCnComV[3773].Len() = 1\n",
"WCnComV[3773][0] = 3398\n",
"WCnComV[3774].Len() = 1\n",
"WCnComV[3774][0] = 3389\n",
"WCnComV[3775].Len() = 1\n",
"WCnComV[3775][0] = 3387\n",
"WCnComV[3776].Len() = 1\n",
"WCnComV[3776][0] = 3385\n",
"WCnComV[3777].Len() = 1\n",
"WCnComV[3777][0] = 3384\n",
"WCnComV[3778].Len() = 1\n",
"WCnComV[3778][0] = 3383\n",
"WCnComV[3779].Len() = 1\n",
"WCnComV[3779][0] = 3380\n",
"WCnComV[3780].Len() = 1\n",
"WCnComV[3780][0] = 3377\n",
"WCnComV[3781].Len() = 1\n",
"WCnComV[3781][0] = 3376\n",
"WCnComV[3782].Len() = 1\n",
"WCnComV[3782][0] = 3374\n",
"WCnComV[3783].Len() = 1\n",
"WCnComV[3783][0] = 3372\n",
"WCnComV[3784].Len() = 1\n",
"WCnComV[3784][0] = 3368\n",
"WCnComV[3785].Len() = 1\n",
"WCnComV[3785][0] = 3360\n",
"WCnComV[3786].Len() = 1\n",
"WCnComV[3786][0] = 3358\n",
"WCnComV[3787].Len() = 1\n",
"WCnComV[3787][0] = 3356\n",
"WCnComV[3788].Len() = 1\n",
"WCnComV[3788][0] = 3352\n",
"WCnComV[3789].Len() = 1\n",
"WCnComV[3789][0] = 3351\n",
"WCnComV[3790].Len() = 1\n",
"WCnComV[3790][0] = 3350\n",
"WCnComV[3791].Len() = 1\n",
"WCnComV[3791][0] = 3348\n",
"WCnComV[3792].Len() = 1\n",
"WCnComV[3792][0] = 3345\n",
"WCnComV[3793].Len() = 1\n",
"WCnComV[3793][0] = 3344\n",
"WCnComV[3794].Len() = 1\n",
"WCnComV[3794][0] = 3343\n",
"WCnComV[3795].Len() = 1\n",
"WCnComV[3795][0] = 3342\n",
"WCnComV[3796].Len() = 1\n",
"WCnComV[3796][0] = 3341\n",
"WCnComV[3797].Len() = 1\n",
"WCnComV[3797][0] = 3336\n",
"WCnComV[3798].Len() = 1\n",
"WCnComV[3798][0] = 3335\n",
"WCnComV[3799].Len() = 1\n",
"WCnComV[3799][0] = 3334\n",
"WCnComV[3800].Len() = 1\n",
"WCnComV[3800][0] = 3330\n",
"WCnComV[3801].Len() = 1\n",
"WCnComV[3801][0] = 3329\n",
"WCnComV[3802].Len() = 1\n",
"WCnComV[3802][0] = 3328\n",
"WCnComV[3803].Len() = 1\n",
"WCnComV[3803][0] = 3325\n",
"WCnComV[3804].Len() = 1\n",
"WCnComV[3804][0] = 3321\n",
"WCnComV[3805].Len() = 1\n",
"WCnComV[3805][0] = 3319\n",
"WCnComV[3806].Len() = 1\n",
"WCnComV[3806][0] = 3316\n",
"WCnComV[3807].Len() = 1\n",
"WCnComV[3807][0] = 3314\n",
"WCnComV[3808].Len() = 1\n",
"WCnComV[3808][0] = 3310\n",
"WCnComV[3809].Len() = 1\n",
"WCnComV[3809][0] = 3309\n",
"WCnComV[3810].Len() = 1\n",
"WCnComV[3810][0] = 3307\n",
"WCnComV[3811].Len() = 1\n",
"WCnComV[3811][0] = 3306\n",
"WCnComV[3812].Len() = 1\n",
"WCnComV[3812][0] = 3305\n",
"WCnComV[3813].Len() = 1\n",
"WCnComV[3813][0] = 3298\n",
"WCnComV[3814].Len() = 1\n",
"WCnComV[3814][0] = 3294\n",
"WCnComV[3815].Len() = 1\n",
"WCnComV[3815][0] = 3291\n",
"WCnComV[3816].Len() = 1\n",
"WCnComV[3816][0] = 3288\n",
"WCnComV[3817].Len() = 1\n",
"WCnComV[3817][0] = 3285\n",
"WCnComV[3818].Len() = 1\n",
"WCnComV[3818][0] = 3283\n",
"WCnComV[3819].Len() = 1\n",
"WCnComV[3819][0] = 3282\n",
"WCnComV[3820].Len() = 1\n",
"WCnComV[3820][0] = 3279\n",
"WCnComV[3821].Len() = 1\n",
"WCnComV[3821][0] = 3267\n",
"WCnComV[3822].Len() = 1\n",
"WCnComV[3822][0] = 3266\n",
"WCnComV[3823].Len() = 1\n",
"WCnComV[3823][0] = 3261\n",
"WCnComV[3824].Len() = 1\n",
"WCnComV[3824][0] = 3260\n",
"WCnComV[3825].Len() = 1\n",
"WCnComV[3825][0] = 3257\n",
"WCnComV[3826].Len() = 1\n",
"WCnComV[3826][0] = 3255\n",
"WCnComV[3827].Len() = 1\n",
"WCnComV[3827][0] = 3253\n",
"WCnComV[3828].Len() = 1\n",
"WCnComV[3828][0] = 3252\n",
"WCnComV[3829].Len() = 1\n",
"WCnComV[3829][0] = 3250\n",
"WCnComV[3830].Len() = 1\n",
"WCnComV[3830][0] = 3249\n",
"WCnComV[3831].Len() = 1\n",
"WCnComV[3831][0] = 3248\n",
"WCnComV[3832].Len() = 1\n",
"WCnComV[3832][0] = 3247\n",
"WCnComV[3833].Len() = 1\n",
"WCnComV[3833][0] = 3240\n",
"WCnComV[3834].Len() = 1\n",
"WCnComV[3834][0] = 3236\n",
"WCnComV[3835].Len() = 1\n",
"WCnComV[3835][0] = 3235\n",
"WCnComV[3836].Len() = 1\n",
"WCnComV[3836][0] = 3233\n",
"WCnComV[3837].Len() = 1\n",
"WCnComV[3837][0] = 3232\n",
"WCnComV[3838].Len() = 1\n",
"WCnComV[3838][0] = 3229\n",
"WCnComV[3839].Len() = 1\n",
"WCnComV[3839][0] = 3228\n",
"WCnComV[3840].Len() = 1\n",
"WCnComV[3840][0] = 3224\n",
"WCnComV[3841].Len() = 1\n",
"WCnComV[3841][0] = 3222\n",
"WCnComV[3842].Len() = 1\n",
"WCnComV[3842][0] = 3217\n",
"WCnComV[3843].Len() = 1\n",
"WCnComV[3843][0] = 3214\n",
"WCnComV[3844].Len() = 1\n",
"WCnComV[3844][0] = 3213\n",
"WCnComV[3845].Len() = 1\n",
"WCnComV[3845][0] = 3210\n",
"WCnComV[3846].Len() = 1\n",
"WCnComV[3846][0] = 3209\n",
"WCnComV[3847].Len() = 1\n",
"WCnComV[3847][0] = 3208\n",
"WCnComV[3848].Len() = 1\n",
"WCnComV[3848][0] = 3204\n",
"WCnComV[3849].Len() = 1\n",
"WCnComV[3849][0] = 3201\n",
"WCnComV[3850].Len() = 1\n",
"WCnComV[3850][0] = 3200\n",
"WCnComV[3851].Len() = 1\n",
"WCnComV[3851][0] = 3199\n",
"WCnComV[3852].Len() = 1\n",
"WCnComV[3852][0] = 3197\n",
"WCnComV[3853].Len() = 1\n",
"WCnComV[3853][0] = 3194\n",
"WCnComV[3854].Len() = 1\n",
"WCnComV[3854][0] = 3192\n",
"WCnComV[3855].Len() = 1\n",
"WCnComV[3855][0] = 3191\n",
"WCnComV[3856].Len() = 1\n",
"WCnComV[3856][0] = 3190\n",
"WCnComV[3857].Len() = 1\n",
"WCnComV[3857][0] = 3189\n",
"WCnComV[3858].Len() = 1\n",
"WCnComV[3858][0] = 3187\n",
"WCnComV[3859].Len() = 1\n",
"WCnComV[3859][0] = 3186\n",
"WCnComV[3860].Len() = 1\n",
"WCnComV[3860][0] = 3181\n",
"WCnComV[3861].Len() = 1\n",
"WCnComV[3861][0] = 3176\n",
"WCnComV[3862].Len() = 1\n",
"WCnComV[3862][0] = 3174\n",
"WCnComV[3863].Len() = 1\n",
"WCnComV[3863][0] = 3172\n",
"WCnComV[3864].Len() = 1\n",
"WCnComV[3864][0] = 3169\n",
"WCnComV[3865].Len() = 1\n",
"WCnComV[3865][0] = 3168\n",
"WCnComV[3866].Len() = 1\n",
"WCnComV[3866][0] = 3167\n",
"WCnComV[3867].Len() = 1\n",
"WCnComV[3867][0] = 3164\n",
"WCnComV[3868].Len() = 1\n",
"WCnComV[3868][0] = 3162\n",
"WCnComV[3869].Len() = 1\n",
"WCnComV[3869][0] = 3161\n",
"WCnComV[3870].Len() = 1\n",
"WCnComV[3870][0] = 3160\n",
"WCnComV[3871].Len() = 1\n",
"WCnComV[3871][0] = 3158\n",
"WCnComV[3872].Len() = 1\n",
"WCnComV[3872][0] = 3148\n",
"WCnComV[3873].Len() = 1\n",
"WCnComV[3873][0] = 3143\n",
"WCnComV[3874].Len() = 1\n",
"WCnComV[3874][0] = 3136\n",
"WCnComV[3875].Len() = 1\n",
"WCnComV[3875][0] = 3135\n",
"WCnComV[3876].Len() = 1\n",
"WCnComV[3876][0] = 3129\n",
"WCnComV[3877].Len() = 1\n",
"WCnComV[3877][0] = 3122\n",
"WCnComV[3878].Len() = 1\n",
"WCnComV[3878][0] = 3121\n",
"WCnComV[3879].Len() = 1\n",
"WCnComV[3879][0] = 3117\n",
"WCnComV[3880].Len() = 1\n",
"WCnComV[3880][0] = 3113\n",
"WCnComV[3881].Len() = 1\n",
"WCnComV[3881][0] = 3112\n",
"WCnComV[3882].Len() = 1\n",
"WCnComV[3882][0] = 3110\n",
"WCnComV[3883].Len() = 1\n",
"WCnComV[3883][0] = 3109\n",
"WCnComV[3884].Len() = 1\n",
"WCnComV[3884][0] = 3107\n",
"WCnComV[3885].Len() = 1\n",
"WCnComV[3885][0] = 3106\n",
"WCnComV[3886].Len() = 1\n",
"WCnComV[3886][0] = 3102\n",
"WCnComV[3887].Len() = 1\n",
"WCnComV[3887][0] = 3100\n",
"WCnComV[3888].Len() = 1\n",
"WCnComV[3888][0] = 3099\n",
"WCnComV[3889].Len() = 1\n",
"WCnComV[3889][0] = 3095\n",
"WCnComV[3890].Len() = 1\n",
"WCnComV[3890][0] = 3094\n",
"WCnComV[3891].Len() = 1\n",
"WCnComV[3891][0] = 3088\n",
"WCnComV[3892].Len() = 1\n",
"WCnComV[3892][0] = 3086\n",
"WCnComV[3893].Len() = 1\n",
"WCnComV[3893][0] = 3084\n",
"WCnComV[3894].Len() = 1\n",
"WCnComV[3894][0] = 3083\n",
"WCnComV[3895].Len() = 1\n",
"WCnComV[3895][0] = 3080\n",
"WCnComV[3896].Len() = 1\n",
"WCnComV[3896][0] = 3079\n",
"WCnComV[3897].Len() = 1\n",
"WCnComV[3897][0] = 3078\n",
"WCnComV[3898].Len() = 1\n",
"WCnComV[3898][0] = 3073\n",
"WCnComV[3899].Len() = 1\n",
"WCnComV[3899][0] = 3070\n",
"WCnComV[3900].Len() = 1\n",
"WCnComV[3900][0] = 3068\n",
"WCnComV[3901].Len() = 1\n",
"WCnComV[3901][0] = 3067\n",
"WCnComV[3902].Len() = 1\n",
"WCnComV[3902][0] = 3062\n",
"WCnComV[3903].Len() = 1\n",
"WCnComV[3903][0] = 3061\n",
"WCnComV[3904].Len() = 1\n",
"WCnComV[3904][0] = 3060\n",
"WCnComV[3905].Len() = 1\n",
"WCnComV[3905][0] = 3058\n",
"WCnComV[3906].Len() = 1\n",
"WCnComV[3906][0] = 3057\n",
"WCnComV[3907].Len() = 1\n",
"WCnComV[3907][0] = 3049\n",
"WCnComV[3908].Len() = 1\n",
"WCnComV[3908][0] = 3047\n",
"WCnComV[3909].Len() = 1\n",
"WCnComV[3909][0] = 3040\n",
"WCnComV[3910].Len() = 1\n",
"WCnComV[3910][0] = 3038\n",
"WCnComV[3911].Len() = 1\n",
"WCnComV[3911][0] = 3037\n",
"WCnComV[3912].Len() = 1\n",
"WCnComV[3912][0] = 3031\n",
"WCnComV[3913].Len() = 1\n",
"WCnComV[3913][0] = 3030\n",
"WCnComV[3914].Len() = 1\n",
"WCnComV[3914][0] = 3029\n",
"WCnComV[3915].Len() = 1\n",
"WCnComV[3915][0] = 3028\n",
"WCnComV[3916].Len() = 1\n",
"WCnComV[3916][0] = 3019\n",
"WCnComV[3917].Len() = 1\n",
"WCnComV[3917][0] = 3018\n",
"WCnComV[3918].Len() = 1\n",
"WCnComV[3918][0] = 3016\n",
"WCnComV[3919].Len() = 1\n",
"WCnComV[3919][0] = 3014\n",
"WCnComV[3920].Len() = 1\n",
"WCnComV[3920][0] = 3011\n",
"WCnComV[3921].Len() = 1\n",
"WCnComV[3921][0] = 3005\n",
"WCnComV[3922].Len() = 1\n",
"WCnComV[3922][0] = 3003\n",
"WCnComV[3923].Len() = 1\n",
"WCnComV[3923][0] = 3002\n",
"WCnComV[3924].Len() = 1\n",
"WCnComV[3924][0] = 2999\n",
"WCnComV[3925].Len() = 1\n",
"WCnComV[3925][0] = 2998\n",
"WCnComV[3926].Len() = 1\n",
"WCnComV[3926][0] = 2996\n",
"WCnComV[3927].Len() = 1\n",
"WCnComV[3927][0] = 2992\n",
"WCnComV[3928].Len() = 1\n",
"WCnComV[3928][0] = 2986\n",
"WCnComV[3929].Len() = 1\n",
"WCnComV[3929][0] = 2985\n",
"WCnComV[3930].Len() = 1\n",
"WCnComV[3930][0] = 2984\n",
"WCnComV[3931].Len() = 1\n",
"WCnComV[3931][0] = 2982\n",
"WCnComV[3932].Len() = 1\n",
"WCnComV[3932][0] = 2980\n",
"WCnComV[3933].Len() = 1\n",
"WCnComV[3933][0] = 2977\n",
"WCnComV[3934].Len() = 1\n",
"WCnComV[3934][0] = 2976\n",
"WCnComV[3935].Len() = 1\n",
"WCnComV[3935][0] = 2975\n",
"WCnComV[3936].Len() = 1\n",
"WCnComV[3936][0] = 2974\n",
"WCnComV[3937].Len() = 1\n",
"WCnComV[3937][0] = 2972\n",
"WCnComV[3938].Len() = 1\n",
"WCnComV[3938][0] = 2968\n",
"WCnComV[3939].Len() = 1\n",
"WCnComV[3939][0] = 2966\n",
"WCnComV[3940].Len() = 1\n",
"WCnComV[3940][0] = 2964\n",
"WCnComV[3941].Len() = 1\n",
"WCnComV[3941][0] = 2956\n",
"WCnComV[3942].Len() = 1\n",
"WCnComV[3942][0] = 2955\n",
"WCnComV[3943].Len() = 1\n",
"WCnComV[3943][0] = 2954\n",
"WCnComV[3944].Len() = 1\n",
"WCnComV[3944][0] = 2952\n",
"WCnComV[3945].Len() = 1\n",
"WCnComV[3945][0] = 2951\n",
"WCnComV[3946].Len() = 1\n",
"WCnComV[3946][0] = 2949\n",
"WCnComV[3947].Len() = 1\n",
"WCnComV[3947][0] = 2945\n",
"WCnComV[3948].Len() = 1\n",
"WCnComV[3948][0] = 2939\n",
"WCnComV[3949].Len() = 1\n",
"WCnComV[3949][0] = 2938\n",
"WCnComV[3950].Len() = 1\n",
"WCnComV[3950][0] = 2933\n",
"WCnComV[3951].Len() = 1\n",
"WCnComV[3951][0] = 2931\n",
"WCnComV[3952].Len() = 1\n",
"WCnComV[3952][0] = 2927\n",
"WCnComV[3953].Len() = 1\n",
"WCnComV[3953][0] = 2926\n",
"WCnComV[3954].Len() = 1\n",
"WCnComV[3954][0] = 2919\n",
"WCnComV[3955].Len() = 1\n",
"WCnComV[3955][0] = 2914\n",
"WCnComV[3956].Len() = 1\n",
"WCnComV[3956][0] = 2913\n",
"WCnComV[3957].Len() = 1\n",
"WCnComV[3957][0] = 2912\n",
"WCnComV[3958].Len() = 1\n",
"WCnComV[3958][0] = 2907\n",
"WCnComV[3959].Len() = 1\n",
"WCnComV[3959][0] = 2905\n",
"WCnComV[3960].Len() = 1\n",
"WCnComV[3960][0] = 2902\n",
"WCnComV[3961].Len() = 1\n",
"WCnComV[3961][0] = 2897\n",
"WCnComV[3962].Len() = 1\n",
"WCnComV[3962][0] = 2894\n",
"WCnComV[3963].Len() = 1\n",
"WCnComV[3963][0] = 2891\n",
"WCnComV[3964].Len() = 1\n",
"WCnComV[3964][0] = 2888\n",
"WCnComV[3965].Len() = 1\n",
"WCnComV[3965][0] = 2884\n",
"WCnComV[3966].Len() = 1\n",
"WCnComV[3966][0] = 2880\n",
"WCnComV[3967].Len() = 1\n",
"WCnComV[3967][0] = 2877\n",
"WCnComV[3968].Len() = 1\n",
"WCnComV[3968][0] = 2874\n",
"WCnComV[3969].Len() = 1\n",
"WCnComV[3969][0] = 2872\n",
"WCnComV[3970].Len() = 1\n",
"WCnComV[3970][0] = 2864\n",
"WCnComV[3971].Len() = 1\n",
"WCnComV[3971][0] = 2863\n",
"WCnComV[3972].Len() = 1\n",
"WCnComV[3972][0] = 2860\n",
"WCnComV[3973].Len() = 1\n",
"WCnComV[3973][0] = 2859\n",
"WCnComV[3974].Len() = 1\n",
"WCnComV[3974][0] = 2858\n",
"WCnComV[3975].Len() = 1\n",
"WCnComV[3975][0] = 2848\n",
"WCnComV[3976].Len() = 1\n",
"WCnComV[3976][0] = 2845\n",
"WCnComV[3977].Len() = 1\n",
"WCnComV[3977][0] = 2841\n",
"WCnComV[3978].Len() = 1\n",
"WCnComV[3978][0] = 2835\n",
"WCnComV[3979].Len() = 1\n",
"WCnComV[3979][0] = 2834\n",
"WCnComV[3980].Len() = 1\n",
"WCnComV[3980][0] = 2833\n",
"WCnComV[3981].Len() = 1\n",
"WCnComV[3981][0] = 2830\n",
"WCnComV[3982].Len() = 1\n",
"WCnComV[3982][0] = 2829\n",
"WCnComV[3983].Len() = 1\n",
"WCnComV[3983][0] = 2827\n",
"WCnComV[3984].Len() = 1\n",
"WCnComV[3984][0] = 2820\n",
"WCnComV[3985].Len() = 1\n",
"WCnComV[3985][0] = 2819\n",
"WCnComV[3986].Len() = 1\n",
"WCnComV[3986][0] = 2817\n",
"WCnComV[3987].Len() = 1\n",
"WCnComV[3987][0] = 2815\n",
"WCnComV[3988].Len() = 1\n",
"WCnComV[3988][0] = 2813\n",
"WCnComV[3989].Len() = 1\n",
"WCnComV[3989][0] = 2810\n",
"WCnComV[3990].Len() = 1\n",
"WCnComV[3990][0] = 2807\n",
"WCnComV[3991].Len() = 1\n",
"WCnComV[3991][0] = 2805\n",
"WCnComV[3992].Len() = 1\n",
"WCnComV[3992][0] = 2803\n",
"WCnComV[3993].Len() = 1\n",
"WCnComV[3993][0] = 2802\n",
"WCnComV[3994].Len() = 1\n",
"WCnComV[3994][0] = 2799\n",
"WCnComV[3995].Len() = 1\n",
"WCnComV[3995][0] = 2798\n",
"WCnComV[3996].Len() = 1\n",
"WCnComV[3996][0] = 2795\n",
"WCnComV[3997].Len() = 1\n",
"WCnComV[3997][0] = 2794\n",
"WCnComV[3998].Len() = 1\n",
"WCnComV[3998][0] = 2793\n",
"WCnComV[3999].Len() = 1\n",
"WCnComV[3999][0] = 2791\n",
"WCnComV[4000].Len() = 1\n",
"WCnComV[4000][0] = 2789\n",
"WCnComV[4001].Len() = 1\n",
"WCnComV[4001][0] = 2786\n",
"WCnComV[4002].Len() = 1\n",
"WCnComV[4002][0] = 2781\n",
"WCnComV[4003].Len() = 1\n",
"WCnComV[4003][0] = 2779\n",
"WCnComV[4004].Len() = 1\n",
"WCnComV[4004][0] = 2770\n",
"WCnComV[4005].Len() = 1\n",
"WCnComV[4005][0] = 2768\n",
"WCnComV[4006].Len() = 1\n",
"WCnComV[4006][0] = 2766\n",
"WCnComV[4007].Len() = 1\n",
"WCnComV[4007][0] = 2763\n",
"WCnComV[4008].Len() = 1\n",
"WCnComV[4008][0] = 2758\n",
"WCnComV[4009].Len() = 1\n",
"WCnComV[4009][0] = 2754\n",
"WCnComV[4010].Len() = 1\n",
"WCnComV[4010][0] = 2752\n",
"WCnComV[4011].Len() = 1\n",
"WCnComV[4011][0] = 2751\n",
"WCnComV[4012].Len() = 1\n",
"WCnComV[4012][0] = 2749\n",
"WCnComV[4013].Len() = 1\n",
"WCnComV[4013][0] = 2748\n",
"WCnComV[4014].Len() = 1\n",
"WCnComV[4014][0] = 2736\n",
"WCnComV[4015].Len() = 1\n",
"WCnComV[4015][0] = 2734\n",
"WCnComV[4016].Len() = 1\n",
"WCnComV[4016][0] = 2733\n",
"WCnComV[4017].Len() = 1\n",
"WCnComV[4017][0] = 2730\n",
"WCnComV[4018].Len() = 1\n",
"WCnComV[4018][0] = 2729\n",
"WCnComV[4019].Len() = 1\n",
"WCnComV[4019][0] = 2725\n",
"WCnComV[4020].Len() = 1\n",
"WCnComV[4020][0] = 2724\n",
"WCnComV[4021].Len() = 1\n",
"WCnComV[4021][0] = 2723\n",
"WCnComV[4022].Len() = 1\n",
"WCnComV[4022][0] = 2722\n",
"WCnComV[4023].Len() = 1\n",
"WCnComV[4023][0] = 2717\n",
"WCnComV[4024].Len() = 1\n",
"WCnComV[4024][0] = 2716\n",
"WCnComV[4025].Len() = 1\n",
"WCnComV[4025][0] = 2711\n",
"WCnComV[4026].Len() = 1\n",
"WCnComV[4026][0] = 2709\n",
"WCnComV[4027].Len() = 1\n",
"WCnComV[4027][0] = 2706\n",
"WCnComV[4028].Len() = 1\n",
"WCnComV[4028][0] = 2705\n",
"WCnComV[4029].Len() = 1\n",
"WCnComV[4029][0] = 2701\n",
"WCnComV[4030].Len() = 1\n",
"WCnComV[4030][0] = 2698\n",
"WCnComV[4031].Len() = 1\n",
"WCnComV[4031][0] = 2697\n",
"WCnComV[4032].Len() = 1\n",
"WCnComV[4032][0] = 2696\n",
"WCnComV[4033].Len() = 1\n",
"WCnComV[4033][0] = 2695\n",
"WCnComV[4034].Len() = 1\n",
"WCnComV[4034][0] = 2693\n",
"WCnComV[4035].Len() = 1\n",
"WCnComV[4035][0] = 2692\n",
"WCnComV[4036].Len() = 1\n",
"WCnComV[4036][0] = 2690\n",
"WCnComV[4037].Len() = 1\n",
"WCnComV[4037][0] = 2689\n",
"WCnComV[4038].Len() = 1\n",
"WCnComV[4038][0] = 2688\n",
"WCnComV[4039].Len() = 1\n",
"WCnComV[4039][0] = 2687\n",
"WCnComV[4040].Len() = 1\n",
"WCnComV[4040][0] = 2685\n",
"WCnComV[4041].Len() = 1\n",
"WCnComV[4041][0] = 2684\n",
"WCnComV[4042].Len() = 1\n",
"WCnComV[4042][0] = 2683\n",
"WCnComV[4043].Len() = 1\n",
"WCnComV[4043][0] = 2677\n",
"WCnComV[4044].Len() = 1\n",
"WCnComV[4044][0] = 2676\n",
"WCnComV[4045].Len() = 1\n",
"WCnComV[4045][0] = 2674\n",
"WCnComV[4046].Len() = 1\n",
"WCnComV[4046][0] = 2671\n",
"WCnComV[4047].Len() = 1\n",
"WCnComV[4047][0] = 2670\n",
"WCnComV[4048].Len() = 1\n",
"WCnComV[4048][0] = 2668\n",
"WCnComV[4049].Len() = 1\n",
"WCnComV[4049][0] = 2666\n",
"WCnComV[4050].Len() = 1\n",
"WCnComV[4050][0] = 2665\n",
"WCnComV[4051].Len() = 1\n",
"WCnComV[4051][0] = 2664\n",
"WCnComV[4052].Len() = 1\n",
"WCnComV[4052][0] = 2660\n",
"WCnComV[4053].Len() = 1\n",
"WCnComV[4053][0] = 2658\n",
"WCnComV[4054].Len() = 1\n",
"WCnComV[4054][0] = 2653\n",
"WCnComV[4055].Len() = 1\n",
"WCnComV[4055][0] = 2649\n",
"WCnComV[4056].Len() = 1\n",
"WCnComV[4056][0] = 2643\n",
"WCnComV[4057].Len() = 1\n",
"WCnComV[4057][0] = 2638\n",
"WCnComV[4058].Len() = 1\n",
"WCnComV[4058][0] = 2633\n",
"WCnComV[4059].Len() = 1\n",
"WCnComV[4059][0] = 2623\n",
"WCnComV[4060].Len() = 1\n",
"WCnComV[4060][0] = 2616\n",
"WCnComV[4061].Len() = 1\n",
"WCnComV[4061][0] = 2615\n",
"WCnComV[4062].Len() = 1\n",
"WCnComV[4062][0] = 2614\n",
"WCnComV[4063].Len() = 1\n",
"WCnComV[4063][0] = 2609\n",
"WCnComV[4064].Len() = 1\n",
"WCnComV[4064][0] = 2608\n",
"WCnComV[4065].Len() = 1\n",
"WCnComV[4065][0] = 2606\n",
"WCnComV[4066].Len() = 1\n",
"WCnComV[4066][0] = 2602\n",
"WCnComV[4067].Len() = 1\n",
"WCnComV[4067][0] = 2600\n",
"WCnComV[4068].Len() = 1\n",
"WCnComV[4068][0] = 2599\n",
"WCnComV[4069].Len() = 1\n",
"WCnComV[4069][0] = 2596\n",
"WCnComV[4070].Len() = 1\n",
"WCnComV[4070][0] = 2595\n",
"WCnComV[4071].Len() = 1\n",
"WCnComV[4071][0] = 2588\n",
"WCnComV[4072].Len() = 1\n",
"WCnComV[4072][0] = 2587\n",
"WCnComV[4073].Len() = 1\n",
"WCnComV[4073][0] = 2583\n",
"WCnComV[4074].Len() = 1\n",
"WCnComV[4074][0] = 2581\n",
"WCnComV[4075].Len() = 1\n",
"WCnComV[4075][0] = 2575\n",
"WCnComV[4076].Len() = 1\n",
"WCnComV[4076][0] = 2574\n",
"WCnComV[4077].Len() = 1\n",
"WCnComV[4077][0] = 2570\n",
"WCnComV[4078].Len() = 1\n",
"WCnComV[4078][0] = 2568\n",
"WCnComV[4079].Len() = 1\n",
"WCnComV[4079][0] = 2567\n",
"WCnComV[4080].Len() = 1\n",
"WCnComV[4080][0] = 2562\n",
"WCnComV[4081].Len() = 1\n",
"WCnComV[4081][0] = 2560\n",
"WCnComV[4082].Len() = 1\n",
"WCnComV[4082][0] = 2559\n",
"WCnComV[4083].Len() = 1\n",
"WCnComV[4083][0] = 2554\n",
"WCnComV[4084].Len() = 1\n",
"WCnComV[4084][0] = 2549\n",
"WCnComV[4085].Len() = 1\n",
"WCnComV[4085][0] = 2548\n",
"WCnComV[4086].Len() = 1\n",
"WCnComV[4086][0] = 2545\n",
"WCnComV[4087].Len() = 1\n",
"WCnComV[4087][0] = 2544\n",
"WCnComV[4088].Len() = 1\n",
"WCnComV[4088][0] = 2543\n",
"WCnComV[4089].Len() = 1\n",
"WCnComV[4089][0] = 2538\n",
"WCnComV[4090].Len() = 1\n",
"WCnComV[4090][0] = 2533\n",
"WCnComV[4091].Len() = 1\n",
"WCnComV[4091][0] = 2532\n",
"WCnComV[4092].Len() = 1\n",
"WCnComV[4092][0] = 2531\n",
"WCnComV[4093].Len() = 1\n",
"WCnComV[4093][0] = 2529\n",
"WCnComV[4094].Len() = 1\n",
"WCnComV[4094][0] = 2527\n",
"WCnComV[4095].Len() = 1\n",
"WCnComV[4095][0] = 2523\n",
"WCnComV[4096].Len() = 1\n",
"WCnComV[4096][0] = 2520\n",
"WCnComV[4097].Len() = 1\n",
"WCnComV[4097][0] = 2517\n",
"WCnComV[4098].Len() = 1\n",
"WCnComV[4098][0] = 2512\n",
"WCnComV[4099].Len() = 1\n",
"WCnComV[4099][0] = 2510\n",
"WCnComV[4100].Len() = 1\n",
"WCnComV[4100][0] = 2508\n",
"WCnComV[4101].Len() = 1\n",
"WCnComV[4101][0] = 2507\n",
"WCnComV[4102].Len() = 1\n",
"WCnComV[4102][0] = 2506\n",
"WCnComV[4103].Len() = 1\n",
"WCnComV[4103][0] = 2504\n",
"WCnComV[4104].Len() = 1\n",
"WCnComV[4104][0] = 2502\n",
"WCnComV[4105].Len() = 1\n",
"WCnComV[4105][0] = 2501\n",
"WCnComV[4106].Len() = 1\n",
"WCnComV[4106][0] = 2499\n",
"WCnComV[4107].Len() = 1\n",
"WCnComV[4107][0] = 2495\n",
"WCnComV[4108].Len() = 1\n",
"WCnComV[4108][0] = 2491\n",
"WCnComV[4109].Len() = 1\n",
"WCnComV[4109][0] = 2488\n",
"WCnComV[4110].Len() = 1\n",
"WCnComV[4110][0] = 2487\n",
"WCnComV[4111].Len() = 1\n",
"WCnComV[4111][0] = 2475\n",
"WCnComV[4112].Len() = 1\n",
"WCnComV[4112][0] = 2474\n",
"WCnComV[4113].Len() = 1\n",
"WCnComV[4113][0] = 2469\n",
"WCnComV[4114].Len() = 1\n",
"WCnComV[4114][0] = 2468\n",
"WCnComV[4115].Len() = 1\n",
"WCnComV[4115][0] = 2464\n",
"WCnComV[4116].Len() = 1\n",
"WCnComV[4116][0] = 2460\n",
"WCnComV[4117].Len() = 1\n",
"WCnComV[4117][0] = 2458\n",
"WCnComV[4118].Len() = 1\n",
"WCnComV[4118][0] = 2455\n",
"WCnComV[4119].Len() = 1\n",
"WCnComV[4119][0] = 2451\n",
"WCnComV[4120].Len() = 1\n",
"WCnComV[4120][0] = 2447\n",
"WCnComV[4121].Len() = 1\n",
"WCnComV[4121][0] = 2444\n",
"WCnComV[4122].Len() = 1\n",
"WCnComV[4122][0] = 2440\n",
"WCnComV[4123].Len() = 1\n",
"WCnComV[4123][0] = 2439\n",
"WCnComV[4124].Len() = 1\n",
"WCnComV[4124][0] = 2438\n",
"WCnComV[4125].Len() = 1\n",
"WCnComV[4125][0] = 2437\n",
"WCnComV[4126].Len() = 1\n",
"WCnComV[4126][0] = 2436\n",
"WCnComV[4127].Len() = 1\n",
"WCnComV[4127][0] = 2429\n",
"WCnComV[4128].Len() = 1\n",
"WCnComV[4128][0] = 2428\n",
"WCnComV[4129].Len() = 1\n",
"WCnComV[4129][0] = 2424\n",
"WCnComV[4130].Len() = 1\n",
"WCnComV[4130][0] = 2423\n",
"WCnComV[4131].Len() = 1\n",
"WCnComV[4131][0] = 2420\n",
"WCnComV[4132].Len() = 1\n",
"WCnComV[4132][0] = 2419\n",
"WCnComV[4133].Len() = 1\n",
"WCnComV[4133][0] = 2412\n",
"WCnComV[4134].Len() = 1\n",
"WCnComV[4134][0] = 2409\n",
"WCnComV[4135].Len() = 1\n",
"WCnComV[4135][0] = 2405\n",
"WCnComV[4136].Len() = 1\n",
"WCnComV[4136][0] = 2404\n",
"WCnComV[4137].Len() = 1\n",
"WCnComV[4137][0] = 2402\n",
"WCnComV[4138].Len() = 1\n",
"WCnComV[4138][0] = 2399\n",
"WCnComV[4139].Len() = 1\n",
"WCnComV[4139][0] = 2396\n",
"WCnComV[4140].Len() = 1\n",
"WCnComV[4140][0] = 2393\n",
"WCnComV[4141].Len() = 1\n",
"WCnComV[4141][0] = 2391\n",
"WCnComV[4142].Len() = 1\n",
"WCnComV[4142][0] = 2389\n",
"WCnComV[4143].Len() = 1\n",
"WCnComV[4143][0] = 2384\n",
"WCnComV[4144].Len() = 1\n",
"WCnComV[4144][0] = 2383\n",
"WCnComV[4145].Len() = 1\n",
"WCnComV[4145][0] = 2381\n",
"WCnComV[4146].Len() = 1\n",
"WCnComV[4146][0] = 2380\n",
"WCnComV[4147].Len() = 1\n",
"WCnComV[4147][0] = 2378\n",
"WCnComV[4148].Len() = 1\n",
"WCnComV[4148][0] = 2376\n",
"WCnComV[4149].Len() = 1\n",
"WCnComV[4149][0] = 2375\n",
"WCnComV[4150].Len() = 1\n",
"WCnComV[4150][0] = 2374\n",
"WCnComV[4151].Len() = 1\n",
"WCnComV[4151][0] = 2373\n",
"WCnComV[4152].Len() = 1\n",
"WCnComV[4152][0] = 2371\n",
"WCnComV[4153].Len() = 1\n",
"WCnComV[4153][0] = 2365\n",
"WCnComV[4154].Len() = 1\n",
"WCnComV[4154][0] = 2363\n",
"WCnComV[4155].Len() = 1\n",
"WCnComV[4155][0] = 2361\n",
"WCnComV[4156].Len() = 1\n",
"WCnComV[4156][0] = 2357\n",
"WCnComV[4157].Len() = 1\n",
"WCnComV[4157][0] = 2356\n",
"WCnComV[4158].Len() = 1\n",
"WCnComV[4158][0] = 2352\n",
"WCnComV[4159].Len() = 1\n",
"WCnComV[4159][0] = 2351\n",
"WCnComV[4160].Len() = 1\n",
"WCnComV[4160][0] = 2350\n",
"WCnComV[4161].Len() = 1\n",
"WCnComV[4161][0] = 2348\n",
"WCnComV[4162].Len() = 1\n",
"WCnComV[4162][0] = 2345\n",
"WCnComV[4163].Len() = 1\n",
"WCnComV[4163][0] = 2344\n",
"WCnComV[4164].Len() = 1\n",
"WCnComV[4164][0] = 2343\n",
"WCnComV[4165].Len() = 1\n",
"WCnComV[4165][0] = 2340\n",
"WCnComV[4166].Len() = 1\n",
"WCnComV[4166][0] = 2339\n",
"WCnComV[4167].Len() = 1\n",
"WCnComV[4167][0] = 2337\n",
"WCnComV[4168].Len() = 1\n",
"WCnComV[4168][0] = 2335\n",
"WCnComV[4169].Len() = 1\n",
"WCnComV[4169][0] = 2330\n",
"WCnComV[4170].Len() = 1\n",
"WCnComV[4170][0] = 2329\n",
"WCnComV[4171].Len() = 1\n",
"WCnComV[4171][0] = 2328\n",
"WCnComV[4172].Len() = 1\n",
"WCnComV[4172][0] = 2327\n",
"WCnComV[4173].Len() = 1\n",
"WCnComV[4173][0] = 2326\n",
"WCnComV[4174].Len() = 1\n",
"WCnComV[4174][0] = 2325\n",
"WCnComV[4175].Len() = 1\n",
"WCnComV[4175][0] = 2321\n",
"WCnComV[4176].Len() = 1\n",
"WCnComV[4176][0] = 2318\n",
"WCnComV[4177].Len() = 1\n",
"WCnComV[4177][0] = 2316\n",
"WCnComV[4178].Len() = 1\n",
"WCnComV[4178][0] = 2315\n",
"WCnComV[4179].Len() = 1\n",
"WCnComV[4179][0] = 2308\n",
"WCnComV[4180].Len() = 1\n",
"WCnComV[4180][0] = 2307\n",
"WCnComV[4181].Len() = 1\n",
"WCnComV[4181][0] = 2305\n",
"WCnComV[4182].Len() = 1\n",
"WCnComV[4182][0] = 2303\n",
"WCnComV[4183].Len() = 1\n",
"WCnComV[4183][0] = 2298\n",
"WCnComV[4184].Len() = 1\n",
"WCnComV[4184][0] = 2289\n",
"WCnComV[4185].Len() = 1\n",
"WCnComV[4185][0] = 2286\n",
"WCnComV[4186].Len() = 1\n",
"WCnComV[4186][0] = 2277\n",
"WCnComV[4187].Len() = 1\n",
"WCnComV[4187][0] = 2275\n",
"WCnComV[4188].Len() = 1\n",
"WCnComV[4188][0] = 2273\n",
"WCnComV[4189].Len() = 1\n",
"WCnComV[4189][0] = 2268\n",
"WCnComV[4190].Len() = 1\n",
"WCnComV[4190][0] = 2266\n",
"WCnComV[4191].Len() = 1\n",
"WCnComV[4191][0] = 2264\n",
"WCnComV[4192].Len() = 1\n",
"WCnComV[4192][0] = 2263\n",
"WCnComV[4193].Len() = 1\n",
"WCnComV[4193][0] = 2260\n",
"WCnComV[4194].Len() = 1\n",
"WCnComV[4194][0] = 2258\n",
"WCnComV[4195].Len() = 1\n",
"WCnComV[4195][0] = 2256\n",
"WCnComV[4196].Len() = 1\n",
"WCnComV[4196][0] = 2250\n",
"WCnComV[4197].Len() = 1\n",
"WCnComV[4197][0] = 2247\n",
"WCnComV[4198].Len() = 1\n",
"WCnComV[4198][0] = 2246\n",
"WCnComV[4199].Len() = 1\n",
"WCnComV[4199][0] = 2244\n",
"WCnComV[4200].Len() = 1\n",
"WCnComV[4200][0] = 2240\n",
"WCnComV[4201].Len() = 1\n",
"WCnComV[4201][0] = 2238\n",
"WCnComV[4202].Len() = 1\n",
"WCnComV[4202][0] = 2234\n",
"WCnComV[4203].Len() = 1\n",
"WCnComV[4203][0] = 2233\n",
"WCnComV[4204].Len() = 1\n",
"WCnComV[4204][0] = 2231\n",
"WCnComV[4205].Len() = 1\n",
"WCnComV[4205][0] = 2228\n",
"WCnComV[4206].Len() = 1\n",
"WCnComV[4206][0] = 2226\n",
"WCnComV[4207].Len() = 1\n",
"WCnComV[4207][0] = 2225\n",
"WCnComV[4208].Len() = 1\n",
"WCnComV[4208][0] = 2224\n",
"WCnComV[4209].Len() = 1\n",
"WCnComV[4209][0] = 2223\n",
"WCnComV[4210].Len() = 1\n",
"WCnComV[4210][0] = 2221\n",
"WCnComV[4211].Len() = 1\n",
"WCnComV[4211][0] = 2220\n",
"WCnComV[4212].Len() = 1\n",
"WCnComV[4212][0] = 2219\n",
"WCnComV[4213].Len() = 1\n",
"WCnComV[4213][0] = 2218\n",
"WCnComV[4214].Len() = 1\n",
"WCnComV[4214][0] = 2217\n",
"WCnComV[4215].Len() = 1\n",
"WCnComV[4215][0] = 2214\n",
"WCnComV[4216].Len() = 1\n",
"WCnComV[4216][0] = 2211\n",
"WCnComV[4217].Len() = 1\n",
"WCnComV[4217][0] = 2210\n",
"WCnComV[4218].Len() = 1\n",
"WCnComV[4218][0] = 2207\n",
"WCnComV[4219].Len() = 1\n",
"WCnComV[4219][0] = 2205\n",
"WCnComV[4220].Len() = 1\n",
"WCnComV[4220][0] = 2204\n",
"WCnComV[4221].Len() = 1\n",
"WCnComV[4221][0] = 2202\n",
"WCnComV[4222].Len() = 1\n",
"WCnComV[4222][0] = 2201\n",
"WCnComV[4223].Len() = 1\n",
"WCnComV[4223][0] = 2198\n",
"WCnComV[4224].Len() = 1\n",
"WCnComV[4224][0] = 2194\n",
"WCnComV[4225].Len() = 1\n",
"WCnComV[4225][0] = 2193\n",
"WCnComV[4226].Len() = 1\n",
"WCnComV[4226][0] = 2190\n",
"WCnComV[4227].Len() = 1\n",
"WCnComV[4227][0] = 2183\n",
"WCnComV[4228].Len() = 1\n",
"WCnComV[4228][0] = 2182\n",
"WCnComV[4229].Len() = 1\n",
"WCnComV[4229][0] = 2180\n",
"WCnComV[4230].Len() = 1\n",
"WCnComV[4230][0] = 2179\n",
"WCnComV[4231].Len() = 1\n",
"WCnComV[4231][0] = 2177\n",
"WCnComV[4232].Len() = 1\n",
"WCnComV[4232][0] = 2168\n",
"WCnComV[4233].Len() = 1\n",
"WCnComV[4233][0] = 2165\n",
"WCnComV[4234].Len() = 1\n",
"WCnComV[4234][0] = 2162\n",
"WCnComV[4235].Len() = 1\n",
"WCnComV[4235][0] = 2161\n",
"WCnComV[4236].Len() = 1\n",
"WCnComV[4236][0] = 2150\n",
"WCnComV[4237].Len() = 1\n",
"WCnComV[4237][0] = 2146\n",
"WCnComV[4238].Len() = 1\n",
"WCnComV[4238][0] = 2139\n",
"WCnComV[4239].Len() = 1\n",
"WCnComV[4239][0] = 2132\n",
"WCnComV[4240].Len() = 1\n",
"WCnComV[4240][0] = 2129\n",
"WCnComV[4241].Len() = 1\n",
"WCnComV[4241][0] = 2128\n",
"WCnComV[4242].Len() = 1\n",
"WCnComV[4242][0] = 2127\n",
"WCnComV[4243].Len() = 1\n",
"WCnComV[4243][0] = 2126\n",
"WCnComV[4244].Len() = 1\n",
"WCnComV[4244][0] = 2125\n",
"WCnComV[4245].Len() = 1\n",
"WCnComV[4245][0] = 2123\n",
"WCnComV[4246].Len() = 1\n",
"WCnComV[4246][0] = 2119\n",
"WCnComV[4247].Len() = 1\n",
"WCnComV[4247][0] = 2117\n",
"WCnComV[4248].Len() = 1\n",
"WCnComV[4248][0] = 2114\n",
"WCnComV[4249].Len() = 1\n",
"WCnComV[4249][0] = 2111\n",
"WCnComV[4250].Len() = 1\n",
"WCnComV[4250][0] = 2109\n",
"WCnComV[4251].Len() = 1\n",
"WCnComV[4251][0] = 2108\n",
"WCnComV[4252].Len() = 1\n",
"WCnComV[4252][0] = 2107\n",
"WCnComV[4253].Len() = 1\n",
"WCnComV[4253][0] = 2106\n",
"WCnComV[4254].Len() = 1\n",
"WCnComV[4254][0] = 2105\n",
"WCnComV[4255].Len() = 1\n",
"WCnComV[4255][0] = 2096\n",
"WCnComV[4256].Len() = 1\n",
"WCnComV[4256][0] = 2093\n",
"WCnComV[4257].Len() = 1\n",
"WCnComV[4257][0] = 2091\n",
"WCnComV[4258].Len() = 1\n",
"WCnComV[4258][0] = 2088\n",
"WCnComV[4259].Len() = 1\n",
"WCnComV[4259][0] = 2086\n",
"WCnComV[4260].Len() = 1\n",
"WCnComV[4260][0] = 2083\n",
"WCnComV[4261].Len() = 1\n",
"WCnComV[4261][0] = 2080\n",
"WCnComV[4262].Len() = 1\n",
"WCnComV[4262][0] = 2079\n",
"WCnComV[4263].Len() = 1\n",
"WCnComV[4263][0] = 2078\n",
"WCnComV[4264].Len() = 1\n",
"WCnComV[4264][0] = 2076\n",
"WCnComV[4265].Len() = 1\n",
"WCnComV[4265][0] = 2075\n",
"WCnComV[4266].Len() = 1\n",
"WCnComV[4266][0] = 2071\n",
"WCnComV[4267].Len() = 1\n",
"WCnComV[4267][0] = 2070\n",
"WCnComV[4268].Len() = 1\n",
"WCnComV[4268][0] = 2066\n",
"WCnComV[4269].Len() = 1\n",
"WCnComV[4269][0] = 2065\n",
"WCnComV[4270].Len() = 1\n",
"WCnComV[4270][0] = 2060\n",
"WCnComV[4271].Len() = 1\n",
"WCnComV[4271][0] = 2056\n",
"WCnComV[4272].Len() = 1\n",
"WCnComV[4272][0] = 2055\n",
"WCnComV[4273].Len() = 1\n",
"WCnComV[4273][0] = 2052\n",
"WCnComV[4274].Len() = 1\n",
"WCnComV[4274][0] = 2049\n",
"WCnComV[4275].Len() = 1\n",
"WCnComV[4275][0] = 2048\n",
"WCnComV[4276].Len() = 1\n",
"WCnComV[4276][0] = 2047\n",
"WCnComV[4277].Len() = 1\n",
"WCnComV[4277][0] = 2045\n",
"WCnComV[4278].Len() = 1\n",
"WCnComV[4278][0] = 2043\n",
"WCnComV[4279].Len() = 1\n",
"WCnComV[4279][0] = 2042\n",
"WCnComV[4280].Len() = 1\n",
"WCnComV[4280][0] = 2041\n",
"WCnComV[4281].Len() = 1\n",
"WCnComV[4281][0] = 2040\n",
"WCnComV[4282].Len() = 1\n",
"WCnComV[4282][0] = 2039\n",
"WCnComV[4283].Len() = 1\n",
"WCnComV[4283][0] = 2036\n",
"WCnComV[4284].Len() = 1\n",
"WCnComV[4284][0] = 2035\n",
"WCnComV[4285].Len() = 1\n",
"WCnComV[4285][0] = 2033\n",
"WCnComV[4286].Len() = 1\n",
"WCnComV[4286][0] = 2032\n",
"WCnComV[4287].Len() = 1\n",
"WCnComV[4287][0] = 2029\n",
"WCnComV[4288].Len() = 1\n",
"WCnComV[4288][0] = 2028\n",
"WCnComV[4289].Len() = 1\n",
"WCnComV[4289][0] = 2024\n",
"WCnComV[4290].Len() = 1\n",
"WCnComV[4290][0] = 2020\n",
"WCnComV[4291].Len() = 1\n",
"WCnComV[4291][0] = 2016\n",
"WCnComV[4292].Len() = 1\n",
"WCnComV[4292][0] = 2015\n",
"WCnComV[4293].Len() = 1\n",
"WCnComV[4293][0] = 2009\n",
"WCnComV[4294].Len() = 1\n",
"WCnComV[4294][0] = 2008\n",
"WCnComV[4295].Len() = 1\n",
"WCnComV[4295][0] = 2004\n",
"WCnComV[4296].Len() = 1\n",
"WCnComV[4296][0] = 2002\n",
"WCnComV[4297].Len() = 1\n",
"WCnComV[4297][0] = 2001\n",
"WCnComV[4298].Len() = 1\n",
"WCnComV[4298][0] = 1999\n",
"WCnComV[4299].Len() = 1\n",
"WCnComV[4299][0] = 1998\n",
"WCnComV[4300].Len() = 1\n",
"WCnComV[4300][0] = 1996\n",
"WCnComV[4301].Len() = 1\n",
"WCnComV[4301][0] = 1985\n",
"WCnComV[4302].Len() = 1\n",
"WCnComV[4302][0] = 1984\n",
"WCnComV[4303].Len() = 1\n",
"WCnComV[4303][0] = 1982\n",
"WCnComV[4304].Len() = 1\n",
"WCnComV[4304][0] = 1981\n",
"WCnComV[4305].Len() = 1\n",
"WCnComV[4305][0] = 1980\n",
"WCnComV[4306].Len() = 1\n",
"WCnComV[4306][0] = 1976\n",
"WCnComV[4307].Len() = 1\n",
"WCnComV[4307][0] = 1972\n",
"WCnComV[4308].Len() = 1\n",
"WCnComV[4308][0] = 1965\n",
"WCnComV[4309].Len() = 1\n",
"WCnComV[4309][0] = 1964\n",
"WCnComV[4310].Len() = 1\n",
"WCnComV[4310][0] = 1961\n",
"WCnComV[4311].Len() = 1\n",
"WCnComV[4311][0] = 1960\n",
"WCnComV[4312].Len() = 1\n",
"WCnComV[4312][0] = 1958\n",
"WCnComV[4313].Len() = 1\n",
"WCnComV[4313][0] = 1957\n",
"WCnComV[4314].Len() = 1\n",
"WCnComV[4314][0] = 1955\n",
"WCnComV[4315].Len() = 1\n",
"WCnComV[4315][0] = 1952\n",
"WCnComV[4316].Len() = 1\n",
"WCnComV[4316][0] = 1950\n",
"WCnComV[4317].Len() = 1\n",
"WCnComV[4317][0] = 1947\n",
"WCnComV[4318].Len() = 1\n",
"WCnComV[4318][0] = 1943\n",
"WCnComV[4319].Len() = 1\n",
"WCnComV[4319][0] = 1942\n",
"WCnComV[4320].Len() = 1\n",
"WCnComV[4320][0] = 1937\n",
"WCnComV[4321].Len() = 1\n",
"WCnComV[4321][0] = 1936\n",
"WCnComV[4322].Len() = 1\n",
"WCnComV[4322][0] = 1935\n",
"WCnComV[4323].Len() = 1\n",
"WCnComV[4323][0] = 1932\n",
"WCnComV[4324].Len() = 1\n",
"WCnComV[4324][0] = 1930\n",
"WCnComV[4325].Len() = 1\n",
"WCnComV[4325][0] = 1926\n",
"WCnComV[4326].Len() = 1\n",
"WCnComV[4326][0] = 1922\n",
"WCnComV[4327].Len() = 1\n",
"WCnComV[4327][0] = 1919\n",
"WCnComV[4328].Len() = 1\n",
"WCnComV[4328][0] = 1918\n",
"WCnComV[4329].Len() = 1\n",
"WCnComV[4329][0] = 1915\n",
"WCnComV[4330].Len() = 1\n",
"WCnComV[4330][0] = 1914\n",
"WCnComV[4331].Len() = 1\n",
"WCnComV[4331][0] = 1911\n",
"WCnComV[4332].Len() = 1\n",
"WCnComV[4332][0] = 1909\n",
"WCnComV[4333].Len() = 1\n",
"WCnComV[4333][0] = 1908\n",
"WCnComV[4334].Len() = 1\n",
"WCnComV[4334][0] = 1906\n",
"WCnComV[4335].Len() = 1\n",
"WCnComV[4335][0] = 1901\n",
"WCnComV[4336].Len() = 1\n",
"WCnComV[4336][0] = 1899\n",
"WCnComV[4337].Len() = 1\n",
"WCnComV[4337][0] = 1897\n",
"WCnComV[4338].Len() = 1\n",
"WCnComV[4338][0] = 1894\n",
"WCnComV[4339].Len() = 1\n",
"WCnComV[4339][0] = 1891\n",
"WCnComV[4340].Len() = 1\n",
"WCnComV[4340][0] = 1889\n",
"WCnComV[4341].Len() = 1\n",
"WCnComV[4341][0] = 1880\n",
"WCnComV[4342].Len() = 1\n",
"WCnComV[4342][0] = 1876\n",
"WCnComV[4343].Len() = 1\n",
"WCnComV[4343][0] = 1874\n",
"WCnComV[4344].Len() = 1\n",
"WCnComV[4344][0] = 1873\n",
"WCnComV[4345].Len() = 1\n",
"WCnComV[4345][0] = 1866\n",
"WCnComV[4346].Len() = 1\n",
"WCnComV[4346][0] = 1863\n",
"WCnComV[4347].Len() = 1\n",
"WCnComV[4347][0] = 1860\n",
"WCnComV[4348].Len() = 1\n",
"WCnComV[4348][0] = 1859\n",
"WCnComV[4349].Len() = 1\n",
"WCnComV[4349][0] = 1855\n",
"WCnComV[4350].Len() = 1\n",
"WCnComV[4350][0] = 1852\n",
"WCnComV[4351].Len() = 1\n",
"WCnComV[4351][0] = 1837\n",
"WCnComV[4352].Len() = 1\n",
"WCnComV[4352][0] = 1829\n",
"WCnComV[4353].Len() = 1\n",
"WCnComV[4353][0] = 1826\n",
"WCnComV[4354].Len() = 1\n",
"WCnComV[4354][0] = 1819\n",
"WCnComV[4355].Len() = 1\n",
"WCnComV[4355][0] = 1816\n",
"WCnComV[4356].Len() = 1\n",
"WCnComV[4356][0] = 1814\n",
"WCnComV[4357].Len() = 1\n",
"WCnComV[4357][0] = 1813\n",
"WCnComV[4358].Len() = 1\n",
"WCnComV[4358][0] = 1811\n",
"WCnComV[4359].Len() = 1\n",
"WCnComV[4359][0] = 1810\n",
"WCnComV[4360].Len() = 1\n",
"WCnComV[4360][0] = 1802\n",
"WCnComV[4361].Len() = 1\n",
"WCnComV[4361][0] = 1801\n",
"WCnComV[4362].Len() = 1\n",
"WCnComV[4362][0] = 1800\n",
"WCnComV[4363].Len() = 1\n",
"WCnComV[4363][0] = 1799\n",
"WCnComV[4364].Len() = 1\n",
"WCnComV[4364][0] = 1793\n",
"WCnComV[4365].Len() = 1\n",
"WCnComV[4365][0] = 1791\n",
"WCnComV[4366].Len() = 1\n",
"WCnComV[4366][0] = 1787\n",
"WCnComV[4367].Len() = 1\n",
"WCnComV[4367][0] = 1781\n",
"WCnComV[4368].Len() = 1\n",
"WCnComV[4368][0] = 1777\n",
"WCnComV[4369].Len() = 1\n",
"WCnComV[4369][0] = 1767\n",
"WCnComV[4370].Len() = 1\n",
"WCnComV[4370][0] = 1764\n",
"WCnComV[4371].Len() = 1\n",
"WCnComV[4371][0] = 1759\n",
"WCnComV[4372].Len() = 1\n",
"WCnComV[4372][0] = 1757\n",
"WCnComV[4373].Len() = 1\n",
"WCnComV[4373][0] = 1750\n",
"WCnComV[4374].Len() = 1\n",
"WCnComV[4374][0] = 1749\n",
"WCnComV[4375].Len() = 1\n",
"WCnComV[4375][0] = 1747\n",
"WCnComV[4376].Len() = 1\n",
"WCnComV[4376][0] = 1745\n",
"WCnComV[4377].Len() = 1\n",
"WCnComV[4377][0] = 1737\n",
"WCnComV[4378].Len() = 1\n",
"WCnComV[4378][0] = 1735\n",
"WCnComV[4379].Len() = 1\n",
"WCnComV[4379][0] = 1726\n",
"WCnComV[4380].Len() = 1\n",
"WCnComV[4380][0] = 1724\n",
"WCnComV[4381].Len() = 1\n",
"WCnComV[4381][0] = 1722\n",
"WCnComV[4382].Len() = 1\n",
"WCnComV[4382][0] = 1721\n",
"WCnComV[4383].Len() = 1\n",
"WCnComV[4383][0] = 1716\n",
"WCnComV[4384].Len() = 1\n",
"WCnComV[4384][0] = 1714\n",
"WCnComV[4385].Len() = 1\n",
"WCnComV[4385][0] = 1713\n",
"WCnComV[4386].Len() = 1\n",
"WCnComV[4386][0] = 1712\n",
"WCnComV[4387].Len() = 1\n",
"WCnComV[4387][0] = 1709\n",
"WCnComV[4388].Len() = 1\n",
"WCnComV[4388][0] = 1703\n",
"WCnComV[4389].Len() = 1\n",
"WCnComV[4389][0] = 1700\n",
"WCnComV[4390].Len() = 1\n",
"WCnComV[4390][0] = 1697\n",
"WCnComV[4391].Len() = 1\n",
"WCnComV[4391][0] = 1696\n",
"WCnComV[4392].Len() = 1\n",
"WCnComV[4392][0] = 1689\n",
"WCnComV[4393].Len() = 1\n",
"WCnComV[4393][0] = 1688\n",
"WCnComV[4394].Len() = 1\n",
"WCnComV[4394][0] = 1686\n",
"WCnComV[4395].Len() = 1\n",
"WCnComV[4395][0] = 1684\n",
"WCnComV[4396].Len() = 1\n",
"WCnComV[4396][0] = 1682\n",
"WCnComV[4397].Len() = 1\n",
"WCnComV[4397][0] = 1679\n",
"WCnComV[4398].Len() = 1\n",
"WCnComV[4398][0] = 1678\n",
"WCnComV[4399].Len() = 1\n",
"WCnComV[4399][0] = 1676\n",
"WCnComV[4400].Len() = 1\n",
"WCnComV[4400][0] = 1672\n",
"WCnComV[4401].Len() = 1\n",
"WCnComV[4401][0] = 1671\n",
"WCnComV[4402].Len() = 1\n",
"WCnComV[4402][0] = 1670\n",
"WCnComV[4403].Len() = 1\n",
"WCnComV[4403][0] = 1669\n",
"WCnComV[4404].Len() = 1\n",
"WCnComV[4404][0] = 1666\n",
"WCnComV[4405].Len() = 1\n",
"WCnComV[4405][0] = 1665\n",
"WCnComV[4406].Len() = 1\n",
"WCnComV[4406][0] = 1662\n",
"WCnComV[4407].Len() = 1\n",
"WCnComV[4407][0] = 1657\n",
"WCnComV[4408].Len() = 1\n",
"WCnComV[4408][0] = 1656\n",
"WCnComV[4409].Len() = 1\n",
"WCnComV[4409][0] = 1654\n",
"WCnComV[4410].Len() = 1\n",
"WCnComV[4410][0] = 1653\n",
"WCnComV[4411].Len() = 1\n",
"WCnComV[4411][0] = 1652\n",
"WCnComV[4412].Len() = 1\n",
"WCnComV[4412][0] = 1650\n",
"WCnComV[4413].Len() = 1\n",
"WCnComV[4413][0] = 1648\n",
"WCnComV[4414].Len() = 1\n",
"WCnComV[4414][0] = 1645\n",
"WCnComV[4415].Len() = 1\n",
"WCnComV[4415][0] = 1644\n",
"WCnComV[4416].Len() = 1\n",
"WCnComV[4416][0] = 1643\n",
"WCnComV[4417].Len() = 1\n",
"WCnComV[4417][0] = 1642\n",
"WCnComV[4418].Len() = 1\n",
"WCnComV[4418][0] = 1640\n",
"WCnComV[4419].Len() = 1\n",
"WCnComV[4419][0] = 1639\n",
"WCnComV[4420].Len() = 1\n",
"WCnComV[4420][0] = 1638\n",
"WCnComV[4421].Len() = 1\n",
"WCnComV[4421][0] = 1637\n",
"WCnComV[4422].Len() = 1\n",
"WCnComV[4422][0] = 1634\n",
"WCnComV[4423].Len() = 1\n",
"WCnComV[4423][0] = 1632\n",
"WCnComV[4424].Len() = 1\n",
"WCnComV[4424][0] = 1630\n",
"WCnComV[4425].Len() = 1\n",
"WCnComV[4425][0] = 1625\n",
"WCnComV[4426].Len() = 1\n",
"WCnComV[4426][0] = 1622\n",
"WCnComV[4427].Len() = 1\n",
"WCnComV[4427][0] = 1620\n",
"WCnComV[4428].Len() = 1\n",
"WCnComV[4428][0] = 1615\n",
"WCnComV[4429].Len() = 1\n",
"WCnComV[4429][0] = 1613\n",
"WCnComV[4430].Len() = 1\n",
"WCnComV[4430][0] = 1607\n",
"WCnComV[4431].Len() = 1\n",
"WCnComV[4431][0] = 1603\n",
"WCnComV[4432].Len() = 1\n",
"WCnComV[4432][0] = 1601\n",
"WCnComV[4433].Len() = 1\n",
"WCnComV[4433][0] = 1596\n",
"WCnComV[4434].Len() = 1\n",
"WCnComV[4434][0] = 1590\n",
"WCnComV[4435].Len() = 1\n",
"WCnComV[4435][0] = 1586\n",
"WCnComV[4436].Len() = 1\n",
"WCnComV[4436][0] = 1583\n",
"WCnComV[4437].Len() = 1\n",
"WCnComV[4437][0] = 1581\n",
"WCnComV[4438].Len() = 1\n",
"WCnComV[4438][0] = 1579\n",
"WCnComV[4439].Len() = 1\n",
"WCnComV[4439][0] = 1576\n",
"WCnComV[4440].Len() = 1\n",
"WCnComV[4440][0] = 1572\n",
"WCnComV[4441].Len() = 1\n",
"WCnComV[4441][0] = 1571\n",
"WCnComV[4442].Len() = 1\n",
"WCnComV[4442][0] = 1567\n",
"WCnComV[4443].Len() = 1\n",
"WCnComV[4443][0] = 1566\n",
"WCnComV[4444].Len() = 1\n",
"WCnComV[4444][0] = 1565\n",
"WCnComV[4445].Len() = 1\n",
"WCnComV[4445][0] = 1561\n",
"WCnComV[4446].Len() = 1\n",
"WCnComV[4446][0] = 1554\n",
"WCnComV[4447].Len() = 1\n",
"WCnComV[4447][0] = 1550\n",
"WCnComV[4448].Len() = 1\n",
"WCnComV[4448][0] = 1546\n",
"WCnComV[4449].Len() = 1\n",
"WCnComV[4449][0] = 1545\n",
"WCnComV[4450].Len() = 1\n",
"WCnComV[4450][0] = 1544\n",
"WCnComV[4451].Len() = 1\n",
"WCnComV[4451][0] = 1542\n",
"WCnComV[4452].Len() = 1\n",
"WCnComV[4452][0] = 1541\n",
"WCnComV[4453].Len() = 1\n",
"WCnComV[4453][0] = 1539\n",
"WCnComV[4454].Len() = 1\n",
"WCnComV[4454][0] = 1538\n",
"WCnComV[4455].Len() = 1\n",
"WCnComV[4455][0] = 1534\n",
"WCnComV[4456].Len() = 1\n",
"WCnComV[4456][0] = 1532\n",
"WCnComV[4457].Len() = 1\n",
"WCnComV[4457][0] = 1529\n",
"WCnComV[4458].Len() = 1\n",
"WCnComV[4458][0] = 1528\n",
"WCnComV[4459].Len() = 1\n",
"WCnComV[4459][0] = 1527\n",
"WCnComV[4460].Len() = 1\n",
"WCnComV[4460][0] = 1526\n",
"WCnComV[4461].Len() = 1\n",
"WCnComV[4461][0] = 1524\n",
"WCnComV[4462].Len() = 1\n",
"WCnComV[4462][0] = 1521\n",
"WCnComV[4463].Len() = 1\n",
"WCnComV[4463][0] = 1520\n",
"WCnComV[4464].Len() = 1\n",
"WCnComV[4464][0] = 1516\n",
"WCnComV[4465].Len() = 1\n",
"WCnComV[4465][0] = 1515\n",
"WCnComV[4466].Len() = 1\n",
"WCnComV[4466][0] = 1509\n",
"WCnComV[4467].Len() = 1\n",
"WCnComV[4467][0] = 1504\n",
"WCnComV[4468].Len() = 1\n",
"WCnComV[4468][0] = 1503\n",
"WCnComV[4469].Len() = 1\n",
"WCnComV[4469][0] = 1502\n",
"WCnComV[4470].Len() = 1\n",
"WCnComV[4470][0] = 1499\n",
"WCnComV[4471].Len() = 1\n",
"WCnComV[4471][0] = 1496\n",
"WCnComV[4472].Len() = 1\n",
"WCnComV[4472][0] = 1495\n",
"WCnComV[4473].Len() = 1\n",
"WCnComV[4473][0] = 1494\n",
"WCnComV[4474].Len() = 1\n",
"WCnComV[4474][0] = 1492\n",
"WCnComV[4475].Len() = 1\n",
"WCnComV[4475][0] = 1490\n",
"WCnComV[4476].Len() = 1\n",
"WCnComV[4476][0] = 1489\n",
"WCnComV[4477].Len() = 1\n",
"WCnComV[4477][0] = 1488\n",
"WCnComV[4478].Len() = 1\n",
"WCnComV[4478][0] = 1482\n",
"WCnComV[4479].Len() = 1\n",
"WCnComV[4479][0] = 1474\n",
"WCnComV[4480].Len() = 1\n",
"WCnComV[4480][0] = 1473\n",
"WCnComV[4481].Len() = 1\n",
"WCnComV[4481][0] = 1471\n",
"WCnComV[4482].Len() = 1\n",
"WCnComV[4482][0] = 1470\n",
"WCnComV[4483].Len() = 1\n",
"WCnComV[4483][0] = 1467\n",
"WCnComV[4484].Len() = 1\n",
"WCnComV[4484][0] = 1466\n",
"WCnComV[4485].Len() = 1\n",
"WCnComV[4485][0] = 1461\n",
"WCnComV[4486].Len() = 1\n",
"WCnComV[4486][0] = 1460\n",
"WCnComV[4487].Len() = 1\n",
"WCnComV[4487][0] = 1459\n",
"WCnComV[4488].Len() = 1\n",
"WCnComV[4488][0] = 1457\n",
"WCnComV[4489].Len() = 1\n",
"WCnComV[4489][0] = 1453\n",
"WCnComV[4490].Len() = 1\n",
"WCnComV[4490][0] = 1450\n",
"WCnComV[4491].Len() = 1\n",
"WCnComV[4491][0] = 1447\n",
"WCnComV[4492].Len() = 1\n",
"WCnComV[4492][0] = 1442\n",
"WCnComV[4493].Len() = 1\n",
"WCnComV[4493][0] = 1438\n",
"WCnComV[4494].Len() = 1\n",
"WCnComV[4494][0] = 1437\n",
"WCnComV[4495].Len() = 1\n",
"WCnComV[4495][0] = 1435\n",
"WCnComV[4496].Len() = 1\n",
"WCnComV[4496][0] = 1434\n",
"WCnComV[4497].Len() = 1\n",
"WCnComV[4497][0] = 1432\n",
"WCnComV[4498].Len() = 1\n",
"WCnComV[4498][0] = 1425\n",
"WCnComV[4499].Len() = 1\n",
"WCnComV[4499][0] = 1423\n",
"WCnComV[4500].Len() = 1\n",
"WCnComV[4500][0] = 1419\n",
"WCnComV[4501].Len() = 1\n",
"WCnComV[4501][0] = 1418\n",
"WCnComV[4502].Len() = 1\n",
"WCnComV[4502][0] = 1417\n",
"WCnComV[4503].Len() = 1\n",
"WCnComV[4503][0] = 1410\n",
"WCnComV[4504].Len() = 1\n",
"WCnComV[4504][0] = 1403\n",
"WCnComV[4505].Len() = 1\n",
"WCnComV[4505][0] = 1402\n",
"WCnComV[4506].Len() = 1\n",
"WCnComV[4506][0] = 1400\n",
"WCnComV[4507].Len() = 1\n",
"WCnComV[4507][0] = 1399\n",
"WCnComV[4508].Len() = 1\n",
"WCnComV[4508][0] = 1397\n",
"WCnComV[4509].Len() = 1\n",
"WCnComV[4509][0] = 1391\n",
"WCnComV[4510].Len() = 1\n",
"WCnComV[4510][0] = 1389\n",
"WCnComV[4511].Len() = 1\n",
"WCnComV[4511][0] = 1385\n",
"WCnComV[4512].Len() = 1\n",
"WCnComV[4512][0] = 1384\n",
"WCnComV[4513].Len() = 1\n",
"WCnComV[4513][0] = 1383\n",
"WCnComV[4514].Len() = 1\n",
"WCnComV[4514][0] = 1382\n",
"WCnComV[4515].Len() = 1\n",
"WCnComV[4515][0] = 1380\n",
"WCnComV[4516].Len() = 1\n",
"WCnComV[4516][0] = 1379\n",
"WCnComV[4517].Len() = 1\n",
"WCnComV[4517][0] = 1376\n",
"WCnComV[4518].Len() = 1\n",
"WCnComV[4518][0] = 1375\n",
"WCnComV[4519].Len() = 1\n",
"WCnComV[4519][0] = 1368\n",
"WCnComV[4520].Len() = 1\n",
"WCnComV[4520][0] = 1362\n",
"WCnComV[4521].Len() = 1\n",
"WCnComV[4521][0] = 1361\n",
"WCnComV[4522].Len() = 1\n",
"WCnComV[4522][0] = 1359\n",
"WCnComV[4523].Len() = 1\n",
"WCnComV[4523][0] = 1357\n",
"WCnComV[4524].Len() = 1\n",
"WCnComV[4524][0] = 1355\n",
"WCnComV[4525].Len() = 1\n",
"WCnComV[4525][0] = 1352\n",
"WCnComV[4526].Len() = 1\n",
"WCnComV[4526][0] = 1344\n",
"WCnComV[4527].Len() = 1\n",
"WCnComV[4527][0] = 1342\n",
"WCnComV[4528].Len() = 1\n",
"WCnComV[4528][0] = 1340\n",
"WCnComV[4529].Len() = 1\n",
"WCnComV[4529][0] = 1339\n",
"WCnComV[4530].Len() = 1\n",
"WCnComV[4530][0] = 1338\n",
"WCnComV[4531].Len() = 1\n",
"WCnComV[4531][0] = 1337\n",
"WCnComV[4532].Len() = 1\n",
"WCnComV[4532][0] = 1336\n",
"WCnComV[4533].Len() = 1\n",
"WCnComV[4533][0] = 1334\n",
"WCnComV[4534].Len() = 1\n",
"WCnComV[4534][0] = 1332\n",
"WCnComV[4535].Len() = 1\n",
"WCnComV[4535][0] = 1331\n",
"WCnComV[4536].Len() = 1\n",
"WCnComV[4536][0] = 1327\n",
"WCnComV[4537].Len() = 1\n",
"WCnComV[4537][0] = 1326\n",
"WCnComV[4538].Len() = 1\n",
"WCnComV[4538][0] = 1324\n",
"WCnComV[4539].Len() = 1\n",
"WCnComV[4539][0] = 1322\n",
"WCnComV[4540].Len() = 1\n",
"WCnComV[4540][0] = 1316\n",
"WCnComV[4541].Len() = 1\n",
"WCnComV[4541][0] = 1313\n",
"WCnComV[4542].Len() = 1\n",
"WCnComV[4542][0] = 1311\n",
"WCnComV[4543].Len() = 1\n",
"WCnComV[4543][0] = 1306\n",
"WCnComV[4544].Len() = 1\n",
"WCnComV[4544][0] = 1305\n",
"WCnComV[4545].Len() = 1\n",
"WCnComV[4545][0] = 1301\n",
"WCnComV[4546].Len() = 1\n",
"WCnComV[4546][0] = 1299\n",
"WCnComV[4547].Len() = 1\n",
"WCnComV[4547][0] = 1296\n",
"WCnComV[4548].Len() = 1\n",
"WCnComV[4548][0] = 1294\n",
"WCnComV[4549].Len() = 1\n",
"WCnComV[4549][0] = 1292\n",
"WCnComV[4550].Len() = 1\n",
"WCnComV[4550][0] = 1291\n",
"WCnComV[4551].Len() = 1\n",
"WCnComV[4551][0] = 1284\n",
"WCnComV[4552].Len() = 1\n",
"WCnComV[4552][0] = 1283\n",
"WCnComV[4553].Len() = 1\n",
"WCnComV[4553][0] = 1281\n",
"WCnComV[4554].Len() = 1\n",
"WCnComV[4554][0] = 1280\n",
"WCnComV[4555].Len() = 1\n",
"WCnComV[4555][0] = 1278\n",
"WCnComV[4556].Len() = 1\n",
"WCnComV[4556][0] = 1272\n",
"WCnComV[4557].Len() = 1\n",
"WCnComV[4557][0] = 1269\n",
"WCnComV[4558].Len() = 1\n",
"WCnComV[4558][0] = 1268\n",
"WCnComV[4559].Len() = 1\n",
"WCnComV[4559][0] = 1264\n",
"WCnComV[4560].Len() = 1\n",
"WCnComV[4560][0] = 1262\n",
"WCnComV[4561].Len() = 1\n",
"WCnComV[4561][0] = 1260\n",
"WCnComV[4562].Len() = 1\n",
"WCnComV[4562][0] = 1257\n",
"WCnComV[4563].Len() = 1\n",
"WCnComV[4563][0] = 1255\n",
"WCnComV[4564].Len() = 1\n",
"WCnComV[4564][0] = 1249\n",
"WCnComV[4565].Len() = 1\n",
"WCnComV[4565][0] = 1244\n",
"WCnComV[4566].Len() = 1\n",
"WCnComV[4566][0] = 1243\n",
"WCnComV[4567].Len() = 1\n",
"WCnComV[4567][0] = 1240\n",
"WCnComV[4568].Len() = 1\n",
"WCnComV[4568][0] = 1237\n",
"WCnComV[4569].Len() = 1\n",
"WCnComV[4569][0] = 1234\n",
"WCnComV[4570].Len() = 1\n",
"WCnComV[4570][0] = 1230\n",
"WCnComV[4571].Len() = 1\n",
"WCnComV[4571][0] = 1229\n",
"WCnComV[4572].Len() = 1\n",
"WCnComV[4572][0] = 1228\n",
"WCnComV[4573].Len() = 1\n",
"WCnComV[4573][0] = 1225\n",
"WCnComV[4574].Len() = 1\n",
"WCnComV[4574][0] = 1223\n",
"WCnComV[4575].Len() = 1\n",
"WCnComV[4575][0] = 1222\n",
"WCnComV[4576].Len() = 1\n",
"WCnComV[4576][0] = 1221\n",
"WCnComV[4577].Len() = 1\n",
"WCnComV[4577][0] = 1218\n",
"WCnComV[4578].Len() = 1\n",
"WCnComV[4578][0] = 1214\n",
"WCnComV[4579].Len() = 1\n",
"WCnComV[4579][0] = 1212\n",
"WCnComV[4580].Len() = 1\n",
"WCnComV[4580][0] = 1211\n",
"WCnComV[4581].Len() = 1\n",
"WCnComV[4581][0] = 1210\n",
"WCnComV[4582].Len() = 1\n",
"WCnComV[4582][0] = 1206\n",
"WCnComV[4583].Len() = 1\n",
"WCnComV[4583][0] = 1203\n",
"WCnComV[4584].Len() = 1\n",
"WCnComV[4584][0] = 1199\n",
"WCnComV[4585].Len() = 1\n",
"WCnComV[4585][0] = 1194\n",
"WCnComV[4586].Len() = 1\n",
"WCnComV[4586][0] = 1193\n",
"WCnComV[4587].Len() = 1\n",
"WCnComV[4587][0] = 1191\n",
"WCnComV[4588].Len() = 1\n",
"WCnComV[4588][0] = 1189\n",
"WCnComV[4589].Len() = 1\n",
"WCnComV[4589][0] = 1187\n",
"WCnComV[4590].Len() = 1\n",
"WCnComV[4590][0] = 1185\n",
"WCnComV[4591].Len() = 1\n",
"WCnComV[4591][0] = 1180\n",
"WCnComV[4592].Len() = 1\n",
"WCnComV[4592][0] = 1177\n",
"WCnComV[4593].Len() = 1\n",
"WCnComV[4593][0] = 1175\n",
"WCnComV[4594].Len() = 1\n",
"WCnComV[4594][0] = 1174\n",
"WCnComV[4595].Len() = 1\n",
"WCnComV[4595][0] = 1171\n",
"WCnComV[4596].Len() = 1\n",
"WCnComV[4596][0] = 1165\n",
"WCnComV[4597].Len() = 1\n",
"WCnComV[4597][0] = 1164\n",
"WCnComV[4598].Len() = 1\n",
"WCnComV[4598][0] = 1163\n",
"WCnComV[4599].Len() = 1\n",
"WCnComV[4599][0] = 1162\n",
"WCnComV[4600].Len() = 1\n",
"WCnComV[4600][0] = 1160\n",
"WCnComV[4601].Len() = 1\n",
"WCnComV[4601][0] = 1159\n",
"WCnComV[4602].Len() = 1\n",
"WCnComV[4602][0] = 1153\n",
"WCnComV[4603].Len() = 1\n",
"WCnComV[4603][0] = 1152\n",
"WCnComV[4604].Len() = 1\n",
"WCnComV[4604][0] = 1151\n",
"WCnComV[4605].Len() = 1\n",
"WCnComV[4605][0] = 1141\n",
"WCnComV[4606].Len() = 1\n",
"WCnComV[4606][0] = 1139\n",
"WCnComV[4607].Len() = 1\n",
"WCnComV[4607][0] = 1135\n",
"WCnComV[4608].Len() = 1\n",
"WCnComV[4608][0] = 1134\n",
"WCnComV[4609].Len() = 1\n",
"WCnComV[4609][0] = 1132\n",
"WCnComV[4610].Len() = 1\n",
"WCnComV[4610][0] = 1129\n",
"WCnComV[4611].Len() = 1\n",
"WCnComV[4611][0] = 1126\n",
"WCnComV[4612].Len() = 1\n",
"WCnComV[4612][0] = 1120\n",
"WCnComV[4613].Len() = 1\n",
"WCnComV[4613][0] = 1119\n",
"WCnComV[4614].Len() = 1\n",
"WCnComV[4614][0] = 1114\n",
"WCnComV[4615].Len() = 1\n",
"WCnComV[4615][0] = 1112\n",
"WCnComV[4616].Len() = 1\n",
"WCnComV[4616][0] = 1107\n",
"WCnComV[4617].Len() = 1\n",
"WCnComV[4617][0] = 1104\n",
"WCnComV[4618].Len() = 1\n",
"WCnComV[4618][0] = 1103\n",
"WCnComV[4619].Len() = 1\n",
"WCnComV[4619][0] = 1102\n",
"WCnComV[4620].Len() = 1\n",
"WCnComV[4620][0] = 1101\n",
"WCnComV[4621].Len() = 1\n",
"WCnComV[4621][0] = 1097\n",
"WCnComV[4622].Len() = 1\n",
"WCnComV[4622][0] = 1096\n",
"WCnComV[4623].Len() = 1\n",
"WCnComV[4623][0] = 1095\n",
"WCnComV[4624].Len() = 1\n",
"WCnComV[4624][0] = 1093\n",
"WCnComV[4625].Len() = 1\n",
"WCnComV[4625][0] = 1091\n",
"WCnComV[4626].Len() = 1\n",
"WCnComV[4626][0] = 1090\n",
"WCnComV[4627].Len() = 1\n",
"WCnComV[4627][0] = 1082\n",
"WCnComV[4628].Len() = 1\n",
"WCnComV[4628][0] = 1078\n",
"WCnComV[4629].Len() = 1\n",
"WCnComV[4629][0] = 1077\n",
"WCnComV[4630].Len() = 1\n",
"WCnComV[4630][0] = 1075\n",
"WCnComV[4631].Len() = 1\n",
"WCnComV[4631][0] = 1073\n",
"WCnComV[4632].Len() = 1\n",
"WCnComV[4632][0] = 1068\n",
"WCnComV[4633].Len() = 1\n",
"WCnComV[4633][0] = 1066\n",
"WCnComV[4634].Len() = 1\n",
"WCnComV[4634][0] = 1060\n",
"WCnComV[4635].Len() = 1\n",
"WCnComV[4635][0] = 1058\n",
"WCnComV[4636].Len() = 1\n",
"WCnComV[4636][0] = 1051\n",
"WCnComV[4637].Len() = 1\n",
"WCnComV[4637][0] = 1050\n",
"WCnComV[4638].Len() = 1\n",
"WCnComV[4638][0] = 1041\n",
"WCnComV[4639].Len() = 1\n",
"WCnComV[4639][0] = 1040\n",
"WCnComV[4640].Len() = 1\n",
"WCnComV[4640][0] = 1033\n",
"WCnComV[4641].Len() = 1\n",
"WCnComV[4641][0] = 1031\n",
"WCnComV[4642].Len() = 1\n",
"WCnComV[4642][0] = 1028\n",
"WCnComV[4643].Len() = 1\n",
"WCnComV[4643][0] = 1027\n",
"WCnComV[4644].Len() = 1\n",
"WCnComV[4644][0] = 1019\n",
"WCnComV[4645].Len() = 1\n",
"WCnComV[4645][0] = 1014\n",
"WCnComV[4646].Len() = 1\n",
"WCnComV[4646][0] = 1012\n",
"WCnComV[4647].Len() = 1\n",
"WCnComV[4647][0] = 1010\n",
"WCnComV[4648].Len() = 1\n",
"WCnComV[4648][0] = 1006\n",
"WCnComV[4649].Len() = 1\n",
"WCnComV[4649][0] = 1004\n",
"WCnComV[4650].Len() = 1\n",
"WCnComV[4650][0] = 1001\n",
"WCnComV[4651].Len() = 1\n",
"WCnComV[4651][0] = 1000\n",
"WCnComV[4652].Len() = 1\n",
"WCnComV[4652][0] = 997\n",
"WCnComV[4653].Len() = 1\n",
"WCnComV[4653][0] = 996\n",
"WCnComV[4654].Len() = 1\n",
"WCnComV[4654][0] = 994\n",
"WCnComV[4655].Len() = 1\n",
"WCnComV[4655][0] = 990\n",
"WCnComV[4656].Len() = 1\n",
"WCnComV[4656][0] = 988\n",
"WCnComV[4657].Len() = 1\n",
"WCnComV[4657][0] = 983\n",
"WCnComV[4658].Len() = 1\n",
"WCnComV[4658][0] = 977\n",
"WCnComV[4659].Len() = 1\n",
"WCnComV[4659][0] = 974\n",
"WCnComV[4660].Len() = 1\n",
"WCnComV[4660][0] = 971\n",
"WCnComV[4661].Len() = 1\n",
"WCnComV[4661][0] = 970\n",
"WCnComV[4662].Len() = 1\n",
"WCnComV[4662][0] = 967\n",
"WCnComV[4663].Len() = 1\n",
"WCnComV[4663][0] = 963\n",
"WCnComV[4664].Len() = 1\n",
"WCnComV[4664][0] = 962\n",
"WCnComV[4665].Len() = 1\n",
"WCnComV[4665][0] = 959\n",
"WCnComV[4666].Len() = 1\n",
"WCnComV[4666][0] = 958\n",
"WCnComV[4667].Len() = 1\n",
"WCnComV[4667][0] = 952\n",
"WCnComV[4668].Len() = 1\n",
"WCnComV[4668][0] = 949\n",
"WCnComV[4669].Len() = 1\n",
"WCnComV[4669][0] = 946\n",
"WCnComV[4670].Len() = 1\n",
"WCnComV[4670][0] = 945\n",
"WCnComV[4671].Len() = 1\n",
"WCnComV[4671][0] = 944\n",
"WCnComV[4672].Len() = 1\n",
"WCnComV[4672][0] = 941\n",
"WCnComV[4673].Len() = 1\n",
"WCnComV[4673][0] = 935\n",
"WCnComV[4674].Len() = 1\n",
"WCnComV[4674][0] = 933\n",
"WCnComV[4675].Len() = 1\n",
"WCnComV[4675][0] = 932\n",
"WCnComV[4676].Len() = 1\n",
"WCnComV[4676][0] = 930\n",
"WCnComV[4677].Len() = 1\n",
"WCnComV[4677][0] = 924\n",
"WCnComV[4678].Len() = 1\n",
"WCnComV[4678][0] = 923\n",
"WCnComV[4679].Len() = 1\n",
"WCnComV[4679][0] = 922\n",
"WCnComV[4680].Len() = 1\n",
"WCnComV[4680][0] = 916\n",
"WCnComV[4681].Len() = 1\n",
"WCnComV[4681][0] = 915\n",
"WCnComV[4682].Len() = 1\n",
"WCnComV[4682][0] = 912\n",
"WCnComV[4683].Len() = 1\n",
"WCnComV[4683][0] = 909\n",
"WCnComV[4684].Len() = 1\n",
"WCnComV[4684][0] = 908\n",
"WCnComV[4685].Len() = 1\n",
"WCnComV[4685][0] = 904\n",
"WCnComV[4686].Len() = 1\n",
"WCnComV[4686][0] = 903\n",
"WCnComV[4687].Len() = 1\n",
"WCnComV[4687][0] = 901\n",
"WCnComV[4688].Len() = 1\n",
"WCnComV[4688][0] = 900\n",
"WCnComV[4689].Len() = 1\n",
"WCnComV[4689][0] = 897\n",
"WCnComV[4690].Len() = 1\n",
"WCnComV[4690][0] = 892\n",
"WCnComV[4691].Len() = 1\n",
"WCnComV[4691][0] = 890\n",
"WCnComV[4692].Len() = 1\n",
"WCnComV[4692][0] = 885\n",
"WCnComV[4693].Len() = 1\n",
"WCnComV[4693][0] = 881\n",
"WCnComV[4694].Len() = 1\n",
"WCnComV[4694][0] = 880\n",
"WCnComV[4695].Len() = 1\n",
"WCnComV[4695][0] = 874\n",
"WCnComV[4696].Len() = 1\n",
"WCnComV[4696][0] = 870\n",
"WCnComV[4697].Len() = 1\n",
"WCnComV[4697][0] = 868\n",
"WCnComV[4698].Len() = 1\n",
"WCnComV[4698][0] = 865\n",
"WCnComV[4699].Len() = 1\n",
"WCnComV[4699][0] = 864\n",
"WCnComV[4700].Len() = 1\n",
"WCnComV[4700][0] = 862\n",
"WCnComV[4701].Len() = 1\n",
"WCnComV[4701][0] = 858\n",
"WCnComV[4702].Len() = 1\n",
"WCnComV[4702][0] = 856\n",
"WCnComV[4703].Len() = 1\n",
"WCnComV[4703][0] = 855\n",
"WCnComV[4704].Len() = 1\n",
"WCnComV[4704][0] = 854\n",
"WCnComV[4705].Len() = 1\n",
"WCnComV[4705][0] = 852\n",
"WCnComV[4706].Len() = 1\n",
"WCnComV[4706][0] = 849\n",
"WCnComV[4707].Len() = 1\n",
"WCnComV[4707][0] = 848\n",
"WCnComV[4708].Len() = 1\n",
"WCnComV[4708][0] = 840\n",
"WCnComV[4709].Len() = 1\n",
"WCnComV[4709][0] = 839\n",
"WCnComV[4710].Len() = 1\n",
"WCnComV[4710][0] = 836\n",
"WCnComV[4711].Len() = 1\n",
"WCnComV[4711][0] = 833\n",
"WCnComV[4712].Len() = 1\n",
"WCnComV[4712][0] = 832\n",
"WCnComV[4713].Len() = 1\n",
"WCnComV[4713][0] = 829\n",
"WCnComV[4714].Len() = 1\n",
"WCnComV[4714][0] = 815\n",
"WCnComV[4715].Len() = 1\n",
"WCnComV[4715][0] = 810\n",
"WCnComV[4716].Len() = 1\n",
"WCnComV[4716][0] = 808\n",
"WCnComV[4717].Len() = 1\n",
"WCnComV[4717][0] = 806\n",
"WCnComV[4718].Len() = 1\n",
"WCnComV[4718][0] = 805\n",
"WCnComV[4719].Len() = 1\n",
"WCnComV[4719][0] = 801\n",
"WCnComV[4720].Len() = 1\n",
"WCnComV[4720][0] = 799\n",
"WCnComV[4721].Len() = 1\n",
"WCnComV[4721][0] = 796\n",
"WCnComV[4722].Len() = 1\n",
"WCnComV[4722][0] = 795\n",
"WCnComV[4723].Len() = 1\n",
"WCnComV[4723][0] = 793\n",
"WCnComV[4724].Len() = 1\n",
"WCnComV[4724][0] = 792\n",
"WCnComV[4725].Len() = 1\n",
"WCnComV[4725][0] = 790\n",
"WCnComV[4726].Len() = 1\n",
"WCnComV[4726][0] = 789\n",
"WCnComV[4727].Len() = 1\n",
"WCnComV[4727][0] = 784\n",
"WCnComV[4728].Len() = 1\n",
"WCnComV[4728][0] = 777\n",
"WCnComV[4729].Len() = 1\n",
"WCnComV[4729][0] = 774\n",
"WCnComV[4730].Len() = 1\n",
"WCnComV[4730][0] = 772\n",
"WCnComV[4731].Len() = 1\n",
"WCnComV[4731][0] = 770\n",
"WCnComV[4732].Len() = 1\n",
"WCnComV[4732][0] = 769\n",
"WCnComV[4733].Len() = 1\n",
"WCnComV[4733][0] = 767\n",
"WCnComV[4734].Len() = 1\n",
"WCnComV[4734][0] = 766\n",
"WCnComV[4735].Len() = 1\n",
"WCnComV[4735][0] = 763\n",
"WCnComV[4736].Len() = 1\n",
"WCnComV[4736][0] = 761\n",
"WCnComV[4737].Len() = 1\n",
"WCnComV[4737][0] = 759\n",
"WCnComV[4738].Len() = 1\n",
"WCnComV[4738][0] = 753\n",
"WCnComV[4739].Len() = 1\n",
"WCnComV[4739][0] = 752\n",
"WCnComV[4740].Len() = 1\n",
"WCnComV[4740][0] = 750\n",
"WCnComV[4741].Len() = 1\n",
"WCnComV[4741][0] = 749\n",
"WCnComV[4742].Len() = 1\n",
"WCnComV[4742][0] = 746\n",
"WCnComV[4743].Len() = 1\n",
"WCnComV[4743][0] = 744\n",
"WCnComV[4744].Len() = 1\n",
"WCnComV[4744][0] = 743\n",
"WCnComV[4745].Len() = 1\n",
"WCnComV[4745][0] = 742\n",
"WCnComV[4746].Len() = 1\n",
"WCnComV[4746][0] = 735\n",
"WCnComV[4747].Len() = 1\n",
"WCnComV[4747][0] = 725\n",
"WCnComV[4748].Len() = 1\n",
"WCnComV[4748][0] = 715\n",
"WCnComV[4749].Len() = 1\n",
"WCnComV[4749][0] = 714\n",
"WCnComV[4750].Len() = 1\n",
"WCnComV[4750][0] = 712\n",
"WCnComV[4751].Len() = 1\n",
"WCnComV[4751][0] = 711\n",
"WCnComV[4752].Len() = 1\n",
"WCnComV[4752][0] = 709\n",
"WCnComV[4753].Len() = 1\n",
"WCnComV[4753][0] = 708\n",
"WCnComV[4754].Len() = 1\n",
"WCnComV[4754][0] = 706\n",
"WCnComV[4755].Len() = 1\n",
"WCnComV[4755][0] = 701\n",
"WCnComV[4756].Len() = 1\n",
"WCnComV[4756][0] = 698\n",
"WCnComV[4757].Len() = 1\n",
"WCnComV[4757][0] = 693\n",
"WCnComV[4758].Len() = 1\n",
"WCnComV[4758][0] = 692\n",
"WCnComV[4759].Len() = 1\n",
"WCnComV[4759][0] = 688\n",
"WCnComV[4760].Len() = 1\n",
"WCnComV[4760][0] = 683\n",
"WCnComV[4761].Len() = 1\n",
"WCnComV[4761][0] = 681\n",
"WCnComV[4762].Len() = 1\n",
"WCnComV[4762][0] = 680\n",
"WCnComV[4763].Len() = 1\n",
"WCnComV[4763][0] = 675\n",
"WCnComV[4764].Len() = 1\n",
"WCnComV[4764][0] = 672\n",
"WCnComV[4765].Len() = 1\n",
"WCnComV[4765][0] = 671\n",
"WCnComV[4766].Len() = 1\n",
"WCnComV[4766][0] = 665\n",
"WCnComV[4767].Len() = 1\n",
"WCnComV[4767][0] = 663\n",
"WCnComV[4768].Len() = 1\n",
"WCnComV[4768][0] = 658\n",
"WCnComV[4769].Len() = 1\n",
"WCnComV[4769][0] = 656\n",
"WCnComV[4770].Len() = 1\n",
"WCnComV[4770][0] = 655\n",
"WCnComV[4771].Len() = 1\n",
"WCnComV[4771][0] = 653\n",
"WCnComV[4772].Len() = 1\n",
"WCnComV[4772][0] = 649\n",
"WCnComV[4773].Len() = 1\n",
"WCnComV[4773][0] = 648\n",
"WCnComV[4774].Len() = 1\n",
"WCnComV[4774][0] = 646\n",
"WCnComV[4775].Len() = 1\n",
"WCnComV[4775][0] = 643\n",
"WCnComV[4776].Len() = 1\n",
"WCnComV[4776][0] = 640\n",
"WCnComV[4777].Len() = 1\n",
"WCnComV[4777][0] = 639\n",
"WCnComV[4778].Len() = 1\n",
"WCnComV[4778][0] = 638\n",
"WCnComV[4779].Len() = 1\n",
"WCnComV[4779][0] = 637\n",
"WCnComV[4780].Len() = 1\n",
"WCnComV[4780][0] = 634\n",
"WCnComV[4781].Len() = 1\n",
"WCnComV[4781][0] = 633\n",
"WCnComV[4782].Len() = 1\n",
"WCnComV[4782][0] = 631\n",
"WCnComV[4783].Len() = 1\n",
"WCnComV[4783][0] = 630\n",
"WCnComV[4784].Len() = 1\n",
"WCnComV[4784][0] = 626\n",
"WCnComV[4785].Len() = 1\n",
"WCnComV[4785][0] = 622\n",
"WCnComV[4786].Len() = 1\n",
"WCnComV[4786][0] = 620\n",
"WCnComV[4787].Len() = 1\n",
"WCnComV[4787][0] = 619\n",
"WCnComV[4788].Len() = 1\n",
"WCnComV[4788][0] = 607\n",
"WCnComV[4789].Len() = 1\n",
"WCnComV[4789][0] = 604\n",
"WCnComV[4790].Len() = 1\n",
"WCnComV[4790][0] = 603\n",
"WCnComV[4791].Len() = 1\n",
"WCnComV[4791][0] = 602\n",
"WCnComV[4792].Len() = 1\n",
"WCnComV[4792][0] = 600\n",
"WCnComV[4793].Len() = 1\n",
"WCnComV[4793][0] = 598\n",
"WCnComV[4794].Len() = 1\n",
"WCnComV[4794][0] = 596\n",
"WCnComV[4795].Len() = 1\n",
"WCnComV[4795][0] = 594\n",
"WCnComV[4796].Len() = 1\n",
"WCnComV[4796][0] = 592\n",
"WCnComV[4797].Len() = 1\n",
"WCnComV[4797][0] = 590\n",
"WCnComV[4798].Len() = 1\n",
"WCnComV[4798][0] = 589\n",
"WCnComV[4799].Len() = 1\n",
"WCnComV[4799][0] = 588\n",
"WCnComV[4800].Len() = 1\n",
"WCnComV[4800][0] = 585\n",
"WCnComV[4801].Len() = 1\n",
"WCnComV[4801][0] = 582\n",
"WCnComV[4802].Len() = 1\n",
"WCnComV[4802][0] = 581\n",
"WCnComV[4803].Len() = 1\n",
"WCnComV[4803][0] = 580\n",
"WCnComV[4804].Len() = 1\n",
"WCnComV[4804][0] = 571\n",
"WCnComV[4805].Len() = 1\n",
"WCnComV[4805][0] = 567\n",
"WCnComV[4806].Len() = 1\n",
"WCnComV[4806][0] = 564\n",
"WCnComV[4807].Len() = 1\n",
"WCnComV[4807][0] = 563\n",
"WCnComV[4808].Len() = 1\n",
"WCnComV[4808][0] = 558\n",
"WCnComV[4809].Len() = 1\n",
"WCnComV[4809][0] = 557\n",
"WCnComV[4810].Len() = 1\n",
"WCnComV[4810][0] = 554\n",
"WCnComV[4811].Len() = 1\n",
"WCnComV[4811][0] = 550\n",
"WCnComV[4812].Len() = 1\n",
"WCnComV[4812][0] = 547\n",
"WCnComV[4813].Len() = 1\n",
"WCnComV[4813][0] = 541\n",
"WCnComV[4814].Len() = 1\n",
"WCnComV[4814][0] = 540\n",
"WCnComV[4815].Len() = 1\n",
"WCnComV[4815][0] = 539\n",
"WCnComV[4816].Len() = 1\n",
"WCnComV[4816][0] = 534\n",
"WCnComV[4817].Len() = 1\n",
"WCnComV[4817][0] = 531\n",
"WCnComV[4818].Len() = 1\n",
"WCnComV[4818][0] = 530\n",
"WCnComV[4819].Len() = 1\n",
"WCnComV[4819][0] = 526\n",
"WCnComV[4820].Len() = 1\n",
"WCnComV[4820][0] = 525\n",
"WCnComV[4821].Len() = 1\n",
"WCnComV[4821][0] = 517\n",
"WCnComV[4822].Len() = 1\n",
"WCnComV[4822][0] = 515\n",
"WCnComV[4823].Len() = 1\n",
"WCnComV[4823][0] = 512\n",
"WCnComV[4824].Len() = 1\n",
"WCnComV[4824][0] = 511\n",
"WCnComV[4825].Len() = 1\n",
"WCnComV[4825][0] = 507\n",
"WCnComV[4826].Len() = 1\n",
"WCnComV[4826][0] = 502\n",
"WCnComV[4827].Len() = 1\n",
"WCnComV[4827][0] = 501\n",
"WCnComV[4828].Len() = 1\n",
"WCnComV[4828][0] = 498\n",
"WCnComV[4829].Len() = 1\n",
"WCnComV[4829][0] = 497\n",
"WCnComV[4830].Len() = 1\n",
"WCnComV[4830][0] = 496\n",
"WCnComV[4831].Len() = 1\n",
"WCnComV[4831][0] = 495\n",
"WCnComV[4832].Len() = 1\n",
"WCnComV[4832][0] = 492\n",
"WCnComV[4833].Len() = 1\n",
"WCnComV[4833][0] = 485\n",
"WCnComV[4834].Len() = 1\n",
"WCnComV[4834][0] = 482\n",
"WCnComV[4835].Len() = 1\n",
"WCnComV[4835][0] = 481\n",
"WCnComV[4836].Len() = 1\n",
"WCnComV[4836][0] = 480\n",
"WCnComV[4837].Len() = 1\n",
"WCnComV[4837][0] = 470\n",
"WCnComV[4838].Len() = 1\n",
"WCnComV[4838][0] = 469\n",
"WCnComV[4839].Len() = 1\n",
"WCnComV[4839][0] = 468\n",
"WCnComV[4840].Len() = 1\n",
"WCnComV[4840][0] = 467\n",
"WCnComV[4841].Len() = 1\n",
"WCnComV[4841][0] = 466\n",
"WCnComV[4842].Len() = 1\n",
"WCnComV[4842][0] = 465\n",
"WCnComV[4843].Len() = 1\n",
"WCnComV[4843][0] = 462\n",
"WCnComV[4844].Len() = 1\n",
"WCnComV[4844][0] = 454\n",
"WCnComV[4845].Len() = 1\n",
"WCnComV[4845][0] = 453\n",
"WCnComV[4846].Len() = 1\n",
"WCnComV[4846][0] = 452\n",
"WCnComV[4847].Len() = 1\n",
"WCnComV[4847][0] = 451\n",
"WCnComV[4848].Len() = 1\n",
"WCnComV[4848][0] = 449\n",
"WCnComV[4849].Len() = 1\n",
"WCnComV[4849][0] = 445\n",
"WCnComV[4850].Len() = 1\n",
"WCnComV[4850][0] = 444\n",
"WCnComV[4851].Len() = 1\n",
"WCnComV[4851][0] = 442\n",
"WCnComV[4852].Len() = 1\n",
"WCnComV[4852][0] = 441\n",
"WCnComV[4853].Len() = 1\n",
"WCnComV[4853][0] = 440\n",
"WCnComV[4854].Len() = 1\n",
"WCnComV[4854][0] = 439\n",
"WCnComV[4855].Len() = 1\n",
"WCnComV[4855][0] = 437\n",
"WCnComV[4856].Len() = 1\n",
"WCnComV[4856][0] = 436\n",
"WCnComV[4857].Len() = 1\n",
"WCnComV[4857][0] = 435\n",
"WCnComV[4858].Len() = 1\n",
"WCnComV[4858][0] = 434\n",
"WCnComV[4859].Len() = 1\n",
"WCnComV[4859][0] = 432\n",
"WCnComV[4860].Len() = 1\n",
"WCnComV[4860][0] = 431\n",
"WCnComV[4861].Len() = 1\n",
"WCnComV[4861][0] = 429\n",
"WCnComV[4862].Len() = 1\n",
"WCnComV[4862][0] = 421\n",
"WCnComV[4863].Len() = 1\n",
"WCnComV[4863][0] = 420\n",
"WCnComV[4864].Len() = 1\n",
"WCnComV[4864][0] = 418\n",
"WCnComV[4865].Len() = 1\n",
"WCnComV[4865][0] = 417\n",
"WCnComV[4866].Len() = 1\n",
"WCnComV[4866][0] = 416\n",
"WCnComV[4867].Len() = 1\n",
"WCnComV[4867][0] = 411\n",
"WCnComV[4868].Len() = 1\n",
"WCnComV[4868][0] = 404\n",
"WCnComV[4869].Len() = 1\n",
"WCnComV[4869][0] = 401\n",
"WCnComV[4870].Len() = 1\n",
"WCnComV[4870][0] = 397\n",
"WCnComV[4871].Len() = 1\n",
"WCnComV[4871][0] = 395\n",
"WCnComV[4872].Len() = 1\n",
"WCnComV[4872][0] = 391\n",
"WCnComV[4873].Len() = 1\n",
"WCnComV[4873][0] = 390\n",
"WCnComV[4874].Len() = 1\n",
"WCnComV[4874][0] = 386\n",
"WCnComV[4875].Len() = 1\n",
"WCnComV[4875][0] = 381\n",
"WCnComV[4876].Len() = 1\n",
"WCnComV[4876][0] = 380\n",
"WCnComV[4877].Len() = 1\n",
"WCnComV[4877][0] = 372\n",
"WCnComV[4878].Len() = 1\n",
"WCnComV[4878][0] = 367\n",
"WCnComV[4879].Len() = 1\n",
"WCnComV[4879][0] = 366\n",
"WCnComV[4880].Len() = 1\n",
"WCnComV[4880][0] = 362\n",
"WCnComV[4881].Len() = 1\n",
"WCnComV[4881][0] = 358\n",
"WCnComV[4882].Len() = 1\n",
"WCnComV[4882][0] = 355\n",
"WCnComV[4883].Len() = 1\n",
"WCnComV[4883][0] = 353\n",
"WCnComV[4884].Len() = 1\n",
"WCnComV[4884][0] = 350\n",
"WCnComV[4885].Len() = 1\n",
"WCnComV[4885][0] = 349\n",
"WCnComV[4886].Len() = 1\n",
"WCnComV[4886][0] = 348\n",
"WCnComV[4887].Len() = 1\n",
"WCnComV[4887][0] = 347\n",
"WCnComV[4888].Len() = 1\n",
"WCnComV[4888][0] = 345\n",
"WCnComV[4889].Len() = 1\n",
"WCnComV[4889][0] = 341\n",
"WCnComV[4890].Len() = 1\n",
"WCnComV[4890][0] = 332\n",
"WCnComV[4891].Len() = 1\n",
"WCnComV[4891][0] = 329\n",
"WCnComV[4892].Len() = 1\n",
"WCnComV[4892][0] = 327\n",
"WCnComV[4893].Len() = 1\n",
"WCnComV[4893][0] = 323\n",
"WCnComV[4894].Len() = 1\n",
"WCnComV[4894][0] = 321\n",
"WCnComV[4895].Len() = 1\n",
"WCnComV[4895][0] = 317\n",
"WCnComV[4896].Len() = 1\n",
"WCnComV[4896][0] = 313\n",
"WCnComV[4897].Len() = 1\n",
"WCnComV[4897][0] = 311\n",
"WCnComV[4898].Len() = 1\n",
"WCnComV[4898][0] = 309\n",
"WCnComV[4899].Len() = 1\n",
"WCnComV[4899][0] = 308\n",
"WCnComV[4900].Len() = 1\n",
"WCnComV[4900][0] = 302\n",
"WCnComV[4901].Len() = 1\n",
"WCnComV[4901][0] = 299\n",
"WCnComV[4902].Len() = 1\n",
"WCnComV[4902][0] = 295\n",
"WCnComV[4903].Len() = 1\n",
"WCnComV[4903][0] = 292\n",
"WCnComV[4904].Len() = 1\n",
"WCnComV[4904][0] = 289\n",
"WCnComV[4905].Len() = 1\n",
"WCnComV[4905][0] = 288\n",
"WCnComV[4906].Len() = 1\n",
"WCnComV[4906][0] = 284\n",
"WCnComV[4907].Len() = 1\n",
"WCnComV[4907][0] = 282\n",
"WCnComV[4908].Len() = 1\n",
"WCnComV[4908][0] = 281\n",
"WCnComV[4909].Len() = 1\n",
"WCnComV[4909][0] = 280\n",
"WCnComV[4910].Len() = 1\n",
"WCnComV[4910][0] = 279\n",
"WCnComV[4911].Len() = 1\n",
"WCnComV[4911][0] = 276\n",
"WCnComV[4912].Len() = 1\n",
"WCnComV[4912][0] = 271\n",
"WCnComV[4913].Len() = 1\n",
"WCnComV[4913][0] = 268\n",
"WCnComV[4914].Len() = 1\n",
"WCnComV[4914][0] = 260\n",
"WCnComV[4915].Len() = 1\n",
"WCnComV[4915][0] = 259\n",
"WCnComV[4916].Len() = 1\n",
"WCnComV[4916][0] = 255\n",
"WCnComV[4917].Len() = 1\n",
"WCnComV[4917][0] = 251\n",
"WCnComV[4918].Len() = 1\n",
"WCnComV[4918][0] = 249\n",
"WCnComV[4919].Len() = 1\n",
"WCnComV[4919][0] = 248\n",
"WCnComV[4920].Len() = 1\n",
"WCnComV[4920][0] = 247\n",
"WCnComV[4921].Len() = 1\n",
"WCnComV[4921][0] = 239\n",
"WCnComV[4922].Len() = 1\n",
"WCnComV[4922][0] = 228\n",
"WCnComV[4923].Len() = 1\n",
"WCnComV[4923][0] = 227\n",
"WCnComV[4924].Len() = 1\n",
"WCnComV[4924][0] = 224\n",
"WCnComV[4925].Len() = 1\n",
"WCnComV[4925][0] = 223\n",
"WCnComV[4926].Len() = 1\n",
"WCnComV[4926][0] = 219\n",
"WCnComV[4927].Len() = 1\n",
"WCnComV[4927][0] = 216\n",
"WCnComV[4928].Len() = 1\n",
"WCnComV[4928][0] = 215\n",
"WCnComV[4929].Len() = 1\n",
"WCnComV[4929][0] = 214\n",
"WCnComV[4930].Len() = 1\n",
"WCnComV[4930][0] = 211\n",
"WCnComV[4931].Len() = 1\n",
"WCnComV[4931][0] = 210\n",
"WCnComV[4932].Len() = 1\n",
"WCnComV[4932][0] = 207\n",
"WCnComV[4933].Len() = 1\n",
"WCnComV[4933][0] = 206\n",
"WCnComV[4934].Len() = 1\n",
"WCnComV[4934][0] = 201\n",
"WCnComV[4935].Len() = 1\n",
"WCnComV[4935][0] = 200\n",
"WCnComV[4936].Len() = 1\n",
"WCnComV[4936][0] = 195\n",
"WCnComV[4937].Len() = 1\n",
"WCnComV[4937][0] = 192\n",
"WCnComV[4938].Len() = 1\n",
"WCnComV[4938][0] = 191\n",
"WCnComV[4939].Len() = 1\n",
"WCnComV[4939][0] = 189\n",
"WCnComV[4940].Len() = 1\n",
"WCnComV[4940][0] = 188\n",
"WCnComV[4941].Len() = 1\n",
"WCnComV[4941][0] = 187\n",
"WCnComV[4942].Len() = 1\n",
"WCnComV[4942][0] = 184\n",
"WCnComV[4943].Len() = 1\n",
"WCnComV[4943][0] = 180\n",
"WCnComV[4944].Len() = 1\n",
"WCnComV[4944][0] = 176\n",
"WCnComV[4945].Len() = 1\n",
"WCnComV[4945][0] = 167\n",
"WCnComV[4946].Len() = 1\n",
"WCnComV[4946][0] = 164\n",
"WCnComV[4947].Len() = 1\n",
"WCnComV[4947][0] = 162\n",
"WCnComV[4948].Len() = 1\n",
"WCnComV[4948][0] = 161\n",
"WCnComV[4949].Len() = 1\n",
"WCnComV[4949][0] = 160\n",
"WCnComV[4950].Len() = 1\n",
"WCnComV[4950][0] = 155\n",
"WCnComV[4951].Len() = 1\n",
"WCnComV[4951][0] = 153\n",
"WCnComV[4952].Len() = 1\n",
"WCnComV[4952][0] = 150\n",
"WCnComV[4953].Len() = 1\n",
"WCnComV[4953][0] = 147\n",
"WCnComV[4954].Len() = 1\n",
"WCnComV[4954][0] = 145\n",
"WCnComV[4955].Len() = 1\n",
"WCnComV[4955][0] = 139\n",
"WCnComV[4956].Len() = 1\n",
"WCnComV[4956][0] = 138\n",
"WCnComV[4957].Len() = 1\n",
"WCnComV[4957][0] = 135\n",
"WCnComV[4958].Len() = 1\n",
"WCnComV[4958][0] = 133\n",
"WCnComV[4959].Len() = 1\n",
"WCnComV[4959][0] = 131\n",
"WCnComV[4960].Len() = 1\n",
"WCnComV[4960][0] = 124\n",
"WCnComV[4961].Len() = 1\n",
"WCnComV[4961][0] = 122\n",
"WCnComV[4962].Len() = 1\n",
"WCnComV[4962][0] = 121\n",
"WCnComV[4963].Len() = 1\n",
"WCnComV[4963][0] = 112\n",
"WCnComV[4964].Len() = 1\n",
"WCnComV[4964][0] = 111\n",
"WCnComV[4965].Len() = 1\n",
"WCnComV[4965][0] = 109\n",
"WCnComV[4966].Len() = 1\n",
"WCnComV[4966][0] = 104\n",
"WCnComV[4967].Len() = 1\n",
"WCnComV[4967][0] = 103\n",
"WCnComV[4968].Len() = 1\n",
"WCnComV[4968][0] = 100\n",
"WCnComV[4969].Len() = 1\n",
"WCnComV[4969][0] = 99\n",
"WCnComV[4970].Len() = 1\n",
"WCnComV[4970][0] = 97\n",
"WCnComV[4971].Len() = 1\n",
"WCnComV[4971][0] = 90\n",
"WCnComV[4972].Len() = 1\n",
"WCnComV[4972][0] = 86\n",
"WCnComV[4973].Len() = 1\n",
"WCnComV[4973][0] = 82\n",
"WCnComV[4974].Len() = 1\n",
"WCnComV[4974][0] = 81\n",
"WCnComV[4975].Len() = 1\n",
"WCnComV[4975][0] = 78\n",
"WCnComV[4976].Len() = 1\n",
"WCnComV[4976][0] = 73\n",
"WCnComV[4977].Len() = 1\n",
"WCnComV[4977][0] = 72\n",
"WCnComV[4978].Len() = 1\n",
"WCnComV[4978][0] = 68\n",
"WCnComV[4979].Len() = 1\n",
"WCnComV[4979][0] = 63\n",
"WCnComV[4980].Len() = 1\n",
"WCnComV[4980][0] = 59\n",
"WCnComV[4981].Len() = 1\n",
"WCnComV[4981][0] = 55\n",
"WCnComV[4982].Len() = 1\n",
"WCnComV[4982][0] = 54\n",
"WCnComV[4983].Len() = 1\n",
"WCnComV[4983][0] = 51\n",
"WCnComV[4984].Len() = 1\n",
"WCnComV[4984][0] = 43\n",
"WCnComV[4985].Len() = 1\n",
"WCnComV[4985][0] = 41\n",
"WCnComV[4986].Len() = 1\n",
"WCnComV[4986][0] = 37\n",
"WCnComV[4987].Len() = 1\n",
"WCnComV[4987][0] = 36\n",
"WCnComV[4988].Len() = 1\n",
"WCnComV[4988][0] = 34\n",
"WCnComV[4989].Len() = 1\n",
"WCnComV[4989][0] = 31\n",
"WCnComV[4990].Len() = 1\n",
"WCnComV[4990][0] = 27\n",
"WCnComV[4991].Len() = 1\n",
"WCnComV[4991][0] = 25\n",
"WCnComV[4992].Len() = 1\n",
"WCnComV[4992][0] = 22\n",
"WCnComV[4993].Len() = 1\n",
"WCnComV[4993][0] = 19\n",
"WCnComV[4994].Len() = 1\n",
"WCnComV[4994][0] = 18\n",
"WCnComV[4995].Len() = 1\n",
"WCnComV[4995][0] = 12\n",
"WCnComV[4996].Len() = 1\n",
"WCnComV[4996][0] = 9\n",
"WCnComV[4997].Len() = 1\n",
"WCnComV[4997][0] = 7\n",
"WCnComV[4998].Len() = 1\n",
"WCnComV[4998][0] = 6\n",
"WCnComV[4999].Len() = 1\n",
"WCnComV[4999][0] = 3\n",
"WCnComV[5000].Len() = 1\n",
"WCnComV[5000][0] = 1\n",
"MxWccSz = 0.02920\n",
"GMx: GetNodes() = 292, GetEdges() = 291\n",
"SCnComV[0].Len() = 1\n",
"SCnComV[1].Len() = 1\n",
"SCnComV[2].Len() = 1\n",
"SCnComV[3].Len() = 1\n",
"SCnComV[4].Len() = 1\n",
"SCnComV[5].Len() = 1\n",
"SCnComV[6].Len() = 1\n",
"SCnComV[7].Len() = 1\n",
"SCnComV[8].Len() = 1\n",
"SCnComV[9].Len() = 1\n",
"SCnComV[10].Len() = 1\n",
"SCnComV[11].Len() = 1\n",
"SCnComV[12].Len() = 1\n",
"SCnComV[13].Len() = 1\n",
"SCnComV[14].Len() = 1\n",
"SCnComV[15].Len() = 1\n",
"SCnComV[16].Len() = 1\n",
"SCnComV[17].Len() = 1\n",
"SCnComV[18].Len() = 1\n",
"SCnComV[19].Len() = 1\n",
"SCnComV[20].Len() = 1\n",
"SCnComV[21].Len() = 1\n",
"SCnComV[22].Len() = 1\n",
"SCnComV[23].Len() = 1\n",
"SCnComV[24].Len() = 1\n",
"SCnComV[25].Len() = 1\n",
"SCnComV[26].Len() = 1\n",
"SCnComV[27].Len() = 1\n",
"SCnComV[28].Len() = 1\n",
"SCnComV[29].Len() = 1\n",
"SCnComV[30].Len() = 1\n",
"SCnComV[31].Len() = 1\n",
"SCnComV[32].Len() = 1\n",
"SCnComV[33].Len() = 1\n",
"SCnComV[34].Len() = 1\n",
"SCnComV[35].Len() = 1\n",
"SCnComV[36].Len() = 1\n",
"SCnComV[37].Len() = 1\n",
"SCnComV[38].Len() = 1\n",
"SCnComV[39].Len() = 1\n",
"SCnComV[40].Len() = 1\n",
"SCnComV[41].Len() = 1\n",
"SCnComV[42].Len() = 1\n",
"SCnComV[43].Len() = 1\n",
"SCnComV[44].Len() = 1\n",
"SCnComV[45].Len() = 1\n",
"SCnComV[46].Len() = 1\n",
"SCnComV[47].Len() = 1\n",
"SCnComV[48].Len() = 1\n",
"SCnComV[49].Len() = 1\n",
"SCnComV[50].Len() = 1\n",
"SCnComV[51].Len() = 1\n",
"SCnComV[52].Len() = 1\n",
"SCnComV[53].Len() = 1\n",
"SCnComV[54].Len() = 1\n",
"SCnComV[55].Len() = 1\n",
"SCnComV[56].Len() = 1\n",
"SCnComV[57].Len() = 1\n",
"SCnComV[58].Len() = 1\n",
"SCnComV[59].Len() = 1\n",
"SCnComV[60].Len() = 1\n",
"SCnComV[61].Len() = 1\n",
"SCnComV[62].Len() = 1\n",
"SCnComV[63].Len() = 1\n",
"SCnComV[64].Len() = 1\n",
"SCnComV[65].Len() = 1\n",
"SCnComV[66].Len() = 1\n",
"SCnComV[67].Len() = 1\n",
"SCnComV[68].Len() = 1\n",
"SCnComV[69].Len() = 1\n",
"SCnComV[70].Len() = 1\n",
"SCnComV[71].Len() = 1\n",
"SCnComV[72].Len() = 1\n",
"SCnComV[73].Len() = 1\n",
"SCnComV[74].Len() = 1\n",
"SCnComV[75].Len() = 1\n",
"SCnComV[76].Len() = 1\n",
"SCnComV[77].Len() = 1\n",
"SCnComV[78].Len() = 1\n",
"SCnComV[79].Len() = 1\n",
"SCnComV[80].Len() = 1\n",
"SCnComV[81].Len() = 1\n",
"SCnComV[82].Len() = 1\n",
"SCnComV[83].Len() = 1\n",
"SCnComV[84].Len() = 1\n",
"SCnComV[85].Len() = 1\n",
"SCnComV[86].Len() = 1\n",
"SCnComV[87].Len() = 1\n",
"SCnComV[88].Len() = 1\n",
"SCnComV[89].Len() = 1\n",
"SCnComV[90].Len() = 1\n",
"SCnComV[91].Len() = 1\n",
"SCnComV[92].Len() = 1\n",
"SCnComV[93].Len() = 1\n",
"SCnComV[94].Len() = 1\n",
"SCnComV[95].Len() = 1\n",
"SCnComV[96].Len() = 1\n",
"SCnComV[97].Len() = 1\n",
"SCnComV[98].Len() = 1\n",
"SCnComV[99].Len() = 1\n",
"SCnComV[100].Len() = 1\n",
"SCnComV[101].Len() = 1\n",
"SCnComV[102].Len() = 1\n",
"SCnComV[103].Len() = 1\n",
"SCnComV[104].Len() = 1\n",
"SCnComV[105].Len() = 1\n",
"SCnComV[106].Len() = 1\n",
"SCnComV[107].Len() = 1\n",
"SCnComV[108].Len() = 1\n",
"SCnComV[109].Len() = 1\n",
"SCnComV[110].Len() = 1\n",
"SCnComV[111].Len() = 1\n",
"SCnComV[112].Len() = 1\n",
"SCnComV[113].Len() = 1\n",
"SCnComV[114].Len() = 1\n",
"SCnComV[115].Len() = 1\n",
"SCnComV[116].Len() = 1\n",
"SCnComV[117].Len() = 1\n",
"SCnComV[118].Len() = 1\n",
"SCnComV[119].Len() = 1\n",
"SCnComV[120].Len() = 1\n",
"SCnComV[121].Len() = 1\n",
"SCnComV[122].Len() = 1\n",
"SCnComV[123].Len() = 1\n",
"SCnComV[124].Len() = 1\n",
"SCnComV[125].Len() = 1\n",
"SCnComV[126].Len() = 1\n",
"SCnComV[127].Len() = 1\n",
"SCnComV[128].Len() = 1\n",
"SCnComV[129].Len() = 1\n",
"SCnComV[130].Len() = 1\n",
"SCnComV[131].Len() = 1\n",
"SCnComV[132].Len() = 1\n",
"SCnComV[133].Len() = 1\n",
"SCnComV[134].Len() = 1\n",
"SCnComV[135].Len() = 1\n",
"SCnComV[136].Len() = 1\n",
"SCnComV[137].Len() = 1\n",
"SCnComV[138].Len() = 1\n",
"SCnComV[139].Len() = 1\n",
"SCnComV[140].Len() = 1\n",
"SCnComV[141].Len() = 1\n",
"SCnComV[142].Len() = 1\n",
"SCnComV[143].Len() = 1\n",
"SCnComV[144].Len() = 1\n",
"SCnComV[145].Len() = 1\n",
"SCnComV[146].Len() = 1\n",
"SCnComV[147].Len() = 1\n",
"SCnComV[148].Len() = 1\n",
"SCnComV[149].Len() = 1\n",
"SCnComV[150].Len() = 1\n",
"SCnComV[151].Len() = 1\n",
"SCnComV[152].Len() = 1\n",
"SCnComV[153].Len() = 1\n",
"SCnComV[154].Len() = 1\n",
"SCnComV[155].Len() = 1\n",
"SCnComV[156].Len() = 1\n",
"SCnComV[157].Len() = 1\n",
"SCnComV[158].Len() = 1\n",
"SCnComV[159].Len() = 1\n",
"SCnComV[160].Len() = 1\n",
"SCnComV[161].Len() = 1\n",
"SCnComV[162].Len() = 1\n",
"SCnComV[163].Len() = 1\n",
"SCnComV[164].Len() = 1\n",
"SCnComV[165].Len() = 1\n",
"SCnComV[166].Len() = 1\n",
"SCnComV[167].Len() = 1\n",
"SCnComV[168].Len() = 1\n",
"SCnComV[169].Len() = 1\n",
"SCnComV[170].Len() = 1\n",
"SCnComV[171].Len() = 1\n",
"SCnComV[172].Len() = 1\n",
"SCnComV[173].Len() = 1\n",
"SCnComV[174].Len() = 1\n",
"SCnComV[175].Len() = 1\n",
"SCnComV[176].Len() = 1\n",
"SCnComV[177].Len() = 1\n",
"SCnComV[178].Len() = 1\n",
"SCnComV[179].Len() = 1\n",
"SCnComV[180].Len() = 1\n",
"SCnComV[181].Len() = 1\n",
"SCnComV[182].Len() = 1\n",
"SCnComV[183].Len() = 1\n",
"SCnComV[184].Len() = 1\n",
"SCnComV[185].Len() = 1\n",
"SCnComV[186].Len() = 1\n",
"SCnComV[187].Len() = 1\n",
"SCnComV[188].Len() = 1\n",
"SCnComV[189].Len() = 1\n",
"SCnComV[190].Len() = 1\n",
"SCnComV[191].Len() = 1\n",
"SCnComV[192].Len() = 1\n",
"SCnComV[193].Len() = 1\n",
"SCnComV[194].Len() = 1\n",
"SCnComV[195].Len() = 1\n",
"SCnComV[196].Len() = 1\n",
"SCnComV[197].Len() = 1\n",
"SCnComV[198].Len() = 1\n",
"SCnComV[199].Len() = 1\n",
"SCnComV[200].Len() = 1\n",
"SCnComV[201].Len() = 1\n",
"SCnComV[202].Len() = 1\n",
"SCnComV[203].Len() = 1\n",
"SCnComV[204].Len() = 1\n",
"SCnComV[205].Len() = 1\n",
"SCnComV[206].Len() = 1\n",
"SCnComV[207].Len() = 1\n",
"SCnComV[208].Len() = 1\n",
"SCnComV[209].Len() = 1\n",
"SCnComV[210].Len() = 1\n",
"SCnComV[211].Len() = 1\n",
"SCnComV[212].Len() = 1\n",
"SCnComV[213].Len() = 1\n",
"SCnComV[214].Len() = 1\n",
"SCnComV[215].Len() = 1\n",
"SCnComV[216].Len() = 1\n",
"SCnComV[217].Len() = 1\n",
"SCnComV[218].Len() = 1\n",
"SCnComV[219].Len() = 1\n",
"SCnComV[220].Len() = 1\n",
"SCnComV[221].Len() = 1\n",
"SCnComV[222].Len() = 1\n",
"SCnComV[223].Len() = 1\n",
"SCnComV[224].Len() = 1\n",
"SCnComV[225].Len() = 1\n",
"SCnComV[226].Len() = 1\n",
"SCnComV[227].Len() = 1\n",
"SCnComV[228].Len() = 1\n",
"SCnComV[229].Len() = 1\n",
"SCnComV[230].Len() = 1\n",
"SCnComV[231].Len() = 1\n",
"SCnComV[232].Len() = 1\n",
"SCnComV[233].Len() = 1\n",
"SCnComV[234].Len() = 1\n",
"SCnComV[235].Len() = 1\n",
"SCnComV[236].Len() = 1\n",
"SCnComV[237].Len() = 1\n",
"SCnComV[238].Len() = 1\n",
"SCnComV[239].Len() = 1\n",
"SCnComV[240].Len() = 1\n",
"SCnComV[241].Len() = 1\n",
"SCnComV[242].Len() = 1\n",
"SCnComV[243].Len() = 1\n",
"SCnComV[244].Len() = 1\n",
"SCnComV[245].Len() = 1\n",
"SCnComV[246].Len() = 1\n",
"SCnComV[247].Len() = 1\n",
"SCnComV[248].Len() = 1\n",
"SCnComV[249].Len() = 1\n",
"SCnComV[250].Len() = 1\n",
"SCnComV[251].Len() = 1\n",
"SCnComV[252].Len() = 1\n",
"SCnComV[253].Len() = 1\n",
"SCnComV[254].Len() = 1\n",
"SCnComV[255].Len() = 1\n",
"SCnComV[256].Len() = 1\n",
"SCnComV[257].Len() = 1\n",
"SCnComV[258].Len() = 1\n",
"SCnComV[259].Len() = 1\n",
"SCnComV[260].Len() = 1\n",
"SCnComV[261].Len() = 1\n",
"SCnComV[262].Len() = 1\n",
"SCnComV[263].Len() = 1\n",
"SCnComV[264].Len() = 1\n",
"SCnComV[265].Len() = 1\n",
"SCnComV[266].Len() = 1\n",
"SCnComV[267].Len() = 1\n",
"SCnComV[268].Len() = 1\n",
"SCnComV[269].Len() = 1\n",
"SCnComV[270].Len() = 1\n",
"SCnComV[271].Len() = 1\n",
"SCnComV[272].Len() = 1\n",
"SCnComV[273].Len() = 1\n",
"SCnComV[274].Len() = 1\n",
"SCnComV[275].Len() = 1\n",
"SCnComV[276].Len() = 1\n",
"SCnComV[277].Len() = 1\n",
"SCnComV[278].Len() = 1\n",
"SCnComV[279].Len() = 1\n",
"SCnComV[280].Len() = 1\n",
"SCnComV[281].Len() = 1\n",
"SCnComV[282].Len() = 1\n",
"SCnComV[283].Len() = 1\n",
"SCnComV[284].Len() = 1\n",
"SCnComV[285].Len() = 1\n",
"SCnComV[286].Len() = 1\n",
"SCnComV[287].Len() = 1\n",
"SCnComV[288].Len() = 1\n",
"SCnComV[289].Len() = 1\n",
"SCnComV[290].Len() = 1\n",
"SCnComV[291].Len() = 1\n",
"SCnComV[292].Len() = 1\n",
"SCnComV[293].Len() = 1\n",
"SCnComV[294].Len() = 1\n",
"SCnComV[295].Len() = 1\n",
"SCnComV[296].Len() = 1\n",
"SCnComV[297].Len() = 1\n",
"SCnComV[298].Len() = 1\n",
"SCnComV[299].Len() = 1\n",
"SCnComV[300].Len() = 1\n",
"SCnComV[301].Len() = 1\n",
"SCnComV[302].Len() = 1\n",
"SCnComV[303].Len() = 1\n",
"SCnComV[304].Len() = 1\n",
"SCnComV[305].Len() = 1\n",
"SCnComV[306].Len() = 1\n",
"SCnComV[307].Len() = 1\n",
"SCnComV[308].Len() = 1\n",
"SCnComV[309].Len() = 1\n",
"SCnComV[310].Len() = 1\n",
"SCnComV[311].Len() = 1\n",
"SCnComV[312].Len() = 1\n",
"SCnComV[313].Len() = 1\n",
"SCnComV[314].Len() = 1\n",
"SCnComV[315].Len() = 1\n",
"SCnComV[316].Len() = 1\n",
"SCnComV[317].Len() = 1\n",
"SCnComV[318].Len() = 1\n",
"SCnComV[319].Len() = 1\n",
"SCnComV[320].Len() = 1\n",
"SCnComV[321].Len() = 1\n",
"SCnComV[322].Len() = 1\n",
"SCnComV[323].Len() = 1\n",
"SCnComV[324].Len() = 1\n",
"SCnComV[325].Len() = 1\n",
"SCnComV[326].Len() = 1\n",
"SCnComV[327].Len() = 1\n",
"SCnComV[328].Len() = 1\n",
"SCnComV[329].Len() = 1\n",
"SCnComV[330].Len() = 1\n",
"SCnComV[331].Len() = 1\n",
"SCnComV[332].Len() = 1\n",
"SCnComV[333].Len() = 1\n",
"SCnComV[334].Len() = 1\n",
"SCnComV[335].Len() = 1\n",
"SCnComV[336].Len() = 1\n",
"SCnComV[337].Len() = 1\n",
"SCnComV[338].Len() = 1\n",
"SCnComV[339].Len() = 1\n",
"SCnComV[340].Len() = 1\n",
"SCnComV[341].Len() = 1\n",
"SCnComV[342].Len() = 1\n",
"SCnComV[343].Len() = 1\n",
"SCnComV[344].Len() = 1\n",
"SCnComV[345].Len() = 1\n",
"SCnComV[346].Len() = 1\n",
"SCnComV[347].Len() = 1\n",
"SCnComV[348].Len() = 1\n",
"SCnComV[349].Len() = 1\n",
"SCnComV[350].Len() = 1\n",
"SCnComV[351].Len() = 1\n",
"SCnComV[352].Len() = 1\n",
"SCnComV[353].Len() = 1\n",
"SCnComV[354].Len() = 1\n",
"SCnComV[355].Len() = 1\n",
"SCnComV[356].Len() = 1\n",
"SCnComV[357].Len() = 1\n",
"SCnComV[358].Len() = 1\n",
"SCnComV[359].Len() = 1\n",
"SCnComV[360].Len() = 1\n",
"SCnComV[361].Len() = 1\n",
"SCnComV[362].Len() = 1\n",
"SCnComV[363].Len() = 1\n",
"SCnComV[364].Len() = 1\n",
"SCnComV[365].Len() = 1\n",
"SCnComV[366].Len() = 1\n",
"SCnComV[367].Len() = 1\n",
"SCnComV[368].Len() = 1\n",
"SCnComV[369].Len() = 1\n",
"SCnComV[370].Len() = 1\n",
"SCnComV[371].Len() = 1\n",
"SCnComV[372].Len() = 1\n",
"SCnComV[373].Len() = 1\n",
"SCnComV[374].Len() = 1\n",
"SCnComV[375].Len() = 1\n",
"SCnComV[376].Len() = 1\n",
"SCnComV[377].Len() = 1\n",
"SCnComV[378].Len() = 1\n",
"SCnComV[379].Len() = 1\n",
"SCnComV[380].Len() = 1\n",
"SCnComV[381].Len() = 1\n",
"SCnComV[382].Len() = 1\n",
"SCnComV[383].Len() = 1\n",
"SCnComV[384].Len() = 1\n",
"SCnComV[385].Len() = 1\n",
"SCnComV[386].Len() = 1\n",
"SCnComV[387].Len() = 1\n",
"SCnComV[388].Len() = 1\n",
"SCnComV[389].Len() = 1\n",
"SCnComV[390].Len() = 1\n",
"SCnComV[391].Len() = 1\n",
"SCnComV[392].Len() = 1\n",
"SCnComV[393].Len() = 1\n",
"SCnComV[394].Len() = 1\n",
"SCnComV[395].Len() = 1\n",
"SCnComV[396].Len() = 1\n",
"SCnComV[397].Len() = 1\n",
"SCnComV[398].Len() = 1\n",
"SCnComV[399].Len() = 1\n",
"SCnComV[400].Len() = 1\n",
"SCnComV[401].Len() = 1\n",
"SCnComV[402].Len() = 1\n",
"SCnComV[403].Len() = 1\n",
"SCnComV[404].Len() = 1\n",
"SCnComV[405].Len() = 1\n",
"SCnComV[406].Len() = 1\n",
"SCnComV[407].Len() = 1\n",
"SCnComV[408].Len() = 1\n",
"SCnComV[409].Len() = 1\n",
"SCnComV[410].Len() = 1\n",
"SCnComV[411].Len() = 1\n",
"SCnComV[412].Len() = 1\n",
"SCnComV[413].Len() = 1\n",
"SCnComV[414].Len() = 1\n",
"SCnComV[415].Len() = 1\n",
"SCnComV[416].Len() = 1\n",
"SCnComV[417].Len() = 1\n",
"SCnComV[418].Len() = 1\n",
"SCnComV[419].Len() = 1\n",
"SCnComV[420].Len() = 1\n",
"SCnComV[421].Len() = 1\n",
"SCnComV[422].Len() = 1\n",
"SCnComV[423].Len() = 1\n",
"SCnComV[424].Len() = 1\n",
"SCnComV[425].Len() = 1\n",
"SCnComV[426].Len() = 1\n",
"SCnComV[427].Len() = 1\n",
"SCnComV[428].Len() = 1\n",
"SCnComV[429].Len() = 1\n",
"SCnComV[430].Len() = 1\n",
"SCnComV[431].Len() = 1\n",
"SCnComV[432].Len() = 1\n",
"SCnComV[433].Len() = 1\n",
"SCnComV[434].Len() = 1\n",
"SCnComV[435].Len() = 1\n",
"SCnComV[436].Len() = 1\n",
"SCnComV[437].Len() = 1\n",
"SCnComV[438].Len() = 1\n",
"SCnComV[439].Len() = 1\n",
"SCnComV[440].Len() = 1\n",
"SCnComV[441].Len() = 1\n",
"SCnComV[442].Len() = 1\n",
"SCnComV[443].Len() = 1\n",
"SCnComV[444].Len() = 1\n",
"SCnComV[445].Len() = 1\n",
"SCnComV[446].Len() = 1\n",
"SCnComV[447].Len() = 1\n",
"SCnComV[448].Len() = 1\n",
"SCnComV[449].Len() = 1\n",
"SCnComV[450].Len() = 1\n",
"SCnComV[451].Len() = 1\n",
"SCnComV[452].Len() = 1\n",
"SCnComV[453].Len() = 1\n",
"SCnComV[454].Len() = 1\n",
"SCnComV[455].Len() = 1\n",
"SCnComV[456].Len() = 1\n",
"SCnComV[457].Len() = 1\n",
"SCnComV[458].Len() = 1\n",
"SCnComV[459].Len() = 1\n",
"SCnComV[460].Len() = 1\n",
"SCnComV[461].Len() = 1\n",
"SCnComV[462].Len() = 1\n",
"SCnComV[463].Len() = 1\n",
"SCnComV[464].Len() = 1\n",
"SCnComV[465].Len() = 1\n",
"SCnComV[466].Len() = 1\n",
"SCnComV[467].Len() = 1\n",
"SCnComV[468].Len() = 1\n",
"SCnComV[469].Len() = 1\n",
"SCnComV[470].Len() = 1\n",
"SCnComV[471].Len() = 1\n",
"SCnComV[472].Len() = 1\n",
"SCnComV[473].Len() = 1\n",
"SCnComV[474].Len() = 1\n",
"SCnComV[475].Len() = 1\n",
"SCnComV[476].Len() = 1\n",
"SCnComV[477].Len() = 1\n",
"SCnComV[478].Len() = 1\n",
"SCnComV[479].Len() = 1\n",
"SCnComV[480].Len() = 1\n",
"SCnComV[481].Len() = 1\n",
"SCnComV[482].Len() = 1\n",
"SCnComV[483].Len() = 1\n",
"SCnComV[484].Len() = 1\n",
"SCnComV[485].Len() = 1\n",
"SCnComV[486].Len() = 1\n",
"SCnComV[487].Len() = 1\n",
"SCnComV[488].Len() = 1\n",
"SCnComV[489].Len() = 1\n",
"SCnComV[490].Len() = 1\n",
"SCnComV[491].Len() = 1\n",
"SCnComV[492].Len() = 1\n",
"SCnComV[493].Len() = 1\n",
"SCnComV[494].Len() = 1\n",
"SCnComV[495].Len() = 1\n",
"SCnComV[496].Len() = 1\n",
"SCnComV[497].Len() = 1\n",
"SCnComV[498].Len() = 1\n",
"SCnComV[499].Len() = 1\n",
"SCnComV[500].Len() = 1\n",
"SCnComV[501].Len() = 1\n",
"SCnComV[502].Len() = 1\n",
"SCnComV[503].Len() = 1\n",
"SCnComV[504].Len() = 1\n",
"SCnComV[505].Len() = 1\n",
"SCnComV[506].Len() = 1\n",
"SCnComV[507].Len() = 1\n",
"SCnComV[508].Len() = 1\n",
"SCnComV[509].Len() = 1\n",
"SCnComV[510].Len() = 1\n",
"SCnComV[511].Len() = 1\n",
"SCnComV[512].Len() = 1\n",
"SCnComV[513].Len() = 1\n",
"SCnComV[514].Len() = 1\n",
"SCnComV[515].Len() = 1\n",
"SCnComV[516].Len() = 1\n",
"SCnComV[517].Len() = 1\n",
"SCnComV[518].Len() = 1\n",
"SCnComV[519].Len() = 1\n",
"SCnComV[520].Len() = 1\n",
"SCnComV[521].Len() = 1\n",
"SCnComV[522].Len() = 1\n",
"SCnComV[523].Len() = 1\n",
"SCnComV[524].Len() = 1\n",
"SCnComV[525].Len() = 1\n",
"SCnComV[526].Len() = 1\n",
"SCnComV[527].Len() = 1\n",
"SCnComV[528].Len() = 1\n",
"SCnComV[529].Len() = 1\n",
"SCnComV[530].Len() = 1\n",
"SCnComV[531].Len() = 1\n",
"SCnComV[532].Len() = 1\n",
"SCnComV[533].Len() = 1\n",
"SCnComV[534].Len() = 1\n",
"SCnComV[535].Len() = 1\n",
"SCnComV[536].Len() = 1\n",
"SCnComV[537].Len() = 1\n",
"SCnComV[538].Len() = 1\n",
"SCnComV[539].Len() = 1\n",
"SCnComV[540].Len() = 1\n",
"SCnComV[541].Len() = 1\n",
"SCnComV[542].Len() = 1\n",
"SCnComV[543].Len() = 1\n",
"SCnComV[544].Len() = 1\n",
"SCnComV[545].Len() = 1\n",
"SCnComV[546].Len() = 1\n",
"SCnComV[547].Len() = 1\n",
"SCnComV[548].Len() = 1\n",
"SCnComV[549].Len() = 1\n",
"SCnComV[550].Len() = 1\n",
"SCnComV[551].Len() = 1\n",
"SCnComV[552].Len() = 1\n",
"SCnComV[553].Len() = 1\n",
"SCnComV[554].Len() = 1\n",
"SCnComV[555].Len() = 1\n",
"SCnComV[556].Len() = 1\n",
"SCnComV[557].Len() = 1\n",
"SCnComV[558].Len() = 1\n",
"SCnComV[559].Len() = 1\n",
"SCnComV[560].Len() = 1\n",
"SCnComV[561].Len() = 1\n",
"SCnComV[562].Len() = 1\n",
"SCnComV[563].Len() = 1\n",
"SCnComV[564].Len() = 1\n",
"SCnComV[565].Len() = 1\n",
"SCnComV[566].Len() = 1\n",
"SCnComV[567].Len() = 1\n",
"SCnComV[568].Len() = 1\n",
"SCnComV[569].Len() = 1\n",
"SCnComV[570].Len() = 1\n",
"SCnComV[571].Len() = 1\n",
"SCnComV[572].Len() = 1\n",
"SCnComV[573].Len() = 1\n",
"SCnComV[574].Len() = 1\n",
"SCnComV[575].Len() = 1\n",
"SCnComV[576].Len() = 1\n",
"SCnComV[577].Len() = 1\n",
"SCnComV[578].Len() = 1\n",
"SCnComV[579].Len() = 1\n",
"SCnComV[580].Len() = 1\n",
"SCnComV[581].Len() = 1\n",
"SCnComV[582].Len() = 1\n",
"SCnComV[583].Len() = 1\n",
"SCnComV[584].Len() = 1\n",
"SCnComV[585].Len() = 1\n",
"SCnComV[586].Len() = 1\n",
"SCnComV[587].Len() = 1\n",
"SCnComV[588].Len() = 1\n",
"SCnComV[589].Len() = 1\n",
"SCnComV[590].Len() = 1\n",
"SCnComV[591].Len() = 1\n",
"SCnComV[592].Len() = 1\n",
"SCnComV[593].Len() = 1\n",
"SCnComV[594].Len() = 1\n",
"SCnComV[595].Len() = 1\n",
"SCnComV[596].Len() = 1\n",
"SCnComV[597].Len() = 1\n",
"SCnComV[598].Len() = 1\n",
"SCnComV[599].Len() = 1\n",
"SCnComV[600].Len() = 1\n",
"SCnComV[601].Len() = 1\n",
"SCnComV[602].Len() = 1\n",
"SCnComV[603].Len() = 1\n",
"SCnComV[604].Len() = 1\n",
"SCnComV[605].Len() = 1\n",
"SCnComV[606].Len() = 1\n",
"SCnComV[607].Len() = 1\n",
"SCnComV[608].Len() = 1\n",
"SCnComV[609].Len() = 1\n",
"SCnComV[610].Len() = 1\n",
"SCnComV[611].Len() = 1\n",
"SCnComV[612].Len() = 1\n",
"SCnComV[613].Len() = 1\n",
"SCnComV[614].Len() = 1\n",
"SCnComV[615].Len() = 1\n",
"SCnComV[616].Len() = 1\n",
"SCnComV[617].Len() = 1\n",
"SCnComV[618].Len() = 1\n",
"SCnComV[619].Len() = 1\n",
"SCnComV[620].Len() = 1\n",
"SCnComV[621].Len() = 1\n",
"SCnComV[622].Len() = 1\n",
"SCnComV[623].Len() = 1\n",
"SCnComV[624].Len() = 1\n",
"SCnComV[625].Len() = 1\n",
"SCnComV[626].Len() = 1\n",
"SCnComV[627].Len() = 1\n",
"SCnComV[628].Len() = 1\n",
"SCnComV[629].Len() = 1\n",
"SCnComV[630].Len() = 1\n",
"SCnComV[631].Len() = 1\n",
"SCnComV[632].Len() = 1\n",
"SCnComV[633].Len() = 1\n",
"SCnComV[634].Len() = 1\n",
"SCnComV[635].Len() = 1\n",
"SCnComV[636].Len() = 1\n",
"SCnComV[637].Len() = 1\n",
"SCnComV[638].Len() = 1\n",
"SCnComV[639].Len() = 1\n",
"SCnComV[640].Len() = 1\n",
"SCnComV[641].Len() = 1\n",
"SCnComV[642].Len() = 1\n",
"SCnComV[643].Len() = 1\n",
"SCnComV[644].Len() = 1\n",
"SCnComV[645].Len() = 1\n",
"SCnComV[646].Len() = 1\n",
"SCnComV[647].Len() = 1\n",
"SCnComV[648].Len() = 1\n",
"SCnComV[649].Len() = 1\n",
"SCnComV[650].Len() = 1\n",
"SCnComV[651].Len() = 1\n",
"SCnComV[652].Len() = 1\n",
"SCnComV[653].Len() = 1\n",
"SCnComV[654].Len() = 1\n",
"SCnComV[655].Len() = 1\n",
"SCnComV[656].Len() = 1\n",
"SCnComV[657].Len() = 1\n",
"SCnComV[658].Len() = 1\n",
"SCnComV[659].Len() = 1\n",
"SCnComV[660].Len() = 1\n",
"SCnComV[661].Len() = 1\n",
"SCnComV[662].Len() = 1\n",
"SCnComV[663].Len() = 1\n",
"SCnComV[664].Len() = 1\n",
"SCnComV[665].Len() = 1\n",
"SCnComV[666].Len() = 1\n",
"SCnComV[667].Len() = 1\n",
"SCnComV[668].Len() = 1\n",
"SCnComV[669].Len() = 1\n",
"SCnComV[670].Len() = 1\n",
"SCnComV[671].Len() = 1\n",
"SCnComV[672].Len() = 1\n",
"SCnComV[673].Len() = 1\n",
"SCnComV[674].Len() = 1\n",
"SCnComV[675].Len() = 1\n",
"SCnComV[676].Len() = 1\n",
"SCnComV[677].Len() = 1\n",
"SCnComV[678].Len() = 1\n",
"SCnComV[679].Len() = 1\n",
"SCnComV[680].Len() = 1\n",
"SCnComV[681].Len() = 1\n",
"SCnComV[682].Len() = 1\n",
"SCnComV[683].Len() = 1\n",
"SCnComV[684].Len() = 1\n",
"SCnComV[685].Len() = 1\n",
"SCnComV[686].Len() = 1\n",
"SCnComV[687].Len() = 1\n",
"SCnComV[688].Len() = 1\n",
"SCnComV[689].Len() = 1\n",
"SCnComV[690].Len() = 1\n",
"SCnComV[691].Len() = 1\n",
"SCnComV[692].Len() = 1\n",
"SCnComV[693].Len() = 1\n",
"SCnComV[694].Len() = 1\n",
"SCnComV[695].Len() = 1\n",
"SCnComV[696].Len() = 1\n",
"SCnComV[697].Len() = 1\n",
"SCnComV[698].Len() = 1\n",
"SCnComV[699].Len() = 1\n",
"SCnComV[700].Len() = 1\n",
"SCnComV[701].Len() = 1\n",
"SCnComV[702].Len() = 1\n",
"SCnComV[703].Len() = 1\n",
"SCnComV[704].Len() = 1\n",
"SCnComV[705].Len() = 1\n",
"SCnComV[706].Len() = 1\n",
"SCnComV[707].Len() = 1\n",
"SCnComV[708].Len() = 1\n",
"SCnComV[709].Len() = 1\n",
"SCnComV[710].Len() = 1\n",
"SCnComV[711].Len() = 1\n",
"SCnComV[712].Len() = 1\n",
"SCnComV[713].Len() = 1\n",
"SCnComV[714].Len() = 1\n",
"SCnComV[715].Len() = 1\n",
"SCnComV[716].Len() = 1\n",
"SCnComV[717].Len() = 1\n",
"SCnComV[718].Len() = 1\n",
"SCnComV[719].Len() = 1\n",
"SCnComV[720].Len() = 1\n",
"SCnComV[721].Len() = 1\n",
"SCnComV[722].Len() = 1\n",
"SCnComV[723].Len() = 1\n",
"SCnComV[724].Len() = 1\n",
"SCnComV[725].Len() = 1\n",
"SCnComV[726].Len() = 1\n",
"SCnComV[727].Len() = 1\n",
"SCnComV[728].Len() = 1\n",
"SCnComV[729].Len() = 1\n",
"SCnComV[730].Len() = 1\n",
"SCnComV[731].Len() = 1\n",
"SCnComV[732].Len() = 1\n",
"SCnComV[733].Len() = 1\n",
"SCnComV[734].Len() = 1\n",
"SCnComV[735].Len() = 1\n",
"SCnComV[736].Len() = 1\n",
"SCnComV[737].Len() = 1\n",
"SCnComV[738].Len() = 1\n",
"SCnComV[739].Len() = 1\n",
"SCnComV[740].Len() = 1\n",
"SCnComV[741].Len() = 1\n",
"SCnComV[742].Len() = 1\n",
"SCnComV[743].Len() = 1\n",
"SCnComV[744].Len() = 1\n",
"SCnComV[745].Len() = 1\n",
"SCnComV[746].Len() = 1\n",
"SCnComV[747].Len() = 1\n",
"SCnComV[748].Len() = 1\n",
"SCnComV[749].Len() = 1\n",
"SCnComV[750].Len() = 1\n",
"SCnComV[751].Len() = 1\n",
"SCnComV[752].Len() = 1\n",
"SCnComV[753].Len() = 1\n",
"SCnComV[754].Len() = 1\n",
"SCnComV[755].Len() = 1\n",
"SCnComV[756].Len() = 1\n",
"SCnComV[757].Len() = 1\n",
"SCnComV[758].Len() = 1\n",
"SCnComV[759].Len() = 1\n",
"SCnComV[760].Len() = 1\n",
"SCnComV[761].Len() = 1\n",
"SCnComV[762].Len() = 1\n",
"SCnComV[763].Len() = 1\n",
"SCnComV[764].Len() = 1\n",
"SCnComV[765].Len() = 1\n",
"SCnComV[766].Len() = 1\n",
"SCnComV[767].Len() = 1\n",
"SCnComV[768].Len() = 1\n",
"SCnComV[769].Len() = 1\n",
"SCnComV[770].Len() = 1\n",
"SCnComV[771].Len() = 1\n",
"SCnComV[772].Len() = 1\n",
"SCnComV[773].Len() = 1\n",
"SCnComV[774].Len() = 1\n",
"SCnComV[775].Len() = 1\n",
"SCnComV[776].Len() = 1\n",
"SCnComV[777].Len() = 1\n",
"SCnComV[778].Len() = 1\n",
"SCnComV[779].Len() = 1\n",
"SCnComV[780].Len() = 1\n",
"SCnComV[781].Len() = 1\n",
"SCnComV[782].Len() = 1\n",
"SCnComV[783].Len() = 1\n",
"SCnComV[784].Len() = 1\n",
"SCnComV[785].Len() = 1\n",
"SCnComV[786].Len() = 1\n",
"SCnComV[787].Len() = 1\n",
"SCnComV[788].Len() = 1\n",
"SCnComV[789].Len() = 1\n",
"SCnComV[790].Len() = 1\n",
"SCnComV[791].Len() = 1\n",
"SCnComV[792].Len() = 1\n",
"SCnComV[793].Len() = 1\n",
"SCnComV[794].Len() = 1\n",
"SCnComV[795].Len() = 1\n",
"SCnComV[796].Len() = 1\n",
"SCnComV[797].Len() = 1\n",
"SCnComV[798].Len() = 1\n",
"SCnComV[799].Len() = 1\n",
"SCnComV[800].Len() = 1\n",
"SCnComV[801].Len() = 1\n",
"SCnComV[802].Len() = 1\n",
"SCnComV[803].Len() = 1\n",
"SCnComV[804].Len() = 1\n",
"SCnComV[805].Len() = 1\n",
"SCnComV[806].Len() = 1\n",
"SCnComV[807].Len() = 1\n",
"SCnComV[808].Len() = 1\n",
"SCnComV[809].Len() = 1\n",
"SCnComV[810].Len() = 1\n",
"SCnComV[811].Len() = 1\n",
"SCnComV[812].Len() = 1\n",
"SCnComV[813].Len() = 1\n",
"SCnComV[814].Len() = 1\n",
"SCnComV[815].Len() = 1\n",
"SCnComV[816].Len() = 1\n",
"SCnComV[817].Len() = 1\n",
"SCnComV[818].Len() = 1\n",
"SCnComV[819].Len() = 1\n",
"SCnComV[820].Len() = 1\n",
"SCnComV[821].Len() = 1\n",
"SCnComV[822].Len() = 1\n",
"SCnComV[823].Len() = 1\n",
"SCnComV[824].Len() = 1\n",
"SCnComV[825].Len() = 1\n",
"SCnComV[826].Len() = 1\n",
"SCnComV[827].Len() = 1\n",
"SCnComV[828].Len() = 1\n",
"SCnComV[829].Len() = 1\n",
"SCnComV[830].Len() = 1\n",
"SCnComV[831].Len() = 1\n",
"SCnComV[832].Len() = 1\n",
"SCnComV[833].Len() = 1\n",
"SCnComV[834].Len() = 1\n",
"SCnComV[835].Len() = 1\n",
"SCnComV[836].Len() = 1\n",
"SCnComV[837].Len() = 1\n",
"SCnComV[838].Len() = 1\n",
"SCnComV[839].Len() = 1\n",
"SCnComV[840].Len() = 1\n",
"SCnComV[841].Len() = 1\n",
"SCnComV[842].Len() = 1\n",
"SCnComV[843].Len() = 1\n",
"SCnComV[844].Len() = 1\n",
"SCnComV[845].Len() = 1\n",
"SCnComV[846].Len() = 1\n",
"SCnComV[847].Len() = 1\n",
"SCnComV[848].Len() = 1\n",
"SCnComV[849].Len() = 1\n",
"SCnComV[850].Len() = 1\n",
"SCnComV[851].Len() = 1\n",
"SCnComV[852].Len() = 1\n",
"SCnComV[853].Len() = 1\n",
"SCnComV[854].Len() = 1\n",
"SCnComV[855].Len() = 1\n",
"SCnComV[856].Len() = 1\n",
"SCnComV[857].Len() = 1\n",
"SCnComV[858].Len() = 1\n",
"SCnComV[859].Len() = 1\n",
"SCnComV[860].Len() = 1\n",
"SCnComV[861].Len() = 1\n",
"SCnComV[862].Len() = 1\n",
"SCnComV[863].Len() = 1\n",
"SCnComV[864].Len() = 1\n",
"SCnComV[865].Len() = 1\n",
"SCnComV[866].Len() = 1\n",
"SCnComV[867].Len() = 1\n",
"SCnComV[868].Len() = 1\n",
"SCnComV[869].Len() = 1\n",
"SCnComV[870].Len() = 1\n",
"SCnComV[871].Len() = 1\n",
"SCnComV[872].Len() = 1\n",
"SCnComV[873].Len() = 1\n",
"SCnComV[874].Len() = 1\n",
"SCnComV[875].Len() = 1\n",
"SCnComV[876].Len() = 1\n",
"SCnComV[877].Len() = 1\n",
"SCnComV[878].Len() = 1\n",
"SCnComV[879].Len() = 1\n",
"SCnComV[880].Len() = 1\n",
"SCnComV[881].Len() = 1\n",
"SCnComV[882].Len() = 1\n",
"SCnComV[883].Len() = 1\n",
"SCnComV[884].Len() = 1\n",
"SCnComV[885].Len() = 1\n",
"SCnComV[886].Len() = 1\n",
"SCnComV[887].Len() = 1\n",
"SCnComV[888].Len() = 1\n",
"SCnComV[889].Len() = 1\n",
"SCnComV[890].Len() = 1\n",
"SCnComV[891].Len() = 1\n",
"SCnComV[892].Len() = 1\n",
"SCnComV[893].Len() = 1\n",
"SCnComV[894].Len() = 1\n",
"SCnComV[895].Len() = 1\n",
"SCnComV[896].Len() = 1\n",
"SCnComV[897].Len() = 1\n",
"SCnComV[898].Len() = 1\n",
"SCnComV[899].Len() = 1\n",
"SCnComV[900].Len() = 1\n",
"SCnComV[901].Len() = 1\n",
"SCnComV[902].Len() = 1\n",
"SCnComV[903].Len() = 1\n",
"SCnComV[904].Len() = 1\n",
"SCnComV[905].Len() = 1\n",
"SCnComV[906].Len() = 1\n",
"SCnComV[907].Len() = 1\n",
"SCnComV[908].Len() = 1\n",
"SCnComV[909].Len() = 1\n",
"SCnComV[910].Len() = 1\n",
"SCnComV[911].Len() = 1\n",
"SCnComV[912].Len() = 1\n",
"SCnComV[913].Len() = 1\n",
"SCnComV[914].Len() = 1\n",
"SCnComV[915].Len() = 1\n",
"SCnComV[916].Len() = 1\n",
"SCnComV[917].Len() = 1\n",
"SCnComV[918].Len() = 1\n",
"SCnComV[919].Len() = 1\n",
"SCnComV[920].Len() = 1\n",
"SCnComV[921].Len() = 1\n",
"SCnComV[922].Len() = 1\n",
"SCnComV[923].Len() = 1\n",
"SCnComV[924].Len() = 1\n",
"SCnComV[925].Len() = 1\n",
"SCnComV[926].Len() = 1\n",
"SCnComV[927].Len() = 1\n",
"SCnComV[928].Len() = 1\n",
"SCnComV[929].Len() = 1\n",
"SCnComV[930].Len() = 1\n",
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"SCnComV[932].Len() = 1\n",
"SCnComV[933].Len() = 1\n",
"SCnComV[934].Len() = 1\n",
"SCnComV[935].Len() = 1\n",
"SCnComV[936].Len() = 1\n",
"SCnComV[937].Len() = 1\n",
"SCnComV[938].Len() = 1\n",
"SCnComV[939].Len() = 1\n",
"SCnComV[940].Len() = 1\n",
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"SCnComV[942].Len() = 1\n",
"SCnComV[943].Len() = 1\n",
"SCnComV[944].Len() = 1\n",
"SCnComV[945].Len() = 1\n",
"SCnComV[946].Len() = 1\n",
"SCnComV[947].Len() = 1\n",
"SCnComV[948].Len() = 1\n",
"SCnComV[949].Len() = 1\n",
"SCnComV[950].Len() = 1\n",
"SCnComV[951].Len() = 1\n",
"SCnComV[952].Len() = 1\n",
"SCnComV[953].Len() = 1\n",
"SCnComV[954].Len() = 1\n",
"SCnComV[955].Len() = 1\n",
"SCnComV[956].Len() = 1\n",
"SCnComV[957].Len() = 1\n",
"SCnComV[958].Len() = 1\n",
"SCnComV[959].Len() = 1\n",
"SCnComV[960].Len() = 1\n",
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"SCnComV[962].Len() = 1\n",
"SCnComV[963].Len() = 1\n",
"SCnComV[964].Len() = 1\n",
"SCnComV[965].Len() = 1\n",
"SCnComV[966].Len() = 1\n",
"SCnComV[967].Len() = 1\n",
"SCnComV[968].Len() = 1\n",
"SCnComV[969].Len() = 1\n",
"SCnComV[970].Len() = 1\n",
"SCnComV[971].Len() = 1\n",
"SCnComV[972].Len() = 1\n",
"SCnComV[973].Len() = 1\n",
"SCnComV[974].Len() = 1\n",
"SCnComV[975].Len() = 1\n",
"SCnComV[976].Len() = 1\n",
"SCnComV[977].Len() = 1\n",
"SCnComV[978].Len() = 1\n",
"SCnComV[979].Len() = 1\n",
"SCnComV[980].Len() = 1\n",
"SCnComV[981].Len() = 1\n",
"SCnComV[982].Len() = 1\n",
"SCnComV[983].Len() = 1\n",
"SCnComV[984].Len() = 1\n",
"SCnComV[985].Len() = 1\n",
"SCnComV[986].Len() = 1\n",
"SCnComV[987].Len() = 1\n",
"SCnComV[988].Len() = 1\n",
"SCnComV[989].Len() = 1\n",
"SCnComV[990].Len() = 1\n",
"SCnComV[991].Len() = 1\n",
"SCnComV[992].Len() = 1\n",
"SCnComV[993].Len() = 1\n",
"SCnComV[994].Len() = 1\n",
"SCnComV[995].Len() = 1\n",
"SCnComV[996].Len() = 1\n",
"SCnComV[997].Len() = 1\n",
"SCnComV[998].Len() = 1\n",
"SCnComV[999].Len() = 1\n",
"SCnComV[1000].Len() = 1\n",
"SCnComV[1001].Len() = 1\n",
"SCnComV[1002].Len() = 1\n",
"SCnComV[1003].Len() = 1\n",
"SCnComV[1004].Len() = 1\n",
"SCnComV[1005].Len() = 1\n",
"SCnComV[1006].Len() = 1\n",
"SCnComV[1007].Len() = 1\n",
"SCnComV[1008].Len() = 1\n",
"SCnComV[1009].Len() = 1\n",
"SCnComV[1010].Len() = 1\n",
"SCnComV[1011].Len() = 1\n",
"SCnComV[1012].Len() = 1\n",
"SCnComV[1013].Len() = 1\n",
"SCnComV[1014].Len() = 1\n",
"SCnComV[1015].Len() = 1\n",
"SCnComV[1016].Len() = 1\n",
"SCnComV[1017].Len() = 1\n",
"SCnComV[1018].Len() = 1\n",
"SCnComV[1019].Len() = 1\n",
"SCnComV[1020].Len() = 1\n",
"SCnComV[1021].Len() = 1\n",
"SCnComV[1022].Len() = 1\n",
"SCnComV[1023].Len() = 1\n",
"SCnComV[1024].Len() = 1\n",
"SCnComV[1025].Len() = 1\n",
"SCnComV[1026].Len() = 1\n",
"SCnComV[1027].Len() = 1\n",
"SCnComV[1028].Len() = 1\n",
"SCnComV[1029].Len() = 1\n",
"SCnComV[1030].Len() = 1\n",
"SCnComV[1031].Len() = 1\n",
"SCnComV[1032].Len() = 1\n",
"SCnComV[1033].Len() = 1\n",
"SCnComV[1034].Len() = 1\n",
"SCnComV[1035].Len() = 1\n",
"SCnComV[1036].Len() = 1\n",
"SCnComV[1037].Len() = 1\n",
"SCnComV[1038].Len() = 1\n",
"SCnComV[1039].Len() = 1\n",
"SCnComV[1040].Len() = 1\n",
"SCnComV[1041].Len() = 1\n",
"SCnComV[1042].Len() = 1\n",
"SCnComV[1043].Len() = 1\n",
"SCnComV[1044].Len() = 1\n",
"SCnComV[1045].Len() = 1\n",
"SCnComV[1046].Len() = 1\n",
"SCnComV[1047].Len() = 1\n",
"SCnComV[1048].Len() = 1\n",
"SCnComV[1049].Len() = 1\n",
"SCnComV[1050].Len() = 1\n",
"SCnComV[1051].Len() = 1\n",
"SCnComV[1052].Len() = 1\n",
"SCnComV[1053].Len() = 1\n",
"SCnComV[1054].Len() = 1\n",
"SCnComV[1055].Len() = 1\n",
"SCnComV[1056].Len() = 1\n",
"SCnComV[1057].Len() = 1\n",
"SCnComV[1058].Len() = 1\n",
"SCnComV[1059].Len() = 1\n",
"SCnComV[1060].Len() = 1\n",
"SCnComV[1061].Len() = 1\n",
"SCnComV[1062].Len() = 1\n",
"SCnComV[1063].Len() = 1\n",
"SCnComV[1064].Len() = 1\n",
"SCnComV[1065].Len() = 1\n",
"SCnComV[1066].Len() = 1\n",
"SCnComV[1067].Len() = 1\n",
"SCnComV[1068].Len() = 1\n",
"SCnComV[1069].Len() = 1\n",
"SCnComV[1070].Len() = 1\n",
"SCnComV[1071].Len() = 1\n",
"SCnComV[1072].Len() = 1\n",
"SCnComV[1073].Len() = 1\n",
"SCnComV[1074].Len() = 1\n",
"SCnComV[1075].Len() = 1\n",
"SCnComV[1076].Len() = 1\n",
"SCnComV[1077].Len() = 1\n",
"SCnComV[1078].Len() = 1\n",
"SCnComV[1079].Len() = 1\n",
"SCnComV[1080].Len() = 1\n",
"SCnComV[1081].Len() = 1\n",
"SCnComV[1082].Len() = 1\n",
"SCnComV[1083].Len() = 1\n",
"SCnComV[1084].Len() = 1\n",
"SCnComV[1085].Len() = 1\n",
"SCnComV[1086].Len() = 1\n",
"SCnComV[1087].Len() = 1\n",
"SCnComV[1088].Len() = 1\n",
"SCnComV[1089].Len() = 1\n",
"SCnComV[1090].Len() = 1\n",
"SCnComV[1091].Len() = 1\n",
"SCnComV[1092].Len() = 1\n",
"SCnComV[1093].Len() = 1\n",
"SCnComV[1094].Len() = 1\n",
"SCnComV[1095].Len() = 1\n",
"SCnComV[1096].Len() = 1\n",
"SCnComV[1097].Len() = 1\n",
"SCnComV[1098].Len() = 1\n",
"SCnComV[1099].Len() = 1\n",
"SCnComV[1100].Len() = 1\n",
"SCnComV[1101].Len() = 1\n",
"SCnComV[1102].Len() = 1\n",
"SCnComV[1103].Len() = 1\n",
"SCnComV[1104].Len() = 1\n",
"SCnComV[1105].Len() = 1\n",
"SCnComV[1106].Len() = 1\n",
"SCnComV[1107].Len() = 1\n",
"SCnComV[1108].Len() = 1\n",
"SCnComV[1109].Len() = 1\n",
"SCnComV[1110].Len() = 1\n",
"SCnComV[1111].Len() = 1\n",
"SCnComV[1112].Len() = 1\n",
"SCnComV[1113].Len() = 1\n",
"SCnComV[1114].Len() = 1\n",
"SCnComV[1115].Len() = 1\n",
"SCnComV[1116].Len() = 1\n",
"SCnComV[1117].Len() = 1\n",
"SCnComV[1118].Len() = 1\n",
"SCnComV[1119].Len() = 1\n",
"SCnComV[1120].Len() = 1\n",
"SCnComV[1121].Len() = 1\n",
"SCnComV[1122].Len() = 1\n",
"SCnComV[1123].Len() = 1\n",
"SCnComV[1124].Len() = 1\n",
"SCnComV[1125].Len() = 1\n",
"SCnComV[1126].Len() = 1\n",
"SCnComV[1127].Len() = 1\n",
"SCnComV[1128].Len() = 1\n",
"SCnComV[1129].Len() = 1\n",
"SCnComV[1130].Len() = 1\n",
"SCnComV[1131].Len() = 1\n",
"SCnComV[1132].Len() = 1\n",
"SCnComV[1133].Len() = 1\n",
"SCnComV[1134].Len() = 1\n",
"SCnComV[1135].Len() = 1\n",
"SCnComV[1136].Len() = 1\n",
"SCnComV[1137].Len() = 1\n",
"SCnComV[1138].Len() = 1\n",
"SCnComV[1139].Len() = 1\n",
"SCnComV[1140].Len() = 1\n",
"SCnComV[1141].Len() = 1\n",
"SCnComV[1142].Len() = 1\n",
"SCnComV[1143].Len() = 1\n",
"SCnComV[1144].Len() = 1\n",
"SCnComV[1145].Len() = 1\n",
"SCnComV[1146].Len() = 1\n",
"SCnComV[1147].Len() = 1\n",
"SCnComV[1148].Len() = 1\n",
"SCnComV[1149].Len() = 1\n",
"SCnComV[1150].Len() = 1\n",
"SCnComV[1151].Len() = 1\n",
"SCnComV[1152].Len() = 1\n",
"SCnComV[1153].Len() = 1\n",
"SCnComV[1154].Len() = 1\n",
"SCnComV[1155].Len() = 1\n",
"SCnComV[1156].Len() = 1\n",
"SCnComV[1157].Len() = 1\n",
"SCnComV[1158].Len() = 1\n",
"SCnComV[1159].Len() = 1\n",
"SCnComV[1160].Len() = 1\n",
"SCnComV[1161].Len() = 1\n",
"SCnComV[1162].Len() = 1\n",
"SCnComV[1163].Len() = 1\n",
"SCnComV[1164].Len() = 1\n",
"SCnComV[1165].Len() = 1\n",
"SCnComV[1166].Len() = 1\n",
"SCnComV[1167].Len() = 1\n",
"SCnComV[1168].Len() = 1\n",
"SCnComV[1169].Len() = 1\n",
"SCnComV[1170].Len() = 1\n",
"SCnComV[1171].Len() = 1\n",
"SCnComV[1172].Len() = 1\n",
"SCnComV[1173].Len() = 1\n",
"SCnComV[1174].Len() = 1\n",
"SCnComV[1175].Len() = 1\n",
"SCnComV[1176].Len() = 1\n",
"SCnComV[1177].Len() = 1\n",
"SCnComV[1178].Len() = 1\n",
"SCnComV[1179].Len() = 1\n",
"SCnComV[1180].Len() = 1\n",
"SCnComV[1181].Len() = 1\n",
"SCnComV[1182].Len() = 1\n",
"SCnComV[1183].Len() = 1\n",
"SCnComV[1184].Len() = 1\n",
"SCnComV[1185].Len() = 1\n",
"SCnComV[1186].Len() = 1\n",
"SCnComV[1187].Len() = 1\n",
"SCnComV[1188].Len() = 1\n",
"SCnComV[1189].Len() = 1\n",
"SCnComV[1190].Len() = 1\n",
"SCnComV[1191].Len() = 1\n",
"SCnComV[1192].Len() = 1\n",
"SCnComV[1193].Len() = 1\n",
"SCnComV[1194].Len() = 1\n",
"SCnComV[1195].Len() = 1\n",
"SCnComV[1196].Len() = 1\n",
"SCnComV[1197].Len() = 1\n",
"SCnComV[1198].Len() = 1\n",
"SCnComV[1199].Len() = 1\n",
"SCnComV[1200].Len() = 1\n",
"SCnComV[1201].Len() = 1\n",
"SCnComV[1202].Len() = 1\n",
"SCnComV[1203].Len() = 1\n",
"SCnComV[1204].Len() = 1\n",
"SCnComV[1205].Len() = 1\n",
"SCnComV[1206].Len() = 1\n",
"SCnComV[1207].Len() = 1\n",
"SCnComV[1208].Len() = 1\n",
"SCnComV[1209].Len() = 1\n",
"SCnComV[1210].Len() = 1\n",
"SCnComV[1211].Len() = 1\n",
"SCnComV[1212].Len() = 1\n",
"SCnComV[1213].Len() = 1\n",
"SCnComV[1214].Len() = 1\n",
"SCnComV[1215].Len() = 1\n",
"SCnComV[1216].Len() = 1\n",
"SCnComV[1217].Len() = 1\n",
"SCnComV[1218].Len() = 1\n",
"SCnComV[1219].Len() = 1\n",
"SCnComV[1220].Len() = 1\n",
"SCnComV[1221].Len() = 1\n",
"SCnComV[1222].Len() = 1\n",
"SCnComV[1223].Len() = 1\n",
"SCnComV[1224].Len() = 1\n",
"SCnComV[1225].Len() = 1\n",
"SCnComV[1226].Len() = 1\n",
"SCnComV[1227].Len() = 1\n",
"SCnComV[1228].Len() = 1\n",
"SCnComV[1229].Len() = 1\n",
"SCnComV[1230].Len() = 1\n",
"SCnComV[1231].Len() = 1\n",
"SCnComV[1232].Len() = 1\n",
"SCnComV[1233].Len() = 1\n",
"SCnComV[1234].Len() = 1\n",
"SCnComV[1235].Len() = 1\n",
"SCnComV[1236].Len() = 1\n",
"SCnComV[1237].Len() = 1\n",
"SCnComV[1238].Len() = 1\n",
"SCnComV[1239].Len() = 1\n",
"SCnComV[1240].Len() = 1\n",
"SCnComV[1241].Len() = 1\n",
"SCnComV[1242].Len() = 1\n",
"SCnComV[1243].Len() = 1\n",
"SCnComV[1244].Len() = 1\n",
"SCnComV[1245].Len() = 1\n",
"SCnComV[1246].Len() = 1\n",
"SCnComV[1247].Len() = 1\n",
"SCnComV[1248].Len() = 1\n",
"SCnComV[1249].Len() = 1\n",
"SCnComV[1250].Len() = 1\n",
"SCnComV[1251].Len() = 1\n",
"SCnComV[1252].Len() = 1\n",
"SCnComV[1253].Len() = 1\n",
"SCnComV[1254].Len() = 1\n",
"SCnComV[1255].Len() = 1\n",
"SCnComV[1256].Len() = 1\n",
"SCnComV[1257].Len() = 1\n",
"SCnComV[1258].Len() = 1\n",
"SCnComV[1259].Len() = 1\n",
"SCnComV[1260].Len() = 1\n",
"SCnComV[1261].Len() = 1\n",
"SCnComV[1262].Len() = 1\n",
"SCnComV[1263].Len() = 1\n",
"SCnComV[1264].Len() = 1\n",
"SCnComV[1265].Len() = 1\n",
"SCnComV[1266].Len() = 1\n",
"SCnComV[1267].Len() = 1\n",
"SCnComV[1268].Len() = 1\n",
"SCnComV[1269].Len() = 1\n",
"SCnComV[1270].Len() = 1\n",
"SCnComV[1271].Len() = 1\n",
"SCnComV[1272].Len() = 1\n",
"SCnComV[1273].Len() = 1\n",
"SCnComV[1274].Len() = 1\n",
"SCnComV[1275].Len() = 1\n",
"SCnComV[1276].Len() = 1\n",
"SCnComV[1277].Len() = 1\n",
"SCnComV[1278].Len() = 1\n",
"SCnComV[1279].Len() = 1\n",
"SCnComV[1280].Len() = 1\n",
"SCnComV[1281].Len() = 1\n",
"SCnComV[1282].Len() = 1\n",
"SCnComV[1283].Len() = 1\n",
"SCnComV[1284].Len() = 1\n",
"SCnComV[1285].Len() = 1\n",
"SCnComV[1286].Len() = 1\n",
"SCnComV[1287].Len() = 1\n",
"SCnComV[1288].Len() = 1\n",
"SCnComV[1289].Len() = 1\n",
"SCnComV[1290].Len() = 1\n",
"SCnComV[1291].Len() = 1\n",
"SCnComV[1292].Len() = 1\n",
"SCnComV[1293].Len() = 1\n",
"SCnComV[1294].Len() = 1\n",
"SCnComV[1295].Len() = 1\n",
"SCnComV[1296].Len() = 1\n",
"SCnComV[1297].Len() = 1\n",
"SCnComV[1298].Len() = 1\n",
"SCnComV[1299].Len() = 1\n",
"SCnComV[1300].Len() = 1\n",
"SCnComV[1301].Len() = 1\n",
"SCnComV[1302].Len() = 1\n",
"SCnComV[1303].Len() = 1\n",
"SCnComV[1304].Len() = 1\n",
"SCnComV[1305].Len() = 1\n",
"SCnComV[1306].Len() = 1\n",
"SCnComV[1307].Len() = 1\n",
"SCnComV[1308].Len() = 1\n",
"SCnComV[1309].Len() = 1\n",
"SCnComV[1310].Len() = 1\n",
"SCnComV[1311].Len() = 1\n",
"SCnComV[1312].Len() = 1\n",
"SCnComV[1313].Len() = 1\n",
"SCnComV[1314].Len() = 1\n",
"SCnComV[1315].Len() = 1\n",
"SCnComV[1316].Len() = 1\n",
"SCnComV[1317].Len() = 1\n",
"SCnComV[1318].Len() = 1\n",
"SCnComV[1319].Len() = 1\n",
"SCnComV[1320].Len() = 1\n",
"SCnComV[1321].Len() = 1\n",
"SCnComV[1322].Len() = 1\n",
"SCnComV[1323].Len() = 1\n",
"SCnComV[1324].Len() = 1\n",
"SCnComV[1325].Len() = 1\n",
"SCnComV[1326].Len() = 1\n",
"SCnComV[1327].Len() = 1\n",
"SCnComV[1328].Len() = 1\n",
"SCnComV[1329].Len() = 1\n",
"SCnComV[1330].Len() = 1\n",
"SCnComV[1331].Len() = 1\n",
"SCnComV[1332].Len() = 1\n",
"SCnComV[1333].Len() = 1\n",
"SCnComV[1334].Len() = 1\n",
"SCnComV[1335].Len() = 1\n",
"SCnComV[1336].Len() = 1\n",
"SCnComV[1337].Len() = 1\n",
"SCnComV[1338].Len() = 1\n",
"SCnComV[1339].Len() = 1\n",
"SCnComV[1340].Len() = 1\n",
"SCnComV[1341].Len() = 1\n",
"SCnComV[1342].Len() = 1\n",
"SCnComV[1343].Len() = 1\n",
"SCnComV[1344].Len() = 1\n",
"SCnComV[1345].Len() = 1\n",
"SCnComV[1346].Len() = 1\n",
"SCnComV[1347].Len() = 1\n",
"SCnComV[1348].Len() = 1\n",
"SCnComV[1349].Len() = 1\n",
"SCnComV[1350].Len() = 1\n",
"SCnComV[1351].Len() = 1\n",
"SCnComV[1352].Len() = 1\n",
"SCnComV[1353].Len() = 1\n",
"SCnComV[1354].Len() = 1\n",
"SCnComV[1355].Len() = 1\n",
"SCnComV[1356].Len() = 1\n",
"SCnComV[1357].Len() = 1\n",
"SCnComV[1358].Len() = 1\n",
"SCnComV[1359].Len() = 1\n",
"SCnComV[1360].Len() = 1\n",
"SCnComV[1361].Len() = 1\n",
"SCnComV[1362].Len() = 1\n",
"SCnComV[1363].Len() = 1\n",
"SCnComV[1364].Len() = 1\n",
"SCnComV[1365].Len() = 1\n",
"SCnComV[1366].Len() = 1\n",
"SCnComV[1367].Len() = 1\n",
"SCnComV[1368].Len() = 1\n",
"SCnComV[1369].Len() = 1\n",
"SCnComV[1370].Len() = 1\n",
"SCnComV[1371].Len() = 1\n",
"SCnComV[1372].Len() = 1\n",
"SCnComV[1373].Len() = 1\n",
"SCnComV[1374].Len() = 1\n",
"SCnComV[1375].Len() = 1\n",
"SCnComV[1376].Len() = 1\n",
"SCnComV[1377].Len() = 1\n",
"SCnComV[1378].Len() = 1\n",
"SCnComV[1379].Len() = 1\n",
"SCnComV[1380].Len() = 1\n",
"SCnComV[1381].Len() = 1\n",
"SCnComV[1382].Len() = 1\n",
"SCnComV[1383].Len() = 1\n",
"SCnComV[1384].Len() = 1\n",
"SCnComV[1385].Len() = 1\n",
"SCnComV[1386].Len() = 1\n",
"SCnComV[1387].Len() = 1\n",
"SCnComV[1388].Len() = 1\n",
"SCnComV[1389].Len() = 1\n",
"SCnComV[1390].Len() = 1\n",
"SCnComV[1391].Len() = 1\n",
"SCnComV[1392].Len() = 1\n",
"SCnComV[1393].Len() = 1\n",
"SCnComV[1394].Len() = 1\n",
"SCnComV[1395].Len() = 1\n",
"SCnComV[1396].Len() = 1\n",
"SCnComV[1397].Len() = 1\n",
"SCnComV[1398].Len() = 1\n",
"SCnComV[1399].Len() = 1\n",
"SCnComV[1400].Len() = 1\n",
"SCnComV[1401].Len() = 1\n",
"SCnComV[1402].Len() = 1\n",
"SCnComV[1403].Len() = 1\n",
"SCnComV[1404].Len() = 1\n",
"SCnComV[1405].Len() = 1\n",
"SCnComV[1406].Len() = 1\n",
"SCnComV[1407].Len() = 1\n",
"SCnComV[1408].Len() = 1\n",
"SCnComV[1409].Len() = 1\n",
"SCnComV[1410].Len() = 1\n",
"SCnComV[1411].Len() = 1\n",
"SCnComV[1412].Len() = 1\n",
"SCnComV[1413].Len() = 1\n",
"SCnComV[1414].Len() = 1\n",
"SCnComV[1415].Len() = 1\n",
"SCnComV[1416].Len() = 1\n",
"SCnComV[1417].Len() = 1\n",
"SCnComV[1418].Len() = 1\n",
"SCnComV[1419].Len() = 1\n",
"SCnComV[1420].Len() = 1\n",
"SCnComV[1421].Len() = 1\n",
"SCnComV[1422].Len() = 1\n",
"SCnComV[1423].Len() = 1\n",
"SCnComV[1424].Len() = 1\n",
"SCnComV[1425].Len() = 1\n",
"SCnComV[1426].Len() = 1\n",
"SCnComV[1427].Len() = 1\n",
"SCnComV[1428].Len() = 1\n",
"SCnComV[1429].Len() = 1\n",
"SCnComV[1430].Len() = 1\n",
"SCnComV[1431].Len() = 1\n",
"SCnComV[1432].Len() = 1\n",
"SCnComV[1433].Len() = 1\n",
"SCnComV[1434].Len() = 1\n",
"SCnComV[1435].Len() = 1\n",
"SCnComV[1436].Len() = 1\n",
"SCnComV[1437].Len() = 1\n",
"SCnComV[1438].Len() = 1\n",
"SCnComV[1439].Len() = 1\n",
"SCnComV[1440].Len() = 1\n",
"SCnComV[1441].Len() = 1\n",
"SCnComV[1442].Len() = 1\n",
"SCnComV[1443].Len() = 1\n",
"SCnComV[1444].Len() = 1\n",
"SCnComV[1445].Len() = 1\n",
"SCnComV[1446].Len() = 1\n",
"SCnComV[1447].Len() = 1\n",
"SCnComV[1448].Len() = 1\n",
"SCnComV[1449].Len() = 1\n",
"SCnComV[1450].Len() = 1\n",
"SCnComV[1451].Len() = 1\n",
"SCnComV[1452].Len() = 1\n",
"SCnComV[1453].Len() = 1\n",
"SCnComV[1454].Len() = 1\n",
"SCnComV[1455].Len() = 1\n",
"SCnComV[1456].Len() = 1\n",
"SCnComV[1457].Len() = 1\n",
"SCnComV[1458].Len() = 1\n",
"SCnComV[1459].Len() = 1\n",
"SCnComV[1460].Len() = 1\n",
"SCnComV[1461].Len() = 1\n",
"SCnComV[1462].Len() = 1\n",
"SCnComV[1463].Len() = 1\n",
"SCnComV[1464].Len() = 1\n",
"SCnComV[1465].Len() = 1\n",
"SCnComV[1466].Len() = 1\n",
"SCnComV[1467].Len() = 1\n",
"SCnComV[1468].Len() = 1\n",
"SCnComV[1469].Len() = 1\n",
"SCnComV[1470].Len() = 1\n",
"SCnComV[1471].Len() = 1\n",
"SCnComV[1472].Len() = 1\n",
"SCnComV[1473].Len() = 1\n",
"SCnComV[1474].Len() = 1\n",
"SCnComV[1475].Len() = 1\n",
"SCnComV[1476].Len() = 1\n",
"SCnComV[1477].Len() = 1\n",
"SCnComV[1478].Len() = 1\n",
"SCnComV[1479].Len() = 1\n",
"SCnComV[1480].Len() = 1\n",
"SCnComV[1481].Len() = 1\n",
"SCnComV[1482].Len() = 1\n",
"SCnComV[1483].Len() = 1\n",
"SCnComV[1484].Len() = 1\n",
"SCnComV[1485].Len() = 1\n",
"SCnComV[1486].Len() = 1\n",
"SCnComV[1487].Len() = 1\n",
"SCnComV[1488].Len() = 1\n",
"SCnComV[1489].Len() = 1\n",
"SCnComV[1490].Len() = 1\n",
"SCnComV[1491].Len() = 1\n",
"SCnComV[1492].Len() = 1\n",
"SCnComV[1493].Len() = 1\n",
"SCnComV[1494].Len() = 1\n",
"SCnComV[1495].Len() = 1\n",
"SCnComV[1496].Len() = 1\n",
"SCnComV[1497].Len() = 1\n",
"SCnComV[1498].Len() = 1\n",
"SCnComV[1499].Len() = 1\n",
"SCnComV[1500].Len() = 1\n",
"SCnComV[1501].Len() = 1\n",
"SCnComV[1502].Len() = 1\n",
"SCnComV[1503].Len() = 1\n",
"SCnComV[1504].Len() = 1\n",
"SCnComV[1505].Len() = 1\n",
"SCnComV[1506].Len() = 1\n",
"SCnComV[1507].Len() = 1\n",
"SCnComV[1508].Len() = 1\n",
"SCnComV[1509].Len() = 1\n",
"SCnComV[1510].Len() = 1\n",
"SCnComV[1511].Len() = 1\n",
"SCnComV[1512].Len() = 1\n",
"SCnComV[1513].Len() = 1\n",
"SCnComV[1514].Len() = 1\n",
"SCnComV[1515].Len() = 1\n",
"SCnComV[1516].Len() = 1\n",
"SCnComV[1517].Len() = 1\n",
"SCnComV[1518].Len() = 1\n",
"SCnComV[1519].Len() = 1\n",
"SCnComV[1520].Len() = 1\n",
"SCnComV[1521].Len() = 1\n",
"SCnComV[1522].Len() = 1\n",
"SCnComV[1523].Len() = 1\n",
"SCnComV[1524].Len() = 1\n",
"SCnComV[1525].Len() = 1\n",
"SCnComV[1526].Len() = 1\n",
"SCnComV[1527].Len() = 1\n",
"SCnComV[1528].Len() = 1\n",
"SCnComV[1529].Len() = 1\n",
"SCnComV[1530].Len() = 1\n",
"SCnComV[1531].Len() = 1\n",
"SCnComV[1532].Len() = 1\n",
"SCnComV[1533].Len() = 1\n",
"SCnComV[1534].Len() = 1\n",
"SCnComV[1535].Len() = 1\n",
"SCnComV[1536].Len() = 1\n",
"SCnComV[1537].Len() = 1\n",
"SCnComV[1538].Len() = 1\n",
"SCnComV[1539].Len() = 1\n",
"SCnComV[1540].Len() = 1\n",
"SCnComV[1541].Len() = 1\n",
"SCnComV[1542].Len() = 1\n",
"SCnComV[1543].Len() = 1\n",
"SCnComV[1544].Len() = 1\n",
"SCnComV[1545].Len() = 1\n",
"SCnComV[1546].Len() = 1\n",
"SCnComV[1547].Len() = 1\n",
"SCnComV[1548].Len() = 1\n",
"SCnComV[1549].Len() = 1\n",
"SCnComV[1550].Len() = 1\n",
"SCnComV[1551].Len() = 1\n",
"SCnComV[1552].Len() = 1\n",
"SCnComV[1553].Len() = 1\n",
"SCnComV[1554].Len() = 1\n",
"SCnComV[1555].Len() = 1\n",
"SCnComV[1556].Len() = 1\n",
"SCnComV[1557].Len() = 1\n",
"SCnComV[1558].Len() = 1\n",
"SCnComV[1559].Len() = 1\n",
"SCnComV[1560].Len() = 1\n",
"SCnComV[1561].Len() = 1\n",
"SCnComV[1562].Len() = 1\n",
"SCnComV[1563].Len() = 1\n",
"SCnComV[1564].Len() = 1\n",
"SCnComV[1565].Len() = 1\n",
"SCnComV[1566].Len() = 1\n",
"SCnComV[1567].Len() = 1\n",
"SCnComV[1568].Len() = 1\n",
"SCnComV[1569].Len() = 1\n",
"SCnComV[1570].Len() = 1\n",
"SCnComV[1571].Len() = 1\n",
"SCnComV[1572].Len() = 1\n",
"SCnComV[1573].Len() = 1\n",
"SCnComV[1574].Len() = 1\n",
"SCnComV[1575].Len() = 1\n",
"SCnComV[1576].Len() = 1\n",
"SCnComV[1577].Len() = 1\n",
"SCnComV[1578].Len() = 1\n",
"SCnComV[1579].Len() = 1\n",
"SCnComV[1580].Len() = 1\n",
"SCnComV[1581].Len() = 1\n",
"SCnComV[1582].Len() = 1\n",
"SCnComV[1583].Len() = 1\n",
"SCnComV[1584].Len() = 1\n",
"SCnComV[1585].Len() = 1\n",
"SCnComV[1586].Len() = 1\n",
"SCnComV[1587].Len() = 1\n",
"SCnComV[1588].Len() = 1\n",
"SCnComV[1589].Len() = 1\n",
"SCnComV[1590].Len() = 1\n",
"SCnComV[1591].Len() = 1\n",
"SCnComV[1592].Len() = 1\n",
"SCnComV[1593].Len() = 1\n",
"SCnComV[1594].Len() = 1\n",
"SCnComV[1595].Len() = 1\n",
"SCnComV[1596].Len() = 1\n",
"SCnComV[1597].Len() = 1\n",
"SCnComV[1598].Len() = 1\n",
"SCnComV[1599].Len() = 1\n",
"SCnComV[1600].Len() = 1\n",
"SCnComV[1601].Len() = 1\n",
"SCnComV[1602].Len() = 1\n",
"SCnComV[1603].Len() = 1\n",
"SCnComV[1604].Len() = 1\n",
"SCnComV[1605].Len() = 1\n",
"SCnComV[1606].Len() = 1\n",
"SCnComV[1607].Len() = 1\n",
"SCnComV[1608].Len() = 1\n",
"SCnComV[1609].Len() = 1\n",
"SCnComV[1610].Len() = 1\n",
"SCnComV[1611].Len() = 1\n",
"SCnComV[1612].Len() = 1\n",
"SCnComV[1613].Len() = 1\n",
"SCnComV[1614].Len() = 1\n",
"SCnComV[1615].Len() = 1\n",
"SCnComV[1616].Len() = 1\n",
"SCnComV[1617].Len() = 1\n",
"SCnComV[1618].Len() = 1\n",
"SCnComV[1619].Len() = 1\n",
"SCnComV[1620].Len() = 1\n",
"SCnComV[1621].Len() = 1\n",
"SCnComV[1622].Len() = 1\n",
"SCnComV[1623].Len() = 1\n",
"SCnComV[1624].Len() = 1\n",
"SCnComV[1625].Len() = 1\n",
"SCnComV[1626].Len() = 1\n",
"SCnComV[1627].Len() = 1\n",
"SCnComV[1628].Len() = 1\n",
"SCnComV[1629].Len() = 1\n",
"SCnComV[1630].Len() = 1\n",
"SCnComV[1631].Len() = 1\n",
"SCnComV[1632].Len() = 1\n",
"SCnComV[1633].Len() = 1\n",
"SCnComV[1634].Len() = 1\n",
"SCnComV[1635].Len() = 1\n",
"SCnComV[1636].Len() = 1\n",
"SCnComV[1637].Len() = 1\n",
"SCnComV[1638].Len() = 1\n",
"SCnComV[1639].Len() = 1\n",
"SCnComV[1640].Len() = 1\n",
"SCnComV[1641].Len() = 1\n",
"SCnComV[1642].Len() = 1\n",
"SCnComV[1643].Len() = 1\n",
"SCnComV[1644].Len() = 1\n",
"SCnComV[1645].Len() = 1\n",
"SCnComV[1646].Len() = 1\n",
"SCnComV[1647].Len() = 1\n",
"SCnComV[1648].Len() = 1\n",
"SCnComV[1649].Len() = 1\n",
"SCnComV[1650].Len() = 1\n",
"SCnComV[1651].Len() = 1\n",
"SCnComV[1652].Len() = 1\n",
"SCnComV[1653].Len() = 1\n",
"SCnComV[1654].Len() = 1\n",
"SCnComV[1655].Len() = 1\n",
"SCnComV[1656].Len() = 1\n",
"SCnComV[1657].Len() = 1\n",
"SCnComV[1658].Len() = 1\n",
"SCnComV[1659].Len() = 1\n",
"SCnComV[1660].Len() = 1\n",
"SCnComV[1661].Len() = 1\n",
"SCnComV[1662].Len() = 1\n",
"SCnComV[1663].Len() = 1\n",
"SCnComV[1664].Len() = 1\n",
"SCnComV[1665].Len() = 1\n",
"SCnComV[1666].Len() = 1\n",
"SCnComV[1667].Len() = 1\n",
"SCnComV[1668].Len() = 1\n",
"SCnComV[1669].Len() = 1\n",
"SCnComV[1670].Len() = 1\n",
"SCnComV[1671].Len() = 1\n",
"SCnComV[1672].Len() = 1\n",
"SCnComV[1673].Len() = 1\n",
"SCnComV[1674].Len() = 1\n",
"SCnComV[1675].Len() = 1\n",
"SCnComV[1676].Len() = 1\n",
"SCnComV[1677].Len() = 1\n",
"SCnComV[1678].Len() = 1\n",
"SCnComV[1679].Len() = 1\n",
"SCnComV[1680].Len() = 1\n",
"SCnComV[1681].Len() = 1\n",
"SCnComV[1682].Len() = 1\n",
"SCnComV[1683].Len() = 1\n",
"SCnComV[1684].Len() = 1\n",
"SCnComV[1685].Len() = 1\n",
"SCnComV[1686].Len() = 1\n",
"SCnComV[1687].Len() = 1\n",
"SCnComV[1688].Len() = 1\n",
"SCnComV[1689].Len() = 1\n",
"SCnComV[1690].Len() = 1\n",
"SCnComV[1691].Len() = 1\n",
"SCnComV[1692].Len() = 1\n",
"SCnComV[1693].Len() = 1\n",
"SCnComV[1694].Len() = 1\n",
"SCnComV[1695].Len() = 1\n",
"SCnComV[1696].Len() = 1\n",
"SCnComV[1697].Len() = 1\n",
"SCnComV[1698].Len() = 1\n",
"SCnComV[1699].Len() = 1\n",
"SCnComV[1700].Len() = 1\n",
"SCnComV[1701].Len() = 1\n",
"SCnComV[1702].Len() = 1\n",
"SCnComV[1703].Len() = 1\n",
"SCnComV[1704].Len() = 1\n",
"SCnComV[1705].Len() = 1\n",
"SCnComV[1706].Len() = 1\n",
"SCnComV[1707].Len() = 1\n",
"SCnComV[1708].Len() = 1\n",
"SCnComV[1709].Len() = 1\n",
"SCnComV[1710].Len() = 1\n",
"SCnComV[1711].Len() = 1\n",
"SCnComV[1712].Len() = 1\n",
"SCnComV[1713].Len() = 1\n",
"SCnComV[1714].Len() = 1\n",
"SCnComV[1715].Len() = 1\n",
"SCnComV[1716].Len() = 1\n",
"SCnComV[1717].Len() = 1\n",
"SCnComV[1718].Len() = 1\n",
"SCnComV[1719].Len() = 1\n",
"SCnComV[1720].Len() = 1\n",
"SCnComV[1721].Len() = 1\n",
"SCnComV[1722].Len() = 1\n",
"SCnComV[1723].Len() = 1\n",
"SCnComV[1724].Len() = 1\n",
"SCnComV[1725].Len() = 1\n",
"SCnComV[1726].Len() = 1\n",
"SCnComV[1727].Len() = 1\n",
"SCnComV[1728].Len() = 1\n",
"SCnComV[1729].Len() = 1\n",
"SCnComV[1730].Len() = 1\n",
"SCnComV[1731].Len() = 1\n",
"SCnComV[1732].Len() = 1\n",
"SCnComV[1733].Len() = 1\n",
"SCnComV[1734].Len() = 1\n",
"SCnComV[1735].Len() = 1\n",
"SCnComV[1736].Len() = 1\n",
"SCnComV[1737].Len() = 1\n",
"SCnComV[1738].Len() = 1\n",
"SCnComV[1739].Len() = 1\n",
"SCnComV[1740].Len() = 1\n",
"SCnComV[1741].Len() = 1\n",
"SCnComV[1742].Len() = 1\n",
"SCnComV[1743].Len() = 1\n",
"SCnComV[1744].Len() = 1\n",
"SCnComV[1745].Len() = 1\n",
"SCnComV[1746].Len() = 1\n",
"SCnComV[1747].Len() = 1\n",
"SCnComV[1748].Len() = 1\n",
"SCnComV[1749].Len() = 1\n",
"SCnComV[1750].Len() = 1\n",
"SCnComV[1751].Len() = 1\n",
"SCnComV[1752].Len() = 1\n",
"SCnComV[1753].Len() = 1\n",
"SCnComV[1754].Len() = 1\n",
"SCnComV[1755].Len() = 1\n",
"SCnComV[1756].Len() = 1\n",
"SCnComV[1757].Len() = 1\n",
"SCnComV[1758].Len() = 1\n",
"SCnComV[1759].Len() = 1\n",
"SCnComV[1760].Len() = 1\n",
"SCnComV[1761].Len() = 1\n",
"SCnComV[1762].Len() = 1\n",
"SCnComV[1763].Len() = 1\n",
"SCnComV[1764].Len() = 1\n",
"SCnComV[1765].Len() = 1\n",
"SCnComV[1766].Len() = 1\n",
"SCnComV[1767].Len() = 1\n",
"SCnComV[1768].Len() = 1\n",
"SCnComV[1769].Len() = 1\n",
"SCnComV[1770].Len() = 1\n",
"SCnComV[1771].Len() = 1\n",
"SCnComV[1772].Len() = 1\n",
"SCnComV[1773].Len() = 1\n",
"SCnComV[1774].Len() = 1\n",
"SCnComV[1775].Len() = 1\n",
"SCnComV[1776].Len() = 1\n",
"SCnComV[1777].Len() = 1\n",
"SCnComV[1778].Len() = 1\n",
"SCnComV[1779].Len() = 1\n",
"SCnComV[1780].Len() = 1\n",
"SCnComV[1781].Len() = 1\n",
"SCnComV[1782].Len() = 1\n",
"SCnComV[1783].Len() = 1\n",
"SCnComV[1784].Len() = 1\n",
"SCnComV[1785].Len() = 1\n",
"SCnComV[1786].Len() = 1\n",
"SCnComV[1787].Len() = 1\n",
"SCnComV[1788].Len() = 1\n",
"SCnComV[1789].Len() = 1\n",
"SCnComV[1790].Len() = 1\n",
"SCnComV[1791].Len() = 1\n",
"SCnComV[1792].Len() = 1\n",
"SCnComV[1793].Len() = 1\n",
"SCnComV[1794].Len() = 1\n",
"SCnComV[1795].Len() = 1\n",
"SCnComV[1796].Len() = 1\n",
"SCnComV[1797].Len() = 1\n",
"SCnComV[1798].Len() = 1\n",
"SCnComV[1799].Len() = 1\n",
"SCnComV[1800].Len() = 1\n",
"SCnComV[1801].Len() = 1\n",
"SCnComV[1802].Len() = 1\n",
"SCnComV[1803].Len() = 1\n",
"SCnComV[1804].Len() = 1\n",
"SCnComV[1805].Len() = 1\n",
"SCnComV[1806].Len() = 1\n",
"SCnComV[1807].Len() = 1\n",
"SCnComV[1808].Len() = 1\n",
"SCnComV[1809].Len() = 1\n",
"SCnComV[1810].Len() = 1\n",
"SCnComV[1811].Len() = 1\n",
"SCnComV[1812].Len() = 1\n",
"SCnComV[1813].Len() = 1\n",
"SCnComV[1814].Len() = 1\n",
"SCnComV[1815].Len() = 1\n",
"SCnComV[1816].Len() = 1\n",
"SCnComV[1817].Len() = 1\n",
"SCnComV[1818].Len() = 1\n",
"SCnComV[1819].Len() = 1\n",
"SCnComV[1820].Len() = 1\n",
"SCnComV[1821].Len() = 1\n",
"SCnComV[1822].Len() = 1\n",
"SCnComV[1823].Len() = 1\n",
"SCnComV[1824].Len() = 1\n",
"SCnComV[1825].Len() = 1\n",
"SCnComV[1826].Len() = 1\n",
"SCnComV[1827].Len() = 1\n",
"SCnComV[1828].Len() = 1\n",
"SCnComV[1829].Len() = 1\n",
"SCnComV[1830].Len() = 1\n",
"SCnComV[1831].Len() = 1\n",
"SCnComV[1832].Len() = 1\n",
"SCnComV[1833].Len() = 1\n",
"SCnComV[1834].Len() = 1\n",
"SCnComV[1835].Len() = 1\n",
"SCnComV[1836].Len() = 1\n",
"SCnComV[1837].Len() = 1\n",
"SCnComV[1838].Len() = 1\n",
"SCnComV[1839].Len() = 1\n",
"SCnComV[1840].Len() = 1\n",
"SCnComV[1841].Len() = 1\n",
"SCnComV[1842].Len() = 1\n",
"SCnComV[1843].Len() = 1\n",
"SCnComV[1844].Len() = 1\n",
"SCnComV[1845].Len() = 1\n",
"SCnComV[1846].Len() = 1\n",
"SCnComV[1847].Len() = 1\n",
"SCnComV[1848].Len() = 1\n",
"SCnComV[1849].Len() = 1\n",
"SCnComV[1850].Len() = 1\n",
"SCnComV[1851].Len() = 1\n",
"SCnComV[1852].Len() = 1\n",
"SCnComV[1853].Len() = 1\n",
"SCnComV[1854].Len() = 1\n",
"SCnComV[1855].Len() = 1\n",
"SCnComV[1856].Len() = 1\n",
"SCnComV[1857].Len() = 1\n",
"SCnComV[1858].Len() = 1\n",
"SCnComV[1859].Len() = 1\n",
"SCnComV[1860].Len() = 1\n",
"SCnComV[1861].Len() = 1\n",
"SCnComV[1862].Len() = 1\n",
"SCnComV[1863].Len() = 1\n",
"SCnComV[1864].Len() = 1\n",
"SCnComV[1865].Len() = 1\n",
"SCnComV[1866].Len() = 1\n",
"SCnComV[1867].Len() = 1\n",
"SCnComV[1868].Len() = 1\n",
"SCnComV[1869].Len() = 1\n",
"SCnComV[1870].Len() = 1\n",
"SCnComV[1871].Len() = 1\n",
"SCnComV[1872].Len() = 1\n",
"SCnComV[1873].Len() = 1\n",
"SCnComV[1874].Len() = 1\n",
"SCnComV[1875].Len() = 1\n",
"SCnComV[1876].Len() = 1\n",
"SCnComV[1877].Len() = 1\n",
"SCnComV[1878].Len() = 1\n",
"SCnComV[1879].Len() = 1\n",
"SCnComV[1880].Len() = 1\n",
"SCnComV[1881].Len() = 1\n",
"SCnComV[1882].Len() = 1\n",
"SCnComV[1883].Len() = 1\n",
"SCnComV[1884].Len() = 1\n",
"SCnComV[1885].Len() = 1\n",
"SCnComV[1886].Len() = 1\n",
"SCnComV[1887].Len() = 1\n",
"SCnComV[1888].Len() = 1\n",
"SCnComV[1889].Len() = 1\n",
"SCnComV[1890].Len() = 1\n",
"SCnComV[1891].Len() = 1\n",
"SCnComV[1892].Len() = 1\n",
"SCnComV[1893].Len() = 1\n",
"SCnComV[1894].Len() = 1\n",
"SCnComV[1895].Len() = 1\n",
"SCnComV[1896].Len() = 1\n",
"SCnComV[1897].Len() = 1\n",
"SCnComV[1898].Len() = 1\n",
"SCnComV[1899].Len() = 1\n",
"SCnComV[1900].Len() = 1\n",
"SCnComV[1901].Len() = 1\n",
"SCnComV[1902].Len() = 1\n",
"SCnComV[1903].Len() = 1\n",
"SCnComV[1904].Len() = 1\n",
"SCnComV[1905].Len() = 1\n",
"SCnComV[1906].Len() = 1\n",
"SCnComV[1907].Len() = 1\n",
"SCnComV[1908].Len() = 1\n",
"SCnComV[1909].Len() = 1\n",
"SCnComV[1910].Len() = 1\n",
"SCnComV[1911].Len() = 1\n",
"SCnComV[1912].Len() = 1\n",
"SCnComV[1913].Len() = 1\n",
"SCnComV[1914].Len() = 1\n",
"SCnComV[1915].Len() = 1\n",
"SCnComV[1916].Len() = 1\n",
"SCnComV[1917].Len() = 1\n",
"SCnComV[1918].Len() = 1\n",
"SCnComV[1919].Len() = 1\n",
"SCnComV[1920].Len() = 1\n",
"SCnComV[1921].Len() = 1\n",
"SCnComV[1922].Len() = 1\n",
"SCnComV[1923].Len() = 1\n",
"SCnComV[1924].Len() = 1\n",
"SCnComV[1925].Len() = 1\n",
"SCnComV[1926].Len() = 1\n",
"SCnComV[1927].Len() = 1\n",
"SCnComV[1928].Len() = 1\n",
"SCnComV[1929].Len() = 1\n",
"SCnComV[1930].Len() = 1\n",
"SCnComV[1931].Len() = 1\n",
"SCnComV[1932].Len() = 1\n",
"SCnComV[1933].Len() = 1\n",
"SCnComV[1934].Len() = 1\n",
"SCnComV[1935].Len() = 1\n",
"SCnComV[1936].Len() = 1\n",
"SCnComV[1937].Len() = 1\n",
"SCnComV[1938].Len() = 1\n",
"SCnComV[1939].Len() = 1\n",
"SCnComV[1940].Len() = 1\n",
"SCnComV[1941].Len() = 1\n",
"SCnComV[1942].Len() = 1\n",
"SCnComV[1943].Len() = 1\n",
"SCnComV[1944].Len() = 1\n",
"SCnComV[1945].Len() = 1\n",
"SCnComV[1946].Len() = 1\n",
"SCnComV[1947].Len() = 1\n",
"SCnComV[1948].Len() = 1\n",
"SCnComV[1949].Len() = 1\n",
"SCnComV[1950].Len() = 1\n",
"SCnComV[1951].Len() = 1\n",
"SCnComV[1952].Len() = 1\n",
"SCnComV[1953].Len() = 1\n",
"SCnComV[1954].Len() = 1\n",
"SCnComV[1955].Len() = 1\n",
"SCnComV[1956].Len() = 1\n",
"SCnComV[1957].Len() = 1\n",
"SCnComV[1958].Len() = 1\n",
"SCnComV[1959].Len() = 1\n",
"SCnComV[1960].Len() = 1\n",
"SCnComV[1961].Len() = 1\n",
"SCnComV[1962].Len() = 1\n",
"SCnComV[1963].Len() = 1\n",
"SCnComV[1964].Len() = 1\n",
"SCnComV[1965].Len() = 1\n",
"SCnComV[1966].Len() = 1\n",
"SCnComV[1967].Len() = 1\n",
"SCnComV[1968].Len() = 1\n",
"SCnComV[1969].Len() = 1\n",
"SCnComV[1970].Len() = 1\n",
"SCnComV[1971].Len() = 1\n",
"SCnComV[1972].Len() = 1\n",
"SCnComV[1973].Len() = 1\n",
"SCnComV[1974].Len() = 1\n",
"SCnComV[1975].Len() = 1\n",
"SCnComV[1976].Len() = 1\n",
"SCnComV[1977].Len() = 1\n",
"SCnComV[1978].Len() = 1\n",
"SCnComV[1979].Len() = 1\n",
"SCnComV[1980].Len() = 1\n",
"SCnComV[1981].Len() = 1\n",
"SCnComV[1982].Len() = 1\n",
"SCnComV[1983].Len() = 1\n",
"SCnComV[1984].Len() = 1\n",
"SCnComV[1985].Len() = 1\n",
"SCnComV[1986].Len() = 1\n",
"SCnComV[1987].Len() = 1\n",
"SCnComV[1988].Len() = 1\n",
"SCnComV[1989].Len() = 1\n",
"SCnComV[1990].Len() = 1\n",
"SCnComV[1991].Len() = 1\n",
"SCnComV[1992].Len() = 1\n",
"SCnComV[1993].Len() = 1\n",
"SCnComV[1994].Len() = 1\n",
"SCnComV[1995].Len() = 1\n",
"SCnComV[1996].Len() = 1\n",
"SCnComV[1997].Len() = 1\n",
"SCnComV[1998].Len() = 1\n",
"SCnComV[1999].Len() = 1\n",
"SCnComV[2000].Len() = 1\n",
"SCnComV[2001].Len() = 1\n",
"SCnComV[2002].Len() = 1\n",
"SCnComV[2003].Len() = 1\n",
"SCnComV[2004].Len() = 1\n",
"SCnComV[2005].Len() = 1\n",
"SCnComV[2006].Len() = 1\n",
"SCnComV[2007].Len() = 1\n",
"SCnComV[2008].Len() = 1\n",
"SCnComV[2009].Len() = 1\n",
"SCnComV[2010].Len() = 1\n",
"SCnComV[2011].Len() = 1\n",
"SCnComV[2012].Len() = 1\n",
"SCnComV[2013].Len() = 1\n",
"SCnComV[2014].Len() = 1\n",
"SCnComV[2015].Len() = 1\n",
"SCnComV[2016].Len() = 1\n",
"SCnComV[2017].Len() = 1\n",
"SCnComV[2018].Len() = 1\n",
"SCnComV[2019].Len() = 1\n",
"SCnComV[2020].Len() = 1\n",
"SCnComV[2021].Len() = 1\n",
"SCnComV[2022].Len() = 1\n",
"SCnComV[2023].Len() = 1\n",
"SCnComV[2024].Len() = 1\n",
"SCnComV[2025].Len() = 1\n",
"SCnComV[2026].Len() = 1\n",
"SCnComV[2027].Len() = 1\n",
"SCnComV[2028].Len() = 1\n",
"SCnComV[2029].Len() = 1\n",
"SCnComV[2030].Len() = 1\n",
"SCnComV[2031].Len() = 1\n",
"SCnComV[2032].Len() = 1\n",
"SCnComV[2033].Len() = 1\n",
"SCnComV[2034].Len() = 1\n",
"SCnComV[2035].Len() = 1\n",
"SCnComV[2036].Len() = 1\n",
"SCnComV[2037].Len() = 1\n",
"SCnComV[2038].Len() = 1\n",
"SCnComV[2039].Len() = 1\n",
"SCnComV[2040].Len() = 1\n",
"SCnComV[2041].Len() = 1\n",
"SCnComV[2042].Len() = 1\n",
"SCnComV[2043].Len() = 1\n",
"SCnComV[2044].Len() = 1\n",
"SCnComV[2045].Len() = 1\n",
"SCnComV[2046].Len() = 1\n",
"SCnComV[2047].Len() = 1\n",
"SCnComV[2048].Len() = 1\n",
"SCnComV[2049].Len() = 1\n",
"SCnComV[2050].Len() = 1\n",
"SCnComV[2051].Len() = 1\n",
"SCnComV[2052].Len() = 1\n",
"SCnComV[2053].Len() = 1\n",
"SCnComV[2054].Len() = 1\n",
"SCnComV[2055].Len() = 1\n",
"SCnComV[2056].Len() = 1\n",
"SCnComV[2057].Len() = 1\n",
"SCnComV[2058].Len() = 1\n",
"SCnComV[2059].Len() = 1\n",
"SCnComV[2060].Len() = 1\n",
"SCnComV[2061].Len() = 1\n",
"SCnComV[2062].Len() = 1\n",
"SCnComV[2063].Len() = 1\n",
"SCnComV[2064].Len() = 1\n",
"SCnComV[2065].Len() = 1\n",
"SCnComV[2066].Len() = 1\n",
"SCnComV[2067].Len() = 1\n",
"SCnComV[2068].Len() = 1\n",
"SCnComV[2069].Len() = 1\n",
"SCnComV[2070].Len() = 1\n",
"SCnComV[2071].Len() = 1\n",
"SCnComV[2072].Len() = 1\n",
"SCnComV[2073].Len() = 1\n",
"SCnComV[2074].Len() = 1\n",
"SCnComV[2075].Len() = 1\n",
"SCnComV[2076].Len() = 1\n",
"SCnComV[2077].Len() = 1\n",
"SCnComV[2078].Len() = 1\n",
"SCnComV[2079].Len() = 1\n",
"SCnComV[2080].Len() = 1\n",
"SCnComV[2081].Len() = 1\n",
"SCnComV[2082].Len() = 1\n",
"SCnComV[2083].Len() = 1\n",
"SCnComV[2084].Len() = 1\n",
"SCnComV[2085].Len() = 1\n",
"SCnComV[2086].Len() = 1\n",
"SCnComV[2087].Len() = 1\n",
"SCnComV[2088].Len() = 1\n",
"SCnComV[2089].Len() = 1\n",
"SCnComV[2090].Len() = 1\n",
"SCnComV[2091].Len() = 1\n",
"SCnComV[2092].Len() = 1\n",
"SCnComV[2093].Len() = 1\n",
"SCnComV[2094].Len() = 1\n",
"SCnComV[2095].Len() = 1\n",
"SCnComV[2096].Len() = 1\n",
"SCnComV[2097].Len() = 1\n",
"SCnComV[2098].Len() = 1\n",
"SCnComV[2099].Len() = 1\n",
"SCnComV[2100].Len() = 1\n",
"SCnComV[2101].Len() = 1\n",
"SCnComV[2102].Len() = 1\n",
"SCnComV[2103].Len() = 1\n",
"SCnComV[2104].Len() = 1\n",
"SCnComV[2105].Len() = 1\n",
"SCnComV[2106].Len() = 1\n",
"SCnComV[2107].Len() = 1\n",
"SCnComV[2108].Len() = 1\n",
"SCnComV[2109].Len() = 1\n",
"SCnComV[2110].Len() = 1\n",
"SCnComV[2111].Len() = 1\n",
"SCnComV[2112].Len() = 1\n",
"SCnComV[2113].Len() = 1\n",
"SCnComV[2114].Len() = 1\n",
"SCnComV[2115].Len() = 1\n",
"SCnComV[2116].Len() = 1\n",
"SCnComV[2117].Len() = 1\n",
"SCnComV[2118].Len() = 1\n",
"SCnComV[2119].Len() = 1\n",
"SCnComV[2120].Len() = 1\n",
"SCnComV[2121].Len() = 1\n",
"SCnComV[2122].Len() = 1\n",
"SCnComV[2123].Len() = 1\n",
"SCnComV[2124].Len() = 1\n",
"SCnComV[2125].Len() = 1\n",
"SCnComV[2126].Len() = 1\n",
"SCnComV[2127].Len() = 1\n",
"SCnComV[2128].Len() = 1\n",
"SCnComV[2129].Len() = 1\n",
"SCnComV[2130].Len() = 1\n",
"SCnComV[2131].Len() = 1\n",
"SCnComV[2132].Len() = 1\n",
"SCnComV[2133].Len() = 1\n",
"SCnComV[2134].Len() = 1\n",
"SCnComV[2135].Len() = 1\n",
"SCnComV[2136].Len() = 1\n",
"SCnComV[2137].Len() = 1\n",
"SCnComV[2138].Len() = 1\n",
"SCnComV[2139].Len() = 1\n",
"SCnComV[2140].Len() = 1\n",
"SCnComV[2141].Len() = 1\n",
"SCnComV[2142].Len() = 1\n",
"SCnComV[2143].Len() = 1\n",
"SCnComV[2144].Len() = 1\n",
"SCnComV[2145].Len() = 1\n",
"SCnComV[2146].Len() = 1\n",
"SCnComV[2147].Len() = 1\n",
"SCnComV[2148].Len() = 1\n",
"SCnComV[2149].Len() = 1\n",
"SCnComV[2150].Len() = 1\n",
"SCnComV[2151].Len() = 1\n",
"SCnComV[2152].Len() = 1\n",
"SCnComV[2153].Len() = 1\n",
"SCnComV[2154].Len() = 1\n",
"SCnComV[2155].Len() = 1\n",
"SCnComV[2156].Len() = 1\n",
"SCnComV[2157].Len() = 1\n",
"SCnComV[2158].Len() = 1\n",
"SCnComV[2159].Len() = 1\n",
"SCnComV[2160].Len() = 1\n",
"SCnComV[2161].Len() = 1\n",
"SCnComV[2162].Len() = 1\n",
"SCnComV[2163].Len() = 1\n",
"SCnComV[2164].Len() = 1\n",
"SCnComV[2165].Len() = 1\n",
"SCnComV[2166].Len() = 1\n",
"SCnComV[2167].Len() = 1\n",
"SCnComV[2168].Len() = 1\n",
"SCnComV[2169].Len() = 1\n",
"SCnComV[2170].Len() = 1\n",
"SCnComV[2171].Len() = 1\n",
"SCnComV[2172].Len() = 1\n",
"SCnComV[2173].Len() = 1\n",
"SCnComV[2174].Len() = 1\n",
"SCnComV[2175].Len() = 1\n",
"SCnComV[2176].Len() = 1\n",
"SCnComV[2177].Len() = 1\n",
"SCnComV[2178].Len() = 1\n",
"SCnComV[2179].Len() = 1\n",
"SCnComV[2180].Len() = 1\n",
"SCnComV[2181].Len() = 1\n",
"SCnComV[2182].Len() = 1\n",
"SCnComV[2183].Len() = 1\n",
"SCnComV[2184].Len() = 1\n",
"SCnComV[2185].Len() = 1\n",
"SCnComV[2186].Len() = 1\n",
"SCnComV[2187].Len() = 1\n",
"SCnComV[2188].Len() = 1\n",
"SCnComV[2189].Len() = 1\n",
"SCnComV[2190].Len() = 1\n",
"SCnComV[2191].Len() = 1\n",
"SCnComV[2192].Len() = 1\n",
"SCnComV[2193].Len() = 1\n",
"SCnComV[2194].Len() = 1\n",
"SCnComV[2195].Len() = 1\n",
"SCnComV[2196].Len() = 1\n",
"SCnComV[2197].Len() = 1\n",
"SCnComV[2198].Len() = 1\n",
"SCnComV[2199].Len() = 1\n",
"SCnComV[2200].Len() = 1\n",
"SCnComV[2201].Len() = 1\n",
"SCnComV[2202].Len() = 1\n",
"SCnComV[2203].Len() = 1\n",
"SCnComV[2204].Len() = 1\n",
"SCnComV[2205].Len() = 1\n",
"SCnComV[2206].Len() = 1\n",
"SCnComV[2207].Len() = 1\n",
"SCnComV[2208].Len() = 1\n",
"SCnComV[2209].Len() = 1\n",
"SCnComV[2210].Len() = 1\n",
"SCnComV[2211].Len() = 1\n",
"SCnComV[2212].Len() = 1\n",
"SCnComV[2213].Len() = 1\n",
"SCnComV[2214].Len() = 1\n",
"SCnComV[2215].Len() = 1\n",
"SCnComV[2216].Len() = 1\n",
"SCnComV[2217].Len() = 1\n",
"SCnComV[2218].Len() = 1\n",
"SCnComV[2219].Len() = 1\n",
"SCnComV[2220].Len() = 1\n",
"SCnComV[2221].Len() = 1\n",
"SCnComV[2222].Len() = 1\n",
"SCnComV[2223].Len() = 1\n",
"SCnComV[2224].Len() = 1\n",
"SCnComV[2225].Len() = 1\n",
"SCnComV[2226].Len() = 1\n",
"SCnComV[2227].Len() = 1\n",
"SCnComV[2228].Len() = 1\n",
"SCnComV[2229].Len() = 1\n",
"SCnComV[2230].Len() = 1\n",
"SCnComV[2231].Len() = 1\n",
"SCnComV[2232].Len() = 1\n",
"SCnComV[2233].Len() = 1\n",
"SCnComV[2234].Len() = 1\n",
"SCnComV[2235].Len() = 1\n",
"SCnComV[2236].Len() = 1\n",
"SCnComV[2237].Len() = 1\n",
"SCnComV[2238].Len() = 1\n",
"SCnComV[2239].Len() = 1\n",
"SCnComV[2240].Len() = 1\n",
"SCnComV[2241].Len() = 1\n",
"SCnComV[2242].Len() = 1\n",
"SCnComV[2243].Len() = 1\n",
"SCnComV[2244].Len() = 1\n",
"SCnComV[2245].Len() = 1\n",
"SCnComV[2246].Len() = 1\n",
"SCnComV[2247].Len() = 1\n",
"SCnComV[2248].Len() = 1\n",
"SCnComV[2249].Len() = 1\n",
"SCnComV[2250].Len() = 1\n",
"SCnComV[2251].Len() = 1\n",
"SCnComV[2252].Len() = 1\n",
"SCnComV[2253].Len() = 1\n",
"SCnComV[2254].Len() = 1\n",
"SCnComV[2255].Len() = 1\n",
"SCnComV[2256].Len() = 1\n",
"SCnComV[2257].Len() = 1\n",
"SCnComV[2258].Len() = 1\n",
"SCnComV[2259].Len() = 1\n",
"SCnComV[2260].Len() = 1\n",
"SCnComV[2261].Len() = 1\n",
"SCnComV[2262].Len() = 1\n",
"SCnComV[2263].Len() = 1\n",
"SCnComV[2264].Len() = 1\n",
"SCnComV[2265].Len() = 1\n",
"SCnComV[2266].Len() = 1\n",
"SCnComV[2267].Len() = 1\n",
"SCnComV[2268].Len() = 1\n",
"SCnComV[2269].Len() = 1\n",
"SCnComV[2270].Len() = 1\n",
"SCnComV[2271].Len() = 1\n",
"SCnComV[2272].Len() = 1\n",
"SCnComV[2273].Len() = 1\n",
"SCnComV[2274].Len() = 1\n",
"SCnComV[2275].Len() = 1\n",
"SCnComV[2276].Len() = 1\n",
"SCnComV[2277].Len() = 1\n",
"SCnComV[2278].Len() = 1\n",
"SCnComV[2279].Len() = 1\n",
"SCnComV[2280].Len() = 1\n",
"SCnComV[2281].Len() = 1\n",
"SCnComV[2282].Len() = 1\n",
"SCnComV[2283].Len() = 1\n",
"SCnComV[2284].Len() = 1\n",
"SCnComV[2285].Len() = 1\n",
"SCnComV[2286].Len() = 1\n",
"SCnComV[2287].Len() = 1\n",
"SCnComV[2288].Len() = 1\n",
"SCnComV[2289].Len() = 1\n",
"SCnComV[2290].Len() = 1\n",
"SCnComV[2291].Len() = 1\n",
"SCnComV[2292].Len() = 1\n",
"SCnComV[2293].Len() = 1\n",
"SCnComV[2294].Len() = 1\n",
"SCnComV[2295].Len() = 1\n",
"SCnComV[2296].Len() = 1\n",
"SCnComV[2297].Len() = 1\n",
"SCnComV[2298].Len() = 1\n",
"SCnComV[2299].Len() = 1\n",
"SCnComV[2300].Len() = 1\n",
"SCnComV[2301].Len() = 1\n",
"SCnComV[2302].Len() = 1\n",
"SCnComV[2303].Len() = 1\n",
"SCnComV[2304].Len() = 1\n",
"SCnComV[2305].Len() = 1\n",
"SCnComV[2306].Len() = 1\n",
"SCnComV[2307].Len() = 1\n",
"SCnComV[2308].Len() = 1\n",
"SCnComV[2309].Len() = 1\n",
"SCnComV[2310].Len() = 1\n",
"SCnComV[2311].Len() = 1\n",
"SCnComV[2312].Len() = 1\n",
"SCnComV[2313].Len() = 1\n",
"SCnComV[2314].Len() = 1\n",
"SCnComV[2315].Len() = 1\n",
"SCnComV[2316].Len() = 1\n",
"SCnComV[2317].Len() = 1\n",
"SCnComV[2318].Len() = 1\n",
"SCnComV[2319].Len() = 1\n",
"SCnComV[2320].Len() = 1\n",
"SCnComV[2321].Len() = 1\n",
"SCnComV[2322].Len() = 1\n",
"SCnComV[2323].Len() = 1\n",
"SCnComV[2324].Len() = 1\n",
"SCnComV[2325].Len() = 1\n",
"SCnComV[2326].Len() = 1\n",
"SCnComV[2327].Len() = 1\n",
"SCnComV[2328].Len() = 1\n",
"SCnComV[2329].Len() = 1\n",
"SCnComV[2330].Len() = 1\n",
"SCnComV[2331].Len() = 1\n",
"SCnComV[2332].Len() = 1\n",
"SCnComV[2333].Len() = 1\n",
"SCnComV[2334].Len() = 1\n",
"SCnComV[2335].Len() = 1\n",
"SCnComV[2336].Len() = 1\n",
"SCnComV[2337].Len() = 1\n",
"SCnComV[2338].Len() = 1\n",
"SCnComV[2339].Len() = 1\n",
"SCnComV[2340].Len() = 1\n",
"SCnComV[2341].Len() = 1\n",
"SCnComV[2342].Len() = 1\n",
"SCnComV[2343].Len() = 1\n",
"SCnComV[2344].Len() = 1\n",
"SCnComV[2345].Len() = 1\n",
"SCnComV[2346].Len() = 1\n",
"SCnComV[2347].Len() = 1\n",
"SCnComV[2348].Len() = 1\n",
"SCnComV[2349].Len() = 1\n",
"SCnComV[2350].Len() = 1\n",
"SCnComV[2351].Len() = 1\n",
"SCnComV[2352].Len() = 1\n",
"SCnComV[2353].Len() = 1\n",
"SCnComV[2354].Len() = 1\n",
"SCnComV[2355].Len() = 1\n",
"SCnComV[2356].Len() = 1\n",
"SCnComV[2357].Len() = 1\n",
"SCnComV[2358].Len() = 1\n",
"SCnComV[2359].Len() = 1\n",
"SCnComV[2360].Len() = 1\n",
"SCnComV[2361].Len() = 1\n",
"SCnComV[2362].Len() = 1\n",
"SCnComV[2363].Len() = 1\n",
"SCnComV[2364].Len() = 1\n",
"SCnComV[2365].Len() = 1\n",
"SCnComV[2366].Len() = 1\n",
"SCnComV[2367].Len() = 1\n",
"SCnComV[2368].Len() = 1\n",
"SCnComV[2369].Len() = 1\n",
"SCnComV[2370].Len() = 1\n",
"SCnComV[2371].Len() = 1\n",
"SCnComV[2372].Len() = 1\n",
"SCnComV[2373].Len() = 1\n",
"SCnComV[2374].Len() = 1\n",
"SCnComV[2375].Len() = 1\n",
"SCnComV[2376].Len() = 1\n",
"SCnComV[2377].Len() = 1\n",
"SCnComV[2378].Len() = 1\n",
"SCnComV[2379].Len() = 1\n",
"SCnComV[2380].Len() = 1\n",
"SCnComV[2381].Len() = 1\n",
"SCnComV[2382].Len() = 1\n",
"SCnComV[2383].Len() = 1\n",
"SCnComV[2384].Len() = 1\n",
"SCnComV[2385].Len() = 1\n",
"SCnComV[2386].Len() = 1\n",
"SCnComV[2387].Len() = 1\n",
"SCnComV[2388].Len() = 1\n",
"SCnComV[2389].Len() = 1\n",
"SCnComV[2390].Len() = 1\n",
"SCnComV[2391].Len() = 1\n",
"SCnComV[2392].Len() = 1\n",
"SCnComV[2393].Len() = 1\n",
"SCnComV[2394].Len() = 1\n",
"SCnComV[2395].Len() = 1\n",
"SCnComV[2396].Len() = 1\n",
"SCnComV[2397].Len() = 1\n",
"SCnComV[2398].Len() = 1\n",
"SCnComV[2399].Len() = 1\n",
"SCnComV[2400].Len() = 1\n",
"SCnComV[2401].Len() = 1\n",
"SCnComV[2402].Len() = 1\n",
"SCnComV[2403].Len() = 1\n",
"SCnComV[2404].Len() = 1\n",
"SCnComV[2405].Len() = 1\n",
"SCnComV[2406].Len() = 1\n",
"SCnComV[2407].Len() = 1\n",
"SCnComV[2408].Len() = 1\n",
"SCnComV[2409].Len() = 1\n",
"SCnComV[2410].Len() = 1\n",
"SCnComV[2411].Len() = 1\n",
"SCnComV[2412].Len() = 1\n",
"SCnComV[2413].Len() = 1\n",
"SCnComV[2414].Len() = 1\n",
"SCnComV[2415].Len() = 1\n",
"SCnComV[2416].Len() = 1\n",
"SCnComV[2417].Len() = 1\n",
"SCnComV[2418].Len() = 1\n",
"SCnComV[2419].Len() = 1\n",
"SCnComV[2420].Len() = 1\n",
"SCnComV[2421].Len() = 1\n",
"SCnComV[2422].Len() = 1\n",
"SCnComV[2423].Len() = 1\n",
"SCnComV[2424].Len() = 1\n",
"SCnComV[2425].Len() = 1\n",
"SCnComV[2426].Len() = 1\n",
"SCnComV[2427].Len() = 1\n",
"SCnComV[2428].Len() = 1\n",
"SCnComV[2429].Len() = 1\n",
"SCnComV[2430].Len() = 1\n",
"SCnComV[2431].Len() = 1\n",
"SCnComV[2432].Len() = 1\n",
"SCnComV[2433].Len() = 1\n",
"SCnComV[2434].Len() = 1\n",
"SCnComV[2435].Len() = 1\n",
"SCnComV[2436].Len() = 1\n",
"SCnComV[2437].Len() = 1\n",
"SCnComV[2438].Len() = 1\n",
"SCnComV[2439].Len() = 1\n",
"SCnComV[2440].Len() = 1\n",
"SCnComV[2441].Len() = 1\n",
"SCnComV[2442].Len() = 1\n",
"SCnComV[2443].Len() = 1\n",
"SCnComV[2444].Len() = 1\n",
"SCnComV[2445].Len() = 1\n",
"SCnComV[2446].Len() = 1\n",
"SCnComV[2447].Len() = 1\n",
"SCnComV[2448].Len() = 1\n",
"SCnComV[2449].Len() = 1\n",
"SCnComV[2450].Len() = 1\n",
"SCnComV[2451].Len() = 1\n",
"SCnComV[2452].Len() = 1\n",
"SCnComV[2453].Len() = 1\n",
"SCnComV[2454].Len() = 1\n",
"SCnComV[2455].Len() = 1\n",
"SCnComV[2456].Len() = 1\n",
"SCnComV[2457].Len() = 1\n",
"SCnComV[2458].Len() = 1\n",
"SCnComV[2459].Len() = 1\n",
"SCnComV[2460].Len() = 1\n",
"SCnComV[2461].Len() = 1\n",
"SCnComV[2462].Len() = 1\n",
"SCnComV[2463].Len() = 1\n",
"SCnComV[2464].Len() = 1\n",
"SCnComV[2465].Len() = 1\n",
"SCnComV[2466].Len() = 1\n",
"SCnComV[2467].Len() = 1\n",
"SCnComV[2468].Len() = 1\n",
"SCnComV[2469].Len() = 1\n",
"SCnComV[2470].Len() = 1\n",
"SCnComV[2471].Len() = 1\n",
"SCnComV[2472].Len() = 1\n",
"SCnComV[2473].Len() = 1\n",
"SCnComV[2474].Len() = 1\n",
"SCnComV[2475].Len() = 1\n",
"SCnComV[2476].Len() = 1\n",
"SCnComV[2477].Len() = 1\n",
"SCnComV[2478].Len() = 1\n",
"SCnComV[2479].Len() = 1\n",
"SCnComV[2480].Len() = 1\n",
"SCnComV[2481].Len() = 1\n",
"SCnComV[2482].Len() = 1\n",
"SCnComV[2483].Len() = 1\n",
"SCnComV[2484].Len() = 1\n",
"SCnComV[2485].Len() = 1\n",
"SCnComV[2486].Len() = 1\n",
"SCnComV[2487].Len() = 1\n",
"SCnComV[2488].Len() = 1\n",
"SCnComV[2489].Len() = 1\n",
"SCnComV[2490].Len() = 1\n",
"SCnComV[2491].Len() = 1\n",
"SCnComV[2492].Len() = 1\n",
"SCnComV[2493].Len() = 1\n",
"SCnComV[2494].Len() = 1\n",
"SCnComV[2495].Len() = 1\n",
"SCnComV[2496].Len() = 1\n",
"SCnComV[2497].Len() = 1\n",
"SCnComV[2498].Len() = 1\n",
"SCnComV[2499].Len() = 1\n",
"SCnComV[2500].Len() = 1\n",
"SCnComV[2501].Len() = 1\n",
"SCnComV[2502].Len() = 1\n",
"SCnComV[2503].Len() = 1\n",
"SCnComV[2504].Len() = 1\n",
"SCnComV[2505].Len() = 1\n",
"SCnComV[2506].Len() = 1\n",
"SCnComV[2507].Len() = 1\n",
"SCnComV[2508].Len() = 1\n",
"SCnComV[2509].Len() = 1\n",
"SCnComV[2510].Len() = 1\n",
"SCnComV[2511].Len() = 1\n",
"SCnComV[2512].Len() = 1\n",
"SCnComV[2513].Len() = 1\n",
"SCnComV[2514].Len() = 1\n",
"SCnComV[2515].Len() = 1\n",
"SCnComV[2516].Len() = 1\n",
"SCnComV[2517].Len() = 1\n",
"SCnComV[2518].Len() = 1\n",
"SCnComV[2519].Len() = 1\n",
"SCnComV[2520].Len() = 1\n",
"SCnComV[2521].Len() = 1\n",
"SCnComV[2522].Len() = 1\n",
"SCnComV[2523].Len() = 1\n",
"SCnComV[2524].Len() = 1\n",
"SCnComV[2525].Len() = 1\n",
"SCnComV[2526].Len() = 1\n",
"SCnComV[2527].Len() = 1\n",
"SCnComV[2528].Len() = 1\n",
"SCnComV[2529].Len() = 1\n",
"SCnComV[2530].Len() = 1\n",
"SCnComV[2531].Len() = 1\n",
"SCnComV[2532].Len() = 1\n",
"SCnComV[2533].Len() = 1\n",
"SCnComV[2534].Len() = 1\n",
"SCnComV[2535].Len() = 1\n",
"SCnComV[2536].Len() = 1\n",
"SCnComV[2537].Len() = 1\n",
"SCnComV[2538].Len() = 1\n",
"SCnComV[2539].Len() = 1\n",
"SCnComV[2540].Len() = 1\n",
"SCnComV[2541].Len() = 1\n",
"SCnComV[2542].Len() = 1\n",
"SCnComV[2543].Len() = 1\n",
"SCnComV[2544].Len() = 1\n",
"SCnComV[2545].Len() = 1\n",
"SCnComV[2546].Len() = 1\n",
"SCnComV[2547].Len() = 1\n",
"SCnComV[2548].Len() = 1\n",
"SCnComV[2549].Len() = 1\n",
"SCnComV[2550].Len() = 1\n",
"SCnComV[2551].Len() = 1\n",
"SCnComV[2552].Len() = 1\n",
"SCnComV[2553].Len() = 1\n",
"SCnComV[2554].Len() = 1\n",
"SCnComV[2555].Len() = 1\n",
"SCnComV[2556].Len() = 1\n",
"SCnComV[2557].Len() = 1\n",
"SCnComV[2558].Len() = 1\n",
"SCnComV[2559].Len() = 1\n",
"SCnComV[2560].Len() = 1\n",
"SCnComV[2561].Len() = 1\n",
"SCnComV[2562].Len() = 1\n",
"SCnComV[2563].Len() = 1\n",
"SCnComV[2564].Len() = 1\n",
"SCnComV[2565].Len() = 1\n",
"SCnComV[2566].Len() = 1\n",
"SCnComV[2567].Len() = 1\n",
"SCnComV[2568].Len() = 1\n",
"SCnComV[2569].Len() = 1\n",
"SCnComV[2570].Len() = 1\n",
"SCnComV[2571].Len() = 1\n",
"SCnComV[2572].Len() = 1\n",
"SCnComV[2573].Len() = 1\n",
"SCnComV[2574].Len() = 1\n",
"SCnComV[2575].Len() = 1\n",
"SCnComV[2576].Len() = 1\n",
"SCnComV[2577].Len() = 1\n",
"SCnComV[2578].Len() = 1\n",
"SCnComV[2579].Len() = 1\n",
"SCnComV[2580].Len() = 1\n",
"SCnComV[2581].Len() = 1\n",
"SCnComV[2582].Len() = 1\n",
"SCnComV[2583].Len() = 1\n",
"SCnComV[2584].Len() = 1\n",
"SCnComV[2585].Len() = 1\n",
"SCnComV[2586].Len() = 1\n",
"SCnComV[2587].Len() = 1\n",
"SCnComV[2588].Len() = 1\n",
"SCnComV[2589].Len() = 1\n",
"SCnComV[2590].Len() = 1\n",
"SCnComV[2591].Len() = 1\n",
"SCnComV[2592].Len() = 1\n",
"SCnComV[2593].Len() = 1\n",
"SCnComV[2594].Len() = 1\n",
"SCnComV[2595].Len() = 1\n",
"SCnComV[2596].Len() = 1\n",
"SCnComV[2597].Len() = 1\n",
"SCnComV[2598].Len() = 1\n",
"SCnComV[2599].Len() = 1\n",
"SCnComV[2600].Len() = 1\n",
"SCnComV[2601].Len() = 1\n",
"SCnComV[2602].Len() = 1\n",
"SCnComV[2603].Len() = 1\n",
"SCnComV[2604].Len() = 1\n",
"SCnComV[2605].Len() = 1\n",
"SCnComV[2606].Len() = 1\n",
"SCnComV[2607].Len() = 1\n",
"SCnComV[2608].Len() = 1\n",
"SCnComV[2609].Len() = 1\n",
"SCnComV[2610].Len() = 1\n",
"SCnComV[2611].Len() = 1\n",
"SCnComV[2612].Len() = 1\n",
"SCnComV[2613].Len() = 1\n",
"SCnComV[2614].Len() = 1\n",
"SCnComV[2615].Len() = 1\n",
"SCnComV[2616].Len() = 1\n",
"SCnComV[2617].Len() = 1\n",
"SCnComV[2618].Len() = 1\n",
"SCnComV[2619].Len() = 1\n",
"SCnComV[2620].Len() = 1\n",
"SCnComV[2621].Len() = 1\n",
"SCnComV[2622].Len() = 1\n",
"SCnComV[2623].Len() = 1\n",
"SCnComV[2624].Len() = 1\n",
"SCnComV[2625].Len() = 1\n",
"SCnComV[2626].Len() = 1\n",
"SCnComV[2627].Len() = 1\n",
"SCnComV[2628].Len() = 1\n",
"SCnComV[2629].Len() = 1\n",
"SCnComV[2630].Len() = 1\n",
"SCnComV[2631].Len() = 1\n",
"SCnComV[2632].Len() = 1\n",
"SCnComV[2633].Len() = 1\n",
"SCnComV[2634].Len() = 1\n",
"SCnComV[2635].Len() = 1\n",
"SCnComV[2636].Len() = 1\n",
"SCnComV[2637].Len() = 1\n",
"SCnComV[2638].Len() = 1\n",
"SCnComV[2639].Len() = 1\n",
"SCnComV[2640].Len() = 1\n",
"SCnComV[2641].Len() = 1\n",
"SCnComV[2642].Len() = 1\n",
"SCnComV[2643].Len() = 1\n",
"SCnComV[2644].Len() = 1\n",
"SCnComV[2645].Len() = 1\n",
"SCnComV[2646].Len() = 1\n",
"SCnComV[2647].Len() = 1\n",
"SCnComV[2648].Len() = 1\n",
"SCnComV[2649].Len() = 1\n",
"SCnComV[2650].Len() = 1\n",
"SCnComV[2651].Len() = 1\n",
"SCnComV[2652].Len() = 1\n",
"SCnComV[2653].Len() = 1\n",
"SCnComV[2654].Len() = 1\n",
"SCnComV[2655].Len() = 1\n",
"SCnComV[2656].Len() = 1\n",
"SCnComV[2657].Len() = 1\n",
"SCnComV[2658].Len() = 1\n",
"SCnComV[2659].Len() = 1\n",
"SCnComV[2660].Len() = 1\n",
"SCnComV[2661].Len() = 1\n",
"SCnComV[2662].Len() = 1\n",
"SCnComV[2663].Len() = 1\n",
"SCnComV[2664].Len() = 1\n",
"SCnComV[2665].Len() = 1\n",
"SCnComV[2666].Len() = 1\n",
"SCnComV[2667].Len() = 1\n",
"SCnComV[2668].Len() = 1\n",
"SCnComV[2669].Len() = 1\n",
"SCnComV[2670].Len() = 1\n",
"SCnComV[2671].Len() = 1\n",
"SCnComV[2672].Len() = 1\n",
"SCnComV[2673].Len() = 1\n",
"SCnComV[2674].Len() = 1\n",
"SCnComV[2675].Len() = 1\n",
"SCnComV[2676].Len() = 1\n",
"SCnComV[2677].Len() = 1\n",
"SCnComV[2678].Len() = 1\n",
"SCnComV[2679].Len() = 1\n",
"SCnComV[2680].Len() = 1\n",
"SCnComV[2681].Len() = 1\n",
"SCnComV[2682].Len() = 1\n",
"SCnComV[2683].Len() = 1\n",
"SCnComV[2684].Len() = 1\n",
"SCnComV[2685].Len() = 1\n",
"SCnComV[2686].Len() = 1\n",
"SCnComV[2687].Len() = 1\n",
"SCnComV[2688].Len() = 1\n",
"SCnComV[2689].Len() = 1\n",
"SCnComV[2690].Len() = 1\n",
"SCnComV[2691].Len() = 1\n",
"SCnComV[2692].Len() = 1\n",
"SCnComV[2693].Len() = 1\n",
"SCnComV[2694].Len() = 1\n",
"SCnComV[2695].Len() = 1\n",
"SCnComV[2696].Len() = 1\n",
"SCnComV[2697].Len() = 1\n",
"SCnComV[2698].Len() = 1\n",
"SCnComV[2699].Len() = 1\n",
"SCnComV[2700].Len() = 1\n",
"SCnComV[2701].Len() = 1\n",
"SCnComV[2702].Len() = 1\n",
"SCnComV[2703].Len() = 1\n",
"SCnComV[2704].Len() = 1\n",
"SCnComV[2705].Len() = 1\n",
"SCnComV[2706].Len() = 1\n",
"SCnComV[2707].Len() = 1\n",
"SCnComV[2708].Len() = 1\n",
"SCnComV[2709].Len() = 1\n",
"SCnComV[2710].Len() = 1\n",
"SCnComV[2711].Len() = 1\n",
"SCnComV[2712].Len() = 1\n",
"SCnComV[2713].Len() = 1\n",
"SCnComV[2714].Len() = 1\n",
"SCnComV[2715].Len() = 1\n",
"SCnComV[2716].Len() = 1\n",
"SCnComV[2717].Len() = 1\n",
"SCnComV[2718].Len() = 1\n",
"SCnComV[2719].Len() = 1\n",
"SCnComV[2720].Len() = 1\n",
"SCnComV[2721].Len() = 1\n",
"SCnComV[2722].Len() = 1\n",
"SCnComV[2723].Len() = 1\n",
"SCnComV[2724].Len() = 1\n",
"SCnComV[2725].Len() = 1\n",
"SCnComV[2726].Len() = 1\n",
"SCnComV[2727].Len() = 1\n",
"SCnComV[2728].Len() = 1\n",
"SCnComV[2729].Len() = 1\n",
"SCnComV[2730].Len() = 1\n",
"SCnComV[2731].Len() = 1\n",
"SCnComV[2732].Len() = 1\n",
"SCnComV[2733].Len() = 1\n",
"SCnComV[2734].Len() = 1\n",
"SCnComV[2735].Len() = 1\n",
"SCnComV[2736].Len() = 1\n",
"SCnComV[2737].Len() = 1\n",
"SCnComV[2738].Len() = 1\n",
"SCnComV[2739].Len() = 1\n",
"SCnComV[2740].Len() = 1\n",
"SCnComV[2741].Len() = 1\n",
"SCnComV[2742].Len() = 1\n",
"SCnComV[2743].Len() = 1\n",
"SCnComV[2744].Len() = 1\n",
"SCnComV[2745].Len() = 1\n",
"SCnComV[2746].Len() = 1\n",
"SCnComV[2747].Len() = 1\n",
"SCnComV[2748].Len() = 1\n",
"SCnComV[2749].Len() = 1\n",
"SCnComV[2750].Len() = 1\n",
"SCnComV[2751].Len() = 1\n",
"SCnComV[2752].Len() = 1\n",
"SCnComV[2753].Len() = 1\n",
"SCnComV[2754].Len() = 1\n",
"SCnComV[2755].Len() = 1\n",
"SCnComV[2756].Len() = 1\n",
"SCnComV[2757].Len() = 1\n",
"SCnComV[2758].Len() = 1\n",
"SCnComV[2759].Len() = 1\n",
"SCnComV[2760].Len() = 1\n",
"SCnComV[2761].Len() = 1\n",
"SCnComV[2762].Len() = 1\n",
"SCnComV[2763].Len() = 1\n",
"SCnComV[2764].Len() = 1\n",
"SCnComV[2765].Len() = 1\n",
"SCnComV[2766].Len() = 1\n",
"SCnComV[2767].Len() = 1\n",
"SCnComV[2768].Len() = 1\n",
"SCnComV[2769].Len() = 1\n",
"SCnComV[2770].Len() = 1\n",
"SCnComV[2771].Len() = 1\n",
"SCnComV[2772].Len() = 1\n",
"SCnComV[2773].Len() = 1\n",
"SCnComV[2774].Len() = 1\n",
"SCnComV[2775].Len() = 1\n",
"SCnComV[2776].Len() = 1\n",
"SCnComV[2777].Len() = 1\n",
"SCnComV[2778].Len() = 1\n",
"SCnComV[2779].Len() = 1\n",
"SCnComV[2780].Len() = 1\n",
"SCnComV[2781].Len() = 1\n",
"SCnComV[2782].Len() = 1\n",
"SCnComV[2783].Len() = 1\n",
"SCnComV[2784].Len() = 1\n",
"SCnComV[2785].Len() = 1\n",
"SCnComV[2786].Len() = 1\n",
"SCnComV[2787].Len() = 1\n",
"SCnComV[2788].Len() = 1\n",
"SCnComV[2789].Len() = 1\n",
"SCnComV[2790].Len() = 1\n",
"SCnComV[2791].Len() = 1\n",
"SCnComV[2792].Len() = 1\n",
"SCnComV[2793].Len() = 1\n",
"SCnComV[2794].Len() = 1\n",
"SCnComV[2795].Len() = 1\n",
"SCnComV[2796].Len() = 1\n",
"SCnComV[2797].Len() = 1\n",
"SCnComV[2798].Len() = 1\n",
"SCnComV[2799].Len() = 1\n",
"SCnComV[2800].Len() = 1\n",
"SCnComV[2801].Len() = 1\n",
"SCnComV[2802].Len() = 1\n",
"SCnComV[2803].Len() = 1\n",
"SCnComV[2804].Len() = 1\n",
"SCnComV[2805].Len() = 1\n",
"SCnComV[2806].Len() = 1\n",
"SCnComV[2807].Len() = 1\n",
"SCnComV[2808].Len() = 1\n",
"SCnComV[2809].Len() = 1\n",
"SCnComV[2810].Len() = 1\n",
"SCnComV[2811].Len() = 1\n",
"SCnComV[2812].Len() = 1\n",
"SCnComV[2813].Len() = 1\n",
"SCnComV[2814].Len() = 1\n",
"SCnComV[2815].Len() = 1\n",
"SCnComV[2816].Len() = 1\n",
"SCnComV[2817].Len() = 1\n",
"SCnComV[2818].Len() = 1\n",
"SCnComV[2819].Len() = 1\n",
"SCnComV[2820].Len() = 1\n",
"SCnComV[2821].Len() = 1\n",
"SCnComV[2822].Len() = 1\n",
"SCnComV[2823].Len() = 1\n",
"SCnComV[2824].Len() = 1\n",
"SCnComV[2825].Len() = 1\n",
"SCnComV[2826].Len() = 1\n",
"SCnComV[2827].Len() = 1\n",
"SCnComV[2828].Len() = 1\n",
"SCnComV[2829].Len() = 1\n",
"SCnComV[2830].Len() = 1\n",
"SCnComV[2831].Len() = 1\n",
"SCnComV[2832].Len() = 1\n",
"SCnComV[2833].Len() = 1\n",
"SCnComV[2834].Len() = 1\n",
"SCnComV[2835].Len() = 1\n",
"SCnComV[2836].Len() = 1\n",
"SCnComV[2837].Len() = 1\n",
"SCnComV[2838].Len() = 1\n",
"SCnComV[2839].Len() = 1\n",
"SCnComV[2840].Len() = 1\n",
"SCnComV[2841].Len() = 1\n",
"SCnComV[2842].Len() = 1\n",
"SCnComV[2843].Len() = 1\n",
"SCnComV[2844].Len() = 1\n",
"SCnComV[2845].Len() = 1\n",
"SCnComV[2846].Len() = 1\n",
"SCnComV[2847].Len() = 1\n",
"SCnComV[2848].Len() = 1\n",
"SCnComV[2849].Len() = 1\n",
"SCnComV[2850].Len() = 1\n",
"SCnComV[2851].Len() = 1\n",
"SCnComV[2852].Len() = 1\n",
"SCnComV[2853].Len() = 1\n",
"SCnComV[2854].Len() = 1\n",
"SCnComV[2855].Len() = 1\n",
"SCnComV[2856].Len() = 1\n",
"SCnComV[2857].Len() = 1\n",
"SCnComV[2858].Len() = 1\n",
"SCnComV[2859].Len() = 1\n",
"SCnComV[2860].Len() = 1\n",
"SCnComV[2861].Len() = 1\n",
"SCnComV[2862].Len() = 1\n",
"SCnComV[2863].Len() = 1\n",
"SCnComV[2864].Len() = 1\n",
"SCnComV[2865].Len() = 1\n",
"SCnComV[2866].Len() = 1\n",
"SCnComV[2867].Len() = 1\n",
"SCnComV[2868].Len() = 1\n",
"SCnComV[2869].Len() = 1\n",
"SCnComV[2870].Len() = 1\n",
"SCnComV[2871].Len() = 1\n",
"SCnComV[2872].Len() = 1\n",
"SCnComV[2873].Len() = 1\n",
"SCnComV[2874].Len() = 1\n",
"SCnComV[2875].Len() = 1\n",
"SCnComV[2876].Len() = 1\n",
"SCnComV[2877].Len() = 1\n",
"SCnComV[2878].Len() = 1\n",
"SCnComV[2879].Len() = 1\n",
"SCnComV[2880].Len() = 1\n",
"SCnComV[2881].Len() = 1\n",
"SCnComV[2882].Len() = 1\n",
"SCnComV[2883].Len() = 1\n",
"SCnComV[2884].Len() = 1\n",
"SCnComV[2885].Len() = 1\n",
"SCnComV[2886].Len() = 1\n",
"SCnComV[2887].Len() = 1\n",
"SCnComV[2888].Len() = 1\n",
"SCnComV[2889].Len() = 1\n",
"SCnComV[2890].Len() = 1\n",
"SCnComV[2891].Len() = 1\n",
"SCnComV[2892].Len() = 1\n",
"SCnComV[2893].Len() = 1\n",
"SCnComV[2894].Len() = 1\n",
"SCnComV[2895].Len() = 1\n",
"SCnComV[2896].Len() = 1\n",
"SCnComV[2897].Len() = 1\n",
"SCnComV[2898].Len() = 1\n",
"SCnComV[2899].Len() = 1\n",
"SCnComV[2900].Len() = 1\n",
"SCnComV[2901].Len() = 1\n",
"SCnComV[2902].Len() = 1\n",
"SCnComV[2903].Len() = 1\n",
"SCnComV[2904].Len() = 1\n",
"SCnComV[2905].Len() = 1\n",
"SCnComV[2906].Len() = 1\n",
"SCnComV[2907].Len() = 1\n",
"SCnComV[2908].Len() = 1\n",
"SCnComV[2909].Len() = 1\n",
"SCnComV[2910].Len() = 1\n",
"SCnComV[2911].Len() = 1\n",
"SCnComV[2912].Len() = 1\n",
"SCnComV[2913].Len() = 1\n",
"SCnComV[2914].Len() = 1\n",
"SCnComV[2915].Len() = 1\n",
"SCnComV[2916].Len() = 1\n",
"SCnComV[2917].Len() = 1\n",
"SCnComV[2918].Len() = 1\n",
"SCnComV[2919].Len() = 1\n",
"SCnComV[2920].Len() = 1\n",
"SCnComV[2921].Len() = 1\n",
"SCnComV[2922].Len() = 1\n",
"SCnComV[2923].Len() = 1\n",
"SCnComV[2924].Len() = 1\n",
"SCnComV[2925].Len() = 1\n",
"SCnComV[2926].Len() = 1\n",
"SCnComV[2927].Len() = 1\n",
"SCnComV[2928].Len() = 1\n",
"SCnComV[2929].Len() = 1\n",
"SCnComV[2930].Len() = 1\n",
"SCnComV[2931].Len() = 1\n",
"SCnComV[2932].Len() = 1\n",
"SCnComV[2933].Len() = 1\n",
"SCnComV[2934].Len() = 1\n",
"SCnComV[2935].Len() = 1\n",
"SCnComV[2936].Len() = 1\n",
"SCnComV[2937].Len() = 1\n",
"SCnComV[2938].Len() = 1\n",
"SCnComV[2939].Len() = 1\n",
"SCnComV[2940].Len() = 1\n",
"SCnComV[2941].Len() = 1\n",
"SCnComV[2942].Len() = 1\n",
"SCnComV[2943].Len() = 1\n",
"SCnComV[2944].Len() = 1\n",
"SCnComV[2945].Len() = 1\n",
"SCnComV[2946].Len() = 1\n",
"SCnComV[2947].Len() = 1\n",
"SCnComV[2948].Len() = 1\n",
"SCnComV[2949].Len() = 1\n",
"SCnComV[2950].Len() = 1\n",
"SCnComV[2951].Len() = 1\n",
"SCnComV[2952].Len() = 1\n",
"SCnComV[2953].Len() = 1\n",
"SCnComV[2954].Len() = 1\n",
"SCnComV[2955].Len() = 1\n",
"SCnComV[2956].Len() = 1\n",
"SCnComV[2957].Len() = 1\n",
"SCnComV[2958].Len() = 1\n",
"SCnComV[2959].Len() = 1\n",
"SCnComV[2960].Len() = 1\n",
"SCnComV[2961].Len() = 1\n",
"SCnComV[2962].Len() = 1\n",
"SCnComV[2963].Len() = 1\n",
"SCnComV[2964].Len() = 1\n",
"SCnComV[2965].Len() = 1\n",
"SCnComV[2966].Len() = 1\n",
"SCnComV[2967].Len() = 1\n",
"SCnComV[2968].Len() = 1\n",
"SCnComV[2969].Len() = 1\n",
"SCnComV[2970].Len() = 1\n",
"SCnComV[2971].Len() = 1\n",
"SCnComV[2972].Len() = 1\n",
"SCnComV[2973].Len() = 1\n",
"SCnComV[2974].Len() = 1\n",
"SCnComV[2975].Len() = 1\n",
"SCnComV[2976].Len() = 1\n",
"SCnComV[2977].Len() = 1\n",
"SCnComV[2978].Len() = 1\n",
"SCnComV[2979].Len() = 1\n",
"SCnComV[2980].Len() = 1\n",
"SCnComV[2981].Len() = 1\n",
"SCnComV[2982].Len() = 1\n",
"SCnComV[2983].Len() = 1\n",
"SCnComV[2984].Len() = 1\n",
"SCnComV[2985].Len() = 1\n",
"SCnComV[2986].Len() = 1\n",
"SCnComV[2987].Len() = 1\n",
"SCnComV[2988].Len() = 1\n",
"SCnComV[2989].Len() = 1\n",
"SCnComV[2990].Len() = 1\n",
"SCnComV[2991].Len() = 1\n",
"SCnComV[2992].Len() = 1\n",
"SCnComV[2993].Len() = 1\n",
"SCnComV[2994].Len() = 1\n",
"SCnComV[2995].Len() = 1\n",
"SCnComV[2996].Len() = 1\n",
"SCnComV[2997].Len() = 1\n",
"SCnComV[2998].Len() = 1\n",
"SCnComV[2999].Len() = 1\n",
"SCnComV[3000].Len() = 1\n",
"SCnComV[3001].Len() = 1\n",
"SCnComV[3002].Len() = 1\n",
"SCnComV[3003].Len() = 1\n",
"SCnComV[3004].Len() = 1\n",
"SCnComV[3005].Len() = 1\n",
"SCnComV[3006].Len() = 1\n",
"SCnComV[3007].Len() = 1\n",
"SCnComV[3008].Len() = 1\n",
"SCnComV[3009].Len() = 1\n",
"SCnComV[3010].Len() = 1\n",
"SCnComV[3011].Len() = 1\n",
"SCnComV[3012].Len() = 1\n",
"SCnComV[3013].Len() = 1\n",
"SCnComV[3014].Len() = 1\n",
"SCnComV[3015].Len() = 1\n",
"SCnComV[3016].Len() = 1\n",
"SCnComV[3017].Len() = 1\n",
"SCnComV[3018].Len() = 1\n",
"SCnComV[3019].Len() = 1\n",
"SCnComV[3020].Len() = 1\n",
"SCnComV[3021].Len() = 1\n",
"SCnComV[3022].Len() = 1\n",
"SCnComV[3023].Len() = 1\n",
"SCnComV[3024].Len() = 1\n",
"SCnComV[3025].Len() = 1\n",
"SCnComV[3026].Len() = 1\n",
"SCnComV[3027].Len() = 1\n",
"SCnComV[3028].Len() = 1\n",
"SCnComV[3029].Len() = 1\n",
"SCnComV[3030].Len() = 1\n",
"SCnComV[3031].Len() = 1\n",
"SCnComV[3032].Len() = 1\n",
"SCnComV[3033].Len() = 1\n",
"SCnComV[3034].Len() = 1\n",
"SCnComV[3035].Len() = 1\n",
"SCnComV[3036].Len() = 1\n",
"SCnComV[3037].Len() = 1\n",
"SCnComV[3038].Len() = 1\n",
"SCnComV[3039].Len() = 1\n",
"SCnComV[3040].Len() = 1\n",
"SCnComV[3041].Len() = 1\n",
"SCnComV[3042].Len() = 1\n",
"SCnComV[3043].Len() = 1\n",
"SCnComV[3044].Len() = 1\n",
"SCnComV[3045].Len() = 1\n",
"SCnComV[3046].Len() = 1\n",
"SCnComV[3047].Len() = 1\n",
"SCnComV[3048].Len() = 1\n",
"SCnComV[3049].Len() = 1\n",
"SCnComV[3050].Len() = 1\n",
"SCnComV[3051].Len() = 1\n",
"SCnComV[3052].Len() = 1\n",
"SCnComV[3053].Len() = 1\n",
"SCnComV[3054].Len() = 1\n",
"SCnComV[3055].Len() = 1\n",
"SCnComV[3056].Len() = 1\n",
"SCnComV[3057].Len() = 1\n",
"SCnComV[3058].Len() = 1\n",
"SCnComV[3059].Len() = 1\n",
"SCnComV[3060].Len() = 1\n",
"SCnComV[3061].Len() = 1\n",
"SCnComV[3062].Len() = 1\n",
"SCnComV[3063].Len() = 1\n",
"SCnComV[3064].Len() = 1\n",
"SCnComV[3065].Len() = 1\n",
"SCnComV[3066].Len() = 1\n",
"SCnComV[3067].Len() = 1\n",
"SCnComV[3068].Len() = 1\n",
"SCnComV[3069].Len() = 1\n",
"SCnComV[3070].Len() = 1\n",
"SCnComV[3071].Len() = 1\n",
"SCnComV[3072].Len() = 1\n",
"SCnComV[3073].Len() = 1\n",
"SCnComV[3074].Len() = 1\n",
"SCnComV[3075].Len() = 1\n",
"SCnComV[3076].Len() = 1\n",
"SCnComV[3077].Len() = 1\n",
"SCnComV[3078].Len() = 1\n",
"SCnComV[3079].Len() = 1\n",
"SCnComV[3080].Len() = 1\n",
"SCnComV[3081].Len() = 1\n",
"SCnComV[3082].Len() = 1\n",
"SCnComV[3083].Len() = 1\n",
"SCnComV[3084].Len() = 1\n",
"SCnComV[3085].Len() = 1\n",
"SCnComV[3086].Len() = 1\n",
"SCnComV[3087].Len() = 1\n",
"SCnComV[3088].Len() = 1\n",
"SCnComV[3089].Len() = 1\n",
"SCnComV[3090].Len() = 1\n",
"SCnComV[3091].Len() = 1\n",
"SCnComV[3092].Len() = 1\n",
"SCnComV[3093].Len() = 1\n",
"SCnComV[3094].Len() = 1\n",
"SCnComV[3095].Len() = 1\n",
"SCnComV[3096].Len() = 1\n",
"SCnComV[3097].Len() = 1\n",
"SCnComV[3098].Len() = 1\n",
"SCnComV[3099].Len() = 1\n",
"SCnComV[3100].Len() = 1\n",
"SCnComV[3101].Len() = 1\n",
"SCnComV[3102].Len() = 1\n",
"SCnComV[3103].Len() = 1\n",
"SCnComV[3104].Len() = 1\n",
"SCnComV[3105].Len() = 1\n",
"SCnComV[3106].Len() = 1\n",
"SCnComV[3107].Len() = 1\n",
"SCnComV[3108].Len() = 1\n",
"SCnComV[3109].Len() = 1\n",
"SCnComV[3110].Len() = 1\n",
"SCnComV[3111].Len() = 1\n",
"SCnComV[3112].Len() = 1\n",
"SCnComV[3113].Len() = 1\n",
"SCnComV[3114].Len() = 1\n",
"SCnComV[3115].Len() = 1\n",
"SCnComV[3116].Len() = 1\n",
"SCnComV[3117].Len() = 1\n",
"SCnComV[3118].Len() = 1\n",
"SCnComV[3119].Len() = 1\n",
"SCnComV[3120].Len() = 1\n",
"SCnComV[3121].Len() = 1\n",
"SCnComV[3122].Len() = 1\n",
"SCnComV[3123].Len() = 1\n",
"SCnComV[3124].Len() = 1\n",
"SCnComV[3125].Len() = 1\n",
"SCnComV[3126].Len() = 1\n",
"SCnComV[3127].Len() = 1\n",
"SCnComV[3128].Len() = 1\n",
"SCnComV[3129].Len() = 1\n",
"SCnComV[3130].Len() = 1\n",
"SCnComV[3131].Len() = 1\n",
"SCnComV[3132].Len() = 1\n",
"SCnComV[3133].Len() = 1\n",
"SCnComV[3134].Len() = 1\n",
"SCnComV[3135].Len() = 1\n",
"SCnComV[3136].Len() = 1\n",
"SCnComV[3137].Len() = 1\n",
"SCnComV[3138].Len() = 1\n",
"SCnComV[3139].Len() = 1\n",
"SCnComV[3140].Len() = 1\n",
"SCnComV[3141].Len() = 1\n",
"SCnComV[3142].Len() = 1\n",
"SCnComV[3143].Len() = 1\n",
"SCnComV[3144].Len() = 1\n",
"SCnComV[3145].Len() = 1\n",
"SCnComV[3146].Len() = 1\n",
"SCnComV[3147].Len() = 1\n",
"SCnComV[3148].Len() = 1\n",
"SCnComV[3149].Len() = 1\n",
"SCnComV[3150].Len() = 1\n",
"SCnComV[3151].Len() = 1\n",
"SCnComV[3152].Len() = 1\n",
"SCnComV[3153].Len() = 1\n",
"SCnComV[3154].Len() = 1\n",
"SCnComV[3155].Len() = 1\n",
"SCnComV[3156].Len() = 1\n",
"SCnComV[3157].Len() = 1\n",
"SCnComV[3158].Len() = 1\n",
"SCnComV[3159].Len() = 1\n",
"SCnComV[3160].Len() = 1\n",
"SCnComV[3161].Len() = 1\n",
"SCnComV[3162].Len() = 1\n",
"SCnComV[3163].Len() = 1\n",
"SCnComV[3164].Len() = 1\n",
"SCnComV[3165].Len() = 1\n",
"SCnComV[3166].Len() = 1\n",
"SCnComV[3167].Len() = 1\n",
"SCnComV[3168].Len() = 1\n",
"SCnComV[3169].Len() = 1\n",
"SCnComV[3170].Len() = 1\n",
"SCnComV[3171].Len() = 1\n",
"SCnComV[3172].Len() = 1\n",
"SCnComV[3173].Len() = 1\n",
"SCnComV[3174].Len() = 1\n",
"SCnComV[3175].Len() = 1\n",
"SCnComV[3176].Len() = 1\n",
"SCnComV[3177].Len() = 1\n",
"SCnComV[3178].Len() = 1\n",
"SCnComV[3179].Len() = 1\n",
"SCnComV[3180].Len() = 1\n",
"SCnComV[3181].Len() = 1\n",
"SCnComV[3182].Len() = 1\n",
"SCnComV[3183].Len() = 1\n",
"SCnComV[3184].Len() = 1\n",
"SCnComV[3185].Len() = 1\n",
"SCnComV[3186].Len() = 1\n",
"SCnComV[3187].Len() = 1\n",
"SCnComV[3188].Len() = 1\n",
"SCnComV[3189].Len() = 1\n",
"SCnComV[3190].Len() = 1\n",
"SCnComV[3191].Len() = 1\n",
"SCnComV[3192].Len() = 1\n",
"SCnComV[3193].Len() = 1\n",
"SCnComV[3194].Len() = 1\n",
"SCnComV[3195].Len() = 1\n",
"SCnComV[3196].Len() = 1\n",
"SCnComV[3197].Len() = 1\n",
"SCnComV[3198].Len() = 1\n",
"SCnComV[3199].Len() = 1\n",
"SCnComV[3200].Len() = 1\n",
"SCnComV[3201].Len() = 1\n",
"SCnComV[3202].Len() = 1\n",
"SCnComV[3203].Len() = 1\n",
"SCnComV[3204].Len() = 1\n",
"SCnComV[3205].Len() = 1\n",
"SCnComV[3206].Len() = 1\n",
"SCnComV[3207].Len() = 1\n",
"SCnComV[3208].Len() = 1\n",
"SCnComV[3209].Len() = 1\n",
"SCnComV[3210].Len() = 1\n",
"SCnComV[3211].Len() = 1\n",
"SCnComV[3212].Len() = 1\n",
"SCnComV[3213].Len() = 1\n",
"SCnComV[3214].Len() = 1\n",
"SCnComV[3215].Len() = 1\n",
"SCnComV[3216].Len() = 1\n",
"SCnComV[3217].Len() = 1\n",
"SCnComV[3218].Len() = 1\n",
"SCnComV[3219].Len() = 1\n",
"SCnComV[3220].Len() = 1\n",
"SCnComV[3221].Len() = 1\n",
"SCnComV[3222].Len() = 1\n",
"SCnComV[3223].Len() = 1\n",
"SCnComV[3224].Len() = 1\n",
"SCnComV[3225].Len() = 1\n",
"SCnComV[3226].Len() = 1\n",
"SCnComV[3227].Len() = 1\n",
"SCnComV[3228].Len() = 1\n",
"SCnComV[3229].Len() = 1\n",
"SCnComV[3230].Len() = 1\n",
"SCnComV[3231].Len() = 1\n",
"SCnComV[3232].Len() = 1\n",
"SCnComV[3233].Len() = 1\n",
"SCnComV[3234].Len() = 1\n",
"SCnComV[3235].Len() = 1\n",
"SCnComV[3236].Len() = 1\n",
"SCnComV[3237].Len() = 1\n",
"SCnComV[3238].Len() = 1\n",
"SCnComV[3239].Len() = 1\n",
"SCnComV[3240].Len() = 1\n",
"SCnComV[3241].Len() = 1\n",
"SCnComV[3242].Len() = 1\n",
"SCnComV[3243].Len() = 1\n",
"SCnComV[3244].Len() = 1\n",
"SCnComV[3245].Len() = 1\n",
"SCnComV[3246].Len() = 1\n",
"SCnComV[3247].Len() = 1\n",
"SCnComV[3248].Len() = 1\n",
"SCnComV[3249].Len() = 1\n",
"SCnComV[3250].Len() = 1\n",
"SCnComV[3251].Len() = 1\n",
"SCnComV[3252].Len() = 1\n",
"SCnComV[3253].Len() = 1\n",
"SCnComV[3254].Len() = 1\n",
"SCnComV[3255].Len() = 1\n",
"SCnComV[3256].Len() = 1\n",
"SCnComV[3257].Len() = 1\n",
"SCnComV[3258].Len() = 1\n",
"SCnComV[3259].Len() = 1\n",
"SCnComV[3260].Len() = 1\n",
"SCnComV[3261].Len() = 1\n",
"SCnComV[3262].Len() = 1\n",
"SCnComV[3263].Len() = 1\n",
"SCnComV[3264].Len() = 1\n",
"SCnComV[3265].Len() = 1\n",
"SCnComV[3266].Len() = 1\n",
"SCnComV[3267].Len() = 1\n",
"SCnComV[3268].Len() = 1\n",
"SCnComV[3269].Len() = 1\n",
"SCnComV[3270].Len() = 1\n",
"SCnComV[3271].Len() = 1\n",
"SCnComV[3272].Len() = 1\n",
"SCnComV[3273].Len() = 1\n",
"SCnComV[3274].Len() = 1\n",
"SCnComV[3275].Len() = 1\n",
"SCnComV[3276].Len() = 1\n",
"SCnComV[3277].Len() = 1\n",
"SCnComV[3278].Len() = 1\n",
"SCnComV[3279].Len() = 1\n",
"SCnComV[3280].Len() = 1\n",
"SCnComV[3281].Len() = 1\n",
"SCnComV[3282].Len() = 1\n",
"SCnComV[3283].Len() = 1\n",
"SCnComV[3284].Len() = 1\n",
"SCnComV[3285].Len() = 1\n",
"SCnComV[3286].Len() = 1\n",
"SCnComV[3287].Len() = 1\n",
"SCnComV[3288].Len() = 1\n",
"SCnComV[3289].Len() = 1\n",
"SCnComV[3290].Len() = 1\n",
"SCnComV[3291].Len() = 1\n",
"SCnComV[3292].Len() = 1\n",
"SCnComV[3293].Len() = 1\n",
"SCnComV[3294].Len() = 1\n",
"SCnComV[3295].Len() = 1\n",
"SCnComV[3296].Len() = 1\n",
"SCnComV[3297].Len() = 1\n",
"SCnComV[3298].Len() = 1\n",
"SCnComV[3299].Len() = 1\n",
"SCnComV[3300].Len() = 1\n",
"SCnComV[3301].Len() = 1\n",
"SCnComV[3302].Len() = 1\n",
"SCnComV[3303].Len() = 1\n",
"SCnComV[3304].Len() = 1\n",
"SCnComV[3305].Len() = 1\n",
"SCnComV[3306].Len() = 1\n",
"SCnComV[3307].Len() = 1\n",
"SCnComV[3308].Len() = 1\n",
"SCnComV[3309].Len() = 1\n",
"SCnComV[3310].Len() = 1\n",
"SCnComV[3311].Len() = 1\n",
"SCnComV[3312].Len() = 1\n",
"SCnComV[3313].Len() = 1\n",
"SCnComV[3314].Len() = 1\n",
"SCnComV[3315].Len() = 1\n",
"SCnComV[3316].Len() = 1\n",
"SCnComV[3317].Len() = 1\n",
"SCnComV[3318].Len() = 1\n",
"SCnComV[3319].Len() = 1\n",
"SCnComV[3320].Len() = 1\n",
"SCnComV[3321].Len() = 1\n",
"SCnComV[3322].Len() = 1\n",
"SCnComV[3323].Len() = 1\n",
"SCnComV[3324].Len() = 1\n",
"SCnComV[3325].Len() = 1\n",
"SCnComV[3326].Len() = 1\n",
"SCnComV[3327].Len() = 1\n",
"SCnComV[3328].Len() = 1\n",
"SCnComV[3329].Len() = 1\n",
"SCnComV[3330].Len() = 1\n",
"SCnComV[3331].Len() = 1\n",
"SCnComV[3332].Len() = 1\n",
"SCnComV[3333].Len() = 1\n",
"SCnComV[3334].Len() = 1\n",
"SCnComV[3335].Len() = 1\n",
"SCnComV[3336].Len() = 1\n",
"SCnComV[3337].Len() = 1\n",
"SCnComV[3338].Len() = 1\n",
"SCnComV[3339].Len() = 1\n",
"SCnComV[3340].Len() = 1\n",
"SCnComV[3341].Len() = 1\n",
"SCnComV[3342].Len() = 1\n",
"SCnComV[3343].Len() = 1\n",
"SCnComV[3344].Len() = 1\n",
"SCnComV[3345].Len() = 1\n",
"SCnComV[3346].Len() = 1\n",
"SCnComV[3347].Len() = 1\n",
"SCnComV[3348].Len() = 1\n",
"SCnComV[3349].Len() = 1\n",
"SCnComV[3350].Len() = 1\n",
"SCnComV[3351].Len() = 1\n",
"SCnComV[3352].Len() = 1\n",
"SCnComV[3353].Len() = 1\n",
"SCnComV[3354].Len() = 1\n",
"SCnComV[3355].Len() = 1\n",
"SCnComV[3356].Len() = 1\n",
"SCnComV[3357].Len() = 1\n",
"SCnComV[3358].Len() = 1\n",
"SCnComV[3359].Len() = 1\n",
"SCnComV[3360].Len() = 1\n",
"SCnComV[3361].Len() = 1\n",
"SCnComV[3362].Len() = 1\n",
"SCnComV[3363].Len() = 1\n",
"SCnComV[3364].Len() = 1\n",
"SCnComV[3365].Len() = 1\n",
"SCnComV[3366].Len() = 1\n",
"SCnComV[3367].Len() = 1\n",
"SCnComV[3368].Len() = 1\n",
"SCnComV[3369].Len() = 1\n",
"SCnComV[3370].Len() = 1\n",
"SCnComV[3371].Len() = 1\n",
"SCnComV[3372].Len() = 1\n",
"SCnComV[3373].Len() = 1\n",
"SCnComV[3374].Len() = 1\n",
"SCnComV[3375].Len() = 1\n",
"SCnComV[3376].Len() = 1\n",
"SCnComV[3377].Len() = 1\n",
"SCnComV[3378].Len() = 1\n",
"SCnComV[3379].Len() = 1\n",
"SCnComV[3380].Len() = 1\n",
"SCnComV[3381].Len() = 1\n",
"SCnComV[3382].Len() = 1\n",
"SCnComV[3383].Len() = 1\n",
"SCnComV[3384].Len() = 1\n",
"SCnComV[3385].Len() = 1\n",
"SCnComV[3386].Len() = 1\n",
"SCnComV[3387].Len() = 1\n",
"SCnComV[3388].Len() = 1\n",
"SCnComV[3389].Len() = 1\n",
"SCnComV[3390].Len() = 1\n",
"SCnComV[3391].Len() = 1\n",
"SCnComV[3392].Len() = 1\n",
"SCnComV[3393].Len() = 1\n",
"SCnComV[3394].Len() = 1\n",
"SCnComV[3395].Len() = 1\n",
"SCnComV[3396].Len() = 1\n",
"SCnComV[3397].Len() = 1\n",
"SCnComV[3398].Len() = 1\n",
"SCnComV[3399].Len() = 1\n",
"SCnComV[3400].Len() = 1\n",
"SCnComV[3401].Len() = 1\n",
"SCnComV[3402].Len() = 1\n",
"SCnComV[3403].Len() = 1\n",
"SCnComV[3404].Len() = 1\n",
"SCnComV[3405].Len() = 1\n",
"SCnComV[3406].Len() = 1\n",
"SCnComV[3407].Len() = 1\n",
"SCnComV[3408].Len() = 1\n",
"SCnComV[3409].Len() = 1\n",
"SCnComV[3410].Len() = 1\n",
"SCnComV[3411].Len() = 1\n",
"SCnComV[3412].Len() = 1\n",
"SCnComV[3413].Len() = 1\n",
"SCnComV[3414].Len() = 1\n",
"SCnComV[3415].Len() = 1\n",
"SCnComV[3416].Len() = 1\n",
"SCnComV[3417].Len() = 1\n",
"SCnComV[3418].Len() = 1\n",
"SCnComV[3419].Len() = 1\n",
"SCnComV[3420].Len() = 1\n",
"SCnComV[3421].Len() = 1\n",
"SCnComV[3422].Len() = 1\n",
"SCnComV[3423].Len() = 1\n",
"SCnComV[3424].Len() = 1\n",
"SCnComV[3425].Len() = 1\n",
"SCnComV[3426].Len() = 1\n",
"SCnComV[3427].Len() = 1\n",
"SCnComV[3428].Len() = 1\n",
"SCnComV[3429].Len() = 1\n",
"SCnComV[3430].Len() = 1\n",
"SCnComV[3431].Len() = 1\n",
"SCnComV[3432].Len() = 1\n",
"SCnComV[3433].Len() = 1\n",
"SCnComV[3434].Len() = 1\n",
"SCnComV[3435].Len() = 1\n",
"SCnComV[3436].Len() = 1\n",
"SCnComV[3437].Len() = 1\n",
"SCnComV[3438].Len() = 1\n",
"SCnComV[3439].Len() = 1\n",
"SCnComV[3440].Len() = 1\n",
"SCnComV[3441].Len() = 1\n",
"SCnComV[3442].Len() = 1\n",
"SCnComV[3443].Len() = 1\n",
"SCnComV[3444].Len() = 1\n",
"SCnComV[3445].Len() = 1\n",
"SCnComV[3446].Len() = 1\n",
"SCnComV[3447].Len() = 1\n",
"SCnComV[3448].Len() = 1\n",
"SCnComV[3449].Len() = 1\n",
"SCnComV[3450].Len() = 1\n",
"SCnComV[3451].Len() = 1\n",
"SCnComV[3452].Len() = 1\n",
"SCnComV[3453].Len() = 1\n",
"SCnComV[3454].Len() = 1\n",
"SCnComV[3455].Len() = 1\n",
"SCnComV[3456].Len() = 1\n",
"SCnComV[3457].Len() = 1\n",
"SCnComV[3458].Len() = 1\n",
"SCnComV[3459].Len() = 1\n",
"SCnComV[3460].Len() = 1\n",
"SCnComV[3461].Len() = 1\n",
"SCnComV[3462].Len() = 1\n",
"SCnComV[3463].Len() = 1\n",
"SCnComV[3464].Len() = 1\n",
"SCnComV[3465].Len() = 1\n",
"SCnComV[3466].Len() = 1\n",
"SCnComV[3467].Len() = 1\n",
"SCnComV[3468].Len() = 1\n",
"SCnComV[3469].Len() = 1\n",
"SCnComV[3470].Len() = 1\n",
"SCnComV[3471].Len() = 1\n",
"SCnComV[3472].Len() = 1\n",
"SCnComV[3473].Len() = 1\n",
"SCnComV[3474].Len() = 1\n",
"SCnComV[3475].Len() = 1\n",
"SCnComV[3476].Len() = 1\n",
"SCnComV[3477].Len() = 1\n",
"SCnComV[3478].Len() = 1\n",
"SCnComV[3479].Len() = 1\n",
"SCnComV[3480].Len() = 1\n",
"SCnComV[3481].Len() = 1\n",
"SCnComV[3482].Len() = 1\n",
"SCnComV[3483].Len() = 1\n",
"SCnComV[3484].Len() = 1\n",
"SCnComV[3485].Len() = 1\n",
"SCnComV[3486].Len() = 1\n",
"SCnComV[3487].Len() = 1\n",
"SCnComV[3488].Len() = 1\n",
"SCnComV[3489].Len() = 1\n",
"SCnComV[3490].Len() = 1\n",
"SCnComV[3491].Len() = 1\n",
"SCnComV[3492].Len() = 1\n",
"SCnComV[3493].Len() = 1\n",
"SCnComV[3494].Len() = 1\n",
"SCnComV[3495].Len() = 1\n",
"SCnComV[3496].Len() = 1\n",
"SCnComV[3497].Len() = 1\n",
"SCnComV[3498].Len() = 1\n",
"SCnComV[3499].Len() = 1\n",
"SCnComV[3500].Len() = 1\n",
"SCnComV[3501].Len() = 1\n",
"SCnComV[3502].Len() = 1\n",
"SCnComV[3503].Len() = 1\n",
"SCnComV[3504].Len() = 1\n",
"SCnComV[3505].Len() = 1\n",
"SCnComV[3506].Len() = 1\n",
"SCnComV[3507].Len() = 1\n",
"SCnComV[3508].Len() = 1\n",
"SCnComV[3509].Len() = 1\n",
"SCnComV[3510].Len() = 1\n",
"SCnComV[3511].Len() = 1\n",
"SCnComV[3512].Len() = 1\n",
"SCnComV[3513].Len() = 1\n",
"SCnComV[3514].Len() = 1\n",
"SCnComV[3515].Len() = 1\n",
"SCnComV[3516].Len() = 1\n",
"SCnComV[3517].Len() = 1\n",
"SCnComV[3518].Len() = 1\n",
"SCnComV[3519].Len() = 1\n",
"SCnComV[3520].Len() = 1\n",
"SCnComV[3521].Len() = 1\n",
"SCnComV[3522].Len() = 1\n",
"SCnComV[3523].Len() = 1\n",
"SCnComV[3524].Len() = 1\n",
"SCnComV[3525].Len() = 1\n",
"SCnComV[3526].Len() = 1\n",
"SCnComV[3527].Len() = 1\n",
"SCnComV[3528].Len() = 1\n",
"SCnComV[3529].Len() = 1\n",
"SCnComV[3530].Len() = 1\n",
"SCnComV[3531].Len() = 1\n",
"SCnComV[3532].Len() = 1\n",
"SCnComV[3533].Len() = 1\n",
"SCnComV[3534].Len() = 1\n",
"SCnComV[3535].Len() = 1\n",
"SCnComV[3536].Len() = 1\n",
"SCnComV[3537].Len() = 1\n",
"SCnComV[3538].Len() = 1\n",
"SCnComV[3539].Len() = 1\n",
"SCnComV[3540].Len() = 1\n",
"SCnComV[3541].Len() = 1\n",
"SCnComV[3542].Len() = 1\n",
"SCnComV[3543].Len() = 1\n",
"SCnComV[3544].Len() = 1\n",
"SCnComV[3545].Len() = 1\n",
"SCnComV[3546].Len() = 1\n",
"SCnComV[3547].Len() = 1\n",
"SCnComV[3548].Len() = 1\n",
"SCnComV[3549].Len() = 1\n",
"SCnComV[3550].Len() = 1\n",
"SCnComV[3551].Len() = 1\n",
"SCnComV[3552].Len() = 1\n",
"SCnComV[3553].Len() = 1\n",
"SCnComV[3554].Len() = 1\n",
"SCnComV[3555].Len() = 1\n",
"SCnComV[3556].Len() = 1\n",
"SCnComV[3557].Len() = 1\n",
"SCnComV[3558].Len() = 1\n",
"SCnComV[3559].Len() = 1\n",
"SCnComV[3560].Len() = 1\n",
"SCnComV[3561].Len() = 1\n",
"SCnComV[3562].Len() = 1\n",
"SCnComV[3563].Len() = 1\n",
"SCnComV[3564].Len() = 1\n",
"SCnComV[3565].Len() = 1\n",
"SCnComV[3566].Len() = 1\n",
"SCnComV[3567].Len() = 1\n",
"SCnComV[3568].Len() = 1\n",
"SCnComV[3569].Len() = 1\n",
"SCnComV[3570].Len() = 1\n",
"SCnComV[3571].Len() = 1\n",
"SCnComV[3572].Len() = 1\n",
"SCnComV[3573].Len() = 1\n",
"SCnComV[3574].Len() = 1\n",
"SCnComV[3575].Len() = 1\n",
"SCnComV[3576].Len() = 1\n",
"SCnComV[3577].Len() = 1\n",
"SCnComV[3578].Len() = 1\n",
"SCnComV[3579].Len() = 1\n",
"SCnComV[3580].Len() = 1\n",
"SCnComV[3581].Len() = 1\n",
"SCnComV[3582].Len() = 1\n",
"SCnComV[3583].Len() = 1\n",
"SCnComV[3584].Len() = 1\n",
"SCnComV[3585].Len() = 1\n",
"SCnComV[3586].Len() = 1\n",
"SCnComV[3587].Len() = 1\n",
"SCnComV[3588].Len() = 1\n",
"SCnComV[3589].Len() = 1\n",
"SCnComV[3590].Len() = 1\n",
"SCnComV[3591].Len() = 1\n",
"SCnComV[3592].Len() = 1\n",
"SCnComV[3593].Len() = 1\n",
"SCnComV[3594].Len() = 1\n",
"SCnComV[3595].Len() = 1\n",
"SCnComV[3596].Len() = 1\n",
"SCnComV[3597].Len() = 1\n",
"SCnComV[3598].Len() = 1\n",
"SCnComV[3599].Len() = 1\n",
"SCnComV[3600].Len() = 1\n",
"SCnComV[3601].Len() = 1\n",
"SCnComV[3602].Len() = 1\n",
"SCnComV[3603].Len() = 1\n",
"SCnComV[3604].Len() = 1\n",
"SCnComV[3605].Len() = 1\n",
"SCnComV[3606].Len() = 1\n",
"SCnComV[3607].Len() = 1\n",
"SCnComV[3608].Len() = 1\n",
"SCnComV[3609].Len() = 1\n",
"SCnComV[3610].Len() = 1\n",
"SCnComV[3611].Len() = 1\n",
"SCnComV[3612].Len() = 1\n",
"SCnComV[3613].Len() = 1\n",
"SCnComV[3614].Len() = 1\n",
"SCnComV[3615].Len() = 1\n",
"SCnComV[3616].Len() = 1\n",
"SCnComV[3617].Len() = 1\n",
"SCnComV[3618].Len() = 1\n",
"SCnComV[3619].Len() = 1\n",
"SCnComV[3620].Len() = 1\n",
"SCnComV[3621].Len() = 1\n",
"SCnComV[3622].Len() = 1\n",
"SCnComV[3623].Len() = 1\n",
"SCnComV[3624].Len() = 1\n",
"SCnComV[3625].Len() = 1\n",
"SCnComV[3626].Len() = 1\n",
"SCnComV[3627].Len() = 1\n",
"SCnComV[3628].Len() = 1\n",
"SCnComV[3629].Len() = 1\n",
"SCnComV[3630].Len() = 1\n",
"SCnComV[3631].Len() = 1\n",
"SCnComV[3632].Len() = 1\n",
"SCnComV[3633].Len() = 1\n",
"SCnComV[3634].Len() = 1\n",
"SCnComV[3635].Len() = 1\n",
"SCnComV[3636].Len() = 1\n",
"SCnComV[3637].Len() = 1\n",
"SCnComV[3638].Len() = 1\n",
"SCnComV[3639].Len() = 1\n",
"SCnComV[3640].Len() = 1\n",
"SCnComV[3641].Len() = 1\n",
"SCnComV[3642].Len() = 1\n",
"SCnComV[3643].Len() = 1\n",
"SCnComV[3644].Len() = 1\n",
"SCnComV[3645].Len() = 1\n",
"SCnComV[3646].Len() = 1\n",
"SCnComV[3647].Len() = 1\n",
"SCnComV[3648].Len() = 1\n",
"SCnComV[3649].Len() = 1\n",
"SCnComV[3650].Len() = 1\n",
"SCnComV[3651].Len() = 1\n",
"SCnComV[3652].Len() = 1\n",
"SCnComV[3653].Len() = 1\n",
"SCnComV[3654].Len() = 1\n",
"SCnComV[3655].Len() = 1\n",
"SCnComV[3656].Len() = 1\n",
"SCnComV[3657].Len() = 1\n",
"SCnComV[3658].Len() = 1\n",
"SCnComV[3659].Len() = 1\n",
"SCnComV[3660].Len() = 1\n",
"SCnComV[3661].Len() = 1\n",
"SCnComV[3662].Len() = 1\n",
"SCnComV[3663].Len() = 1\n",
"SCnComV[3664].Len() = 1\n",
"SCnComV[3665].Len() = 1\n",
"SCnComV[3666].Len() = 1\n",
"SCnComV[3667].Len() = 1\n",
"SCnComV[3668].Len() = 1\n",
"SCnComV[3669].Len() = 1\n",
"SCnComV[3670].Len() = 1\n",
"SCnComV[3671].Len() = 1\n",
"SCnComV[3672].Len() = 1\n",
"SCnComV[3673].Len() = 1\n",
"SCnComV[3674].Len() = 1\n",
"SCnComV[3675].Len() = 1\n",
"SCnComV[3676].Len() = 1\n",
"SCnComV[3677].Len() = 1\n",
"SCnComV[3678].Len() = 1\n",
"SCnComV[3679].Len() = 1\n",
"SCnComV[3680].Len() = 1\n",
"SCnComV[3681].Len() = 1\n",
"SCnComV[3682].Len() = 1\n",
"SCnComV[3683].Len() = 1\n",
"SCnComV[3684].Len() = 1\n",
"SCnComV[3685].Len() = 1\n",
"SCnComV[3686].Len() = 1\n",
"SCnComV[3687].Len() = 1\n",
"SCnComV[3688].Len() = 1\n",
"SCnComV[3689].Len() = 1\n",
"SCnComV[3690].Len() = 1\n",
"SCnComV[3691].Len() = 1\n",
"SCnComV[3692].Len() = 1\n",
"SCnComV[3693].Len() = 1\n",
"SCnComV[3694].Len() = 1\n",
"SCnComV[3695].Len() = 1\n",
"SCnComV[3696].Len() = 1\n",
"SCnComV[3697].Len() = 1\n",
"SCnComV[3698].Len() = 1\n",
"SCnComV[3699].Len() = 1\n",
"SCnComV[3700].Len() = 1\n",
"SCnComV[3701].Len() = 1\n",
"SCnComV[3702].Len() = 1\n",
"SCnComV[3703].Len() = 1\n",
"SCnComV[3704].Len() = 1\n",
"SCnComV[3705].Len() = 1\n",
"SCnComV[3706].Len() = 1\n",
"SCnComV[3707].Len() = 1\n",
"SCnComV[3708].Len() = 1\n",
"SCnComV[3709].Len() = 1\n",
"SCnComV[3710].Len() = 1\n",
"SCnComV[3711].Len() = 1\n",
"SCnComV[3712].Len() = 1\n",
"SCnComV[3713].Len() = 1\n",
"SCnComV[3714].Len() = 1\n",
"SCnComV[3715].Len() = 1\n",
"SCnComV[3716].Len() = 1\n",
"SCnComV[3717].Len() = 1\n",
"SCnComV[3718].Len() = 1\n",
"SCnComV[3719].Len() = 1\n",
"SCnComV[3720].Len() = 1\n",
"SCnComV[3721].Len() = 1\n",
"SCnComV[3722].Len() = 1\n",
"SCnComV[3723].Len() = 1\n",
"SCnComV[3724].Len() = 1\n",
"SCnComV[3725].Len() = 1\n",
"SCnComV[3726].Len() = 1\n",
"SCnComV[3727].Len() = 1\n",
"SCnComV[3728].Len() = 1\n",
"SCnComV[3729].Len() = 1\n",
"SCnComV[3730].Len() = 1\n",
"SCnComV[3731].Len() = 1\n",
"SCnComV[3732].Len() = 1\n",
"SCnComV[3733].Len() = 1\n",
"SCnComV[3734].Len() = 1\n",
"SCnComV[3735].Len() = 1\n",
"SCnComV[3736].Len() = 1\n",
"SCnComV[3737].Len() = 1\n",
"SCnComV[3738].Len() = 1\n",
"SCnComV[3739].Len() = 1\n",
"SCnComV[3740].Len() = 1\n",
"SCnComV[3741].Len() = 1\n",
"SCnComV[3742].Len() = 1\n",
"SCnComV[3743].Len() = 1\n",
"SCnComV[3744].Len() = 1\n",
"SCnComV[3745].Len() = 1\n",
"SCnComV[3746].Len() = 1\n",
"SCnComV[3747].Len() = 1\n",
"SCnComV[3748].Len() = 1\n",
"SCnComV[3749].Len() = 1\n",
"SCnComV[3750].Len() = 1\n",
"SCnComV[3751].Len() = 1\n",
"SCnComV[3752].Len() = 1\n",
"SCnComV[3753].Len() = 1\n",
"SCnComV[3754].Len() = 1\n",
"SCnComV[3755].Len() = 1\n",
"SCnComV[3756].Len() = 1\n",
"SCnComV[3757].Len() = 1\n",
"SCnComV[3758].Len() = 1\n",
"SCnComV[3759].Len() = 1\n",
"SCnComV[3760].Len() = 1\n",
"SCnComV[3761].Len() = 1\n",
"SCnComV[3762].Len() = 1\n",
"SCnComV[3763].Len() = 1\n",
"SCnComV[3764].Len() = 1\n",
"SCnComV[3765].Len() = 1\n",
"SCnComV[3766].Len() = 1\n",
"SCnComV[3767].Len() = 1\n",
"SCnComV[3768].Len() = 1\n",
"SCnComV[3769].Len() = 1\n",
"SCnComV[3770].Len() = 1\n",
"SCnComV[3771].Len() = 1\n",
"SCnComV[3772].Len() = 1\n",
"SCnComV[3773].Len() = 1\n",
"SCnComV[3774].Len() = 1\n",
"SCnComV[3775].Len() = 1\n",
"SCnComV[3776].Len() = 1\n",
"SCnComV[3777].Len() = 1\n",
"SCnComV[3778].Len() = 1\n",
"SCnComV[3779].Len() = 1\n",
"SCnComV[3780].Len() = 1\n",
"SCnComV[3781].Len() = 1\n",
"SCnComV[3782].Len() = 1\n",
"SCnComV[3783].Len() = 1\n",
"SCnComV[3784].Len() = 1\n",
"SCnComV[3785].Len() = 1\n",
"SCnComV[3786].Len() = 1\n",
"SCnComV[3787].Len() = 1\n",
"SCnComV[3788].Len() = 1\n",
"SCnComV[3789].Len() = 1\n",
"SCnComV[3790].Len() = 1\n",
"SCnComV[3791].Len() = 1\n",
"SCnComV[3792].Len() = 1\n",
"SCnComV[3793].Len() = 1\n",
"SCnComV[3794].Len() = 1\n",
"SCnComV[3795].Len() = 1\n",
"SCnComV[3796].Len() = 1\n",
"SCnComV[3797].Len() = 1\n",
"SCnComV[3798].Len() = 1\n",
"SCnComV[3799].Len() = 1\n",
"SCnComV[3800].Len() = 1\n",
"SCnComV[3801].Len() = 1\n",
"SCnComV[3802].Len() = 1\n",
"SCnComV[3803].Len() = 1\n",
"SCnComV[3804].Len() = 1\n",
"SCnComV[3805].Len() = 1\n",
"SCnComV[3806].Len() = 1\n",
"SCnComV[3807].Len() = 1\n",
"SCnComV[3808].Len() = 1\n",
"SCnComV[3809].Len() = 1\n",
"SCnComV[3810].Len() = 1\n",
"SCnComV[3811].Len() = 1\n",
"SCnComV[3812].Len() = 1\n",
"SCnComV[3813].Len() = 1\n",
"SCnComV[3814].Len() = 1\n",
"SCnComV[3815].Len() = 1\n",
"SCnComV[3816].Len() = 1\n",
"SCnComV[3817].Len() = 1\n",
"SCnComV[3818].Len() = 1\n",
"SCnComV[3819].Len() = 1\n",
"SCnComV[3820].Len() = 1\n",
"SCnComV[3821].Len() = 1\n",
"SCnComV[3822].Len() = 1\n",
"SCnComV[3823].Len() = 1\n",
"SCnComV[3824].Len() = 1\n",
"SCnComV[3825].Len() = 1\n",
"SCnComV[3826].Len() = 1\n",
"SCnComV[3827].Len() = 1\n",
"SCnComV[3828].Len() = 1\n",
"SCnComV[3829].Len() = 1\n",
"SCnComV[3830].Len() = 1\n",
"SCnComV[3831].Len() = 1\n",
"SCnComV[3832].Len() = 1\n",
"SCnComV[3833].Len() = 1\n",
"SCnComV[3834].Len() = 1\n",
"SCnComV[3835].Len() = 1\n",
"SCnComV[3836].Len() = 1\n",
"SCnComV[3837].Len() = 1\n",
"SCnComV[3838].Len() = 1\n",
"SCnComV[3839].Len() = 1\n",
"SCnComV[3840].Len() = 1\n",
"SCnComV[3841].Len() = 1\n",
"SCnComV[3842].Len() = 1\n",
"SCnComV[3843].Len() = 1\n",
"SCnComV[3844].Len() = 1\n",
"SCnComV[3845].Len() = 1\n",
"SCnComV[3846].Len() = 1\n",
"SCnComV[3847].Len() = 1\n",
"SCnComV[3848].Len() = 1\n",
"SCnComV[3849].Len() = 1\n",
"SCnComV[3850].Len() = 1\n",
"SCnComV[3851].Len() = 1\n",
"SCnComV[3852].Len() = 1\n",
"SCnComV[3853].Len() = 1\n",
"SCnComV[3854].Len() = 1\n",
"SCnComV[3855].Len() = 1\n",
"SCnComV[3856].Len() = 1\n",
"SCnComV[3857].Len() = 1\n",
"SCnComV[3858].Len() = 1\n",
"SCnComV[3859].Len() = 1\n",
"SCnComV[3860].Len() = 1\n",
"SCnComV[3861].Len() = 1\n",
"SCnComV[3862].Len() = 1\n",
"SCnComV[3863].Len() = 1\n",
"SCnComV[3864].Len() = 1\n",
"SCnComV[3865].Len() = 1\n",
"SCnComV[3866].Len() = 1\n",
"SCnComV[3867].Len() = 1\n",
"SCnComV[3868].Len() = 1\n",
"SCnComV[3869].Len() = 1\n",
"SCnComV[3870].Len() = 1\n",
"SCnComV[3871].Len() = 1\n",
"SCnComV[3872].Len() = 1\n",
"SCnComV[3873].Len() = 1\n",
"SCnComV[3874].Len() = 1\n",
"SCnComV[3875].Len() = 1\n",
"SCnComV[3876].Len() = 1\n",
"SCnComV[3877].Len() = 1\n",
"SCnComV[3878].Len() = 1\n",
"SCnComV[3879].Len() = 1\n",
"SCnComV[3880].Len() = 1\n",
"SCnComV[3881].Len() = 1\n",
"SCnComV[3882].Len() = 1\n",
"SCnComV[3883].Len() = 1\n",
"SCnComV[3884].Len() = 1\n",
"SCnComV[3885].Len() = 1\n",
"SCnComV[3886].Len() = 1\n",
"SCnComV[3887].Len() = 1\n",
"SCnComV[3888].Len() = 1\n",
"SCnComV[3889].Len() = 1\n",
"SCnComV[3890].Len() = 1\n",
"SCnComV[3891].Len() = 1\n",
"SCnComV[3892].Len() = 1\n",
"SCnComV[3893].Len() = 1\n",
"SCnComV[3894].Len() = 1\n",
"SCnComV[3895].Len() = 1\n",
"SCnComV[3896].Len() = 1\n",
"SCnComV[3897].Len() = 1\n",
"SCnComV[3898].Len() = 1\n",
"SCnComV[3899].Len() = 1\n",
"SCnComV[3900].Len() = 1\n",
"SCnComV[3901].Len() = 1\n",
"SCnComV[3902].Len() = 1\n",
"SCnComV[3903].Len() = 1\n",
"SCnComV[3904].Len() = 1\n",
"SCnComV[3905].Len() = 1\n",
"SCnComV[3906].Len() = 1\n",
"SCnComV[3907].Len() = 1\n",
"SCnComV[3908].Len() = 1\n",
"SCnComV[3909].Len() = 1\n",
"SCnComV[3910].Len() = 1\n",
"SCnComV[3911].Len() = 1\n",
"SCnComV[3912].Len() = 1\n",
"SCnComV[3913].Len() = 1\n",
"SCnComV[3914].Len() = 1\n",
"SCnComV[3915].Len() = 1\n",
"SCnComV[3916].Len() = 1\n",
"SCnComV[3917].Len() = 1\n",
"SCnComV[3918].Len() = 1\n",
"SCnComV[3919].Len() = 1\n",
"SCnComV[3920].Len() = 1\n",
"SCnComV[3921].Len() = 1\n",
"SCnComV[3922].Len() = 1\n",
"SCnComV[3923].Len() = 1\n",
"SCnComV[3924].Len() = 1\n",
"SCnComV[3925].Len() = 1\n",
"SCnComV[3926].Len() = 1\n",
"SCnComV[3927].Len() = 1\n",
"SCnComV[3928].Len() = 1\n",
"SCnComV[3929].Len() = 1\n",
"SCnComV[3930].Len() = 1\n",
"SCnComV[3931].Len() = 1\n",
"SCnComV[3932].Len() = 1\n",
"SCnComV[3933].Len() = 1\n",
"SCnComV[3934].Len() = 1\n",
"SCnComV[3935].Len() = 1\n",
"SCnComV[3936].Len() = 1\n",
"SCnComV[3937].Len() = 1\n",
"SCnComV[3938].Len() = 1\n",
"SCnComV[3939].Len() = 1\n",
"SCnComV[3940].Len() = 1\n",
"SCnComV[3941].Len() = 1\n",
"SCnComV[3942].Len() = 1\n",
"SCnComV[3943].Len() = 1\n",
"SCnComV[3944].Len() = 1\n",
"SCnComV[3945].Len() = 1\n",
"SCnComV[3946].Len() = 1\n",
"SCnComV[3947].Len() = 1\n",
"SCnComV[3948].Len() = 1\n",
"SCnComV[3949].Len() = 1\n",
"SCnComV[3950].Len() = 1\n",
"SCnComV[3951].Len() = 1\n",
"SCnComV[3952].Len() = 1\n",
"SCnComV[3953].Len() = 1\n",
"SCnComV[3954].Len() = 1\n",
"SCnComV[3955].Len() = 1\n",
"SCnComV[3956].Len() = 1\n",
"SCnComV[3957].Len() = 1\n",
"SCnComV[3958].Len() = 1\n",
"SCnComV[3959].Len() = 1\n",
"SCnComV[3960].Len() = 1\n",
"SCnComV[3961].Len() = 1\n",
"SCnComV[3962].Len() = 1\n",
"SCnComV[3963].Len() = 1\n",
"SCnComV[3964].Len() = 1\n",
"SCnComV[3965].Len() = 1\n",
"SCnComV[3966].Len() = 1\n",
"SCnComV[3967].Len() = 1\n",
"SCnComV[3968].Len() = 1\n",
"SCnComV[3969].Len() = 1\n",
"SCnComV[3970].Len() = 1\n",
"SCnComV[3971].Len() = 1\n",
"SCnComV[3972].Len() = 1\n",
"SCnComV[3973].Len() = 1\n",
"SCnComV[3974].Len() = 1\n",
"SCnComV[3975].Len() = 1\n",
"SCnComV[3976].Len() = 1\n",
"SCnComV[3977].Len() = 1\n",
"SCnComV[3978].Len() = 1\n",
"SCnComV[3979].Len() = 1\n",
"SCnComV[3980].Len() = 1\n",
"SCnComV[3981].Len() = 1\n",
"SCnComV[3982].Len() = 1\n",
"SCnComV[3983].Len() = 1\n",
"SCnComV[3984].Len() = 1\n",
"SCnComV[3985].Len() = 1\n",
"SCnComV[3986].Len() = 1\n",
"SCnComV[3987].Len() = 1\n",
"SCnComV[3988].Len() = 1\n",
"SCnComV[3989].Len() = 1\n",
"SCnComV[3990].Len() = 1\n",
"SCnComV[3991].Len() = 1\n",
"SCnComV[3992].Len() = 1\n",
"SCnComV[3993].Len() = 1\n",
"SCnComV[3994].Len() = 1\n",
"SCnComV[3995].Len() = 1\n",
"SCnComV[3996].Len() = 1\n",
"SCnComV[3997].Len() = 1\n",
"SCnComV[3998].Len() = 1\n",
"SCnComV[3999].Len() = 1\n",
"SCnComV[4000].Len() = 1\n",
"SCnComV[4001].Len() = 1\n",
"SCnComV[4002].Len() = 1\n",
"SCnComV[4003].Len() = 1\n",
"SCnComV[4004].Len() = 1\n",
"SCnComV[4005].Len() = 1\n",
"SCnComV[4006].Len() = 1\n",
"SCnComV[4007].Len() = 1\n",
"SCnComV[4008].Len() = 1\n",
"SCnComV[4009].Len() = 1\n",
"SCnComV[4010].Len() = 1\n",
"SCnComV[4011].Len() = 1\n",
"SCnComV[4012].Len() = 1\n",
"SCnComV[4013].Len() = 1\n",
"SCnComV[4014].Len() = 1\n",
"SCnComV[4015].Len() = 1\n",
"SCnComV[4016].Len() = 1\n",
"SCnComV[4017].Len() = 1\n",
"SCnComV[4018].Len() = 1\n",
"SCnComV[4019].Len() = 1\n",
"SCnComV[4020].Len() = 1\n",
"SCnComV[4021].Len() = 1\n",
"SCnComV[4022].Len() = 1\n",
"SCnComV[4023].Len() = 1\n",
"SCnComV[4024].Len() = 1\n",
"SCnComV[4025].Len() = 1\n",
"SCnComV[4026].Len() = 1\n",
"SCnComV[4027].Len() = 1\n",
"SCnComV[4028].Len() = 1\n",
"SCnComV[4029].Len() = 1\n",
"SCnComV[4030].Len() = 1\n",
"SCnComV[4031].Len() = 1\n",
"SCnComV[4032].Len() = 1\n",
"SCnComV[4033].Len() = 1\n",
"SCnComV[4034].Len() = 1\n",
"SCnComV[4035].Len() = 1\n",
"SCnComV[4036].Len() = 1\n",
"SCnComV[4037].Len() = 1\n",
"SCnComV[4038].Len() = 1\n",
"SCnComV[4039].Len() = 1\n",
"SCnComV[4040].Len() = 1\n",
"SCnComV[4041].Len() = 1\n",
"SCnComV[4042].Len() = 1\n",
"SCnComV[4043].Len() = 1\n",
"SCnComV[4044].Len() = 1\n",
"SCnComV[4045].Len() = 1\n",
"SCnComV[4046].Len() = 1\n",
"SCnComV[4047].Len() = 1\n",
"SCnComV[4048].Len() = 1\n",
"SCnComV[4049].Len() = 1\n",
"SCnComV[4050].Len() = 1\n",
"SCnComV[4051].Len() = 1\n",
"SCnComV[4052].Len() = 1\n",
"SCnComV[4053].Len() = 1\n",
"SCnComV[4054].Len() = 1\n",
"SCnComV[4055].Len() = 1\n",
"SCnComV[4056].Len() = 1\n",
"SCnComV[4057].Len() = 1\n",
"SCnComV[4058].Len() = 1\n",
"SCnComV[4059].Len() = 1\n",
"SCnComV[4060].Len() = 1\n",
"SCnComV[4061].Len() = 1\n",
"SCnComV[4062].Len() = 1\n",
"SCnComV[4063].Len() = 1\n",
"SCnComV[4064].Len() = 1\n",
"SCnComV[4065].Len() = 1\n",
"SCnComV[4066].Len() = 1\n",
"SCnComV[4067].Len() = 1\n",
"SCnComV[4068].Len() = 1\n",
"SCnComV[4069].Len() = 1\n",
"SCnComV[4070].Len() = 1\n",
"SCnComV[4071].Len() = 1\n",
"SCnComV[4072].Len() = 1\n",
"SCnComV[4073].Len() = 1\n",
"SCnComV[4074].Len() = 1\n",
"SCnComV[4075].Len() = 1\n",
"SCnComV[4076].Len() = 1\n",
"SCnComV[4077].Len() = 1\n",
"SCnComV[4078].Len() = 1\n",
"SCnComV[4079].Len() = 1\n",
"SCnComV[4080].Len() = 1\n",
"SCnComV[4081].Len() = 1\n",
"SCnComV[4082].Len() = 1\n",
"SCnComV[4083].Len() = 1\n",
"SCnComV[4084].Len() = 1\n",
"SCnComV[4085].Len() = 1\n",
"SCnComV[4086].Len() = 1\n",
"SCnComV[4087].Len() = 1\n",
"SCnComV[4088].Len() = 1\n",
"SCnComV[4089].Len() = 1\n",
"SCnComV[4090].Len() = 1\n",
"SCnComV[4091].Len() = 1\n",
"SCnComV[4092].Len() = 1\n",
"SCnComV[4093].Len() = 1\n",
"SCnComV[4094].Len() = 1\n",
"SCnComV[4095].Len() = 1\n",
"SCnComV[4096].Len() = 1\n",
"SCnComV[4097].Len() = 1\n",
"SCnComV[4098].Len() = 1\n",
"SCnComV[4099].Len() = 1\n",
"SCnComV[4100].Len() = 1\n",
"SCnComV[4101].Len() = 1\n",
"SCnComV[4102].Len() = 1\n",
"SCnComV[4103].Len() = 1\n",
"SCnComV[4104].Len() = 1\n",
"SCnComV[4105].Len() = 1\n",
"SCnComV[4106].Len() = 1\n",
"SCnComV[4107].Len() = 1\n",
"SCnComV[4108].Len() = 1\n",
"SCnComV[4109].Len() = 1\n",
"SCnComV[4110].Len() = 1\n",
"SCnComV[4111].Len() = 1\n",
"SCnComV[4112].Len() = 1\n",
"SCnComV[4113].Len() = 1\n",
"SCnComV[4114].Len() = 1\n",
"SCnComV[4115].Len() = 1\n",
"SCnComV[4116].Len() = 1\n",
"SCnComV[4117].Len() = 1\n",
"SCnComV[4118].Len() = 1\n",
"SCnComV[4119].Len() = 1\n",
"SCnComV[4120].Len() = 1\n",
"SCnComV[4121].Len() = 1\n",
"SCnComV[4122].Len() = 1\n",
"SCnComV[4123].Len() = 1\n",
"SCnComV[4124].Len() = 1\n",
"SCnComV[4125].Len() = 1\n",
"SCnComV[4126].Len() = 1\n",
"SCnComV[4127].Len() = 1\n",
"SCnComV[4128].Len() = 1\n",
"SCnComV[4129].Len() = 1\n",
"SCnComV[4130].Len() = 1\n",
"SCnComV[4131].Len() = 1\n",
"SCnComV[4132].Len() = 1\n",
"SCnComV[4133].Len() = 1\n",
"SCnComV[4134].Len() = 1\n",
"SCnComV[4135].Len() = 1\n",
"SCnComV[4136].Len() = 1\n",
"SCnComV[4137].Len() = 1\n",
"SCnComV[4138].Len() = 1\n",
"SCnComV[4139].Len() = 1\n",
"SCnComV[4140].Len() = 1\n",
"SCnComV[4141].Len() = 1\n",
"SCnComV[4142].Len() = 1\n",
"SCnComV[4143].Len() = 1\n",
"SCnComV[4144].Len() = 1\n",
"SCnComV[4145].Len() = 1\n",
"SCnComV[4146].Len() = 1\n",
"SCnComV[4147].Len() = 1\n",
"SCnComV[4148].Len() = 1\n",
"SCnComV[4149].Len() = 1\n",
"SCnComV[4150].Len() = 1\n",
"SCnComV[4151].Len() = 1\n",
"SCnComV[4152].Len() = 1\n",
"SCnComV[4153].Len() = 1\n",
"SCnComV[4154].Len() = 1\n",
"SCnComV[4155].Len() = 1\n",
"SCnComV[4156].Len() = 1\n",
"SCnComV[4157].Len() = 1\n",
"SCnComV[4158].Len() = 1\n",
"SCnComV[4159].Len() = 1\n",
"SCnComV[4160].Len() = 1\n",
"SCnComV[4161].Len() = 1\n",
"SCnComV[4162].Len() = 1\n",
"SCnComV[4163].Len() = 1\n",
"SCnComV[4164].Len() = 1\n",
"SCnComV[4165].Len() = 1\n",
"SCnComV[4166].Len() = 1\n",
"SCnComV[4167].Len() = 1\n",
"SCnComV[4168].Len() = 1\n",
"SCnComV[4169].Len() = 1\n",
"SCnComV[4170].Len() = 1\n",
"SCnComV[4171].Len() = 1\n",
"SCnComV[4172].Len() = 1\n",
"SCnComV[4173].Len() = 1\n",
"SCnComV[4174].Len() = 1\n",
"SCnComV[4175].Len() = 1\n",
"SCnComV[4176].Len() = 1\n",
"SCnComV[4177].Len() = 1\n",
"SCnComV[4178].Len() = 1\n",
"SCnComV[4179].Len() = 1\n",
"SCnComV[4180].Len() = 1\n",
"SCnComV[4181].Len() = 1\n",
"SCnComV[4182].Len() = 1\n",
"SCnComV[4183].Len() = 1\n",
"SCnComV[4184].Len() = 1\n",
"SCnComV[4185].Len() = 1\n",
"SCnComV[4186].Len() = 1\n",
"SCnComV[4187].Len() = 1\n",
"SCnComV[4188].Len() = 1\n",
"SCnComV[4189].Len() = 1\n",
"SCnComV[4190].Len() = 1\n",
"SCnComV[4191].Len() = 1\n",
"SCnComV[4192].Len() = 1\n",
"SCnComV[4193].Len() = 1\n",
"SCnComV[4194].Len() = 1\n",
"SCnComV[4195].Len() = 1\n",
"SCnComV[4196].Len() = 1\n",
"SCnComV[4197].Len() = 1\n",
"SCnComV[4198].Len() = 1\n",
"SCnComV[4199].Len() = 1\n",
"SCnComV[4200].Len() = 1\n",
"SCnComV[4201].Len() = 1\n",
"SCnComV[4202].Len() = 1\n",
"SCnComV[4203].Len() = 1\n",
"SCnComV[4204].Len() = 1\n",
"SCnComV[4205].Len() = 1\n",
"SCnComV[4206].Len() = 1\n",
"SCnComV[4207].Len() = 1\n",
"SCnComV[4208].Len() = 1\n",
"SCnComV[4209].Len() = 1\n",
"SCnComV[4210].Len() = 1\n",
"SCnComV[4211].Len() = 1\n",
"SCnComV[4212].Len() = 1\n",
"SCnComV[4213].Len() = 1\n",
"SCnComV[4214].Len() = 1\n",
"SCnComV[4215].Len() = 1\n",
"SCnComV[4216].Len() = 1\n",
"SCnComV[4217].Len() = 1\n",
"SCnComV[4218].Len() = 1\n",
"SCnComV[4219].Len() = 1\n",
"SCnComV[4220].Len() = 1\n",
"SCnComV[4221].Len() = 1\n",
"SCnComV[4222].Len() = 1\n",
"SCnComV[4223].Len() = 1\n",
"SCnComV[4224].Len() = 1\n",
"SCnComV[4225].Len() = 1\n",
"SCnComV[4226].Len() = 1\n",
"SCnComV[4227].Len() = 1\n",
"SCnComV[4228].Len() = 1\n",
"SCnComV[4229].Len() = 1\n",
"SCnComV[4230].Len() = 1\n",
"SCnComV[4231].Len() = 1\n",
"SCnComV[4232].Len() = 1\n",
"SCnComV[4233].Len() = 1\n",
"SCnComV[4234].Len() = 1\n",
"SCnComV[4235].Len() = 1\n",
"SCnComV[4236].Len() = 1\n",
"SCnComV[4237].Len() = 1\n",
"SCnComV[4238].Len() = 1\n",
"SCnComV[4239].Len() = 1\n",
"SCnComV[4240].Len() = 1\n",
"SCnComV[4241].Len() = 1\n",
"SCnComV[4242].Len() = 1\n",
"SCnComV[4243].Len() = 1\n",
"SCnComV[4244].Len() = 1\n",
"SCnComV[4245].Len() = 1\n",
"SCnComV[4246].Len() = 1\n",
"SCnComV[4247].Len() = 1\n",
"SCnComV[4248].Len() = 1\n",
"SCnComV[4249].Len() = 1\n",
"SCnComV[4250].Len() = 1\n",
"SCnComV[4251].Len() = 1\n",
"SCnComV[4252].Len() = 1\n",
"SCnComV[4253].Len() = 1\n",
"SCnComV[4254].Len() = 1\n",
"SCnComV[4255].Len() = 1\n",
"SCnComV[4256].Len() = 1\n",
"SCnComV[4257].Len() = 1\n",
"SCnComV[4258].Len() = 1\n",
"SCnComV[4259].Len() = 1\n",
"SCnComV[4260].Len() = 1\n",
"SCnComV[4261].Len() = 1\n",
"SCnComV[4262].Len() = 1\n",
"SCnComV[4263].Len() = 1\n",
"SCnComV[4264].Len() = 1\n",
"SCnComV[4265].Len() = 1\n",
"SCnComV[4266].Len() = 1\n",
"SCnComV[4267].Len() = 1\n",
"SCnComV[4268].Len() = 1\n",
"SCnComV[4269].Len() = 1\n",
"SCnComV[4270].Len() = 1\n",
"SCnComV[4271].Len() = 1\n",
"SCnComV[4272].Len() = 1\n",
"SCnComV[4273].Len() = 1\n",
"SCnComV[4274].Len() = 1\n",
"SCnComV[4275].Len() = 1\n",
"SCnComV[4276].Len() = 1\n",
"SCnComV[4277].Len() = 1\n",
"SCnComV[4278].Len() = 1\n",
"SCnComV[4279].Len() = 1\n",
"SCnComV[4280].Len() = 1\n",
"SCnComV[4281].Len() = 1\n",
"SCnComV[4282].Len() = 1\n",
"SCnComV[4283].Len() = 1\n",
"SCnComV[4284].Len() = 1\n",
"SCnComV[4285].Len() = 1\n",
"SCnComV[4286].Len() = 1\n",
"SCnComV[4287].Len() = 1\n",
"SCnComV[4288].Len() = 1\n",
"SCnComV[4289].Len() = 1\n",
"SCnComV[4290].Len() = 1\n",
"SCnComV[4291].Len() = 1\n",
"SCnComV[4292].Len() = 1\n",
"SCnComV[4293].Len() = 1\n",
"SCnComV[4294].Len() = 1\n",
"SCnComV[4295].Len() = 1\n",
"SCnComV[4296].Len() = 1\n",
"SCnComV[4297].Len() = 1\n",
"SCnComV[4298].Len() = 1\n",
"SCnComV[4299].Len() = 1\n",
"SCnComV[4300].Len() = 1\n",
"SCnComV[4301].Len() = 1\n",
"SCnComV[4302].Len() = 1\n",
"SCnComV[4303].Len() = 1\n",
"SCnComV[4304].Len() = 1\n",
"SCnComV[4305].Len() = 1\n",
"SCnComV[4306].Len() = 1\n",
"SCnComV[4307].Len() = 1\n",
"SCnComV[4308].Len() = 1\n",
"SCnComV[4309].Len() = 1\n",
"SCnComV[4310].Len() = 1\n",
"SCnComV[4311].Len() = 1\n",
"SCnComV[4312].Len() = 1\n",
"SCnComV[4313].Len() = 1\n",
"SCnComV[4314].Len() = 1\n",
"SCnComV[4315].Len() = 1\n",
"SCnComV[4316].Len() = 1\n",
"SCnComV[4317].Len() = 1\n",
"SCnComV[4318].Len() = 1\n",
"SCnComV[4319].Len() = 1\n",
"SCnComV[4320].Len() = 1\n",
"SCnComV[4321].Len() = 1\n",
"SCnComV[4322].Len() = 1\n",
"SCnComV[4323].Len() = 1\n",
"SCnComV[4324].Len() = 1\n",
"SCnComV[4325].Len() = 1\n",
"SCnComV[4326].Len() = 1\n",
"SCnComV[4327].Len() = 1\n",
"SCnComV[4328].Len() = 1\n",
"SCnComV[4329].Len() = 1\n",
"SCnComV[4330].Len() = 1\n",
"SCnComV[4331].Len() = 1\n",
"SCnComV[4332].Len() = 1\n",
"SCnComV[4333].Len() = 1\n",
"SCnComV[4334].Len() = 1\n",
"SCnComV[4335].Len() = 1\n",
"SCnComV[4336].Len() = 1\n",
"SCnComV[4337].Len() = 1\n",
"SCnComV[4338].Len() = 1\n",
"SCnComV[4339].Len() = 1\n",
"SCnComV[4340].Len() = 1\n",
"SCnComV[4341].Len() = 1\n",
"SCnComV[4342].Len() = 1\n",
"SCnComV[4343].Len() = 1\n",
"SCnComV[4344].Len() = 1\n",
"SCnComV[4345].Len() = 1\n",
"SCnComV[4346].Len() = 1\n",
"SCnComV[4347].Len() = 1\n",
"SCnComV[4348].Len() = 1\n",
"SCnComV[4349].Len() = 1\n",
"SCnComV[4350].Len() = 1\n",
"SCnComV[4351].Len() = 1\n",
"SCnComV[4352].Len() = 1\n",
"SCnComV[4353].Len() = 1\n",
"SCnComV[4354].Len() = 1\n",
"SCnComV[4355].Len() = 1\n",
"SCnComV[4356].Len() = 1\n",
"SCnComV[4357].Len() = 1\n",
"SCnComV[4358].Len() = 1\n",
"SCnComV[4359].Len() = 1\n",
"SCnComV[4360].Len() = 1\n",
"SCnComV[4361].Len() = 1\n",
"SCnComV[4362].Len() = 1\n",
"SCnComV[4363].Len() = 1\n",
"SCnComV[4364].Len() = 1\n",
"SCnComV[4365].Len() = 1\n",
"SCnComV[4366].Len() = 1\n",
"SCnComV[4367].Len() = 1\n",
"SCnComV[4368].Len() = 1\n",
"SCnComV[4369].Len() = 1\n",
"SCnComV[4370].Len() = 1\n",
"SCnComV[4371].Len() = 1\n",
"SCnComV[4372].Len() = 1\n",
"SCnComV[4373].Len() = 1\n",
"SCnComV[4374].Len() = 1\n",
"SCnComV[4375].Len() = 1\n",
"SCnComV[4376].Len() = 1\n",
"SCnComV[4377].Len() = 1\n",
"SCnComV[4378].Len() = 1\n",
"SCnComV[4379].Len() = 1\n",
"SCnComV[4380].Len() = 1\n",
"SCnComV[4381].Len() = 1\n",
"SCnComV[4382].Len() = 1\n",
"SCnComV[4383].Len() = 1\n",
"SCnComV[4384].Len() = 1\n",
"SCnComV[4385].Len() = 1\n",
"SCnComV[4386].Len() = 1\n",
"SCnComV[4387].Len() = 1\n",
"SCnComV[4388].Len() = 1\n",
"SCnComV[4389].Len() = 1\n",
"SCnComV[4390].Len() = 1\n",
"SCnComV[4391].Len() = 1\n",
"SCnComV[4392].Len() = 1\n",
"SCnComV[4393].Len() = 1\n",
"SCnComV[4394].Len() = 1\n",
"SCnComV[4395].Len() = 1\n",
"SCnComV[4396].Len() = 1\n",
"SCnComV[4397].Len() = 1\n",
"SCnComV[4398].Len() = 1\n",
"SCnComV[4399].Len() = 1\n",
"SCnComV[4400].Len() = 1\n",
"SCnComV[4401].Len() = 1\n",
"SCnComV[4402].Len() = 1\n",
"SCnComV[4403].Len() = 1\n",
"SCnComV[4404].Len() = 1\n",
"SCnComV[4405].Len() = 1\n",
"SCnComV[4406].Len() = 1\n",
"SCnComV[4407].Len() = 1\n",
"SCnComV[4408].Len() = 1\n",
"SCnComV[4409].Len() = 1\n",
"SCnComV[4410].Len() = 1\n",
"SCnComV[4411].Len() = 1\n",
"SCnComV[4412].Len() = 1\n",
"SCnComV[4413].Len() = 1\n",
"SCnComV[4414].Len() = 1\n",
"SCnComV[4415].Len() = 1\n",
"SCnComV[4416].Len() = 1\n",
"SCnComV[4417].Len() = 1\n",
"SCnComV[4418].Len() = 1\n",
"SCnComV[4419].Len() = 1\n",
"SCnComV[4420].Len() = 1\n",
"SCnComV[4421].Len() = 1\n",
"SCnComV[4422].Len() = 1\n",
"SCnComV[4423].Len() = 1\n",
"SCnComV[4424].Len() = 1\n",
"SCnComV[4425].Len() = 1\n",
"SCnComV[4426].Len() = 1\n",
"SCnComV[4427].Len() = 1\n",
"SCnComV[4428].Len() = 1\n",
"SCnComV[4429].Len() = 1\n",
"SCnComV[4430].Len() = 1\n",
"SCnComV[4431].Len() = 1\n",
"SCnComV[4432].Len() = 1\n",
"SCnComV[4433].Len() = 1\n",
"SCnComV[4434].Len() = 1\n",
"SCnComV[4435].Len() = 1\n",
"SCnComV[4436].Len() = 1\n",
"SCnComV[4437].Len() = 1\n",
"SCnComV[4438].Len() = 1\n",
"SCnComV[4439].Len() = 1\n",
"SCnComV[4440].Len() = 1\n",
"SCnComV[4441].Len() = 1\n",
"SCnComV[4442].Len() = 1\n",
"SCnComV[4443].Len() = 1\n",
"SCnComV[4444].Len() = 1\n",
"SCnComV[4445].Len() = 1\n",
"SCnComV[4446].Len() = 1\n",
"SCnComV[4447].Len() = 1\n",
"SCnComV[4448].Len() = 1\n",
"SCnComV[4449].Len() = 1\n",
"SCnComV[4450].Len() = 1\n",
"SCnComV[4451].Len() = 1\n",
"SCnComV[4452].Len() = 1\n",
"SCnComV[4453].Len() = 1\n",
"SCnComV[4454].Len() = 1\n",
"SCnComV[4455].Len() = 1\n",
"SCnComV[4456].Len() = 1\n",
"SCnComV[4457].Len() = 1\n",
"SCnComV[4458].Len() = 1\n",
"SCnComV[4459].Len() = 1\n",
"SCnComV[4460].Len() = 1\n",
"SCnComV[4461].Len() = 1\n",
"SCnComV[4462].Len() = 1\n",
"SCnComV[4463].Len() = 1\n",
"SCnComV[4464].Len() = 1\n",
"SCnComV[4465].Len() = 1\n",
"SCnComV[4466].Len() = 1\n",
"SCnComV[4467].Len() = 1\n",
"SCnComV[4468].Len() = 1\n",
"SCnComV[4469].Len() = 1\n",
"SCnComV[4470].Len() = 1\n",
"SCnComV[4471].Len() = 1\n",
"SCnComV[4472].Len() = 1\n",
"SCnComV[4473].Len() = 1\n",
"SCnComV[4474].Len() = 1\n",
"SCnComV[4475].Len() = 1\n",
"SCnComV[4476].Len() = 1\n",
"SCnComV[4477].Len() = 1\n",
"SCnComV[4478].Len() = 1\n",
"SCnComV[4479].Len() = 1\n",
"SCnComV[4480].Len() = 1\n",
"SCnComV[4481].Len() = 1\n",
"SCnComV[4482].Len() = 1\n",
"SCnComV[4483].Len() = 1\n",
"SCnComV[4484].Len() = 1\n",
"SCnComV[4485].Len() = 1\n",
"SCnComV[4486].Len() = 1\n",
"SCnComV[4487].Len() = 1\n",
"SCnComV[4488].Len() = 1\n",
"SCnComV[4489].Len() = 1\n",
"SCnComV[4490].Len() = 1\n",
"SCnComV[4491].Len() = 1\n",
"SCnComV[4492].Len() = 1\n",
"SCnComV[4493].Len() = 1\n",
"SCnComV[4494].Len() = 1\n",
"SCnComV[4495].Len() = 1\n",
"SCnComV[4496].Len() = 1\n",
"SCnComV[4497].Len() = 1\n",
"SCnComV[4498].Len() = 1\n",
"SCnComV[4499].Len() = 1\n",
"SCnComV[4500].Len() = 1\n",
"SCnComV[4501].Len() = 1\n",
"SCnComV[4502].Len() = 1\n",
"SCnComV[4503].Len() = 1\n",
"SCnComV[4504].Len() = 1\n",
"SCnComV[4505].Len() = 1\n",
"SCnComV[4506].Len() = 1\n",
"SCnComV[4507].Len() = 1\n",
"SCnComV[4508].Len() = 1\n",
"SCnComV[4509].Len() = 1\n",
"SCnComV[4510].Len() = 1\n",
"SCnComV[4511].Len() = 1\n",
"SCnComV[4512].Len() = 1\n",
"SCnComV[4513].Len() = 1\n",
"SCnComV[4514].Len() = 1\n",
"SCnComV[4515].Len() = 1\n",
"SCnComV[4516].Len() = 1\n",
"SCnComV[4517].Len() = 1\n",
"SCnComV[4518].Len() = 1\n",
"SCnComV[4519].Len() = 1\n",
"SCnComV[4520].Len() = 1\n",
"SCnComV[4521].Len() = 1\n",
"SCnComV[4522].Len() = 1\n",
"SCnComV[4523].Len() = 1\n",
"SCnComV[4524].Len() = 1\n",
"SCnComV[4525].Len() = 1\n",
"SCnComV[4526].Len() = 1\n",
"SCnComV[4527].Len() = 1\n",
"SCnComV[4528].Len() = 1\n",
"SCnComV[4529].Len() = 1\n",
"SCnComV[4530].Len() = 1\n",
"SCnComV[4531].Len() = 1\n",
"SCnComV[4532].Len() = 1\n",
"SCnComV[4533].Len() = 1\n",
"SCnComV[4534].Len() = 1\n",
"SCnComV[4535].Len() = 1\n",
"SCnComV[4536].Len() = 1\n",
"SCnComV[4537].Len() = 1\n",
"SCnComV[4538].Len() = 1\n",
"SCnComV[4539].Len() = 1\n",
"SCnComV[4540].Len() = 1\n",
"SCnComV[4541].Len() = 1\n",
"SCnComV[4542].Len() = 1\n",
"SCnComV[4543].Len() = 1\n",
"SCnComV[4544].Len() = 1\n",
"SCnComV[4545].Len() = 1\n",
"SCnComV[4546].Len() = 1\n",
"SCnComV[4547].Len() = 1\n",
"SCnComV[4548].Len() = 1\n",
"SCnComV[4549].Len() = 1\n",
"SCnComV[4550].Len() = 1\n",
"SCnComV[4551].Len() = 1\n",
"SCnComV[4552].Len() = 1\n",
"SCnComV[4553].Len() = 1\n",
"SCnComV[4554].Len() = 1\n",
"SCnComV[4555].Len() = 1\n",
"SCnComV[4556].Len() = 1\n",
"SCnComV[4557].Len() = 1\n",
"SCnComV[4558].Len() = 1\n",
"SCnComV[4559].Len() = 1\n",
"SCnComV[4560].Len() = 1\n",
"SCnComV[4561].Len() = 1\n",
"SCnComV[4562].Len() = 1\n",
"SCnComV[4563].Len() = 1\n",
"SCnComV[4564].Len() = 1\n",
"SCnComV[4565].Len() = 1\n",
"SCnComV[4566].Len() = 1\n",
"SCnComV[4567].Len() = 1\n",
"SCnComV[4568].Len() = 1\n",
"SCnComV[4569].Len() = 1\n",
"SCnComV[4570].Len() = 1\n",
"SCnComV[4571].Len() = 1\n",
"SCnComV[4572].Len() = 1\n",
"SCnComV[4573].Len() = 1\n",
"SCnComV[4574].Len() = 1\n",
"SCnComV[4575].Len() = 1\n",
"SCnComV[4576].Len() = 1\n",
"SCnComV[4577].Len() = 1\n",
"SCnComV[4578].Len() = 1\n",
"SCnComV[4579].Len() = 1\n",
"SCnComV[4580].Len() = 1\n",
"SCnComV[4581].Len() = 1\n",
"SCnComV[4582].Len() = 1\n",
"SCnComV[4583].Len() = 1\n",
"SCnComV[4584].Len() = 1\n",
"SCnComV[4585].Len() = 1\n",
"SCnComV[4586].Len() = 1\n",
"SCnComV[4587].Len() = 1\n",
"SCnComV[4588].Len() = 1\n",
"SCnComV[4589].Len() = 1\n",
"SCnComV[4590].Len() = 1\n",
"SCnComV[4591].Len() = 1\n",
"SCnComV[4592].Len() = 1\n",
"SCnComV[4593].Len() = 1\n",
"SCnComV[4594].Len() = 1\n",
"SCnComV[4595].Len() = 1\n",
"SCnComV[4596].Len() = 1\n",
"SCnComV[4597].Len() = 1\n",
"SCnComV[4598].Len() = 1\n",
"SCnComV[4599].Len() = 1\n",
"SCnComV[4600].Len() = 1\n",
"SCnComV[4601].Len() = 1\n",
"SCnComV[4602].Len() = 1\n",
"SCnComV[4603].Len() = 1\n",
"SCnComV[4604].Len() = 1\n",
"SCnComV[4605].Len() = 1\n",
"SCnComV[4606].Len() = 1\n",
"SCnComV[4607].Len() = 1\n",
"SCnComV[4608].Len() = 1\n",
"SCnComV[4609].Len() = 1\n",
"SCnComV[4610].Len() = 1\n",
"SCnComV[4611].Len() = 1\n",
"SCnComV[4612].Len() = 1\n",
"SCnComV[4613].Len() = 1\n",
"SCnComV[4614].Len() = 1\n",
"SCnComV[4615].Len() = 1\n",
"SCnComV[4616].Len() = 1\n",
"SCnComV[4617].Len() = 1\n",
"SCnComV[4618].Len() = 1\n",
"SCnComV[4619].Len() = 1\n",
"SCnComV[4620].Len() = 1\n",
"SCnComV[4621].Len() = 1\n",
"SCnComV[4622].Len() = 1\n",
"SCnComV[4623].Len() = 1\n",
"SCnComV[4624].Len() = 1\n",
"SCnComV[4625].Len() = 1\n",
"SCnComV[4626].Len() = 1\n",
"SCnComV[4627].Len() = 1\n",
"SCnComV[4628].Len() = 1\n",
"SCnComV[4629].Len() = 1\n",
"SCnComV[4630].Len() = 1\n",
"SCnComV[4631].Len() = 1\n",
"SCnComV[4632].Len() = 1\n",
"SCnComV[4633].Len() = 1\n",
"SCnComV[4634].Len() = 1\n",
"SCnComV[4635].Len() = 1\n",
"SCnComV[4636].Len() = 1\n",
"SCnComV[4637].Len() = 1\n",
"SCnComV[4638].Len() = 1\n",
"SCnComV[4639].Len() = 1\n",
"SCnComV[4640].Len() = 1\n",
"SCnComV[4641].Len() = 1\n",
"SCnComV[4642].Len() = 1\n",
"SCnComV[4643].Len() = 1\n",
"SCnComV[4644].Len() = 1\n",
"SCnComV[4645].Len() = 1\n",
"SCnComV[4646].Len() = 1\n",
"SCnComV[4647].Len() = 1\n",
"SCnComV[4648].Len() = 1\n",
"SCnComV[4649].Len() = 1\n",
"SCnComV[4650].Len() = 1\n",
"SCnComV[4651].Len() = 1\n",
"SCnComV[4652].Len() = 1\n",
"SCnComV[4653].Len() = 1\n",
"SCnComV[4654].Len() = 1\n",
"SCnComV[4655].Len() = 1\n",
"SCnComV[4656].Len() = 1\n",
"SCnComV[4657].Len() = 1\n",
"SCnComV[4658].Len() = 1\n",
"SCnComV[4659].Len() = 1\n",
"SCnComV[4660].Len() = 1\n",
"SCnComV[4661].Len() = 1\n",
"SCnComV[4662].Len() = 1\n",
"SCnComV[4663].Len() = 1\n",
"SCnComV[4664].Len() = 1\n",
"SCnComV[4665].Len() = 1\n",
"SCnComV[4666].Len() = 1\n",
"SCnComV[4667].Len() = 1\n",
"SCnComV[4668].Len() = 1\n",
"SCnComV[4669].Len() = 1\n",
"SCnComV[4670].Len() = 1\n",
"SCnComV[4671].Len() = 1\n",
"SCnComV[4672].Len() = 1\n",
"SCnComV[4673].Len() = 1\n",
"SCnComV[4674].Len() = 1\n",
"SCnComV[4675].Len() = 1\n",
"SCnComV[4676].Len() = 1\n",
"SCnComV[4677].Len() = 1\n",
"SCnComV[4678].Len() = 1\n",
"SCnComV[4679].Len() = 1\n",
"SCnComV[4680].Len() = 1\n",
"SCnComV[4681].Len() = 1\n",
"SCnComV[4682].Len() = 1\n",
"SCnComV[4683].Len() = 1\n",
"SCnComV[4684].Len() = 1\n",
"SCnComV[4685].Len() = 1\n",
"SCnComV[4686].Len() = 1\n",
"SCnComV[4687].Len() = 1\n",
"SCnComV[4688].Len() = 1\n",
"SCnComV[4689].Len() = 1\n",
"SCnComV[4690].Len() = 1\n",
"SCnComV[4691].Len() = 1\n",
"SCnComV[4692].Len() = 1\n",
"SCnComV[4693].Len() = 1\n",
"SCnComV[4694].Len() = 1\n",
"SCnComV[4695].Len() = 1\n",
"SCnComV[4696].Len() = 1\n",
"SCnComV[4697].Len() = 1\n",
"SCnComV[4698].Len() = 1\n",
"SCnComV[4699].Len() = 1\n",
"SCnComV[4700].Len() = 1\n",
"SCnComV[4701].Len() = 1\n",
"SCnComV[4702].Len() = 1\n",
"SCnComV[4703].Len() = 1\n",
"SCnComV[4704].Len() = 1\n",
"SCnComV[4705].Len() = 1\n",
"SCnComV[4706].Len() = 1\n",
"SCnComV[4707].Len() = 1\n",
"SCnComV[4708].Len() = 1\n",
"SCnComV[4709].Len() = 1\n",
"SCnComV[4710].Len() = 1\n",
"SCnComV[4711].Len() = 1\n",
"SCnComV[4712].Len() = 1\n",
"SCnComV[4713].Len() = 1\n",
"SCnComV[4714].Len() = 1\n",
"SCnComV[4715].Len() = 1\n",
"SCnComV[4716].Len() = 1\n",
"SCnComV[4717].Len() = 1\n",
"SCnComV[4718].Len() = 1\n",
"SCnComV[4719].Len() = 1\n",
"SCnComV[4720].Len() = 1\n",
"SCnComV[4721].Len() = 1\n",
"SCnComV[4722].Len() = 1\n",
"SCnComV[4723].Len() = 1\n",
"SCnComV[4724].Len() = 1\n",
"SCnComV[4725].Len() = 1\n",
"SCnComV[4726].Len() = 1\n",
"SCnComV[4727].Len() = 1\n",
"SCnComV[4728].Len() = 1\n",
"SCnComV[4729].Len() = 1\n",
"SCnComV[4730].Len() = 1\n",
"SCnComV[4731].Len() = 1\n",
"SCnComV[4732].Len() = 1\n",
"SCnComV[4733].Len() = 1\n",
"SCnComV[4734].Len() = 1\n",
"SCnComV[4735].Len() = 1\n",
"SCnComV[4736].Len() = 1\n",
"SCnComV[4737].Len() = 1\n",
"SCnComV[4738].Len() = 1\n",
"SCnComV[4739].Len() = 1\n",
"SCnComV[4740].Len() = 1\n",
"SCnComV[4741].Len() = 1\n",
"SCnComV[4742].Len() = 1\n",
"SCnComV[4743].Len() = 1\n",
"SCnComV[4744].Len() = 1\n",
"SCnComV[4745].Len() = 1\n",
"SCnComV[4746].Len() = 1\n",
"SCnComV[4747].Len() = 1\n",
"SCnComV[4748].Len() = 1\n",
"SCnComV[4749].Len() = 1\n",
"SCnComV[4750].Len() = 1\n",
"SCnComV[4751].Len() = 1\n",
"SCnComV[4752].Len() = 1\n",
"SCnComV[4753].Len() = 1\n",
"SCnComV[4754].Len() = 1\n",
"SCnComV[4755].Len() = 1\n",
"SCnComV[4756].Len() = 1\n",
"SCnComV[4757].Len() = 1\n",
"SCnComV[4758].Len() = 1\n",
"SCnComV[4759].Len() = 1\n",
"SCnComV[4760].Len() = 1\n",
"SCnComV[4761].Len() = 1\n",
"SCnComV[4762].Len() = 1\n",
"SCnComV[4763].Len() = 1\n",
"SCnComV[4764].Len() = 1\n",
"SCnComV[4765].Len() = 1\n",
"SCnComV[4766].Len() = 1\n",
"SCnComV[4767].Len() = 1\n",
"SCnComV[4768].Len() = 1\n",
"SCnComV[4769].Len() = 1\n",
"SCnComV[4770].Len() = 1\n",
"SCnComV[4771].Len() = 1\n",
"SCnComV[4772].Len() = 1\n",
"SCnComV[4773].Len() = 1\n",
"SCnComV[4774].Len() = 1\n",
"SCnComV[4775].Len() = 1\n",
"SCnComV[4776].Len() = 1\n",
"SCnComV[4777].Len() = 1\n",
"SCnComV[4778].Len() = 1\n",
"SCnComV[4779].Len() = 1\n",
"SCnComV[4780].Len() = 1\n",
"SCnComV[4781].Len() = 1\n",
"SCnComV[4782].Len() = 1\n",
"SCnComV[4783].Len() = 1\n",
"SCnComV[4784].Len() = 1\n",
"SCnComV[4785].Len() = 1\n",
"SCnComV[4786].Len() = 1\n",
"SCnComV[4787].Len() = 1\n",
"SCnComV[4788].Len() = 1\n",
"SCnComV[4789].Len() = 1\n",
"SCnComV[4790].Len() = 1\n",
"SCnComV[4791].Len() = 1\n",
"SCnComV[4792].Len() = 1\n",
"SCnComV[4793].Len() = 1\n",
"SCnComV[4794].Len() = 1\n",
"SCnComV[4795].Len() = 1\n",
"SCnComV[4796].Len() = 1\n",
"SCnComV[4797].Len() = 1\n",
"SCnComV[4798].Len() = 1\n",
"SCnComV[4799].Len() = 1\n",
"SCnComV[4800].Len() = 1\n",
"SCnComV[4801].Len() = 1\n",
"SCnComV[4802].Len() = 1\n",
"SCnComV[4803].Len() = 1\n",
"SCnComV[4804].Len() = 1\n",
"SCnComV[4805].Len() = 1\n",
"SCnComV[4806].Len() = 1\n",
"SCnComV[4807].Len() = 1\n",
"SCnComV[4808].Len() = 1\n",
"SCnComV[4809].Len() = 1\n",
"SCnComV[4810].Len() = 1\n",
"SCnComV[4811].Len() = 1\n",
"SCnComV[4812].Len() = 1\n",
"SCnComV[4813].Len() = 1\n",
"SCnComV[4814].Len() = 1\n",
"SCnComV[4815].Len() = 1\n",
"SCnComV[4816].Len() = 1\n",
"SCnComV[4817].Len() = 1\n",
"SCnComV[4818].Len() = 1\n",
"SCnComV[4819].Len() = 1\n",
"SCnComV[4820].Len() = 1\n",
"SCnComV[4821].Len() = 1\n",
"SCnComV[4822].Len() = 1\n",
"SCnComV[4823].Len() = 1\n",
"SCnComV[4824].Len() = 1\n",
"SCnComV[4825].Len() = 1\n",
"SCnComV[4826].Len() = 1\n",
"SCnComV[4827].Len() = 1\n",
"SCnComV[4828].Len() = 1\n",
"SCnComV[4829].Len() = 1\n",
"SCnComV[4830].Len() = 1\n",
"SCnComV[4831].Len() = 1\n",
"SCnComV[4832].Len() = 1\n",
"SCnComV[4833].Len() = 1\n",
"SCnComV[4834].Len() = 1\n",
"SCnComV[4835].Len() = 1\n",
"SCnComV[4836].Len() = 1\n",
"SCnComV[4837].Len() = 1\n",
"SCnComV[4838].Len() = 1\n",
"SCnComV[4839].Len() = 1\n",
"SCnComV[4840].Len() = 1\n",
"SCnComV[4841].Len() = 1\n",
"SCnComV[4842].Len() = 1\n",
"SCnComV[4843].Len() = 1\n",
"SCnComV[4844].Len() = 1\n",
"SCnComV[4845].Len() = 1\n",
"SCnComV[4846].Len() = 1\n",
"SCnComV[4847].Len() = 1\n",
"SCnComV[4848].Len() = 1\n",
"SCnComV[4849].Len() = 1\n",
"SCnComV[4850].Len() = 1\n",
"SCnComV[4851].Len() = 1\n",
"SCnComV[4852].Len() = 1\n",
"SCnComV[4853].Len() = 1\n",
"SCnComV[4854].Len() = 1\n",
"SCnComV[4855].Len() = 1\n",
"SCnComV[4856].Len() = 1\n",
"SCnComV[4857].Len() = 1\n",
"SCnComV[4858].Len() = 1\n",
"SCnComV[4859].Len() = 1\n",
"SCnComV[4860].Len() = 1\n",
"SCnComV[4861].Len() = 1\n",
"SCnComV[4862].Len() = 1\n",
"SCnComV[4863].Len() = 1\n",
"SCnComV[4864].Len() = 1\n",
"SCnComV[4865].Len() = 1\n",
"SCnComV[4866].Len() = 1\n",
"SCnComV[4867].Len() = 1\n",
"SCnComV[4868].Len() = 1\n",
"SCnComV[4869].Len() = 1\n",
"SCnComV[4870].Len() = 1\n",
"SCnComV[4871].Len() = 1\n",
"SCnComV[4872].Len() = 1\n",
"SCnComV[4873].Len() = 1\n",
"SCnComV[4874].Len() = 1\n",
"SCnComV[4875].Len() = 1\n",
"SCnComV[4876].Len() = 1\n",
"SCnComV[4877].Len() = 1\n",
"SCnComV[4878].Len() = 1\n",
"SCnComV[4879].Len() = 1\n",
"SCnComV[4880].Len() = 1\n",
"SCnComV[4881].Len() = 1\n",
"SCnComV[4882].Len() = 1\n",
"SCnComV[4883].Len() = 1\n",
"SCnComV[4884].Len() = 1\n",
"SCnComV[4885].Len() = 1\n",
"SCnComV[4886].Len() = 1\n",
"SCnComV[4887].Len() = 1\n",
"SCnComV[4888].Len() = 1\n",
"SCnComV[4889].Len() = 1\n",
"SCnComV[4890].Len() = 1\n",
"SCnComV[4891].Len() = 1\n",
"SCnComV[4892].Len() = 1\n",
"SCnComV[4893].Len() = 1\n",
"SCnComV[4894].Len() = 1\n",
"SCnComV[4895].Len() = 1\n",
"SCnComV[4896].Len() = 1\n",
"SCnComV[4897].Len() = 1\n",
"SCnComV[4898].Len() = 1\n",
"SCnComV[4899].Len() = 1\n",
"SCnComV[4900].Len() = 1\n",
"SCnComV[4901].Len() = 1\n",
"SCnComV[4902].Len() = 1\n",
"SCnComV[4903].Len() = 1\n",
"SCnComV[4904].Len() = 1\n",
"SCnComV[4905].Len() = 1\n",
"SCnComV[4906].Len() = 1\n",
"SCnComV[4907].Len() = 1\n",
"SCnComV[4908].Len() = 1\n",
"SCnComV[4909].Len() = 1\n",
"SCnComV[4910].Len() = 1\n",
"SCnComV[4911].Len() = 1\n",
"SCnComV[4912].Len() = 1\n",
"SCnComV[4913].Len() = 1\n",
"SCnComV[4914].Len() = 1\n",
"SCnComV[4915].Len() = 1\n",
"SCnComV[4916].Len() = 1\n",
"SCnComV[4917].Len() = 1\n",
"SCnComV[4918].Len() = 1\n",
"SCnComV[4919].Len() = 1\n",
"SCnComV[4920].Len() = 1\n",
"SCnComV[4921].Len() = 1\n",
"SCnComV[4922].Len() = 1\n",
"SCnComV[4923].Len() = 1\n",
"SCnComV[4924].Len() = 1\n",
"SCnComV[4925].Len() = 1\n",
"SCnComV[4926].Len() = 1\n",
"SCnComV[4927].Len() = 1\n",
"SCnComV[4928].Len() = 1\n",
"SCnComV[4929].Len() = 1\n",
"SCnComV[4930].Len() = 1\n",
"SCnComV[4931].Len() = 1\n",
"SCnComV[4932].Len() = 1\n",
"SCnComV[4933].Len() = 1\n",
"SCnComV[4934].Len() = 1\n",
"SCnComV[4935].Len() = 1\n",
"SCnComV[4936].Len() = 1\n",
"SCnComV[4937].Len() = 1\n",
"SCnComV[4938].Len() = 1\n",
"SCnComV[4939].Len() = 1\n",
"SCnComV[4940].Len() = 1\n",
"SCnComV[4941].Len() = 1\n",
"SCnComV[4942].Len() = 1\n",
"SCnComV[4943].Len() = 1\n",
"SCnComV[4944].Len() = 1\n",
"SCnComV[4945].Len() = 1\n",
"SCnComV[4946].Len() = 1\n",
"SCnComV[4947].Len() = 1\n",
"SCnComV[4948].Len() = 1\n",
"SCnComV[4949].Len() = 1\n",
"SCnComV[4950].Len() = 1\n",
"SCnComV[4951].Len() = 1\n",
"SCnComV[4952].Len() = 1\n",
"SCnComV[4953].Len() = 1\n",
"SCnComV[4954].Len() = 1\n",
"SCnComV[4955].Len() = 1\n",
"SCnComV[4956].Len() = 1\n",
"SCnComV[4957].Len() = 1\n",
"SCnComV[4958].Len() = 1\n",
"SCnComV[4959].Len() = 1\n",
"SCnComV[4960].Len() = 1\n",
"SCnComV[4961].Len() = 1\n",
"SCnComV[4962].Len() = 1\n",
"SCnComV[4963].Len() = 1\n",
"SCnComV[4964].Len() = 1\n",
"SCnComV[4965].Len() = 1\n",
"SCnComV[4966].Len() = 1\n",
"SCnComV[4967].Len() = 1\n",
"SCnComV[4968].Len() = 1\n",
"SCnComV[4969].Len() = 1\n",
"SCnComV[4970].Len() = 1\n",
"SCnComV[4971].Len() = 1\n",
"SCnComV[4972].Len() = 1\n",
"SCnComV[4973].Len() = 1\n",
"SCnComV[4974].Len() = 1\n",
"SCnComV[4975].Len() = 1\n",
"SCnComV[4976].Len() = 1\n",
"SCnComV[4977].Len() = 1\n",
"SCnComV[4978].Len() = 1\n",
"SCnComV[4979].Len() = 1\n",
"SCnComV[4980].Len() = 1\n",
"SCnComV[4981].Len() = 1\n",
"SCnComV[4982].Len() = 1\n",
"SCnComV[4983].Len() = 1\n",
"SCnComV[4984].Len() = 1\n",
"SCnComV[4985].Len() = 1\n",
"SCnComV[4986].Len() = 1\n",
"SCnComV[4987].Len() = 1\n",
"SCnComV[4988].Len() = 1\n",
"SCnComV[4989].Len() = 1\n",
"SCnComV[4990].Len() = 1\n",
"SCnComV[4991].Len() = 1\n",
"SCnComV[4992].Len() = 1\n",
"SCnComV[4993].Len() = 1\n",
"SCnComV[4994].Len() = 1\n",
"SCnComV[4995].Len() = 1\n",
"SCnComV[4996].Len() = 1\n",
"SCnComV[4997].Len() = 1\n",
"SCnComV[4998].Len() = 1\n",
"SCnComV[4999].Len() = 1\n",
"SCnComV[5000].Len() = 1\n",
"SCnComV[5001].Len() = 1\n",
"SCnComV[5002].Len() = 1\n",
"SCnComV[5003].Len() = 1\n",
"SCnComV[5004].Len() = 1\n",
"SCnComV[5005].Len() = 1\n",
"SCnComV[5006].Len() = 1\n",
"SCnComV[5007].Len() = 1\n",
"SCnComV[5008].Len() = 1\n",
"SCnComV[5009].Len() = 1\n",
"SCnComV[5010].Len() = 1\n",
"SCnComV[5011].Len() = 1\n",
"SCnComV[5012].Len() = 1\n",
"SCnComV[5013].Len() = 1\n",
"SCnComV[5014].Len() = 1\n",
"SCnComV[5015].Len() = 1\n",
"SCnComV[5016].Len() = 1\n",
"SCnComV[5017].Len() = 1\n",
"SCnComV[5018].Len() = 1\n",
"SCnComV[5019].Len() = 1\n",
"SCnComV[5020].Len() = 1\n",
"SCnComV[5021].Len() = 1\n",
"SCnComV[5022].Len() = 1\n",
"SCnComV[5023].Len() = 1\n",
"SCnComV[5024].Len() = 1\n",
"SCnComV[5025].Len() = 1\n",
"SCnComV[5026].Len() = 1\n",
"SCnComV[5027].Len() = 1\n",
"SCnComV[5028].Len() = 1\n",
"SCnComV[5029].Len() = 1\n",
"SCnComV[5030].Len() = 1\n",
"SCnComV[5031].Len() = 1\n",
"SCnComV[5032].Len() = 1\n",
"SCnComV[5033].Len() = 1\n",
"SCnComV[5034].Len() = 1\n",
"SCnComV[5035].Len() = 1\n",
"SCnComV[5036].Len() = 1\n",
"SCnComV[5037].Len() = 1\n",
"SCnComV[5038].Len() = 1\n",
"SCnComV[5039].Len() = 1\n",
"SCnComV[5040].Len() = 1\n",
"SCnComV[5041].Len() = 1\n",
"SCnComV[5042].Len() = 1\n",
"SCnComV[5043].Len() = 1\n",
"SCnComV[5044].Len() = 1\n",
"SCnComV[5045].Len() = 1\n",
"SCnComV[5046].Len() = 1\n",
"SCnComV[5047].Len() = 1\n",
"SCnComV[5048].Len() = 1\n",
"SCnComV[5049].Len() = 1\n",
"SCnComV[5050].Len() = 1\n",
"SCnComV[5051].Len() = 1\n",
"SCnComV[5052].Len() = 1\n",
"SCnComV[5053].Len() = 1\n",
"SCnComV[5054].Len() = 1\n",
"SCnComV[5055].Len() = 1\n",
"SCnComV[5056].Len() = 1\n",
"SCnComV[5057].Len() = 1\n",
"SCnComV[5058].Len() = 1\n",
"SCnComV[5059].Len() = 1\n",
"SCnComV[5060].Len() = 1\n",
"SCnComV[5061].Len() = 1\n",
"SCnComV[5062].Len() = 1\n",
"SCnComV[5063].Len() = 1\n",
"SCnComV[5064].Len() = 1\n",
"SCnComV[5065].Len() = 1\n",
"SCnComV[5066].Len() = 1\n",
"SCnComV[5067].Len() = 1\n",
"SCnComV[5068].Len() = 1\n",
"SCnComV[5069].Len() = 1\n",
"SCnComV[5070].Len() = 1\n",
"SCnComV[5071].Len() = 1\n",
"SCnComV[5072].Len() = 1\n",
"SCnComV[5073].Len() = 1\n",
"SCnComV[5074].Len() = 1\n",
"SCnComV[5075].Len() = 1\n",
"SCnComV[5076].Len() = 1\n",
"SCnComV[5077].Len() = 1\n",
"SCnComV[5078].Len() = 1\n",
"SCnComV[5079].Len() = 1\n",
"SCnComV[5080].Len() = 1\n",
"SCnComV[5081].Len() = 1\n",
"SCnComV[5082].Len() = 1\n",
"SCnComV[5083].Len() = 1\n",
"SCnComV[5084].Len() = 1\n",
"SCnComV[5085].Len() = 1\n",
"SCnComV[5086].Len() = 1\n",
"SCnComV[5087].Len() = 1\n",
"SCnComV[5088].Len() = 1\n",
"SCnComV[5089].Len() = 1\n",
"SCnComV[5090].Len() = 1\n",
"SCnComV[5091].Len() = 1\n",
"SCnComV[5092].Len() = 1\n",
"SCnComV[5093].Len() = 1\n",
"SCnComV[5094].Len() = 1\n",
"SCnComV[5095].Len() = 1\n",
"SCnComV[5096].Len() = 1\n",
"SCnComV[5097].Len() = 1\n",
"SCnComV[5098].Len() = 1\n",
"SCnComV[5099].Len() = 1\n",
"SCnComV[5100].Len() = 1\n",
"SCnComV[5101].Len() = 1\n",
"SCnComV[5102].Len() = 1\n",
"SCnComV[5103].Len() = 1\n",
"SCnComV[5104].Len() = 1\n",
"SCnComV[5105].Len() = 1\n",
"SCnComV[5106].Len() = 1\n",
"SCnComV[5107].Len() = 1\n",
"SCnComV[5108].Len() = 1\n",
"SCnComV[5109].Len() = 1\n",
"SCnComV[5110].Len() = 1\n",
"SCnComV[5111].Len() = 1\n",
"SCnComV[5112].Len() = 1\n",
"SCnComV[5113].Len() = 1\n",
"SCnComV[5114].Len() = 1\n",
"SCnComV[5115].Len() = 1\n",
"SCnComV[5116].Len() = 1\n",
"SCnComV[5117].Len() = 1\n",
"SCnComV[5118].Len() = 1\n",
"SCnComV[5119].Len() = 1\n",
"SCnComV[5120].Len() = 1\n",
"SCnComV[5121].Len() = 1\n",
"SCnComV[5122].Len() = 1\n",
"SCnComV[5123].Len() = 1\n",
"SCnComV[5124].Len() = 1\n",
"SCnComV[5125].Len() = 1\n",
"SCnComV[5126].Len() = 1\n",
"SCnComV[5127].Len() = 1\n",
"SCnComV[5128].Len() = 1\n",
"SCnComV[5129].Len() = 1\n",
"SCnComV[5130].Len() = 1\n",
"SCnComV[5131].Len() = 1\n",
"SCnComV[5132].Len() = 1\n",
"SCnComV[5133].Len() = 1\n",
"SCnComV[5134].Len() = 1\n",
"SCnComV[5135].Len() = 1\n",
"SCnComV[5136].Len() = 1\n",
"SCnComV[5137].Len() = 1\n",
"SCnComV[5138].Len() = 1\n",
"SCnComV[5139].Len() = 1\n",
"SCnComV[5140].Len() = 1\n",
"SCnComV[5141].Len() = 1\n",
"SCnComV[5142].Len() = 1\n",
"SCnComV[5143].Len() = 1\n",
"SCnComV[5144].Len() = 1\n",
"SCnComV[5145].Len() = 1\n",
"SCnComV[5146].Len() = 1\n",
"SCnComV[5147].Len() = 1\n",
"SCnComV[5148].Len() = 1\n",
"SCnComV[5149].Len() = 1\n",
"SCnComV[5150].Len() = 1\n",
"SCnComV[5151].Len() = 1\n",
"SCnComV[5152].Len() = 1\n",
"SCnComV[5153].Len() = 1\n",
"SCnComV[5154].Len() = 1\n",
"SCnComV[5155].Len() = 1\n",
"SCnComV[5156].Len() = 1\n",
"SCnComV[5157].Len() = 1\n",
"SCnComV[5158].Len() = 1\n",
"SCnComV[5159].Len() = 1\n",
"SCnComV[5160].Len() = 1\n",
"SCnComV[5161].Len() = 1\n",
"SCnComV[5162].Len() = 1\n",
"SCnComV[5163].Len() = 1\n",
"SCnComV[5164].Len() = 1\n",
"SCnComV[5165].Len() = 1\n",
"SCnComV[5166].Len() = 1\n",
"SCnComV[5167].Len() = 1\n",
"SCnComV[5168].Len() = 1\n",
"SCnComV[5169].Len() = 1\n",
"SCnComV[5170].Len() = 1\n",
"SCnComV[5171].Len() = 1\n",
"SCnComV[5172].Len() = 1\n",
"SCnComV[5173].Len() = 1\n",
"SCnComV[5174].Len() = 1\n",
"SCnComV[5175].Len() = 1\n",
"SCnComV[5176].Len() = 1\n",
"SCnComV[5177].Len() = 1\n",
"SCnComV[5178].Len() = 1\n",
"SCnComV[5179].Len() = 1\n",
"SCnComV[5180].Len() = 1\n",
"SCnComV[5181].Len() = 1\n",
"SCnComV[5182].Len() = 1\n",
"SCnComV[5183].Len() = 1\n",
"SCnComV[5184].Len() = 1\n",
"SCnComV[5185].Len() = 1\n",
"SCnComV[5186].Len() = 1\n",
"SCnComV[5187].Len() = 1\n",
"SCnComV[5188].Len() = 1\n",
"SCnComV[5189].Len() = 1\n",
"SCnComV[5190].Len() = 1\n",
"SCnComV[5191].Len() = 1\n",
"SCnComV[5192].Len() = 1\n",
"SCnComV[5193].Len() = 1\n",
"SCnComV[5194].Len() = 1\n",
"SCnComV[5195].Len() = 1\n",
"SCnComV[5196].Len() = 1\n",
"SCnComV[5197].Len() = 1\n",
"SCnComV[5198].Len() = 1\n",
"SCnComV[5199].Len() = 1\n",
"SCnComV[5200].Len() = 1\n",
"SCnComV[5201].Len() = 1\n",
"SCnComV[5202].Len() = 1\n",
"SCnComV[5203].Len() = 1\n",
"SCnComV[5204].Len() = 1\n",
"SCnComV[5205].Len() = 1\n",
"SCnComV[5206].Len() = 1\n",
"SCnComV[5207].Len() = 1\n",
"SCnComV[5208].Len() = 1\n",
"SCnComV[5209].Len() = 1\n",
"SCnComV[5210].Len() = 1\n",
"SCnComV[5211].Len() = 1\n",
"SCnComV[5212].Len() = 1\n",
"SCnComV[5213].Len() = 1\n",
"SCnComV[5214].Len() = 1\n",
"SCnComV[5215].Len() = 1\n",
"SCnComV[5216].Len() = 1\n",
"SCnComV[5217].Len() = 1\n",
"SCnComV[5218].Len() = 1\n",
"SCnComV[5219].Len() = 1\n",
"SCnComV[5220].Len() = 1\n",
"SCnComV[5221].Len() = 1\n",
"SCnComV[5222].Len() = 1\n",
"SCnComV[5223].Len() = 1\n",
"SCnComV[5224].Len() = 1\n",
"SCnComV[5225].Len() = 1\n",
"SCnComV[5226].Len() = 1\n",
"SCnComV[5227].Len() = 1\n",
"SCnComV[5228].Len() = 1\n",
"SCnComV[5229].Len() = 1\n",
"SCnComV[5230].Len() = 1\n",
"SCnComV[5231].Len() = 1\n",
"SCnComV[5232].Len() = 1\n",
"SCnComV[5233].Len() = 1\n",
"SCnComV[5234].Len() = 1\n",
"SCnComV[5235].Len() = 1\n",
"SCnComV[5236].Len() = 1\n",
"SCnComV[5237].Len() = 1\n",
"SCnComV[5238].Len() = 1\n",
"SCnComV[5239].Len() = 1\n",
"SCnComV[5240].Len() = 1\n",
"SCnComV[5241].Len() = 1\n",
"SCnComV[5242].Len() = 1\n",
"SCnComV[5243].Len() = 1\n",
"SCnComV[5244].Len() = 1\n",
"SCnComV[5245].Len() = 1\n",
"SCnComV[5246].Len() = 1\n",
"SCnComV[5247].Len() = 1\n",
"SCnComV[5248].Len() = 1\n",
"SCnComV[5249].Len() = 1\n",
"SCnComV[5250].Len() = 1\n",
"SCnComV[5251].Len() = 1\n",
"SCnComV[5252].Len() = 1\n",
"SCnComV[5253].Len() = 1\n",
"SCnComV[5254].Len() = 1\n",
"SCnComV[5255].Len() = 1\n",
"SCnComV[5256].Len() = 1\n",
"SCnComV[5257].Len() = 1\n",
"SCnComV[5258].Len() = 1\n",
"SCnComV[5259].Len() = 1\n",
"SCnComV[5260].Len() = 1\n",
"SCnComV[5261].Len() = 1\n",
"SCnComV[5262].Len() = 1\n",
"SCnComV[5263].Len() = 1\n",
"SCnComV[5264].Len() = 1\n",
"SCnComV[5265].Len() = 1\n",
"SCnComV[5266].Len() = 1\n",
"SCnComV[5267].Len() = 1\n",
"SCnComV[5268].Len() = 1\n",
"SCnComV[5269].Len() = 1\n",
"SCnComV[5270].Len() = 1\n",
"SCnComV[5271].Len() = 1\n",
"SCnComV[5272].Len() = 1\n",
"SCnComV[5273].Len() = 1\n",
"SCnComV[5274].Len() = 1\n",
"SCnComV[5275].Len() = 1\n",
"SCnComV[5276].Len() = 1\n",
"SCnComV[5277].Len() = 1\n",
"SCnComV[5278].Len() = 1\n",
"SCnComV[5279].Len() = 1\n",
"SCnComV[5280].Len() = 1\n",
"SCnComV[5281].Len() = 1\n",
"SCnComV[5282].Len() = 1\n",
"SCnComV[5283].Len() = 1\n",
"SCnComV[5284].Len() = 1\n",
"SCnComV[5285].Len() = 1\n",
"SCnComV[5286].Len() = 1\n",
"SCnComV[5287].Len() = 1\n",
"SCnComV[5288].Len() = 1\n",
"SCnComV[5289].Len() = 1\n",
"SCnComV[5290].Len() = 1\n",
"SCnComV[5291].Len() = 1\n",
"SCnComV[5292].Len() = 1\n",
"SCnComV[5293].Len() = 1\n",
"SCnComV[5294].Len() = 1\n",
"SCnComV[5295].Len() = 1\n",
"SCnComV[5296].Len() = 1\n",
"SCnComV[5297].Len() = 1\n",
"SCnComV[5298].Len() = 1\n",
"SCnComV[5299].Len() = 1\n",
"SCnComV[5300].Len() = 1\n",
"SCnComV[5301].Len() = 1\n",
"SCnComV[5302].Len() = 1\n",
"SCnComV[5303].Len() = 1\n",
"SCnComV[5304].Len() = 1\n",
"SCnComV[5305].Len() = 1\n",
"SCnComV[5306].Len() = 1\n",
"SCnComV[5307].Len() = 1\n",
"SCnComV[5308].Len() = 1\n",
"SCnComV[5309].Len() = 1\n",
"SCnComV[5310].Len() = 1\n",
"SCnComV[5311].Len() = 1\n",
"SCnComV[5312].Len() = 1\n",
"SCnComV[5313].Len() = 1\n",
"SCnComV[5314].Len() = 1\n",
"SCnComV[5315].Len() = 1\n",
"SCnComV[5316].Len() = 1\n",
"SCnComV[5317].Len() = 1\n",
"SCnComV[5318].Len() = 1\n",
"SCnComV[5319].Len() = 1\n",
"SCnComV[5320].Len() = 1\n",
"SCnComV[5321].Len() = 1\n",
"SCnComV[5322].Len() = 1\n",
"SCnComV[5323].Len() = 1\n",
"SCnComV[5324].Len() = 1\n",
"SCnComV[5325].Len() = 1\n",
"SCnComV[5326].Len() = 1\n",
"SCnComV[5327].Len() = 1\n",
"SCnComV[5328].Len() = 1\n",
"SCnComV[5329].Len() = 1\n",
"SCnComV[5330].Len() = 1\n",
"SCnComV[5331].Len() = 1\n",
"SCnComV[5332].Len() = 1\n",
"SCnComV[5333].Len() = 1\n",
"SCnComV[5334].Len() = 1\n",
"SCnComV[5335].Len() = 1\n",
"SCnComV[5336].Len() = 1\n",
"SCnComV[5337].Len() = 1\n",
"SCnComV[5338].Len() = 1\n",
"SCnComV[5339].Len() = 1\n",
"SCnComV[5340].Len() = 1\n",
"SCnComV[5341].Len() = 1\n",
"SCnComV[5342].Len() = 1\n",
"SCnComV[5343].Len() = 1\n",
"SCnComV[5344].Len() = 1\n",
"SCnComV[5345].Len() = 1\n",
"SCnComV[5346].Len() = 1\n",
"SCnComV[5347].Len() = 1\n",
"SCnComV[5348].Len() = 1\n",
"SCnComV[5349].Len() = 1\n",
"SCnComV[5350].Len() = 1\n",
"SCnComV[5351].Len() = 1\n",
"SCnComV[5352].Len() = 1\n",
"SCnComV[5353].Len() = 1\n",
"SCnComV[5354].Len() = 1\n",
"SCnComV[5355].Len() = 1\n",
"SCnComV[5356].Len() = 1\n",
"SCnComV[5357].Len() = 1\n",
"SCnComV[5358].Len() = 1\n",
"SCnComV[5359].Len() = 1\n",
"SCnComV[5360].Len() = 1\n",
"SCnComV[5361].Len() = 1\n",
"SCnComV[5362].Len() = 1\n",
"SCnComV[5363].Len() = 1\n",
"SCnComV[5364].Len() = 1\n",
"SCnComV[5365].Len() = 1\n",
"SCnComV[5366].Len() = 1\n",
"SCnComV[5367].Len() = 1\n",
"SCnComV[5368].Len() = 1\n",
"SCnComV[5369].Len() = 1\n",
"SCnComV[5370].Len() = 1\n",
"SCnComV[5371].Len() = 1\n",
"SCnComV[5372].Len() = 1\n",
"SCnComV[5373].Len() = 1\n",
"SCnComV[5374].Len() = 1\n",
"SCnComV[5375].Len() = 1\n",
"SCnComV[5376].Len() = 1\n",
"SCnComV[5377].Len() = 1\n",
"SCnComV[5378].Len() = 1\n",
"SCnComV[5379].Len() = 1\n",
"SCnComV[5380].Len() = 1\n",
"SCnComV[5381].Len() = 1\n",
"SCnComV[5382].Len() = 1\n",
"SCnComV[5383].Len() = 1\n",
"SCnComV[5384].Len() = 1\n",
"SCnComV[5385].Len() = 1\n",
"SCnComV[5386].Len() = 1\n",
"SCnComV[5387].Len() = 1\n",
"SCnComV[5388].Len() = 1\n",
"SCnComV[5389].Len() = 1\n",
"SCnComV[5390].Len() = 1\n",
"SCnComV[5391].Len() = 1\n",
"SCnComV[5392].Len() = 1\n",
"SCnComV[5393].Len() = 1\n",
"SCnComV[5394].Len() = 1\n",
"SCnComV[5395].Len() = 1\n",
"SCnComV[5396].Len() = 1\n",
"SCnComV[5397].Len() = 1\n",
"SCnComV[5398].Len() = 1\n",
"SCnComV[5399].Len() = 1\n",
"SCnComV[5400].Len() = 1\n",
"SCnComV[5401].Len() = 1\n",
"SCnComV[5402].Len() = 1\n",
"SCnComV[5403].Len() = 1\n",
"SCnComV[5404].Len() = 1\n",
"SCnComV[5405].Len() = 1\n",
"SCnComV[5406].Len() = 1\n",
"SCnComV[5407].Len() = 1\n",
"SCnComV[5408].Len() = 1\n",
"SCnComV[5409].Len() = 1\n",
"SCnComV[5410].Len() = 1\n",
"SCnComV[5411].Len() = 1\n",
"SCnComV[5412].Len() = 1\n",
"SCnComV[5413].Len() = 1\n",
"SCnComV[5414].Len() = 1\n",
"SCnComV[5415].Len() = 1\n",
"SCnComV[5416].Len() = 1\n",
"SCnComV[5417].Len() = 1\n",
"SCnComV[5418].Len() = 1\n",
"SCnComV[5419].Len() = 1\n",
"SCnComV[5420].Len() = 1\n",
"SCnComV[5421].Len() = 1\n",
"SCnComV[5422].Len() = 1\n",
"SCnComV[5423].Len() = 1\n",
"SCnComV[5424].Len() = 1\n",
"SCnComV[5425].Len() = 1\n",
"SCnComV[5426].Len() = 1\n",
"SCnComV[5427].Len() = 1\n",
"SCnComV[5428].Len() = 1\n",
"SCnComV[5429].Len() = 1\n",
"SCnComV[5430].Len() = 1\n",
"SCnComV[5431].Len() = 1\n",
"SCnComV[5432].Len() = 1\n",
"SCnComV[5433].Len() = 1\n",
"SCnComV[5434].Len() = 1\n",
"SCnComV[5435].Len() = 1\n",
"SCnComV[5436].Len() = 1\n",
"SCnComV[5437].Len() = 1\n",
"SCnComV[5438].Len() = 1\n",
"SCnComV[5439].Len() = 1\n",
"SCnComV[5440].Len() = 1\n",
"SCnComV[5441].Len() = 1\n",
"SCnComV[5442].Len() = 1\n",
"SCnComV[5443].Len() = 1\n",
"SCnComV[5444].Len() = 1\n",
"SCnComV[5445].Len() = 1\n",
"SCnComV[5446].Len() = 1\n",
"SCnComV[5447].Len() = 1\n",
"SCnComV[5448].Len() = 1\n",
"SCnComV[5449].Len() = 1\n",
"SCnComV[5450].Len() = 1\n",
"SCnComV[5451].Len() = 1\n",
"SCnComV[5452].Len() = 1\n",
"SCnComV[5453].Len() = 1\n",
"SCnComV[5454].Len() = 1\n",
"SCnComV[5455].Len() = 1\n",
"SCnComV[5456].Len() = 1\n",
"SCnComV[5457].Len() = 1\n",
"SCnComV[5458].Len() = 1\n",
"SCnComV[5459].Len() = 1\n",
"SCnComV[5460].Len() = 1\n",
"SCnComV[5461].Len() = 1\n",
"SCnComV[5462].Len() = 1\n",
"SCnComV[5463].Len() = 1\n",
"SCnComV[5464].Len() = 1\n",
"SCnComV[5465].Len() = 1\n",
"SCnComV[5466].Len() = 1\n",
"SCnComV[5467].Len() = 1\n",
"SCnComV[5468].Len() = 1\n",
"SCnComV[5469].Len() = 1\n",
"SCnComV[5470].Len() = 1\n",
"SCnComV[5471].Len() = 1\n",
"SCnComV[5472].Len() = 1\n",
"SCnComV[5473].Len() = 1\n",
"SCnComV[5474].Len() = 1\n",
"SCnComV[5475].Len() = 1\n",
"SCnComV[5476].Len() = 1\n",
"SCnComV[5477].Len() = 1\n",
"SCnComV[5478].Len() = 1\n",
"SCnComV[5479].Len() = 1\n",
"SCnComV[5480].Len() = 1\n",
"SCnComV[5481].Len() = 1\n",
"SCnComV[5482].Len() = 1\n",
"SCnComV[5483].Len() = 1\n",
"SCnComV[5484].Len() = 1\n",
"SCnComV[5485].Len() = 1\n",
"SCnComV[5486].Len() = 1\n",
"SCnComV[5487].Len() = 1\n",
"SCnComV[5488].Len() = 1\n",
"SCnComV[5489].Len() = 1\n",
"SCnComV[5490].Len() = 1\n",
"SCnComV[5491].Len() = 1\n",
"SCnComV[5492].Len() = 1\n",
"SCnComV[5493].Len() = 1\n",
"SCnComV[5494].Len() = 1\n",
"SCnComV[5495].Len() = 1\n",
"SCnComV[5496].Len() = 1\n",
"SCnComV[5497].Len() = 1\n",
"SCnComV[5498].Len() = 1\n",
"SCnComV[5499].Len() = 1\n",
"SCnComV[5500].Len() = 1\n",
"SCnComV[5501].Len() = 1\n",
"SCnComV[5502].Len() = 1\n",
"SCnComV[5503].Len() = 1\n",
"SCnComV[5504].Len() = 1\n",
"SCnComV[5505].Len() = 1\n",
"SCnComV[5506].Len() = 1\n",
"SCnComV[5507].Len() = 1\n",
"SCnComV[5508].Len() = 1\n",
"SCnComV[5509].Len() = 1\n",
"SCnComV[5510].Len() = 1\n",
"SCnComV[5511].Len() = 1\n",
"SCnComV[5512].Len() = 1\n",
"SCnComV[5513].Len() = 1\n",
"SCnComV[5514].Len() = 1\n",
"SCnComV[5515].Len() = 1\n",
"SCnComV[5516].Len() = 1\n",
"SCnComV[5517].Len() = 1\n",
"SCnComV[5518].Len() = 1\n",
"SCnComV[5519].Len() = 1\n",
"SCnComV[5520].Len() = 1\n",
"SCnComV[5521].Len() = 1\n",
"SCnComV[5522].Len() = 1\n",
"SCnComV[5523].Len() = 1\n",
"SCnComV[5524].Len() = 1\n",
"SCnComV[5525].Len() = 1\n",
"SCnComV[5526].Len() = 1\n",
"SCnComV[5527].Len() = 1\n",
"SCnComV[5528].Len() = 1\n",
"SCnComV[5529].Len() = 1\n",
"SCnComV[5530].Len() = 1\n",
"SCnComV[5531].Len() = 1\n",
"SCnComV[5532].Len() = 1\n",
"SCnComV[5533].Len() = 1\n",
"SCnComV[5534].Len() = 1\n",
"SCnComV[5535].Len() = 1\n",
"SCnComV[5536].Len() = 1\n",
"SCnComV[5537].Len() = 1\n",
"SCnComV[5538].Len() = 1\n",
"SCnComV[5539].Len() = 1\n",
"SCnComV[5540].Len() = 1\n",
"SCnComV[5541].Len() = 1\n",
"SCnComV[5542].Len() = 1\n",
"SCnComV[5543].Len() = 1\n",
"SCnComV[5544].Len() = 1\n",
"SCnComV[5545].Len() = 1\n",
"SCnComV[5546].Len() = 1\n",
"SCnComV[5547].Len() = 1\n",
"SCnComV[5548].Len() = 1\n",
"SCnComV[5549].Len() = 1\n",
"SCnComV[5550].Len() = 1\n",
"SCnComV[5551].Len() = 1\n",
"SCnComV[5552].Len() = 1\n",
"SCnComV[5553].Len() = 1\n",
"SCnComV[5554].Len() = 1\n",
"SCnComV[5555].Len() = 1\n",
"SCnComV[5556].Len() = 1\n",
"SCnComV[5557].Len() = 1\n",
"SCnComV[5558].Len() = 1\n",
"SCnComV[5559].Len() = 1\n",
"SCnComV[5560].Len() = 1\n",
"SCnComV[5561].Len() = 1\n",
"SCnComV[5562].Len() = 1\n",
"SCnComV[5563].Len() = 1\n",
"SCnComV[5564].Len() = 1\n",
"SCnComV[5565].Len() = 1\n",
"SCnComV[5566].Len() = 1\n",
"SCnComV[5567].Len() = 1\n",
"SCnComV[5568].Len() = 1\n",
"SCnComV[5569].Len() = 1\n",
"SCnComV[5570].Len() = 1\n",
"SCnComV[5571].Len() = 1\n",
"SCnComV[5572].Len() = 1\n",
"SCnComV[5573].Len() = 1\n",
"SCnComV[5574].Len() = 1\n",
"SCnComV[5575].Len() = 1\n",
"SCnComV[5576].Len() = 1\n",
"SCnComV[5577].Len() = 1\n",
"SCnComV[5578].Len() = 1\n",
"SCnComV[5579].Len() = 1\n",
"SCnComV[5580].Len() = 1\n",
"SCnComV[5581].Len() = 1\n",
"SCnComV[5582].Len() = 1\n",
"SCnComV[5583].Len() = 1\n",
"SCnComV[5584].Len() = 1\n",
"SCnComV[5585].Len() = 1\n",
"SCnComV[5586].Len() = 1\n",
"SCnComV[5587].Len() = 1\n",
"SCnComV[5588].Len() = 1\n",
"SCnComV[5589].Len() = 1\n",
"SCnComV[5590].Len() = 1\n",
"SCnComV[5591].Len() = 1\n",
"SCnComV[5592].Len() = 1\n",
"SCnComV[5593].Len() = 1\n",
"SCnComV[5594].Len() = 1\n",
"SCnComV[5595].Len() = 1\n",
"SCnComV[5596].Len() = 1\n",
"SCnComV[5597].Len() = 1\n",
"SCnComV[5598].Len() = 1\n",
"SCnComV[5599].Len() = 1\n",
"SCnComV[5600].Len() = 1\n",
"SCnComV[5601].Len() = 1\n",
"SCnComV[5602].Len() = 1\n",
"SCnComV[5603].Len() = 1\n",
"SCnComV[5604].Len() = 1\n",
"SCnComV[5605].Len() = 1\n",
"SCnComV[5606].Len() = 1\n",
"SCnComV[5607].Len() = 1\n",
"SCnComV[5608].Len() = 1\n",
"SCnComV[5609].Len() = 1\n",
"SCnComV[5610].Len() = 1\n",
"SCnComV[5611].Len() = 1\n",
"SCnComV[5612].Len() = 1\n",
"SCnComV[5613].Len() = 1\n",
"SCnComV[5614].Len() = 1\n",
"SCnComV[5615].Len() = 1\n",
"SCnComV[5616].Len() = 1\n",
"SCnComV[5617].Len() = 1\n",
"SCnComV[5618].Len() = 1\n",
"SCnComV[5619].Len() = 1\n",
"SCnComV[5620].Len() = 1\n",
"SCnComV[5621].Len() = 1\n",
"SCnComV[5622].Len() = 1\n",
"SCnComV[5623].Len() = 1\n",
"SCnComV[5624].Len() = 1\n",
"SCnComV[5625].Len() = 1\n",
"SCnComV[5626].Len() = 1\n",
"SCnComV[5627].Len() = 1\n",
"SCnComV[5628].Len() = 1\n",
"SCnComV[5629].Len() = 1\n",
"SCnComV[5630].Len() = 1\n",
"SCnComV[5631].Len() = 1\n",
"SCnComV[5632].Len() = 1\n",
"SCnComV[5633].Len() = 1\n",
"SCnComV[5634].Len() = 1\n",
"SCnComV[5635].Len() = 1\n",
"SCnComV[5636].Len() = 1\n",
"SCnComV[5637].Len() = 1\n",
"SCnComV[5638].Len() = 1\n",
"SCnComV[5639].Len() = 1\n",
"SCnComV[5640].Len() = 1\n",
"SCnComV[5641].Len() = 1\n",
"SCnComV[5642].Len() = 1\n",
"SCnComV[5643].Len() = 1\n",
"SCnComV[5644].Len() = 1\n",
"SCnComV[5645].Len() = 1\n",
"SCnComV[5646].Len() = 1\n",
"SCnComV[5647].Len() = 1\n",
"SCnComV[5648].Len() = 1\n",
"SCnComV[5649].Len() = 1\n",
"SCnComV[5650].Len() = 1\n",
"SCnComV[5651].Len() = 1\n",
"SCnComV[5652].Len() = 1\n",
"SCnComV[5653].Len() = 1\n",
"SCnComV[5654].Len() = 1\n",
"SCnComV[5655].Len() = 1\n",
"SCnComV[5656].Len() = 1\n",
"SCnComV[5657].Len() = 1\n",
"SCnComV[5658].Len() = 1\n",
"SCnComV[5659].Len() = 1\n",
"SCnComV[5660].Len() = 1\n",
"SCnComV[5661].Len() = 1\n",
"SCnComV[5662].Len() = 1\n",
"SCnComV[5663].Len() = 1\n",
"SCnComV[5664].Len() = 1\n",
"SCnComV[5665].Len() = 1\n",
"SCnComV[5666].Len() = 1\n",
"SCnComV[5667].Len() = 1\n",
"SCnComV[5668].Len() = 1\n",
"SCnComV[5669].Len() = 1\n",
"SCnComV[5670].Len() = 1\n",
"SCnComV[5671].Len() = 1\n",
"SCnComV[5672].Len() = 1\n",
"SCnComV[5673].Len() = 1\n",
"SCnComV[5674].Len() = 1\n",
"SCnComV[5675].Len() = 1\n",
"SCnComV[5676].Len() = 1\n",
"SCnComV[5677].Len() = 1\n",
"SCnComV[5678].Len() = 1\n",
"SCnComV[5679].Len() = 1\n",
"SCnComV[5680].Len() = 1\n",
"SCnComV[5681].Len() = 1\n",
"SCnComV[5682].Len() = 1\n",
"SCnComV[5683].Len() = 1\n",
"SCnComV[5684].Len() = 1\n",
"SCnComV[5685].Len() = 1\n",
"SCnComV[5686].Len() = 1\n",
"SCnComV[5687].Len() = 1\n",
"SCnComV[5688].Len() = 1\n",
"SCnComV[5689].Len() = 1\n",
"SCnComV[5690].Len() = 1\n",
"SCnComV[5691].Len() = 1\n",
"SCnComV[5692].Len() = 1\n",
"SCnComV[5693].Len() = 1\n",
"SCnComV[5694].Len() = 1\n",
"SCnComV[5695].Len() = 1\n",
"SCnComV[5696].Len() = 1\n",
"SCnComV[5697].Len() = 1\n",
"SCnComV[5698].Len() = 1\n",
"SCnComV[5699].Len() = 1\n",
"SCnComV[5700].Len() = 1\n",
"SCnComV[5701].Len() = 1\n",
"SCnComV[5702].Len() = 1\n",
"SCnComV[5703].Len() = 1\n",
"SCnComV[5704].Len() = 1\n",
"SCnComV[5705].Len() = 1\n",
"SCnComV[5706].Len() = 1\n",
"SCnComV[5707].Len() = 1\n",
"SCnComV[5708].Len() = 1\n",
"SCnComV[5709].Len() = 1\n",
"SCnComV[5710].Len() = 1\n",
"SCnComV[5711].Len() = 1\n",
"SCnComV[5712].Len() = 1\n",
"SCnComV[5713].Len() = 1\n",
"SCnComV[5714].Len() = 1\n",
"SCnComV[5715].Len() = 1\n",
"SCnComV[5716].Len() = 1\n",
"SCnComV[5717].Len() = 1\n",
"SCnComV[5718].Len() = 1\n",
"SCnComV[5719].Len() = 1\n",
"SCnComV[5720].Len() = 1\n",
"SCnComV[5721].Len() = 1\n",
"SCnComV[5722].Len() = 1\n",
"SCnComV[5723].Len() = 1\n",
"SCnComV[5724].Len() = 1\n",
"SCnComV[5725].Len() = 1\n",
"SCnComV[5726].Len() = 1\n",
"SCnComV[5727].Len() = 1\n",
"SCnComV[5728].Len() = 1\n",
"SCnComV[5729].Len() = 1\n",
"SCnComV[5730].Len() = 1\n",
"SCnComV[5731].Len() = 1\n",
"SCnComV[5732].Len() = 1\n",
"SCnComV[5733].Len() = 1\n",
"SCnComV[5734].Len() = 1\n",
"SCnComV[5735].Len() = 1\n",
"SCnComV[5736].Len() = 1\n",
"SCnComV[5737].Len() = 1\n",
"SCnComV[5738].Len() = 1\n",
"SCnComV[5739].Len() = 1\n",
"SCnComV[5740].Len() = 1\n",
"SCnComV[5741].Len() = 1\n",
"SCnComV[5742].Len() = 1\n",
"SCnComV[5743].Len() = 1\n",
"SCnComV[5744].Len() = 1\n",
"SCnComV[5745].Len() = 1\n",
"SCnComV[5746].Len() = 1\n",
"SCnComV[5747].Len() = 1\n",
"SCnComV[5748].Len() = 1\n",
"SCnComV[5749].Len() = 1\n",
"SCnComV[5750].Len() = 1\n",
"SCnComV[5751].Len() = 1\n",
"SCnComV[5752].Len() = 1\n",
"SCnComV[5753].Len() = 1\n",
"SCnComV[5754].Len() = 1\n",
"SCnComV[5755].Len() = 1\n",
"SCnComV[5756].Len() = 1\n",
"SCnComV[5757].Len() = 1\n",
"SCnComV[5758].Len() = 1\n",
"SCnComV[5759].Len() = 1\n",
"SCnComV[5760].Len() = 1\n",
"SCnComV[5761].Len() = 1\n",
"SCnComV[5762].Len() = 1\n",
"SCnComV[5763].Len() = 1\n",
"SCnComV[5764].Len() = 1\n",
"SCnComV[5765].Len() = 1\n",
"SCnComV[5766].Len() = 1\n",
"SCnComV[5767].Len() = 1\n",
"SCnComV[5768].Len() = 1\n",
"SCnComV[5769].Len() = 1\n",
"SCnComV[5770].Len() = 1\n",
"SCnComV[5771].Len() = 1\n",
"SCnComV[5772].Len() = 1\n",
"SCnComV[5773].Len() = 1\n",
"SCnComV[5774].Len() = 1\n",
"SCnComV[5775].Len() = 1\n",
"SCnComV[5776].Len() = 1\n",
"SCnComV[5777].Len() = 1\n",
"SCnComV[5778].Len() = 1\n",
"SCnComV[5779].Len() = 1\n",
"SCnComV[5780].Len() = 1\n",
"SCnComV[5781].Len() = 1\n",
"SCnComV[5782].Len() = 1\n",
"SCnComV[5783].Len() = 1\n",
"SCnComV[5784].Len() = 1\n",
"SCnComV[5785].Len() = 1\n",
"SCnComV[5786].Len() = 1\n",
"SCnComV[5787].Len() = 1\n",
"SCnComV[5788].Len() = 1\n",
"SCnComV[5789].Len() = 1\n",
"SCnComV[5790].Len() = 1\n",
"SCnComV[5791].Len() = 1\n",
"SCnComV[5792].Len() = 1\n",
"SCnComV[5793].Len() = 1\n",
"SCnComV[5794].Len() = 1\n",
"SCnComV[5795].Len() = 1\n",
"SCnComV[5796].Len() = 1\n",
"SCnComV[5797].Len() = 1\n",
"SCnComV[5798].Len() = 1\n",
"SCnComV[5799].Len() = 1\n",
"SCnComV[5800].Len() = 1\n",
"SCnComV[5801].Len() = 1\n",
"SCnComV[5802].Len() = 1\n",
"SCnComV[5803].Len() = 1\n",
"SCnComV[5804].Len() = 1\n",
"SCnComV[5805].Len() = 1\n",
"SCnComV[5806].Len() = 1\n",
"SCnComV[5807].Len() = 1\n",
"SCnComV[5808].Len() = 1\n",
"SCnComV[5809].Len() = 1\n",
"SCnComV[5810].Len() = 1\n",
"SCnComV[5811].Len() = 1\n",
"SCnComV[5812].Len() = 1\n",
"SCnComV[5813].Len() = 1\n",
"SCnComV[5814].Len() = 1\n",
"SCnComV[5815].Len() = 1\n",
"SCnComV[5816].Len() = 1\n",
"SCnComV[5817].Len() = 1\n",
"SCnComV[5818].Len() = 1\n",
"SCnComV[5819].Len() = 1\n",
"SCnComV[5820].Len() = 1\n",
"SCnComV[5821].Len() = 1\n",
"SCnComV[5822].Len() = 1\n",
"SCnComV[5823].Len() = 1\n",
"SCnComV[5824].Len() = 1\n",
"SCnComV[5825].Len() = 1\n",
"SCnComV[5826].Len() = 1\n",
"SCnComV[5827].Len() = 1\n",
"SCnComV[5828].Len() = 1\n",
"SCnComV[5829].Len() = 1\n",
"SCnComV[5830].Len() = 1\n",
"SCnComV[5831].Len() = 1\n",
"SCnComV[5832].Len() = 1\n",
"SCnComV[5833].Len() = 1\n",
"SCnComV[5834].Len() = 1\n",
"SCnComV[5835].Len() = 1\n",
"SCnComV[5836].Len() = 1\n",
"SCnComV[5837].Len() = 1\n",
"SCnComV[5838].Len() = 1\n",
"SCnComV[5839].Len() = 1\n",
"SCnComV[5840].Len() = 1\n",
"SCnComV[5841].Len() = 1\n",
"SCnComV[5842].Len() = 1\n",
"SCnComV[5843].Len() = 1\n",
"SCnComV[5844].Len() = 1\n",
"SCnComV[5845].Len() = 1\n",
"SCnComV[5846].Len() = 1\n",
"SCnComV[5847].Len() = 1\n",
"SCnComV[5848].Len() = 1\n",
"SCnComV[5849].Len() = 1\n",
"SCnComV[5850].Len() = 1\n",
"SCnComV[5851].Len() = 1\n",
"SCnComV[5852].Len() = 1\n",
"SCnComV[5853].Len() = 1\n",
"SCnComV[5854].Len() = 1\n",
"SCnComV[5855].Len() = 1\n",
"SCnComV[5856].Len() = 1\n",
"SCnComV[5857].Len() = 1\n",
"SCnComV[5858].Len() = 1\n",
"SCnComV[5859].Len() = 1\n",
"SCnComV[5860].Len() = 1\n",
"SCnComV[5861].Len() = 1\n",
"SCnComV[5862].Len() = 1\n",
"SCnComV[5863].Len() = 1\n",
"SCnComV[5864].Len() = 1\n",
"SCnComV[5865].Len() = 1\n",
"SCnComV[5866].Len() = 1\n",
"SCnComV[5867].Len() = 1\n",
"SCnComV[5868].Len() = 1\n",
"SCnComV[5869].Len() = 1\n",
"SCnComV[5870].Len() = 1\n",
"SCnComV[5871].Len() = 1\n",
"SCnComV[5872].Len() = 1\n",
"SCnComV[5873].Len() = 1\n",
"SCnComV[5874].Len() = 1\n",
"SCnComV[5875].Len() = 1\n",
"SCnComV[5876].Len() = 1\n",
"SCnComV[5877].Len() = 1\n",
"SCnComV[5878].Len() = 1\n",
"SCnComV[5879].Len() = 1\n",
"SCnComV[5880].Len() = 1\n",
"SCnComV[5881].Len() = 1\n",
"SCnComV[5882].Len() = 1\n",
"SCnComV[5883].Len() = 1\n",
"SCnComV[5884].Len() = 1\n",
"SCnComV[5885].Len() = 1\n",
"SCnComV[5886].Len() = 1\n",
"SCnComV[5887].Len() = 1\n",
"SCnComV[5888].Len() = 1\n",
"SCnComV[5889].Len() = 1\n",
"SCnComV[5890].Len() = 1\n",
"SCnComV[5891].Len() = 1\n",
"SCnComV[5892].Len() = 1\n",
"SCnComV[5893].Len() = 1\n",
"SCnComV[5894].Len() = 1\n",
"SCnComV[5895].Len() = 1\n",
"SCnComV[5896].Len() = 1\n",
"SCnComV[5897].Len() = 1\n",
"SCnComV[5898].Len() = 1\n",
"SCnComV[5899].Len() = 1\n",
"SCnComV[5900].Len() = 1\n",
"SCnComV[5901].Len() = 1\n",
"SCnComV[5902].Len() = 1\n",
"SCnComV[5903].Len() = 1\n",
"SCnComV[5904].Len() = 1\n",
"SCnComV[5905].Len() = 1\n",
"SCnComV[5906].Len() = 1\n",
"SCnComV[5907].Len() = 1\n",
"SCnComV[5908].Len() = 1\n",
"SCnComV[5909].Len() = 1\n",
"SCnComV[5910].Len() = 1\n",
"SCnComV[5911].Len() = 1\n",
"SCnComV[5912].Len() = 1\n",
"SCnComV[5913].Len() = 1\n",
"SCnComV[5914].Len() = 1\n",
"SCnComV[5915].Len() = 1\n",
"SCnComV[5916].Len() = 1\n",
"SCnComV[5917].Len() = 1\n",
"SCnComV[5918].Len() = 1\n",
"SCnComV[5919].Len() = 1\n",
"SCnComV[5920].Len() = 1\n",
"SCnComV[5921].Len() = 1\n",
"SCnComV[5922].Len() = 1\n",
"SCnComV[5923].Len() = 1\n",
"SCnComV[5924].Len() = 1\n",
"SCnComV[5925].Len() = 1\n",
"SCnComV[5926].Len() = 1\n",
"SCnComV[5927].Len() = 1\n",
"SCnComV[5928].Len() = 1\n",
"SCnComV[5929].Len() = 1\n",
"SCnComV[5930].Len() = 1\n",
"SCnComV[5931].Len() = 1\n",
"SCnComV[5932].Len() = 1\n",
"SCnComV[5933].Len() = 1\n",
"SCnComV[5934].Len() = 1\n",
"SCnComV[5935].Len() = 1\n",
"SCnComV[5936].Len() = 1\n",
"SCnComV[5937].Len() = 1\n",
"SCnComV[5938].Len() = 1\n",
"SCnComV[5939].Len() = 1\n",
"SCnComV[5940].Len() = 1\n",
"SCnComV[5941].Len() = 1\n",
"SCnComV[5942].Len() = 1\n",
"SCnComV[5943].Len() = 1\n",
"SCnComV[5944].Len() = 1\n",
"SCnComV[5945].Len() = 1\n",
"SCnComV[5946].Len() = 1\n",
"SCnComV[5947].Len() = 1\n",
"SCnComV[5948].Len() = 1\n",
"SCnComV[5949].Len() = 1\n",
"SCnComV[5950].Len() = 1\n",
"SCnComV[5951].Len() = 1\n",
"SCnComV[5952].Len() = 1\n",
"SCnComV[5953].Len() = 1\n",
"SCnComV[5954].Len() = 1\n",
"SCnComV[5955].Len() = 1\n",
"SCnComV[5956].Len() = 1\n",
"SCnComV[5957].Len() = 1\n",
"SCnComV[5958].Len() = 1\n",
"SCnComV[5959].Len() = 1\n",
"SCnComV[5960].Len() = 1\n",
"SCnComV[5961].Len() = 1\n",
"SCnComV[5962].Len() = 1\n",
"SCnComV[5963].Len() = 1\n",
"SCnComV[5964].Len() = 1\n",
"SCnComV[5965].Len() = 1\n",
"SCnComV[5966].Len() = 1\n",
"SCnComV[5967].Len() = 1\n",
"SCnComV[5968].Len() = 1\n",
"SCnComV[5969].Len() = 1\n",
"SCnComV[5970].Len() = 1\n",
"SCnComV[5971].Len() = 1\n",
"SCnComV[5972].Len() = 1\n",
"SCnComV[5973].Len() = 1\n",
"SCnComV[5974].Len() = 1\n",
"SCnComV[5975].Len() = 1\n",
"SCnComV[5976].Len() = 1\n",
"SCnComV[5977].Len() = 1\n",
"SCnComV[5978].Len() = 1\n",
"SCnComV[5979].Len() = 1\n",
"SCnComV[5980].Len() = 1\n",
"SCnComV[5981].Len() = 1\n",
"SCnComV[5982].Len() = 1\n",
"SCnComV[5983].Len() = 1\n",
"SCnComV[5984].Len() = 1\n",
"SCnComV[5985].Len() = 1\n",
"SCnComV[5986].Len() = 1\n",
"SCnComV[5987].Len() = 1\n",
"SCnComV[5988].Len() = 1\n",
"SCnComV[5989].Len() = 1\n",
"SCnComV[5990].Len() = 1\n",
"SCnComV[5991].Len() = 1\n",
"SCnComV[5992].Len() = 1\n",
"SCnComV[5993].Len() = 1\n",
"SCnComV[5994].Len() = 1\n",
"SCnComV[5995].Len() = 1\n",
"SCnComV[5996].Len() = 1\n",
"SCnComV[5997].Len() = 1\n",
"SCnComV[5998].Len() = 1\n",
"SCnComV[5999].Len() = 1\n",
"SCnComV[6000].Len() = 1\n",
"SCnComV[6001].Len() = 1\n",
"SCnComV[6002].Len() = 1\n",
"SCnComV[6003].Len() = 1\n",
"SCnComV[6004].Len() = 1\n",
"SCnComV[6005].Len() = 1\n",
"SCnComV[6006].Len() = 1\n",
"SCnComV[6007].Len() = 1\n",
"SCnComV[6008].Len() = 1\n",
"SCnComV[6009].Len() = 1\n",
"SCnComV[6010].Len() = 1\n",
"SCnComV[6011].Len() = 1\n",
"SCnComV[6012].Len() = 1\n",
"SCnComV[6013].Len() = 1\n",
"SCnComV[6014].Len() = 1\n",
"SCnComV[6015].Len() = 1\n",
"SCnComV[6016].Len() = 1\n",
"SCnComV[6017].Len() = 1\n",
"SCnComV[6018].Len() = 1\n",
"SCnComV[6019].Len() = 1\n",
"SCnComV[6020].Len() = 1\n",
"SCnComV[6021].Len() = 1\n",
"SCnComV[6022].Len() = 1\n",
"SCnComV[6023].Len() = 1\n",
"SCnComV[6024].Len() = 1\n",
"SCnComV[6025].Len() = 1\n",
"SCnComV[6026].Len() = 1\n",
"SCnComV[6027].Len() = 1\n",
"SCnComV[6028].Len() = 1\n",
"SCnComV[6029].Len() = 1\n",
"SCnComV[6030].Len() = 1\n",
"SCnComV[6031].Len() = 1\n",
"SCnComV[6032].Len() = 1\n",
"SCnComV[6033].Len() = 1\n",
"SCnComV[6034].Len() = 1\n",
"SCnComV[6035].Len() = 1\n",
"SCnComV[6036].Len() = 1\n",
"SCnComV[6037].Len() = 1\n",
"SCnComV[6038].Len() = 1\n",
"SCnComV[6039].Len() = 1\n",
"SCnComV[6040].Len() = 1\n",
"SCnComV[6041].Len() = 1\n",
"SCnComV[6042].Len() = 1\n",
"SCnComV[6043].Len() = 1\n",
"SCnComV[6044].Len() = 1\n",
"SCnComV[6045].Len() = 1\n",
"SCnComV[6046].Len() = 1\n",
"SCnComV[6047].Len() = 1\n",
"SCnComV[6048].Len() = 1\n",
"SCnComV[6049].Len() = 1\n",
"SCnComV[6050].Len() = 1\n",
"SCnComV[6051].Len() = 1\n",
"SCnComV[6052].Len() = 1\n",
"SCnComV[6053].Len() = 1\n",
"SCnComV[6054].Len() = 1\n",
"SCnComV[6055].Len() = 1\n",
"SCnComV[6056].Len() = 1\n",
"SCnComV[6057].Len() = 1\n",
"SCnComV[6058].Len() = 1\n",
"SCnComV[6059].Len() = 1\n",
"SCnComV[6060].Len() = 1\n",
"SCnComV[6061].Len() = 1\n",
"SCnComV[6062].Len() = 1\n",
"SCnComV[6063].Len() = 1\n",
"SCnComV[6064].Len() = 1\n",
"SCnComV[6065].Len() = 1\n",
"SCnComV[6066].Len() = 1\n",
"SCnComV[6067].Len() = 1\n",
"SCnComV[6068].Len() = 1\n",
"SCnComV[6069].Len() = 1\n",
"SCnComV[6070].Len() = 1\n",
"SCnComV[6071].Len() = 1\n",
"SCnComV[6072].Len() = 1\n",
"SCnComV[6073].Len() = 1\n",
"SCnComV[6074].Len() = 1\n",
"SCnComV[6075].Len() = 1\n",
"SCnComV[6076].Len() = 1\n",
"SCnComV[6077].Len() = 1\n",
"SCnComV[6078].Len() = 1\n",
"SCnComV[6079].Len() = 1\n",
"SCnComV[6080].Len() = 1\n",
"SCnComV[6081].Len() = 1\n",
"SCnComV[6082].Len() = 1\n",
"SCnComV[6083].Len() = 1\n",
"SCnComV[6084].Len() = 1\n",
"SCnComV[6085].Len() = 1\n",
"SCnComV[6086].Len() = 1\n",
"SCnComV[6087].Len() = 1\n",
"SCnComV[6088].Len() = 1\n",
"SCnComV[6089].Len() = 1\n",
"SCnComV[6090].Len() = 1\n",
"SCnComV[6091].Len() = 1\n",
"SCnComV[6092].Len() = 1\n",
"SCnComV[6093].Len() = 1\n",
"SCnComV[6094].Len() = 1\n",
"SCnComV[6095].Len() = 1\n",
"SCnComV[6096].Len() = 1\n",
"SCnComV[6097].Len() = 1\n",
"SCnComV[6098].Len() = 1\n",
"SCnComV[6099].Len() = 1\n",
"SCnComV[6100].Len() = 1\n",
"SCnComV[6101].Len() = 1\n",
"SCnComV[6102].Len() = 1\n",
"SCnComV[6103].Len() = 1\n",
"SCnComV[6104].Len() = 1\n",
"SCnComV[6105].Len() = 1\n",
"SCnComV[6106].Len() = 1\n",
"SCnComV[6107].Len() = 1\n",
"SCnComV[6108].Len() = 1\n",
"SCnComV[6109].Len() = 1\n",
"SCnComV[6110].Len() = 1\n",
"SCnComV[6111].Len() = 1\n",
"SCnComV[6112].Len() = 1\n",
"SCnComV[6113].Len() = 1\n",
"SCnComV[6114].Len() = 1\n",
"SCnComV[6115].Len() = 1\n",
"SCnComV[6116].Len() = 1\n",
"SCnComV[6117].Len() = 1\n",
"SCnComV[6118].Len() = 1\n",
"SCnComV[6119].Len() = 1\n",
"SCnComV[6120].Len() = 1\n",
"SCnComV[6121].Len() = 1\n",
"SCnComV[6122].Len() = 1\n",
"SCnComV[6123].Len() = 1\n",
"SCnComV[6124].Len() = 1\n",
"SCnComV[6125].Len() = 1\n",
"SCnComV[6126].Len() = 1\n",
"SCnComV[6127].Len() = 1\n",
"SCnComV[6128].Len() = 1\n",
"SCnComV[6129].Len() = 1\n",
"SCnComV[6130].Len() = 1\n",
"SCnComV[6131].Len() = 1\n",
"SCnComV[6132].Len() = 1\n",
"SCnComV[6133].Len() = 1\n",
"SCnComV[6134].Len() = 1\n",
"SCnComV[6135].Len() = 1\n",
"SCnComV[6136].Len() = 1\n",
"SCnComV[6137].Len() = 1\n",
"SCnComV[6138].Len() = 1\n",
"SCnComV[6139].Len() = 1\n",
"SCnComV[6140].Len() = 1\n",
"SCnComV[6141].Len() = 1\n",
"SCnComV[6142].Len() = 1\n",
"SCnComV[6143].Len() = 1\n",
"SCnComV[6144].Len() = 1\n",
"SCnComV[6145].Len() = 1\n",
"SCnComV[6146].Len() = 1\n",
"SCnComV[6147].Len() = 1\n",
"SCnComV[6148].Len() = 1\n",
"SCnComV[6149].Len() = 1\n",
"SCnComV[6150].Len() = 1\n",
"SCnComV[6151].Len() = 1\n",
"SCnComV[6152].Len() = 1\n",
"SCnComV[6153].Len() = 1\n",
"SCnComV[6154].Len() = 1\n",
"SCnComV[6155].Len() = 1\n",
"SCnComV[6156].Len() = 1\n",
"SCnComV[6157].Len() = 1\n",
"SCnComV[6158].Len() = 1\n",
"SCnComV[6159].Len() = 1\n",
"SCnComV[6160].Len() = 1\n",
"SCnComV[6161].Len() = 1\n",
"SCnComV[6162].Len() = 1\n",
"SCnComV[6163].Len() = 1\n",
"SCnComV[6164].Len() = 1\n",
"SCnComV[6165].Len() = 1\n",
"SCnComV[6166].Len() = 1\n",
"SCnComV[6167].Len() = 1\n",
"SCnComV[6168].Len() = 1\n",
"SCnComV[6169].Len() = 1\n",
"SCnComV[6170].Len() = 1\n",
"SCnComV[6171].Len() = 1\n",
"SCnComV[6172].Len() = 1\n",
"SCnComV[6173].Len() = 1\n",
"SCnComV[6174].Len() = 1\n",
"SCnComV[6175].Len() = 1\n",
"SCnComV[6176].Len() = 1\n",
"SCnComV[6177].Len() = 1\n",
"SCnComV[6178].Len() = 1\n",
"SCnComV[6179].Len() = 1\n",
"SCnComV[6180].Len() = 1\n",
"SCnComV[6181].Len() = 1\n",
"SCnComV[6182].Len() = 1\n",
"SCnComV[6183].Len() = 1\n",
"SCnComV[6184].Len() = 1\n",
"SCnComV[6185].Len() = 1\n",
"SCnComV[6186].Len() = 1\n",
"SCnComV[6187].Len() = 1\n",
"SCnComV[6188].Len() = 1\n",
"SCnComV[6189].Len() = 1\n",
"SCnComV[6190].Len() = 1\n",
"SCnComV[6191].Len() = 1\n",
"SCnComV[6192].Len() = 1\n",
"SCnComV[6193].Len() = 1\n",
"SCnComV[6194].Len() = 1\n",
"SCnComV[6195].Len() = 1\n",
"SCnComV[6196].Len() = 1\n",
"SCnComV[6197].Len() = 1\n",
"SCnComV[6198].Len() = 1\n",
"SCnComV[6199].Len() = 1\n",
"SCnComV[6200].Len() = 1\n",
"SCnComV[6201].Len() = 1\n",
"SCnComV[6202].Len() = 1\n",
"SCnComV[6203].Len() = 1\n",
"SCnComV[6204].Len() = 1\n",
"SCnComV[6205].Len() = 1\n",
"SCnComV[6206].Len() = 1\n",
"SCnComV[6207].Len() = 1\n",
"SCnComV[6208].Len() = 1\n",
"SCnComV[6209].Len() = 1\n",
"SCnComV[6210].Len() = 1\n",
"SCnComV[6211].Len() = 1\n",
"SCnComV[6212].Len() = 1\n",
"SCnComV[6213].Len() = 1\n",
"SCnComV[6214].Len() = 1\n",
"SCnComV[6215].Len() = 1\n",
"SCnComV[6216].Len() = 1\n",
"SCnComV[6217].Len() = 1\n",
"SCnComV[6218].Len() = 1\n",
"SCnComV[6219].Len() = 1\n",
"SCnComV[6220].Len() = 1\n",
"SCnComV[6221].Len() = 1\n",
"SCnComV[6222].Len() = 1\n",
"SCnComV[6223].Len() = 1\n",
"SCnComV[6224].Len() = 1\n",
"SCnComV[6225].Len() = 1\n",
"SCnComV[6226].Len() = 1\n",
"SCnComV[6227].Len() = 1\n",
"SCnComV[6228].Len() = 1\n",
"SCnComV[6229].Len() = 1\n",
"SCnComV[6230].Len() = 1\n",
"SCnComV[6231].Len() = 1\n",
"SCnComV[6232].Len() = 1\n",
"SCnComV[6233].Len() = 1\n",
"SCnComV[6234].Len() = 1\n",
"SCnComV[6235].Len() = 1\n",
"SCnComV[6236].Len() = 1\n",
"SCnComV[6237].Len() = 1\n",
"SCnComV[6238].Len() = 1\n",
"SCnComV[6239].Len() = 1\n",
"SCnComV[6240].Len() = 1\n",
"SCnComV[6241].Len() = 1\n",
"SCnComV[6242].Len() = 1\n",
"SCnComV[6243].Len() = 1\n",
"SCnComV[6244].Len() = 1\n",
"SCnComV[6245].Len() = 1\n",
"SCnComV[6246].Len() = 1\n",
"SCnComV[6247].Len() = 1\n",
"SCnComV[6248].Len() = 1\n",
"SCnComV[6249].Len() = 1\n",
"SCnComV[6250].Len() = 1\n",
"SCnComV[6251].Len() = 1\n",
"SCnComV[6252].Len() = 1\n",
"SCnComV[6253].Len() = 1\n",
"SCnComV[6254].Len() = 1\n",
"SCnComV[6255].Len() = 1\n",
"SCnComV[6256].Len() = 1\n",
"SCnComV[6257].Len() = 1\n",
"SCnComV[6258].Len() = 1\n",
"SCnComV[6259].Len() = 1\n",
"SCnComV[6260].Len() = 1\n",
"SCnComV[6261].Len() = 1\n",
"SCnComV[6262].Len() = 1\n",
"SCnComV[6263].Len() = 1\n",
"SCnComV[6264].Len() = 1\n",
"SCnComV[6265].Len() = 1\n",
"SCnComV[6266].Len() = 1\n",
"SCnComV[6267].Len() = 1\n",
"SCnComV[6268].Len() = 1\n",
"SCnComV[6269].Len() = 1\n",
"SCnComV[6270].Len() = 1\n",
"SCnComV[6271].Len() = 1\n",
"SCnComV[6272].Len() = 1\n",
"SCnComV[6273].Len() = 1\n",
"SCnComV[6274].Len() = 1\n",
"SCnComV[6275].Len() = 1\n",
"SCnComV[6276].Len() = 1\n",
"SCnComV[6277].Len() = 1\n",
"SCnComV[6278].Len() = 1\n",
"SCnComV[6279].Len() = 1\n",
"SCnComV[6280].Len() = 1\n",
"SCnComV[6281].Len() = 1\n",
"SCnComV[6282].Len() = 1\n",
"SCnComV[6283].Len() = 1\n",
"SCnComV[6284].Len() = 1\n",
"SCnComV[6285].Len() = 1\n",
"SCnComV[6286].Len() = 1\n",
"SCnComV[6287].Len() = 1\n",
"SCnComV[6288].Len() = 1\n",
"SCnComV[6289].Len() = 1\n",
"SCnComV[6290].Len() = 1\n",
"SCnComV[6291].Len() = 1\n",
"SCnComV[6292].Len() = 1\n",
"SCnComV[6293].Len() = 1\n",
"SCnComV[6294].Len() = 1\n",
"SCnComV[6295].Len() = 1\n",
"SCnComV[6296].Len() = 1\n",
"SCnComV[6297].Len() = 1\n",
"SCnComV[6298].Len() = 1\n",
"SCnComV[6299].Len() = 1\n",
"SCnComV[6300].Len() = 1\n",
"SCnComV[6301].Len() = 1\n",
"SCnComV[6302].Len() = 1\n",
"SCnComV[6303].Len() = 1\n",
"SCnComV[6304].Len() = 1\n",
"SCnComV[6305].Len() = 1\n",
"SCnComV[6306].Len() = 1\n",
"SCnComV[6307].Len() = 1\n",
"SCnComV[6308].Len() = 1\n",
"SCnComV[6309].Len() = 1\n",
"SCnComV[6310].Len() = 1\n",
"SCnComV[6311].Len() = 1\n",
"SCnComV[6312].Len() = 1\n",
"SCnComV[6313].Len() = 1\n",
"SCnComV[6314].Len() = 1\n",
"SCnComV[6315].Len() = 1\n",
"SCnComV[6316].Len() = 1\n",
"SCnComV[6317].Len() = 1\n",
"SCnComV[6318].Len() = 1\n",
"SCnComV[6319].Len() = 1\n",
"SCnComV[6320].Len() = 1\n",
"SCnComV[6321].Len() = 1\n",
"SCnComV[6322].Len() = 1\n",
"SCnComV[6323].Len() = 1\n",
"SCnComV[6324].Len() = 1\n",
"SCnComV[6325].Len() = 1\n",
"SCnComV[6326].Len() = 1\n",
"SCnComV[6327].Len() = 1\n",
"SCnComV[6328].Len() = 1\n",
"SCnComV[6329].Len() = 1\n",
"SCnComV[6330].Len() = 1\n",
"SCnComV[6331].Len() = 1\n",
"SCnComV[6332].Len() = 1\n",
"SCnComV[6333].Len() = 1\n",
"SCnComV[6334].Len() = 1\n",
"SCnComV[6335].Len() = 1\n",
"SCnComV[6336].Len() = 1\n",
"SCnComV[6337].Len() = 1\n",
"SCnComV[6338].Len() = 1\n",
"SCnComV[6339].Len() = 1\n",
"SCnComV[6340].Len() = 1\n",
"SCnComV[6341].Len() = 1\n",
"SCnComV[6342].Len() = 1\n",
"SCnComV[6343].Len() = 1\n",
"SCnComV[6344].Len() = 1\n",
"SCnComV[6345].Len() = 1\n",
"SCnComV[6346].Len() = 1\n",
"SCnComV[6347].Len() = 1\n",
"SCnComV[6348].Len() = 1\n",
"SCnComV[6349].Len() = 1\n",
"SCnComV[6350].Len() = 1\n",
"SCnComV[6351].Len() = 1\n",
"SCnComV[6352].Len() = 1\n",
"SCnComV[6353].Len() = 1\n",
"SCnComV[6354].Len() = 1\n",
"SCnComV[6355].Len() = 1\n",
"SCnComV[6356].Len() = 1\n",
"SCnComV[6357].Len() = 1\n",
"SCnComV[6358].Len() = 1\n",
"SCnComV[6359].Len() = 1\n",
"SCnComV[6360].Len() = 1\n",
"SCnComV[6361].Len() = 1\n",
"SCnComV[6362].Len() = 1\n",
"SCnComV[6363].Len() = 1\n",
"SCnComV[6364].Len() = 1\n",
"SCnComV[6365].Len() = 1\n",
"SCnComV[6366].Len() = 1\n",
"SCnComV[6367].Len() = 1\n",
"SCnComV[6368].Len() = 1\n",
"SCnComV[6369].Len() = 1\n",
"SCnComV[6370].Len() = 1\n",
"SCnComV[6371].Len() = 1\n",
"SCnComV[6372].Len() = 1\n",
"SCnComV[6373].Len() = 1\n",
"SCnComV[6374].Len() = 1\n",
"SCnComV[6375].Len() = 1\n",
"SCnComV[6376].Len() = 1\n",
"SCnComV[6377].Len() = 1\n",
"SCnComV[6378].Len() = 1\n",
"SCnComV[6379].Len() = 1\n",
"SCnComV[6380].Len() = 1\n",
"SCnComV[6381].Len() = 1\n",
"SCnComV[6382].Len() = 1\n",
"SCnComV[6383].Len() = 1\n",
"SCnComV[6384].Len() = 1\n",
"SCnComV[6385].Len() = 1\n",
"SCnComV[6386].Len() = 1\n",
"SCnComV[6387].Len() = 1\n",
"SCnComV[6388].Len() = 1\n",
"SCnComV[6389].Len() = 1\n",
"SCnComV[6390].Len() = 1\n",
"SCnComV[6391].Len() = 1\n",
"SCnComV[6392].Len() = 1\n",
"SCnComV[6393].Len() = 1\n",
"SCnComV[6394].Len() = 1\n",
"SCnComV[6395].Len() = 1\n",
"SCnComV[6396].Len() = 1\n",
"SCnComV[6397].Len() = 1\n",
"SCnComV[6398].Len() = 1\n",
"SCnComV[6399].Len() = 1\n",
"SCnComV[6400].Len() = 1\n",
"SCnComV[6401].Len() = 1\n",
"SCnComV[6402].Len() = 1\n",
"SCnComV[6403].Len() = 1\n",
"SCnComV[6404].Len() = 1\n",
"SCnComV[6405].Len() = 1\n",
"SCnComV[6406].Len() = 1\n",
"SCnComV[6407].Len() = 1\n",
"SCnComV[6408].Len() = 1\n",
"SCnComV[6409].Len() = 1\n",
"SCnComV[6410].Len() = 1\n",
"SCnComV[6411].Len() = 1\n",
"SCnComV[6412].Len() = 1\n",
"SCnComV[6413].Len() = 1\n",
"SCnComV[6414].Len() = 1\n",
"SCnComV[6415].Len() = 1\n",
"SCnComV[6416].Len() = 1\n",
"SCnComV[6417].Len() = 1\n",
"SCnComV[6418].Len() = 1\n",
"SCnComV[6419].Len() = 1\n",
"SCnComV[6420].Len() = 1\n",
"SCnComV[6421].Len() = 1\n",
"SCnComV[6422].Len() = 1\n",
"SCnComV[6423].Len() = 1\n",
"SCnComV[6424].Len() = 1\n",
"SCnComV[6425].Len() = 1\n",
"SCnComV[6426].Len() = 1\n",
"SCnComV[6427].Len() = 1\n",
"SCnComV[6428].Len() = 1\n",
"SCnComV[6429].Len() = 1\n",
"SCnComV[6430].Len() = 1\n",
"SCnComV[6431].Len() = 1\n",
"SCnComV[6432].Len() = 1\n",
"SCnComV[6433].Len() = 1\n",
"SCnComV[6434].Len() = 1\n",
"SCnComV[6435].Len() = 1\n",
"SCnComV[6436].Len() = 1\n",
"SCnComV[6437].Len() = 1\n",
"SCnComV[6438].Len() = 1\n",
"SCnComV[6439].Len() = 1\n",
"SCnComV[6440].Len() = 1\n",
"SCnComV[6441].Len() = 1\n",
"SCnComV[6442].Len() = 1\n",
"SCnComV[6443].Len() = 1\n",
"SCnComV[6444].Len() = 1\n",
"SCnComV[6445].Len() = 1\n",
"SCnComV[6446].Len() = 1\n",
"SCnComV[6447].Len() = 1\n",
"SCnComV[6448].Len() = 1\n",
"SCnComV[6449].Len() = 1\n",
"SCnComV[6450].Len() = 1\n",
"SCnComV[6451].Len() = 1\n",
"SCnComV[6452].Len() = 1\n",
"SCnComV[6453].Len() = 1\n",
"SCnComV[6454].Len() = 1\n",
"SCnComV[6455].Len() = 1\n",
"SCnComV[6456].Len() = 1\n",
"SCnComV[6457].Len() = 1\n",
"SCnComV[6458].Len() = 1\n",
"SCnComV[6459].Len() = 1\n",
"SCnComV[6460].Len() = 1\n",
"SCnComV[6461].Len() = 1\n",
"SCnComV[6462].Len() = 1\n",
"SCnComV[6463].Len() = 1\n",
"SCnComV[6464].Len() = 1\n",
"SCnComV[6465].Len() = 1\n",
"SCnComV[6466].Len() = 1\n",
"SCnComV[6467].Len() = 1\n",
"SCnComV[6468].Len() = 1\n",
"SCnComV[6469].Len() = 1\n",
"SCnComV[6470].Len() = 1\n",
"SCnComV[6471].Len() = 1\n",
"SCnComV[6472].Len() = 1\n",
"SCnComV[6473].Len() = 1\n",
"SCnComV[6474].Len() = 1\n",
"SCnComV[6475].Len() = 1\n",
"SCnComV[6476].Len() = 1\n",
"SCnComV[6477].Len() = 1\n",
"SCnComV[6478].Len() = 1\n",
"SCnComV[6479].Len() = 1\n",
"SCnComV[6480].Len() = 1\n",
"SCnComV[6481].Len() = 1\n",
"SCnComV[6482].Len() = 1\n",
"SCnComV[6483].Len() = 1\n",
"SCnComV[6484].Len() = 1\n",
"SCnComV[6485].Len() = 1\n",
"SCnComV[6486].Len() = 1\n",
"SCnComV[6487].Len() = 1\n",
"SCnComV[6488].Len() = 1\n",
"SCnComV[6489].Len() = 1\n",
"SCnComV[6490].Len() = 1\n",
"SCnComV[6491].Len() = 1\n",
"SCnComV[6492].Len() = 1\n",
"SCnComV[6493].Len() = 1\n",
"SCnComV[6494].Len() = 1\n",
"SCnComV[6495].Len() = 1\n",
"SCnComV[6496].Len() = 1\n",
"SCnComV[6497].Len() = 1\n",
"SCnComV[6498].Len() = 1\n",
"SCnComV[6499].Len() = 1\n",
"SCnComV[6500].Len() = 1\n",
"SCnComV[6501].Len() = 1\n",
"SCnComV[6502].Len() = 1\n",
"SCnComV[6503].Len() = 1\n",
"SCnComV[6504].Len() = 1\n",
"SCnComV[6505].Len() = 1\n",
"SCnComV[6506].Len() = 1\n",
"SCnComV[6507].Len() = 1\n",
"SCnComV[6508].Len() = 1\n",
"SCnComV[6509].Len() = 1\n",
"SCnComV[6510].Len() = 1\n",
"SCnComV[6511].Len() = 1\n",
"SCnComV[6512].Len() = 1\n",
"SCnComV[6513].Len() = 1\n",
"SCnComV[6514].Len() = 1\n",
"SCnComV[6515].Len() = 1\n",
"SCnComV[6516].Len() = 1\n",
"SCnComV[6517].Len() = 1\n",
"SCnComV[6518].Len() = 1\n",
"SCnComV[6519].Len() = 1\n",
"SCnComV[6520].Len() = 1\n",
"SCnComV[6521].Len() = 1\n",
"SCnComV[6522].Len() = 1\n",
"SCnComV[6523].Len() = 1\n",
"SCnComV[6524].Len() = 1\n",
"SCnComV[6525].Len() = 1\n",
"SCnComV[6526].Len() = 1\n",
"SCnComV[6527].Len() = 1\n",
"SCnComV[6528].Len() = 1\n",
"SCnComV[6529].Len() = 1\n",
"SCnComV[6530].Len() = 1\n",
"SCnComV[6531].Len() = 1\n",
"SCnComV[6532].Len() = 1\n",
"SCnComV[6533].Len() = 1\n",
"SCnComV[6534].Len() = 1\n",
"SCnComV[6535].Len() = 1\n",
"SCnComV[6536].Len() = 1\n",
"SCnComV[6537].Len() = 1\n",
"SCnComV[6538].Len() = 1\n",
"SCnComV[6539].Len() = 1\n",
"SCnComV[6540].Len() = 1\n",
"SCnComV[6541].Len() = 1\n",
"SCnComV[6542].Len() = 1\n",
"SCnComV[6543].Len() = 1\n",
"SCnComV[6544].Len() = 1\n",
"SCnComV[6545].Len() = 1\n",
"SCnComV[6546].Len() = 1\n",
"SCnComV[6547].Len() = 1\n",
"SCnComV[6548].Len() = 1\n",
"SCnComV[6549].Len() = 1\n",
"SCnComV[6550].Len() = 1\n",
"SCnComV[6551].Len() = 1\n",
"SCnComV[6552].Len() = 1\n",
"SCnComV[6553].Len() = 1\n",
"SCnComV[6554].Len() = 1\n",
"SCnComV[6555].Len() = 1\n",
"SCnComV[6556].Len() = 1\n",
"SCnComV[6557].Len() = 1\n",
"SCnComV[6558].Len() = 1\n",
"SCnComV[6559].Len() = 1\n",
"SCnComV[6560].Len() = 1\n",
"SCnComV[6561].Len() = 1\n",
"SCnComV[6562].Len() = 1\n",
"SCnComV[6563].Len() = 1\n",
"SCnComV[6564].Len() = 1\n",
"SCnComV[6565].Len() = 1\n",
"SCnComV[6566].Len() = 1\n",
"SCnComV[6567].Len() = 1\n",
"SCnComV[6568].Len() = 1\n",
"SCnComV[6569].Len() = 1\n",
"SCnComV[6570].Len() = 1\n",
"SCnComV[6571].Len() = 1\n",
"SCnComV[6572].Len() = 1\n",
"SCnComV[6573].Len() = 1\n",
"SCnComV[6574].Len() = 1\n",
"SCnComV[6575].Len() = 1\n",
"SCnComV[6576].Len() = 1\n",
"SCnComV[6577].Len() = 1\n",
"SCnComV[6578].Len() = 1\n",
"SCnComV[6579].Len() = 1\n",
"SCnComV[6580].Len() = 1\n",
"SCnComV[6581].Len() = 1\n",
"SCnComV[6582].Len() = 1\n",
"SCnComV[6583].Len() = 1\n",
"SCnComV[6584].Len() = 1\n",
"SCnComV[6585].Len() = 1\n",
"SCnComV[6586].Len() = 1\n",
"SCnComV[6587].Len() = 1\n",
"SCnComV[6588].Len() = 1\n",
"SCnComV[6589].Len() = 1\n",
"SCnComV[6590].Len() = 1\n",
"SCnComV[6591].Len() = 1\n",
"SCnComV[6592].Len() = 1\n",
"SCnComV[6593].Len() = 1\n",
"SCnComV[6594].Len() = 1\n",
"SCnComV[6595].Len() = 1\n",
"SCnComV[6596].Len() = 1\n",
"SCnComV[6597].Len() = 1\n",
"SCnComV[6598].Len() = 1\n",
"SCnComV[6599].Len() = 1\n",
"SCnComV[6600].Len() = 1\n",
"SCnComV[6601].Len() = 1\n",
"SCnComV[6602].Len() = 1\n",
"SCnComV[6603].Len() = 1\n",
"SCnComV[6604].Len() = 1\n",
"SCnComV[6605].Len() = 1\n",
"SCnComV[6606].Len() = 1\n",
"SCnComV[6607].Len() = 1\n",
"SCnComV[6608].Len() = 1\n",
"SCnComV[6609].Len() = 1\n",
"SCnComV[6610].Len() = 1\n",
"SCnComV[6611].Len() = 1\n",
"SCnComV[6612].Len() = 1\n",
"SCnComV[6613].Len() = 1\n",
"SCnComV[6614].Len() = 1\n",
"SCnComV[6615].Len() = 1\n",
"SCnComV[6616].Len() = 1\n",
"SCnComV[6617].Len() = 1\n",
"SCnComV[6618].Len() = 1\n",
"SCnComV[6619].Len() = 1\n",
"SCnComV[6620].Len() = 1\n",
"SCnComV[6621].Len() = 1\n",
"SCnComV[6622].Len() = 1\n",
"SCnComV[6623].Len() = 1\n",
"SCnComV[6624].Len() = 1\n",
"SCnComV[6625].Len() = 1\n",
"SCnComV[6626].Len() = 1\n",
"SCnComV[6627].Len() = 1\n",
"SCnComV[6628].Len() = 1\n",
"SCnComV[6629].Len() = 1\n",
"SCnComV[6630].Len() = 1\n",
"SCnComV[6631].Len() = 1\n",
"SCnComV[6632].Len() = 1\n",
"SCnComV[6633].Len() = 1\n",
"SCnComV[6634].Len() = 1\n",
"SCnComV[6635].Len() = 1\n",
"SCnComV[6636].Len() = 1\n",
"SCnComV[6637].Len() = 1\n",
"SCnComV[6638].Len() = 1\n",
"SCnComV[6639].Len() = 1\n",
"SCnComV[6640].Len() = 1\n",
"SCnComV[6641].Len() = 1\n",
"SCnComV[6642].Len() = 1\n",
"SCnComV[6643].Len() = 1\n",
"SCnComV[6644].Len() = 1\n",
"SCnComV[6645].Len() = 1\n",
"SCnComV[6646].Len() = 1\n",
"SCnComV[6647].Len() = 1\n",
"SCnComV[6648].Len() = 1\n",
"SCnComV[6649].Len() = 1\n",
"SCnComV[6650].Len() = 1\n",
"SCnComV[6651].Len() = 1\n",
"SCnComV[6652].Len() = 1\n",
"SCnComV[6653].Len() = 1\n",
"SCnComV[6654].Len() = 1\n",
"SCnComV[6655].Len() = 1\n",
"SCnComV[6656].Len() = 1\n",
"SCnComV[6657].Len() = 1\n",
"SCnComV[6658].Len() = 1\n",
"SCnComV[6659].Len() = 1\n",
"SCnComV[6660].Len() = 1\n",
"SCnComV[6661].Len() = 1\n",
"SCnComV[6662].Len() = 1\n",
"SCnComV[6663].Len() = 1\n",
"SCnComV[6664].Len() = 1\n",
"SCnComV[6665].Len() = 1\n",
"SCnComV[6666].Len() = 1\n",
"SCnComV[6667].Len() = 1\n",
"SCnComV[6668].Len() = 1\n",
"SCnComV[6669].Len() = 1\n",
"SCnComV[6670].Len() = 1\n",
"SCnComV[6671].Len() = 1\n",
"SCnComV[6672].Len() = 1\n",
"SCnComV[6673].Len() = 1\n",
"SCnComV[6674].Len() = 1\n",
"SCnComV[6675].Len() = 1\n",
"SCnComV[6676].Len() = 1\n",
"SCnComV[6677].Len() = 1\n",
"SCnComV[6678].Len() = 1\n",
"SCnComV[6679].Len() = 1\n",
"SCnComV[6680].Len() = 1\n",
"SCnComV[6681].Len() = 1\n",
"SCnComV[6682].Len() = 1\n",
"SCnComV[6683].Len() = 1\n",
"SCnComV[6684].Len() = 1\n",
"SCnComV[6685].Len() = 1\n",
"SCnComV[6686].Len() = 1\n",
"SCnComV[6687].Len() = 1\n",
"SCnComV[6688].Len() = 1\n",
"SCnComV[6689].Len() = 1\n",
"SCnComV[6690].Len() = 1\n",
"SCnComV[6691].Len() = 1\n",
"SCnComV[6692].Len() = 1\n",
"SCnComV[6693].Len() = 1\n",
"SCnComV[6694].Len() = 1\n",
"SCnComV[6695].Len() = 1\n",
"SCnComV[6696].Len() = 1\n",
"SCnComV[6697].Len() = 1\n",
"SCnComV[6698].Len() = 1\n",
"SCnComV[6699].Len() = 1\n",
"SCnComV[6700].Len() = 1\n",
"SCnComV[6701].Len() = 1\n",
"SCnComV[6702].Len() = 1\n",
"SCnComV[6703].Len() = 1\n",
"SCnComV[6704].Len() = 1\n",
"SCnComV[6705].Len() = 1\n",
"SCnComV[6706].Len() = 1\n",
"SCnComV[6707].Len() = 1\n",
"SCnComV[6708].Len() = 1\n",
"SCnComV[6709].Len() = 1\n",
"SCnComV[6710].Len() = 1\n",
"SCnComV[6711].Len() = 1\n",
"SCnComV[6712].Len() = 1\n",
"SCnComV[6713].Len() = 1\n",
"SCnComV[6714].Len() = 1\n",
"SCnComV[6715].Len() = 1\n",
"SCnComV[6716].Len() = 1\n",
"SCnComV[6717].Len() = 1\n",
"SCnComV[6718].Len() = 1\n",
"SCnComV[6719].Len() = 1\n",
"SCnComV[6720].Len() = 1\n",
"SCnComV[6721].Len() = 1\n",
"SCnComV[6722].Len() = 1\n",
"SCnComV[6723].Len() = 1\n",
"SCnComV[6724].Len() = 1\n",
"SCnComV[6725].Len() = 1\n",
"SCnComV[6726].Len() = 1\n",
"SCnComV[6727].Len() = 1\n",
"SCnComV[6728].Len() = 1\n",
"SCnComV[6729].Len() = 1\n",
"SCnComV[6730].Len() = 1\n",
"SCnComV[6731].Len() = 1\n",
"SCnComV[6732].Len() = 1\n",
"SCnComV[6733].Len() = 1\n",
"SCnComV[6734].Len() = 1\n",
"SCnComV[6735].Len() = 1\n",
"SCnComV[6736].Len() = 1\n",
"SCnComV[6737].Len() = 1\n",
"SCnComV[6738].Len() = 1\n",
"SCnComV[6739].Len() = 1\n",
"SCnComV[6740].Len() = 1\n",
"SCnComV[6741].Len() = 1\n",
"SCnComV[6742].Len() = 1\n",
"SCnComV[6743].Len() = 1\n",
"SCnComV[6744].Len() = 1\n",
"SCnComV[6745].Len() = 1\n",
"SCnComV[6746].Len() = 1\n",
"SCnComV[6747].Len() = 1\n",
"SCnComV[6748].Len() = 1\n",
"SCnComV[6749].Len() = 1\n",
"SCnComV[6750].Len() = 1\n",
"SCnComV[6751].Len() = 1\n",
"SCnComV[6752].Len() = 1\n",
"SCnComV[6753].Len() = 1\n",
"SCnComV[6754].Len() = 1\n",
"SCnComV[6755].Len() = 1\n",
"SCnComV[6756].Len() = 1\n",
"SCnComV[6757].Len() = 1\n",
"SCnComV[6758].Len() = 1\n",
"SCnComV[6759].Len() = 1\n",
"SCnComV[6760].Len() = 1\n",
"SCnComV[6761].Len() = 1\n",
"SCnComV[6762].Len() = 1\n",
"SCnComV[6763].Len() = 1\n",
"SCnComV[6764].Len() = 1\n",
"SCnComV[6765].Len() = 1\n",
"SCnComV[6766].Len() = 1\n",
"SCnComV[6767].Len() = 1\n",
"SCnComV[6768].Len() = 1\n",
"SCnComV[6769].Len() = 1\n",
"SCnComV[6770].Len() = 1\n",
"SCnComV[6771].Len() = 1\n",
"SCnComV[6772].Len() = 1\n",
"SCnComV[6773].Len() = 1\n",
"SCnComV[6774].Len() = 1\n",
"SCnComV[6775].Len() = 1\n",
"SCnComV[6776].Len() = 1\n",
"SCnComV[6777].Len() = 1\n",
"SCnComV[6778].Len() = 1\n",
"SCnComV[6779].Len() = 1\n",
"SCnComV[6780].Len() = 1\n",
"SCnComV[6781].Len() = 1\n",
"SCnComV[6782].Len() = 1\n",
"SCnComV[6783].Len() = 1\n",
"SCnComV[6784].Len() = 1\n",
"SCnComV[6785].Len() = 1\n",
"SCnComV[6786].Len() = 1\n",
"SCnComV[6787].Len() = 1\n",
"SCnComV[6788].Len() = 1\n",
"SCnComV[6789].Len() = 1\n",
"SCnComV[6790].Len() = 1\n",
"SCnComV[6791].Len() = 1\n",
"SCnComV[6792].Len() = 1\n",
"SCnComV[6793].Len() = 1\n",
"SCnComV[6794].Len() = 1\n",
"SCnComV[6795].Len() = 1\n",
"SCnComV[6796].Len() = 1\n",
"SCnComV[6797].Len() = 1\n",
"SCnComV[6798].Len() = 1\n",
"SCnComV[6799].Len() = 1\n",
"SCnComV[6800].Len() = 1\n",
"SCnComV[6801].Len() = 1\n",
"SCnComV[6802].Len() = 1\n",
"SCnComV[6803].Len() = 1\n",
"SCnComV[6804].Len() = 1\n",
"SCnComV[6805].Len() = 1\n",
"SCnComV[6806].Len() = 1\n",
"SCnComV[6807].Len() = 1\n",
"SCnComV[6808].Len() = 1\n",
"SCnComV[6809].Len() = 1\n",
"SCnComV[6810].Len() = 1\n",
"SCnComV[6811].Len() = 1\n",
"SCnComV[6812].Len() = 1\n",
"SCnComV[6813].Len() = 1\n",
"SCnComV[6814].Len() = 1\n",
"SCnComV[6815].Len() = 1\n",
"SCnComV[6816].Len() = 1\n",
"SCnComV[6817].Len() = 1\n",
"SCnComV[6818].Len() = 1\n",
"SCnComV[6819].Len() = 1\n",
"SCnComV[6820].Len() = 1\n",
"SCnComV[6821].Len() = 1\n",
"SCnComV[6822].Len() = 1\n",
"SCnComV[6823].Len() = 1\n",
"SCnComV[6824].Len() = 1\n",
"SCnComV[6825].Len() = 1\n",
"SCnComV[6826].Len() = 1\n",
"SCnComV[6827].Len() = 1\n",
"SCnComV[6828].Len() = 1\n",
"SCnComV[6829].Len() = 1\n",
"SCnComV[6830].Len() = 1\n",
"SCnComV[6831].Len() = 1\n",
"SCnComV[6832].Len() = 1\n",
"SCnComV[6833].Len() = 1\n",
"SCnComV[6834].Len() = 1\n",
"SCnComV[6835].Len() = 1\n",
"SCnComV[6836].Len() = 1\n",
"SCnComV[6837].Len() = 1\n",
"SCnComV[6838].Len() = 1\n",
"SCnComV[6839].Len() = 1\n",
"SCnComV[6840].Len() = 1\n",
"SCnComV[6841].Len() = 1\n",
"SCnComV[6842].Len() = 1\n",
"SCnComV[6843].Len() = 1\n",
"SCnComV[6844].Len() = 1\n",
"SCnComV[6845].Len() = 1\n",
"SCnComV[6846].Len() = 1\n",
"SCnComV[6847].Len() = 1\n",
"SCnComV[6848].Len() = 1\n",
"SCnComV[6849].Len() = 1\n",
"SCnComV[6850].Len() = 1\n",
"SCnComV[6851].Len() = 1\n",
"SCnComV[6852].Len() = 1\n",
"SCnComV[6853].Len() = 1\n",
"SCnComV[6854].Len() = 1\n",
"SCnComV[6855].Len() = 1\n",
"SCnComV[6856].Len() = 1\n",
"SCnComV[6857].Len() = 1\n",
"SCnComV[6858].Len() = 1\n",
"SCnComV[6859].Len() = 1\n",
"SCnComV[6860].Len() = 1\n",
"SCnComV[6861].Len() = 1\n",
"SCnComV[6862].Len() = 1\n",
"SCnComV[6863].Len() = 1\n",
"SCnComV[6864].Len() = 1\n",
"SCnComV[6865].Len() = 1\n",
"SCnComV[6866].Len() = 1\n",
"SCnComV[6867].Len() = 1\n",
"SCnComV[6868].Len() = 1\n",
"SCnComV[6869].Len() = 1\n",
"SCnComV[6870].Len() = 1\n",
"SCnComV[6871].Len() = 1\n",
"SCnComV[6872].Len() = 1\n",
"SCnComV[6873].Len() = 1\n",
"SCnComV[6874].Len() = 1\n",
"SCnComV[6875].Len() = 1\n",
"SCnComV[6876].Len() = 1\n",
"SCnComV[6877].Len() = 1\n",
"SCnComV[6878].Len() = 1\n",
"SCnComV[6879].Len() = 1\n",
"SCnComV[6880].Len() = 1\n",
"SCnComV[6881].Len() = 1\n",
"SCnComV[6882].Len() = 1\n",
"SCnComV[6883].Len() = 1\n",
"SCnComV[6884].Len() = 1\n",
"SCnComV[6885].Len() = 1\n",
"SCnComV[6886].Len() = 1\n",
"SCnComV[6887].Len() = 1\n",
"SCnComV[6888].Len() = 1\n",
"SCnComV[6889].Len() = 1\n",
"SCnComV[6890].Len() = 1\n",
"SCnComV[6891].Len() = 1\n",
"SCnComV[6892].Len() = 1\n",
"SCnComV[6893].Len() = 1\n",
"SCnComV[6894].Len() = 1\n",
"SCnComV[6895].Len() = 1\n",
"SCnComV[6896].Len() = 1\n",
"SCnComV[6897].Len() = 1\n",
"SCnComV[6898].Len() = 1\n",
"SCnComV[6899].Len() = 1\n",
"SCnComV[6900].Len() = 1\n",
"SCnComV[6901].Len() = 1\n",
"SCnComV[6902].Len() = 1\n",
"SCnComV[6903].Len() = 1\n",
"SCnComV[6904].Len() = 1\n",
"SCnComV[6905].Len() = 1\n",
"SCnComV[6906].Len() = 1\n",
"SCnComV[6907].Len() = 1\n",
"SCnComV[6908].Len() = 1\n",
"SCnComV[6909].Len() = 1\n",
"SCnComV[6910].Len() = 1\n",
"SCnComV[6911].Len() = 1\n",
"SCnComV[6912].Len() = 1\n",
"SCnComV[6913].Len() = 1\n",
"SCnComV[6914].Len() = 1\n",
"SCnComV[6915].Len() = 1\n",
"SCnComV[6916].Len() = 1\n",
"SCnComV[6917].Len() = 1\n",
"SCnComV[6918].Len() = 1\n",
"SCnComV[6919].Len() = 1\n",
"SCnComV[6920].Len() = 1\n",
"SCnComV[6921].Len() = 1\n",
"SCnComV[6922].Len() = 1\n",
"SCnComV[6923].Len() = 1\n",
"SCnComV[6924].Len() = 1\n",
"SCnComV[6925].Len() = 1\n",
"SCnComV[6926].Len() = 1\n",
"SCnComV[6927].Len() = 1\n",
"SCnComV[6928].Len() = 1\n",
"SCnComV[6929].Len() = 1\n",
"SCnComV[6930].Len() = 1\n",
"SCnComV[6931].Len() = 1\n",
"SCnComV[6932].Len() = 1\n",
"SCnComV[6933].Len() = 1\n",
"SCnComV[6934].Len() = 1\n",
"SCnComV[6935].Len() = 1\n",
"SCnComV[6936].Len() = 1\n",
"SCnComV[6937].Len() = 1\n",
"SCnComV[6938].Len() = 1\n",
"SCnComV[6939].Len() = 1\n",
"SCnComV[6940].Len() = 1\n",
"SCnComV[6941].Len() = 1\n",
"SCnComV[6942].Len() = 1\n",
"SCnComV[6943].Len() = 1\n",
"SCnComV[6944].Len() = 1\n",
"SCnComV[6945].Len() = 1\n",
"SCnComV[6946].Len() = 1\n",
"SCnComV[6947].Len() = 1\n",
"SCnComV[6948].Len() = 1\n",
"SCnComV[6949].Len() = 1\n",
"SCnComV[6950].Len() = 1\n",
"SCnComV[6951].Len() = 1\n",
"SCnComV[6952].Len() = 1\n",
"SCnComV[6953].Len() = 1\n",
"SCnComV[6954].Len() = 1\n",
"SCnComV[6955].Len() = 1\n",
"SCnComV[6956].Len() = 1\n",
"SCnComV[6957].Len() = 1\n",
"SCnComV[6958].Len() = 1\n",
"SCnComV[6959].Len() = 1\n",
"SCnComV[6960].Len() = 1\n",
"SCnComV[6961].Len() = 1\n",
"SCnComV[6962].Len() = 1\n",
"SCnComV[6963].Len() = 1\n",
"SCnComV[6964].Len() = 1\n",
"SCnComV[6965].Len() = 1\n",
"SCnComV[6966].Len() = 1\n",
"SCnComV[6967].Len() = 1\n",
"SCnComV[6968].Len() = 1\n",
"SCnComV[6969].Len() = 1\n",
"SCnComV[6970].Len() = 1\n",
"SCnComV[6971].Len() = 1\n",
"SCnComV[6972].Len() = 1\n",
"SCnComV[6973].Len() = 1\n",
"SCnComV[6974].Len() = 1\n",
"SCnComV[6975].Len() = 1\n",
"SCnComV[6976].Len() = 1\n",
"SCnComV[6977].Len() = 1\n",
"SCnComV[6978].Len() = 1\n",
"SCnComV[6979].Len() = 1\n",
"SCnComV[6980].Len() = 1\n",
"SCnComV[6981].Len() = 1\n",
"SCnComV[6982].Len() = 1\n",
"SCnComV[6983].Len() = 1\n",
"SCnComV[6984].Len() = 1\n",
"SCnComV[6985].Len() = 1\n",
"SCnComV[6986].Len() = 1\n",
"SCnComV[6987].Len() = 1\n",
"SCnComV[6988].Len() = 1\n",
"SCnComV[6989].Len() = 1\n",
"SCnComV[6990].Len() = 1\n",
"SCnComV[6991].Len() = 1\n",
"SCnComV[6992].Len() = 1\n",
"SCnComV[6993].Len() = 1\n",
"SCnComV[6994].Len() = 1\n",
"SCnComV[6995].Len() = 1\n",
"SCnComV[6996].Len() = 1\n",
"SCnComV[6997].Len() = 1\n",
"SCnComV[6998].Len() = 1\n",
"SCnComV[6999].Len() = 1\n",
"SCnComV[7000].Len() = 1\n",
"SCnComV[7001].Len() = 1\n",
"SCnComV[7002].Len() = 1\n",
"SCnComV[7003].Len() = 1\n",
"SCnComV[7004].Len() = 1\n",
"SCnComV[7005].Len() = 1\n",
"SCnComV[7006].Len() = 1\n",
"SCnComV[7007].Len() = 1\n",
"SCnComV[7008].Len() = 1\n",
"SCnComV[7009].Len() = 1\n",
"SCnComV[7010].Len() = 1\n",
"SCnComV[7011].Len() = 1\n",
"SCnComV[7012].Len() = 1\n",
"SCnComV[7013].Len() = 1\n",
"SCnComV[7014].Len() = 1\n",
"SCnComV[7015].Len() = 1\n",
"SCnComV[7016].Len() = 1\n",
"SCnComV[7017].Len() = 1\n",
"SCnComV[7018].Len() = 1\n",
"SCnComV[7019].Len() = 1\n",
"SCnComV[7020].Len() = 1\n",
"SCnComV[7021].Len() = 1\n",
"SCnComV[7022].Len() = 1\n",
"SCnComV[7023].Len() = 1\n",
"SCnComV[7024].Len() = 1\n",
"SCnComV[7025].Len() = 1\n",
"SCnComV[7026].Len() = 1\n",
"SCnComV[7027].Len() = 1\n",
"SCnComV[7028].Len() = 1\n",
"SCnComV[7029].Len() = 1\n",
"SCnComV[7030].Len() = 1\n",
"SCnComV[7031].Len() = 1\n",
"SCnComV[7032].Len() = 1\n",
"SCnComV[7033].Len() = 1\n",
"SCnComV[7034].Len() = 1\n",
"SCnComV[7035].Len() = 1\n",
"SCnComV[7036].Len() = 1\n",
"SCnComV[7037].Len() = 1\n",
"SCnComV[7038].Len() = 1\n",
"SCnComV[7039].Len() = 1\n",
"SCnComV[7040].Len() = 1\n",
"SCnComV[7041].Len() = 1\n",
"SCnComV[7042].Len() = 1\n",
"SCnComV[7043].Len() = 1\n",
"SCnComV[7044].Len() = 1\n",
"SCnComV[7045].Len() = 1\n",
"SCnComV[7046].Len() = 1\n",
"SCnComV[7047].Len() = 1\n",
"SCnComV[7048].Len() = 1\n",
"SCnComV[7049].Len() = 1\n",
"SCnComV[7050].Len() = 1\n",
"SCnComV[7051].Len() = 1\n",
"SCnComV[7052].Len() = 1\n",
"SCnComV[7053].Len() = 1\n",
"SCnComV[7054].Len() = 1\n",
"SCnComV[7055].Len() = 1\n",
"SCnComV[7056].Len() = 1\n",
"SCnComV[7057].Len() = 1\n",
"SCnComV[7058].Len() = 1\n",
"SCnComV[7059].Len() = 1\n",
"SCnComV[7060].Len() = 1\n",
"SCnComV[7061].Len() = 1\n",
"SCnComV[7062].Len() = 1\n",
"SCnComV[7063].Len() = 1\n",
"SCnComV[7064].Len() = 1\n",
"SCnComV[7065].Len() = 1\n",
"SCnComV[7066].Len() = 1\n",
"SCnComV[7067].Len() = 1\n",
"SCnComV[7068].Len() = 1\n",
"SCnComV[7069].Len() = 1\n",
"SCnComV[7070].Len() = 1\n",
"SCnComV[7071].Len() = 1\n",
"SCnComV[7072].Len() = 1\n",
"SCnComV[7073].Len() = 1\n",
"SCnComV[7074].Len() = 1\n",
"SCnComV[7075].Len() = 1\n",
"SCnComV[7076].Len() = 1\n",
"SCnComV[7077].Len() = 1\n",
"SCnComV[7078].Len() = 1\n",
"SCnComV[7079].Len() = 1\n",
"SCnComV[7080].Len() = 1\n",
"SCnComV[7081].Len() = 1\n",
"SCnComV[7082].Len() = 1\n",
"SCnComV[7083].Len() = 1\n",
"SCnComV[7084].Len() = 1\n",
"SCnComV[7085].Len() = 1\n",
"SCnComV[7086].Len() = 1\n",
"SCnComV[7087].Len() = 1\n",
"SCnComV[7088].Len() = 1\n",
"SCnComV[7089].Len() = 1\n",
"SCnComV[7090].Len() = 1\n",
"SCnComV[7091].Len() = 1\n",
"SCnComV[7092].Len() = 1\n",
"SCnComV[7093].Len() = 1\n",
"SCnComV[7094].Len() = 1\n",
"SCnComV[7095].Len() = 1\n",
"SCnComV[7096].Len() = 1\n",
"SCnComV[7097].Len() = 1\n",
"SCnComV[7098].Len() = 1\n",
"SCnComV[7099].Len() = 1\n",
"SCnComV[7100].Len() = 1\n",
"SCnComV[7101].Len() = 1\n",
"SCnComV[7102].Len() = 1\n",
"SCnComV[7103].Len() = 1\n",
"SCnComV[7104].Len() = 1\n",
"SCnComV[7105].Len() = 1\n",
"SCnComV[7106].Len() = 1\n",
"SCnComV[7107].Len() = 1\n",
"SCnComV[7108].Len() = 1\n",
"SCnComV[7109].Len() = 1\n",
"SCnComV[7110].Len() = 1\n",
"SCnComV[7111].Len() = 1\n",
"SCnComV[7112].Len() = 1\n",
"SCnComV[7113].Len() = 1\n",
"SCnComV[7114].Len() = 1\n",
"SCnComV[7115].Len() = 1\n",
"SCnComV[7116].Len() = 1\n",
"SCnComV[7117].Len() = 1\n",
"SCnComV[7118].Len() = 1\n",
"SCnComV[7119].Len() = 1\n",
"SCnComV[7120].Len() = 1\n",
"SCnComV[7121].Len() = 1\n",
"SCnComV[7122].Len() = 1\n",
"SCnComV[7123].Len() = 1\n",
"SCnComV[7124].Len() = 1\n",
"SCnComV[7125].Len() = 1\n",
"SCnComV[7126].Len() = 1\n",
"SCnComV[7127].Len() = 1\n",
"SCnComV[7128].Len() = 1\n",
"SCnComV[7129].Len() = 1\n",
"SCnComV[7130].Len() = 1\n",
"SCnComV[7131].Len() = 1\n",
"SCnComV[7132].Len() = 1\n",
"SCnComV[7133].Len() = 1\n",
"SCnComV[7134].Len() = 1\n",
"SCnComV[7135].Len() = 1\n",
"SCnComV[7136].Len() = 1\n",
"SCnComV[7137].Len() = 1\n",
"SCnComV[7138].Len() = 1\n",
"SCnComV[7139].Len() = 1\n",
"SCnComV[7140].Len() = 1\n",
"SCnComV[7141].Len() = 1\n",
"SCnComV[7142].Len() = 1\n",
"SCnComV[7143].Len() = 1\n",
"SCnComV[7144].Len() = 1\n",
"SCnComV[7145].Len() = 1\n",
"SCnComV[7146].Len() = 1\n",
"SCnComV[7147].Len() = 1\n",
"SCnComV[7148].Len() = 1\n",
"SCnComV[7149].Len() = 1\n",
"SCnComV[7150].Len() = 1\n",
"SCnComV[7151].Len() = 1\n",
"SCnComV[7152].Len() = 1\n",
"SCnComV[7153].Len() = 1\n",
"SCnComV[7154].Len() = 1\n",
"SCnComV[7155].Len() = 1\n",
"SCnComV[7156].Len() = 1\n",
"SCnComV[7157].Len() = 1\n",
"SCnComV[7158].Len() = 1\n",
"SCnComV[7159].Len() = 1\n",
"SCnComV[7160].Len() = 1\n",
"SCnComV[7161].Len() = 1\n",
"SCnComV[7162].Len() = 1\n",
"SCnComV[7163].Len() = 1\n",
"SCnComV[7164].Len() = 1\n",
"SCnComV[7165].Len() = 1\n",
"SCnComV[7166].Len() = 1\n",
"SCnComV[7167].Len() = 1\n",
"SCnComV[7168].Len() = 1\n",
"SCnComV[7169].Len() = 1\n",
"SCnComV[7170].Len() = 1\n",
"SCnComV[7171].Len() = 1\n",
"SCnComV[7172].Len() = 1\n",
"SCnComV[7173].Len() = 1\n",
"SCnComV[7174].Len() = 1\n",
"SCnComV[7175].Len() = 1\n",
"SCnComV[7176].Len() = 1\n",
"SCnComV[7177].Len() = 1\n",
"SCnComV[7178].Len() = 1\n",
"SCnComV[7179].Len() = 1\n",
"SCnComV[7180].Len() = 1\n",
"SCnComV[7181].Len() = 1\n",
"SCnComV[7182].Len() = 1\n",
"SCnComV[7183].Len() = 1\n",
"SCnComV[7184].Len() = 1\n",
"SCnComV[7185].Len() = 1\n",
"SCnComV[7186].Len() = 1\n",
"SCnComV[7187].Len() = 1\n",
"SCnComV[7188].Len() = 1\n",
"SCnComV[7189].Len() = 1\n",
"SCnComV[7190].Len() = 1\n",
"SCnComV[7191].Len() = 1\n",
"SCnComV[7192].Len() = 1\n",
"SCnComV[7193].Len() = 1\n",
"SCnComV[7194].Len() = 1\n",
"SCnComV[7195].Len() = 1\n",
"SCnComV[7196].Len() = 1\n",
"SCnComV[7197].Len() = 1\n",
"SCnComV[7198].Len() = 1\n",
"SCnComV[7199].Len() = 1\n",
"SCnComV[7200].Len() = 1\n",
"SCnComV[7201].Len() = 1\n",
"SCnComV[7202].Len() = 1\n",
"SCnComV[7203].Len() = 1\n",
"SCnComV[7204].Len() = 1\n",
"SCnComV[7205].Len() = 1\n",
"SCnComV[7206].Len() = 1\n",
"SCnComV[7207].Len() = 1\n",
"SCnComV[7208].Len() = 1\n",
"SCnComV[7209].Len() = 1\n",
"SCnComV[7210].Len() = 1\n",
"SCnComV[7211].Len() = 1\n",
"SCnComV[7212].Len() = 1\n",
"SCnComV[7213].Len() = 1\n",
"SCnComV[7214].Len() = 1\n",
"SCnComV[7215].Len() = 1\n",
"SCnComV[7216].Len() = 1\n",
"SCnComV[7217].Len() = 1\n",
"SCnComV[7218].Len() = 1\n",
"SCnComV[7219].Len() = 1\n",
"SCnComV[7220].Len() = 1\n",
"SCnComV[7221].Len() = 1\n",
"SCnComV[7222].Len() = 1\n",
"SCnComV[7223].Len() = 1\n",
"SCnComV[7224].Len() = 1\n",
"SCnComV[7225].Len() = 1\n",
"SCnComV[7226].Len() = 1\n",
"SCnComV[7227].Len() = 1\n",
"SCnComV[7228].Len() = 1\n",
"SCnComV[7229].Len() = 1\n",
"SCnComV[7230].Len() = 1\n",
"SCnComV[7231].Len() = 1\n",
"SCnComV[7232].Len() = 1\n",
"SCnComV[7233].Len() = 1\n",
"SCnComV[7234].Len() = 1\n",
"SCnComV[7235].Len() = 1\n",
"SCnComV[7236].Len() = 1\n",
"SCnComV[7237].Len() = 1\n",
"SCnComV[7238].Len() = 1\n",
"SCnComV[7239].Len() = 1\n",
"SCnComV[7240].Len() = 1\n",
"SCnComV[7241].Len() = 1\n",
"SCnComV[7242].Len() = 1\n",
"SCnComV[7243].Len() = 1\n",
"SCnComV[7244].Len() = 1\n",
"SCnComV[7245].Len() = 1\n",
"SCnComV[7246].Len() = 1\n",
"SCnComV[7247].Len() = 1\n",
"SCnComV[7248].Len() = 1\n",
"SCnComV[7249].Len() = 1\n",
"SCnComV[7250].Len() = 1\n",
"SCnComV[7251].Len() = 1\n",
"SCnComV[7252].Len() = 1\n",
"SCnComV[7253].Len() = 1\n",
"SCnComV[7254].Len() = 1\n",
"SCnComV[7255].Len() = 1\n",
"SCnComV[7256].Len() = 1\n",
"SCnComV[7257].Len() = 1\n",
"SCnComV[7258].Len() = 1\n",
"SCnComV[7259].Len() = 1\n",
"SCnComV[7260].Len() = 1\n",
"SCnComV[7261].Len() = 1\n",
"SCnComV[7262].Len() = 1\n",
"SCnComV[7263].Len() = 1\n",
"SCnComV[7264].Len() = 1\n",
"SCnComV[7265].Len() = 1\n",
"SCnComV[7266].Len() = 1\n",
"SCnComV[7267].Len() = 1\n",
"SCnComV[7268].Len() = 1\n",
"SCnComV[7269].Len() = 1\n",
"SCnComV[7270].Len() = 1\n",
"SCnComV[7271].Len() = 1\n",
"SCnComV[7272].Len() = 1\n",
"SCnComV[7273].Len() = 1\n",
"SCnComV[7274].Len() = 1\n",
"SCnComV[7275].Len() = 1\n",
"SCnComV[7276].Len() = 1\n",
"SCnComV[7277].Len() = 1\n",
"SCnComV[7278].Len() = 1\n",
"SCnComV[7279].Len() = 1\n",
"SCnComV[7280].Len() = 1\n",
"SCnComV[7281].Len() = 1\n",
"SCnComV[7282].Len() = 1\n",
"SCnComV[7283].Len() = 1\n",
"SCnComV[7284].Len() = 1\n",
"SCnComV[7285].Len() = 1\n",
"SCnComV[7286].Len() = 1\n",
"SCnComV[7287].Len() = 1\n",
"SCnComV[7288].Len() = 1\n",
"SCnComV[7289].Len() = 1\n",
"SCnComV[7290].Len() = 1\n",
"SCnComV[7291].Len() = 1\n",
"SCnComV[7292].Len() = 1\n",
"SCnComV[7293].Len() = 1\n",
"SCnComV[7294].Len() = 1\n",
"SCnComV[7295].Len() = 1\n",
"SCnComV[7296].Len() = 1\n",
"SCnComV[7297].Len() = 1\n",
"SCnComV[7298].Len() = 1\n",
"SCnComV[7299].Len() = 1\n",
"SCnComV[7300].Len() = 1\n",
"SCnComV[7301].Len() = 1\n",
"SCnComV[7302].Len() = 1\n",
"SCnComV[7303].Len() = 1\n",
"SCnComV[7304].Len() = 1\n",
"SCnComV[7305].Len() = 1\n",
"SCnComV[7306].Len() = 1\n",
"SCnComV[7307].Len() = 1\n",
"SCnComV[7308].Len() = 1\n",
"SCnComV[7309].Len() = 1\n",
"SCnComV[7310].Len() = 1\n",
"SCnComV[7311].Len() = 1\n",
"SCnComV[7312].Len() = 1\n",
"SCnComV[7313].Len() = 1\n",
"SCnComV[7314].Len() = 1\n",
"SCnComV[7315].Len() = 1\n",
"SCnComV[7316].Len() = 1\n",
"SCnComV[7317].Len() = 1\n",
"SCnComV[7318].Len() = 1\n",
"SCnComV[7319].Len() = 1\n",
"SCnComV[7320].Len() = 1\n",
"SCnComV[7321].Len() = 1\n",
"SCnComV[7322].Len() = 1\n",
"SCnComV[7323].Len() = 1\n",
"SCnComV[7324].Len() = 1\n",
"SCnComV[7325].Len() = 1\n",
"SCnComV[7326].Len() = 1\n",
"SCnComV[7327].Len() = 1\n",
"SCnComV[7328].Len() = 1\n",
"SCnComV[7329].Len() = 1\n",
"SCnComV[7330].Len() = 1\n",
"SCnComV[7331].Len() = 1\n",
"SCnComV[7332].Len() = 1\n",
"SCnComV[7333].Len() = 1\n",
"SCnComV[7334].Len() = 1\n",
"SCnComV[7335].Len() = 1\n",
"SCnComV[7336].Len() = 1\n",
"SCnComV[7337].Len() = 1\n",
"SCnComV[7338].Len() = 1\n",
"SCnComV[7339].Len() = 1\n",
"SCnComV[7340].Len() = 1\n",
"SCnComV[7341].Len() = 1\n",
"SCnComV[7342].Len() = 1\n",
"SCnComV[7343].Len() = 1\n",
"SCnComV[7344].Len() = 1\n",
"SCnComV[7345].Len() = 1\n",
"SCnComV[7346].Len() = 1\n",
"SCnComV[7347].Len() = 1\n",
"SCnComV[7348].Len() = 1\n",
"SCnComV[7349].Len() = 1\n",
"SCnComV[7350].Len() = 1\n",
"SCnComV[7351].Len() = 1\n",
"SCnComV[7352].Len() = 1\n",
"SCnComV[7353].Len() = 1\n",
"SCnComV[7354].Len() = 1\n",
"SCnComV[7355].Len() = 1\n",
"SCnComV[7356].Len() = 1\n",
"SCnComV[7357].Len() = 1\n",
"SCnComV[7358].Len() = 1\n",
"SCnComV[7359].Len() = 1\n",
"SCnComV[7360].Len() = 1\n",
"SCnComV[7361].Len() = 1\n",
"SCnComV[7362].Len() = 1\n",
"SCnComV[7363].Len() = 1\n",
"SCnComV[7364].Len() = 1\n",
"SCnComV[7365].Len() = 1\n",
"SCnComV[7366].Len() = 1\n",
"SCnComV[7367].Len() = 1\n",
"SCnComV[7368].Len() = 1\n",
"SCnComV[7369].Len() = 1\n",
"SCnComV[7370].Len() = 1\n",
"SCnComV[7371].Len() = 1\n",
"SCnComV[7372].Len() = 1\n",
"SCnComV[7373].Len() = 1\n",
"SCnComV[7374].Len() = 1\n",
"SCnComV[7375].Len() = 1\n",
"SCnComV[7376].Len() = 1\n",
"SCnComV[7377].Len() = 1\n",
"SCnComV[7378].Len() = 1\n",
"SCnComV[7379].Len() = 1\n",
"SCnComV[7380].Len() = 1\n",
"SCnComV[7381].Len() = 1\n",
"SCnComV[7382].Len() = 1\n",
"SCnComV[7383].Len() = 1\n",
"SCnComV[7384].Len() = 1\n",
"SCnComV[7385].Len() = 1\n",
"SCnComV[7386].Len() = 1\n",
"SCnComV[7387].Len() = 1\n",
"SCnComV[7388].Len() = 1\n",
"SCnComV[7389].Len() = 1\n",
"SCnComV[7390].Len() = 1\n",
"SCnComV[7391].Len() = 1\n",
"SCnComV[7392].Len() = 1\n",
"SCnComV[7393].Len() = 1\n",
"SCnComV[7394].Len() = 1\n",
"SCnComV[7395].Len() = 1\n",
"SCnComV[7396].Len() = 1\n",
"SCnComV[7397].Len() = 1\n",
"SCnComV[7398].Len() = 1\n",
"SCnComV[7399].Len() = 1\n",
"SCnComV[7400].Len() = 1\n",
"SCnComV[7401].Len() = 1\n",
"SCnComV[7402].Len() = 1\n",
"SCnComV[7403].Len() = 1\n",
"SCnComV[7404].Len() = 1\n",
"SCnComV[7405].Len() = 1\n",
"SCnComV[7406].Len() = 1\n",
"SCnComV[7407].Len() = 1\n",
"SCnComV[7408].Len() = 1\n",
"SCnComV[7409].Len() = 1\n",
"SCnComV[7410].Len() = 1\n",
"SCnComV[7411].Len() = 1\n",
"SCnComV[7412].Len() = 1\n",
"SCnComV[7413].Len() = 1\n",
"SCnComV[7414].Len() = 1\n",
"SCnComV[7415].Len() = 1\n",
"SCnComV[7416].Len() = 1\n",
"SCnComV[7417].Len() = 1\n",
"SCnComV[7418].Len() = 1\n",
"SCnComV[7419].Len() = 1\n",
"SCnComV[7420].Len() = 1\n",
"SCnComV[7421].Len() = 1\n",
"SCnComV[7422].Len() = 1\n",
"SCnComV[7423].Len() = 1\n",
"SCnComV[7424].Len() = 1\n",
"SCnComV[7425].Len() = 1\n",
"SCnComV[7426].Len() = 1\n",
"SCnComV[7427].Len() = 1\n",
"SCnComV[7428].Len() = 1\n",
"SCnComV[7429].Len() = 1\n",
"SCnComV[7430].Len() = 1\n",
"SCnComV[7431].Len() = 1\n",
"SCnComV[7432].Len() = 1\n",
"SCnComV[7433].Len() = 1\n",
"SCnComV[7434].Len() = 1\n",
"SCnComV[7435].Len() = 1\n",
"SCnComV[7436].Len() = 1\n",
"SCnComV[7437].Len() = 1\n",
"SCnComV[7438].Len() = 1\n",
"SCnComV[7439].Len() = 1\n",
"SCnComV[7440].Len() = 1\n",
"SCnComV[7441].Len() = 1\n",
"SCnComV[7442].Len() = 1\n",
"SCnComV[7443].Len() = 1\n",
"SCnComV[7444].Len() = 1\n",
"SCnComV[7445].Len() = 1\n",
"SCnComV[7446].Len() = 1\n",
"SCnComV[7447].Len() = 1\n",
"SCnComV[7448].Len() = 1\n",
"SCnComV[7449].Len() = 1\n",
"SCnComV[7450].Len() = 1\n",
"SCnComV[7451].Len() = 1\n",
"SCnComV[7452].Len() = 1\n",
"SCnComV[7453].Len() = 1\n",
"SCnComV[7454].Len() = 1\n",
"SCnComV[7455].Len() = 1\n",
"SCnComV[7456].Len() = 1\n",
"SCnComV[7457].Len() = 1\n",
"SCnComV[7458].Len() = 1\n",
"SCnComV[7459].Len() = 1\n",
"SCnComV[7460].Len() = 1\n",
"SCnComV[7461].Len() = 1\n",
"SCnComV[7462].Len() = 1\n",
"SCnComV[7463].Len() = 1\n",
"SCnComV[7464].Len() = 1\n",
"SCnComV[7465].Len() = 1\n",
"SCnComV[7466].Len() = 1\n",
"SCnComV[7467].Len() = 1\n",
"SCnComV[7468].Len() = 1\n",
"SCnComV[7469].Len() = 1\n",
"SCnComV[7470].Len() = 1\n",
"SCnComV[7471].Len() = 1\n",
"SCnComV[7472].Len() = 1\n",
"SCnComV[7473].Len() = 1\n",
"SCnComV[7474].Len() = 1\n",
"SCnComV[7475].Len() = 1\n",
"SCnComV[7476].Len() = 1\n",
"SCnComV[7477].Len() = 1\n",
"SCnComV[7478].Len() = 1\n",
"SCnComV[7479].Len() = 1\n",
"SCnComV[7480].Len() = 1\n",
"SCnComV[7481].Len() = 1\n",
"SCnComV[7482].Len() = 1\n",
"SCnComV[7483].Len() = 1\n",
"SCnComV[7484].Len() = 1\n",
"SCnComV[7485].Len() = 1\n",
"SCnComV[7486].Len() = 1\n",
"SCnComV[7487].Len() = 1\n",
"SCnComV[7488].Len() = 1\n",
"SCnComV[7489].Len() = 1\n",
"SCnComV[7490].Len() = 1\n",
"SCnComV[7491].Len() = 1\n",
"SCnComV[7492].Len() = 1\n",
"SCnComV[7493].Len() = 1\n",
"SCnComV[7494].Len() = 1\n",
"SCnComV[7495].Len() = 1\n",
"SCnComV[7496].Len() = 1\n",
"SCnComV[7497].Len() = 1\n",
"SCnComV[7498].Len() = 1\n",
"SCnComV[7499].Len() = 1\n",
"SCnComV[7500].Len() = 1\n",
"SCnComV[7501].Len() = 1\n",
"SCnComV[7502].Len() = 1\n",
"SCnComV[7503].Len() = 1\n",
"SCnComV[7504].Len() = 1\n",
"SCnComV[7505].Len() = 1\n",
"SCnComV[7506].Len() = 1\n",
"SCnComV[7507].Len() = 1\n",
"SCnComV[7508].Len() = 1\n",
"SCnComV[7509].Len() = 1\n",
"SCnComV[7510].Len() = 1\n",
"SCnComV[7511].Len() = 1\n",
"SCnComV[7512].Len() = 1\n",
"SCnComV[7513].Len() = 1\n",
"SCnComV[7514].Len() = 1\n",
"SCnComV[7515].Len() = 1\n",
"SCnComV[7516].Len() = 1\n",
"SCnComV[7517].Len() = 1\n",
"SCnComV[7518].Len() = 1\n",
"SCnComV[7519].Len() = 1\n",
"SCnComV[7520].Len() = 1\n",
"SCnComV[7521].Len() = 1\n",
"SCnComV[7522].Len() = 1\n",
"SCnComV[7523].Len() = 1\n",
"SCnComV[7524].Len() = 1\n",
"SCnComV[7525].Len() = 1\n",
"SCnComV[7526].Len() = 1\n",
"SCnComV[7527].Len() = 1\n",
"SCnComV[7528].Len() = 1\n",
"SCnComV[7529].Len() = 1\n",
"SCnComV[7530].Len() = 1\n",
"SCnComV[7531].Len() = 1\n",
"SCnComV[7532].Len() = 1\n",
"SCnComV[7533].Len() = 1\n",
"SCnComV[7534].Len() = 1\n",
"SCnComV[7535].Len() = 1\n",
"SCnComV[7536].Len() = 1\n",
"SCnComV[7537].Len() = 1\n",
"SCnComV[7538].Len() = 1\n",
"SCnComV[7539].Len() = 1\n",
"SCnComV[7540].Len() = 1\n",
"SCnComV[7541].Len() = 1\n",
"SCnComV[7542].Len() = 1\n",
"SCnComV[7543].Len() = 1\n",
"SCnComV[7544].Len() = 1\n",
"SCnComV[7545].Len() = 1\n",
"SCnComV[7546].Len() = 1\n",
"SCnComV[7547].Len() = 1\n",
"SCnComV[7548].Len() = 1\n",
"SCnComV[7549].Len() = 1\n",
"SCnComV[7550].Len() = 1\n",
"SCnComV[7551].Len() = 1\n",
"SCnComV[7552].Len() = 1\n",
"SCnComV[7553].Len() = 1\n",
"SCnComV[7554].Len() = 1\n",
"SCnComV[7555].Len() = 1\n",
"SCnComV[7556].Len() = 1\n",
"SCnComV[7557].Len() = 1\n",
"SCnComV[7558].Len() = 1\n",
"SCnComV[7559].Len() = 1\n",
"SCnComV[7560].Len() = 1\n",
"SCnComV[7561].Len() = 1\n",
"SCnComV[7562].Len() = 1\n",
"SCnComV[7563].Len() = 1\n",
"SCnComV[7564].Len() = 1\n",
"SCnComV[7565].Len() = 1\n",
"SCnComV[7566].Len() = 1\n",
"SCnComV[7567].Len() = 1\n",
"SCnComV[7568].Len() = 1\n",
"SCnComV[7569].Len() = 1\n",
"SCnComV[7570].Len() = 1\n",
"SCnComV[7571].Len() = 1\n",
"SCnComV[7572].Len() = 1\n",
"SCnComV[7573].Len() = 1\n",
"SCnComV[7574].Len() = 1\n",
"SCnComV[7575].Len() = 1\n",
"SCnComV[7576].Len() = 1\n",
"SCnComV[7577].Len() = 1\n",
"SCnComV[7578].Len() = 1\n",
"SCnComV[7579].Len() = 1\n",
"SCnComV[7580].Len() = 1\n",
"SCnComV[7581].Len() = 1\n",
"SCnComV[7582].Len() = 1\n",
"SCnComV[7583].Len() = 1\n",
"SCnComV[7584].Len() = 1\n",
"SCnComV[7585].Len() = 1\n",
"SCnComV[7586].Len() = 1\n",
"SCnComV[7587].Len() = 1\n",
"SCnComV[7588].Len() = 1\n",
"SCnComV[7589].Len() = 1\n",
"SCnComV[7590].Len() = 1\n",
"SCnComV[7591].Len() = 1\n",
"SCnComV[7592].Len() = 1\n",
"SCnComV[7593].Len() = 1\n",
"SCnComV[7594].Len() = 1\n",
"SCnComV[7595].Len() = 1\n",
"SCnComV[7596].Len() = 1\n",
"SCnComV[7597].Len() = 1\n",
"SCnComV[7598].Len() = 1\n",
"SCnComV[7599].Len() = 1\n",
"SCnComV[7600].Len() = 1\n",
"SCnComV[7601].Len() = 1\n",
"SCnComV[7602].Len() = 1\n",
"SCnComV[7603].Len() = 1\n",
"SCnComV[7604].Len() = 1\n",
"SCnComV[7605].Len() = 1\n",
"SCnComV[7606].Len() = 1\n",
"SCnComV[7607].Len() = 1\n",
"SCnComV[7608].Len() = 1\n",
"SCnComV[7609].Len() = 1\n",
"SCnComV[7610].Len() = 1\n",
"SCnComV[7611].Len() = 1\n",
"SCnComV[7612].Len() = 1\n",
"SCnComV[7613].Len() = 1\n",
"SCnComV[7614].Len() = 1\n",
"SCnComV[7615].Len() = 1\n",
"SCnComV[7616].Len() = 1\n",
"SCnComV[7617].Len() = 1\n",
"SCnComV[7618].Len() = 1\n",
"SCnComV[7619].Len() = 1\n",
"SCnComV[7620].Len() = 1\n",
"SCnComV[7621].Len() = 1\n",
"SCnComV[7622].Len() = 1\n",
"SCnComV[7623].Len() = 1\n",
"SCnComV[7624].Len() = 1\n",
"SCnComV[7625].Len() = 1\n",
"SCnComV[7626].Len() = 1\n",
"SCnComV[7627].Len() = 1\n",
"SCnComV[7628].Len() = 1\n",
"SCnComV[7629].Len() = 1\n",
"SCnComV[7630].Len() = 1\n",
"SCnComV[7631].Len() = 1\n",
"SCnComV[7632].Len() = 1\n",
"SCnComV[7633].Len() = 1\n",
"SCnComV[7634].Len() = 1\n",
"SCnComV[7635].Len() = 1\n",
"SCnComV[7636].Len() = 1\n",
"SCnComV[7637].Len() = 1\n",
"SCnComV[7638].Len() = 1\n",
"SCnComV[7639].Len() = 1\n",
"SCnComV[7640].Len() = 1\n",
"SCnComV[7641].Len() = 1\n",
"SCnComV[7642].Len() = 1\n",
"SCnComV[7643].Len() = 1\n",
"SCnComV[7644].Len() = 1\n",
"SCnComV[7645].Len() = 1\n",
"SCnComV[7646].Len() = 1\n",
"SCnComV[7647].Len() = 1\n",
"SCnComV[7648].Len() = 1\n",
"SCnComV[7649].Len() = 1\n",
"SCnComV[7650].Len() = 1\n",
"SCnComV[7651].Len() = 1\n",
"SCnComV[7652].Len() = 1\n",
"SCnComV[7653].Len() = 1\n",
"SCnComV[7654].Len() = 1\n",
"SCnComV[7655].Len() = 1\n",
"SCnComV[7656].Len() = 1\n",
"SCnComV[7657].Len() = 1\n",
"SCnComV[7658].Len() = 1\n",
"SCnComV[7659].Len() = 1\n",
"SCnComV[7660].Len() = 1\n",
"SCnComV[7661].Len() = 1\n",
"SCnComV[7662].Len() = 1\n",
"SCnComV[7663].Len() = 1\n",
"SCnComV[7664].Len() = 1\n",
"SCnComV[7665].Len() = 1\n",
"SCnComV[7666].Len() = 1\n",
"SCnComV[7667].Len() = 1\n",
"SCnComV[7668].Len() = 1\n",
"SCnComV[7669].Len() = 1\n",
"SCnComV[7670].Len() = 1\n",
"SCnComV[7671].Len() = 1\n",
"SCnComV[7672].Len() = 1\n",
"SCnComV[7673].Len() = 1\n",
"SCnComV[7674].Len() = 1\n",
"SCnComV[7675].Len() = 1\n",
"SCnComV[7676].Len() = 1\n",
"SCnComV[7677].Len() = 1\n",
"SCnComV[7678].Len() = 1\n",
"SCnComV[7679].Len() = 1\n",
"SCnComV[7680].Len() = 1\n",
"SCnComV[7681].Len() = 1\n",
"SCnComV[7682].Len() = 1\n",
"SCnComV[7683].Len() = 1\n",
"SCnComV[7684].Len() = 1\n",
"SCnComV[7685].Len() = 1\n",
"SCnComV[7686].Len() = 1\n",
"SCnComV[7687].Len() = 1\n",
"SCnComV[7688].Len() = 1\n",
"SCnComV[7689].Len() = 1\n",
"SCnComV[7690].Len() = 1\n",
"SCnComV[7691].Len() = 1\n",
"SCnComV[7692].Len() = 1\n",
"SCnComV[7693].Len() = 1\n",
"SCnComV[7694].Len() = 1\n",
"SCnComV[7695].Len() = 1\n",
"SCnComV[7696].Len() = 1\n",
"SCnComV[7697].Len() = 1\n",
"SCnComV[7698].Len() = 1\n",
"SCnComV[7699].Len() = 1\n",
"SCnComV[7700].Len() = 1\n",
"SCnComV[7701].Len() = 1\n",
"SCnComV[7702].Len() = 1\n",
"SCnComV[7703].Len() = 1\n",
"SCnComV[7704].Len() = 1\n",
"SCnComV[7705].Len() = 1\n",
"SCnComV[7706].Len() = 1\n",
"SCnComV[7707].Len() = 1\n",
"SCnComV[7708].Len() = 1\n",
"SCnComV[7709].Len() = 1\n",
"SCnComV[7710].Len() = 1\n",
"SCnComV[7711].Len() = 1\n",
"SCnComV[7712].Len() = 1\n",
"SCnComV[7713].Len() = 1\n",
"SCnComV[7714].Len() = 1\n",
"SCnComV[7715].Len() = 1\n",
"SCnComV[7716].Len() = 1\n",
"SCnComV[7717].Len() = 1\n",
"SCnComV[7718].Len() = 1\n",
"SCnComV[7719].Len() = 1\n",
"SCnComV[7720].Len() = 1\n",
"SCnComV[7721].Len() = 1\n",
"SCnComV[7722].Len() = 1\n",
"SCnComV[7723].Len() = 1\n",
"SCnComV[7724].Len() = 1\n",
"SCnComV[7725].Len() = 1\n",
"SCnComV[7726].Len() = 1\n",
"SCnComV[7727].Len() = 1\n",
"SCnComV[7728].Len() = 1\n",
"SCnComV[7729].Len() = 1\n",
"SCnComV[7730].Len() = 1\n",
"SCnComV[7731].Len() = 1\n",
"SCnComV[7732].Len() = 1\n",
"SCnComV[7733].Len() = 1\n",
"SCnComV[7734].Len() = 1\n",
"SCnComV[7735].Len() = 1\n",
"SCnComV[7736].Len() = 1\n",
"SCnComV[7737].Len() = 1\n",
"SCnComV[7738].Len() = 1\n",
"SCnComV[7739].Len() = 1\n",
"SCnComV[7740].Len() = 1\n",
"SCnComV[7741].Len() = 1\n",
"SCnComV[7742].Len() = 1\n",
"SCnComV[7743].Len() = 1\n",
"SCnComV[7744].Len() = 1\n",
"SCnComV[7745].Len() = 1\n",
"SCnComV[7746].Len() = 1\n",
"SCnComV[7747].Len() = 1\n",
"SCnComV[7748].Len() = 1\n",
"SCnComV[7749].Len() = 1\n",
"SCnComV[7750].Len() = 1\n",
"SCnComV[7751].Len() = 1\n",
"SCnComV[7752].Len() = 1\n",
"SCnComV[7753].Len() = 1\n",
"SCnComV[7754].Len() = 1\n",
"SCnComV[7755].Len() = 1\n",
"SCnComV[7756].Len() = 1\n",
"SCnComV[7757].Len() = 1\n",
"SCnComV[7758].Len() = 1\n",
"SCnComV[7759].Len() = 1\n",
"SCnComV[7760].Len() = 1\n",
"SCnComV[7761].Len() = 1\n",
"SCnComV[7762].Len() = 1\n",
"SCnComV[7763].Len() = 1\n",
"SCnComV[7764].Len() = 1\n",
"SCnComV[7765].Len() = 1\n",
"SCnComV[7766].Len() = 1\n",
"SCnComV[7767].Len() = 1\n",
"SCnComV[7768].Len() = 1\n",
"SCnComV[7769].Len() = 1\n",
"SCnComV[7770].Len() = 1\n",
"SCnComV[7771].Len() = 1\n",
"SCnComV[7772].Len() = 1\n",
"SCnComV[7773].Len() = 1\n",
"SCnComV[7774].Len() = 1\n",
"SCnComV[7775].Len() = 1\n",
"SCnComV[7776].Len() = 1\n",
"SCnComV[7777].Len() = 1\n",
"SCnComV[7778].Len() = 1\n",
"SCnComV[7779].Len() = 1\n",
"SCnComV[7780].Len() = 1\n",
"SCnComV[7781].Len() = 1\n",
"SCnComV[7782].Len() = 1\n",
"SCnComV[7783].Len() = 1\n",
"SCnComV[7784].Len() = 1\n",
"SCnComV[7785].Len() = 1\n",
"SCnComV[7786].Len() = 1\n",
"SCnComV[7787].Len() = 1\n",
"SCnComV[7788].Len() = 1\n",
"SCnComV[7789].Len() = 1\n",
"SCnComV[7790].Len() = 1\n",
"SCnComV[7791].Len() = 1\n",
"SCnComV[7792].Len() = 1\n",
"SCnComV[7793].Len() = 1\n",
"SCnComV[7794].Len() = 1\n",
"SCnComV[7795].Len() = 1\n",
"SCnComV[7796].Len() = 1\n",
"SCnComV[7797].Len() = 1\n",
"SCnComV[7798].Len() = 1\n",
"SCnComV[7799].Len() = 1\n",
"SCnComV[7800].Len() = 1\n",
"SCnComV[7801].Len() = 1\n",
"SCnComV[7802].Len() = 1\n",
"SCnComV[7803].Len() = 1\n",
"SCnComV[7804].Len() = 1\n",
"SCnComV[7805].Len() = 1\n",
"SCnComV[7806].Len() = 1\n",
"SCnComV[7807].Len() = 1\n",
"SCnComV[7808].Len() = 1\n",
"SCnComV[7809].Len() = 1\n",
"SCnComV[7810].Len() = 1\n",
"SCnComV[7811].Len() = 1\n",
"SCnComV[7812].Len() = 1\n",
"SCnComV[7813].Len() = 1\n",
"SCnComV[7814].Len() = 1\n",
"SCnComV[7815].Len() = 1\n",
"SCnComV[7816].Len() = 1\n",
"SCnComV[7817].Len() = 1\n",
"SCnComV[7818].Len() = 1\n",
"SCnComV[7819].Len() = 1\n",
"SCnComV[7820].Len() = 1\n",
"SCnComV[7821].Len() = 1\n",
"SCnComV[7822].Len() = 1\n",
"SCnComV[7823].Len() = 1\n",
"SCnComV[7824].Len() = 1\n",
"SCnComV[7825].Len() = 1\n",
"SCnComV[7826].Len() = 1\n",
"SCnComV[7827].Len() = 1\n",
"SCnComV[7828].Len() = 1\n",
"SCnComV[7829].Len() = 1\n",
"SCnComV[7830].Len() = 1\n",
"SCnComV[7831].Len() = 1\n",
"SCnComV[7832].Len() = 1\n",
"SCnComV[7833].Len() = 1\n",
"SCnComV[7834].Len() = 1\n",
"SCnComV[7835].Len() = 1\n",
"SCnComV[7836].Len() = 1\n",
"SCnComV[7837].Len() = 1\n",
"SCnComV[7838].Len() = 1\n",
"SCnComV[7839].Len() = 1\n",
"SCnComV[7840].Len() = 1\n",
"SCnComV[7841].Len() = 1\n",
"SCnComV[7842].Len() = 1\n",
"SCnComV[7843].Len() = 1\n",
"SCnComV[7844].Len() = 1\n",
"SCnComV[7845].Len() = 1\n",
"SCnComV[7846].Len() = 1\n",
"SCnComV[7847].Len() = 1\n",
"SCnComV[7848].Len() = 1\n",
"SCnComV[7849].Len() = 1\n",
"SCnComV[7850].Len() = 1\n",
"SCnComV[7851].Len() = 1\n",
"SCnComV[7852].Len() = 1\n",
"SCnComV[7853].Len() = 1\n",
"SCnComV[7854].Len() = 1\n",
"SCnComV[7855].Len() = 1\n",
"SCnComV[7856].Len() = 1\n",
"SCnComV[7857].Len() = 1\n",
"SCnComV[7858].Len() = 1\n",
"SCnComV[7859].Len() = 1\n",
"SCnComV[7860].Len() = 1\n",
"SCnComV[7861].Len() = 1\n",
"SCnComV[7862].Len() = 1\n",
"SCnComV[7863].Len() = 1\n",
"SCnComV[7864].Len() = 1\n",
"SCnComV[7865].Len() = 1\n",
"SCnComV[7866].Len() = 1\n",
"SCnComV[7867].Len() = 1\n",
"SCnComV[7868].Len() = 1\n",
"SCnComV[7869].Len() = 1\n",
"SCnComV[7870].Len() = 1\n",
"SCnComV[7871].Len() = 1\n",
"SCnComV[7872].Len() = 1\n",
"SCnComV[7873].Len() = 1\n",
"SCnComV[7874].Len() = 1\n",
"SCnComV[7875].Len() = 1\n",
"SCnComV[7876].Len() = 1\n",
"SCnComV[7877].Len() = 1\n",
"SCnComV[7878].Len() = 1\n",
"SCnComV[7879].Len() = 1\n",
"SCnComV[7880].Len() = 1\n",
"SCnComV[7881].Len() = 1\n",
"SCnComV[7882].Len() = 1\n",
"SCnComV[7883].Len() = 1\n",
"SCnComV[7884].Len() = 1\n",
"SCnComV[7885].Len() = 1\n",
"SCnComV[7886].Len() = 1\n",
"SCnComV[7887].Len() = 1\n",
"SCnComV[7888].Len() = 1\n",
"SCnComV[7889].Len() = 1\n",
"SCnComV[7890].Len() = 1\n",
"SCnComV[7891].Len() = 1\n",
"SCnComV[7892].Len() = 1\n",
"SCnComV[7893].Len() = 1\n",
"SCnComV[7894].Len() = 1\n",
"SCnComV[7895].Len() = 1\n",
"SCnComV[7896].Len() = 1\n",
"SCnComV[7897].Len() = 1\n",
"SCnComV[7898].Len() = 1\n",
"SCnComV[7899].Len() = 1\n",
"SCnComV[7900].Len() = 1\n",
"SCnComV[7901].Len() = 1\n",
"SCnComV[7902].Len() = 1\n",
"SCnComV[7903].Len() = 1\n",
"SCnComV[7904].Len() = 1\n",
"SCnComV[7905].Len() = 1\n",
"SCnComV[7906].Len() = 1\n",
"SCnComV[7907].Len() = 1\n",
"SCnComV[7908].Len() = 1\n",
"SCnComV[7909].Len() = 1\n",
"SCnComV[7910].Len() = 1\n",
"SCnComV[7911].Len() = 1\n",
"SCnComV[7912].Len() = 1\n",
"SCnComV[7913].Len() = 1\n",
"SCnComV[7914].Len() = 1\n",
"SCnComV[7915].Len() = 1\n",
"SCnComV[7916].Len() = 1\n",
"SCnComV[7917].Len() = 1\n",
"SCnComV[7918].Len() = 1\n",
"SCnComV[7919].Len() = 1\n",
"SCnComV[7920].Len() = 1\n",
"SCnComV[7921].Len() = 1\n",
"SCnComV[7922].Len() = 1\n",
"SCnComV[7923].Len() = 1\n",
"SCnComV[7924].Len() = 1\n",
"SCnComV[7925].Len() = 1\n",
"SCnComV[7926].Len() = 1\n",
"SCnComV[7927].Len() = 1\n",
"SCnComV[7928].Len() = 1\n",
"SCnComV[7929].Len() = 1\n",
"SCnComV[7930].Len() = 1\n",
"SCnComV[7931].Len() = 1\n",
"SCnComV[7932].Len() = 1\n",
"SCnComV[7933].Len() = 1\n",
"SCnComV[7934].Len() = 1\n",
"SCnComV[7935].Len() = 1\n",
"SCnComV[7936].Len() = 1\n",
"SCnComV[7937].Len() = 1\n",
"SCnComV[7938].Len() = 1\n",
"SCnComV[7939].Len() = 1\n",
"SCnComV[7940].Len() = 1\n",
"SCnComV[7941].Len() = 1\n",
"SCnComV[7942].Len() = 1\n",
"SCnComV[7943].Len() = 1\n",
"SCnComV[7944].Len() = 1\n",
"SCnComV[7945].Len() = 1\n",
"SCnComV[7946].Len() = 1\n",
"SCnComV[7947].Len() = 1\n",
"SCnComV[7948].Len() = 1\n",
"SCnComV[7949].Len() = 1\n",
"SCnComV[7950].Len() = 1\n",
"SCnComV[7951].Len() = 1\n",
"SCnComV[7952].Len() = 1\n",
"SCnComV[7953].Len() = 1\n",
"SCnComV[7954].Len() = 1\n",
"SCnComV[7955].Len() = 1\n",
"SCnComV[7956].Len() = 1\n",
"SCnComV[7957].Len() = 1\n",
"SCnComV[7958].Len() = 1\n",
"SCnComV[7959].Len() = 1\n",
"SCnComV[7960].Len() = 1\n",
"SCnComV[7961].Len() = 1\n",
"SCnComV[7962].Len() = 1\n",
"SCnComV[7963].Len() = 1\n",
"SCnComV[7964].Len() = 1\n",
"SCnComV[7965].Len() = 1\n",
"SCnComV[7966].Len() = 1\n",
"SCnComV[7967].Len() = 1\n",
"SCnComV[7968].Len() = 1\n",
"SCnComV[7969].Len() = 1\n",
"SCnComV[7970].Len() = 1\n",
"SCnComV[7971].Len() = 1\n",
"SCnComV[7972].Len() = 1\n",
"SCnComV[7973].Len() = 1\n",
"SCnComV[7974].Len() = 1\n",
"SCnComV[7975].Len() = 1\n",
"SCnComV[7976].Len() = 1\n",
"SCnComV[7977].Len() = 1\n",
"SCnComV[7978].Len() = 1\n",
"SCnComV[7979].Len() = 1\n",
"SCnComV[7980].Len() = 1\n",
"SCnComV[7981].Len() = 1\n",
"SCnComV[7982].Len() = 1\n",
"SCnComV[7983].Len() = 1\n",
"SCnComV[7984].Len() = 1\n",
"SCnComV[7985].Len() = 1\n",
"SCnComV[7986].Len() = 1\n",
"SCnComV[7987].Len() = 1\n",
"SCnComV[7988].Len() = 1\n",
"SCnComV[7989].Len() = 1\n",
"SCnComV[7990].Len() = 1\n",
"SCnComV[7991].Len() = 1\n",
"SCnComV[7992].Len() = 1\n",
"SCnComV[7993].Len() = 1\n",
"SCnComV[7994].Len() = 1\n",
"SCnComV[7995].Len() = 1\n",
"SCnComV[7996].Len() = 1\n",
"SCnComV[7997].Len() = 1\n",
"SCnComV[7998].Len() = 1\n",
"SCnComV[7999].Len() = 1\n",
"SCnComV[8000].Len() = 1\n",
"SCnComV[8001].Len() = 1\n",
"SCnComV[8002].Len() = 1\n",
"SCnComV[8003].Len() = 1\n",
"SCnComV[8004].Len() = 1\n",
"SCnComV[8005].Len() = 1\n",
"SCnComV[8006].Len() = 1\n",
"SCnComV[8007].Len() = 1\n",
"SCnComV[8008].Len() = 1\n",
"SCnComV[8009].Len() = 1\n",
"SCnComV[8010].Len() = 1\n",
"SCnComV[8011].Len() = 1\n",
"SCnComV[8012].Len() = 1\n",
"SCnComV[8013].Len() = 1\n",
"SCnComV[8014].Len() = 1\n",
"SCnComV[8015].Len() = 1\n",
"SCnComV[8016].Len() = 1\n",
"SCnComV[8017].Len() = 1\n",
"SCnComV[8018].Len() = 1\n",
"SCnComV[8019].Len() = 1\n",
"SCnComV[8020].Len() = 1\n",
"SCnComV[8021].Len() = 1\n",
"SCnComV[8022].Len() = 1\n",
"SCnComV[8023].Len() = 1\n",
"SCnComV[8024].Len() = 1\n",
"SCnComV[8025].Len() = 1\n",
"SCnComV[8026].Len() = 1\n",
"SCnComV[8027].Len() = 1\n",
"SCnComV[8028].Len() = 1\n",
"SCnComV[8029].Len() = 1\n",
"SCnComV[8030].Len() = 1\n",
"SCnComV[8031].Len() = 1\n",
"SCnComV[8032].Len() = 1\n",
"SCnComV[8033].Len() = 1\n",
"SCnComV[8034].Len() = 1\n",
"SCnComV[8035].Len() = 1\n",
"SCnComV[8036].Len() = 1\n",
"SCnComV[8037].Len() = 1\n",
"SCnComV[8038].Len() = 1\n",
"SCnComV[8039].Len() = 1\n",
"SCnComV[8040].Len() = 1\n",
"SCnComV[8041].Len() = 1\n",
"SCnComV[8042].Len() = 1\n",
"SCnComV[8043].Len() = 1\n",
"SCnComV[8044].Len() = 1\n",
"SCnComV[8045].Len() = 1\n",
"SCnComV[8046].Len() = 1\n",
"SCnComV[8047].Len() = 1\n",
"SCnComV[8048].Len() = 1\n",
"SCnComV[8049].Len() = 1\n",
"SCnComV[8050].Len() = 1\n",
"SCnComV[8051].Len() = 1\n",
"SCnComV[8052].Len() = 1\n",
"SCnComV[8053].Len() = 1\n",
"SCnComV[8054].Len() = 1\n",
"SCnComV[8055].Len() = 1\n",
"SCnComV[8056].Len() = 1\n",
"SCnComV[8057].Len() = 1\n",
"SCnComV[8058].Len() = 1\n",
"SCnComV[8059].Len() = 1\n",
"SCnComV[8060].Len() = 1\n",
"SCnComV[8061].Len() = 1\n",
"SCnComV[8062].Len() = 1\n",
"SCnComV[8063].Len() = 1\n",
"SCnComV[8064].Len() = 1\n",
"SCnComV[8065].Len() = 1\n",
"SCnComV[8066].Len() = 1\n",
"SCnComV[8067].Len() = 1\n",
"SCnComV[8068].Len() = 1\n",
"SCnComV[8069].Len() = 1\n",
"SCnComV[8070].Len() = 1\n",
"SCnComV[8071].Len() = 1\n",
"SCnComV[8072].Len() = 1\n",
"SCnComV[8073].Len() = 1\n",
"SCnComV[8074].Len() = 1\n",
"SCnComV[8075].Len() = 1\n",
"SCnComV[8076].Len() = 1\n",
"SCnComV[8077].Len() = 1\n",
"SCnComV[8078].Len() = 1\n",
"SCnComV[8079].Len() = 1\n",
"SCnComV[8080].Len() = 1\n",
"SCnComV[8081].Len() = 1\n",
"SCnComV[8082].Len() = 1\n",
"SCnComV[8083].Len() = 1\n",
"SCnComV[8084].Len() = 1\n",
"SCnComV[8085].Len() = 1\n",
"SCnComV[8086].Len() = 1\n",
"SCnComV[8087].Len() = 1\n",
"SCnComV[8088].Len() = 1\n",
"SCnComV[8089].Len() = 1\n",
"SCnComV[8090].Len() = 1\n",
"SCnComV[8091].Len() = 1\n",
"SCnComV[8092].Len() = 1\n",
"SCnComV[8093].Len() = 1\n",
"SCnComV[8094].Len() = 1\n",
"SCnComV[8095].Len() = 1\n",
"SCnComV[8096].Len() = 1\n",
"SCnComV[8097].Len() = 1\n",
"SCnComV[8098].Len() = 1\n",
"SCnComV[8099].Len() = 1\n",
"SCnComV[8100].Len() = 1\n",
"SCnComV[8101].Len() = 1\n",
"SCnComV[8102].Len() = 1\n",
"SCnComV[8103].Len() = 1\n",
"SCnComV[8104].Len() = 1\n",
"SCnComV[8105].Len() = 1\n",
"SCnComV[8106].Len() = 1\n",
"SCnComV[8107].Len() = 1\n",
"SCnComV[8108].Len() = 1\n",
"SCnComV[8109].Len() = 1\n",
"SCnComV[8110].Len() = 1\n",
"SCnComV[8111].Len() = 1\n",
"SCnComV[8112].Len() = 1\n",
"SCnComV[8113].Len() = 1\n",
"SCnComV[8114].Len() = 1\n",
"SCnComV[8115].Len() = 1\n",
"SCnComV[8116].Len() = 1\n",
"SCnComV[8117].Len() = 1\n",
"SCnComV[8118].Len() = 1\n",
"SCnComV[8119].Len() = 1\n",
"SCnComV[8120].Len() = 1\n",
"SCnComV[8121].Len() = 1\n",
"SCnComV[8122].Len() = 1\n",
"SCnComV[8123].Len() = 1\n",
"SCnComV[8124].Len() = 1\n",
"SCnComV[8125].Len() = 1\n",
"SCnComV[8126].Len() = 1\n",
"SCnComV[8127].Len() = 1\n",
"SCnComV[8128].Len() = 1\n",
"SCnComV[8129].Len() = 1\n",
"SCnComV[8130].Len() = 1\n",
"SCnComV[8131].Len() = 1\n",
"SCnComV[8132].Len() = 1\n",
"SCnComV[8133].Len() = 1\n",
"SCnComV[8134].Len() = 1\n",
"SCnComV[8135].Len() = 1\n",
"SCnComV[8136].Len() = 1\n",
"SCnComV[8137].Len() = 1\n",
"SCnComV[8138].Len() = 1\n",
"SCnComV[8139].Len() = 1\n",
"SCnComV[8140].Len() = 1\n",
"SCnComV[8141].Len() = 1\n",
"SCnComV[8142].Len() = 1\n",
"SCnComV[8143].Len() = 1\n",
"SCnComV[8144].Len() = 1\n",
"SCnComV[8145].Len() = 1\n",
"SCnComV[8146].Len() = 1\n",
"SCnComV[8147].Len() = 1\n",
"SCnComV[8148].Len() = 1\n",
"SCnComV[8149].Len() = 1\n",
"SCnComV[8150].Len() = 1\n",
"SCnComV[8151].Len() = 1\n",
"SCnComV[8152].Len() = 1\n",
"SCnComV[8153].Len() = 1\n",
"SCnComV[8154].Len() = 1\n",
"SCnComV[8155].Len() = 1\n",
"SCnComV[8156].Len() = 1\n",
"SCnComV[8157].Len() = 1\n",
"SCnComV[8158].Len() = 1\n",
"SCnComV[8159].Len() = 1\n",
"SCnComV[8160].Len() = 1\n",
"SCnComV[8161].Len() = 1\n",
"SCnComV[8162].Len() = 1\n",
"SCnComV[8163].Len() = 1\n",
"SCnComV[8164].Len() = 1\n",
"SCnComV[8165].Len() = 1\n",
"SCnComV[8166].Len() = 1\n",
"SCnComV[8167].Len() = 1\n",
"SCnComV[8168].Len() = 1\n",
"SCnComV[8169].Len() = 1\n",
"SCnComV[8170].Len() = 1\n",
"SCnComV[8171].Len() = 1\n",
"SCnComV[8172].Len() = 1\n",
"SCnComV[8173].Len() = 1\n",
"SCnComV[8174].Len() = 1\n",
"SCnComV[8175].Len() = 1\n",
"SCnComV[8176].Len() = 1\n",
"SCnComV[8177].Len() = 1\n",
"SCnComV[8178].Len() = 1\n",
"SCnComV[8179].Len() = 1\n",
"SCnComV[8180].Len() = 1\n",
"SCnComV[8181].Len() = 1\n",
"SCnComV[8182].Len() = 1\n",
"SCnComV[8183].Len() = 1\n",
"SCnComV[8184].Len() = 1\n",
"SCnComV[8185].Len() = 1\n",
"SCnComV[8186].Len() = 1\n",
"SCnComV[8187].Len() = 1\n",
"SCnComV[8188].Len() = 1\n",
"SCnComV[8189].Len() = 1\n",
"SCnComV[8190].Len() = 1\n",
"SCnComV[8191].Len() = 1\n",
"SCnComV[8192].Len() = 1\n",
"SCnComV[8193].Len() = 1\n",
"SCnComV[8194].Len() = 1\n",
"SCnComV[8195].Len() = 1\n",
"SCnComV[8196].Len() = 1\n",
"SCnComV[8197].Len() = 1\n",
"SCnComV[8198].Len() = 1\n",
"SCnComV[8199].Len() = 1\n",
"SCnComV[8200].Len() = 1\n",
"SCnComV[8201].Len() = 1\n",
"SCnComV[8202].Len() = 1\n",
"SCnComV[8203].Len() = 1\n",
"SCnComV[8204].Len() = 1\n",
"SCnComV[8205].Len() = 1\n",
"SCnComV[8206].Len() = 1\n",
"SCnComV[8207].Len() = 1\n",
"SCnComV[8208].Len() = 1\n",
"SCnComV[8209].Len() = 1\n",
"SCnComV[8210].Len() = 1\n",
"SCnComV[8211].Len() = 1\n",
"SCnComV[8212].Len() = 1\n",
"SCnComV[8213].Len() = 1\n",
"SCnComV[8214].Len() = 1\n",
"SCnComV[8215].Len() = 1\n",
"SCnComV[8216].Len() = 1\n",
"SCnComV[8217].Len() = 1\n",
"SCnComV[8218].Len() = 1\n",
"SCnComV[8219].Len() = 1\n",
"SCnComV[8220].Len() = 1\n",
"SCnComV[8221].Len() = 1\n",
"SCnComV[8222].Len() = 1\n",
"SCnComV[8223].Len() = 1\n",
"SCnComV[8224].Len() = 1\n",
"SCnComV[8225].Len() = 1\n",
"SCnComV[8226].Len() = 1\n",
"SCnComV[8227].Len() = 1\n",
"SCnComV[8228].Len() = 1\n",
"SCnComV[8229].Len() = 1\n",
"SCnComV[8230].Len() = 1\n",
"SCnComV[8231].Len() = 1\n",
"SCnComV[8232].Len() = 1\n",
"SCnComV[8233].Len() = 1\n",
"SCnComV[8234].Len() = 1\n",
"SCnComV[8235].Len() = 1\n",
"SCnComV[8236].Len() = 1\n",
"SCnComV[8237].Len() = 1\n",
"SCnComV[8238].Len() = 1\n",
"SCnComV[8239].Len() = 1\n",
"SCnComV[8240].Len() = 1\n",
"SCnComV[8241].Len() = 1\n",
"SCnComV[8242].Len() = 1\n",
"SCnComV[8243].Len() = 1\n",
"SCnComV[8244].Len() = 1\n",
"SCnComV[8245].Len() = 1\n",
"SCnComV[8246].Len() = 1\n",
"SCnComV[8247].Len() = 1\n",
"SCnComV[8248].Len() = 1\n",
"SCnComV[8249].Len() = 1\n",
"SCnComV[8250].Len() = 1\n",
"SCnComV[8251].Len() = 1\n",
"SCnComV[8252].Len() = 1\n",
"SCnComV[8253].Len() = 1\n",
"SCnComV[8254].Len() = 1\n",
"SCnComV[8255].Len() = 1\n",
"SCnComV[8256].Len() = 1\n",
"SCnComV[8257].Len() = 1\n",
"SCnComV[8258].Len() = 1\n",
"SCnComV[8259].Len() = 1\n",
"SCnComV[8260].Len() = 1\n",
"SCnComV[8261].Len() = 1\n",
"SCnComV[8262].Len() = 1\n",
"SCnComV[8263].Len() = 1\n",
"SCnComV[8264].Len() = 1\n",
"SCnComV[8265].Len() = 1\n",
"SCnComV[8266].Len() = 1\n",
"SCnComV[8267].Len() = 1\n",
"SCnComV[8268].Len() = 1\n",
"SCnComV[8269].Len() = 1\n",
"SCnComV[8270].Len() = 1\n",
"SCnComV[8271].Len() = 1\n",
"SCnComV[8272].Len() = 1\n",
"SCnComV[8273].Len() = 1\n",
"SCnComV[8274].Len() = 1\n",
"SCnComV[8275].Len() = 1\n",
"SCnComV[8276].Len() = 1\n",
"SCnComV[8277].Len() = 1\n",
"SCnComV[8278].Len() = 1\n",
"SCnComV[8279].Len() = 1\n",
"SCnComV[8280].Len() = 1\n",
"SCnComV[8281].Len() = 1\n",
"SCnComV[8282].Len() = 1\n",
"SCnComV[8283].Len() = 1\n",
"SCnComV[8284].Len() = 1\n",
"SCnComV[8285].Len() = 1\n",
"SCnComV[8286].Len() = 1\n",
"SCnComV[8287].Len() = 1\n",
"SCnComV[8288].Len() = 1\n",
"SCnComV[8289].Len() = 1\n",
"SCnComV[8290].Len() = 1\n",
"SCnComV[8291].Len() = 1\n",
"SCnComV[8292].Len() = 1\n",
"SCnComV[8293].Len() = 1\n",
"SCnComV[8294].Len() = 1\n",
"SCnComV[8295].Len() = 1\n",
"SCnComV[8296].Len() = 1\n",
"SCnComV[8297].Len() = 1\n",
"SCnComV[8298].Len() = 1\n",
"SCnComV[8299].Len() = 1\n",
"SCnComV[8300].Len() = 1\n",
"SCnComV[8301].Len() = 1\n",
"SCnComV[8302].Len() = 1\n",
"SCnComV[8303].Len() = 1\n",
"SCnComV[8304].Len() = 1\n",
"SCnComV[8305].Len() = 1\n",
"SCnComV[8306].Len() = 1\n",
"SCnComV[8307].Len() = 1\n",
"SCnComV[8308].Len() = 1\n",
"SCnComV[8309].Len() = 1\n",
"SCnComV[8310].Len() = 1\n",
"SCnComV[8311].Len() = 1\n",
"SCnComV[8312].Len() = 1\n",
"SCnComV[8313].Len() = 1\n",
"SCnComV[8314].Len() = 1\n",
"SCnComV[8315].Len() = 1\n",
"SCnComV[8316].Len() = 1\n",
"SCnComV[8317].Len() = 1\n",
"SCnComV[8318].Len() = 1\n",
"SCnComV[8319].Len() = 1\n",
"SCnComV[8320].Len() = 1\n",
"SCnComV[8321].Len() = 1\n",
"SCnComV[8322].Len() = 1\n",
"SCnComV[8323].Len() = 1\n",
"SCnComV[8324].Len() = 1\n",
"SCnComV[8325].Len() = 1\n",
"SCnComV[8326].Len() = 1\n",
"SCnComV[8327].Len() = 1\n",
"SCnComV[8328].Len() = 1\n",
"SCnComV[8329].Len() = 1\n",
"SCnComV[8330].Len() = 1\n",
"SCnComV[8331].Len() = 1\n",
"SCnComV[8332].Len() = 1\n",
"SCnComV[8333].Len() = 1\n",
"SCnComV[8334].Len() = 1\n",
"SCnComV[8335].Len() = 1\n",
"SCnComV[8336].Len() = 1\n",
"SCnComV[8337].Len() = 1\n",
"SCnComV[8338].Len() = 1\n",
"SCnComV[8339].Len() = 1\n",
"SCnComV[8340].Len() = 1\n",
"SCnComV[8341].Len() = 1\n",
"SCnComV[8342].Len() = 1\n",
"SCnComV[8343].Len() = 1\n",
"SCnComV[8344].Len() = 1\n",
"SCnComV[8345].Len() = 1\n",
"SCnComV[8346].Len() = 1\n",
"SCnComV[8347].Len() = 1\n",
"SCnComV[8348].Len() = 1\n",
"SCnComV[8349].Len() = 1\n",
"SCnComV[8350].Len() = 1\n",
"SCnComV[8351].Len() = 1\n",
"SCnComV[8352].Len() = 1\n",
"SCnComV[8353].Len() = 1\n",
"SCnComV[8354].Len() = 1\n",
"SCnComV[8355].Len() = 1\n",
"SCnComV[8356].Len() = 1\n",
"SCnComV[8357].Len() = 1\n",
"SCnComV[8358].Len() = 1\n",
"SCnComV[8359].Len() = 1\n",
"SCnComV[8360].Len() = 1\n",
"SCnComV[8361].Len() = 1\n",
"SCnComV[8362].Len() = 1\n",
"SCnComV[8363].Len() = 1\n",
"SCnComV[8364].Len() = 1\n",
"SCnComV[8365].Len() = 1\n",
"SCnComV[8366].Len() = 1\n",
"SCnComV[8367].Len() = 1\n",
"SCnComV[8368].Len() = 1\n",
"SCnComV[8369].Len() = 1\n",
"SCnComV[8370].Len() = 1\n",
"SCnComV[8371].Len() = 1\n",
"SCnComV[8372].Len() = 1\n",
"SCnComV[8373].Len() = 1\n",
"SCnComV[8374].Len() = 1\n",
"SCnComV[8375].Len() = 1\n",
"SCnComV[8376].Len() = 1\n",
"SCnComV[8377].Len() = 1\n",
"SCnComV[8378].Len() = 1\n",
"SCnComV[8379].Len() = 1\n",
"SCnComV[8380].Len() = 1\n",
"SCnComV[8381].Len() = 1\n",
"SCnComV[8382].Len() = 1\n",
"SCnComV[8383].Len() = 1\n",
"SCnComV[8384].Len() = 1\n",
"SCnComV[8385].Len() = 1\n",
"SCnComV[8386].Len() = 1\n",
"SCnComV[8387].Len() = 1\n",
"SCnComV[8388].Len() = 1\n",
"SCnComV[8389].Len() = 1\n",
"SCnComV[8390].Len() = 1\n",
"SCnComV[8391].Len() = 1\n",
"SCnComV[8392].Len() = 1\n",
"SCnComV[8393].Len() = 1\n",
"SCnComV[8394].Len() = 1\n",
"SCnComV[8395].Len() = 1\n",
"SCnComV[8396].Len() = 1\n",
"SCnComV[8397].Len() = 1\n",
"SCnComV[8398].Len() = 1\n",
"SCnComV[8399].Len() = 1\n",
"SCnComV[8400].Len() = 1\n",
"SCnComV[8401].Len() = 1\n",
"SCnComV[8402].Len() = 1\n",
"SCnComV[8403].Len() = 1\n",
"SCnComV[8404].Len() = 1\n",
"SCnComV[8405].Len() = 1\n",
"SCnComV[8406].Len() = 1\n",
"SCnComV[8407].Len() = 1\n",
"SCnComV[8408].Len() = 1\n",
"SCnComV[8409].Len() = 1\n",
"SCnComV[8410].Len() = 1\n",
"SCnComV[8411].Len() = 1\n",
"SCnComV[8412].Len() = 1\n",
"SCnComV[8413].Len() = 1\n",
"SCnComV[8414].Len() = 1\n",
"SCnComV[8415].Len() = 1\n",
"SCnComV[8416].Len() = 1\n",
"SCnComV[8417].Len() = 1\n",
"SCnComV[8418].Len() = 1\n",
"SCnComV[8419].Len() = 1\n",
"SCnComV[8420].Len() = 1\n",
"SCnComV[8421].Len() = 1\n",
"SCnComV[8422].Len() = 1\n",
"SCnComV[8423].Len() = 1\n",
"SCnComV[8424].Len() = 1\n",
"SCnComV[8425].Len() = 1\n",
"SCnComV[8426].Len() = 1\n",
"SCnComV[8427].Len() = 1\n",
"SCnComV[8428].Len() = 1\n",
"SCnComV[8429].Len() = 1\n",
"SCnComV[8430].Len() = 1\n",
"SCnComV[8431].Len() = 1\n",
"SCnComV[8432].Len() = 1\n",
"SCnComV[8433].Len() = 1\n",
"SCnComV[8434].Len() = 1\n",
"SCnComV[8435].Len() = 1\n",
"SCnComV[8436].Len() = 1\n",
"SCnComV[8437].Len() = 1\n",
"SCnComV[8438].Len() = 1\n",
"SCnComV[8439].Len() = 1\n",
"SCnComV[8440].Len() = 1\n",
"SCnComV[8441].Len() = 1\n",
"SCnComV[8442].Len() = 1\n",
"SCnComV[8443].Len() = 1\n",
"SCnComV[8444].Len() = 1\n",
"SCnComV[8445].Len() = 1\n",
"SCnComV[8446].Len() = 1\n",
"SCnComV[8447].Len() = 1\n",
"SCnComV[8448].Len() = 1\n",
"SCnComV[8449].Len() = 1\n",
"SCnComV[8450].Len() = 1\n",
"SCnComV[8451].Len() = 1\n",
"SCnComV[8452].Len() = 1\n",
"SCnComV[8453].Len() = 1\n",
"SCnComV[8454].Len() = 1\n",
"SCnComV[8455].Len() = 1\n",
"SCnComV[8456].Len() = 1\n",
"SCnComV[8457].Len() = 1\n",
"SCnComV[8458].Len() = 1\n",
"SCnComV[8459].Len() = 1\n",
"SCnComV[8460].Len() = 1\n",
"SCnComV[8461].Len() = 1\n",
"SCnComV[8462].Len() = 1\n",
"SCnComV[8463].Len() = 1\n",
"SCnComV[8464].Len() = 1\n",
"SCnComV[8465].Len() = 1\n",
"SCnComV[8466].Len() = 1\n",
"SCnComV[8467].Len() = 1\n",
"SCnComV[8468].Len() = 1\n",
"SCnComV[8469].Len() = 1\n",
"SCnComV[8470].Len() = 1\n",
"SCnComV[8471].Len() = 1\n",
"SCnComV[8472].Len() = 1\n",
"SCnComV[8473].Len() = 1\n",
"SCnComV[8474].Len() = 1\n",
"SCnComV[8475].Len() = 1\n",
"SCnComV[8476].Len() = 1\n",
"SCnComV[8477].Len() = 1\n",
"SCnComV[8478].Len() = 1\n",
"SCnComV[8479].Len() = 1\n",
"SCnComV[8480].Len() = 1\n",
"SCnComV[8481].Len() = 1\n",
"SCnComV[8482].Len() = 1\n",
"SCnComV[8483].Len() = 1\n",
"SCnComV[8484].Len() = 1\n",
"SCnComV[8485].Len() = 1\n",
"SCnComV[8486].Len() = 1\n",
"SCnComV[8487].Len() = 1\n",
"SCnComV[8488].Len() = 1\n",
"SCnComV[8489].Len() = 1\n",
"SCnComV[8490].Len() = 1\n",
"SCnComV[8491].Len() = 1\n",
"SCnComV[8492].Len() = 1\n",
"SCnComV[8493].Len() = 1\n",
"SCnComV[8494].Len() = 1\n",
"SCnComV[8495].Len() = 1\n",
"SCnComV[8496].Len() = 1\n",
"SCnComV[8497].Len() = 1\n",
"SCnComV[8498].Len() = 1\n",
"SCnComV[8499].Len() = 1\n",
"SCnComV[8500].Len() = 1\n",
"SCnComV[8501].Len() = 1\n",
"SCnComV[8502].Len() = 1\n",
"SCnComV[8503].Len() = 1\n",
"SCnComV[8504].Len() = 1\n",
"SCnComV[8505].Len() = 1\n",
"SCnComV[8506].Len() = 1\n",
"SCnComV[8507].Len() = 1\n",
"SCnComV[8508].Len() = 1\n",
"SCnComV[8509].Len() = 1\n",
"SCnComV[8510].Len() = 1\n",
"SCnComV[8511].Len() = 1\n",
"SCnComV[8512].Len() = 1\n",
"SCnComV[8513].Len() = 1\n",
"SCnComV[8514].Len() = 1\n",
"SCnComV[8515].Len() = 1\n",
"SCnComV[8516].Len() = 1\n",
"SCnComV[8517].Len() = 1\n",
"SCnComV[8518].Len() = 1\n",
"SCnComV[8519].Len() = 1\n",
"SCnComV[8520].Len() = 1\n",
"SCnComV[8521].Len() = 1\n",
"SCnComV[8522].Len() = 1\n",
"SCnComV[8523].Len() = 1\n",
"SCnComV[8524].Len() = 1\n",
"SCnComV[8525].Len() = 1\n",
"SCnComV[8526].Len() = 1\n",
"SCnComV[8527].Len() = 1\n",
"SCnComV[8528].Len() = 1\n",
"SCnComV[8529].Len() = 1\n",
"SCnComV[8530].Len() = 1\n",
"SCnComV[8531].Len() = 1\n",
"SCnComV[8532].Len() = 1\n",
"SCnComV[8533].Len() = 1\n",
"SCnComV[8534].Len() = 1\n",
"SCnComV[8535].Len() = 1\n",
"SCnComV[8536].Len() = 1\n",
"SCnComV[8537].Len() = 1\n",
"SCnComV[8538].Len() = 1\n",
"SCnComV[8539].Len() = 1\n",
"SCnComV[8540].Len() = 1\n",
"SCnComV[8541].Len() = 1\n",
"SCnComV[8542].Len() = 1\n",
"SCnComV[8543].Len() = 1\n",
"SCnComV[8544].Len() = 1\n",
"SCnComV[8545].Len() = 1\n",
"SCnComV[8546].Len() = 1\n",
"SCnComV[8547].Len() = 1\n",
"SCnComV[8548].Len() = 1\n",
"SCnComV[8549].Len() = 1\n",
"SCnComV[8550].Len() = 1\n",
"SCnComV[8551].Len() = 1\n",
"SCnComV[8552].Len() = 1\n",
"SCnComV[8553].Len() = 1\n",
"SCnComV[8554].Len() = 1\n",
"SCnComV[8555].Len() = 1\n",
"SCnComV[8556].Len() = 1\n",
"SCnComV[8557].Len() = 1\n",
"SCnComV[8558].Len() = 1\n",
"SCnComV[8559].Len() = 1\n",
"SCnComV[8560].Len() = 1\n",
"SCnComV[8561].Len() = 1\n",
"SCnComV[8562].Len() = 1\n",
"SCnComV[8563].Len() = 1\n",
"SCnComV[8564].Len() = 1\n",
"SCnComV[8565].Len() = 1\n",
"SCnComV[8566].Len() = 1\n",
"SCnComV[8567].Len() = 1\n",
"SCnComV[8568].Len() = 1\n",
"SCnComV[8569].Len() = 1\n",
"SCnComV[8570].Len() = 1\n",
"SCnComV[8571].Len() = 1\n",
"SCnComV[8572].Len() = 1\n",
"SCnComV[8573].Len() = 1\n",
"SCnComV[8574].Len() = 1\n",
"SCnComV[8575].Len() = 1\n",
"SCnComV[8576].Len() = 1\n",
"SCnComV[8577].Len() = 1\n",
"SCnComV[8578].Len() = 1\n",
"SCnComV[8579].Len() = 1\n",
"SCnComV[8580].Len() = 1\n",
"SCnComV[8581].Len() = 1\n",
"SCnComV[8582].Len() = 1\n",
"SCnComV[8583].Len() = 1\n",
"SCnComV[8584].Len() = 1\n",
"SCnComV[8585].Len() = 1\n",
"SCnComV[8586].Len() = 1\n",
"SCnComV[8587].Len() = 1\n",
"SCnComV[8588].Len() = 1\n",
"SCnComV[8589].Len() = 1\n",
"SCnComV[8590].Len() = 1\n",
"SCnComV[8591].Len() = 1\n",
"SCnComV[8592].Len() = 1\n",
"SCnComV[8593].Len() = 1\n",
"SCnComV[8594].Len() = 1\n",
"SCnComV[8595].Len() = 1\n",
"SCnComV[8596].Len() = 1\n",
"SCnComV[8597].Len() = 1\n",
"SCnComV[8598].Len() = 1\n",
"SCnComV[8599].Len() = 1\n",
"SCnComV[8600].Len() = 1\n",
"SCnComV[8601].Len() = 1\n",
"SCnComV[8602].Len() = 1\n",
"SCnComV[8603].Len() = 1\n",
"SCnComV[8604].Len() = 1\n",
"SCnComV[8605].Len() = 1\n",
"SCnComV[8606].Len() = 1\n",
"SCnComV[8607].Len() = 1\n",
"SCnComV[8608].Len() = 1\n",
"SCnComV[8609].Len() = 1\n",
"SCnComV[8610].Len() = 1\n",
"SCnComV[8611].Len() = 1\n",
"SCnComV[8612].Len() = 1\n",
"SCnComV[8613].Len() = 1\n",
"SCnComV[8614].Len() = 1\n",
"SCnComV[8615].Len() = 1\n",
"SCnComV[8616].Len() = 1\n",
"SCnComV[8617].Len() = 1\n",
"SCnComV[8618].Len() = 1\n",
"SCnComV[8619].Len() = 1\n",
"SCnComV[8620].Len() = 1\n",
"SCnComV[8621].Len() = 1\n",
"SCnComV[8622].Len() = 1\n",
"SCnComV[8623].Len() = 1\n",
"SCnComV[8624].Len() = 1\n",
"SCnComV[8625].Len() = 1\n",
"SCnComV[8626].Len() = 1\n",
"SCnComV[8627].Len() = 1\n",
"SCnComV[8628].Len() = 1\n",
"SCnComV[8629].Len() = 1\n",
"SCnComV[8630].Len() = 1\n",
"SCnComV[8631].Len() = 1\n",
"SCnComV[8632].Len() = 1\n",
"SCnComV[8633].Len() = 1\n",
"SCnComV[8634].Len() = 1\n",
"SCnComV[8635].Len() = 1\n",
"SCnComV[8636].Len() = 1\n",
"SCnComV[8637].Len() = 1\n",
"SCnComV[8638].Len() = 1\n",
"SCnComV[8639].Len() = 1\n",
"SCnComV[8640].Len() = 1\n",
"SCnComV[8641].Len() = 1\n",
"SCnComV[8642].Len() = 1\n",
"SCnComV[8643].Len() = 1\n",
"SCnComV[8644].Len() = 1\n",
"SCnComV[8645].Len() = 1\n",
"SCnComV[8646].Len() = 1\n",
"SCnComV[8647].Len() = 1\n",
"SCnComV[8648].Len() = 1\n",
"SCnComV[8649].Len() = 1\n",
"SCnComV[8650].Len() = 1\n",
"SCnComV[8651].Len() = 1\n",
"SCnComV[8652].Len() = 1\n",
"SCnComV[8653].Len() = 1\n",
"SCnComV[8654].Len() = 1\n",
"SCnComV[8655].Len() = 1\n",
"SCnComV[8656].Len() = 1\n",
"SCnComV[8657].Len() = 1\n",
"SCnComV[8658].Len() = 1\n",
"SCnComV[8659].Len() = 1\n",
"SCnComV[8660].Len() = 1\n",
"SCnComV[8661].Len() = 1\n",
"SCnComV[8662].Len() = 1\n",
"SCnComV[8663].Len() = 1\n",
"SCnComV[8664].Len() = 1\n",
"SCnComV[8665].Len() = 1\n",
"SCnComV[8666].Len() = 1\n",
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"SCnComV[8668].Len() = 1\n",
"SCnComV[8669].Len() = 1\n",
"SCnComV[8670].Len() = 1\n",
"SCnComV[8671].Len() = 1\n",
"SCnComV[8672].Len() = 1\n",
"SCnComV[8673].Len() = 1\n",
"SCnComV[8674].Len() = 1\n",
"SCnComV[8675].Len() = 1\n",
"SCnComV[8676].Len() = 1\n",
"SCnComV[8677].Len() = 1\n",
"SCnComV[8678].Len() = 1\n",
"SCnComV[8679].Len() = 1\n",
"SCnComV[8680].Len() = 1\n",
"SCnComV[8681].Len() = 1\n",
"SCnComV[8682].Len() = 1\n",
"SCnComV[8683].Len() = 1\n",
"SCnComV[8684].Len() = 1\n",
"SCnComV[8685].Len() = 1\n",
"SCnComV[8686].Len() = 1\n",
"SCnComV[8687].Len() = 1\n",
"SCnComV[8688].Len() = 1\n",
"SCnComV[8689].Len() = 1\n",
"SCnComV[8690].Len() = 1\n",
"SCnComV[8691].Len() = 1\n",
"SCnComV[8692].Len() = 1\n",
"SCnComV[8693].Len() = 1\n",
"SCnComV[8694].Len() = 1\n",
"SCnComV[8695].Len() = 1\n",
"SCnComV[8696].Len() = 1\n",
"SCnComV[8697].Len() = 1\n",
"SCnComV[8698].Len() = 1\n",
"SCnComV[8699].Len() = 1\n",
"SCnComV[8700].Len() = 1\n",
"SCnComV[8701].Len() = 1\n",
"SCnComV[8702].Len() = 1\n",
"SCnComV[8703].Len() = 1\n",
"SCnComV[8704].Len() = 1\n",
"SCnComV[8705].Len() = 1\n",
"SCnComV[8706].Len() = 1\n",
"SCnComV[8707].Len() = 1\n",
"SCnComV[8708].Len() = 1\n",
"SCnComV[8709].Len() = 1\n",
"SCnComV[8710].Len() = 1\n",
"SCnComV[8711].Len() = 1\n",
"SCnComV[8712].Len() = 1\n",
"SCnComV[8713].Len() = 1\n",
"SCnComV[8714].Len() = 1\n",
"SCnComV[8715].Len() = 1\n",
"SCnComV[8716].Len() = 1\n",
"SCnComV[8717].Len() = 1\n",
"SCnComV[8718].Len() = 1\n",
"SCnComV[8719].Len() = 1\n",
"SCnComV[8720].Len() = 1\n",
"SCnComV[8721].Len() = 1\n",
"SCnComV[8722].Len() = 1\n",
"SCnComV[8723].Len() = 1\n",
"SCnComV[8724].Len() = 1\n",
"SCnComV[8725].Len() = 1\n",
"SCnComV[8726].Len() = 1\n",
"SCnComV[8727].Len() = 1\n",
"SCnComV[8728].Len() = 1\n",
"SCnComV[8729].Len() = 1\n",
"SCnComV[8730].Len() = 1\n",
"SCnComV[8731].Len() = 1\n",
"SCnComV[8732].Len() = 1\n",
"SCnComV[8733].Len() = 1\n",
"SCnComV[8734].Len() = 1\n",
"SCnComV[8735].Len() = 1\n",
"SCnComV[8736].Len() = 1\n",
"SCnComV[8737].Len() = 1\n",
"SCnComV[8738].Len() = 1\n",
"SCnComV[8739].Len() = 1\n",
"SCnComV[8740].Len() = 1\n",
"SCnComV[8741].Len() = 1\n",
"SCnComV[8742].Len() = 1\n",
"SCnComV[8743].Len() = 1\n",
"SCnComV[8744].Len() = 1\n",
"SCnComV[8745].Len() = 1\n",
"SCnComV[8746].Len() = 1\n",
"SCnComV[8747].Len() = 1\n",
"SCnComV[8748].Len() = 1\n",
"SCnComV[8749].Len() = 1\n",
"SCnComV[8750].Len() = 1\n",
"SCnComV[8751].Len() = 1\n",
"SCnComV[8752].Len() = 1\n",
"SCnComV[8753].Len() = 1\n",
"SCnComV[8754].Len() = 1\n",
"SCnComV[8755].Len() = 1\n",
"SCnComV[8756].Len() = 1\n",
"SCnComV[8757].Len() = 1\n",
"SCnComV[8758].Len() = 1\n",
"SCnComV[8759].Len() = 1\n",
"SCnComV[8760].Len() = 1\n",
"SCnComV[8761].Len() = 1\n",
"SCnComV[8762].Len() = 1\n",
"SCnComV[8763].Len() = 1\n",
"SCnComV[8764].Len() = 1\n",
"SCnComV[8765].Len() = 1\n",
"SCnComV[8766].Len() = 1\n",
"SCnComV[8767].Len() = 1\n",
"SCnComV[8768].Len() = 1\n",
"SCnComV[8769].Len() = 1\n",
"SCnComV[8770].Len() = 1\n",
"SCnComV[8771].Len() = 1\n",
"SCnComV[8772].Len() = 1\n",
"SCnComV[8773].Len() = 1\n",
"SCnComV[8774].Len() = 1\n",
"SCnComV[8775].Len() = 1\n",
"SCnComV[8776].Len() = 1\n",
"SCnComV[8777].Len() = 1\n",
"SCnComV[8778].Len() = 1\n",
"SCnComV[8779].Len() = 1\n",
"SCnComV[8780].Len() = 1\n",
"SCnComV[8781].Len() = 1\n",
"SCnComV[8782].Len() = 1\n",
"SCnComV[8783].Len() = 1\n",
"SCnComV[8784].Len() = 1\n",
"SCnComV[8785].Len() = 1\n",
"SCnComV[8786].Len() = 1\n",
"SCnComV[8787].Len() = 1\n",
"SCnComV[8788].Len() = 1\n",
"SCnComV[8789].Len() = 1\n",
"SCnComV[8790].Len() = 1\n",
"SCnComV[8791].Len() = 1\n",
"SCnComV[8792].Len() = 1\n",
"SCnComV[8793].Len() = 1\n",
"SCnComV[8794].Len() = 1\n",
"SCnComV[8795].Len() = 1\n",
"SCnComV[8796].Len() = 1\n",
"SCnComV[8797].Len() = 1\n",
"SCnComV[8798].Len() = 1\n",
"SCnComV[8799].Len() = 1\n",
"SCnComV[8800].Len() = 1\n",
"SCnComV[8801].Len() = 1\n",
"SCnComV[8802].Len() = 1\n",
"SCnComV[8803].Len() = 1\n",
"SCnComV[8804].Len() = 1\n",
"SCnComV[8805].Len() = 1\n",
"SCnComV[8806].Len() = 1\n",
"SCnComV[8807].Len() = 1\n",
"SCnComV[8808].Len() = 1\n",
"SCnComV[8809].Len() = 1\n",
"SCnComV[8810].Len() = 1\n",
"SCnComV[8811].Len() = 1\n",
"SCnComV[8812].Len() = 1\n",
"SCnComV[8813].Len() = 1\n",
"SCnComV[8814].Len() = 1\n",
"SCnComV[8815].Len() = 1\n",
"SCnComV[8816].Len() = 1\n",
"SCnComV[8817].Len() = 1\n",
"SCnComV[8818].Len() = 1\n",
"SCnComV[8819].Len() = 1\n",
"SCnComV[8820].Len() = 1\n",
"SCnComV[8821].Len() = 1\n",
"SCnComV[8822].Len() = 1\n",
"SCnComV[8823].Len() = 1\n",
"SCnComV[8824].Len() = 1\n",
"SCnComV[8825].Len() = 1\n",
"SCnComV[8826].Len() = 1\n",
"SCnComV[8827].Len() = 1\n",
"SCnComV[8828].Len() = 1\n",
"SCnComV[8829].Len() = 1\n",
"SCnComV[8830].Len() = 1\n",
"SCnComV[8831].Len() = 1\n",
"SCnComV[8832].Len() = 1\n",
"SCnComV[8833].Len() = 1\n",
"SCnComV[8834].Len() = 1\n",
"SCnComV[8835].Len() = 1\n",
"SCnComV[8836].Len() = 1\n",
"SCnComV[8837].Len() = 1\n",
"SCnComV[8838].Len() = 1\n",
"SCnComV[8839].Len() = 1\n",
"SCnComV[8840].Len() = 1\n",
"SCnComV[8841].Len() = 1\n",
"SCnComV[8842].Len() = 1\n",
"SCnComV[8843].Len() = 1\n",
"SCnComV[8844].Len() = 1\n",
"SCnComV[8845].Len() = 1\n",
"SCnComV[8846].Len() = 1\n",
"SCnComV[8847].Len() = 1\n",
"SCnComV[8848].Len() = 1\n",
"SCnComV[8849].Len() = 1\n",
"SCnComV[8850].Len() = 1\n",
"SCnComV[8851].Len() = 1\n",
"SCnComV[8852].Len() = 1\n",
"SCnComV[8853].Len() = 1\n",
"SCnComV[8854].Len() = 1\n",
"SCnComV[8855].Len() = 1\n",
"SCnComV[8856].Len() = 1\n",
"SCnComV[8857].Len() = 1\n",
"SCnComV[8858].Len() = 1\n",
"SCnComV[8859].Len() = 1\n",
"SCnComV[8860].Len() = 1\n",
"SCnComV[8861].Len() = 1\n",
"SCnComV[8862].Len() = 1\n",
"SCnComV[8863].Len() = 1\n",
"SCnComV[8864].Len() = 1\n",
"SCnComV[8865].Len() = 1\n",
"SCnComV[8866].Len() = 1\n",
"SCnComV[8867].Len() = 1\n",
"SCnComV[8868].Len() = 1\n",
"SCnComV[8869].Len() = 1\n",
"SCnComV[8870].Len() = 1\n",
"SCnComV[8871].Len() = 1\n",
"SCnComV[8872].Len() = 1\n",
"SCnComV[8873].Len() = 1\n",
"SCnComV[8874].Len() = 1\n",
"SCnComV[8875].Len() = 1\n",
"SCnComV[8876].Len() = 1\n",
"SCnComV[8877].Len() = 1\n",
"SCnComV[8878].Len() = 1\n",
"SCnComV[8879].Len() = 1\n",
"SCnComV[8880].Len() = 1\n",
"SCnComV[8881].Len() = 1\n",
"SCnComV[8882].Len() = 1\n",
"SCnComV[8883].Len() = 1\n",
"SCnComV[8884].Len() = 1\n",
"SCnComV[8885].Len() = 1\n",
"SCnComV[8886].Len() = 1\n",
"SCnComV[8887].Len() = 1\n",
"SCnComV[8888].Len() = 1\n",
"SCnComV[8889].Len() = 1\n",
"SCnComV[8890].Len() = 1\n",
"SCnComV[8891].Len() = 1\n",
"SCnComV[8892].Len() = 1\n",
"SCnComV[8893].Len() = 1\n",
"SCnComV[8894].Len() = 1\n",
"SCnComV[8895].Len() = 1\n",
"SCnComV[8896].Len() = 1\n",
"SCnComV[8897].Len() = 1\n",
"SCnComV[8898].Len() = 1\n",
"SCnComV[8899].Len() = 1\n",
"SCnComV[8900].Len() = 1\n",
"SCnComV[8901].Len() = 1\n",
"SCnComV[8902].Len() = 1\n",
"SCnComV[8903].Len() = 1\n",
"SCnComV[8904].Len() = 1\n",
"SCnComV[8905].Len() = 1\n",
"SCnComV[8906].Len() = 1\n",
"SCnComV[8907].Len() = 1\n",
"SCnComV[8908].Len() = 1\n",
"SCnComV[8909].Len() = 1\n",
"SCnComV[8910].Len() = 1\n",
"SCnComV[8911].Len() = 1\n",
"SCnComV[8912].Len() = 1\n",
"SCnComV[8913].Len() = 1\n",
"SCnComV[8914].Len() = 1\n",
"SCnComV[8915].Len() = 1\n",
"SCnComV[8916].Len() = 1\n",
"SCnComV[8917].Len() = 1\n",
"SCnComV[8918].Len() = 1\n",
"SCnComV[8919].Len() = 1\n",
"SCnComV[8920].Len() = 1\n",
"SCnComV[8921].Len() = 1\n",
"SCnComV[8922].Len() = 1\n",
"SCnComV[8923].Len() = 1\n",
"SCnComV[8924].Len() = 1\n",
"SCnComV[8925].Len() = 1\n",
"SCnComV[8926].Len() = 1\n",
"SCnComV[8927].Len() = 1\n",
"SCnComV[8928].Len() = 1\n",
"SCnComV[8929].Len() = 1\n",
"SCnComV[8930].Len() = 1\n",
"SCnComV[8931].Len() = 1\n",
"SCnComV[8932].Len() = 1\n",
"SCnComV[8933].Len() = 1\n",
"SCnComV[8934].Len() = 1\n",
"SCnComV[8935].Len() = 1\n",
"SCnComV[8936].Len() = 1\n",
"SCnComV[8937].Len() = 1\n",
"SCnComV[8938].Len() = 1\n",
"SCnComV[8939].Len() = 1\n",
"SCnComV[8940].Len() = 1\n",
"SCnComV[8941].Len() = 1\n",
"SCnComV[8942].Len() = 1\n",
"SCnComV[8943].Len() = 1\n",
"SCnComV[8944].Len() = 1\n",
"SCnComV[8945].Len() = 1\n",
"SCnComV[8946].Len() = 1\n",
"SCnComV[8947].Len() = 1\n",
"SCnComV[8948].Len() = 1\n",
"SCnComV[8949].Len() = 1\n",
"SCnComV[8950].Len() = 1\n",
"SCnComV[8951].Len() = 1\n",
"SCnComV[8952].Len() = 1\n",
"SCnComV[8953].Len() = 1\n",
"SCnComV[8954].Len() = 1\n",
"SCnComV[8955].Len() = 1\n",
"SCnComV[8956].Len() = 1\n",
"SCnComV[8957].Len() = 1\n",
"SCnComV[8958].Len() = 1\n",
"SCnComV[8959].Len() = 1\n",
"SCnComV[8960].Len() = 1\n",
"SCnComV[8961].Len() = 1\n",
"SCnComV[8962].Len() = 1\n",
"SCnComV[8963].Len() = 1\n",
"SCnComV[8964].Len() = 1\n",
"SCnComV[8965].Len() = 1\n",
"SCnComV[8966].Len() = 1\n",
"SCnComV[8967].Len() = 1\n",
"SCnComV[8968].Len() = 1\n",
"SCnComV[8969].Len() = 1\n",
"SCnComV[8970].Len() = 1\n",
"SCnComV[8971].Len() = 1\n",
"SCnComV[8972].Len() = 1\n",
"SCnComV[8973].Len() = 1\n",
"SCnComV[8974].Len() = 1\n",
"SCnComV[8975].Len() = 1\n",
"SCnComV[8976].Len() = 1\n",
"SCnComV[8977].Len() = 1\n",
"SCnComV[8978].Len() = 1\n",
"SCnComV[8979].Len() = 1\n",
"SCnComV[8980].Len() = 1\n",
"SCnComV[8981].Len() = 1\n",
"SCnComV[8982].Len() = 1\n",
"SCnComV[8983].Len() = 1\n",
"SCnComV[8984].Len() = 1\n",
"SCnComV[8985].Len() = 1\n",
"SCnComV[8986].Len() = 1\n",
"SCnComV[8987].Len() = 1\n",
"SCnComV[8988].Len() = 1\n",
"SCnComV[8989].Len() = 1\n",
"SCnComV[8990].Len() = 1\n",
"SCnComV[8991].Len() = 1\n",
"SCnComV[8992].Len() = 1\n",
"SCnComV[8993].Len() = 1\n",
"SCnComV[8994].Len() = 1\n",
"SCnComV[8995].Len() = 1\n",
"SCnComV[8996].Len() = 1\n",
"SCnComV[8997].Len() = 1\n",
"SCnComV[8998].Len() = 1\n",
"SCnComV[8999].Len() = 1\n",
"SCnComV[9000].Len() = 1\n",
"SCnComV[9001].Len() = 1\n",
"SCnComV[9002].Len() = 1\n",
"SCnComV[9003].Len() = 1\n",
"SCnComV[9004].Len() = 1\n",
"SCnComV[9005].Len() = 1\n",
"SCnComV[9006].Len() = 1\n",
"SCnComV[9007].Len() = 1\n",
"SCnComV[9008].Len() = 1\n",
"SCnComV[9009].Len() = 1\n",
"SCnComV[9010].Len() = 1\n",
"SCnComV[9011].Len() = 1\n",
"SCnComV[9012].Len() = 1\n",
"SCnComV[9013].Len() = 1\n",
"SCnComV[9014].Len() = 1\n",
"SCnComV[9015].Len() = 1\n",
"SCnComV[9016].Len() = 1\n",
"SCnComV[9017].Len() = 1\n",
"SCnComV[9018].Len() = 1\n",
"SCnComV[9019].Len() = 1\n",
"SCnComV[9020].Len() = 1\n",
"SCnComV[9021].Len() = 1\n",
"SCnComV[9022].Len() = 1\n",
"SCnComV[9023].Len() = 1\n",
"SCnComV[9024].Len() = 1\n",
"SCnComV[9025].Len() = 1\n",
"SCnComV[9026].Len() = 1\n",
"SCnComV[9027].Len() = 1\n",
"SCnComV[9028].Len() = 1\n",
"SCnComV[9029].Len() = 1\n",
"SCnComV[9030].Len() = 1\n",
"SCnComV[9031].Len() = 1\n",
"SCnComV[9032].Len() = 1\n",
"SCnComV[9033].Len() = 1\n",
"SCnComV[9034].Len() = 1\n",
"SCnComV[9035].Len() = 1\n",
"SCnComV[9036].Len() = 1\n",
"SCnComV[9037].Len() = 1\n",
"SCnComV[9038].Len() = 1\n",
"SCnComV[9039].Len() = 1\n",
"SCnComV[9040].Len() = 1\n",
"SCnComV[9041].Len() = 1\n",
"SCnComV[9042].Len() = 1\n",
"SCnComV[9043].Len() = 1\n",
"SCnComV[9044].Len() = 1\n",
"SCnComV[9045].Len() = 1\n",
"SCnComV[9046].Len() = 1\n",
"SCnComV[9047].Len() = 1\n",
"SCnComV[9048].Len() = 1\n",
"SCnComV[9049].Len() = 1\n",
"SCnComV[9050].Len() = 1\n",
"SCnComV[9051].Len() = 1\n",
"SCnComV[9052].Len() = 1\n",
"SCnComV[9053].Len() = 1\n",
"SCnComV[9054].Len() = 1\n",
"SCnComV[9055].Len() = 1\n",
"SCnComV[9056].Len() = 1\n",
"SCnComV[9057].Len() = 1\n",
"SCnComV[9058].Len() = 1\n",
"SCnComV[9059].Len() = 1\n",
"SCnComV[9060].Len() = 1\n",
"SCnComV[9061].Len() = 1\n",
"SCnComV[9062].Len() = 1\n",
"SCnComV[9063].Len() = 1\n",
"SCnComV[9064].Len() = 1\n",
"SCnComV[9065].Len() = 1\n",
"SCnComV[9066].Len() = 1\n",
"SCnComV[9067].Len() = 1\n",
"SCnComV[9068].Len() = 1\n",
"SCnComV[9069].Len() = 1\n",
"SCnComV[9070].Len() = 1\n",
"SCnComV[9071].Len() = 1\n",
"SCnComV[9072].Len() = 1\n",
"SCnComV[9073].Len() = 1\n",
"SCnComV[9074].Len() = 1\n",
"SCnComV[9075].Len() = 1\n",
"SCnComV[9076].Len() = 1\n",
"SCnComV[9077].Len() = 1\n",
"SCnComV[9078].Len() = 1\n",
"SCnComV[9079].Len() = 1\n",
"SCnComV[9080].Len() = 1\n",
"SCnComV[9081].Len() = 1\n",
"SCnComV[9082].Len() = 1\n",
"SCnComV[9083].Len() = 1\n",
"SCnComV[9084].Len() = 1\n",
"SCnComV[9085].Len() = 1\n",
"SCnComV[9086].Len() = 1\n",
"SCnComV[9087].Len() = 1\n",
"SCnComV[9088].Len() = 1\n",
"SCnComV[9089].Len() = 1\n",
"SCnComV[9090].Len() = 1\n",
"SCnComV[9091].Len() = 1\n",
"SCnComV[9092].Len() = 1\n",
"SCnComV[9093].Len() = 1\n",
"SCnComV[9094].Len() = 1\n",
"SCnComV[9095].Len() = 1\n",
"SCnComV[9096].Len() = 1\n",
"SCnComV[9097].Len() = 1\n",
"SCnComV[9098].Len() = 1\n",
"SCnComV[9099].Len() = 1\n",
"SCnComV[9100].Len() = 1\n",
"SCnComV[9101].Len() = 1\n",
"SCnComV[9102].Len() = 1\n",
"SCnComV[9103].Len() = 1\n",
"SCnComV[9104].Len() = 1\n",
"SCnComV[9105].Len() = 1\n",
"SCnComV[9106].Len() = 1\n",
"SCnComV[9107].Len() = 1\n",
"SCnComV[9108].Len() = 1\n",
"SCnComV[9109].Len() = 1\n",
"SCnComV[9110].Len() = 1\n",
"SCnComV[9111].Len() = 1\n",
"SCnComV[9112].Len() = 1\n",
"SCnComV[9113].Len() = 1\n",
"SCnComV[9114].Len() = 1\n",
"SCnComV[9115].Len() = 1\n",
"SCnComV[9116].Len() = 1\n",
"SCnComV[9117].Len() = 1\n",
"SCnComV[9118].Len() = 1\n",
"SCnComV[9119].Len() = 1\n",
"SCnComV[9120].Len() = 1\n",
"SCnComV[9121].Len() = 1\n",
"SCnComV[9122].Len() = 1\n",
"SCnComV[9123].Len() = 1\n",
"SCnComV[9124].Len() = 1\n",
"SCnComV[9125].Len() = 1\n",
"SCnComV[9126].Len() = 1\n",
"SCnComV[9127].Len() = 1\n",
"SCnComV[9128].Len() = 1\n",
"SCnComV[9129].Len() = 1\n",
"SCnComV[9130].Len() = 1\n",
"SCnComV[9131].Len() = 1\n",
"SCnComV[9132].Len() = 1\n",
"SCnComV[9133].Len() = 1\n",
"SCnComV[9134].Len() = 1\n",
"SCnComV[9135].Len() = 1\n",
"SCnComV[9136].Len() = 1\n",
"SCnComV[9137].Len() = 1\n",
"SCnComV[9138].Len() = 1\n",
"SCnComV[9139].Len() = 1\n",
"SCnComV[9140].Len() = 1\n",
"SCnComV[9141].Len() = 1\n",
"SCnComV[9142].Len() = 1\n",
"SCnComV[9143].Len() = 1\n",
"SCnComV[9144].Len() = 1\n",
"SCnComV[9145].Len() = 1\n",
"SCnComV[9146].Len() = 1\n",
"SCnComV[9147].Len() = 1\n",
"SCnComV[9148].Len() = 1\n",
"SCnComV[9149].Len() = 1\n",
"SCnComV[9150].Len() = 1\n",
"SCnComV[9151].Len() = 1\n",
"SCnComV[9152].Len() = 1\n",
"SCnComV[9153].Len() = 1\n",
"SCnComV[9154].Len() = 1\n",
"SCnComV[9155].Len() = 1\n",
"SCnComV[9156].Len() = 1\n",
"SCnComV[9157].Len() = 1\n",
"SCnComV[9158].Len() = 1\n",
"SCnComV[9159].Len() = 1\n",
"SCnComV[9160].Len() = 1\n",
"SCnComV[9161].Len() = 1\n",
"SCnComV[9162].Len() = 1\n",
"SCnComV[9163].Len() = 1\n",
"SCnComV[9164].Len() = 1\n",
"SCnComV[9165].Len() = 1\n",
"SCnComV[9166].Len() = 1\n",
"SCnComV[9167].Len() = 1\n",
"SCnComV[9168].Len() = 1\n",
"SCnComV[9169].Len() = 1\n",
"SCnComV[9170].Len() = 1\n",
"SCnComV[9171].Len() = 1\n",
"SCnComV[9172].Len() = 1\n",
"SCnComV[9173].Len() = 1\n",
"SCnComV[9174].Len() = 1\n",
"SCnComV[9175].Len() = 1\n",
"SCnComV[9176].Len() = 1\n",
"SCnComV[9177].Len() = 1\n",
"SCnComV[9178].Len() = 1\n",
"SCnComV[9179].Len() = 1\n",
"SCnComV[9180].Len() = 1\n",
"SCnComV[9181].Len() = 1\n",
"SCnComV[9182].Len() = 1\n",
"SCnComV[9183].Len() = 1\n",
"SCnComV[9184].Len() = 1\n",
"SCnComV[9185].Len() = 1\n",
"SCnComV[9186].Len() = 1\n",
"SCnComV[9187].Len() = 1\n",
"SCnComV[9188].Len() = 1\n",
"SCnComV[9189].Len() = 1\n",
"SCnComV[9190].Len() = 1\n",
"SCnComV[9191].Len() = 1\n",
"SCnComV[9192].Len() = 1\n",
"SCnComV[9193].Len() = 1\n",
"SCnComV[9194].Len() = 1\n",
"SCnComV[9195].Len() = 1\n",
"SCnComV[9196].Len() = 1\n",
"SCnComV[9197].Len() = 1\n",
"SCnComV[9198].Len() = 1\n",
"SCnComV[9199].Len() = 1\n",
"SCnComV[9200].Len() = 1\n",
"SCnComV[9201].Len() = 1\n",
"SCnComV[9202].Len() = 1\n",
"SCnComV[9203].Len() = 1\n",
"SCnComV[9204].Len() = 1\n",
"SCnComV[9205].Len() = 1\n",
"SCnComV[9206].Len() = 1\n",
"SCnComV[9207].Len() = 1\n",
"SCnComV[9208].Len() = 1\n",
"SCnComV[9209].Len() = 1\n",
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"SCnComV[9211].Len() = 1\n",
"SCnComV[9212].Len() = 1\n",
"SCnComV[9213].Len() = 1\n",
"SCnComV[9214].Len() = 1\n",
"SCnComV[9215].Len() = 1\n",
"SCnComV[9216].Len() = 1\n",
"SCnComV[9217].Len() = 1\n",
"SCnComV[9218].Len() = 1\n",
"SCnComV[9219].Len() = 1\n",
"SCnComV[9220].Len() = 1\n",
"SCnComV[9221].Len() = 1\n",
"SCnComV[9222].Len() = 1\n",
"SCnComV[9223].Len() = 1\n",
"SCnComV[9224].Len() = 1\n",
"SCnComV[9225].Len() = 1\n",
"SCnComV[9226].Len() = 1\n",
"SCnComV[9227].Len() = 1\n",
"SCnComV[9228].Len() = 1\n",
"SCnComV[9229].Len() = 1\n",
"SCnComV[9230].Len() = 1\n",
"SCnComV[9231].Len() = 1\n",
"SCnComV[9232].Len() = 1\n",
"SCnComV[9233].Len() = 1\n",
"SCnComV[9234].Len() = 1\n",
"SCnComV[9235].Len() = 1\n",
"SCnComV[9236].Len() = 1\n",
"SCnComV[9237].Len() = 1\n",
"SCnComV[9238].Len() = 1\n",
"SCnComV[9239].Len() = 1\n",
"SCnComV[9240].Len() = 1\n",
"SCnComV[9241].Len() = 1\n",
"SCnComV[9242].Len() = 1\n",
"SCnComV[9243].Len() = 1\n",
"SCnComV[9244].Len() = 1\n",
"SCnComV[9245].Len() = 1\n",
"SCnComV[9246].Len() = 1\n",
"SCnComV[9247].Len() = 1\n",
"SCnComV[9248].Len() = 1\n",
"SCnComV[9249].Len() = 1\n",
"SCnComV[9250].Len() = 1\n",
"SCnComV[9251].Len() = 1\n",
"SCnComV[9252].Len() = 1\n",
"SCnComV[9253].Len() = 1\n",
"SCnComV[9254].Len() = 1\n",
"SCnComV[9255].Len() = 1\n",
"SCnComV[9256].Len() = 1\n",
"SCnComV[9257].Len() = 1\n",
"SCnComV[9258].Len() = 1\n",
"SCnComV[9259].Len() = 1\n",
"SCnComV[9260].Len() = 1\n",
"SCnComV[9261].Len() = 1\n",
"SCnComV[9262].Len() = 1\n",
"SCnComV[9263].Len() = 1\n",
"SCnComV[9264].Len() = 1\n",
"SCnComV[9265].Len() = 1\n",
"SCnComV[9266].Len() = 1\n",
"SCnComV[9267].Len() = 1\n",
"SCnComV[9268].Len() = 1\n",
"SCnComV[9269].Len() = 1\n",
"SCnComV[9270].Len() = 1\n",
"SCnComV[9271].Len() = 1\n",
"SCnComV[9272].Len() = 1\n",
"SCnComV[9273].Len() = 1\n",
"SCnComV[9274].Len() = 1\n",
"SCnComV[9275].Len() = 1\n",
"SCnComV[9276].Len() = 1\n",
"SCnComV[9277].Len() = 1\n",
"SCnComV[9278].Len() = 1\n",
"SCnComV[9279].Len() = 1\n",
"SCnComV[9280].Len() = 1\n",
"SCnComV[9281].Len() = 1\n",
"SCnComV[9282].Len() = 1\n",
"SCnComV[9283].Len() = 1\n",
"SCnComV[9284].Len() = 1\n",
"SCnComV[9285].Len() = 1\n",
"SCnComV[9286].Len() = 1\n",
"SCnComV[9287].Len() = 1\n",
"SCnComV[9288].Len() = 1\n",
"SCnComV[9289].Len() = 1\n",
"SCnComV[9290].Len() = 1\n",
"SCnComV[9291].Len() = 1\n",
"SCnComV[9292].Len() = 1\n",
"SCnComV[9293].Len() = 1\n",
"SCnComV[9294].Len() = 1\n",
"SCnComV[9295].Len() = 1\n",
"SCnComV[9296].Len() = 1\n",
"SCnComV[9297].Len() = 1\n",
"SCnComV[9298].Len() = 1\n",
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"SCnComV[9300].Len() = 1\n",
"SCnComV[9301].Len() = 1\n",
"SCnComV[9302].Len() = 1\n",
"SCnComV[9303].Len() = 1\n",
"SCnComV[9304].Len() = 1\n",
"SCnComV[9305].Len() = 1\n",
"SCnComV[9306].Len() = 1\n",
"SCnComV[9307].Len() = 1\n",
"SCnComV[9308].Len() = 1\n",
"SCnComV[9309].Len() = 1\n",
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"SCnComV[9311].Len() = 1\n",
"SCnComV[9312].Len() = 1\n",
"SCnComV[9313].Len() = 1\n",
"SCnComV[9314].Len() = 1\n",
"SCnComV[9315].Len() = 1\n",
"SCnComV[9316].Len() = 1\n",
"SCnComV[9317].Len() = 1\n",
"SCnComV[9318].Len() = 1\n",
"SCnComV[9319].Len() = 1\n",
"SCnComV[9320].Len() = 1\n",
"SCnComV[9321].Len() = 1\n",
"SCnComV[9322].Len() = 1\n",
"SCnComV[9323].Len() = 1\n",
"SCnComV[9324].Len() = 1\n",
"SCnComV[9325].Len() = 1\n",
"SCnComV[9326].Len() = 1\n",
"SCnComV[9327].Len() = 1\n",
"SCnComV[9328].Len() = 1\n",
"SCnComV[9329].Len() = 1\n",
"SCnComV[9330].Len() = 1\n",
"SCnComV[9331].Len() = 1\n",
"SCnComV[9332].Len() = 1\n",
"SCnComV[9333].Len() = 1\n",
"SCnComV[9334].Len() = 1\n",
"SCnComV[9335].Len() = 1\n",
"SCnComV[9336].Len() = 1\n",
"SCnComV[9337].Len() = 1\n",
"SCnComV[9338].Len() = 1\n",
"SCnComV[9339].Len() = 1\n",
"SCnComV[9340].Len() = 1\n",
"SCnComV[9341].Len() = 1\n",
"SCnComV[9342].Len() = 1\n",
"SCnComV[9343].Len() = 1\n",
"SCnComV[9344].Len() = 1\n",
"SCnComV[9345].Len() = 1\n",
"SCnComV[9346].Len() = 1\n",
"SCnComV[9347].Len() = 1\n",
"SCnComV[9348].Len() = 1\n",
"SCnComV[9349].Len() = 1\n",
"SCnComV[9350].Len() = 1\n",
"SCnComV[9351].Len() = 1\n",
"SCnComV[9352].Len() = 1\n",
"SCnComV[9353].Len() = 1\n",
"SCnComV[9354].Len() = 1\n",
"SCnComV[9355].Len() = 1\n",
"SCnComV[9356].Len() = 1\n",
"SCnComV[9357].Len() = 1\n",
"SCnComV[9358].Len() = 1\n",
"SCnComV[9359].Len() = 1\n",
"SCnComV[9360].Len() = 1\n",
"SCnComV[9361].Len() = 1\n",
"SCnComV[9362].Len() = 1\n",
"SCnComV[9363].Len() = 1\n",
"SCnComV[9364].Len() = 1\n",
"SCnComV[9365].Len() = 1\n",
"SCnComV[9366].Len() = 1\n",
"SCnComV[9367].Len() = 1\n",
"SCnComV[9368].Len() = 1\n",
"SCnComV[9369].Len() = 1\n",
"SCnComV[9370].Len() = 1\n",
"SCnComV[9371].Len() = 1\n",
"SCnComV[9372].Len() = 1\n",
"SCnComV[9373].Len() = 1\n",
"SCnComV[9374].Len() = 1\n",
"SCnComV[9375].Len() = 1\n",
"SCnComV[9376].Len() = 1\n",
"SCnComV[9377].Len() = 1\n",
"SCnComV[9378].Len() = 1\n",
"SCnComV[9379].Len() = 1\n",
"SCnComV[9380].Len() = 1\n",
"SCnComV[9381].Len() = 1\n",
"SCnComV[9382].Len() = 1\n",
"SCnComV[9383].Len() = 1\n",
"SCnComV[9384].Len() = 1\n",
"SCnComV[9385].Len() = 1\n",
"SCnComV[9386].Len() = 1\n",
"SCnComV[9387].Len() = 1\n",
"SCnComV[9388].Len() = 1\n",
"SCnComV[9389].Len() = 1\n",
"SCnComV[9390].Len() = 1\n",
"SCnComV[9391].Len() = 1\n",
"SCnComV[9392].Len() = 1\n",
"SCnComV[9393].Len() = 1\n",
"SCnComV[9394].Len() = 1\n",
"SCnComV[9395].Len() = 1\n",
"SCnComV[9396].Len() = 1\n",
"SCnComV[9397].Len() = 1\n",
"SCnComV[9398].Len() = 1\n",
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"SCnComV[9400].Len() = 1\n",
"SCnComV[9401].Len() = 1\n",
"SCnComV[9402].Len() = 1\n",
"SCnComV[9403].Len() = 1\n",
"SCnComV[9404].Len() = 1\n",
"SCnComV[9405].Len() = 1\n",
"SCnComV[9406].Len() = 1\n",
"SCnComV[9407].Len() = 1\n",
"SCnComV[9408].Len() = 1\n",
"SCnComV[9409].Len() = 1\n",
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"SCnComV[9411].Len() = 1\n",
"SCnComV[9412].Len() = 1\n",
"SCnComV[9413].Len() = 1\n",
"SCnComV[9414].Len() = 1\n",
"SCnComV[9415].Len() = 1\n",
"SCnComV[9416].Len() = 1\n",
"SCnComV[9417].Len() = 1\n",
"SCnComV[9418].Len() = 1\n",
"SCnComV[9419].Len() = 1\n",
"SCnComV[9420].Len() = 1\n",
"SCnComV[9421].Len() = 1\n",
"SCnComV[9422].Len() = 1\n",
"SCnComV[9423].Len() = 1\n",
"SCnComV[9424].Len() = 1\n",
"SCnComV[9425].Len() = 1\n",
"SCnComV[9426].Len() = 1\n",
"SCnComV[9427].Len() = 1\n",
"SCnComV[9428].Len() = 1\n",
"SCnComV[9429].Len() = 1\n",
"SCnComV[9430].Len() = 1\n",
"SCnComV[9431].Len() = 1\n",
"SCnComV[9432].Len() = 1\n",
"SCnComV[9433].Len() = 1\n",
"SCnComV[9434].Len() = 1\n",
"SCnComV[9435].Len() = 1\n",
"SCnComV[9436].Len() = 1\n",
"SCnComV[9437].Len() = 1\n",
"SCnComV[9438].Len() = 1\n",
"SCnComV[9439].Len() = 1\n",
"SCnComV[9440].Len() = 1\n",
"SCnComV[9441].Len() = 1\n",
"SCnComV[9442].Len() = 1\n",
"SCnComV[9443].Len() = 1\n",
"SCnComV[9444].Len() = 1\n",
"SCnComV[9445].Len() = 1\n",
"SCnComV[9446].Len() = 1\n",
"SCnComV[9447].Len() = 1\n",
"SCnComV[9448].Len() = 1\n",
"SCnComV[9449].Len() = 1\n",
"SCnComV[9450].Len() = 1\n",
"SCnComV[9451].Len() = 1\n",
"SCnComV[9452].Len() = 1\n",
"SCnComV[9453].Len() = 1\n",
"SCnComV[9454].Len() = 1\n",
"SCnComV[9455].Len() = 1\n",
"SCnComV[9456].Len() = 1\n",
"SCnComV[9457].Len() = 1\n",
"SCnComV[9458].Len() = 1\n",
"SCnComV[9459].Len() = 1\n",
"SCnComV[9460].Len() = 1\n",
"SCnComV[9461].Len() = 1\n",
"SCnComV[9462].Len() = 1\n",
"SCnComV[9463].Len() = 1\n",
"SCnComV[9464].Len() = 1\n",
"SCnComV[9465].Len() = 1\n",
"SCnComV[9466].Len() = 1\n",
"SCnComV[9467].Len() = 1\n",
"SCnComV[9468].Len() = 1\n",
"SCnComV[9469].Len() = 1\n",
"SCnComV[9470].Len() = 1\n",
"SCnComV[9471].Len() = 1\n",
"SCnComV[9472].Len() = 1\n",
"SCnComV[9473].Len() = 1\n",
"SCnComV[9474].Len() = 1\n",
"SCnComV[9475].Len() = 1\n",
"SCnComV[9476].Len() = 1\n",
"SCnComV[9477].Len() = 1\n",
"SCnComV[9478].Len() = 1\n",
"SCnComV[9479].Len() = 1\n",
"SCnComV[9480].Len() = 1\n",
"SCnComV[9481].Len() = 1\n",
"SCnComV[9482].Len() = 1\n",
"SCnComV[9483].Len() = 1\n",
"SCnComV[9484].Len() = 1\n",
"SCnComV[9485].Len() = 1\n",
"SCnComV[9486].Len() = 1\n",
"SCnComV[9487].Len() = 1\n",
"SCnComV[9488].Len() = 1\n",
"SCnComV[9489].Len() = 1\n",
"SCnComV[9490].Len() = 1\n",
"SCnComV[9491].Len() = 1\n",
"SCnComV[9492].Len() = 1\n",
"SCnComV[9493].Len() = 1\n",
"SCnComV[9494].Len() = 1\n",
"SCnComV[9495].Len() = 1\n",
"SCnComV[9496].Len() = 1\n",
"SCnComV[9497].Len() = 1\n",
"SCnComV[9498].Len() = 1\n",
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"SCnComV[9502].Len() = 1\n",
"SCnComV[9503].Len() = 1\n",
"SCnComV[9504].Len() = 1\n",
"SCnComV[9505].Len() = 1\n",
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"SCnComV[9507].Len() = 1\n",
"SCnComV[9508].Len() = 1\n",
"SCnComV[9509].Len() = 1\n",
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"SCnComV[9516].Len() = 1\n",
"SCnComV[9517].Len() = 1\n",
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"SCnComV[9520].Len() = 1\n",
"SCnComV[9521].Len() = 1\n",
"SCnComV[9522].Len() = 1\n",
"SCnComV[9523].Len() = 1\n",
"SCnComV[9524].Len() = 1\n",
"SCnComV[9525].Len() = 1\n",
"SCnComV[9526].Len() = 1\n",
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"SCnComV[9528].Len() = 1\n",
"SCnComV[9529].Len() = 1\n",
"SCnComV[9530].Len() = 1\n",
"SCnComV[9531].Len() = 1\n",
"SCnComV[9532].Len() = 1\n",
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"SCnComV[9534].Len() = 1\n",
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"SCnComV[9536].Len() = 1\n",
"SCnComV[9537].Len() = 1\n",
"SCnComV[9538].Len() = 1\n",
"SCnComV[9539].Len() = 1\n",
"SCnComV[9540].Len() = 1\n",
"SCnComV[9541].Len() = 1\n",
"SCnComV[9542].Len() = 1\n",
"SCnComV[9543].Len() = 1\n",
"SCnComV[9544].Len() = 1\n",
"SCnComV[9545].Len() = 1\n",
"SCnComV[9546].Len() = 1\n",
"SCnComV[9547].Len() = 1\n",
"SCnComV[9548].Len() = 1\n",
"SCnComV[9549].Len() = 1\n",
"SCnComV[9550].Len() = 1\n",
"SCnComV[9551].Len() = 1\n",
"SCnComV[9552].Len() = 1\n",
"SCnComV[9553].Len() = 1\n",
"SCnComV[9554].Len() = 1\n",
"SCnComV[9555].Len() = 1\n",
"SCnComV[9556].Len() = 1\n",
"SCnComV[9557].Len() = 1\n",
"SCnComV[9558].Len() = 1\n",
"SCnComV[9559].Len() = 1\n",
"SCnComV[9560].Len() = 1\n",
"SCnComV[9561].Len() = 1\n",
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"SCnComV[9563].Len() = 1\n",
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"SCnComV[9572].Len() = 1\n",
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"SCnComV[9575].Len() = 1\n",
"SCnComV[9576].Len() = 1\n",
"SCnComV[9577].Len() = 1\n",
"SCnComV[9578].Len() = 1\n",
"SCnComV[9579].Len() = 1\n",
"SCnComV[9580].Len() = 1\n",
"SCnComV[9581].Len() = 1\n",
"SCnComV[9582].Len() = 1\n",
"SCnComV[9583].Len() = 1\n",
"SCnComV[9584].Len() = 1\n",
"SCnComV[9585].Len() = 1\n",
"SCnComV[9586].Len() = 1\n",
"SCnComV[9587].Len() = 1\n",
"SCnComV[9588].Len() = 1\n",
"SCnComV[9589].Len() = 1\n",
"SCnComV[9590].Len() = 1\n",
"SCnComV[9591].Len() = 1\n",
"SCnComV[9592].Len() = 1\n",
"SCnComV[9593].Len() = 1\n",
"SCnComV[9594].Len() = 1\n",
"SCnComV[9595].Len() = 1\n",
"SCnComV[9596].Len() = 1\n",
"SCnComV[9597].Len() = 1\n",
"SCnComV[9598].Len() = 1\n",
"SCnComV[9599].Len() = 1\n",
"SCnComV[9600].Len() = 1\n",
"SCnComV[9601].Len() = 1\n",
"SCnComV[9602].Len() = 1\n",
"SCnComV[9603].Len() = 1\n",
"SCnComV[9604].Len() = 1\n",
"SCnComV[9605].Len() = 1\n",
"SCnComV[9606].Len() = 1\n",
"SCnComV[9607].Len() = 1\n",
"SCnComV[9608].Len() = 1\n",
"SCnComV[9609].Len() = 1\n",
"SCnComV[9610].Len() = 1\n",
"SCnComV[9611].Len() = 1\n",
"SCnComV[9612].Len() = 1\n",
"SCnComV[9613].Len() = 1\n",
"SCnComV[9614].Len() = 1\n",
"SCnComV[9615].Len() = 1\n",
"SCnComV[9616].Len() = 1\n",
"SCnComV[9617].Len() = 1\n",
"SCnComV[9618].Len() = 1\n",
"SCnComV[9619].Len() = 1\n",
"SCnComV[9620].Len() = 1\n",
"SCnComV[9621].Len() = 1\n",
"SCnComV[9622].Len() = 1\n",
"SCnComV[9623].Len() = 1\n",
"SCnComV[9624].Len() = 1\n",
"SCnComV[9625].Len() = 1\n",
"SCnComV[9626].Len() = 1\n",
"SCnComV[9627].Len() = 1\n",
"SCnComV[9628].Len() = 1\n",
"SCnComV[9629].Len() = 1\n",
"SCnComV[9630].Len() = 1\n",
"SCnComV[9631].Len() = 1\n",
"SCnComV[9632].Len() = 1\n",
"SCnComV[9633].Len() = 1\n",
"SCnComV[9634].Len() = 1\n",
"SCnComV[9635].Len() = 1\n",
"SCnComV[9636].Len() = 1\n",
"SCnComV[9637].Len() = 1\n",
"SCnComV[9638].Len() = 1\n",
"SCnComV[9639].Len() = 1\n",
"SCnComV[9640].Len() = 1\n",
"SCnComV[9641].Len() = 1\n",
"SCnComV[9642].Len() = 1\n",
"SCnComV[9643].Len() = 1\n",
"SCnComV[9644].Len() = 1\n",
"SCnComV[9645].Len() = 1\n",
"SCnComV[9646].Len() = 1\n",
"SCnComV[9647].Len() = 1\n",
"SCnComV[9648].Len() = 1\n",
"SCnComV[9649].Len() = 1\n",
"SCnComV[9650].Len() = 1\n",
"SCnComV[9651].Len() = 1\n",
"SCnComV[9652].Len() = 1\n",
"SCnComV[9653].Len() = 1\n",
"SCnComV[9654].Len() = 1\n",
"SCnComV[9655].Len() = 1\n",
"SCnComV[9656].Len() = 1\n",
"SCnComV[9657].Len() = 1\n",
"SCnComV[9658].Len() = 1\n",
"SCnComV[9659].Len() = 1\n",
"SCnComV[9660].Len() = 1\n",
"SCnComV[9661].Len() = 1\n",
"SCnComV[9662].Len() = 1\n",
"SCnComV[9663].Len() = 1\n",
"SCnComV[9664].Len() = 1\n",
"SCnComV[9665].Len() = 1\n",
"SCnComV[9666].Len() = 1\n",
"SCnComV[9667].Len() = 1\n",
"SCnComV[9668].Len() = 1\n",
"SCnComV[9669].Len() = 1\n",
"SCnComV[9670].Len() = 1\n",
"SCnComV[9671].Len() = 1\n",
"SCnComV[9672].Len() = 1\n",
"SCnComV[9673].Len() = 1\n",
"SCnComV[9674].Len() = 1\n",
"SCnComV[9675].Len() = 1\n",
"SCnComV[9676].Len() = 1\n",
"SCnComV[9677].Len() = 1\n",
"SCnComV[9678].Len() = 1\n",
"SCnComV[9679].Len() = 1\n",
"SCnComV[9680].Len() = 1\n",
"SCnComV[9681].Len() = 1\n",
"SCnComV[9682].Len() = 1\n",
"SCnComV[9683].Len() = 1\n",
"SCnComV[9684].Len() = 1\n",
"SCnComV[9685].Len() = 1\n",
"SCnComV[9686].Len() = 1\n",
"SCnComV[9687].Len() = 1\n",
"SCnComV[9688].Len() = 1\n",
"SCnComV[9689].Len() = 1\n",
"SCnComV[9690].Len() = 1\n",
"SCnComV[9691].Len() = 1\n",
"SCnComV[9692].Len() = 1\n",
"SCnComV[9693].Len() = 1\n",
"SCnComV[9694].Len() = 1\n",
"SCnComV[9695].Len() = 1\n",
"SCnComV[9696].Len() = 1\n",
"SCnComV[9697].Len() = 1\n",
"SCnComV[9698].Len() = 1\n",
"SCnComV[9699].Len() = 1\n",
"SCnComV[9700].Len() = 1\n",
"SCnComV[9701].Len() = 1\n",
"SCnComV[9702].Len() = 1\n",
"SCnComV[9703].Len() = 1\n",
"SCnComV[9704].Len() = 1\n",
"SCnComV[9705].Len() = 1\n",
"SCnComV[9706].Len() = 1\n",
"SCnComV[9707].Len() = 1\n",
"SCnComV[9708].Len() = 1\n",
"SCnComV[9709].Len() = 1\n",
"SCnComV[9710].Len() = 1\n",
"SCnComV[9711].Len() = 1\n",
"SCnComV[9712].Len() = 1\n",
"SCnComV[9713].Len() = 1\n",
"SCnComV[9714].Len() = 1\n",
"SCnComV[9715].Len() = 1\n",
"SCnComV[9716].Len() = 1\n",
"SCnComV[9717].Len() = 1\n",
"SCnComV[9718].Len() = 1\n",
"SCnComV[9719].Len() = 1\n",
"SCnComV[9720].Len() = 1\n",
"SCnComV[9721].Len() = 1\n",
"SCnComV[9722].Len() = 1\n",
"SCnComV[9723].Len() = 1\n",
"SCnComV[9724].Len() = 1\n",
"SCnComV[9725].Len() = 1\n",
"SCnComV[9726].Len() = 1\n",
"SCnComV[9727].Len() = 1\n",
"SCnComV[9728].Len() = 1\n",
"SCnComV[9729].Len() = 1\n",
"SCnComV[9730].Len() = 1\n",
"SCnComV[9731].Len() = 1\n",
"SCnComV[9732].Len() = 1\n",
"SCnComV[9733].Len() = 1\n",
"SCnComV[9734].Len() = 1\n",
"SCnComV[9735].Len() = 1\n",
"SCnComV[9736].Len() = 1\n",
"SCnComV[9737].Len() = 1\n",
"SCnComV[9738].Len() = 1\n",
"SCnComV[9739].Len() = 1\n",
"SCnComV[9740].Len() = 1\n",
"SCnComV[9741].Len() = 1\n",
"SCnComV[9742].Len() = 1\n",
"SCnComV[9743].Len() = 1\n",
"SCnComV[9744].Len() = 1\n",
"SCnComV[9745].Len() = 1\n",
"SCnComV[9746].Len() = 1\n",
"SCnComV[9747].Len() = 1\n",
"SCnComV[9748].Len() = 1\n",
"SCnComV[9749].Len() = 1\n",
"SCnComV[9750].Len() = 1\n",
"SCnComV[9751].Len() = 1\n",
"SCnComV[9752].Len() = 1\n",
"SCnComV[9753].Len() = 1\n",
"SCnComV[9754].Len() = 1\n",
"SCnComV[9755].Len() = 1\n",
"SCnComV[9756].Len() = 1\n",
"SCnComV[9757].Len() = 1\n",
"SCnComV[9758].Len() = 1\n",
"SCnComV[9759].Len() = 1\n",
"SCnComV[9760].Len() = 1\n",
"SCnComV[9761].Len() = 1\n",
"SCnComV[9762].Len() = 1\n",
"SCnComV[9763].Len() = 1\n",
"SCnComV[9764].Len() = 1\n",
"SCnComV[9765].Len() = 1\n",
"SCnComV[9766].Len() = 1\n",
"SCnComV[9767].Len() = 1\n",
"SCnComV[9768].Len() = 1\n",
"SCnComV[9769].Len() = 1\n",
"SCnComV[9770].Len() = 1\n",
"SCnComV[9771].Len() = 1\n",
"SCnComV[9772].Len() = 1\n",
"SCnComV[9773].Len() = 1\n",
"SCnComV[9774].Len() = 1\n",
"SCnComV[9775].Len() = 1\n",
"SCnComV[9776].Len() = 1\n",
"SCnComV[9777].Len() = 1\n",
"SCnComV[9778].Len() = 1\n",
"SCnComV[9779].Len() = 1\n",
"SCnComV[9780].Len() = 1\n",
"SCnComV[9781].Len() = 1\n",
"SCnComV[9782].Len() = 1\n",
"SCnComV[9783].Len() = 1\n",
"SCnComV[9784].Len() = 1\n",
"SCnComV[9785].Len() = 1\n",
"SCnComV[9786].Len() = 1\n",
"SCnComV[9787].Len() = 1\n",
"SCnComV[9788].Len() = 1\n",
"SCnComV[9789].Len() = 1\n",
"SCnComV[9790].Len() = 1\n",
"SCnComV[9791].Len() = 1\n",
"SCnComV[9792].Len() = 1\n",
"SCnComV[9793].Len() = 1\n",
"SCnComV[9794].Len() = 1\n",
"SCnComV[9795].Len() = 1\n",
"SCnComV[9796].Len() = 1\n",
"SCnComV[9797].Len() = 1\n",
"SCnComV[9798].Len() = 1\n",
"SCnComV[9799].Len() = 1\n",
"SCnComV[9800].Len() = 1\n",
"SCnComV[9801].Len() = 1\n",
"SCnComV[9802].Len() = 1\n",
"SCnComV[9803].Len() = 1\n",
"SCnComV[9804].Len() = 1\n",
"SCnComV[9805].Len() = 1\n",
"SCnComV[9806].Len() = 1\n",
"SCnComV[9807].Len() = 1\n",
"SCnComV[9808].Len() = 1\n",
"SCnComV[9809].Len() = 1\n",
"SCnComV[9810].Len() = 1\n",
"SCnComV[9811].Len() = 1\n",
"SCnComV[9812].Len() = 1\n",
"SCnComV[9813].Len() = 1\n",
"SCnComV[9814].Len() = 1\n",
"SCnComV[9815].Len() = 1\n",
"SCnComV[9816].Len() = 1\n",
"SCnComV[9817].Len() = 1\n",
"SCnComV[9818].Len() = 1\n",
"SCnComV[9819].Len() = 1\n",
"SCnComV[9820].Len() = 1\n",
"SCnComV[9821].Len() = 1\n",
"SCnComV[9822].Len() = 1\n",
"SCnComV[9823].Len() = 1\n",
"SCnComV[9824].Len() = 1\n",
"SCnComV[9825].Len() = 1\n",
"SCnComV[9826].Len() = 1\n",
"SCnComV[9827].Len() = 1\n",
"SCnComV[9828].Len() = 1\n",
"SCnComV[9829].Len() = 1\n",
"SCnComV[9830].Len() = 1\n",
"SCnComV[9831].Len() = 1\n",
"SCnComV[9832].Len() = 1\n",
"SCnComV[9833].Len() = 1\n",
"SCnComV[9834].Len() = 1\n",
"SCnComV[9835].Len() = 1\n",
"SCnComV[9836].Len() = 1\n",
"SCnComV[9837].Len() = 1\n",
"SCnComV[9838].Len() = 1\n",
"SCnComV[9839].Len() = 1\n",
"SCnComV[9840].Len() = 1\n",
"SCnComV[9841].Len() = 1\n",
"SCnComV[9842].Len() = 1\n",
"SCnComV[9843].Len() = 1\n",
"SCnComV[9844].Len() = 1\n",
"SCnComV[9845].Len() = 1\n",
"SCnComV[9846].Len() = 1\n",
"SCnComV[9847].Len() = 1\n",
"SCnComV[9848].Len() = 1\n",
"SCnComV[9849].Len() = 1\n",
"SCnComV[9850].Len() = 1\n",
"SCnComV[9851].Len() = 1\n",
"SCnComV[9852].Len() = 1\n",
"SCnComV[9853].Len() = 1\n",
"SCnComV[9854].Len() = 1\n",
"SCnComV[9855].Len() = 1\n",
"SCnComV[9856].Len() = 1\n",
"SCnComV[9857].Len() = 1\n",
"SCnComV[9858].Len() = 1\n",
"SCnComV[9859].Len() = 1\n",
"SCnComV[9860].Len() = 1\n",
"SCnComV[9861].Len() = 1\n",
"SCnComV[9862].Len() = 1\n",
"SCnComV[9863].Len() = 1\n",
"SCnComV[9864].Len() = 1\n",
"SCnComV[9865].Len() = 1\n",
"SCnComV[9866].Len() = 1\n",
"SCnComV[9867].Len() = 1\n",
"SCnComV[9868].Len() = 1\n",
"SCnComV[9869].Len() = 1\n",
"SCnComV[9870].Len() = 1\n",
"SCnComV[9871].Len() = 1\n",
"SCnComV[9872].Len() = 1\n",
"SCnComV[9873].Len() = 1\n",
"SCnComV[9874].Len() = 1\n",
"SCnComV[9875].Len() = 1\n",
"SCnComV[9876].Len() = 1\n",
"SCnComV[9877].Len() = 1\n",
"SCnComV[9878].Len() = 1\n",
"SCnComV[9879].Len() = 1\n",
"SCnComV[9880].Len() = 1\n",
"SCnComV[9881].Len() = 1\n",
"SCnComV[9882].Len() = 1\n",
"SCnComV[9883].Len() = 1\n",
"SCnComV[9884].Len() = 1\n",
"SCnComV[9885].Len() = 1\n",
"SCnComV[9886].Len() = 1\n",
"SCnComV[9887].Len() = 1\n",
"SCnComV[9888].Len() = 1\n",
"SCnComV[9889].Len() = 1\n",
"SCnComV[9890].Len() = 1\n",
"SCnComV[9891].Len() = 1\n",
"SCnComV[9892].Len() = 1\n",
"SCnComV[9893].Len() = 1\n",
"SCnComV[9894].Len() = 1\n",
"SCnComV[9895].Len() = 1\n",
"SCnComV[9896].Len() = 1\n",
"SCnComV[9897].Len() = 1\n",
"SCnComV[9898].Len() = 1\n",
"SCnComV[9899].Len() = 1\n",
"SCnComV[9900].Len() = 1\n",
"SCnComV[9901].Len() = 1\n",
"SCnComV[9902].Len() = 1\n",
"SCnComV[9903].Len() = 1\n",
"SCnComV[9904].Len() = 1\n",
"SCnComV[9905].Len() = 1\n",
"SCnComV[9906].Len() = 1\n",
"SCnComV[9907].Len() = 1\n",
"SCnComV[9908].Len() = 1\n",
"SCnComV[9909].Len() = 1\n",
"SCnComV[9910].Len() = 1\n",
"SCnComV[9911].Len() = 1\n",
"SCnComV[9912].Len() = 1\n",
"SCnComV[9913].Len() = 1\n",
"SCnComV[9914].Len() = 1\n",
"SCnComV[9915].Len() = 1\n",
"SCnComV[9916].Len() = 1\n",
"SCnComV[9917].Len() = 1\n",
"SCnComV[9918].Len() = 1\n",
"SCnComV[9919].Len() = 1\n",
"SCnComV[9920].Len() = 1\n",
"SCnComV[9921].Len() = 1\n",
"SCnComV[9922].Len() = 1\n",
"SCnComV[9923].Len() = 1\n",
"SCnComV[9924].Len() = 1\n",
"SCnComV[9925].Len() = 1\n",
"SCnComV[9926].Len() = 1\n",
"SCnComV[9927].Len() = 1\n",
"SCnComV[9928].Len() = 1\n",
"SCnComV[9929].Len() = 1\n",
"SCnComV[9930].Len() = 1\n",
"SCnComV[9931].Len() = 1\n",
"SCnComV[9932].Len() = 1\n",
"SCnComV[9933].Len() = 1\n",
"SCnComV[9934].Len() = 1\n",
"SCnComV[9935].Len() = 1\n",
"SCnComV[9936].Len() = 1\n",
"SCnComV[9937].Len() = 1\n",
"SCnComV[9938].Len() = 1\n",
"SCnComV[9939].Len() = 1\n",
"SCnComV[9940].Len() = 1\n",
"SCnComV[9941].Len() = 1\n",
"SCnComV[9942].Len() = 1\n",
"SCnComV[9943].Len() = 1\n",
"SCnComV[9944].Len() = 1\n",
"SCnComV[9945].Len() = 1\n",
"SCnComV[9946].Len() = 1\n",
"SCnComV[9947].Len() = 1\n",
"SCnComV[9948].Len() = 1\n",
"SCnComV[9949].Len() = 1\n",
"SCnComV[9950].Len() = 1\n",
"SCnComV[9951].Len() = 1\n",
"SCnComV[9952].Len() = 1\n",
"SCnComV[9953].Len() = 1\n",
"SCnComV[9954].Len() = 1\n",
"SCnComV[9955].Len() = 1\n",
"SCnComV[9956].Len() = 1\n",
"SCnComV[9957].Len() = 1\n",
"SCnComV[9958].Len() = 1\n",
"SCnComV[9959].Len() = 1\n",
"SCnComV[9960].Len() = 1\n",
"SCnComV[9961].Len() = 1\n",
"SCnComV[9962].Len() = 1\n",
"SCnComV[9963].Len() = 1\n",
"SCnComV[9964].Len() = 1\n",
"SCnComV[9965].Len() = 1\n",
"SCnComV[9966].Len() = 1\n",
"SCnComV[9967].Len() = 1\n",
"SCnComV[9968].Len() = 1\n",
"SCnComV[9969].Len() = 1\n",
"SCnComV[9970].Len() = 1\n",
"SCnComV[9971].Len() = 1\n",
"SCnComV[9972].Len() = 1\n",
"SCnComV[9973].Len() = 1\n",
"SCnComV[9974].Len() = 1\n",
"SCnComV[9975].Len() = 1\n",
"SCnComV[9976].Len() = 1\n",
"SCnComV[9977].Len() = 1\n",
"SCnComV[9978].Len() = 1\n",
"SCnComV[9979].Len() = 1\n",
"SCnComV[9980].Len() = 1\n",
"SCnComV[9981].Len() = 1\n",
"SCnComV[9982].Len() = 1\n",
"SCnComV[9983].Len() = 1\n",
"SCnComV[9984].Len() = 1\n",
"SCnComV[9985].Len() = 1\n",
"SCnComV[9986].Len() = 1\n",
"SCnComV[9987].Len() = 1\n",
"SCnComV[9988].Len() = 1\n",
"SCnComV[9989].Len() = 1\n",
"SCnComV[9990].Len() = 1\n",
"SCnComV[9991].Len() = 1\n",
"SCnComV[9992].Len() = 1\n",
"SCnComV[9993].Len() = 1\n",
"SCnComV[9994].Len() = 1\n",
"SCnComV[9995].Len() = 1\n",
"SCnComV[9996].Len() = 1\n",
"SCnComV[9997].Len() = 1\n",
"SCnComV[9998].Len() = 1\n",
"SCnComV[9999].Len() = 1\n",
"GMxBi: GetNodes() = 6, GetEdges() = 6\n"
]
}
],
"source": [
"import snap\n",
"\n",
"# create a random directed graph\n",
"G = snap.GenRndGnm(snap.PNGraph, 10000, 5000)\n",
"\n",
"# test if the graph is connected or weakly connected\n",
"print \"IsConnected(G) =\", snap.IsConnected(G)\n",
"print \"IsWeaklyConnected(G) =\", snap.IsWeaklyConn(G)\n",
"\n",
"# get the weakly connected component counts\n",
"WccSzCnt = snap.TIntPrV()\n",
"snap.GetWccSzCnt(G, WccSzCnt)\n",
"for i in range (0, WccSzCnt.Len()):\n",
" print \"WccSzCnt[%d] = (%d, %d)\" % (\n",
" i, WccSzCnt[i].Val1.Val, WccSzCnt[i].Val2.Val)\n",
"\n",
"# return nodes in the same weakly connected component as node 1\n",
"CnCom = snap.TIntV()\n",
"snap.GetNodeWcc(G, 1, CnCom)\n",
"print \"CnCom.Len() = %d\" % (CnCom.Len())\n",
"\n",
"# get nodes in weakly connected components\n",
"WCnComV = snap.TCnComV()\n",
"snap.GetWccs(G, WCnComV)\n",
"for i in range(0, WCnComV.Len()):\n",
" print \"WCnComV[%d].Len() = %d\" % (i, WCnComV[i].Len())\n",
" for j in range (0, WCnComV[i].Len()):\n",
" print \"WCnComV[%d][%d] = %d\" % (i, j, WCnComV[i][j])\n",
"\n",
"# get the size of the maximum weakly connected component\n",
"MxWccSz = snap.GetMxWccSz(G);\n",
"print \"MxWccSz = %.5f\" % (MxWccSz)\n",
"\n",
"# get the graph with the largest weakly connected component\n",
"GMx = snap.GetMxWcc(G);\n",
"print \"GMx: GetNodes() = %d, GetEdges() = %d\" % (\n",
" GMx.GetNodes(), GMx.GetEdges())\n",
"\n",
"# get strongly connected components\n",
"SCnComV = snap.TCnComV()\n",
"snap.GetSccs(G, SCnComV)\n",
"for i in range(0, SCnComV.Len()):\n",
" print \"SCnComV[%d].Len() = %d\" % (i, SCnComV[i].Len())\n",
"\n",
"# get the graph representing the largest bi-connected component\n",
"GMxBi = snap.GetMxBiCon(G)\n",
"print \"GMxBi: GetNodes() = %d, GetEdges() = %d\" % (\n",
" GMxBi.GetNodes(), GMxBi.GetEdges())\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"node 0, NValInt 0, NValFlt 0.00, NValStr 0\n",
"edge 0 (0,1), EValInt 0\n",
"edge 1 (0,2), EValInt 1\n",
"edge 2 (0,3), EValInt 2\n",
"edge 3 (0,4), EValInt 3\n",
"edge 4 (0,5), EValInt 4\n",
"edge 5 (0,6), EValInt 5\n",
"edge 6 (0,7), EValInt 6\n",
"edge 7 (0,8), EValInt 7\n",
"edge 8 (0,9), EValInt 8\n",
"node 1, NValInt 1, NValFlt 1.00, NValStr 1\n",
"edge 9 (1,0), EValInt 9\n",
"edge 10 (1,2), EValInt 10\n",
"edge 11 (1,3), EValInt 11\n",
"edge 12 (1,4), EValInt 12\n",
"edge 13 (1,5), EValInt 13\n",
"edge 14 (1,6), EValInt 14\n",
"edge 15 (1,7), EValInt 15\n",
"edge 16 (1,8), EValInt 16\n",
"edge 17 (1,9), EValInt 17\n",
"node 2, NValInt 2, NValFlt 2.00, NValStr 2\n",
"edge 18 (2,0), EValInt 18\n",
"edge 19 (2,1), EValInt 19\n",
"edge 20 (2,3), EValInt 20\n",
"edge 21 (2,4), EValInt 21\n",
"edge 22 (2,5), EValInt 22\n",
"edge 23 (2,6), EValInt 23\n",
"edge 24 (2,7), EValInt 24\n",
"edge 25 (2,8), EValInt 25\n",
"edge 26 (2,9), EValInt 26\n",
"node 3, NValInt 3, NValFlt 3.00, NValStr 3\n",
"edge 27 (3,0), EValInt 27\n",
"edge 28 (3,1), EValInt 28\n",
"edge 29 (3,2), EValInt 29\n",
"edge 30 (3,4), EValInt 30\n",
"edge 31 (3,5), EValInt 31\n",
"edge 32 (3,6), EValInt 32\n",
"edge 33 (3,7), EValInt 33\n",
"edge 34 (3,8), EValInt 34\n",
"edge 35 (3,9), EValInt 35\n",
"node 4, NValInt 4, NValFlt 4.00, NValStr 4\n",
"edge 36 (4,0), EValInt 36\n",
"edge 37 (4,1), EValInt 37\n",
"edge 38 (4,2), EValInt 38\n",
"edge 39 (4,3), EValInt 39\n",
"edge 40 (4,5), EValInt 40\n",
"edge 41 (4,6), EValInt 41\n",
"edge 42 (4,7), EValInt 42\n",
"edge 43 (4,8), EValInt 43\n",
"edge 44 (4,9), EValInt 44\n",
"node 5, NValInt 5, NValFlt 5.00, NValStr 5\n",
"edge 45 (5,0), EValInt 45\n",
"edge 46 (5,1), EValInt 46\n",
"edge 47 (5,2), EValInt 47\n",
"edge 48 (5,3), EValInt 48\n",
"edge 49 (5,4), EValInt 49\n",
"edge 50 (5,6), EValInt 50\n",
"edge 51 (5,7), EValInt 51\n",
"edge 52 (5,8), EValInt 52\n",
"edge 53 (5,9), EValInt 53\n",
"node 6, NValInt 6, NValFlt 6.00, NValStr 6\n",
"edge 54 (6,0), EValInt 54\n",
"edge 55 (6,1), EValInt 55\n",
"edge 56 (6,2), EValInt 56\n",
"edge 57 (6,3), EValInt 57\n",
"edge 58 (6,4), EValInt 58\n",
"edge 59 (6,5), EValInt 59\n",
"edge 60 (6,7), EValInt 60\n",
"edge 61 (6,8), EValInt 61\n",
"edge 62 (6,9), EValInt 62\n",
"node 7, NValInt 7, NValFlt 7.00, NValStr 7\n",
"edge 63 (7,0), EValInt 63\n",
"edge 64 (7,1), EValInt 64\n",
"edge 65 (7,2), EValInt 65\n",
"edge 66 (7,3), EValInt 66\n",
"edge 67 (7,4), EValInt 67\n",
"edge 68 (7,5), EValInt 68\n",
"edge 69 (7,6), EValInt 69\n",
"edge 70 (7,8), EValInt 70\n",
"edge 71 (7,9), EValInt 71\n",
"node 8, NValInt 8, NValFlt 8.00, NValStr 8\n",
"edge 72 (8,0), EValInt 72\n",
"edge 73 (8,1), EValInt 73\n",
"edge 74 (8,2), EValInt 74\n",
"edge 75 (8,3), EValInt 75\n",
"edge 76 (8,4), EValInt 76\n",
"edge 77 (8,5), EValInt 77\n",
"edge 78 (8,6), EValInt 78\n",
"edge 79 (8,7), EValInt 79\n",
"edge 80 (8,9), EValInt 80\n",
"node 9, NValInt 9, NValFlt 9.00, NValStr 9\n",
"edge 81 (9,0), EValInt 81\n",
"edge 82 (9,1), EValInt 82\n",
"edge 83 (9,2), EValInt 83\n",
"edge 84 (9,3), EValInt 84\n",
"edge 85 (9,4), EValInt 85\n",
"edge 86 (9,5), EValInt 86\n",
"edge 87 (9,6), EValInt 87\n",
"edge 88 (9,7), EValInt 88\n",
"edge 89 (9,8), EValInt 89\n"
]
}
],
"source": [
"import snap\n",
"\n",
"nodes = 10\n",
"G = snap.GenFull(snap.PNEANet,nodes)\n",
"\n",
"# define int, float and str attributes on nodes\n",
"G.AddIntAttrN(\"NValInt\", 0)\n",
"G.AddFltAttrN(\"NValFlt\", 0.0)\n",
"G.AddStrAttrN(\"NValStr\", \"0\")\n",
"\n",
"# define an int attribute on edges\n",
"G.AddIntAttrE(\"EValInt\", 0)\n",
"\n",
"# add attribute values, node ID for nodes, edge ID for edges\n",
"\n",
"for NI in G.Nodes():\n",
" nid = NI.GetId()\n",
" val = nid\n",
" G.AddIntAttrDatN(nid, val, \"NValInt\")\n",
" G.AddFltAttrDatN(nid, float(val), \"NValFlt\")\n",
" G.AddStrAttrDatN(nid, str(val), \"NValStr\")\n",
"\n",
" for nid1 in NI.GetOutEdges():\n",
" eid = G.GetEId(nid,nid1)\n",
" val = eid\n",
" G.AddIntAttrDatE(eid, val, \"EValInt\")\n",
"\n",
"# print out attribute values\n",
"\n",
"for NI in G.Nodes():\n",
" nid = NI.GetId()\n",
" ival = G.GetIntAttrDatN(nid, \"NValInt\")\n",
" fval = G.GetFltAttrDatN(nid, \"NValFlt\")\n",
" sval = G.GetStrAttrDatN(nid, \"NValStr\")\n",
" print \"node %d, NValInt %d, NValFlt %.2f, NValStr %s\" % (nid, ival, fval, sval)\n",
"\n",
" for nid1 in NI.GetOutEdges():\n",
" eid = G.GetEId(nid, nid1)\n",
" val = G.GetIntAttrDatE(eid, \"EValInt\")\n",
" print \"edge %d (%d,%d), EValInt %d\" % (eid, nid, nid1, val)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
karissa/pyeda | ipynb/BDDs.ipynb | 4 | 10689 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%install_ext https://raw.github.com/cjdrake/ipython-magic/master/gvmagic.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%load_ext gvmagic"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pyeda\n",
"from pyeda.inter import *"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a, b, c, d = map(bddvar, \"abcd\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A binary decision diagram is a directed acyclic graph used to represent a Boolean function.\n",
"They were originally introduced by Lee, and later by Akers.\n",
"In 1986, Randal Bryant introduced the reduced, ordered BDD (ROBDD).\n",
"\n",
"Let's take a look at some basic BDDs."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Zero and One\n",
"%dotobjs pyeda.boolalg.bdd.BDDZERO, pyeda.boolalg.bdd.BDDONE"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Complement and Variable\n",
"%dotobjs ~a, a"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A BDD is a full tree of Shannon cofactor expansions of the inputs variables from top (first variable) to bottom (last variable).\n",
"\n",
"This is what it would look like if you do not merge isomorphic sub-trees."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%dot graph {\n",
" a [label=a,shape=circle]\n",
" b0 [label=b,shape=circle]\n",
" b1 [label=b,shape=circle]\n",
" c00 [label=c,shape=circle]\n",
" c01 [label=c,shape=circle]\n",
" c10 [label=c,shape=circle]\n",
" c11 [label=c,shape=circle]\n",
"\n",
" zero000 [label=0,shape=box]\n",
" one001 [label=1,shape=box]\n",
" one010 [label=1,shape=box]\n",
" one011 [label=1,shape=box]\n",
" one100 [label=1,shape=box]\n",
" one101 [label=1,shape=box]\n",
" one110 [label=1,shape=box]\n",
" one111 [label=1,shape=box]\n",
"\n",
" a -- b0 [label=0]\n",
" a -- b1 [label=1]\n",
"\n",
" b0 -- c00 [label=0]\n",
" b0 -- c01 [label=1]\n",
" b1 -- c10 [label=0]\n",
" b1 -- c11 [label=1]\n",
"\n",
" c00 -- zero000 [label=0]\n",
" c00 -- one001 [label=1]\n",
" c01 -- one010 [label=0]\n",
" c01 -- one011 [label=1]\n",
" c10 -- one100 [label=0]\n",
" c10 -- one101 [label=1]\n",
" c11 -- one110 [label=0]\n",
" c11 -- one111 [label=1]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Join isomorphic `1` nodes:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%dot graph {\n",
" a [label=a,shape=circle]\n",
" b0 [label=b,shape=circle]\n",
" b1 [label=b,shape=circle]\n",
" c00 [label=c,shape=circle]\n",
" c01 [label=c,shape=circle]\n",
" c10 [label=c,shape=circle]\n",
" c11 [label=c,shape=circle]\n",
"\n",
" zero [label=0,shape=box]\n",
" one [label=1,shape=box]\n",
"\n",
" a -- b0 [label=0,style=dashed]\n",
" a -- b1 [label=1]\n",
"\n",
" b0 -- c00 [label=0,style=dashed]\n",
" b0 -- c01 [label=1]\n",
" b1 -- c10 [label=0,style=dashed]\n",
" b1 -- c11 [label=1]\n",
"\n",
" c00 -- zero [label=0,style=dashed]\n",
" c00 -- one [label=1]\n",
" c01 -- one [label=0,style=dashed]\n",
" c01 -- one [label=1]\n",
" c10 -- one [label=0,style=dashed]\n",
" c10 -- one [label=1]\n",
" c11 -- one [label=0,style=dashed]\n",
" c11 -- one [label=1]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Join isomorphic `c` nodes:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%dot graph {\n",
" a [label=a,shape=circle]\n",
" b0 [label=b,shape=circle]\n",
" b1 [label=b,shape=circle]\n",
" c00 [label=c,shape=circle]\n",
"\n",
" zero [label=0,shape=box]\n",
" one [label=1,shape=box]\n",
"\n",
" a -- b0 [label=0,style=dashed]\n",
" a -- b1 [label=1]\n",
"\n",
" b0 -- c00 [label=0,style=dashed]\n",
" b0 -- one [label=1]\n",
" b1 -- one [label=0,style=dashed]\n",
" b1 -- one [label=1]\n",
"\n",
" c00 -- zero [label=0,style=dashed]\n",
" c00 -- one [label=1]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Join isomorphic `b` nodes:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%%dot graph {\n",
" a [label=a,shape=circle]\n",
" b0 [label=b,shape=circle]\n",
" c00 [label=c,shape=circle]\n",
"\n",
" zero [label=0,shape=box]\n",
" one [label=1,shape=box]\n",
"\n",
" a -- b0 [label=0,style=dashed]\n",
" a -- one [label=1]\n",
"\n",
" b0 -- c00 [label=0,style=dashed]\n",
" b0 -- one [label=1]\n",
"\n",
" c00 -- zero [label=0,style=dashed]\n",
" c00 -- one [label=1]\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Some examples:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%dotobj a | b | c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%dotobj a & b & c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%dotobj a ^ b ^ c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Equal-3\n",
"%dotobj ~a & ~b & ~c | a & b & c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# Majority-3\n",
"%dotobj a & b | a & c | b & c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# OneHot-3\n",
"%dotobj (~a | ~b) & (~a | ~c) & (~b | ~c) & (a | b | c)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"BDDs are a *canonical* form.\n",
"Given an identical ordering of input variables,\n",
"equivalent functions will always produce identical BDDs.\n",
"\n",
"This makes testing for SAT and UNSAT trivial.\n",
"A function is SAT if its BDD is not $0$,\n",
"and it is UNSAT if its BDD is not $1$."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# A full minterm cover is unity.\n",
"~a & ~b | ~a & b | a & ~b | a & b"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# a full maxterm cover is empty\n",
"(~a | ~b) & (~a | b) & (a | ~b) & (a | b)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Formal equivalence checking is also trivial.\n",
"You can test whether two BDDs are equivalent by using the `equivalent` method,\n",
"or the Python `is` operator."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"F1 = a ^ b ^ c\n",
"F2 = ~a & ~b & c | ~a & b & ~c | a & ~b & ~c | a & b & c"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"F1.equivalent(F2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"F1 is F2"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"The downside of BDDs is memory usage.\n",
"\n",
"The size of some functions is heavily dependent on the ordering of the input variables,\n",
"but determining an optimal ordering is known to be a hard problem.\n",
"\n",
"Certain functions,\n",
"no matter how cleverly you order their input variables,\n",
"will result in an exponentially-sized graph.\n",
"One example is multiplication."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X = bddvars('x', 6)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%dotobj X[0] & X[1] | X[2] & X[3] | X[4] & X[5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%dotobj X[0] & X[3] | X[1] & X[4] | X[2] & X[5]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.4.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| bsd-2-clause |
DJCordhose/ai | notebooks/tf2/tf-low-level.ipynb | 1 | 437662 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "tf-low-level.ipynb",
"version": "0.3.2",
"provenance": [],
"toc_visible": true,
"include_colab_link": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/github/DJCordhose/ai/blob/master/notebooks/tf2/tf-low-level.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XdfXfXK2-CiG",
"colab_type": "text"
},
"source": [
"# Introduction to Neural Networks with Low Level TensorFlow 2\n",
"\n",
"Based on \n",
"* This thread is a crash course on everything you need to know to use TensorFlow 2.0 + Keras for deep learning research: https://twitter.com/fchollet/status/1105139360226140160\n",
"* Colab Notebook _tf.keras for Researchers_: https://colab.research.google.com/drive/17u-pRZJnKN0gO5XZmq8n5A2bKGrfKEUg#scrollTo=UHOOlixcQ9Gl\n",
"* Effective TensorFlow 2: https://www.tensorflow.org/alpha/guide/effective_tf2\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "u8IonVMfAelY",
"colab_type": "code",
"colab": {}
},
"source": [
"!pip install -q tf-nightly-gpu-2.0-preview"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "0rEa6M11-CiY",
"colab_type": "code",
"outputId": "9fd4b7ad-8d55-425c-f67b-3a385e5d30a7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"import tensorflow as tf\n",
"print(tf.__version__)"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"2.0.0-dev20190513\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "I35W9tmrRWF3",
"colab_type": "code",
"outputId": "277caf92-8700-42e0-b10b-f36444bf97b7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# a small sanity check, does tf seem to work ok?\n",
"hello = tf.constant('Hello TF!')\n",
"print(\"This works: {}\".format(hello))"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"This works: b'Hello TF!'\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "DpXHERPSQvMD",
"colab_type": "code",
"outputId": "126ed516-7b09-4cc3-a6c7-332727b36729",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# this should return True even on Colab\n",
"tf.test.is_gpu_available()"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "hEoP6O2r3Jvf",
"colab_type": "code",
"outputId": "af83fb4b-a7c4-40b1-c245-753e815a3709",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"tf.test.is_built_with_cuda()"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "N5djlq79Lc5P",
"colab_type": "code",
"outputId": "578a5a17-2ce1-4f39-e5bf-b74c217cbbec",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 289
}
},
"source": [
"!nvidia-smi"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"Mon May 13 14:00:55 2019 \n",
"+-----------------------------------------------------------------------------+\n",
"| NVIDIA-SMI 418.56 Driver Version: 410.79 CUDA Version: 10.0 |\n",
"|-------------------------------+----------------------+----------------------+\n",
"| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n",
"| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n",
"|===============================+======================+======================|\n",
"| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n",
"| N/A 66C P0 31W / 70W | 129MiB / 15079MiB | 0% Default |\n",
"+-------------------------------+----------------------+----------------------+\n",
" \n",
"+-----------------------------------------------------------------------------+\n",
"| Processes: GPU Memory |\n",
"| GPU PID Type Process name Usage |\n",
"|=============================================================================|\n",
"+-----------------------------------------------------------------------------+\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PqalzDXr3OiQ",
"colab_type": "code",
"outputId": "008634a4-119b-4b17-b68c-4fc323f64006",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"tf.executing_eagerly()"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iZgqH7oOh73-",
"colab_type": "text"
},
"source": [
"## Transforming an input to a known output"
]
},
{
"cell_type": "code",
"metadata": {
"id": "VsTh2muvh8vi",
"colab_type": "code",
"colab": {}
},
"source": [
"input = [[-1], [0], [1], [2], [3], [4]]\n",
"output = [[2], [1], [0], [-1], [-2], [-3]]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "WJ70YqfqiBrt",
"colab_type": "code",
"outputId": "4fb5fe63-9327-4e3f-80d3-86cf66173a27",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
}
},
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.xlabel('input')\n",
"plt.ylabel('output')\n",
"\n",
"plt.plot(input, output, 'ro')"
],
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fac90185a20>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 9
},
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEKCAYAAAASByJ7AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqHAAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IVyyWLjeiPU0",
"colab_type": "text"
},
"source": [
"### relation between input and output is linear"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XBljtpGIiCOj",
"colab_type": "code",
"outputId": "5654294e-7c2c-439f-9bee-9b4bdd478b62",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286
}
},
"source": [
"plt.plot(input, output)\n",
"plt.plot(input, output, 'ro')"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fac9d77f320>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
},
{
"output_type": "display_data",
"data": {
"image/png": 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sKUb1b8nLAxI4ePQkfcfMY/i0NRw94e1QMRW7iEghXdaoKrNSE+nfNpbxc76i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\nMip2EZEg87/WD0zYxuhUlAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WKxxou9oirbE",
"colab_type": "text"
},
"source": [
"## Defining the model to train\n",
"\n",
"untrained single unit (neuron) also outputs a line from same input, although another one \n",
"\n",
"### The Artificial Neuron: Foundation of Deep Neural Networks (simplified, more later)\n",
"\n",
"* a neuron takes a number of numerical inputs\n",
"* multiplies each with a weight, sums up all weighted input and \n",
"* adds bias (constant) to that sum\n",
"* from this it creates a single numerical output\n",
"* for one input (one dimension) this would be a description of a line\n",
"* for more dimensions this describes a hyper plane that can serve as a decision boundary\n",
"* this is typically expressed as a matrix multplication plus an addition\n",
"\n",
"\n",
"<img src='https://djcordhose.github.io/ai/img/insurance/neuron211.jpg'>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "O6oo7hM_0pv5",
"colab_type": "text"
},
"source": [
"### This can be expressed using a matrix multiplication"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7h8MQieO0vay",
"colab_type": "code",
"outputId": "9cf316eb-8622-46cb-fd87-ae61d5bcc9a0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"w = tf.constant([[1.5], [-2], [1]], dtype='float32')\n",
"x = tf.constant([[10, 6, 8]], dtype='float32')\n",
"b = tf.constant([6], dtype='float32')\n",
"\n",
"y = tf.matmul(x, w) + b\n",
"print(y)"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"tf.Tensor([[17.]], shape=(1, 1), dtype=float32)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IGylP_16koS6",
"colab_type": "text"
},
"source": [
"### Defining a layer with a random number of neurons and inputs"
]
},
{
"cell_type": "code",
"metadata": {
"id": "aMCDcq4JelaE",
"colab_type": "code",
"colab": {}
},
"source": [
"from tensorflow.keras.layers import Layer\n",
"\n",
"class LinearLayer(Layer):\n",
" \"\"\"y = w.x + b\"\"\"\n",
"\n",
" def __init__(self, units=1, input_dim=1):\n",
" super(LinearLayer, self).__init__()\n",
" w_init = tf.random_normal_initializer(stddev=2)\n",
" self.w = tf.Variable(\n",
" initial_value = w_init(shape=(input_dim, units), dtype='float32'),\n",
" trainable=True)\n",
" b_init = tf.zeros_initializer()\n",
" self.b = tf.Variable(\n",
" initial_value = b_init(shape=(units,), dtype='float32'),\n",
" trainable=True)\n",
"\n",
" def call(self, inputs):\n",
" return tf.matmul(inputs, self.w) + self.b\n",
" \n",
"linear_layer = LinearLayer()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "IlrodnQbkmXZ",
"colab_type": "text"
},
"source": [
"### Output of a single untrained neuron"
]
},
{
"cell_type": "code",
"metadata": {
"id": "d1FpEmPgjM76",
"colab_type": "code",
"outputId": "a8b262e0-50c6-4055-9224-4f8adfefb0a5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
}
},
"source": [
"x = tf.constant(input, dtype=tf.float32)\n",
"y_true = tf.constant(output, dtype=tf.float32)\n",
"y_true"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tf.Tensor: id=31, shape=(6, 1), dtype=float32, numpy=\n",
"array([[ 2.],\n",
" [ 1.],\n",
" [ 0.],\n",
" [-1.],\n",
" [-2.],\n",
" [-3.]], dtype=float32)>"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "wJaw2t3ef7Xl",
"colab_type": "code",
"outputId": "5b81c49c-3a25-4ea7-8eac-69ff0c347cfb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
}
},
"source": [
"y_pred = linear_layer(x)\n",
"y_pred"
],
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tf.Tensor: id=37, shape=(6, 1), dtype=float32, numpy=\n",
"array([[ 2.3813493],\n",
" [ 0. ],\n",
" [-2.3813493],\n",
" [-4.7626987],\n",
" [-7.1440477],\n",
" [-9.525397 ]], dtype=float32)>"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "A5no0NPli-dh",
"colab_type": "code",
"outputId": "fa16e329-9766-4f39-8783-d4b5ee86c561",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286
}
},
"source": [
"plt.plot(x, y_pred)\n",
"plt.plot(input, output, 'ro')"
],
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fac9d778518>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
},
{
"output_type": "display_data",
"data": {
"image/png": 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olZTMp3O3qIxN5AYo8CXbCAkxHm6SVsZWp0xB/vbVKn4/Yi4b95/wezSRbEGBL9lOVKFI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\nxzqsAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zAuxRMiMkyX3",
"colab_type": "text"
},
"source": [
"## Loss - Mean Squared Error\n",
"\n",
"Loss function is the prerequisite to training. We need an objective to optimize for. We calculate the difference between what we get as output and what we would like to get.\n",
"\n",
"### Mean Squared Error\n",
"\n",
"$MSE = {\\frac {1}{n}}\\sum _{i=1}^{n}(Y_{i}-{\\hat {Y_{i}}})^{2}$\n",
"\n",
"\n",
"https://en.wikipedia.org/wiki/Mean_squared_error\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "S5qBUY18u2BK",
"colab_type": "code",
"colab": {}
},
"source": [
"loss_fn = tf.losses.mean_squared_error\n",
"# loss_fn = tf.losses.mean_absolute_error"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "pIGGjpC4jdjZ",
"colab_type": "code",
"outputId": "295629aa-249e-45d5-8f4f-eedad2d6bfbf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"loss = loss_fn(y_true=tf.squeeze(y_true), y_pred=tf.squeeze(y_pred))\n",
"print(loss)"
],
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"text": [
"tf.Tensor(15.002698, shape=(), dtype=float32)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6DGusRmhk1JU",
"colab_type": "code",
"outputId": "51342996-0a48-4177-eb06-65606e397135",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"tf.keras.losses.mean_squared_error == tf.losses.mean_squared_error"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"True"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QzcQowwMsZqg",
"colab_type": "text"
},
"source": [
"### Minimize Loss by changing parameters of neuron\n",
"\n",
"Move in parameter space in the direction of a descent\n",
"\n",
"<img src='https://djcordhose.github.io/ai/img/gradients.jpg'>\n",
"\n",
"https://twitter.com/colindcarroll/status/1090266016259534848\n",
"\n",
"### Job of the optimizer\n",
"\n",
"<img src='https://djcordhose.github.io/ai/img/manning/optimizer.png' height=500>\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xukGkwFTET56",
"colab_type": "text"
},
"source": [
"### For this we need partial derivations\n",
"\n",
"TensorFlow offers automatic differentiation: https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/GradientTape\n",
"\n",
"* tape will record operations for automatic differentiation\n",
"* either by making it record explicily (watch) or \n",
"* by declaring a varible to be trainable (which we did in the layer above) \n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "J9C_Whp94Zq2",
"colab_type": "code",
"outputId": "a7e9ce60-cc48-430e-a1f6-d568ecc16d98",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"# a simple example\n",
"\n",
"# f(x) = x^2\n",
"# f'(x) = 2x\n",
"# x = 4\n",
"# f(4) = 16\n",
"# f'(4) = 8 (that's what we expect)\n",
"def tape_sample():\n",
" x = tf.constant(4.0)\n",
" # open a GradientTape\n",
" with tf.GradientTape() as tape:\n",
" tape.watch(x)\n",
" y = x * x\n",
" dy_dx = tape.gradient(y, x)\n",
" print(dy_dx)\n",
" \n",
"# just a function in order not to interfere with x on the global scope \n",
"tape_sample()"
],
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": [
"tf.Tensor(8.0, shape=(), dtype=float32)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BlVOagySSwyO",
"colab_type": "text"
},
"source": [
"## Training"
]
},
{
"cell_type": "code",
"metadata": {
"id": "V36dlfViTMZR",
"colab_type": "code",
"outputId": "99256f1b-03c4-4808-fd82-4a9bb2846083",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"linear_layer = LinearLayer()\n",
"linear_layer.w, linear_layer.b"
],
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(<tf.Variable 'Variable:0' shape=(1, 1) dtype=float32, numpy=array([[-3.5483046]], dtype=float32)>,\n",
" <tf.Variable 'Variable:0' shape=(1,) dtype=float32, numpy=array([0.], dtype=float32)>)"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "WbTfKBTMTm1B",
"colab_type": "code",
"outputId": "33b4cf62-13a4-4781-9844-a4942ce8456d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"linear_layer.trainable_weights"
],
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<tf.Variable 'Variable:0' shape=(1, 1) dtype=float32, numpy=array([[-3.5483046]], dtype=float32)>,\n",
" <tf.Variable 'Variable:0' shape=(1,) dtype=float32, numpy=array([0.], dtype=float32)>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "B988o-LuC2wf",
"colab_type": "code",
"colab": {}
},
"source": [
"EPOCHS = 200\n",
"learning_rate = 1e-2\n",
"\n",
"losses = []\n",
"weights = []\n",
"biases = []\n",
"weights_gradient = []\n",
"biases_gradient = []\n",
"\n",
"for step in range(EPOCHS):\n",
" with tf.GradientTape() as tape:\n",
"\n",
" # forward pass\n",
" y_pred = linear_layer(x)\n",
"\n",
" # loss value for this batch\n",
" loss = loss_fn(y_true=tf.squeeze(y_true), y_pred=tf.squeeze(y_pred))\n",
" \n",
" # just for logging\n",
" losses.append(loss.numpy())\n",
" weights.append(linear_layer.w.numpy()[0][0])\n",
" biases.append(linear_layer.b.numpy()[0])\n",
"\n",
" # get gradients of weights wrt the loss\n",
" gradients = tape.gradient(loss, linear_layer.trainable_weights)\n",
" weights_gradient.append(gradients[0].numpy()[0][0])\n",
" biases_gradient.append(gradients[1].numpy()[0])\n",
" \n",
" # backward pass, changing trainable weights\n",
" linear_layer.w.assign_sub(learning_rate * gradients[0])\n",
" linear_layer.b.assign_sub(learning_rate * gradients[1])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "dnapizSr1ufF",
"colab_type": "code",
"outputId": "84e28a32-d7fe-4b6f-c356-4b85a92ccb8e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"print(loss)"
],
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"text": [
"tf.Tensor(0.00023834866, shape=(), dtype=float32)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "HVU5pKnVxvch",
"colab_type": "code",
"outputId": "c06c16ee-6b73-4230-cf3d-baa6fffcd168",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
}
},
"source": [
"plt.xlabel('epochs')\n",
"plt.ylabel('loss')\n",
"\n",
"# plt.yscale('log')\n",
"\n",
"plt.plot(losses)"
],
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fac6ea024e0>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
},
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEKCAYAAAAfGVI8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-d1PI3AzXqf5",
"colab_type": "code",
"outputId": "29abcf73-a287-4cc1-8bfa-b83d6da1eb98",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 632
}
},
"source": [
"plt.figure(figsize=(20, 10))\n",
"\n",
"plt.plot(weights)\n",
"plt.plot(biases)\n",
"plt.plot(weights_gradient)\n",
"plt.plot(biases_gradient)\n",
"\n",
"plt.legend(['slope', 'offset', 'gradient slope', 'gradient offset'])"
],
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fac6e96c940>"
]
},
"metadata": {
"tags": []
},
"execution_count": 25
},
{
"output_type": "display_data",
"data": {
"image/png": 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gFm9Ap5Tm6N4rT9XHZpUoM3V4vvRNy97QoTvukOnxaPQ99yj3yiti+tStvfV7\ndeO/b1R1a7V+MP8HumrqVTGdFwAAAACAoURRlIC2VDTosbf26cWyQzIkfXRWif5jwQTNGps3bBms\nQEBVDzyguj88rtQZM1T6858rddLEYTv+QKypXKOb37xZKc4U/emSP+nUolPtjgQAAAAAwLCiKEog\n6/fX6Zev79bKPTXKSnXpC+dM0PXnTFRJXnQuTN2XQGOjKm6+RS1vvaW8T39KI2+/XY6UlGHNEKl/\n7vqnfvT2jzQhd4IeXvywSrJK7I4EAAAAAMCwoyhKAGXl9fr5v3Zp+a5qjchK1W2XTtenzxqn3HT3\nsGfx7Htf5V//urzl5Rp1993Kv+aTw54hEqZl6pfv/VJ/3PJHnVN6ju4//35lpWTZHQsAAAAAAFtQ\nFMWx7Yca9cBru/TatiPKz3Dr9kun63PzJyg9xWlLnuYVK1Rx8y0yXC6N/+PjyjjzTFtyhMtn+vS9\nt76nl/e/rGumXaPbzrpNLgd/JAAAAAAAyYvfiuNQVVO7fvbKTj31XrmyUl26+aKTdP25E5U1TBeo\n7k3d//urjtx7r1JPOkljH35I7tJS27KEwxvw6pblt+iNg2/opjk36fqTr+ei1QAAAACApEdRFEe8\nflN/Wv2+fvXvPfL4A7rhvEm68YIpys0Y/lPMOliWpZpf/1o1v/mtsi5crNL77pMjI7ZvJd/ub9e3\n3vyWVlWs0h3z7tCnp3/a7kgAAAAAAMQEiqI48e/tR/Sjpdv1fk2LFk8v1vc+PEOTiuy9lo4VCOjw\n//yP6v/+D+V+4iqNvusuGa7Y/pZq9bXqG8u+oXWH1+nuBXfryqlX2h0JAAAAAICYEdu/1UNVTe36\n72e36pWthzWpKFN/uv5MXTCt2O5YsrxeVXz3u2p6+RUV3vAlFX372zF/6laTt0lff/3r2lyzWT8+\n78f6yKSP2B0JAAAAAICYQlEUoyzL0tPvVeiHL25Tmy+gWy+Zpi+dO0kpLofd0WS2tKj8m/+lllWr\nVPyd76jwi1+wO9IJtfpa9dXXvqpttdv0s4U/00XjL7I7EgAAAAAAMYeiKAZV1rfpjmc2682d1Zoz\nPl/3feI0Tbb5NLMOZmurDnzlK2p7b4NG33OP8q6K/VO3fAGfbnrzJm2p3aIHFj6gxeMX2x0JAAAA\nAICYRFEUY/75brl+8PxWBUxLP/joTH1u/gQ5HbFxSpfp8ejgjTeq7b0NKv35/cq59FK7I51QwAzo\njpV3aHXlav1wwQ8piQAAAAAA6AdFUYxo8fh157Nb9PSGCs2bWKD7r56lsQWxc/cwy+tVxTf/S61v\nv6OSn9wbFyWRZVm6d+29emV3y+V8AAAgAElEQVT/K/r2nG/riqlX2B0JAAAAAICYRlEUA7ZWNugb\nf9ug/bUt+taFU/WNRVNj5lNEkmT5/aq4+RY1L1+uUXffrdzLL7c7Ulh+s+k3+sfOf+j6U67X9adc\nb3ccAAAAAABiHkWRjSzL0v97+wP9aOl25We49cSXztb8yYV2x+rGCgRUedvtanrtNY284w7lX/NJ\nuyOF5YntT+iRTY/oyqlX6qYzbrI7DgAAAAAAcYGiyCbtvoBufapMz2+q1IemFen+q2epMCvV7ljd\nWJalw3f/UI0vvqiim7+tgs9dZ3eksKysWKn71t2nRWMX6c6z75RhxM6nswAAAAAAiGUURTY40tiu\nL/9lvcoqGvSdi6fpawsnyxFDp5p1qPvDH1T/5JMq/PKXNeKGG+yOE5b3G97Xrctv1Un5J+ne8+6V\ny8G3OAAAAAAA4eK36GG2ubxBN/xlvRrbffrdtXO05ORRdkfqVeMrr6rq/p8r57LLVHTTt+yOE5Ym\nb5O+ueybcjvdevBDDyrDHTsXAwcAAAAAIB5QFA2jpWWHdPP/bVRhZqqe+uoCzSzJsTtSr9o2bVLl\nd7+r9NNP1+h7fxwXp24FzIBuXXGrypvK9fuLf6+SrBK7IwEAAAAAEHcoioaBZVn6zZt79bNXd+qM\ncXn63XVzVZQdW9cj6uAtr9DBr98oV3Gxxjz8kBypsZmzpwc3PKiVFSt159l3as7IOXbHAQAAAAAg\nLlEURZllWfrJyzv0uxX79LFZJbrvE6cpze20O1avAk1NOvjVr8jy+TT2d4/IVVBgd6SwvLjvRf1x\nyx91zbRr9Mlp8XFXNgAAAAAAYhFFURQFTEvff3aL/nftAV139njd/bGTY/Ki1ZJkBQKq+PbN8u7/\nQON+/5hSJ02yO1JY9tbv1d2r79ackXP03bO+a3ccAAAAAADiGkVRlPgCpr795Ca9sKlSX79gsr5z\n8bSYvtZPzSOPqOWttzTqrruUefbZdscJiyfg0XdWfEcZ7gzdv/B+uR1uuyMBAAAAABDXKIqioN0X\n0NefeE/LdlTpu5dM19cumGx3pH61rFmjmoceVs7HPqq8a+Ln1K0H1j+g3Ud36+HFD2tE+gi74wAA\nAAAAEPcoioaYZVn66l/f1fJd1brnilP02Xnj7Y7UL9+RKlXc8h2lTJ6k0XfdFdOfeurqzYNv6m87\n/qZrZ1yr88ecb3ccAAAAAAASAkXREDMMQ9efM1FXnF6qy2eX2h2nX5bfr8qbb5bZ2qrxf/6THBkZ\ndkcKS1Vrle5cdaemF0zXTXNusjsOAAAAAAAJg6IoChaeVGR3hLBUP/grta5fr5L7fqrUKVPsjhMW\n0zJ1x8o75Al4dN/59ynFmWJ3JAAAAAAAEobD7gCwR9Obb6r2sceUd/XVyv3Yx+yOE7Y/bvmj3jn0\njm476zZNzJ1odxwAAAAAABIKRVES8ldX69Bttyt1xgyN/N4ddscJ2866nXpow0NaMn6Jrphyhd1x\nAAAAAABIOBRFScayLB26626Zra0qvf9ncqSl2R0pLAEzoLtW36Wc1BzdefadcXPRbQAAAAAA4glF\nUZJpfP55Nf/73yr61reUOnmy3XHC9sT2J7SldotuP+t25aXl2R0HAAAAAICERFGURHxHjujwPT9W\n+umnq+Dzn7M7TtjKm8r10MaHtHDMQl084WK74wAAAAAAkLAoipKEZVk69N//LcvrVcm9P5bhdNod\nKSyWZemHa34oh+HQ98/+PqecAQAAAAAQRRRFSaLh6afVsnyFir/9baVMmGB3nLC9sO8FrTm0Rt86\n41salTnK7jgAAAAAACQ0iqIk4Kus1JF7f6KMuXOVf+1n7Y4Tttq2Wt237j7NLpqtT077pN1xAAAA\nAABIeBRFCc6yLB36/p2yTFOj7/2xDEf8fMl/uvanavW16u4Fd8thxE9uAAAAAADiFb99J7jGpS+p\nZfVqFd/8baWMHWt3nLCtqVyjl/e/rBtOu0GT8ibZHQcAAAAAgKRAUZTAAs0tqrrvPqXNnKn8T33K\n7jhh85t+3bfuPpVmleoLp3zB7jgAAAAAACQNl90BED01v/mN/FVVGvOrB+PmLmeS9PTup7Wnfo8e\nuOABpTpT7Y4DAAAAAEDS4BNFCcqzZ4/q/vIX5V51pdJnz7Y7TtiavE16aMNDmjNyji4cd6HdcQAA\nAAAASCoURQnIsiwd/tE9cmRkqPjmm+2OE5FHyx5Vvade3znzOzIMw+44AAAAAAAkFYqiBNT0yitq\nffttFf3XN+UqKLA7TtgONB7QX7f/VZdPuVwnF55sdxwAAAAAAJIORVGCMVtadOQnP1XqjBlxdQFr\nSXrg3Qfkdrj1zdO/aXcUAAAAAACSEkVRgql55BH5jxzRqDvvjKsLWK89tFb/PvBv3XDqDSrKKLI7\nDgAAAAAASYmiKIF4DxxQ7Z/+rNyPf1wZZ5xud5ywBcyA7lt3n0ZnjtZ1M6+zOw4AAAAAAEkrakWR\nYRh3GYZRYRjGxtDjsmgdC0HVv/q1DKdTRTfdZHeUiLz0/kvaeXSnbppzk9JcaXbHAQAAAAAgabmi\nPP4vLMu6P8rHgKT2HTvU+OKLKrzhBrlHFtsdJ2x+069HNj2iafnTdPGEi+2OAwAAAABAUuPUswRR\n/YtfypGTo8IvfdHuKBFZum+pDjQd0Ndmf00Og29HAAAAAADsFO3fzP/TMIwywzAeNwwjv7cNDMP4\nsmEY6w3DWF9dXR3lOImpdf16NS9frsIbviRnbq7dccLmM336XdnvNKNghhaNXWR3HAAAAAAAkt6g\niiLDMF43DGNLL4/LJf1W0mRJsyUdkvTz3sawLOtRy7LmWpY1t6iIu11FyrIsVT3wC7mKilRw7bV2\nx4nIi3tf1MGmg/rarK/JMAy74wAAAAAAkPQGdY0iy7IuDGc7wzAek/TiYI6F3jW/+aba3ntPo+76\ngRzp6XbHCVvHp4lmFs7UBWMvsDsOAAAAAABQdO96NrrL7BWStkTrWMnKMk1V/+KXco8bp7yrrrI7\nTkSe3/O8KpordOPsG/k0EQAAAAAAMSKadz27zzCM2ZIsSfslfSWKx0pKjUuXyrNrl0p+fr8Mt9vu\nOGHzBXx6tOxRnTriVJ1Xep7dcQAAAAAAQEjUiiLLsq6L1tiQLK9X1Q/+SqkzZijn0kvtjhORZ/Y8\no8qWSt05/04+TQQAAAAAQAyJ5ieKEEUNzz8vX3m5xv7uERmO+LmtvDfg1WObH9NpRafpnJJz7I4D\nAAAAAAC6iJ+GAZ2sQEC1v/+DUmfOUOb559sdJyIv7H1Bh1sO68ZZXJsIAAAAAIBYQ1EUh5pee13e\n/fs14oYb4qpsMS1Tf9n2F80omKH5JfPtjgMAAAAAAHqgKIozlmWp9rHH5B4/TtlLltgdJyIrK1Zq\nX8M+XTfzurgquAAAAAAASBYURXGmdc0atW/dqsIvflGG02l3nIj8ZetfVJxRrEsmXmJ3FAAAAAAA\n0AuKojhT8+hjchUVKffjH7c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ZTh1xqgzDsDsKAAAAAACIURRFSaDJ26R9Dfu4PhEAAAAA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\ngg==\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "PCTnMgCh1ala"
},
"source": [
"### Line drawn by neuron after training\n",
"\n",
"* result after training is not perfect, but almost looks like the same line\n",
"* https://en.wikipedia.org/wiki/Linear_equation#Slope%E2%80%93intercept_form\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"outputId": "ed152ffc-af21-461a-dce9-c804ce631dc6",
"id": "WDwLh7RP1alX",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
}
},
"source": [
"y_pred = linear_layer(x)\n",
"y_pred"
],
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tf.Tensor: id=12139, shape=(6, 1), dtype=float32, numpy=\n",
"array([[ 1.9732319 ],\n",
" [ 0.97976017],\n",
" [-0.01371157],\n",
" [-1.0071833 ],\n",
" [-2.0006552 ],\n",
" [-2.9941268 ]], dtype=float32)>"
]
},
"metadata": {
"tags": []
},
"execution_count": 26
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"outputId": "d200088f-4aea-4982-b975-574622ed2cff",
"id": "D6IsHQhf1alP",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286
}
},
"source": [
"plt.plot(x, y_pred)\n",
"plt.plot(input, output, 'ro')"
],
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fac6ea02828>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
},
{
"output_type": "display_data",
"data": {
"image/png": 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gJ3JDW3s1LM3rW/1m0yF0T0zB4i3sWyV9MdiJ3JTft7rw6S6oU6UCnp6zESM/Tcdvp8/p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\n2IZm6JOijwAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab_type": "code",
"outputId": "87530771-d709-4960-d845-c442da8a7fde",
"id": "FtRUMsQf1alI",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"# single neuron and single input: one weight and one bias\n",
"# slope m ~ -1\n",
"# y-axis offset y0 ~ 1\n",
"# https://en.wikipedia.org/wiki/Linear_equation#Slope%E2%80%93intercept_form\n",
"\n",
"linear_layer.trainable_weights"
],
"execution_count": 28,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<tf.Variable 'Variable:0' shape=(1, 1) dtype=float32, numpy=array([[-0.99347174]], dtype=float32)>,\n",
" <tf.Variable 'Variable:0' shape=(1,) dtype=float32, numpy=array([0.97976017], dtype=float32)>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 28
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vbOJfrtgDQiM",
"colab_type": "text"
},
"source": [
"### Prebuilt Optimizers do this job (but a bit more efficient and sohpisticated)"
]
},
{
"cell_type": "code",
"metadata": {
"id": "eD2YbeHdDM9y",
"colab_type": "code",
"colab": {}
},
"source": [
"optimizer = tf.keras.optimizers.SGD(learning_rate=1e-2)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "cEuzqGgktmut",
"colab_type": "code",
"colab": {}
},
"source": [
"EPOCHS = 500\n",
"\n",
"losses = []\n",
"\n",
"linear_layer = LinearLayer()\n",
"\n",
"for step in range(EPOCHS):\n",
" with tf.GradientTape() as tape:\n",
"\n",
" # Forward pass.\n",
" y_pred = linear_layer(x)\n",
"\n",
" # Loss value for this batch.\n",
" loss = loss_fn(y_true=tf.squeeze(y_true), y_pred=tf.squeeze(y_pred))\n",
" \n",
" losses.append(loss)\n",
" \n",
" # Get gradients of weights wrt the loss.\n",
" gradients = tape.gradient(loss, linear_layer.trainable_weights)\n",
" \n",
" # Update the weights of our linear layer.\n",
" optimizer.apply_gradients(zip(gradients, linear_layer.trainable_weights))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "cqA4saqR0kbq",
"colab_type": "code",
"outputId": "defedc89-6534-4077-b34c-56b5f1650137",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
}
},
"source": [
"# plt.yscale('log')\n",
"plt.ylabel(\"loss\")\n",
"plt.xlabel(\"epochs\")\n",
"\n",
"plt.plot(losses)"
],
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fac6e8802e8>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 31
},
{
"output_type": "display_data",
"data": {
"image/png": 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MzawuynoOh5lx7VuPZ9POfTywQqMFERndIgsFM0sDC4GLgdnAfDOb3Ue7cuA64KmoajlS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\nWQoFERHJUiiIiEjW/wdKVnZ3wyF9RQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "uAQ-LCTW2KkH",
"colab_type": "code",
"outputId": "f2cd0819-7693-4f9a-d049-9530c6c3a1b8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303
}
},
"source": [
"y_pred = linear_layer(x)\n",
"plt.plot(x, y_pred)\n",
"plt.plot(input, output, 'ro')\n",
"linear_layer.trainable_weights"
],
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<tf.Variable 'Variable:0' shape=(1, 1) dtype=float32, numpy=array([[-0.9979986]], dtype=float32)>,\n",
" <tf.Variable 'Variable:0' shape=(1,) dtype=float32, numpy=array([0.99379504], dtype=float32)>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 32
},
{
"output_type": "display_data",
"data": {
"image/png": 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qTB3agTnDEsg/XsSgGat4bNEWCk5WnbEEKnYRkYvwk6sakD4hiVuvi2X2yk/p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\nFxEJMip2EZEgo2IXEQky/wevXHDKpy6cXgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dWIMUIQZObYK",
"colab_type": "text"
},
"source": [
"## More data points, more noisy"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Wq0pg9TvOa0k",
"colab_type": "code",
"outputId": "53717eb6-a148-481a-e263-5f0bce9f50ca",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 286
}
},
"source": [
"import numpy as np\n",
"\n",
"a = -1\n",
"b = 1\n",
"n = 50\n",
"\n",
"x = tf.constant(np.random.uniform(0, 1, n), dtype='float32')\n",
"y = tf.constant(a*x+b + 0.1 * np.random.normal(0, 1, n), dtype='float32')\n",
"\n",
"plt.scatter(x, y)"
],
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fac6e7b99b0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 33
},
{
"output_type": "display_data",
"data": {
"image/png": 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YzsxnI+JdwO8CPz9o2ZLGw2rbhY7qU/YoKaJFcD1wNjMfz8zngHuBfZ0nZOYDmfls++3D\nwJYCypU0JlyFtFpFBIIp4ImO9+fax3q5DfiHlT6IiP0RMRsRswsLCwVUTdIoqNt2oU1TamdxRLwd\nmAZet9LnmXkEOAIwPT2dJVZNUsVMAVWniEAwD2zteL+lfewSEfEG4DeA12Xm/xVQriStalSWp6ha\nEYHgJLA9Iq6lFQBuAd7WeUJE7AL+DNibmU8VUKakMbHazXqQG/koLU9RtYEDQWY+HxG3A8eBCeCe\nzDwVEXcBs5k5AxwGvgX4m4gA+HJm3jRo2ZJG22o3a2CgG/koLwJXtkL6CDLzGHCs69idHa/fUEQ5\nksbLWmsJDXIjH6XlKarmonOSKrPazXrQG/koLwJXNgOBpMqsdrNe7bOjc/PsPnSCaw/ez+5DJzg6\nd9n4FOcmrIOBQNKL0s/NeC2r3ax7ffYT37+ZO+57jPnFJZKLfQfd5Ts3oX8uOidp3YoakdPPip3d\nn62nE9i5Cf0xEEhatyJH5HQHg+WO4uWbePf3vfsT/7Li99gJ/OKZGpK0bkWOyFluXayV6llmJ3Dx\nDASS1q3Im/F6t6O0E7h4BgJJ61bkzXi9rQs7gYtnH4GkdStyW8bV9iJYrXxv/MUxEEh6UYq6GY/y\npu/jwkAgqVKjvOl7EeqwQqqBQFLlmprqqcsKqQYCSSpRZwvgiggu5KV7cFWxQqqBQJJK0t0C6A4C\ny8qeHOfwUUkqyUpzJlZS9uQ4A4EklaSfJ/0qRkwZCCSpJL2e9CciKp0cZx+BJJWk15yJqmdGGwgk\nqSR1nTNhIJCkEtVxzoR9BJLUcAYCSWq4QgJBROyNiDMRcTYiDq7w+TdFxCfan38uIrYVUa4kaXAD\nB4KImADuBt4E7ARujYidXafdBjydmd8H/AHwwUHLlSQVo4gWwfXA2cx8PDOfA+4F9nWdsw/4WPv1\np4DXR0QUULYkaUBFBIIp4ImO9+fax1Y8JzOfB54Bvq2AsiVJA6pVZ3FE7I+I2YiYXVhYqLo6ktQI\nRQSCeWBrx/st7WMrnhMRVwKvAL7a/UWZeSQzpzNzevPmzQVUTZK0liICwUlge0RcGxFXAbcAM13n\nzADvaL/+WeBEZo/1VyVJpRp4ZnFmPh8RtwPHgQngnsw8FRF3AbOZOQP8OfDxiDgLfI1WsJAk1UAh\nS0xk5jHgWNexOztefx34uSLKkiQVq1adxZKk8hkIJKnhDASS1HAGAklqOAOBJDWcgUCSGs5AIEkN\nZyCQpIYzEEhSw7l5vSQN6OjcPIePn+HJxSWu3jjJgT07ardB/WoMBJI0gKNz89xx32Msnb8AwPzi\nEnfc9xjAyAQDU0OSNIDDx8+8EASWLZ2/wOHjZyqq0frZIpCkATy5uNTX8Tqnj2wRSNIArt44uebx\n5fTR/OISycX00dG57j28qmEgkKQBHNizg8kNE5ccm9wwwYE9O154X/f0kakhSRrAcnpntbRPv+mj\nqhgIJGlAN++aWjXff/XGSeZXuOn3SiuVzdSQJA1ZP+mjKtkikKQh6yd9VCUDgSSVYK30UZVMDUlS\nwxkIJKnhBgoEEfGtEfFPEfHF9n9fucI5PxgRn42IUxHxaET8/CBlSpKKNWiL4CDw6czcDny6/b7b\ns8AvZOYPAHuBP4yIjQOWK0kqyKCBYB/wsfbrjwE3d5+QmV/IzC+2Xz8JPAVsHrBcSVJBBg0E35GZ\nX2m//m/gO1Y7OSKuB64C/mPAciVJBVlz+GhE/DPwnSt89BudbzIzIyJX+Z7vAj4OvCMzv9HjnP3A\nfoBrrrlmrapJkgoQmT3v3Wv/cMQZ4IbM/Er7Rv9gZl42VS4iXg48CPxOZn6qz+9eAP7rRVdufGwC\n/qfqStSE1+Iir8VFXouLNgHfnJnrSr8POqFsBngHcKj937/vPiEirgL+DviLfoMAwHp/kXEVEbOZ\nOV11PerAa3GR1+Iir8VF7Wuxbb0/N2gfwSHgjRHxReAN7fdExHREfKR9zluBHwfeGRH/0v73gwOW\nK0kqyEAtgsz8KvD6FY7PAr/Ufv2XwF8OUo4kaXicWVx/R6quQI14LS7yWlzktbjoRV2LgTqLJUmj\nzxaBJDWcgaAmImJvRJyJiLMRcdlSHRHxnog43V6v6dMR8d1V1LMMa12LjvPeEhEZEWM7YqSfaxER\nb23/bZyKiL8qu45l6OP/j2si4oGImGv/P3JjFfUsQ0TcExFPRcTne3weEfHH7Wv1aES8ds0vzUz/\nVfwPmKA12/p7aM28/ldgZ9c5PwG8tP36XcAnqq53Vdeifd7LgIeAh4Hpqutd4d/FdmAOeGX7/bdX\nXe+KrsMR4F3t1zuB/6y63kO8Hj8OvBb4fI/PbwT+AQjgR4HPrfWdtgjq4XrgbGY+npnPAffSWsfp\nBZn5QGY+2377MLCl5DqWZc1r0fZbwAeBr5dZuZL1cy1+Gbg7M58GyMynSq5jGfq5Dgm8vP36FcCT\nJdavVJn5EPC1VU7ZR2veVmbmw8DG9oTfngwE9TAFPNHx/lz7WC+30Yr442jNa9Fu6m7NzPvLrFgF\n+vm7eBXwqoj4TEQ8HBF7S6tdefq5Du8H3h4R54BjwK+WU7VaWu/9xK0qR01EvB2YBl5XdV2qEBFX\nAL8PvLPiqtTFlbTSQzfQaiU+FBHXZeZipbUq363ARzPz9yLix4CPR8Rrsse6ZrqULYJ6mAe2drzf\n0j52iYh4A63F/m7KzP8rqW5lW+tavAx4DfBgRPwnrRzozJh2GPfzd3EOmMnM85n5JeALtALDOOnn\nOtwGfBIgMz8LvITWujtN1Nf9pJOBoB5OAtsj4tr22ky30FrH6QURsQv4M1pBYBzzwMtWvRaZ+Uxm\nbsrMbdlaU+VhWtdktprqDtWafxfAUVqtASJiE61U0eNlVrIE/VyHL9Ne5SAiXk0rECyUWsv6mAF+\noT166EeBZ/LidgErMjVUA5n5fETcDhynNULinsw8FRF3AbOZOQMcBr4F+JuIAPhyZt5UWaWHpM9r\n0Qh9XovjwE9FxGngAnAgW0u/jI0+r8N7gQ9HxLtpdRy/M9tDaMZNRPw1reC/qd0n8j5gA0BmfohW\nH8mNwFlaO0T+4prfOabXSpLUJ1NDktRwBgJJajgDgSQ1nIFAkhrOQCBJDWcgkKSGMxBIUsMZCCSp\n4f4fCUfvZOg4HMgAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "HZOMvTFwR0gv",
"colab_type": "code",
"colab": {}
},
"source": [
"x = tf.reshape(x, (n, 1))\n",
"y_true = tf.reshape(y, (n, 1))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "wvSwOhsbu3Rm",
"colab_type": "code",
"outputId": "d420182e-7ebf-4c88-85d9-54d654198e82",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303
}
},
"source": [
"linear_layer = LinearLayer()\n",
"\n",
"a = linear_layer.w.numpy()[0][0]\n",
"b = linear_layer.b.numpy()[0]\n",
"\n",
"def plot_line(a, b, x, y_true):\n",
" fig, ax = plt.subplots()\n",
" y_pred = a * x + b\n",
" \n",
" line = ax.plot(x, y_pred)\n",
" ax.plot(x, y_true, 'ro')\n",
" return fig, line\n",
"\n",
"plot_line(a, b, x, y_true)"
],
"execution_count": 35,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(<Figure size 432x288 with 1 Axes>,\n",
" [<matplotlib.lines.Line2D at 0x7fac6e798cc0>])"
]
},
"metadata": {
"tags": []
},
"execution_count": 35
},
{
"output_type": "display_data",
"data": {
"image/png": 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VfPXTu3PhMWNiro2IpF2kAWBmDwI7BRTNBG7sfsE3s7fc/cMBx9jV3Vea2SjgYWCiu79S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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "WW7xVyGVQeLU",
"colab_type": "code",
"colab": {}
},
"source": [
"# the problem is a little bit harder, train for a little longer\n",
"EPOCHS = 2000\n",
"\n",
"losses = []\n",
"lines = []\n",
"\n",
"linear_layer = LinearLayer()\n",
"\n",
"for step in range(EPOCHS):\n",
" # Open a GradientTape.\n",
" with tf.GradientTape() as tape:\n",
"\n",
" # Forward pass.\n",
" y_pred = linear_layer(x)\n",
"\n",
" # Loss value for this batch.\n",
" loss = loss_fn(y_true=tf.squeeze(y_true), y_pred=tf.squeeze(y_pred))\n",
" \n",
" losses.append(loss)\n",
" \n",
" a = linear_layer.w.numpy()[0][0]\n",
" b = linear_layer.b.numpy()[0]\n",
" lines.append((a, b))\n",
" \n",
" # Get gradients of weights wrt the loss.\n",
" gradients = tape.gradient(loss, linear_layer.trainable_weights)\n",
" \n",
" # Update the weights of our linear layer.\n",
" optimizer.apply_gradients(zip(gradients, linear_layer.trainable_weights))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VA_TMwwsTvIE",
"colab_type": "code",
"outputId": "a1e0073f-c085-4271-e76d-15911ed5ef36",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"print(loss)"
],
"execution_count": 37,
"outputs": [
{
"output_type": "stream",
"text": [
"tf.Tensor(0.012210889, shape=(), dtype=float32)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "073RfK8UQrAT",
"colab_type": "code",
"outputId": "9e52e4ed-e72a-43fd-e157-e159912206f9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
}
},
"source": [
"# plt.yscale('log')\n",
"plt.ylabel(\"loss\")\n",
"plt.xlabel(\"epochs\")\n",
"\n",
"plt.plot(losses)"
],
"execution_count": 38,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fac6e7cd048>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 38
},
{
"output_type": "display_data",
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAGq9JREFUeJzt3X2QJPV93/H3Z2b24Z7huD0Z7hAH\n6ER8fgDBhuAgyaSQ8EHZnGMkAdEDlomppESCLMUJKlRYRfKHZMVO2RFBQpFKQsZghKL4yj4ZSQij\nii0Ey4MQBzpxnJA4BNzy4OPhbm+fvvmje2Z7Z2dmd++2p3fpz6tqa3t6ftP93Z7d+eyvf/2giMDM\nzAygUnQBZma2eDgUzMyswaFgZmYNDgUzM2twKJiZWYNDwczMGhwKZmbW4FAwM7MGh4KZmTXUii5g\nvtatWxebNm0qugwzsyXl/vvvfz4iBmZrt+RCYdOmTQwNDRVdhpnZkiLpp3Np591HZmbW4FAwM7MG\nh4KZmTU4FMzMrMGhYGZmDQ4FMzNrcCiYmVlDaULhvidf5E++uYuxicmiSzEzW7RKEwoP/uwl/ud3\ndjM67lAwM2unNKFQrSQ/6vhEFFyJmdnilVsoSPqipH2SHmnzvCT9uaTdkh6WdHpetQD0VAXA+KR7\nCmZm7eTZU/gSsLXD8+cDm9OvK4AbcqyFaqUeCu4pmJm1k1soRMR3gRc7NNkG3BSJe4CjJB2bVz09\n9d1HDgUzs7aKHFPYADyVebw3nTeDpCskDUkaGh4ePqyVNXoKPvrIzKytJTHQHBE3RsRgRAwODMx6\nOfCWalXvPjIzm02RofA0cHzm8cZ0Xi5qPvrIzGxWRYbCduAD6VFIZwH7I+KZvFZW89FHZmazyu3O\na5JuAc4B1knaC/wR0AMQEZ8FdgAXALuBA8AH86oFoNYYU3BPwcysndxCISIuneX5AD6U1/qb1ao+\n+sjMbDZLYqB5IdR89JGZ2axKFwoT7imYmbVVnlBIB5rHHApmZm2VJxTSQ1InfPSRmVlbpQmF+hnN\nYz76yMysrdKEQk+13lNwKJiZtVOaUJjqKXj3kZlZO6UJhfr9FNxTMDNrrzShUPUZzWZmsypNKNR8\nPwUzs1mVJxR8QTwzs1mVJxS8+8jMbFblCYXGBfHcUzAza6c8oVDxndfMzGZTvlDw7iMzs7ZKEwpV\n9xTMzGZVmlCQRK0i30/BzKyD0oQCJL0Fn9FsZtZeqUKhp1rx7iMzsw5KFQpV7z4yM+uoVKHQU5V7\nCmZmHZQqFJKegkPBzKydUoVCreIxBTOzTsoVClX5MhdmZh2UKxQqHlMwM+ukZKFQ8dFHZmYdlCsU\nqj55zcysk3KFQkWM+egjM7O2yhUK1Yp7CmZmHZQqFKoVMeYxBTOztnINBUlbJe2StFvS1S2ef6Ok\nuyQ9KOlhSRfkWU+PxxTMzDrKLRQkVYHrgfOBLcClkrY0Nfs4cFtEvAW4BPhfedUDUK1UGHMomJm1\nlWdP4Uxgd0TsiYhR4FZgW1ObAFan02uAn+dYDz0VMeGT18zM2sozFDYAT2Ue703nZX0CeJ+kvcAO\n4D+0WpCkKyQNSRoaHh4+7IJ87SMzs86KHmi+FPhSRGwELgC+ImlGTRFxY0QMRsTgwMDAYa/M91Mw\nM+ssz1B4Gjg+83hjOi/rcuA2gIj4HtAPrMurIN9PwcysszxD4T5gs6QTJfWSDCRvb2rzM+BcAEm/\nSBIKh79/aBY130/BzKyj3EIhIsaBK4E7gMdIjjLaKek6SRemzT4K/L6kHwC3AL8bEbl9atc8pmBm\n1lEtz4VHxA6SAeTsvGsz048CZ+dZQ1bNYwpmZh0VPdDcVcmlsz2mYGbWTslCocKEdx+ZmbVVqlDo\nqYpRH31kZtZWyULBYwpmZp2ULhQmJsMXxTMza6NcoVATgC+fbWbWRqlCobea/LgOBTOz1koVCj1p\nKIyOOxTMzFopZSj4Ps1mZq2VLBQ8pmBm1kmpQqG3lu4+ciiYmbVUrlDwQLOZWUelCoXGmMK4xxTM\nzFopVyh495GZWUflCgUPNJuZdVSqUOj1eQpmZh2VKhR6PNBsZtaRQ8HMzBpKFQpT5yn46CMzs1bK\nFQqNQ1LdUzAza6VUoeBLZ5uZdVauUPCYgplZR6UMBY8pmJm1VqpQ8HkKZmadlSoUfEazmVlnpQqF\nakVIDgUzs3ZKFQqS6K1WfEE8M7M2ShUKkIwr+NLZZmatlS4UemoV7z4yM2ujfKFQlUPBzKyNXENB\n0lZJuyTtlnR1mzbvkfSopJ2S/jLPeiA5V8FjCmZmrdXyWrCkKnA98E5gL3CfpO0R8WimzWbgY8DZ\nEfGSpPV51VPXW634PAUzszby7CmcCeyOiD0RMQrcCmxravP7wPUR8RJAROzLsR4g6Sl495GZWWt5\nhsIG4KnM473pvKw3A2+W9A+S7pG0tdWCJF0haUjS0PDw8BEV1VMTY77MhZlZS0UPNNeAzcA5wKXA\n5yUd1dwoIm6MiMGIGBwYGDiiFbqnYGbWXp6h8DRwfObxxnRe1l5ge0SMRcRPgB+ThERuPKZgZtZe\nnqFwH7BZ0omSeoFLgO1Nbf4vSS8BSetIdiftybEmen2egplZW7mFQkSMA1cCdwCPAbdFxE5J10m6\nMG12B/CCpEeBu4A/jIgX8qoJ6ruPPKZgZtZKboekAkTEDmBH07xrM9MBfCT96gqfvGZm1l7RA81d\n55PXzMzaK10o9FYrHBpzKJiZtVK6UOjrcU/BzKydOYWCpKskrVbiC5IekHRe3sXloa9W5dDYRNFl\nmJktSnPtKfxeRLwMnAccDbwf+GRuVeWor6fCiM9TMDNraa6hoPT7BcBXImJnZt6S0lerMjo+SXLg\nk5mZZc01FO6X9E2SULhD0ipgSf673d+T/MiH3FswM5thrucpXA6cBuyJiAOS1gIfzK+s/PTVqgAc\nGpukv6dacDVmZovLXHsKvwbsioh/kvQ+4OPA/vzKyk9frd5T8GCzmVmzuYbCDcABSacCHwWeAG7K\nraoc1XsH3n1kZjbTXENhPL0kxTbgMxFxPbAqv7LyU+8pjPiwVDOzGeY6pvCKpI+RHIr6NkkVoCe/\nsvIztfvIPQUzs2Zz7SlcDBwiOV/hWZJ7I3w6t6pyNLX7yD0FM7NmcwqFNAhuBtZI+k1gJCKW5JjC\n1O4j9xTMzJrN9TIX7wHuBd4NvAf4vqR35VlYXvrcUzAza2uuYwrXAP88IvYBSBoAvg3cnldheWmc\nvOaegpnZDHMdU6jUAyH1wjxeu6jUT14bcU/BzGyGufYU/k7SHcAt6eOLabqj2lLROPrIPQUzsxnm\nFAoR8YeSLgLOTmfdGBFfz6+s/NSPPvJ5CmZmM835Hs0R8TXgaznW0hU+T8HMrL2OoSDpFaDVNaYF\nRESszqWqHDkUzMza6xgKEbEkL2XRSa1aoVaRdx+ZmbWwJI8gOlJ9tYp7CmZmLZQzFHqqPnnNzKyF\nUoZCf63iy1yYmbVQylBIegoOBTOzZuUMhVqFQx5oNjOboZyh0FNlxD0FM7MZyhkK7imYmbVU2lBw\nT8HMbKZcQ0HSVkm7JO2WdHWHdhdJCkmDedZTt7y3ysHR8W6sysxsScktFCRVgeuB84EtwKWStrRo\ntwq4Cvh+XrU0W9Fb48Codx+ZmTXLs6dwJrA7IvZExChwK7CtRbv/CnwKGMmxlmmW9VY56FAwM5sh\nz1DYADyVebw3ndcg6XTg+Ij4204LknSFpCFJQ8PDw0dc2PLeKq9595GZ2QyFDTRLqgB/Cnx0trYR\ncWNEDEbE4MDAwBGve3lvjZGxSSYnW10A1sysvPIMhaeB4zOPN6bz6lYBvwz8vaQngbOA7d0YbF7e\nm9xo56APSzUzmybPULgP2CzpREm9wCXA9vqTEbE/ItZFxKaI2ATcA1wYEUM51gRMhYIHm83Mpsst\nFCJiHLgSuAN4DLgtInZKuk7ShXmtdy6W9ya3kTjgcQUzs2nmfDvOwxERO4AdTfOubdP2nDxryXJP\nwcystVKe0bzMoWBm1lIpQ2FFn3cfmZm1UspQWNbjnoKZWSulDIXGIakOBTOzaUoZCvXdRz6r2cxs\nulKGwjL3FMzMWiplKCz3mIKZWUulDIVatUJvreLdR2ZmTUoZClC/0Y57CmZmWaUNBd9ox8xsptKG\ngm+0Y2Y2U2lDYUVvlVcOeUzBzCyrtKGwelkPr4yMFV2GmdmiUtpQWNVf45UR9xTMzLJKGwqr+3t4\n+aB7CmZmWaUNBfcUzMxmKm0orO7v4eDYBGMTk0WXYma2aJQ2FFb1JxfFc2/BzGxKiUOhB8BHIJmZ\nZZQ2FFYvS0Lh5YPuKZiZ1ZU2FKZ2H7mnYGZWV9pQWJ3uPnrZoWBm1lDaUKj3FF72QLOZWUNpQ2Fq\nTME9BTOzutKGwso+H5JqZtastKFQrYhVfTWPKZiZZZQ2FCDZhbTfu4/MzBpKHQpHr+jhxddGiy7D\nzGzRKHUorF3R51AwM8sodSisW9HLC686FMzM6nINBUlbJe2StFvS1S2e/4ikRyU9LOlOSSfkWU+z\ntSt63VMwM8vILRQkVYHrgfOBLcClkrY0NXsQGIyIXwVuB/44r3paWbuyl4NjExwcnejmas3MFq08\newpnArsjYk9EjAK3AtuyDSLirog4kD68B9iYYz0zHLOiF4AXXjvUzdWamS1aeYbCBuCpzOO96bx2\nLge+0eoJSVdIGpI0NDw8vGAFrl3RB+BdSGZmqUUx0CzpfcAg8OlWz0fEjRExGBGDAwMDC7betY2e\ngkPBzAygluOynwaOzzzemM6bRtI7gGuAX4+Iru7Hqe8+etFHIJmZAfn2FO4DNks6UVIvcAmwPdtA\n0luAzwEXRsS+HGtpae3KNBTcUzAzA3IMhYgYB64E7gAeA26LiJ2SrpN0Ydrs08BK4KuSHpK0vc3i\ncrGqr0ZvtcLzHmg2MwPy3X1EROwAdjTNuzYz/Y481z8bSbxhTR/P7h8psgwzs0VjUQw0F+nYNct4\n5p8cCmZm4FDguDX9/Hz/waLLMDNbFEofCr+wZhnPvTzC5GQUXYqZWeFKHwrHHdXP2ER4sNnMDIcC\nx65ZBuBxBTMzHAocu6YfgGc8rmBm5lA47qikp/C0ewpmZg6Fo5f3sKq/xpPPv1Z0KWZmhSt9KEji\npIGV7Hn+1aJLMTMrXOlDAeCkdSvYM+yegpmZQ4EkFJ7ZP8KB0fGiSzEzK5RDAThpYCWAewtmVnoO\nBeDk9SsAeGLY4wpmVm4OBeDkgZX01Sr8cO/+oksxMyuUQwHoqVbYctxqHnYomFnJORRSp248ikd+\nvp8JXxjPzErMoZD6lQ1rODA6weP7Xim6FDOzwjgUUoObjgbgnideKLgSM7PiOBRSJxyzgk3HLOfu\nHw8XXYqZWWEcChnnnLKe7+15gZGxiaJLMTMrhEMh41/9s/WMjE3y97v2FV2KmVkhHAoZZ598DOtX\n9fHVob1Fl2JmVgiHQkatWuGiMzZy1659PPXigaLLMTPrOodCk8t+bRO1aoU/u/PxoksxM+s6h0KT\nX1jTzwfOOoGvPbCXe3/yYtHlmJl1lUOhhT9455t549rl/MdbHvRuJDMrFYdCCyv6atzw3jM4ODbB\nxZ/7Hvf/1D0GMysHh0IbW45bzc3/9l9QqYiLbvge//4v7uc7P3rO5zCY2etaregCFrNf3rCGb1z1\nNj7/3T186R+f5BuPPEutIt60fiUnrlvB+lV9rF/dz6r+Gv09VZb3Jl/9PVV6qxVq1Qq1iuipVqhW\nRE9V1KoVeirJ91pV9FSS77WKkFT0j2xmJaeIpXVV0MHBwRgaGur6ekfHJ/nHJ55n6MmX2Pnz/fzs\nxQPse+UQr4ws3C08K4JqRVQkqhVRlahWk++V+uPK1Fe9fbVSoVqhQ7vM43S6UkmCKHluar219LmK\nkuVXlIRVfboioOlx8ny2/fTvndpUKiCybbPt59gm3RakbZISlX6fei1Nj+vTU/NJ281xOS2eS18+\nvW2L5SDmV2tzO/8DYfMk6f6IGJytXa49BUlbgT8DqsD/johPNj3fB9wEnAG8AFwcEU/mWdPh6q1V\nOOeU9Zxzyvpp80fGJnj10DgHRyc4MDrBgdFxDo5NMD4RjE9OMjYRjenp8yYZn4xp05MRyffJYGIy\nmIhkuv7cxGQwMQkTk5NMBNPaTaTTk5np0fHJxjKSNulrJ4PJoNEuu66JCCYmggAmI9IviPT7ZARL\n7P+I173m0MjOb0wz7UGryUyIaca8mW3Vcn77ZR/+MtVmBe2Xk50/v/VOW5NmTi/UtpnD7JZ1XXXu\nZn7r1OPavGJh5BYKkqrA9cA7gb3AfZK2R8SjmWaXAy9FxJskXQJ8Crg4r5ry0N+T7C4qm2xI1INi\nMhsck+l32rSZzD5O2k1b5iRNr0meg6nXN4dVfTkEBMlrI2gsO/meNKjPh3qbmNZ2xvym5TDjNdMf\n15Oz8Vxm/W3X0fS4vp3bPpcuIPvc1PuTea+mvW/Z+TMbTW97+Mtr94/DtGXOcznt2tOu/REsc17b\npu0yWrdvXfncnlizrKfdKxZMnj2FM4HdEbEHQNKtwDYgGwrbgE+k07cDn5GkWGr7tEpIElVBte3/\nOWa2FOV59NEG4KnM473pvJZtImIc2A8c07wgSVdIGpI0NDzsS1ubmeVlSRySGhE3RsRgRAwODAwU\nXY6Z2etWnqHwNHB85vHGdF7LNpJqwBqSAWczMytAnqFwH7BZ0omSeoFLgO1NbbYDl6XT7wK+4/EE\nM7Pi5DbQHBHjkq4E7iA5JPWLEbFT0nXAUERsB74AfEXSbuBFkuAwM7OC5HqeQkTsAHY0zbs2Mz0C\nvDvPGszMbO6WxECzmZl1h0PBzMwalty1jyQNAz89zJevA55fwHIWiuuan8VaFyze2lzX/Lwe6zoh\nImY9pn/JhcKRkDQ0lwtCdZvrmp/FWhcs3tpc1/yUuS7vPjIzswaHgpmZNZQtFG4suoA2XNf8LNa6\nYPHW5rrmp7R1lWpMwczMOitbT8HMzDooTShI2ippl6Tdkq7u8rqPl3SXpEcl7ZR0VTr/E5KelvRQ\n+nVB5jUfS2vdJek3cqztSUk/TNc/lM5bK+lbkh5Pvx+dzpekP0/reljS6TnVdEpmmzwk6WVJHy5i\ne0n6oqR9kh7JzJv39pF0Wdr+cUmXtVrXAtT1aUk/Stf9dUlHpfM3STqY2W6fzbzmjPT9353WfkQ3\nyGhT17zft4X+e21T119lanpS0kPp/G5ur3afDcX9jiV3dnp9f5Fce+kJ4CSgF/gBsKWL6z8WOD2d\nXgX8GNhCcoOh/9Si/Za0xj7gxLT2ak61PQmsa5r3x8DV6fTVwKfS6QuAb5DcQfAs4Ptdeu+eBU4o\nYnsBbwdOBx453O0DrAX2pN+PTqePzqGu84BaOv2pTF2bsu2alnNvWqvS2s/Poa55vW95/L22qqvp\n+T8Bri1ge7X7bCjsd6wsPYXGXeAiYhSo3wWuKyLimYh4IJ1+BXiMmTccytoG3BoRhyLiJ8Bukp+h\nW7YBX06nvwz8dmb+TZG4BzhK0rE513Iu8EREdDphMbftFRHfJblYY/P65rN9fgP4VkS8GBEvAd8C\nti50XRHxzUhuVgVwD8nl6ttKa1sdEfdE8slyU+ZnWbC6Omj3vi3432unutL/9t8D3NJpGTltr3af\nDYX9jpUlFOZyF7iukLQJeAvw/XTWlWk38Iv1LiLdrTeAb0q6X9IV6bw3RMQz6fSzwBsKqKvuEqb/\nsRa9vWD+26eI7fZ7JP9R1p0o6UFJd0t6WzpvQ1pLN+qaz/vW7e31NuC5iHg8M6/r26vps6Gw37Gy\nhMKiIGkl8DXgwxHxMnADcDJwGvAMSRe2294aEacD5wMfkvT27JPpf0SFHKKm5D4cFwJfTWcthu01\nTZHbpx1J1wDjwM3prGeAN0bEW4CPAH8paXUXS1p071uTS5n+j0fXt1eLz4aGbv+OlSUU5nIXuFxJ\n6iF502+OiP8DEBHPRcREREwCn2dql0fX6o2Ip9Pv+4CvpzU8V98tlH7f1+26UucDD0TEc2mNhW+v\n1Hy3T9fqk/S7wG8C700/TEh3z7yQTt9Psr/+zWkN2V1MudR1GO9bN7dXDfgd4K8y9XZ1e7X6bKDA\n37GyhMJc7gKXm3Sf5ReAxyLiTzPzs/vj/zVQPzJiO3CJpD5JJwKbSQa4FrquFZJW1adJBiofYfod\n8S4D/jpT1wfSIyDOAvZnurh5mPYfXNHbK2O+2+cO4DxJR6e7Ts5L5y0oSVuB/wxcGBEHMvMHJFXT\n6ZNIts+etLaXJZ2V/o5+IPOzLGRd833fuvn3+g7gRxHR2C3Uze3V7rOBIn/HjmTkfCl9kYza/5gk\n9a/p8rrfStL9exh4KP26APgK8MN0/nbg2Mxrrklr3cURHuHQoa6TSI7s+AGws75dgGOAO4HHgW8D\na9P5Aq5P6/ohMJjjNltBcr/uNZl5Xd9eJKH0DDBGsp/28sPZPiT7+HenXx/Mqa7dJPuV679jn03b\nXpS+vw8BDwC/lVnOIMmH9BPAZ0hPaF3guub9vi3032urutL5XwL+XVPbbm6vdp8Nhf2O+YxmMzNr\nKMvuIzMzmwOHgpmZNTgUzMyswaFgZmYNDgUzM2twKJjlTNI5kv6m6DrM5sKhYGZmDQ4Fs5Sk90m6\nV8k19D8nqSrpVUn/Q8m17u+UNJC2PU3SPZq6d0H9evdvkvRtST+Q9ICkk9PFr5R0u5L7HdycnsmK\npE8quZb+w5L+e0E/ulmDQ8EMkPSLwMXA2RFxGjABvJfkzOqhiPgl4G7gj9KX3AT8l4j4VZIzS+vz\nbwauj4hTgX9JchYtJFe//DDJtfJPAs6WdAzJZR9+KV3Of8v3pzSbnUPBLHEucAZwn5I7cJ1L8uE9\nydTF0v4CeKukNcBREXF3Ov/LwNvT60htiIivA0TESExdg+jeiNgbyUXhHiK5kct+YAT4gqTfARrX\nKzIrikPBLCHgyxFxWvp1SkR8okW7w70uzKHM9ATJHdLGSa4YejvJlU3/7jCXbbZgHApmiTuBd0la\nD4175J5A8jfyrrTNvwH+X0TsB17K3Hzl/cDdkdw5a6+k306X0SdpebsVptfQXxMRO4A/AE7N4wcz\nm49a0QWYLQYR8aikj5Pcha5CcjXNDwGvAWemz+0jGXeA5HLGn00/9PcAH0znvx/4nKTr0mW8u8Nq\nVwF/LamfpKfykQX+sczmzVdJNetA0qsRsbLoOsy6xbuPzMyswT0FMzNrcE/BzMwaHApmZtbgUDAz\nswaHgpmZNTgUzMyswaFgZmYN/x/u2pz1aountgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BJsSFRwCzAfm",
"colab_type": "text"
},
"source": [
"### Lines model draws over time"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VHOreJd4VrKm",
"colab_type": "text"
},
"source": [
"#### Initial Step"
]
},
{
"cell_type": "code",
"metadata": {
"id": "s7uTZVaIy2C5",
"colab_type": "code",
"outputId": "eb1f9f93-344a-4443-cbea-77d6f9f4e01d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303
}
},
"source": [
"a, b = lines[0]\n",
"\n",
"plot_line(a, b, x, y_true)"
],
"execution_count": 39,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(<Figure size 432x288 with 1 Axes>,\n",
" [<matplotlib.lines.Line2D at 0x7fac6e61d9e8>])"
]
},
"metadata": {
"tags": []
},
"execution_count": 39
},
{
"output_type": "display_data",
"data": {
"image/png": 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RP3lAf331ncgyL35vtNjlEkBPEP458MSSlfrMtQ9Glrnh1BE6cth2gWoEIO8I/xxqu/DO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\n4Q8ABfT/XrXBSv1RyUMAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "I0ozRjCuVvOy",
"colab_type": "text"
},
"source": [
"#### After 500 Steps"
]
},
{
"cell_type": "code",
"metadata": {
"id": "EGuY0tV7zCb4",
"colab_type": "code",
"outputId": "55e36ac6-77c1-4824-c1d2-2daea0aec8c7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303
}
},
"source": [
"a, b = lines[500]\n",
"\n",
"plot_line(a, b, x, y_true)"
],
"execution_count": 40,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(<Figure size 432x288 with 1 Axes>,\n",
" [<matplotlib.lines.Line2D at 0x7fac6e56fd68>])"
]
},
"metadata": {
"tags": []
},
"execution_count": 40
},
{
"output_type": "display_data",
"data": {
"image/png": 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GjruUsMQPk+a//3HJ0Xp3kJ9637Ki7VKCHvCqCgruEq6+fGMHeDl3IEEm7yr8MCm3LPHv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\nCu4iIimk4C4ikkIK7iIiKfT/AUrvhF2yW6dxAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Qxd-OfjaV-lW",
"colab_type": "text"
},
"source": [
"#### Final Step"
]
},
{
"cell_type": "code",
"metadata": {
"id": "1Ps9g06MzHtY",
"colab_type": "code",
"outputId": "d52a9794-fe28-4db9-cbd9-a68359087634",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 303
}
},
"source": [
"a, b = lines[1999]\n",
"\n",
"plot_line(a, b, x, y_true)"
],
"execution_count": 41,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(<Figure size 432x288 with 1 Axes>,\n",
" [<matplotlib.lines.Line2D at 0x7fac6e547630>])"
]
},
"metadata": {
"tags": []
},
"execution_count": 41
},
{
"output_type": "display_data",
"data": {
"image/png": 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zBzc/uKY09agGZbaPQLVScJdwBf3FjnoBrzwfGjXTp9HV2sglH01llH/3kbXhLVZWbaL8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\naAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "axg318pV9GJ3",
"colab_type": "text"
},
"source": [
"## Understandinging the effect of activation functions\n",
"\n",
"Typically, the output of a neuron is transformed using an activation function which compresses the output to a value between 0 and 1 (sigmoid), or between -1 and 1 (tanh) or sets all negative values to zero (relu).\n",
"\n",
"<img src='https://raw.githubusercontent.com/DJCordhose/deep-learning-crash-course-notebooks/master/img/neuron.jpg'>\n",
"\n",
"### Typical Activation Functions\n",
"\n",
"<img src='https://djcordhose.github.io/ai/img/activation-functions.jpg'>\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "qTjQQECGVLAm",
"colab_type": "code",
"outputId": "4b190ddb-ac7f-4a07-c91c-eb4ea25e0b3d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 612
}
},
"source": [
"import numpy as np\n",
"\n",
"x = tf.reshape(tf.constant(np.arange(-1, 4, 0.1), dtype='float32'), (50, 1))\n",
"y_pred = linear_layer(x)\n",
"\n",
"plt.figure(figsize=(20, 10))\n",
"\n",
"plt.plot(x, y_pred)\n",
"\n",
"y_pred_relu = tf.nn.relu(y_pred)\n",
"plt.plot(x, y_pred_relu)\n",
"\n",
"y_pred_sigmoid = tf.nn.sigmoid(y_pred)\n",
"plt.plot(x, y_pred_sigmoid)\n",
"\n",
"y_pred_tanh = tf.nn.tanh(y_pred)\n",
"plt.plot(x, y_pred_tanh)\n",
"\n",
"plt.plot(input, output, 'ro')\n",
"\n",
"plt.legend(['no activation', 'relu', 'sigmoid', 'tanh'])"
],
"execution_count": 42,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fac6e4a9eb8>"
]
},
"metadata": {
"tags": []
},
"execution_count": 42
},
{
"output_type": "display_data",
"data": {
"image/png": 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V7xXw+yJdIgAAAAAAPaK8vFxOp1PXX3+97rzzTn300Ufhz2bOnKl33nlH9fX1CgQCeu65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\nyP8FKZuGPo8gp7AAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6CUXuf_xWghl",
"colab_type": "text"
},
"source": [
"## Logictic Regression\n",
"\n",
"So far we were inferring a continous value for another, now we want to classify. Imagine we have a line that separates two categories in two dimensions."
]
},
{
"cell_type": "code",
"metadata": {
"id": "y0z1ymltS_a7",
"colab_type": "code",
"outputId": "8adc0ad1-0ed3-4352-973b-19e54d177dc2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 368
}
},
"source": [
"from matplotlib.colors import ListedColormap\n",
"\n",
"a = -1\n",
"b = 1\n",
"n = 100\n",
"\n",
"# all points\n",
"X = np.random.uniform(0, 1, (n, 2))\n",
"\n",
"# our line\n",
"line_x = np.random.uniform(0, 1, n)\n",
"line_y = a*line_x+b\n",
"plt.plot(line_x, line_y, 'r')\n",
"\n",
"# below and above line\n",
"y = X[:, 1] > a*X[:, 0]+b\n",
"y = y.astype(int)\n",
"\n",
"plt.xlabel(\"x1\")\n",
"plt.ylabel(\"x2\")\n",
"\n",
"plt.scatter(X[:,0], X[:,1], c=y, cmap=ListedColormap(['#AA6666', '#6666AA']), marker='o', edgecolors='k')\n",
"y"
],
"execution_count": 43,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1,\n",
" 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0,\n",
" 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0,\n",
" 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0,\n",
" 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1])"
]
},
"metadata": {
"tags": []
},
"execution_count": 43
},
{
"output_type": "display_data",
"data": {
"image/png": 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Czs4tvy0x8Tr7939NbOytcvGmTc4+kjRn3ryivYOhQ1U+1hAREYGfXycyM2sy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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BEDW2lpFXRmn",
"colab_type": "text"
},
"source": [
"### We compress output between 0 and 1 using sigmoid to match y\n",
"* everything below 0.5 counts as 0, everthing above as 1"
]
},
{
"cell_type": "code",
"metadata": {
"id": "tHaML5z9SI6o",
"colab_type": "code",
"colab": {}
},
"source": [
"class SigmoidLayer(LinearLayer):\n",
" \"\"\"y = sigmoid(w.x + b)\"\"\"\n",
"\n",
" def __init__(self, **kwargs):\n",
" super(SigmoidLayer, self).__init__(**kwargs)\n",
"\n",
" def call(self, inputs):\n",
" return tf.sigmoid(super().call(inputs))\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "gZBtAidEYTNO",
"colab_type": "text"
},
"source": [
"### We have 2d input now"
]
},
{
"cell_type": "code",
"metadata": {
"id": "a9IjwBVlvnCk",
"colab_type": "code",
"outputId": "154a2b63-1330-4b3e-f0c7-6b6e96a7f7c8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"x = tf.constant(X, dtype='float32')\n",
"y_true = tf.constant(y, dtype='float32')\n",
"x.shape"
],
"execution_count": 45,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TensorShape([100, 2])"
]
},
"metadata": {
"tags": []
},
"execution_count": 45
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ycMPuy0ed3Nx",
"colab_type": "code",
"colab": {}
},
"source": [
"model = SigmoidLayer(input_dim=2)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "WLfqyTcecuoo",
"colab_type": "text"
},
"source": [
"### Reconsidering the loss function\n",
"\n",
"_cross entropy is an alternative to squared error_ \n",
"\n",
"* cross entropy can be used as an error measure when a network's outputs can be thought of as representing independent hypotheses\n",
"* activations can be understood as representing the probability that each hypothesis might be true\n",
"* the loss indicates the distance between what the network believes this distribution should be, and what the teacher says it should be \n",
"\n",
"http://www.cse.unsw.edu.au/~billw/cs9444/crossentropy.html"
]
},
{
"cell_type": "code",
"metadata": {
"id": "tLFVcOeRd50X",
"colab_type": "code",
"colab": {}
},
"source": [
"loss_fn = tf.losses.binary_crossentropy"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nLK7C1L9d8Jk",
"colab_type": "code",
"colab": {}
},
"source": [
"# standard optimizer using advanced properties\n",
"optimizer = tf.keras.optimizers.Adam(learning_rate=1e-1)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "TVhpXvlld_IG",
"colab_type": "code",
"colab": {}
},
"source": [
"# https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/metrics/Accuracy\n",
"m = tf.keras.metrics.Accuracy()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7uF1V08yqXi-",
"colab_type": "code",
"colab": {}
},
"source": [
"EPOCHS = 1000\n",
"\n",
"losses = []\n",
"accuracies = []\n",
"\n",
"for step in range(EPOCHS):\n",
" # Open a GradientTape.\n",
" with tf.GradientTape() as tape:\n",
"\n",
" # Forward pass.\n",
" y_pred = model(x)\n",
"\n",
" # Loss value for this batch.\n",
" loss = loss_fn(y_true=tf.squeeze(y_true), y_pred=tf.squeeze(y_pred))\n",
"\n",
" y_pred_binary = (tf.squeeze(y_pred) > 0.5).numpy().astype(float)\n",
" m.update_state(tf.squeeze(y_true), y_pred_binary)\n",
" accuracy = m.result().numpy()\n",
"\n",
" losses.append(loss)\n",
" accuracies.append(accuracy)\n",
" \n",
" # Get gradients of weights wrt the loss.\n",
" gradients = tape.gradient(loss, model.trainable_weights)\n",
" \n",
" # Update the weights of our linear layer.\n",
" optimizer.apply_gradients(zip(gradients, model.trainable_weights))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-asNXUGQ0HTx",
"colab_type": "code",
"outputId": "5940b557-6b85-4347-a335-c25cfcbe9012",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"print(loss)"
],
"execution_count": 51,
"outputs": [
{
"output_type": "stream",
"text": [
"tf.Tensor(0.049787622, shape=(), dtype=float32)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "-HMbyRAR3qkw",
"colab_type": "code",
"outputId": "e24dff87-1f85-4564-c356-c7ffe25193fe",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"print(accuracy)"
],
"execution_count": 52,
"outputs": [
{
"output_type": "stream",
"text": [
"0.98066\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "uxM3-mu_xF5L",
"colab_type": "code",
"outputId": "041ec992-bbba-4324-8c04-3c788f18c4f2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
}
},
"source": [
"plt.yscale('log')\n",
"plt.ylabel(\"loss\")\n",
"plt.xlabel(\"epochs\")\n",
"\n",
"plt.plot(losses)"
],
"execution_count": 53,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fac6e6e6d68>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 53
},
{
"output_type": "display_data",
"data": {
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xF02tHV6XJyJDRMEhAxYVEc7nLpzMa19ZzjVzslj51gE++N3XeWzdYTo0/iESdBQcMmjS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\ncIiISEAUHCIiEpD/D3PNotRViDH+AAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "d7sGM5vW4Dcf",
"colab_type": "code",
"outputId": "ff93aa09-2c0e-4cb6-9780-b4388fa00171",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
}
},
"source": [
"plt.ylabel(\"accuracy\")\n",
"plt.xlabel(\"epochs\")\n",
"\n",
"plt.plot(accuracies)"
],
"execution_count": 54,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fac6e353358>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 54
},
{
"output_type": "display_data",
"data": {
"image/png": 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CD11zdqrLERE5hsJiEnCP3W58MDLCv7ztZWRrQFtEJhmFxSTwsw0HeHzzIT59w2IW1pSk\nuhwRkeMoLFKsdzDCF3/eyPm1ZbznlfNTXY6IyAnpaqgU+9rj2zjYNch/vOMSnX4SkUlLRxYptLO1\nh5VP7eJtl9RxyTw9CFBEJi+FRQr9y6NbycvO4m+XLU51KSIip6SwSJEXmjv5+fMHuONVC6gpzU91\nOSIip6SwSJF/Xr2FiqJc3neVnk8hIpOfwiIF/rS3nSe3tvKB15xFWUFuqssREUlIYZEC9z65g/LC\nXN6l52iLyBShsJhgO1p7eLTxIO++Yh7F+bpyWUSmBoXFBPvmb3eSm53F7foCnohMIQqLCXSoa4Af\nrWvmbZfUUV2iK6BEZOoINSzMbJmZbTGz7WZ25wmWzzWzJ8zsWTPbYGY3Bu3zzazfzJ4LXt8Is86J\n8q3f7SISjbJcV0CJyBQT2klzM8sG7gGuA5qAtWa2yt0b47r9PfADd/+6mS0FHgHmB8t2uPtFYdU3\n0Tr7h7n/6b28/sLZzJtWnOpyREROS5hHFpcC2919p7sPAQ8CN43p40BZMF0O7A+xnpT63tN76BmM\n8IHX6KhCRKaeMMOiFtgXN98UtMW7G3inmTURO6r4cNyyBcHpqSfN7NUh1hm6geERVj61i9ecU8N5\ns8tTXY6IyGlL9QD3rcB/uXsdcCPwXTPLAg4Ac939YuATwP+YWdnYlc1suZk1mFlDa2vrhBZ+On64\nrom23iE+8JqzUl2KiMgZCTMsmoE5cfN1QVu8O4AfALj7H4ACoNrdB929LWhfB+wAzhn7Bu5+n7vX\nu3t9TU1NCJvw0o1EnW/9dicvm1PB5QurUl2OiMgZCTMs1gKLzGyBmeUBtwCrxvTZC1wLYGZLiIVF\nq5nVBAPkmNlCYBGwM8RaQ/NYYwu72/pY/uqFmOl5FSIyNYV2NZS7R8zsQ8BqIBtY6e4bzWwF0ODu\nq4BPAv9pZh8nNtj9Hnd3M7sKWGFmw0AU+IC7Hwmr1jDd95udzKkqZNn5M1NdiojIGQv1fhPu/gix\ngev4ts/GTTcCV55gvR8BPwqztomwbs8R/rS3g39403l6Cp6ITGlJnYYysx+b2euDwWdJ0n2/2UlF\nUS5vq69LdSkiIi9Jsh/+/wHcBmwzsy+Z2bkh1pQW9rb18WjjQd552TyK8nTDQBGZ2pIKC3f/lbu/\nA3g5sBv4lZn93szea2Z6IMMJPLB2L1lmvFO3IReRNJD0aSUzmwa8B3gf8CzwVWLh8VgolU1hQ5Eo\nP2zYxzWLpzOzvCDV5YiIvGRJnR8xs58A5wLfBd7o7geCRd83s4awipuq1mw6yOGeIW67dG6qSxER\nGRfJnkz/mrs/caIF7l4/jvWkhR807GN2eQFXnTM5vygoInK6kj0NtdTMKkZnzKzSzP4mpJqmtCO9\nQ/x222HedFGtLpcVkbSRbFi83907RmfcvR14fzglTW2/fKGFSNR548tmpboUEZFxk2xYZFvcvSqC\nW3HkhVPS1Paz9ftZWFPM0lnH3fdQRGTKSjYsfklsMPtaM7sWeCBokziHugZ4elcbb7xwtu4DJSJp\nJdkB7r8D/gr462D+MeCboVQ0hf1yYwvu6BSUiKSdpMLC3aPA14OXnMQvnm/h7OklnD29NNWliIiM\nq2TvDbXIzB4ys0Yz2zn6Cru4qaStZ5BndrXxOt1dVkTSULJjFt8mdlQRAf4M+A7wvbCKmooeazxI\n1NGtyEUkLSUbFoXuvgYwd9/j7ncDrw+vrKnnFy+0MLeqSFdBiUhaSnaAezC4Pfm24IFGzUBJeGVN\nLZ39w/x+x2H+8soFugpKRNJSskcWHwWKgI8AlwDvBG4Pq6ipZs2mgwyPuE5BiUjaSnhkEXwB7+3u\n/imgB3hv6FVNMas3tjCzrICX1VUk7iwiMgUlPLJw9xHgVRNQy5Q0MDzCb7cd5rVLp5Ole0GJSJpK\n9jTUs2a2yszeZWZvGX0lWsnMlpnZFjPbbmZ3nmD5XDN7wsyeNbMNZnZj3LK7gvW2mNkNp7FNE+rp\nnW30DY1w7ZIZqS5FRCQ0yQ5wFwBtwDVxbQ78+GQrBKev7gGuA5qAtWa2yt0b47r9PfADd/+6mS0F\nHgHmB9O3AOcBs4k9me+c4ChnUlmz6RCFudlcsXBaqksREQlNst/gPpNxikuB7e6+E8DMHgRuAuLD\nwoHRa03Lgf3B9E3Ag+4+COwys+3B7/vDGdQRGndnzaaDvGpRNQW52akuR0QkNMk+Ke/bxD7Yj+Hu\nf3mK1WqBfXHzTcBlY/rcDTxqZh8GioHXxq379Jh1a5OpdSJtPdjD/s4BPnLtolSXIiISqmTHLB4G\nfh681hA7GugZh/e/Ffgvd68DbgS+G3yfIylmttzMGsysobW1dRzKOT1/2HEYgCvPrp7w9xYRmUjJ\nnob6Ufy8mT0APJVgtWZgTtx8XdAW7w5gWfAefzCzAqA6yXVx9/uA+wDq6+uPO/IJ29M7j1BXWcic\nqqKJfmsRkQmV9P/ix1gETE/QZy2wyMwWmFkesQHrVWP67AWuBTCzJcQG0luDfreYWb6ZLQje749n\nWGsoolHnmV1tXK6BbRHJAMmOWXRz7JhFC7FnXJyUu0eCW4OsBrKBle6+0cxWAA3uvgr4JPCfZvbx\n4Pe/x90d2GhmPyA2GB4BPjjZroTaeqib9r5hhYWIZIRkT0Od0QMa3P0RYpfDxrd9Nm66EbjyJOt+\nEfjimbzvRFi76wgAly2oSnElIiLhS/Z5Fm82s/K4+Qozuzm8sia/5/Z1Ul2ST11lYapLEREJXbJj\nFp9z987RGXfvAD4XTklTw/qmDi6aU667zIpIRkg2LE7UL9lvf6edroFhdrT26MaBIpIxkg2LBjP7\nVzM7K3j9K7AuzMImsxeaOnGHl81RWIhIZkg2LD4MDAHfBx4EBoAPhlXUZPdcUwcAF9aVJ+gpIpIe\nkr0aqhc47q6xmer5pk7mTSuioigv1aWIiEyIZK+GeszMKuLmK81sdXhlTW4b93dx/mwdVYhI5kj2\nNFR1cAUUAO7eTuJvcKelroFh9h7pY+nsssSdRUTSRLJhETWzuaMzZjafE9yFNhM07u8CUFiISEZJ\n9vLXzwBPmdmTgAGvBpaHVtUkNhoW5yksRCSDJDvA/UszqycWEM8CPwX6wyxsstq4v4ua0nymlxak\nuhQRkQmT7I0E3wd8lNitwp8DLif21LprTrVeOmo80MWSWTqqEJHMkuyYxUeBVwB73P3PgIuBjlOv\nkn5Gos6O1h4Wzzyj+yqKiExZyYbFgLsPAJhZvrtvBs4Nr6zJae+RPoYiUc6eXpLqUkREJlSyA9xN\nwfcsfgo8ZmbtwJ7wypqcth3sBuCcGTqyEJHMkuwA95uDybvN7AmgHPhlaFVNUtsOxR47riMLEck0\np33nWHd/MoxCpoJtB7uZXV5ASX7G3nBXRDLUmT6DOyNtO9TDIp2CEpEMpLBI0kjU2X6oh0U6BSUi\nGUhhkaSm9j4GI1ENbotIRgo1LMxsmZltMbPtZnbcLc7N7Ctm9lzw2mpmHXHLRuKWrQqzzmRsOxgM\nbs/QkYWIZJ7QRmrNLBu4B7gOaALWmtkqd28c7ePuH4/r/2FiX/Yb1e/uF4VV3+naeih22ayuhBKR\nTBTmkcWlwHZ33+nuQ8SesHfTKfrfCjwQYj0vyfaDPcwqL6CsIDfVpYiITLgww6IW2Bc33xS0HcfM\n5gELgMfjmgvMrMHMnjazm0+y3vKgT0Nra+t41X1C2w716KhCRDLWZBngvgV4yN1H4trmuXs9cBvw\nb2Z21tiV3P0+d6939/qamprQiosGV0JpcFtEMlWYYdEMzImbrwvaTuQWxpyCcvfm4OdO4NccO54x\noZo7+ukfHtFlsyKSscIMi7XAIjNbYGZ5xALhuKuazGwxUEnsluejbZVmlh9MVwNXAo1j150om1ti\ng9v6Qp6IZKrQroZy94iZfQhYDWQDK919o5mtABrcfTQ4bgEedPf4x7QuAe41syixQPtS/FVUE23T\ngS7M0K3JRSRjhXqTI3d/BHhkTNtnx8zffYL1fg9cEGZtp6NxfxfzpxVTrHtCiUiGmiwD3JPappYu\nlszSUYWIZC6FRQLdA8PsaetjqR6lKiIZTGGRwNbggUeLZyosRCRzKSwSGL0S6lwNbotIBlNYJLCl\npZuS/BzqKgtTXYqISMooLBLY3NLNOTNKMLNUlyIikjIKi1Nwd7a0dHOuxitEJMMpLE6htWeQzv5h\nztEzLEQkwyksTmFPWx8AC6qLU1yJiEhqKSxOYffhXgDmT1NYiEhmU1icwp62PrKzjFpdCSUiGU5h\ncQq723qprSgkN1t/TCKS2fQpeAp72vqYN60o1WWIiKScwuIk3J3dbb0arxARQWFxUh19w3QPRHRk\nISKCwuKkdrfpSigRkVEKi5MY/Y7F/GodWYiIKCxOYndbL2ZQV6mwEBFRWJzEztZeZpcXUpCbnepS\nRERSLtSwMLNlZrbFzLab2Z0nWP4VM3sueG01s464Zbeb2bbgdXuYdZ5I7AaCeoaFiAhATli/2Myy\ngXuA64AmYK2ZrXL3xtE+7v7xuP4fBi4OpquAzwH1gAPrgnXbw6o33lAkyo7WHq5ZMn0i3k5EZNIL\n88jiUmC7u+909yHgQeCmU/S/FXggmL4BeMzdjwQB8RiwLMRaj7HzcA+RqHPuDB1ZiIhAuGFRC+yL\nm28K2o5jZvOABcDjp7OumS03swYza2htbR2XoiF2Cgr0KFURkVGTZYD7FuAhdx85nZXc/T53r3f3\n+pqamnErZtOBbnKyjLNq9BwLEREINyyagTlx83VB24ncwounoE533XH3QnMni2eVkpczWbJURCS1\nwvw0XAssMrMFZpZHLBBWje1kZouBSuAPcc2rgevNrNLMKoHrg7bQuTsbmjq4oLZiIt5ORGRKCO1q\nKHePmNmHiH3IZwMr3X2jma0AGtx9NDhuAR50d49b94iZfZ5Y4ACscPcjYdUab++RProGIlxYVz4R\nbyciMiWEFhYA7v4I8MiYts+Omb/7JOuuBFaGVtxJbGjqBOCCWoWFiMgonZQf4/nmTvJysnQllIhI\nHIXFGBv3d7JkZqmejiciEkefiGPsPtzHgmrdllxEJJ7CIs5QJMqBzn7m6hkWIiLHUFjEaWrvI+ow\nr0q3JRcRiaewiLPnSOyBR3P1KFURkWMoLOLsC8JCRxYiIsdSWMTZ09ZHQW4WNaX5qS5FRGRSUVjE\naW7vp7aiEDNLdSkiIpOKwiLO4Z5BppcWpLoMEZFJR2ERp7VnkGqdghIROY7CIs7h7kFqShQWIiJj\nKSwCfUMReodGqC7NS3UpIiKTjsIicLh7CEBHFiIiJ6CwCLT2DADoslkRkRNQWARagyOLah1ZiIgc\nR2ERaO0ZBGC6jixERI6jsAgc7h7EDKqKNcAtIjKWwiLQ2jNIVVEeOXrokYjIcUL9ZDSzZWa2xcy2\nm9mdJ+nzF2bWaGYbzex/4tpHzOy54LUqzDohdmSh8QoRkRPLCesXm1k2cA9wHdAErDWzVe7eGNdn\nEXAXcKW7t5vZ9Lhf0e/uF4VV31itPYO6EkpE5CTCPLK4FNju7jvdfQh4ELhpTJ/3A/e4ezuAux8K\nsZ5Tau0epLpE4xUiIicSZljUAvvi5puCtnjnAOeY2e/M7GkzWxa3rMDMGoL2m0Osk5Go09I5wOyK\nwjDfRkRkygrtNNRpvP8i4GqgDviNmV3g7h3APHdvNrOFwONm9ry774hf2cyWA8sB5s6de8ZFHOoe\nIBJ1aisVFiIiJxLmkUUzMCduvi5oi9cErHL3YXffBWwlFh64e3Pwcyfwa+DisW/g7ve5e72719fU\n1JxxoXvbYk/Im1OpJ+SJiJxImGGxFlhkZgvMLA+4BRh7VdNPiR1VYGbVxE5L7TSzSjPLj2u/Emgk\nJLvbegFYUF0c1luIiExpoZ2GcveImX0IWA1kAyvdfaOZrQAa3H1VsOx6M2sERoBPu3ubmb0SuNfM\nosQC7UvxV1GNt12H+8jLztKYhYjISYQ6ZuHujwCPjGn7bNy0A58IXvF9fg9cEGZt8XYf7mVOVSHZ\nWXqcqojIiejrysD+zn5qNV4hInJSCgugZyBCWUGqLwwTEZm8FBZA92CEUoWFiMhJKSyIHVmU5Css\nREROJuPDYiTq9A+PUJKfm+pSREQmrYwPi57BCAAlOg0lInJSGR8WOLzhwlksml6S6kpERCatjP/v\ndHlRLv9+28tTXYaIyKSmIwvCI8xUAAAGZklEQVQREUlIYSEiIgkpLEREJCGFhYiIJKSwEBGRhBQW\nIiKSkMJCREQSUliIiEhCFnv+0NRnZq3AnpfwK6qBw+NUzlShbU5/mba9oG0+XfPcvSZRp7QJi5fK\nzBrcvT7VdUwkbXP6y7TtBW1zWHQaSkREElJYiIhIQgqLF92X6gJSQNuc/jJte0HbHAqNWYiISEI6\nshARkYQyPizMbJmZbTGz7WZ2Z6rrGS9mNsfMnjCzRjPbaGYfDdqrzOwxM9sW/KwM2s3Mvhb8OWww\nsyn7kA8zyzazZ83s4WB+gZk9E2zb980sL2jPD+a3B8vnp7LuM2VmFWb2kJltNrNNZnZFuu9nM/t4\n8Pf6BTN7wMwK0m0/m9lKMztkZi/EtZ32fjWz24P+28zs9jOtJ6PDwsyygXuA1wFLgVvNbGlqqxo3\nEeCT7r4UuBz4YLBtdwJr3H0RsCaYh9ifwaLgtRz4+sSXPG4+CmyKm/8n4CvufjbQDtwRtN8BtAft\nXwn6TUVfBX7p7ouBlxHb9rTdz2ZWC3wEqHf384Fs4BbSbz//F7BsTNtp7VczqwI+B1wGXAp8bjRg\nTpu7Z+wLuAJYHTd/F3BXqusKaVv/F7gO2ALMCtpmAVuC6XuBW+P6H+03lV5AXfCP6BrgYcCIfVkp\nZ+w+B1YDVwTTOUE/S/U2nOb2lgO7xtadzvsZqAX2AVXBfnsYuCEd9zMwH3jhTPcrcCtwb1z7Mf1O\n55XRRxa8+JduVFPQllaCw+6LgWeAGe5+IFjUAswIptPlz+LfgL8FosH8NKDD3SPBfPx2Hd3mYHln\n0H8qWQC0At8OTr1908yKSeP97O7NwJeBvcABYvttHem9n0ed7n4dt/2d6WGR9sysBPgR8DF374pf\n5rH/aqTN5XBm9gbgkLuvS3UtEygHeDnwdXe/GOjlxVMTQFru50rgJmJBORso5vjTNWlvovdrpodF\nMzAnbr4uaEsLZpZLLCjud/cfB80HzWxWsHwWcChoT4c/iyuBN5nZbuBBYqeivgpUmFlO0Cd+u45u\nc7C8HGibyILHQRPQ5O7PBPMPEQuPdN7PrwV2uXuruw8DPya279N5P4863f06bvs708NiLbAouIoi\nj9gg2aoU1zQuzMyAbwGb3P1f4xatAkaviLid2FjGaPu7g6sqLgc64w53pwR3v8vd69x9PrF9+bi7\nvwN4Anhr0G3sNo/+Wbw16D+l/gfu7i3APjM7N2i6FmgkjfczsdNPl5tZUfD3fHSb03Y/xznd/boa\nuN7MKoMjsuuDttOX6gGcVL+AG4GtwA7gM6muZxy361XEDlE3AM8FrxuJnatdA2wDfgVUBf2N2JVh\nO4DniV1pkvLteAnbfzXwcDC9EPgjsB34IZAftBcE89uD5QtTXfcZbutFQEOwr38KVKb7fgb+AdgM\nvAB8F8hPt/0MPEBsTGaY2BHkHWeyX4G/DLZ9O/DeM61H3+AWEZGEMv00lIiIJEFhISIiCSksREQk\nIYWFiIgkpLAQEZGEFBYiKWRmV4/eHVdkMlNYiIhIQgoLkSSY2TvN7I9m9pyZ3Rs8M6PHzL4SPFdh\njZnVBH0vMrOng+cK/CTumQNnm9mvzGy9mf3JzM4Kfn1J3PMo7g++lYyZfclizyPZYGZfTtGmiwAK\nC5GEzGwJ8HbgSne/CBgB3kHsBnYN7n4e8CSx5wYAfAf4O3e/kNi3aUfb7wfucfeXAa8k9u1ciN0R\n+GPEnqmyELjSzKYBbwbOC37PF8LdSpFTU1iIJHYtcAmw1syeC+YXErsN+veDPt8DXmVm5UCFuz8Z\ntP83cJWZlQK17v4TAHcfcPe+oM8f3b3J3aPEbssyn9httAeAb5nZW4DRviIpobAQScyA/3b3i4LX\nue5+9wn6nem9cwbjpkeIPcAnQuzJZg8BbwB+eYa/W2RcKCxEElsDvNXMpsPR5yDPI/bvZ/Qup7cB\nT7l7J9BuZq8O2t8FPOnu3UCTmd0c/I58Mys62RsGzyEpd/dHgI8Te1yqSMrkJO4iktncvdHM/h54\n1MyyiN0F9IPEHjR0abDsELFxDYjdOvobQRjsBN4btL8LuNfMVgS/422neNtS4H/NrIDYkc0nxnmz\nRE6L7jorcobMrMfdS1Jdh8hE0GkoERFJSEcWIiKSkI4sREQkIYWFiIgkpLAQEZGEFBYiIpKQwkJE\nRBJSWIiISEL/H4kz59qGCOCFAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "3aYL-GyJY_37",
"colab_type": "code",
"outputId": "f71910dc-c6a7-45ef-9483-6fe8ec8653f1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
}
},
"source": [
"y_pred = model(x)\n",
"y_pred_binary = (tf.squeeze(y_pred) > 0.5).numpy().astype(float)\n",
"y_pred_binary"
],
"execution_count": 55,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([1., 1., 1., 0., 1., 0., 0., 0., 0., 0., 1., 1., 0., 0., 1., 0., 0.,\n",
" 0., 1., 1., 1., 1., 1., 0., 0., 1., 0., 1., 1., 1., 0., 1., 1., 0.,\n",
" 0., 1., 1., 0., 1., 0., 1., 0., 1., 0., 1., 1., 0., 1., 1., 1., 0.,\n",
" 1., 1., 1., 1., 0., 1., 0., 1., 0., 1., 0., 0., 1., 1., 0., 1., 1.,\n",
" 0., 0., 0., 1., 0., 1., 1., 1., 1., 0., 0., 0., 0., 1., 1., 0., 1.,\n",
" 1., 0., 0., 1., 0., 0., 0., 1., 1., 1., 0., 0., 0., 0., 1.])"
]
},
"metadata": {
"tags": []
},
"execution_count": 55
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5O9WZSTuZBW3",
"colab_type": "code",
"outputId": "16b26da3-85b9-408f-e3c4-d32a4e33b33a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 153
}
},
"source": [
"y_true - y_pred_binary"
],
"execution_count": 56,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<tf.Tensor: id=281144, shape=(100,), dtype=float32, numpy=\n",
"array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" dtype=float32)>"
]
},
"metadata": {
"tags": []
},
"execution_count": 56
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IiIut--IYxWz",
"colab_type": "code",
"outputId": "9d74879d-c419-4ce5-943f-76d2cfa653d9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
}
},
"source": [
"# below and above line\n",
"\n",
"plt.xlabel(\"x1\")\n",
"plt.ylabel(\"x2\")\n",
"\n",
"plt.scatter(X[:,0], X[:,1], c=y_pred_binary, cmap=ListedColormap(['#AA6666', '#6666AA']), marker='o', edgecolors='k')"
],
"execution_count": 57,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fac6e2b0da0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 57
},
{
"output_type": "display_data",
"data": {
"image/png": 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aNGDp6dN8N2sWc+bOLXO8pqYmiifcERYpFNV+p7N69eqxdOniZz6+V69exMbe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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jZzGU633WNvt",
"colab_type": "text"
},
"source": [
"## The same solution using high level Keas API"
]
},
{
"cell_type": "code",
"metadata": {
"id": "STfOU_LmVAnj",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 187
},
"outputId": "8350644f-64b3-4881-c06d-cde819504f2e"
},
"source": [
"from tensorflow.keras.layers import Dense\n",
" \n",
"model = tf.keras.Sequential()\n",
"\n",
"model.add(Dense(units=1, activation='sigmoid', input_dim=2))\n",
"\n",
"model.summary()"
],
"execution_count": 58,
"outputs": [
{
"output_type": "stream",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
"Layer (type) Output Shape Param # \n",
"=================================================================\n",
"dense (Dense) (None, 1) 3 \n",
"=================================================================\n",
"Total params: 3\n",
"Trainable params: 3\n",
"Non-trainable params: 0\n",
"_________________________________________________________________\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_Vffpso6WpgK",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "f4966e68-c0a5-4cdb-c00e-cecc69939f91"
},
"source": [
"%%time \n",
"\n",
"model.compile(loss=loss_fn, # binary cross entropy, unchanged from low level example\n",
" optimizer=optimizer, # adam, unchanged from low level example\n",
" metrics=['accuracy'])\n",
"\n",
"# does a similar thing internally as our loop from above\n",
"history = model.fit(x, y_true, epochs=EPOCHS, verbose=0)"
],
"execution_count": 59,
"outputs": [
{
"output_type": "stream",
"text": [
"CPU times: user 10.1 s, sys: 795 ms, total: 10.9 s\n",
"Wall time: 8.69 s\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "NfKDwhCiWkRI",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "fd7f3d2c-f98f-4270-bfac-d04276f99b35"
},
"source": [
"loss, accuracy = model.evaluate(x, y_true)\n",
"loss, accuracy"
],
"execution_count": 60,
"outputs": [
{
"output_type": "stream",
"text": [
"100/100 [==============================] - 0s 422us/sample - loss: 0.0379 - accuracy: 1.0000\n"
],
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(0.03793137133121491, 1.0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 60
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "N2U7B9ZEaKAz",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"outputId": "bd4f8ef0-b3db-4843-c3d5-777161d59cb0"
},
"source": [
"plt.yscale('log')\n",
"plt.ylabel(\"accuracy\")\n",
"plt.xlabel(\"epochs\")\n",
"\n",
"plt.plot(history.history['accuracy'])"
],
"execution_count": 61,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fac63e57780>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 61
},
{
"output_type": "display_data",
"data": {
"image/png": 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hk5pYs/U9IPghrN/WlfybGnwmNNWwdO225LJsxjZU01jb3wcRdIz2nykdknIm3FRXxfjw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\nJouSAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "O61MfB85aMT6",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"outputId": "9d90dc89-dac2-4e4b-f31e-4ec435983218"
},
"source": [
"plt.yscale('log')\n",
"plt.ylabel(\"loss\")\n",
"plt.xlabel(\"epochs\")\n",
"\n",
"plt.plot(history.history['loss'])"
],
"execution_count": 62,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7fac63dbbfd0>]"
]
},
"metadata": {
"tags": []
},
"execution_count": 62
},
{
"output_type": "display_data",
"data": {
"image/png": 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PprR8xvaP483kAxwuKKNXTDgfbjpU6zMeeG+L9/u92cWs2J3jfb18Zxbpnj1S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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "qh8dTuD0a0C3",
"colab_type": "code",
"colab": {}
},
"source": [
"y_pred = model.predict(x)\n",
"y_pred_binary = (tf.squeeze(y_pred) > 0.5).numpy().astype(float)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "K5YAfqbaa2-n",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 300
},
"outputId": "d607b99d-66ac-4ec2-8065-30fbb377246c"
},
"source": [
"# below and above line\n",
"\n",
"plt.xlabel(\"x1\")\n",
"plt.ylabel(\"x2\")\n",
"\n",
"plt.scatter(X[:,0], X[:,1], c=y_pred_binary, cmap=ListedColormap(['#AA6666', '#6666AA']), marker='o', edgecolors='k')"
],
"execution_count": 64,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.collections.PathCollection at 0x7fac63c83400>"
]
},
"metadata": {
"tags": []
},
"execution_count": 64
},
{
"output_type": "display_data",
"data": {
"image/png": 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aNGDp6dN8N2sWc+bOLXO8pqYmiifcERYpFNV+p7N69eqxdOniZz6+V69exMbe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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HlPMzDleu4aM",
"colab_type": "text"
},
"source": [
"## From single neuron to network in the TensorFlow Playground\n",
"\n",
"<img src='https://djcordhose.github.io/ai/img/tf-plaground.png'>\n",
"\n",
"https://playground.tensorflow.org/#activation=linear&batchSize=10&dataset=circle®Dataset=reg-plane&learningRate=0.01®ularizationRate=0&noise=0&networkShape=1&seed=0.98437&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false"
]
}
]
} | mit |
daniel-acuna/python_data_science_intro | notebooks/feature_engineering.ipynb | 1 | 62965 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import sys"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"sys.path.append('/Users/deacuna/Documents/workspace/pyspark_pipes')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import pyspark_pipes"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"pyspark_pipes.patch()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from pyspark.sql import SparkSession"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"spark = SparkSession.builder.getOrCreate()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from pyspark.ml import feature\n",
"from pyspark.sql import Row\n",
"from pyspark.ml import classification\n",
"from pyspark.ml import Pipeline"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"sc = spark.sparkContext"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2018-04-09 18:48:32-- https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv\n",
"Resolving raw.githubusercontent.com... 151.101.116.133\n",
"Connecting to raw.githubusercontent.com|151.101.116.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 60302 (59K) [text/plain]\n",
"Saving to: ‘titanic.csv.1’\n",
"\n",
"titanic.csv.1 100%[===================>] 58.89K --.-KB/s in 0.01s \n",
"\n",
"2018-04-09 18:48:32 (3.96 MB/s) - ‘titanic.csv.1’ saved [60302/60302]\n",
"\n"
]
}
],
"source": [
"!wget 'https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv'"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"titanic_df = spark.read.csv('titanic.csv', header=True, inferSchema=True)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"root\n",
" |-- PassengerId: integer (nullable = true)\n",
" |-- Survived: integer (nullable = true)\n",
" |-- Pclass: integer (nullable = true)\n",
" |-- Name: string (nullable = true)\n",
" |-- Sex: string (nullable = true)\n",
" |-- Age: double (nullable = true)\n",
" |-- SibSp: integer (nullable = true)\n",
" |-- Parch: integer (nullable = true)\n",
" |-- Ticket: string (nullable = true)\n",
" |-- Fare: double (nullable = true)\n",
" |-- Cabin: string (nullable = true)\n",
" |-- Embarked: string (nullable = true)\n",
"\n"
]
}
],
"source": [
"titanic_df.printSchema()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Process title**"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"title_list_reg=\"|\".join(['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',\n",
" 'Dr', 'Ms', 'Mlle','Col', 'Capt', 'Mme', 'Countess',\n",
" 'Don', 'Jonkheer']).lower()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"title = feature.RegexTokenizer(pattern=\"\\\\b(\" + title_list_reg + \")\\\\b\", gaps=False, inputCol='Name') | feature.CountVectorizer()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"gender = feature.StringIndexer(inputCol='Sex')"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"StringIndexer_47f3b8f35cbdb7783496"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gender.fit(titanic_df)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+------+\n",
"| Sex|\n",
"+------+\n",
"| male|\n",
"|female|\n",
"|female|\n",
"|female|\n",
"| male|\n",
"+------+\n",
"only showing top 5 rows\n",
"\n"
]
}
],
"source": [
"titanic_df.select('Sex').show(5)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"pipe = (title, \n",
" gender,\n",
" feature.StringIndexer(inputCol='Embarked') | feature.OneHotEncoder()\n",
") | feature.VectorAssembler() | classification.LogisticRegression(labelCol='Survived')"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"features = ((title, \n",
" gender,\n",
" feature.StringIndexer(inputCol='Embarked') | feature.OneHotEncoder()\n",
") | feature.VectorAssembler()).fit(titanic_df.dropna(subset=['Name', 'Embarked', 'Survived', 'Sex']))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+-------------------------------------------+--------------------------------------------+------------------------------------------+------------------------------------------+------------------------------------------+--------------------------------------------+\n",
"|PassengerId|Survived|Pclass| Name| Sex| Age|SibSp|Parch| Ticket| Fare|Cabin|Embarked|RegexTokenizer_478b82139c086ee88a71__output|CountVectorizer_4a258c4635cb00f9a8be__output|StringIndexer_47f3b8f35cbdb7783496__output|StringIndexer_496ea18e068a4e241bd2__output|OneHotEncoder_483d8b698a83e8f8aa1b__output|VectorAssembler_48a2b6ff1d5dcf03e7d3__output|\n",
"+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+-------------------------------------------+--------------------------------------------+------------------------------------------+------------------------------------------+------------------------------------------+--------------------------------------------+\n",
"| 1| 0| 3|Braund, Mr. Owen ...| male|22.0| 1| 0| A/5 21171| 7.25| null| S| [mr]| (15,[0],[1.0])| 0.0| 0.0| (2,[0],[1.0])| (18,[0,16],[1.0,1...|\n",
"| 2| 1| 1|Cumings, Mrs. Joh...|female|38.0| 1| 0| PC 17599|71.2833| C85| C| [mrs]| (15,[2],[1.0])| 1.0| 1.0| (2,[1],[1.0])| (18,[2,15,17],[1....|\n",
"| 3| 1| 3|Heikkinen, Miss. ...|female|26.0| 0| 0|STON/O2. 3101282| 7.925| null| S| [miss]| (15,[1],[1.0])| 1.0| 0.0| (2,[0],[1.0])| (18,[1,15,16],[1....|\n",
"| 4| 1| 1|Futrelle, Mrs. Ja...|female|35.0| 1| 0| 113803| 53.1| C123| S| [mrs]| (15,[2],[1.0])| 1.0| 0.0| (2,[0],[1.0])| (18,[2,15,16],[1....|\n",
"+-----------+--------+------+--------------------+------+----+-----+-----+----------------+-------+-----+--------+-------------------------------------------+--------------------------------------------+------------------------------------------+------------------------------------------+------------------------------------------+--------------------------------------------+\n",
"only showing top 4 rows\n",
"\n"
]
}
],
"source": [
"features.transform(titanic_df).show(4)"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [],
"source": [
"s = features.stages[0].stages[-1]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"featires"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [
{
"ename": "Py4JJavaError",
"evalue": "An error occurred while calling o2277.fit.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 528.0 failed 1 times, most recent failure: Lost task 0.0 in stage 528.0 (TID 521, localhost, executor driver): org.apache.spark.SparkException: Failed to execute user defined function($anonfun$5: (string) => double)\n\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)\n\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)\n\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1038)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:969)\n\tat org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:760)\n\tat org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:285)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:108)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: org.apache.spark.SparkException: StringIndexer encountered NULL value. To handle or skip NULLS, try setting StringIndexer.handleInvalid.\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:213)\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:208)\n\t... 23 more\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)\n\tat scala.Option.foreach(Option.scala:257)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2119)\n\tat org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1026)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:362)\n\tat org.apache.spark.rdd.RDD.reduce(RDD.scala:1008)\n\tat org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1.apply(RDD.scala:1151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:362)\n\tat org.apache.spark.rdd.RDD.treeAggregate(RDD.scala:1128)\n\tat org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:517)\n\tat org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:487)\n\tat org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:278)\n\tat org.apache.spark.ml.Predictor.fit(Predictor.scala:118)\n\tat org.apache.spark.ml.Predictor.fit(Predictor.scala:82)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun$5: (string) => double)\n\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)\n\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)\n\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1038)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:969)\n\tat org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:760)\n\tat org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:285)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:108)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\t... 1 more\nCaused by: org.apache.spark.SparkException: StringIndexer encountered NULL value. To handle or skip NULLS, try setting StringIndexer.handleInvalid.\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:213)\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:208)\n\t... 23 more\n",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mPy4JJavaError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-127-167ee6f0d48c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mgender\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mfeature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStringIndexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputCol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Embarked'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0mfeature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOneHotEncoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m ) | feature.VectorAssembler() | classification.LogisticRegression(labelCol='Survived')).fit(titanic_df)\n\u001b[0m",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pyspark/ml/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, dataset, params)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m raise ValueError(\"Params must be either a param map or a list/tuple of param maps, \"\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pyspark/ml/pipeline.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# must be an Estimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 109\u001b[0m \u001b[0mtransformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mindexOfLastEstimator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pyspark/ml/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, dataset, params)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m raise ValueError(\"Params must be either a param map or a list/tuple of param maps, \"\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pyspark/ml/wrapper.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m \u001b[0mjava_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_java\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 266\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjava_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pyspark/ml/wrapper.py\u001b[0m in \u001b[0;36m_fit_java\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 260\u001b[0m \"\"\"\n\u001b[1;32m 261\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transfer_params_to_java\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_java_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1131\u001b[0m \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1132\u001b[0m return_value = get_return_value(\n\u001b[0;32m-> 1133\u001b[0;31m answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[1;32m 1134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1135\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pyspark/sql/utils.py\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 64\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/py4j/protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[0;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[1;32m 317\u001b[0m raise Py4JJavaError(\n\u001b[1;32m 318\u001b[0m \u001b[0;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m format(target_id, \".\", name), value)\n\u001b[0m\u001b[1;32m 320\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 321\u001b[0m raise Py4JError(\n",
"\u001b[0;31mPy4JJavaError\u001b[0m: An error occurred while calling o2277.fit.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 528.0 failed 1 times, most recent failure: Lost task 0.0 in stage 528.0 (TID 521, localhost, executor driver): org.apache.spark.SparkException: Failed to execute user defined function($anonfun$5: (string) => double)\n\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)\n\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)\n\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1038)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:969)\n\tat org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:760)\n\tat org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:285)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:108)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: org.apache.spark.SparkException: StringIndexer encountered NULL value. To handle or skip NULLS, try setting StringIndexer.handleInvalid.\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:213)\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:208)\n\t... 23 more\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)\n\tat scala.Option.foreach(Option.scala:257)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2119)\n\tat org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1026)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:362)\n\tat org.apache.spark.rdd.RDD.reduce(RDD.scala:1008)\n\tat org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1.apply(RDD.scala:1151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:362)\n\tat org.apache.spark.rdd.RDD.treeAggregate(RDD.scala:1128)\n\tat org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:517)\n\tat org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:487)\n\tat org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:278)\n\tat org.apache.spark.ml.Predictor.fit(Predictor.scala:118)\n\tat org.apache.spark.ml.Predictor.fit(Predictor.scala:82)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun$5: (string) => double)\n\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)\n\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)\n\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1038)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:969)\n\tat org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:760)\n\tat org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:285)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:108)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\t... 1 more\nCaused by: org.apache.spark.SparkException: StringIndexer encountered NULL value. To handle or skip NULLS, try setting StringIndexer.handleInvalid.\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:213)\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:208)\n\t... 23 more\n"
]
}
],
"source": [
"((title, \n",
" gender,\n",
" feature.StringIndexer(inputCol='Embarked') | feature.OneHotEncoder()\n",
") | feature.VectorAssembler() | classification.LogisticRegression(labelCol='Survived')).fit(titanic_df)"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"root\n",
" |-- PassengerId: integer (nullable = true)\n",
" |-- Survived: integer (nullable = true)\n",
" |-- Pclass: integer (nullable = true)\n",
" |-- Name: string (nullable = true)\n",
" |-- Sex: string (nullable = true)\n",
" |-- Age: double (nullable = true)\n",
" |-- SibSp: integer (nullable = true)\n",
" |-- Parch: integer (nullable = true)\n",
" |-- Ticket: string (nullable = true)\n",
" |-- Fare: double (nullable = true)\n",
" |-- Cabin: string (nullable = true)\n",
" |-- Embarked: string (nullable = true)\n",
"\n"
]
}
],
"source": [
"titanic_df.printSchema()"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"ename": "Py4JJavaError",
"evalue": "An error occurred while calling o594.fit.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 65.0 failed 1 times, most recent failure: Lost task 0.0 in stage 65.0 (TID 235, localhost, executor driver): org.apache.spark.SparkException: Failed to execute user defined function($anonfun$5: (string) => double)\n\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)\n\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)\n\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1038)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:969)\n\tat org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:760)\n\tat org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:285)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:108)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: org.apache.spark.SparkException: StringIndexer encountered NULL value. To handle or skip NULLS, try setting StringIndexer.handleInvalid.\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:213)\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:208)\n\t... 23 more\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)\n\tat scala.Option.foreach(Option.scala:257)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2119)\n\tat org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1026)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:362)\n\tat org.apache.spark.rdd.RDD.reduce(RDD.scala:1008)\n\tat org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1.apply(RDD.scala:1151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:362)\n\tat org.apache.spark.rdd.RDD.treeAggregate(RDD.scala:1128)\n\tat org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:517)\n\tat org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:487)\n\tat org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:278)\n\tat org.apache.spark.ml.Predictor.fit(Predictor.scala:118)\n\tat org.apache.spark.ml.Predictor.fit(Predictor.scala:82)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun$5: (string) => double)\n\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)\n\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)\n\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1038)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:969)\n\tat org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:760)\n\tat org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:285)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:108)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\t... 1 more\nCaused by: org.apache.spark.SparkException: StringIndexer encountered NULL value. To handle or skip NULLS, try setting StringIndexer.handleInvalid.\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:213)\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:208)\n\t... 23 more\n",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mPy4JJavaError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-105-c4a701232fcb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpipe_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitanic_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pyspark/ml/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, dataset, params)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m raise ValueError(\"Params must be either a param map or a list/tuple of param maps, \"\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pyspark/ml/pipeline.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 107\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# must be an Estimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 109\u001b[0m \u001b[0mtransformers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mindexOfLastEstimator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pyspark/ml/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, dataset, params)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 65\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 66\u001b[0m raise ValueError(\"Params must be either a param map or a list/tuple of param maps, \"\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pyspark/ml/wrapper.py\u001b[0m in \u001b[0;36m_fit\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 265\u001b[0;31m \u001b[0mjava_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_java\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 266\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjava_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 267\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pyspark/ml/wrapper.py\u001b[0m in \u001b[0;36m_fit_java\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m 260\u001b[0m \"\"\"\n\u001b[1;32m 261\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_transfer_params_to_java\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_java_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 263\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 264\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_fit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1131\u001b[0m \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1132\u001b[0m return_value = get_return_value(\n\u001b[0;32m-> 1133\u001b[0;31m answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[1;32m 1134\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1135\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/pyspark/sql/utils.py\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 61\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mdeco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 64\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 65\u001b[0m \u001b[0ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_exception\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoString\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/anaconda3/lib/python3.5/site-packages/py4j/protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[0;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[1;32m 317\u001b[0m raise Py4JJavaError(\n\u001b[1;32m 318\u001b[0m \u001b[0;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m format(target_id, \".\", name), value)\n\u001b[0m\u001b[1;32m 320\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 321\u001b[0m raise Py4JError(\n",
"\u001b[0;31mPy4JJavaError\u001b[0m: An error occurred while calling o594.fit.\n: org.apache.spark.SparkException: Job aborted due to stage failure: Task 0 in stage 65.0 failed 1 times, most recent failure: Lost task 0.0 in stage 65.0 (TID 235, localhost, executor driver): org.apache.spark.SparkException: Failed to execute user defined function($anonfun$5: (string) => double)\n\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)\n\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)\n\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1038)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:969)\n\tat org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:760)\n\tat org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:285)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:108)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: org.apache.spark.SparkException: StringIndexer encountered NULL value. To handle or skip NULLS, try setting StringIndexer.handleInvalid.\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:213)\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:208)\n\t... 23 more\n\nDriver stacktrace:\n\tat org.apache.spark.scheduler.DAGScheduler.org$apache$spark$scheduler$DAGScheduler$$failJobAndIndependentStages(DAGScheduler.scala:1499)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1487)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$abortStage$1.apply(DAGScheduler.scala:1486)\n\tat scala.collection.mutable.ResizableArray$class.foreach(ResizableArray.scala:59)\n\tat scala.collection.mutable.ArrayBuffer.foreach(ArrayBuffer.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.abortStage(DAGScheduler.scala:1486)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)\n\tat org.apache.spark.scheduler.DAGScheduler$$anonfun$handleTaskSetFailed$1.apply(DAGScheduler.scala:814)\n\tat scala.Option.foreach(Option.scala:257)\n\tat org.apache.spark.scheduler.DAGScheduler.handleTaskSetFailed(DAGScheduler.scala:814)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.doOnReceive(DAGScheduler.scala:1714)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1669)\n\tat org.apache.spark.scheduler.DAGSchedulerEventProcessLoop.onReceive(DAGScheduler.scala:1658)\n\tat org.apache.spark.util.EventLoop$$anon$1.run(EventLoop.scala:48)\n\tat org.apache.spark.scheduler.DAGScheduler.runJob(DAGScheduler.scala:630)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2022)\n\tat org.apache.spark.SparkContext.runJob(SparkContext.scala:2119)\n\tat org.apache.spark.rdd.RDD$$anonfun$reduce$1.apply(RDD.scala:1026)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:362)\n\tat org.apache.spark.rdd.RDD.reduce(RDD.scala:1008)\n\tat org.apache.spark.rdd.RDD$$anonfun$treeAggregate$1.apply(RDD.scala:1151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:151)\n\tat org.apache.spark.rdd.RDDOperationScope$.withScope(RDDOperationScope.scala:112)\n\tat org.apache.spark.rdd.RDD.withScope(RDD.scala:362)\n\tat org.apache.spark.rdd.RDD.treeAggregate(RDD.scala:1128)\n\tat org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:517)\n\tat org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:487)\n\tat org.apache.spark.ml.classification.LogisticRegression.train(LogisticRegression.scala:278)\n\tat org.apache.spark.ml.Predictor.fit(Predictor.scala:118)\n\tat org.apache.spark.ml.Predictor.fit(Predictor.scala:82)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:280)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:214)\n\tat java.lang.Thread.run(Thread.java:745)\nCaused by: org.apache.spark.SparkException: Failed to execute user defined function($anonfun$5: (string) => double)\n\tat org.apache.spark.sql.catalyst.expressions.GeneratedClass$GeneratedIterator.processNext(Unknown Source)\n\tat org.apache.spark.sql.execution.BufferedRowIterator.hasNext(BufferedRowIterator.java:43)\n\tat org.apache.spark.sql.execution.WholeStageCodegenExec$$anonfun$8$$anon$1.hasNext(WholeStageCodegenExec.scala:395)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat scala.collection.Iterator$$anon$11.hasNext(Iterator.scala:408)\n\tat org.apache.spark.storage.memory.MemoryStore.putIteratorAsValues(MemoryStore.scala:215)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1038)\n\tat org.apache.spark.storage.BlockManager$$anonfun$doPutIterator$1.apply(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.doPut(BlockManager.scala:969)\n\tat org.apache.spark.storage.BlockManager.doPutIterator(BlockManager.scala:1029)\n\tat org.apache.spark.storage.BlockManager.getOrElseUpdate(BlockManager.scala:760)\n\tat org.apache.spark.rdd.RDD.getOrCompute(RDD.scala:334)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:285)\n\tat org.apache.spark.rdd.MapPartitionsRDD.compute(MapPartitionsRDD.scala:38)\n\tat org.apache.spark.rdd.RDD.computeOrReadCheckpoint(RDD.scala:323)\n\tat org.apache.spark.rdd.RDD.iterator(RDD.scala:287)\n\tat org.apache.spark.scheduler.ResultTask.runTask(ResultTask.scala:87)\n\tat org.apache.spark.scheduler.Task.run(Task.scala:108)\n\tat org.apache.spark.executor.Executor$TaskRunner.run(Executor.scala:335)\n\tat java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)\n\tat java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)\n\t... 1 more\nCaused by: org.apache.spark.SparkException: StringIndexer encountered NULL value. To handle or skip NULLS, try setting StringIndexer.handleInvalid.\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:213)\n\tat org.apache.spark.ml.feature.StringIndexerModel$$anonfun$5.apply(StringIndexer.scala:208)\n\t... 23 more\n"
]
}
],
"source": [
"pipe_model = pipe.fit(titanic_df)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:anaconda3]",
"language": "python",
"name": "conda-env-anaconda3-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
amirziai/learning | scala/Optional side-effect.ipynb | 1 | 2497 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Optional side-effect"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36mnameOpt\u001b[0m: \u001b[32mOption\u001b[0m[\u001b[32mString\u001b[0m] = \u001b[33mSome\u001b[0m(\u001b[32m\"Amir\"\u001b[0m)\n",
"defined \u001b[32mfunction \u001b[36mprintName\u001b[0m"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val nameOpt = Option(\"Amir\")\n",
"\n",
"def printName(name: String) = println(name)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Amir"
]
},
{
"data": {
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"nameOpt.foreach(printName)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[36manotherOpt\u001b[0m: \u001b[32mNone\u001b[0m.type = None"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"val anotherOpt = None"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": []
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"anotherOpt.foreach(printName)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a great pattern for optinally generating side-effects given an option that you take from the command line, namely in conjunction with [scallop](https://github.com/scallop/scallop)."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Scala 2.11",
"language": "scala211",
"name": "scala211"
},
"language_info": {
"codemirror_mode": "text/x-scala",
"file_extension": ".scala",
"mimetype": "text/x-scala",
"name": "scala211",
"pygments_lexer": "scala",
"version": "2.11.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
gedman4b/MachineLearning | Python/FordAsleep.ipynb | 1 | 14615 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Stay Alert! The Ford Challenge \n",
"\n",
"by Scott Josephson\n",
"\n",
"Driving while distracted, fatigued or drowsy may lead to accidents. Activities that divert the driver's attention from the road ahead, such as engaging in a conversation with other passengers in the car, making or receiving phone calls, sending or receiving text messages, eating while driving or events outside the car may cause driver distraction. Fatigue and drowsiness can result from driving long hours or from lack of sleep.\n",
"\n",
"The data for this Kaggle challenge shows the results of a number of \"trials\", each one representing about 2 minutes of sequential data that are recorded every 100 ms during a driving session on the road or in a driving simulator. The trials are samples from some 100 drivers of both genders, and of different ages and ethnic backgrounds. The files are structured as follows:\n",
"\n",
"\n",
" The first column is the Trial ID - each period of around 2 minutes of sequential data has a unique trial ID. For instance, the first 1210 observations represent sequential observations every 100ms, and therefore all have the same trial ID\n",
" The second column is the observation number - this is a sequentially increasing number within one trial ID \n",
"The third column has a value X for each row where\n",
"\n",
" X = 1 if the driver is alert\n",
"\n",
" X = 0 if the driver is not alert\n",
"\n",
"The next 8 columns with headers P1, P2 , …….., P8 represent physiological data;\n",
"\n",
"The next 11 columns with headers E1, E2, …….., E11 represent environmental data;\n",
"\n",
"The next 11 columns with headers V1, V2, …….., V11 represent vehicular data;\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import accuracy_score, classification_report"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get the Data\n",
"**Read in the fordtrain.csv file and set it to a data frame called ford_train.**"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"** Split the data into training set and testing set using train_test_split**"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"ford_train = pd.read_csv('fordtrain.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Check the head of ad_data**"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: left;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>TrialID</th>\n",
" <th>ObsNum</th>\n",
" <th>IsAlert</th>\n",
" <th>P1</th>\n",
" <th>P2</th>\n",
" <th>P3</th>\n",
" <th>P4</th>\n",
" <th>P5</th>\n",
" <th>P6</th>\n",
" <th>P7</th>\n",
" <th>...</th>\n",
" <th>V2</th>\n",
" <th>V3</th>\n",
" <th>V4</th>\n",
" <th>V5</th>\n",
" <th>V6</th>\n",
" <th>V7</th>\n",
" <th>V8</th>\n",
" <th>V9</th>\n",
" <th>V10</th>\n",
" <th>V11</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>34.7406</td>\n",
" <td>9.84593</td>\n",
" <td>1400</td>\n",
" <td>42.8571</td>\n",
" <td>0.290601</td>\n",
" <td>572</td>\n",
" <td>104.895</td>\n",
" <td>...</td>\n",
" <td>0.175</td>\n",
" <td>752</td>\n",
" <td>5.99375</td>\n",
" <td>0</td>\n",
" <td>2005</td>\n",
" <td>0</td>\n",
" <td>13.4</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>14.8004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>34.4215</td>\n",
" <td>13.41120</td>\n",
" <td>1400</td>\n",
" <td>42.8571</td>\n",
" <td>0.290601</td>\n",
" <td>572</td>\n",
" <td>104.895</td>\n",
" <td>...</td>\n",
" <td>0.455</td>\n",
" <td>752</td>\n",
" <td>5.99375</td>\n",
" <td>0</td>\n",
" <td>2007</td>\n",
" <td>0</td>\n",
" <td>13.4</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>14.7729</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>34.3447</td>\n",
" <td>15.18520</td>\n",
" <td>1400</td>\n",
" <td>42.8571</td>\n",
" <td>0.290601</td>\n",
" <td>576</td>\n",
" <td>104.167</td>\n",
" <td>...</td>\n",
" <td>0.280</td>\n",
" <td>752</td>\n",
" <td>5.99375</td>\n",
" <td>0</td>\n",
" <td>2011</td>\n",
" <td>0</td>\n",
" <td>13.4</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>14.7736</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>34.3421</td>\n",
" <td>8.84696</td>\n",
" <td>1400</td>\n",
" <td>42.8571</td>\n",
" <td>0.290601</td>\n",
" <td>576</td>\n",
" <td>104.167</td>\n",
" <td>...</td>\n",
" <td>0.070</td>\n",
" <td>752</td>\n",
" <td>5.99375</td>\n",
" <td>0</td>\n",
" <td>2015</td>\n",
" <td>0</td>\n",
" <td>13.4</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>14.7667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>34.3322</td>\n",
" <td>14.69940</td>\n",
" <td>1400</td>\n",
" <td>42.8571</td>\n",
" <td>0.290601</td>\n",
" <td>576</td>\n",
" <td>104.167</td>\n",
" <td>...</td>\n",
" <td>0.175</td>\n",
" <td>752</td>\n",
" <td>5.99375</td>\n",
" <td>0</td>\n",
" <td>2017</td>\n",
" <td>0</td>\n",
" <td>13.4</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>14.7757</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 33 columns</p>\n",
"</div>"
],
"text/plain": [
" TrialID ObsNum IsAlert P1 P2 P3 P4 P5 P6 \\\n",
"0 0 0 0 34.7406 9.84593 1400 42.8571 0.290601 572 \n",
"1 0 1 0 34.4215 13.41120 1400 42.8571 0.290601 572 \n",
"2 0 2 0 34.3447 15.18520 1400 42.8571 0.290601 576 \n",
"3 0 3 0 34.3421 8.84696 1400 42.8571 0.290601 576 \n",
"4 0 4 0 34.3322 14.69940 1400 42.8571 0.290601 576 \n",
"\n",
" P7 ... V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 \n",
"0 104.895 ... 0.175 752 5.99375 0 2005 0 13.4 0 4 14.8004 \n",
"1 104.895 ... 0.455 752 5.99375 0 2007 0 13.4 0 4 14.7729 \n",
"2 104.167 ... 0.280 752 5.99375 0 2011 0 13.4 0 4 14.7736 \n",
"3 104.167 ... 0.070 752 5.99375 0 2015 0 13.4 0 4 14.7667 \n",
"4 104.167 ... 0.175 752 5.99375 0 2017 0 13.4 0 4 14.7757 \n",
"\n",
"[5 rows x 33 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ford_train.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 604329 entries, 0 to 604328\n",
"Data columns (total 33 columns):\n",
"TrialID 604329 non-null int64\n",
"ObsNum 604329 non-null int64\n",
"IsAlert 604329 non-null int64\n",
"P1 604329 non-null float64\n",
"P2 604329 non-null float64\n",
"P3 604329 non-null int64\n",
"P4 604329 non-null float64\n",
"P5 604329 non-null float64\n",
"P6 604329 non-null int64\n",
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"V11 604329 non-null float64\n",
"dtypes: float64(14), int64(19)\n",
"memory usage: 152.2 MB\n"
]
}
],
"source": [
"ford_train.info()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistic Regression\n",
"\n",
"Now it's time to do a train test split, and train our model!\n",
"\n",
"Choose columns that you want to train on!"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(ford_train.drop('IsAlert',axis=1),ford_train['IsAlert'],\n",
" test_size=0.30,random_state=101)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Train and fit a logistic regression model on the training set.**"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"logmodel = LogisticRegression()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
" penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
" verbose=0, warm_start=False)"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"logmodel.fit(X_train, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Predictions and Evaluations\n",
"** Now predict values for the testing data.**"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"predictions = logmodel.predict(X_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"** Create a classification report for the model.**"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.82 0.73 0.77 76334\n",
" 1 0.82 0.88 0.85 104965\n",
"\n",
"avg / total 0.82 0.82 0.82 181299\n",
"\n"
]
}
],
"source": [
"print(classification_report(y_test,predictions))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| gpl-3.0 |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_custom_inverse_solver.ipynb | 1 | 8839 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n# Source localization with a custom inverse solver\n\n\nThe objective of this example is to show how to plug a custom inverse solver\nin MNE in order to facilate empirical comparison with the methods MNE already\nimplements (wMNE, dSPM, sLORETA, LCMV, (TF-)MxNE etc.).\n\nThis script is educational and shall be used for methods\nevaluations and new developments. It is not meant to be an example\nof good practice to analyse your data.\n\nThe example makes use of 2 functions ``apply_solver`` and ``solver``\nso changes can be limited to the ``solver`` function (which only takes three\nparameters: the whitened data, the gain matrix, and the number of orientations)\nin order to try out another inverse algorithm.\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"import numpy as np\nfrom scipy import linalg\nimport mne\nfrom mne.datasets import sample\nfrom mne.viz import plot_sparse_source_estimates\n\n\ndata_path = sample.data_path()\nfwd_fname = data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif'\nave_fname = data_path + '/MEG/sample/sample_audvis-ave.fif'\ncov_fname = data_path + '/MEG/sample/sample_audvis-shrunk-cov.fif'\nsubjects_dir = data_path + '/subjects'\ncondition = 'Left Auditory'\n\n# Read noise covariance matrix\nnoise_cov = mne.read_cov(cov_fname)\n# Handling average file\nevoked = mne.read_evokeds(ave_fname, condition=condition, baseline=(None, 0))\nevoked.crop(tmin=0.04, tmax=0.18)\n\nevoked = evoked.pick_types(eeg=False, meg=True)\n# Handling forward solution\nforward = mne.read_forward_solution(fwd_fname)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Auxiliary function to run the solver\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"def apply_solver(solver, evoked, forward, noise_cov, loose=0.2, depth=0.8):\n \"\"\"Function to call a custom solver on evoked data\n\n This function does all the necessary computation:\n\n - to select the channels in the forward given the available ones in\n the data\n - to take into account the noise covariance and do the spatial whitening\n - to apply loose orientation constraint as MNE solvers\n - to apply a weigthing of the columns of the forward operator as in the\n weighted Minimum Norm formulation in order to limit the problem\n of depth bias.\n\n Parameters\n ----------\n solver : callable\n The solver takes 3 parameters: data M, gain matrix G, number of\n dipoles orientations per location (1 or 3). A solver shall return\n 2 variables: X which contains the time series of the active dipoles\n and an active set which is a boolean mask to specify what dipoles are\n present in X.\n evoked : instance of mne.Evoked\n The evoked data\n forward : instance of Forward\n The forward solution.\n noise_cov : instance of Covariance\n The noise covariance.\n loose : float in [0, 1] | 'auto'\n Value that weights the source variances of the dipole components\n that are parallel (tangential) to the cortical surface. If loose\n is 0 then the solution is computed with fixed orientation.\n If loose is 1, it corresponds to free orientations.\n The default value ('auto') is set to 0.2 for surface-oriented source\n space and set to 1.0 for volumic or discrete source space.\n depth : None | float in [0, 1]\n Depth weighting coefficients. If None, no depth weighting is performed.\n\n Returns\n -------\n stc : instance of SourceEstimate\n The source estimates.\n \"\"\"\n # Import the necessary private functions\n from mne.inverse_sparse.mxne_inverse import \\\n (_prepare_gain, _check_loose_forward, is_fixed_orient,\n _reapply_source_weighting, _make_sparse_stc)\n\n all_ch_names = evoked.ch_names\n\n loose, forward = _check_loose_forward(loose, forward)\n\n # put the forward solution in fixed orientation if it's not already\n if loose == 0. and not is_fixed_orient(forward):\n forward = mne.convert_forward_solution(\n forward, surf_ori=True, force_fixed=True, copy=True, use_cps=True)\n\n # Handle depth weighting and whitening (here is no weights)\n gain, gain_info, whitener, source_weighting, mask = _prepare_gain(\n forward, evoked.info, noise_cov, pca=False, depth=depth,\n loose=loose, weights=None, weights_min=None)\n\n # Select channels of interest\n sel = [all_ch_names.index(name) for name in gain_info['ch_names']]\n M = evoked.data[sel]\n\n # Whiten data\n M = np.dot(whitener, M)\n\n n_orient = 1 if is_fixed_orient(forward) else 3\n X, active_set = solver(M, gain, n_orient)\n X = _reapply_source_weighting(X, source_weighting, active_set, n_orient)\n\n stc = _make_sparse_stc(X, active_set, forward, tmin=evoked.times[0],\n tstep=1. / evoked.info['sfreq'])\n\n return stc"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Define your solver\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"def solver(M, G, n_orient):\n \"\"\"Dummy solver\n\n It just runs L2 penalized regression and keep the 10 strongest locations\n\n Parameters\n ----------\n M : array, shape (n_channels, n_times)\n The whitened data.\n G : array, shape (n_channels, n_dipoles)\n The gain matrix a.k.a. the forward operator. The number of locations\n is n_dipoles / n_orient. n_orient will be 1 for a fixed orientation\n constraint or 3 when using a free orientation model.\n n_orient : int\n Can be 1 or 3 depending if one works with fixed or free orientations.\n If n_orient is 3, then ``G[:, 2::3]`` corresponds to the dipoles that\n are normal to the cortex.\n\n Returns\n -------\n X : array, (n_active_dipoles, n_times)\n The time series of the dipoles in the active set.\n active_set : array (n_dipoles)\n Array of bool. Entry j is True if dipole j is in the active set.\n We have ``X_full[active_set] == X`` where X_full is the full X matrix\n such that ``M = G X_full``.\n \"\"\"\n K = linalg.solve(np.dot(G, G.T) + 1e15 * np.eye(G.shape[0]), G).T\n K /= np.linalg.norm(K, axis=1)[:, None]\n X = np.dot(K, M)\n\n indices = np.argsort(np.sum(X ** 2, axis=1))[-10:]\n active_set = np.zeros(G.shape[1], dtype=bool)\n for idx in indices:\n idx -= idx % n_orient\n active_set[idx:idx + n_orient] = True\n X = X[active_set]\n return X, active_set"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"Apply your custom solver\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"# loose, depth = 0.2, 0.8 # corresponds to loose orientation\nloose, depth = 1., 0. # corresponds to free orientation\nstc = apply_solver(solver, evoked, forward, noise_cov, loose, depth)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"View in 2D and 3D (\"glass\" brain like 3D plot)\n\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"execution_count": null,
"cell_type": "code",
"source": [
"plot_sparse_source_estimates(forward['src'], stc, bgcolor=(1, 1, 1),\n opacity=0.1)"
],
"outputs": [],
"metadata": {
"collapsed": false
}
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"name": "python2",
"language": "python"
},
"language_info": {
"mimetype": "text/x-python",
"nbconvert_exporter": "python",
"name": "python",
"file_extension": ".py",
"version": "2.7.14",
"pygments_lexer": "ipython2",
"codemirror_mode": {
"version": 2,
"name": "ipython"
}
}
}
} | bsd-3-clause |
dgleich/CS520-2017 | Lecture-1-Raptor.ipynb | 1 | 1952274 | null | mit |
peastman/deepchem | examples/tutorials/Advanced_model_training_using_hyperopt.ipynb | 2 | 30688 | {
"cells": [
{
"cell_type": "markdown",
"source": [
"#**Advanced model training using hyperopt**\n",
"\n",
"In the Advanced Model Training tutorial we have already taken a look into hyperparameter optimasation using GridHyperparamOpt in the deepchem pacakge. In this tutorial, we will take a look into another hyperparameter tuning library called hyperopt.\n",
"\n",
"## Colab\n",
"\n",
"This tutorial and the rest in this sequence can be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
"\n",
"[](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/Hyperopt_training.ipynb)\n",
"\n",
"## Setup\n",
"\n",
"To run DeepChem and Hyperopt within Colab, you'll need to run the following installation commands. You can of course run this tutorial locally if you prefer. In that case, don't run these cells since they will download and install DeepChem and Hyperopt in your local machine again.\n",
"\n",
"\n"
],
"metadata": {
"id": "VjoSNeI9RUKi"
}
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "c70xCmMITvql",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 730
},
"outputId": "a0332832-4eba-499e-e02d-d7b56bcb17bb"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting deepchem\n",
" Downloading deepchem-2.6.1-py3-none-any.whl (608 kB)\n",
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"\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from deepchem) (1.4.1)\n",
"Collecting numpy>=1.21\n",
" Downloading numpy-1.21.5-cp37-cp37m-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (15.7 MB)\n",
"\u001b[K |████████████████████████████████| 15.7 MB 25.3 MB/s \n",
"\u001b[?25hRequirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from deepchem) (1.0.2)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from deepchem) (1.3.5)\n",
"Collecting rdkit-pypi\n",
" Downloading rdkit_pypi-2021.9.4-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (20.6 MB)\n",
"\u001b[K |████████████████████████████████| 20.6 MB 1.4 MB/s \n",
"\u001b[?25hRequirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from deepchem) (1.1.0)\n",
"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->deepchem) (2018.9)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->deepchem) (2.8.2)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->deepchem) (1.15.0)\n",
"Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from rdkit-pypi->deepchem) (7.1.2)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->deepchem) (3.1.0)\n",
"Installing collected packages: numpy, rdkit-pypi, deepchem\n",
" Attempting uninstall: numpy\n",
" Found existing installation: numpy 1.19.5\n",
" Uninstalling numpy-1.19.5:\n",
" Successfully uninstalled numpy-1.19.5\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
"yellowbrick 1.3.post1 requires numpy<1.20,>=1.16.0, but you have numpy 1.21.5 which is incompatible.\n",
"datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
"albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
"Successfully installed deepchem-2.6.1 numpy-1.21.5 rdkit-pypi-2021.9.4\n"
]
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"numpy"
]
}
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: hyperopt in /usr/local/lib/python3.7/dist-packages (0.1.2)\n",
"Requirement already satisfied: networkx in /usr/local/lib/python3.7/dist-packages (from hyperopt) (2.6.3)\n",
"Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from hyperopt) (0.16.0)\n",
"Requirement already satisfied: pymongo in /usr/local/lib/python3.7/dist-packages (from hyperopt) (4.0.1)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from hyperopt) (1.4.1)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from hyperopt) (1.21.5)\n",
"Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from hyperopt) (4.62.3)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from hyperopt) (1.15.0)\n"
]
}
],
"source": [
"!pip install deepchem\n",
"!pip install hyperopt"
]
},
{
"cell_type": "markdown",
"source": [
"## Hyperparameter Optimization via hyperopt\n",
"\n",
"Let's start by loading the HIV dataset. It classifies over 40,000 molecules based on whether they inhibit HIV replication.\n",
"\n"
],
"metadata": {
"id": "ZRpJ-BvHRTIE"
}
},
{
"cell_type": "code",
"source": [
"import deepchem as dc\n",
"tasks, datasets, transformers = dc.molnet.load_hiv(featurizer='ECFP', split='scaffold')\n",
"train_dataset, valid_dataset, test_dataset = datasets\n"
],
"metadata": {
"id": "e0aDZ3aY6dkk",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "22e75f47-5d63-455c-b4da-47b420f204f9"
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"'split' is deprecated. Use 'splitter' instead.\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"Now, lets import the hyperopt library, which we will be using to fund the best parameters"
],
"metadata": {
"id": "vnr75060q1j9"
}
},
{
"cell_type": "code",
"source": [
"from hyperopt import hp, fmin, tpe, Trials"
],
"metadata": {
"id": "xM7G0LS1q1V_"
},
"execution_count": 3,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Then we have to declare a dictionary with all the hyperparameters and their range that you will be tuning them in. This dictionary will serve as the search space for the hyperopt. \n",
"Some basic ways of declaring the ranges in the dictionary are:\n",
"\n",
"\n",
"\n",
"* hp.choice('label',[*choices*]) : this is used to specify a list of choices\n",
"* hp.uniform('label' ,low=*low_value* ,high=*high_value*) : this is used to specify a uniform distibution between the low and high values. The values between them can be any real number, not necessaarily an integer.\n",
"\n",
"Here, we are going to use a multitaskclassifier to classify the HIV dataset and hence the appropriate search space is as follows.\n",
"\n",
"\n",
"\n",
"\n"
],
"metadata": {
"id": "ztSRJo3krDUm"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "vGeRn7oWUWo_"
},
"outputs": [],
"source": [
"search_space = {\n",
" 'layer_sizes': hp.choice('layer_sizes',[[500], [1000], [2000],[1000,1000]]),\n",
" 'dropouts': hp.uniform('dropout',low=0.2, high=0.5),\n",
" 'learning_rate': hp.uniform('learning_rate',high=0.001, low=0.0001)\n",
"}"
]
},
{
"cell_type": "markdown",
"source": [
"We should then declare a function to be minimized by the hyperopt. So, here we should use the function to minimize our multitaskclassifier model. Additionally, we are using a validation callback to validate the classifier for every 1000 steps, then we are passing the best score as the return. The metric used here is 'roc_auc_score', which needs to be maximized. To maximize a non-negative value is equivalent to minimize its opposite number, hence we are returning the negative of the validation score.\n",
"\n",
"\n"
],
"metadata": {
"id": "HJzohvgFs70N"
}
},
{
"cell_type": "code",
"source": [
"import tempfile\n",
"#tempfile is used to save the best checkpoint later in the program.\n",
"\n",
"metric = dc.metrics.Metric(dc.metrics.roc_auc_score)\n",
"\n",
"def fm(args):\n",
" save_dir = tempfile.mkdtemp()\n",
" model = dc.models.MultitaskClassifier(n_tasks=len(tasks),n_features=1024,layer_sizes=args['layer_sizes'],dropouts=args['dropouts'],learning_rate=args['learning_rate'])\n",
" #validation callback that saves the best checkpoint, i.e the one with the maximum score.\n",
" validation=dc.models.ValidationCallback(valid_dataset, 1000, [metric],save_dir=save_dir,transformers=transformers,save_on_minimum=False)\n",
" \n",
" model.fit(train_dataset, nb_epoch=25,callbacks=validation)\n",
"\n",
" #restoring the best checkpoint and passing the negative of its validation score to be minimized.\n",
" model.restore(model_dir=save_dir)\n",
" valid_score = model.evaluate(valid_dataset, [metric], transformers)\n",
"\n",
" return -1*valid_score['roc_auc_score']"
],
"metadata": {
"id": "iEFj6HuetG1P"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Here, we are calling the fmin function of the hyperopt, where we pass on the function to be minimized, the algorithm to be followed, max number of evals and a trials object. The Trials object is used to keep All hyperparameters, loss, and other information, this means you can access them after running optimization. Also, trials can help you to save important information and later load and then resume the optimization process.\n",
"\n",
"Moreover, for the algorithm there are three choice which can be used without any additional configuration. they are :- \n",
"\n",
"\n",
"* Random Search - rand.suggest\n",
"* TPE (Tree Parzen Estimators) - tpe.suggest\n",
"* Adaptive TPE - atpe.suggest\n",
"\n",
"\n",
"\n",
"\n"
],
"metadata": {
"id": "McC5wuJax6IR"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "e1i3yc7ECWVq",
"outputId": "15c74c9e-8a15-49eb-bf97-a20cc9523043"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\r 0%| | 0/15 [00:00<?, ?it/s, best loss: ?]Step 1000 validation: roc_auc_score=0.777648\n",
"Step 2000 validation: roc_auc_score=0.755485\n",
"Step 3000 validation: roc_auc_score=0.739519\n",
"Step 4000 validation: roc_auc_score=0.764756\n",
"Step 5000 validation: roc_auc_score=0.757006\n",
"Step 6000 validation: roc_auc_score=0.752609\n",
"Step 7000 validation: roc_auc_score=0.763002\n",
"Step 8000 validation: roc_auc_score=0.749202\n",
" 7%|▋ | 1/15 [05:37<1:18:46, 337.58s/it, best loss: -0.7776476459925534]Step 1000 validation: roc_auc_score=0.750455\n",
"Step 2000 validation: roc_auc_score=0.783594\n",
"Step 3000 validation: roc_auc_score=0.775872\n",
"Step 4000 validation: roc_auc_score=0.768825\n",
"Step 5000 validation: roc_auc_score=0.769555\n",
"Step 6000 validation: roc_auc_score=0.765324\n",
"Step 7000 validation: roc_auc_score=0.771146\n",
"Step 8000 validation: roc_auc_score=0.760138\n",
" 13%|█▎ | 2/15 [07:05<41:16, 190.51s/it, best loss: -0.7835939030962179] Step 1000 validation: roc_auc_score=0.744178\n",
"Step 2000 validation: roc_auc_score=0.765406\n",
"Step 3000 validation: roc_auc_score=0.76532\n",
"Step 4000 validation: roc_auc_score=0.769255\n",
"Step 5000 validation: roc_auc_score=0.77029\n",
"Step 6000 validation: roc_auc_score=0.768024\n",
"Step 7000 validation: roc_auc_score=0.764157\n",
"Step 8000 validation: roc_auc_score=0.756805\n",
" 20%|██ | 3/15 [09:40<34:53, 174.42s/it, best loss: -0.7835939030962179]Step 1000 validation: roc_auc_score=0.714572\n",
"Step 2000 validation: roc_auc_score=0.770712\n",
"Step 3000 validation: roc_auc_score=0.777914\n",
"Step 4000 validation: roc_auc_score=0.76923\n",
"Step 5000 validation: roc_auc_score=0.774823\n",
"Step 6000 validation: roc_auc_score=0.775927\n",
"Step 7000 validation: roc_auc_score=0.777054\n",
"Step 8000 validation: roc_auc_score=0.778508\n",
" 27%|██▋ | 4/15 [12:12<30:22, 165.66s/it, best loss: -0.7835939030962179]Step 1000 validation: roc_auc_score=0.743939\n",
"Step 2000 validation: roc_auc_score=0.759478\n",
"Step 3000 validation: roc_auc_score=0.738839\n",
"Step 4000 validation: roc_auc_score=0.751084\n",
"Step 5000 validation: roc_auc_score=0.740504\n",
"Step 6000 validation: roc_auc_score=0.753612\n",
"Step 7000 validation: roc_auc_score=0.71802\n",
"Step 8000 validation: roc_auc_score=0.761025\n",
" 33%|███▎ | 5/15 [17:40<37:21, 224.16s/it, best loss: -0.7835939030962179]Step 1000 validation: roc_auc_score=0.74099\n",
"Step 2000 validation: roc_auc_score=0.767516\n",
"Step 3000 validation: roc_auc_score=0.767338\n",
"Step 4000 validation: roc_auc_score=0.775691\n",
"Step 5000 validation: roc_auc_score=0.768731\n",
"Step 6000 validation: roc_auc_score=0.755029\n",
"Step 7000 validation: roc_auc_score=0.767115\n",
"Step 8000 validation: roc_auc_score=0.764744\n",
" 40%|████ | 6/15 [22:48<37:54, 252.71s/it, best loss: -0.7835939030962179]Step 1000 validation: roc_auc_score=0.713761\n",
"Step 2000 validation: roc_auc_score=0.759518\n",
"Step 3000 validation: roc_auc_score=0.765853\n",
"Step 4000 validation: roc_auc_score=0.771976\n",
"Step 5000 validation: roc_auc_score=0.772762\n",
"Step 6000 validation: roc_auc_score=0.773206\n",
"Step 7000 validation: roc_auc_score=0.775565\n",
"Step 8000 validation: roc_auc_score=0.768521\n",
" 47%|████▋ | 7/15 [27:53<35:58, 269.84s/it, best loss: -0.7835939030962179]Step 1000 validation: roc_auc_score=0.717178\n",
"Step 2000 validation: roc_auc_score=0.754258\n",
"Step 3000 validation: roc_auc_score=0.767905\n",
"Step 4000 validation: roc_auc_score=0.762917\n",
"Step 5000 validation: roc_auc_score=0.766162\n",
"Step 6000 validation: roc_auc_score=0.767581\n",
"Step 7000 validation: roc_auc_score=0.770746\n",
"Step 8000 validation: roc_auc_score=0.77597\n",
" 53%|█████▎ | 8/15 [30:36<27:29, 235.64s/it, best loss: -0.7835939030962179]Step 1000 validation: roc_auc_score=0.74314\n",
"Step 2000 validation: roc_auc_score=0.757408\n",
"Step 3000 validation: roc_auc_score=0.76668\n",
"Step 4000 validation: roc_auc_score=0.768104\n",
"Step 5000 validation: roc_auc_score=0.746377\n",
"Step 6000 validation: roc_auc_score=0.745282\n",
"Step 7000 validation: roc_auc_score=0.74113\n",
"Step 8000 validation: roc_auc_score=0.734482\n",
" 60%|██████ | 9/15 [36:53<28:00, 280.04s/it, best loss: -0.7835939030962179]Step 1000 validation: roc_auc_score=0.743204\n",
"Step 2000 validation: roc_auc_score=0.76912\n",
"Step 3000 validation: roc_auc_score=0.769981\n",
"Step 4000 validation: roc_auc_score=0.784163\n",
"Step 5000 validation: roc_auc_score=0.77536\n",
"Step 6000 validation: roc_auc_score=0.779237\n",
"Step 7000 validation: roc_auc_score=0.782344\n",
"Step 8000 validation: roc_auc_score=0.779085\n",
" 67%|██████▋ | 10/15 [38:23<18:26, 221.33s/it, best loss: -0.7841634210268469]Step 1000 validation: roc_auc_score=0.743565\n",
"Step 2000 validation: roc_auc_score=0.765063\n",
"Step 3000 validation: roc_auc_score=0.75284\n",
"Step 4000 validation: roc_auc_score=0.759978\n",
"Step 5000 validation: roc_auc_score=0.74255\n",
"Step 6000 validation: roc_auc_score=0.721809\n",
"Step 7000 validation: roc_auc_score=0.729863\n",
"Step 8000 validation: roc_auc_score=0.73075\n",
" 73%|███████▎ | 11/15 [44:07<17:15, 258.91s/it, best loss: -0.7841634210268469]Step 1000 validation: roc_auc_score=0.695949\n",
"Step 2000 validation: roc_auc_score=0.765082\n",
"Step 3000 validation: roc_auc_score=0.756256\n",
"Step 4000 validation: roc_auc_score=0.771923\n",
"Step 5000 validation: roc_auc_score=0.758841\n",
"Step 6000 validation: roc_auc_score=0.759393\n",
"Step 7000 validation: roc_auc_score=0.765971\n",
"Step 8000 validation: roc_auc_score=0.747064\n",
" 80%|████████ | 12/15 [48:54<13:21, 267.23s/it, best loss: -0.7841634210268469]Step 1000 validation: roc_auc_score=0.757871\n",
"Step 2000 validation: roc_auc_score=0.765296\n",
"Step 3000 validation: roc_auc_score=0.769748\n",
"Step 4000 validation: roc_auc_score=0.776487\n",
"Step 5000 validation: roc_auc_score=0.775009\n",
"Step 6000 validation: roc_auc_score=0.779539\n",
"Step 7000 validation: roc_auc_score=0.763165\n",
"Step 8000 validation: roc_auc_score=0.772093\n",
" 87%|████████▋ | 13/15 [50:22<07:06, 213.15s/it, best loss: -0.7841634210268469]Step 1000 validation: roc_auc_score=0.720166\n",
"Step 2000 validation: roc_auc_score=0.768489\n",
"Step 3000 validation: roc_auc_score=0.782853\n",
"Step 4000 validation: roc_auc_score=0.785556\n",
"Step 5000 validation: roc_auc_score=0.78583\n",
"Step 6000 validation: roc_auc_score=0.786569\n",
"Step 7000 validation: roc_auc_score=0.779249\n",
"Step 8000 validation: roc_auc_score=0.783423\n",
" 93%|█████████▎| 14/15 [51:52<02:55, 175.93s/it, best loss: -0.7865693280913189]Step 1000 validation: roc_auc_score=0.743232\n",
"Step 2000 validation: roc_auc_score=0.762007\n",
"Step 3000 validation: roc_auc_score=0.771809\n",
"Step 4000 validation: roc_auc_score=0.755023\n",
"Step 5000 validation: roc_auc_score=0.769812\n",
"Step 6000 validation: roc_auc_score=0.769867\n",
"Step 7000 validation: roc_auc_score=0.777354\n",
"Step 8000 validation: roc_auc_score=0.775313\n",
"100%|██████████| 15/15 [56:47<00:00, 227.13s/it, best loss: -0.7865693280913189]\n"
]
}
],
"source": [
"trials=Trials()\n",
"best = fmin(fm,\n",
" \t\tspace= search_space,\n",
" \t\talgo=tpe.suggest,\n",
" \t\tmax_evals=15,\n",
" \t\ttrials = trials)\n"
]
},
{
"cell_type": "markdown",
"source": [
"The code below is used to print the best hyperparameters found by the hyperopt."
],
"metadata": {
"id": "5I9CzaylyjUw"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aPeJaH89GD67",
"outputId": "1e87f5a0-edbb-45ce-b407-66e7e37059dc"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Best: {'dropout': 0.3749846096922802, 'layer_sizes': 0, 'learning_rate': 0.0007544819475363869}\n"
]
}
],
"source": [
"print(\"Best: {}\".format(best))\n"
]
},
{
"cell_type": "markdown",
"source": [
"The hyperparameter found here may not be necessarily the best one, but gives a general idea on which parameters are effective. To get mroe accurate results, one has to increase the number of validation epochs and the epochs the model fit. But doing so may increase the time in finding the best hyperparameters."
],
"metadata": {
"id": "VD1ET6q6_naf"
}
},
{
"cell_type": "markdown",
"source": [
"# Congratulations! Time to join the Community!\n",
"\n",
"Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n",
"\n",
"## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
"This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n",
"\n",
"## Join the DeepChem Gitter\n",
"The DeepChem [Gitter](https://gitter.im/deepchem/Lobby) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!"
],
"metadata": {
"id": "Ng2npVmkyvpQ"
}
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "Advanced model training using hyperopt.ipynb",
"provenance": []
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
},
"language_info": {
"name": "python"
}
},
"nbformat": 4,
"nbformat_minor": 0
} | mit |
rishuatgithub/MLPy | torch/PYTORCH_NOTEBOOKS/00-Crash-Course-Topics/01-Crash-Course-Pandas/04-Groupby.ipynb | 1 | 22786 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"___\n",
"\n",
"<a href='http://www.pieriandata.com'><img src='../Pierian_Data_Logo.png'/></a>\n",
"___\n",
"<center><em>Copyright Pierian Data</em></center>\n",
"<center><em>For more information, visit us at <a href='http://www.pieriandata.com'>www.pieriandata.com</a></em></center>"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"# Groupby\n",
"\n",
"The groupby method allows you to group rows of data together and call aggregate functions"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"# Create dataframe\n",
"data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],\n",
" 'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],\n",
" 'Sales':[200,120,340,124,243,350]}"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(data)"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Company</th>\n",
" <th>Person</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>GOOG</td>\n",
" <td>Sam</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>GOOG</td>\n",
" <td>Charlie</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>MSFT</td>\n",
" <td>Amy</td>\n",
" <td>340</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>MSFT</td>\n",
" <td>Vanessa</td>\n",
" <td>124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>FB</td>\n",
" <td>Carl</td>\n",
" <td>243</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>FB</td>\n",
" <td>Sarah</td>\n",
" <td>350</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Company Person Sales\n",
"0 GOOG Sam 200\n",
"1 GOOG Charlie 120\n",
"2 MSFT Amy 340\n",
"3 MSFT Vanessa 124\n",
"4 FB Carl 243\n",
"5 FB Sarah 350"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<strong>Now you can use the .groupby() method to group rows together based off of a column name.<br>For instance let's group based off of Company. This will create a DataFrameGroupBy object:</strong>"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<pandas.core.groupby.DataFrameGroupBy object at 0x113014128>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('Company')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can save this object as a new variable:"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"by_comp = df.groupby(\"Company\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And then call aggregate methods off the object:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Company</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>FB</th>\n",
" <td>296.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GOOG</th>\n",
" <td>160.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSFT</th>\n",
" <td>232.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sales\n",
"Company \n",
"FB 296.5\n",
"GOOG 160.0\n",
"MSFT 232.0"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_comp.mean()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Company</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>FB</th>\n",
" <td>296.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GOOG</th>\n",
" <td>160.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSFT</th>\n",
" <td>232.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sales\n",
"Company \n",
"FB 296.5\n",
"GOOG 160.0\n",
"MSFT 232.0"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby('Company').mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"More examples of aggregate methods:"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Company</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>FB</th>\n",
" <td>75.660426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GOOG</th>\n",
" <td>56.568542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSFT</th>\n",
" <td>152.735065</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sales\n",
"Company \n",
"FB 75.660426\n",
"GOOG 56.568542\n",
"MSFT 152.735065"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_comp.std()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Person</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Company</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>FB</th>\n",
" <td>Carl</td>\n",
" <td>243</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GOOG</th>\n",
" <td>Charlie</td>\n",
" <td>120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSFT</th>\n",
" <td>Amy</td>\n",
" <td>124</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Person Sales\n",
"Company \n",
"FB Carl 243\n",
"GOOG Charlie 120\n",
"MSFT Amy 124"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_comp.min()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Person</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Company</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>FB</th>\n",
" <td>Sarah</td>\n",
" <td>350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GOOG</th>\n",
" <td>Sam</td>\n",
" <td>200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSFT</th>\n",
" <td>Vanessa</td>\n",
" <td>340</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Person Sales\n",
"Company \n",
"FB Sarah 350\n",
"GOOG Sam 200\n",
"MSFT Vanessa 340"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_comp.max()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Person</th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Company</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>FB</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>GOOG</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>MSFT</th>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Person Sales\n",
"Company \n",
"FB 2 2\n",
"GOOG 2 2\n",
"MSFT 2 2"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_comp.count()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Company</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th rowspan=\"8\" valign=\"top\">FB</th>\n",
" <th>count</th>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>296.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>75.660426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>243.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>269.750000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>296.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>323.250000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>350.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"8\" valign=\"top\">GOOG</th>\n",
" <th>count</th>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>160.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>56.568542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>120.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>140.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>160.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>180.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>200.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"8\" valign=\"top\">MSFT</th>\n",
" <th>count</th>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>232.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>152.735065</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>124.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>178.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>232.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>286.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>340.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sales\n",
"Company \n",
"FB count 2.000000\n",
" mean 296.500000\n",
" std 75.660426\n",
" min 243.000000\n",
" 25% 269.750000\n",
" 50% 296.500000\n",
" 75% 323.250000\n",
" max 350.000000\n",
"GOOG count 2.000000\n",
" mean 160.000000\n",
" std 56.568542\n",
" min 120.000000\n",
" 25% 140.000000\n",
" 50% 160.000000\n",
" 75% 180.000000\n",
" max 200.000000\n",
"MSFT count 2.000000\n",
" mean 232.000000\n",
" std 152.735065\n",
" min 124.000000\n",
" 25% 178.000000\n",
" 50% 232.000000\n",
" 75% 286.000000\n",
" max 340.000000"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_comp.describe()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th>Company</th>\n",
" <th colspan=\"8\" halign=\"left\">FB</th>\n",
" <th colspan=\"5\" halign=\"left\">GOOG</th>\n",
" <th colspan=\"8\" halign=\"left\">MSFT</th>\n",
" </tr>\n",
" <tr>\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>...</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Sales</th>\n",
" <td>2.0</td>\n",
" <td>296.5</td>\n",
" <td>75.660426</td>\n",
" <td>243.0</td>\n",
" <td>269.75</td>\n",
" <td>296.5</td>\n",
" <td>323.25</td>\n",
" <td>350.0</td>\n",
" <td>2.0</td>\n",
" <td>160.0</td>\n",
" <td>...</td>\n",
" <td>180.0</td>\n",
" <td>200.0</td>\n",
" <td>2.0</td>\n",
" <td>232.0</td>\n",
" <td>152.735065</td>\n",
" <td>124.0</td>\n",
" <td>178.0</td>\n",
" <td>232.0</td>\n",
" <td>286.0</td>\n",
" <td>340.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1 rows × 24 columns</p>\n",
"</div>"
],
"text/plain": [
"Company FB GOOG \\\n",
" count mean std min 25% 50% 75% max count \n",
"Sales 2.0 296.5 75.660426 243.0 269.75 296.5 323.25 350.0 2.0 \n",
"\n",
"Company ... MSFT \\\n",
" mean ... 75% max count mean std min 25% \n",
"Sales 160.0 ... 180.0 200.0 2.0 232.0 152.735065 124.0 178.0 \n",
"\n",
"Company \n",
" 50% 75% max \n",
"Sales 232.0 286.0 340.0 \n",
"\n",
"[1 rows x 24 columns]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_comp.describe().transpose()"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>count</th>\n",
" <th>mean</th>\n",
" <th>std</th>\n",
" <th>min</th>\n",
" <th>25%</th>\n",
" <th>50%</th>\n",
" <th>75%</th>\n",
" <th>max</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Sales</th>\n",
" <td>2.0</td>\n",
" <td>160.0</td>\n",
" <td>56.568542</td>\n",
" <td>120.0</td>\n",
" <td>140.0</td>\n",
" <td>160.0</td>\n",
" <td>180.0</td>\n",
" <td>200.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" count mean std min 25% 50% 75% max\n",
"Sales 2.0 160.0 56.568542 120.0 140.0 160.0 180.0 200.0"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"by_comp.describe().transpose()['GOOG']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Great Job!"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}
| apache-2.0 |
PhilHarnish/forge | src/puzzle/examples/puzzle_boat/4_warmup/petruchio.ipynb | 1 | 6566 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Petruchio\n",
"Petruchio used JDate, but needed to convert first.\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING\n",
"Max fringe size was: 2619\n"
]
}
],
"source": [
"import forge\n",
"from puzzle.puzzlepedia import puzzlepedia\n",
"\n",
"puzzle = puzzlepedia.parse(\"\"\"\n",
"LURCH (Tree in the shape of a bend after July 3rd (5) [2003])\n",
"Acetal chloride is somewhat a soft substance (4)\n",
"ASIA (Endless oasis and a continent (4))\n",
"Wrecked a trailer of some vessels (8)\n",
"Military group’s keen without Europe (4)\n",
"Easy, confused smile around April 2nd (6) [1995]\n",
"Incomplete recollection of Atlanta university (5)\n",
"Sends unwanted messages to 31-Across covering essence of rumor (5)\n",
"Short trip for club after January 5th (5) [1922]\n",
"ASPIRE (Hope for bad praise (6))\n",
"Son holding edge of easily adorned item (4-2)\n",
"Bite upset bridal gown designer Vera (4)\n",
"Abundant part of trifecta (4)\n",
"Maestro done traveling around end of July (5) [2011]\n",
"Indian city’s 50% concerned with food production (4)\n",
"Small step in healthy places (4)\n",
"Planters peanuts finally remade worse (6)\n",
"In a spooky way, Riley goes mad after November 4th (6) [2008]\n",
"FRETS (Worries about parts of a guitar (5))\n",
"Mix up alcohol in large cups? Just the opposite (5)\n",
"Small O trimmed (5)\n",
"SIERRA (In part, tarsier rappelling mountain range (6))\n",
"Possible lunch option: piece of food for dipping (around February 5th) (4) [1992]\n",
"Literary movement about...um... fruits (8)\n",
"Relative adopting Republican guys (4)\n",
"Grape juice…mmmmmmm… before October 3rd (4) [1989]\n",
"Andrew’s announced company that makes mints (5)\n",
"\"\"\", hint=\"cryptic\")\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import forge\n",
"from puzzle.puzzlepedia import puzzlepedia\n",
"\n",
"puzzle = puzzlepedia.parse(\"\"\"\n",
"Quote from Cymbeline (4)\n",
"A place to remember...a getaway hole (5)\n",
"Tangled roots around glider tail and helicopter parts (6)\n",
"Alberts’ swamped with duties and religious performances (8)\n",
"Zorro’s mark in grass is unclear (4)\n",
"Secret meeting to hear saint (5)\n",
"Over operation after a time (4)\n",
"Red swindler covering edges (7)\n",
"Goals: sloth story (4)\n",
"Throw around a soft young tree (7)\n",
"Take cap off medicine for diseases (4)\n",
"A key for getting rid of singer (5)\n",
"Fumbler’s covered in pelts (4)\n",
"Sounds of disapproval about young lads (5)\n",
"An antelope’s recorded over (4)\n",
"Poison that doesn’t include copper is unusual (4)\n",
"Bit of pastry in tart in tube (4)\n",
"Small flower’s piously odd (4)\n",
"\"\"\", hint=\"cryptic\")\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import forge\n",
"from puzzle.puzzlepedia import puzzlepedia\n",
"\n",
"puzzle = puzzlepedia.parse(\"\"\"\n",
"Soaring wildly around regular group of soldiers (8)\n",
"Shakes with silver and straws primarily (4)\n",
"Joker’s way off and dog’s eating last of bone (7)\n",
"Instrument contains ancient sluggard (7)\n",
"Back a motion for a short amount of time (6)\n",
"Lets up, stops, doesn’t start (5)\n",
"Introduction of Tim Meadows coming first...that’s most low (5)\n",
"Agreement over damaged cruet (5)\n",
"Hiccups in format identify leaders in sound quality (2-2)\n",
"Some studies of exploding star (4)\n",
"Neckwear worn to relax in discussion (4)\n",
"Parent at a museum in New York (4)\n",
"Work with odd, unconverted 44-Across (4)\n",
"\"\"\", hint=\"cryptic\")\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import forge\n",
"from puzzle.puzzlepedia import puzzlepedia\n",
"\n",
"# In original order:\n",
"puzzle = puzzlepedia.parse(\"\"\"\n",
"LARCH\n",
"SIMPLE\n",
"FORAY\n",
"DOYEN\n",
"EERILY\n",
"SOUP\n",
"MUST\n",
"\"\"\", hint=\"acrostic\")\n",
"\n",
"# cladist, heroism, lioness."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import forge\n",
"from puzzle.puzzlepedia import puzzlepedia\n",
"\n",
"# In chronological order:\n",
"puzzle = puzzlepedia.parse(\"\"\"\n",
"FORAY\n",
"MUST\n",
"SOUP\n",
"SIMPLE\n",
"LARCH\n",
"EERILY\n",
"DOYEN\n",
"\"\"\", hint=\"acrostic\")\n",
"\n",
"# aspirin.\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'[foray][must][soup][simple][larch][eerily][doyen]'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# As regex, chronological:\n",
"'[%s]' % ']['.join(\"\"\"\n",
"FORAY\n",
"MUST\n",
"SOUP\n",
"SIMPLE\n",
"LARCH\n",
"EERILY\n",
"DOYEN\n",
"\"\"\".strip().lower().split('\\n'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.0"
},
"widgets": {
"state": {
"9dee8fcb1629441b866c65d5d092e482": {
"views": [
{
"cell_index": 5
}
]
}
},
"version": "1.2.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
reata/MachineLearning | k-Means Clustering Algorithm.ipynb | 1 | 3360 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# $k$ 均值聚类 k-Means Clustering Algorithm\n",
"\n",
"在聚类问题中，给定训练集 $\\{x^{(1)},\\cdots,x^{(m)}\\}$，算法试图将数据集分为若干组紧密结合的簇。这里，$x^{(i)} \\in \\mathbb{R}^n$，但数据不包含标签 $y^{(i)}$。所以聚类是一个非监督学习问题。\n",
"\n",
"$k$ 均值聚类的算法过程如下：\n",
"1. 随机初始化**聚类质心 cluster centroids** $\\mu_1,\\mu_2,\\cdots,\\mu_k \\in \\mathbb{R}^n$\n",
"2. 重复以下步骤直至收敛：1）对每一个 $i$，令 $c^{(i)}=arg\\min_j||x^{(i)}-\\mu_j||^2$；2）对每一个 $j$，令 $\\mu_j=\\frac{\\sum_{i=1}^m 1\\{c^{(i)}=j\\}x^{(i)}}{\\sum_{i=1}^m 1\\{c^{(i)}=j\\}}$\n",
"\n",
"在上面的算法中，$k$ 作为算法的一个参数，代表我们所需要找到的聚类数量；聚类质心 $\\mu_j$ 代表当时算法对每一个聚类中心点位置的猜测；在算法的第一步初始化聚类质心时，可以随机选择 $k$ 个训练样本作为各自聚类的质心。（也可以选用其它随机方法）\n",
"\n",
"算法最内层的循环包含两步：1）将训练样本 $x^{(i)}$ 分配到距离它最近的质心 $\\mu_j$；2）将质心 $\\mu_j$ 重新设置为分配到该聚类的所有点的均值位置。\n",
"\n",
"$k$ 均值聚类算法能否保证收敛呢？从某种意义上是可以的。定义**畸变函数distortion function**\n",
"$$ J(c,\\mu)=\\sum_{i=1}^m ||x^{(i)}-\\mu_{c^{(i)}}||^2 $$\n",
"这里 $J$ 衡量的是所有训练样本 $x^{(i)}$ 距其聚类质心 $\\mu_{c^{(i)}}$ 距离的平方和。可以证实，k均值聚类就是针对 $J$ 的坐标下降法。具体来说，算法最里层的循环，重复地先锁定 $\\mu$ 不变，针对 $c$ 来最小化 $J$，之后再锁定 $c$ 不变，针对 $\\mu$ 来最小化 $J$。所以整个过程中，$J$ 是单调下降的，$J$ 的值肯定会收敛。（通常来说，这意味着 $c, \\mu$ 也会收敛。理论上，k均值聚类可能会在少数不同的聚类结果之间震荡，即不同的 $c,\\mu$ 值，但 $J$ 的值相同。但实际中，这种情况几乎不会从不出现）\n",
"\n",
"畸变函数 $J$ 是一个非凸函数，所以针对 $J$ 的坐标下降法并不能保证收敛于全局最小值，也就是说，$k$ 均值聚类易受到局部最小值问题的影响。但实际中 $k$ 均值聚类会给出非常好的聚类结果。如果实在担心收敛于比较糟糕的局部最小值，一个常见的策略是多执行几次 $k$ 均值聚类过程，每次使用不同的随机初始值，然后在所有的聚类结果中，挑选 $J(c,\\mu)$ 最小的结果。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
| mit |
wmorning/EvilLens | examples/CompositeLensTest.ipynb | 1 | 93763 | {
"metadata": {
"name": "",
"signature": "sha256:9469df8496ebf17bf588ad5d5217b67437734f29def2be0d7b78e63ecb45ee23"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Composite image test. Here we will produce a composite lens as a sum of three lenses with the same velocity dispersions and different centroids.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import evillens as evil\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lens1 = evil.AnalyticSIELens(0.4,1.5)\n",
"lens2 = evil.AnalyticSIELens(0.4,1.5)\n",
"lens3 = evil.AnalyticSIELens(0.4,1.5)\n",
"\n",
"lens1.build_kappa_map(sigma=200.0, r_c=0.1)\n",
"lens2.build_kappa_map(sigma=170.0, q = 0.45, centroid=[-1.51,0.51])\n",
"lens3.build_kappa_map(sigma =100.0, centroid=[1.01,1.01])\n",
"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 38
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lens4 = lens1+lens2\n",
"lens5 = lens3+lens4\n",
"lens5.plot('kappa')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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mKre2MOucgRwzv5jUTDXvRKTYnR7uXLSYC6aN5eIZ49EIGpweNzcteoE7rn6Y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\n",
"text": [
"<matplotlib.figure.Figure at 0x105506a90>"
]
}
],
"prompt_number": 39
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lens5.deflect()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 40
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lens5.source = evil.Source(1.5)\n",
"lens5.source.Read_Source_from(\"analytic_SIE_kappa.fits\")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 41
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lens5.raytrace()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 42
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lens5.plot('non-lensed image')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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Cf4UAYGfqWn7WaTnT+KGh7je/oy85UyfYUZ+rnd8hEmWdr1rfu2mA7z0K6IoV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\n44eNKQAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x101bbb890>"
]
}
],
"prompt_number": 43
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lens5.plot('lensed image')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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WY2+IRzHOBfGPKzh2E+Mk73djnOLy2wG8rIJxhRBiKThOgmgBeAfGLmhV80qM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\nQoia+f/uDey6BEaJFwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1054b7bd0>"
]
}
],
"prompt_number": 44
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
} | gpl-2.0 |
acdarby/Python_lectures | Lecture_1.ipynb | 1 | 20087 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "f5c4202e-9f93-4c10-bf59-f4ab83b18e0f"
}
},
"source": [
"# Introduction to Programming in Python 3"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "3525a0d4-2eb3-454e-b49e-fa05ea808b59"
}
},
"source": [
"# What is Python?"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "a41e5f96-3a2d-4d16-a01f-55e466f4c33f"
}
},
"source": [
"- A programming language \n",
" - Set of commands that can be interpreted by a piece of software\n",
"- Python is also the name of the software that interprets the commands (3 is a version)\n",
"- Python can be installed on any computer (running Windows, Mac, linux OS) like any other piece of software"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "c8ae3abc-8a37-49d0-8d83-44dd56ecdb4c"
}
},
"source": [
"FREE!! Graphical User Interface (GUI) \n",
"\n",
"Anaconda - Download and installation \n",
"https://www.continuum.io/downloads\n",
"\n",
"Pycharm – Download and installation\n",
"https://www.jetbrains.com/pycharm/\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbpresent": {
"id": "64ded700-2d4f-43bf-a9d5-dac9859f5389"
}
},
"source": [
"- PYTHON 2.7 \n",
" - Not supported but still popular \n",
" \n",
"- **PYTHON 3.5**\n",
" - Latest version and supported \n",
" \n",
"- There are differences in the code and you need to be careful!\n",
"- We will use `PYTHON 3.4` of higher in this module."
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python Shells/Consoles\n",
"\n",
"- Friendly test zone\n",
"- For testing code\n",
"\n",
"- OPEN CONSOLE ->\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Python will evaluate Expressions that it is given"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"8"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"3+5"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"2.3"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"5.5 -3.2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"15"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"3*5"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"5>4"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# If Python can't evaluate an expression *error* "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"ename": "ZeroDivisionError",
"evalue": "division by zero",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mZeroDivisionError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-5-2b706ee9dd8e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m3\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
]
}
],
"source": [
"3 / 0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In python you can save values as ** variables ** using the ** = ** sign\n",
"This variable can be used instead of the full value expressions are evaluated before being stored "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a=3\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"a*2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"b=5*2\n",
"b"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"c =a+b\n",
"c"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Virtually everything in python is an **Object**.\n",
"\n",
"Objects come in different types here are some examples:\n",
"\n",
"- `bool` (short for boolean) is a True or False\n",
"- `int` (short for integer) is a whole number\n",
"- `str` (short for string) is text\n",
"- `float` is a number with a decimal point.\n",
"\n",
"Python will automatically assign a type but you can and should override this when needed.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"Operations on variables"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"first_name = \"Alistair\"\n",
"last_name = \"Darby\"\n",
"fullname = first_name + last_name\n",
"print (fullname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"When used with strings the '+' operator will concatenate them\n",
"\n",
"When used with numbers '+' will behave as you would expect"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"aphids = 5\n",
"beetles = 12\n",
"total_insects = aphids + beetles\n",
"print (\"Number of insects = \", total_insects)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"aphids = 5\n",
"beetles = 12\n",
"total_insects = Aphids + Beetles\n",
"total_insects_string = str(total_insects)\n",
"print(\"Number of insects = \" + total_insects_string)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `str()` function will attempt to convert a value into a human-readable string (e.g. 1 becomes “1”) which can then be concatenated.\n",
"In this case it is only necessary to convert to a string because we are using concatenate (+) in the print statement. When using the normal (,) print will automatically interpret the variable."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"aphids = \"5\"\n",
"plants = \"12\"\n",
"total_insects = aphids * plants\n",
"print(\"Number of insects = \", total_insects)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"aphids = \"5\"\n",
"plants = \"12\"\n",
"total_insects = int(aphids) * int(plants)\n",
"print(\"Number of aphids = \" , total_insects)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `int()` function will attempt to convert a value into integer (e.g. “1” becomes 1) which can then be used in mathematical operations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Functions in Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A function is some Python code that has been pre-written to do something useful (you will learn how to write your own later)\n",
"\n",
"Some simple functions are built in:\n",
"- `print()` \tprints out a string to the screen\n",
"- `len()`\treturns the length (e.g. characters in a string)\n",
"- `str()`\tattempts to convert a value to a string\n",
"- `int()`\tattempts to convert a value to an integer\n",
"- `float()`\tattempts to convert a value to a floating-point \n",
"\n",
"Functions can also be associated with certain types of value. These are known as methods (more about these later!)\n",
"\n",
"- `str.upper()` converts a string to upper case"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using some Python functions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"some_dna = \"atcgacgtacgtg\"\n",
"dna_length = len(some_dna)\n",
"big_dna = str.upper(some_dna)\n",
"print (\"The length of \" , big_dna , \" is \" , dna_length , \"\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Special characters in Strings"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Strings can contain non-text characters with special meanings\n",
"Most useful are:\n",
"\n",
"\\n\tnewline (end of line character)\n",
"\\t \ttab\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"print(\"Hello, World!\\n\") # adds a new line at the end\n",
"print(\"Hello\\tWorld!\") # puts a tab character in"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Comments and documentation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Very important to document your scripts\n",
"Add useful comments to explain purpose\n",
"Makes it easier for you and easier for someone else to re-use or understand what you’re doing\n",
"\n",
"‘#’ makes the program ignore everything that follows it on that line"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# For example…"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# This code demonstrates some basic string operations\n",
"some_dna = \"atcgacgtacgtg\" # define the dna string\n",
"\n",
"# the following section contains the string operations\n",
"dna_length = len(some_dna) # return the string length\n",
"big_dna = str.upper(some_dna) # convert the string to uppercase\n",
"\n",
"# print the outputs and convert dna_length to a string\n",
"print(\"The length of \" , big_dna , \" is \" , str(dna_length) , \"\\n\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Getting user input"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `input()` function makes the program wait for the user to input something on the keyboard and hit enter before continuing"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"name = input()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can also add an input prompt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"name = input(\"What is your name?\")\n",
"print(\"Hello \", name, \"!\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Incorporating input"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# This script calculates the number of cats you need\n",
"rooms = input(\"How many rooms do you have? \")\n",
"cats = input(\"How many cats fit in a room? \")\n",
"Total_cats = int(rooms) * int(cats)\n",
"print(\"You require \" , Total_cats , \" cats\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Modules in Python"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- There is no need to re-invent the wheel - unless you’re learning!\n",
"- Modules are pre-written pieces of code (such as useful functions) that are designed for specific tasks\n",
"- You need to call these at the top of the script \n",
"- Before you use a module you have to reference it in your script like this:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from math import *"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- `math` is the name of the module\n",
"- An asterisk in this context means ‘everything in the module’. \n",
"Alternatively you can put the specific function you want to use here"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from math import *\n",
"a = log10(1000000)\n",
"print(str(a))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An asterisk in this context means ‘everything in the module’. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from math import log10\n",
"a = log10(1000000)\n",
"print(str(a))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Alternatively you can put the specific function you want to load here."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Regular expressions"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- These are incredibly useful (topic of a later lecture)\n",
" - Way of matching and manipulating strings e.g. “Find and replace” operation\n",
"- One example is sub()\n",
" - Substitutes one pattern in a string for another"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from re import sub\n",
"string = \"Al Darbi\"\n",
"string_mod1 = sub('l','C',string)\n",
"string_mod2 = sub('i','y',string_mod1)\n",
"print(string)\n",
"print(string_mod2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You will hear lots more about regular expressions later on, but there is just one example included here, in case you’re interested in reading more about them (might also be useful for the part of the assignment...)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Making errors / refining scripts"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Scripts almost never work first time\n",
"- Usually require fixing small mistakes:\n",
" - Syntax (i.e. Python gives an error when you try to run)\n",
" - Runtime (something goes wrong while it is running)\n",
" - Logic (script doesn’t behave as you expect)\n",
"- Often error messages are not very informative\n",
" - Takes a bit of practice to work out what might be causing an error\n",
"\n",
"Tip: One way to check for syntax errors is to read each line of code from the end to the beginning. This forces you to look at each line without attaching meaning.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Syntax Errors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Most syntax errors are due to incorrect formatting like:\n",
"Missing matching quotes "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"name = \"Darby\n",
"print(name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Missing brackets"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = (y + z"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Variable name spelling or capitalisation\n",
"Indentation (covered in a later lecture)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Runtime errors"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = 6\n",
"y = 0\n",
"z = x / y\n",
"print (z)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# logic errors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Logic errors are harder to identify\n",
" - May cause unintended behaviour rather than a crash\n",
" - Example: ordering of math operations"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = 3\n",
"y = 7\n",
"average = x + y / 2\n",
"print(average)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: Knowing how to fix errors in the logic comes with practice, good commenting of your code and planning your code before you write it (e.g. Draw a flow diagram)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"x = 3\n",
"y = 7\n",
"average = (x + y) / 2\n",
"print(average)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Summary"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- Brief intro to Python and the shell\n",
"- Variables \n",
"- Operations on variables\n",
"- Variable types and type setting\n",
"- Built-in Functions\n",
"- User input\n",
"- Fixing basic errors\n",
"\n",
"** Now you have to try these out for yourself... **\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
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| gpl-3.0 |
CompPhysics/MachineLearning | doc/src/LectureNotes/_build/jupyter_execute/chapter5.ipynb | 1 | 56759 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistic Regression\n",
"\n",
"## Introduction\n",
"In linear regression our main interest was centered on learning the\n",
"coefficients of a functional fit (say a polynomial) in order to be\n",
"able to predict the response of a continuous variable on some unseen\n",
"data. The fit to the continuous variable $y_i$ is based on some\n",
"independent variables $\\hat{x}_i$. Linear regression resulted in\n",
"analytical expressions for standard ordinary Least Squares or Ridge\n",
"regression (in terms of matrices to invert) for several quantities,\n",
"ranging from the variance and thereby the confidence intervals of the\n",
"parameters $\\hat{\\beta}$ to the mean squared error. If we can invert\n",
"the product of the design matrices, linear regression gives then a\n",
"simple recipe for fitting our data.\n",
"\n",
"\n",
"Classification problems, however, are concerned with outcomes taking\n",
"the form of discrete variables (i.e. categories). We may for example,\n",
"on the basis of DNA sequencing for a number of patients, like to find\n",
"out which mutations are important for a certain disease; or based on\n",
"scans of various patients' brains, figure out if there is a tumor or\n",
"not; or given a specific physical system, we'd like to identify its\n",
"state, say whether it is an ordered or disordered system (typical\n",
"situation in solid state physics); or classify the status of a\n",
"patient, whether she/he has a stroke or not and many other similar\n",
"situations.\n",
"\n",
"The most common situation we encounter when we apply logistic\n",
"regression is that of two possible outcomes, normally denoted as a\n",
"binary outcome, true or false, positive or negative, success or\n",
"failure etc.\n",
"\n",
"Logistic regression will also serve as our stepping stone towards\n",
"neural network algorithms and supervised deep learning. For logistic\n",
"learning, the minimization of the cost function leads to a non-linear\n",
"equation in the parameters $\\hat{\\beta}$. The optimization of the\n",
"problem calls therefore for minimization algorithms. This forms the\n",
"bottle neck of all machine learning algorithms, namely how to find\n",
"reliable minima of a multi-variable function. This leads us to the\n",
"family of gradient descent methods. The latter are the working horses\n",
"of basically all modern machine learning algorithms.\n",
"\n",
"We note also that many of the topics discussed here on logistic \n",
"regression are also commonly used in modern supervised Deep Learning\n",
"models, as we will see later.\n",
"\n",
"\n",
"\n",
"## Basics\n",
"\n",
"We consider the case where the dependent variables, also called the\n",
"responses or the outcomes, $y_i$ are discrete and only take values\n",
"from $k=0,\\dots,K-1$ (i.e. $K$ classes).\n",
"\n",
"The goal is to predict the\n",
"output classes from the design matrix $\\hat{X}\\in\\mathbb{R}^{n\\times p}$\n",
"made of $n$ samples, each of which carries $p$ features or predictors. The\n",
"primary goal is to identify the classes to which new unseen samples\n",
"belong.\n",
"\n",
"Let us specialize to the case of two classes only, with outputs\n",
"$y_i=0$ and $y_i=1$. Our outcomes could represent the status of a\n",
"credit card user that could default or not on her/his credit card\n",
"debt. That is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"y_i = \\begin{bmatrix} 0 & \\mathrm{no}\\\\ 1 & \\mathrm{yes} \\end{bmatrix}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before moving to the logistic model, let us try to use our linear\n",
"regression model to classify these two outcomes. We could for example\n",
"fit a linear model to the default case if $y_i > 0.5$ and the no\n",
"default case $y_i \\leq 0.5$.\n",
"\n",
"We would then have our \n",
"weighted linear combination, namely"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Equation labels as ordinary links -->\n",
"<div id=\"_auto1\"></div>\n",
"\n",
"$$\n",
"\\begin{equation}\n",
"\\hat{y} = \\hat{X}^T\\hat{\\beta} + \\hat{\\epsilon},\n",
"\\label{_auto1} \\tag{1}\n",
"\\end{equation}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\hat{y}$ is a vector representing the possible outcomes, $\\hat{X}$ is our\n",
"$n\\times p$ design matrix and $\\hat{\\beta}$ represents our estimators/predictors.\n",
"\n",
"\n",
"The main problem with our function is that it takes values on the\n",
"entire real axis. In the case of logistic regression, however, the\n",
"labels $y_i$ are discrete variables. A typical example is the credit\n",
"card data discussed below here, where we can set the state of\n",
"defaulting the debt to $y_i=1$ and not to $y_i=0$ for one the persons\n",
"in the data set (see the full example below).\n",
"\n",
"One simple way to get a discrete output is to have sign\n",
"functions that map the output of a linear regressor to values $\\{0,1\\}$,\n",
"$f(s_i)=sign(s_i)=1$ if $s_i\\ge 0$ and 0 if otherwise. \n",
"We will encounter this model in our first demonstration of neural networks. Historically it is called the \"perceptron\" model in the machine learning\n",
"literature. This model is extremely simple. However, in many cases it is more\n",
"favorable to use a ``soft\" classifier that outputs\n",
"the probability of a given category. This leads us to the logistic function.\n",
"\n",
"\n",
"\n",
"## The logistic function\n",
"\n",
"The perceptron is an example of a ``hard classification\" model. We\n",
"will encounter this model when we discuss neural networks as\n",
"well. Each datapoint is deterministically assigned to a category (i.e\n",
"$y_i=0$ or $y_i=1$). In many cases, it is favorable to have a \"soft\"\n",
"classifier that outputs the probability of a given category rather\n",
"than a single value. For example, given $x_i$, the classifier\n",
"outputs the probability of being in a category $k$. Logistic regression\n",
"is the most common example of a so-called soft classifier. In logistic\n",
"regression, the probability that a data point $x_i$\n",
"belongs to a category $y_i=\\{0,1\\}$ is given by the so-called logit function (or Sigmoid) which is meant to represent the likelihood for a given event,"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"p(t) = \\frac{1}{1+\\mathrm \\exp{-t}}=\\frac{\\exp{t}}{1+\\mathrm \\exp{t}}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that $1-p(t)= p(-t)$.\n",
"\n",
"\n",
"The following code plots the logistic function, the step function and other functions we will encounter from here and on."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
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"image/png": "/Users/hjensen/Teaching/FYS-STK4150/doc/src/LectureNotes/_build/jupyter_execute/chapter5_7_0.png"
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},
{
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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
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"image/png": "/Users/hjensen/Teaching/FYS-STK4150/doc/src/LectureNotes/_build/jupyter_execute/chapter5_7_1.png"
},
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},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"filenames": {
"image/png": "/Users/hjensen/Teaching/FYS-STK4150/doc/src/LectureNotes/_build/jupyter_execute/chapter5_7_2.png"
},
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"\"\"\"The sigmoid function (or the logistic curve) is a\n",
"function that takes any real number, z, and outputs a number (0,1).\n",
"It is useful in neural networks for assigning weights on a relative scale.\n",
"The value z is the weighted sum of parameters involved in the learning algorithm.\"\"\"\n",
"\n",
"import numpy\n",
"import matplotlib.pyplot as plt\n",
"import math as mt\n",
"\n",
"z = numpy.arange(-5, 5, .1)\n",
"sigma_fn = numpy.vectorize(lambda z: 1/(1+numpy.exp(-z)))\n",
"sigma = sigma_fn(z)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(z, sigma)\n",
"ax.set_ylim([-0.1, 1.1])\n",
"ax.set_xlim([-5,5])\n",
"ax.grid(True)\n",
"ax.set_xlabel('z')\n",
"ax.set_title('sigmoid function')\n",
"\n",
"plt.show()\n",
"\n",
"\"\"\"Step Function\"\"\"\n",
"z = numpy.arange(-5, 5, .02)\n",
"step_fn = numpy.vectorize(lambda z: 1.0 if z >= 0.0 else 0.0)\n",
"step = step_fn(z)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(z, step)\n",
"ax.set_ylim([-0.5, 1.5])\n",
"ax.set_xlim([-5,5])\n",
"ax.grid(True)\n",
"ax.set_xlabel('z')\n",
"ax.set_title('step function')\n",
"\n",
"plt.show()\n",
"\n",
"\"\"\"tanh Function\"\"\"\n",
"z = numpy.arange(-2*mt.pi, 2*mt.pi, 0.1)\n",
"t = numpy.tanh(z)\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.plot(z, t)\n",
"ax.set_ylim([-1.0, 1.0])\n",
"ax.set_xlim([-2*mt.pi,2*mt.pi])\n",
"ax.grid(True)\n",
"ax.set_xlabel('z')\n",
"ax.set_title('tanh function')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Two parameters\n",
"\n",
"We assume now that we have two classes with $y_i$ either $0$ or $1$. Furthermore we assume also that we have only two parameters $\\beta$ in our fitting of the Sigmoid function, that is we define probabilities"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*}\n",
"p(y_i=1|x_i,\\hat{\\beta}) &= \\frac{\\exp{(\\beta_0+\\beta_1x_i)}}{1+\\exp{(\\beta_0+\\beta_1x_i)}},\\nonumber\\\\\n",
"p(y_i=0|x_i,\\hat{\\beta}) &= 1 - p(y_i=1|x_i,\\hat{\\beta}),\n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"where $\\hat{\\beta}$ are the weights we wish to extract from data, in our case $\\beta_0$ and $\\beta_1$. \n",
"\n",
"Note that we used"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"p(y_i=0\\vert x_i, \\hat{\\beta}) = 1-p(y_i=1\\vert x_i, \\hat{\\beta}).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Maximum likelihood\n",
"\n",
"In order to define the total likelihood for all possible outcomes from a \n",
"dataset $\\mathcal{D}=\\{(y_i,x_i)\\}$, with the binary labels\n",
"$y_i\\in\\{0,1\\}$ and where the data points are drawn independently, we use the so-called [Maximum Likelihood Estimation](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation) (MLE) principle. \n",
"We aim thus at maximizing \n",
"the probability of seeing the observed data. We can then approximate the \n",
"likelihood in terms of the product of the individual probabilities of a specific outcome $y_i$, that is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\begin{align*}\n",
"P(\\mathcal{D}|\\hat{\\beta})& = \\prod_{i=1}^n \\left[p(y_i=1|x_i,\\hat{\\beta})\\right]^{y_i}\\left[1-p(y_i=1|x_i,\\hat{\\beta}))\\right]^{1-y_i}\\nonumber \\\\\n",
"\\end{align*}\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"from which we obtain the log-likelihood and our **cost/loss** function"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mathcal{C}(\\hat{\\beta}) = \\sum_{i=1}^n \\left( y_i\\log{p(y_i=1|x_i,\\hat{\\beta})} + (1-y_i)\\log\\left[1-p(y_i=1|x_i,\\hat{\\beta}))\\right]\\right).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reordering the logarithms, we can rewrite the **cost/loss** function as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mathcal{C}(\\hat{\\beta}) = \\sum_{i=1}^n \\left(y_i(\\beta_0+\\beta_1x_i) -\\log{(1+\\exp{(\\beta_0+\\beta_1x_i)})}\\right).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The maximum likelihood estimator is defined as the set of parameters that maximize the log-likelihood where we maximize with respect to $\\beta$.\n",
"Since the cost (error) function is just the negative log-likelihood, for logistic regression we have that"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\mathcal{C}(\\hat{\\beta})=-\\sum_{i=1}^n \\left(y_i(\\beta_0+\\beta_1x_i) -\\log{(1+\\exp{(\\beta_0+\\beta_1x_i)})}\\right).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This equation is known in statistics as the **cross entropy**. Finally, we note that just as in linear regression, \n",
"in practice we often supplement the cross-entropy with additional regularization terms, usually $L_1$ and $L_2$ regularization as we did for Ridge and Lasso regression.\n",
"\n",
"\n",
"The cross entropy is a convex function of the weights $\\hat{\\beta}$ and,\n",
"therefore, any local minimizer is a global minimizer. \n",
"\n",
"\n",
"Minimizing this\n",
"cost function with respect to the two parameters $\\beta_0$ and $\\beta_1$ we obtain"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial \\mathcal{C}(\\hat{\\beta})}{\\partial \\beta_0} = -\\sum_{i=1}^n \\left(y_i -\\frac{\\exp{(\\beta_0+\\beta_1x_i)}}{1+\\exp{(\\beta_0+\\beta_1x_i)}}\\right),\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial \\mathcal{C}(\\hat{\\beta})}{\\partial \\beta_1} = -\\sum_{i=1}^n \\left(y_ix_i -x_i\\frac{\\exp{(\\beta_0+\\beta_1x_i)}}{1+\\exp{(\\beta_0+\\beta_1x_i)}}\\right).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let us now define a vector $\\hat{y}$ with $n$ elements $y_i$, an\n",
"$n\\times p$ matrix $\\hat{X}$ which contains the $x_i$ values and a\n",
"vector $\\hat{p}$ of fitted probabilities $p(y_i\\vert x_i,\\hat{\\beta})$. We can rewrite in a more compact form the first\n",
"derivative of cost function as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial \\mathcal{C}(\\hat{\\beta})}{\\partial \\hat{\\beta}} = -\\hat{X}^T\\left(\\hat{y}-\\hat{p}\\right).\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we in addition define a diagonal matrix $\\hat{W}$ with elements \n",
"$p(y_i\\vert x_i,\\hat{\\beta})(1-p(y_i\\vert x_i,\\hat{\\beta})$, we can obtain a compact expression of the second derivative as"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\frac{\\partial^2 \\mathcal{C}(\\hat{\\beta})}{\\partial \\hat{\\beta}\\partial \\hat{\\beta}^T} = \\hat{X}^T\\hat{W}\\hat{X}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Within a binary classification problem, we can easily expand our model to include multiple predictors. Our ratio between likelihoods is then with $p$ predictors"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\log{ \\frac{p(\\hat{\\beta}\\hat{x})}{1-p(\\hat{\\beta}\\hat{x})}} = \\beta_0+\\beta_1x_1+\\beta_2x_2+\\dots+\\beta_px_p.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here we defined $\\hat{x}=[1,x_1,x_2,\\dots,x_p]$ and $\\hat{\\beta}=[\\beta_0, \\beta_1, \\dots, \\beta_p]$ leading to"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"p(\\hat{\\beta}\\hat{x})=\\frac{ \\exp{(\\beta_0+\\beta_1x_1+\\beta_2x_2+\\dots+\\beta_px_p)}}{1+\\exp{(\\beta_0+\\beta_1x_1+\\beta_2x_2+\\dots+\\beta_px_p)}}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Including more classes\n",
"\n",
"Till now we have mainly focused on two classes, the so-called binary\n",
"system. Suppose we wish to extend to $K$ classes. Let us for the sake\n",
"of simplicity assume we have only two predictors. We have then\n",
"following model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1\n",
"5\n",
" \n",
"<\n",
"<\n",
"<\n",
"!\n",
"!\n",
"M\n",
"A\n",
"T\n",
"H\n",
"_\n",
"B\n",
"L\n",
"O\n",
"C\n",
"K"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\log{\\frac{p(C=2\\vert x)}{p(K\\vert x)}} = \\beta_{20}+\\beta_{21}x_1,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and so on till the class $C=K-1$ class"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"\\log{\\frac{p(C=K-1\\vert x)}{p(K\\vert x)}} = \\beta_{(K-1)0}+\\beta_{(K-1)1}x_1,\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and the model is specified in term of $K-1$ so-called log-odds or\n",
"**logit** transformations.\n",
"\n",
"\n",
"\n",
"In our discussion of neural networks we will encounter the above again\n",
"in terms of a slightly modified function, the so-called **Softmax** function.\n",
"\n",
"The softmax function is used in various multiclass classification\n",
"methods, such as multinomial logistic regression (also known as\n",
"softmax regression), multiclass linear discriminant analysis, naive\n",
"Bayes classifiers, and artificial neural networks. Specifically, in\n",
"multinomial logistic regression and linear discriminant analysis, the\n",
"input to the function is the result of $K$ distinct linear functions,\n",
"and the predicted probability for the $k$-th class given a sample\n",
"vector $\\hat{x}$ and a weighting vector $\\hat{\\beta}$ is (with two\n",
"predictors):"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"p(C=k\\vert \\mathbf {x} )=\\frac{\\exp{(\\beta_{k0}+\\beta_{k1}x_1)}}{1+\\sum_{l=1}^{K-1}\\exp{(\\beta_{l0}+\\beta_{l1}x_1)}}.\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It is easy to extend to more predictors. The final class is"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$\n",
"p(C=K\\vert \\mathbf {x} )=\\frac{1}{1+\\sum_{l=1}^{K-1}\\exp{(\\beta_{l0}+\\beta_{l1}x_1)}},\n",
"$$"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and they sum to one. Our earlier discussions were all specialized to\n",
"the case with two classes only. It is easy to see from the above that\n",
"what we derived earlier is compatible with these equations.\n",
"\n",
"To find the optimal parameters we would typically use a gradient\n",
"descent method. Newton's method and gradient descent methods are\n",
"discussed in the material on [optimization\n",
"methods](https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html)."
]
}
],
"metadata": {
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
} | cc0-1.0 |
tensorflow/docs-l10n | site/ja/quantum/tutorials/mnist.ipynb | 1 | 34718 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "xLOXFOT5Q40E"
},
"source": [
"##### Copyright 2020 The TensorFlow Authors."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
"id": "iiQkM5ZgQ8r2"
},
"outputs": [],
"source": [
"#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"# you may not use this file except in compliance with the License.\n",
"# You may obtain a copy of the License at\n",
"#\n",
"# https://www.apache.org/licenses/LICENSE-2.0\n",
"#\n",
"# Unless required by applicable law or agreed to in writing, software\n",
"# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
"# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
"# See the License for the specific language governing permissions and\n",
"# limitations under the License."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j6331ZSsQGY3"
},
"source": [
"# MNIST の分類"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i9Jcnb8bQQyd"
},
"source": [
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td> <a target=\"_blank\" href=\"https://www.tensorflow.org/quantum/tutorials/mnist\"> <img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\"> TensorFlow.org で表示</a>\n",
"</td>\n",
" <td> <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/ja/quantum/tutorials/mnist.ipynb\"> <img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\"> Google Colab で実行</a>\n",
"</td>\n",
" <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/ja/quantum/tutorials/mnist.ipynb\"> <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\"> GitHubでソースを表示</a></td>\n",
" <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/ja/quantum/tutorials/mnist.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\">ノートブックをダウンロード</a></td>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "udLObUVeGfTs"
},
"source": [
"このチュートリアルでは、簡略化された MNIST バージョンを分類する、<a href=\"https://arxiv.org/pdf/1802.06002.pdf\" class=\"external\">Farhi et al</a> で使用されたアプローチに似た量子ニューラルネットワーク（QNN）を構築し、古典的なデータ問題における量子ニューラルネットワークのパフォーマンスを従来のニューラルネットワークと比較します。"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "X35qHdh5Gzqg"
},
"source": [
"## セットアップ"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TorxE5tnkvb2"
},
"outputs": [],
"source": [
"!pip install tensorflow==2.7.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FxkQA6oblNqI"
},
"source": [
"TensorFlow Quantum をインストールします。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "saFHsRDpkvkH"
},
"outputs": [],
"source": [
"!pip install tensorflow-quantum"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "4Ql5PW-ACO0J"
},
"outputs": [],
"source": [
"# Update package resources to account for version changes.\n",
"import importlib, pkg_resources\n",
"importlib.reload(pkg_resources)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "hdgMMZEBGqyl"
},
"source": [
"次に、TensorFlow とモジュールの依存関係をインポートします。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "enZ300Bflq80"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import tensorflow_quantum as tfq\n",
"\n",
"import cirq\n",
"import sympy\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import collections\n",
"\n",
"# visualization tools\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from cirq.contrib.svg import SVGCircuit"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b08Mmbs8lr81"
},
"source": [
"## 1. データを読み込む\n",
"\n",
"このチュートリアルでは、<a href=\"https://arxiv.org/pdf/1802.06002.pdf\" class=\"external\">Farhi et al.</a> に従って、数字の 3 と 6 を区別する二項分類器を構築します。このセクションでは、次を行うデータ処理を説明します。\n",
"\n",
"- Keras から生データを読み込みます。\n",
"- データセットを 3 と 6 に絞り込みます。\n",
"- 画像が量子コンピュータに適合するように、画像を縮小します。\n",
"- 矛盾するサンプルを取り除きます。\n",
"- バイナリ画像を Cirq 回路に変換します。\n",
"- Cirq 回路を TensorFlow Quantum 回路に変換します。 "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pDUdGxn-ojgy"
},
"source": [
"### 1.1 生データを読み込む"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xZyGXlaKojgz"
},
"source": [
"Keras で配布された MNIST データセットを読み込みます。 "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "d9OSExvCojg0"
},
"outputs": [],
"source": [
"(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
"\n",
"# Rescale the images from [0,255] to the [0.0,1.0] range.\n",
"x_train, x_test = x_train[..., np.newaxis]/255.0, x_test[..., np.newaxis]/255.0\n",
"\n",
"print(\"Number of original training examples:\", len(x_train))\n",
"print(\"Number of original test examples:\", len(x_test))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fZpbygdGojg3"
},
"source": [
"3 と 6 の数字のみを保持してほかのクラスを取り除くように、データセットを絞り込みます。同時に、`3` を `True`、6 を `False` というように、ラベル `y` をブール型に変換します。 "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "hOw68cCZojg4"
},
"outputs": [],
"source": [
"def filter_36(x, y):\n",
" keep = (y == 3) | (y == 6)\n",
" x, y = x[keep], y[keep]\n",
" y = y == 3\n",
" return x,y"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "p-XEU8egGL6q"
},
"outputs": [],
"source": [
"x_train, y_train = filter_36(x_train, y_train)\n",
"x_test, y_test = filter_36(x_test, y_test)\n",
"\n",
"print(\"Number of filtered training examples:\", len(x_train))\n",
"print(\"Number of filtered test examples:\", len(x_test))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3wyiaP0Xojg_"
},
"source": [
"最初のサンプルを表示します。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "j5STP7MbojhA"
},
"outputs": [],
"source": [
"print(y_train[0])\n",
"\n",
"plt.imshow(x_train[0, :, :, 0])\n",
"plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wNS9sVPQojhC"
},
"source": [
"### 1.2 画像を縮小する"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "fmmtplIFGL6t"
},
"source": [
"現在の量子コンピュータでは、画像サイズ 28x28 は大きすぎるため、4x4 に縮小します。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lbhUdBFWojhE"
},
"outputs": [],
"source": [
"x_train_small = tf.image.resize(x_train, (4,4)).numpy()\n",
"x_test_small = tf.image.resize(x_test, (4,4)).numpy()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pOMd7zIjGL6x"
},
"source": [
"サイズ変更を行ったら、もう一度最初のサンプルを表示します。 "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "YIYOtCRIGL6y"
},
"outputs": [],
"source": [
"print(y_train[0])\n",
"\n",
"plt.imshow(x_train_small[0,:,:,0], vmin=0, vmax=1)\n",
"plt.colorbar()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gGeF1_qtojhK"
},
"source": [
"### 1.3 矛盾するサンプルを取り除く"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7ZLkq2yeojhL"
},
"source": [
"<a href=\"https://arxiv.org/pdf/1802.06002.pdf\" class=\"external\">Farhi et al.</a> の *3.3 Learning to Distinguish Digits* セクションに説明されているように、データセットから両方のクラスに属するラベルが付けられた画像を取り除きます。\n",
"\n",
"これは標準的な機械学習の手順ではありませんが、論文の手順に従う目的で追加している手順です。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LqOPW0C7ojhL"
},
"outputs": [],
"source": [
"def remove_contradicting(xs, ys):\n",
" mapping = collections.defaultdict(set)\n",
" orig_x = {}\n",
" # Determine the set of labels for each unique image:\n",
" for x,y in zip(xs,ys):\n",
" orig_x[tuple(x.flatten())] = x\n",
" mapping[tuple(x.flatten())].add(y)\n",
" \n",
" new_x = []\n",
" new_y = []\n",
" for flatten_x in mapping:\n",
" x = orig_x[flatten_x]\n",
" labels = mapping[flatten_x]\n",
" if len(labels) == 1:\n",
" new_x.append(x)\n",
" new_y.append(next(iter(labels)))\n",
" else:\n",
" # Throw out images that match more than one label.\n",
" pass\n",
" \n",
" num_uniq_3 = sum(1 for value in mapping.values() if len(value) == 1 and True in value)\n",
" num_uniq_6 = sum(1 for value in mapping.values() if len(value) == 1 and False in value)\n",
" num_uniq_both = sum(1 for value in mapping.values() if len(value) == 2)\n",
"\n",
" print(\"Number of unique images:\", len(mapping.values()))\n",
" print(\"Number of unique 3s: \", num_uniq_3)\n",
" print(\"Number of unique 6s: \", num_uniq_6)\n",
" print(\"Number of unique contradicting labels (both 3 and 6): \", num_uniq_both)\n",
" print()\n",
" print(\"Initial number of images: \", len(xs))\n",
" print(\"Remaining non-contradicting unique images: \", len(new_x))\n",
" \n",
" return np.array(new_x), np.array(new_y)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "VMOiJfz_ojhP"
},
"source": [
"取り除いた結果の数量は、レポートされている値に密に一致していませんが、これは正確な手順が指定されていないためです。\n",
"\n",
"また、この時点で、矛盾するサンプルをフィルタリングすることで、矛盾するトレーニングサンプルがモデルに絶対に送られないということではありません。次のデータの二項化のステップでは、さらに競合が発生します。 "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "zpnsAssWojhP"
},
"outputs": [],
"source": [
"x_train_nocon, y_train_nocon = remove_contradicting(x_train_small, y_train)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SlJ5NVaPojhT"
},
"source": [
"### 1.4 データを量子回路としてエンコードする\n",
"\n",
"<a href=\"https://arxiv.org/pdf/1802.06002.pdf\" class=\"external\">Farhi et al.</a> は、量子コンピュータを使って画像を処理するには、ピクセルの値に応じた状態で、各ピクセルをキュービットで表現するように提案しています。最初のステップでは、バイナリエンコーディングへの変換を行います。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "1z8J7OyDojhV"
},
"outputs": [],
"source": [
"THRESHOLD = 0.5\n",
"\n",
"x_train_bin = np.array(x_train_nocon > THRESHOLD, dtype=np.float32)\n",
"x_test_bin = np.array(x_test_small > THRESHOLD, dtype=np.float32)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SlJ5NVaPojhU"
},
"source": [
"この時点で矛盾した画像を取り除く場合、193 個しか画像が残らず、これでは有効なトレーニングを行える数量とは言えません。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "1z8J7OyDojhW"
},
"outputs": [],
"source": [
"_ = remove_contradicting(x_train_bin, y_train_nocon)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oLyxS9KlojhZ"
},
"source": [
"しきい値を超える値を持つピクセルインデックスのキュービットは、$X$ ゲートを介して循環します。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aOu_3-3ZGL61"
},
"outputs": [],
"source": [
"def convert_to_circuit(image):\n",
" \"\"\"Encode truncated classical image into quantum datapoint.\"\"\"\n",
" values = np.ndarray.flatten(image)\n",
" qubits = cirq.GridQubit.rect(4, 4)\n",
" circuit = cirq.Circuit()\n",
" for i, value in enumerate(values):\n",
" if value:\n",
" circuit.append(cirq.X(qubits[i]))\n",
" return circuit\n",
"\n",
"\n",
"x_train_circ = [convert_to_circuit(x) for x in x_train_bin]\n",
"x_test_circ = [convert_to_circuit(x) for x in x_test_bin]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zSCXqzOzojhd"
},
"source": [
"これは最初のサンプルに作成された回路です（回路図には、ゼロゲートのキュービットは表示されていません）。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "w3POmUEUojhe"
},
"outputs": [],
"source": [
"SVGCircuit(x_train_circ[0])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AEQMxCcBojhg"
},
"source": [
"この回路を、画像の値がしきい値を超えるインデックスを比較します。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "TBIsiXdtojhh"
},
"outputs": [],
"source": [
"bin_img = x_train_bin[0,:,:,0]\n",
"indices = np.array(np.where(bin_img)).T\n",
"indices"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "mWZ24w1Oojhk"
},
"source": [
"これらの `Cirq` 回路を `tfq` のテンソルに変換します。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "IZStEMk4ojhk"
},
"outputs": [],
"source": [
"x_train_tfcirc = tfq.convert_to_tensor(x_train_circ)\n",
"x_test_tfcirc = tfq.convert_to_tensor(x_test_circ)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "4USiqeOqGL67"
},
"source": [
"## 2. 量子ニューラルネットワーク\n",
"\n",
"画像を分類する量子回路構造に関するガイダンスはほとんどありません。分類は読み出されるキュービットの期待に基づいて行われるため、<a href=\"https://arxiv.org/pdf/1802.06002.pdf\" class=\"external\">Farhi et al.</a> は、2 つのキュービットゲートを使用して、読み出しキュービットが必ず作用されるようにすることを提案しています。これはある意味、ピクセル全体に小さな<a href=\"https://arxiv.org/abs/1511.06464\" class=\"external\">ユニタリ RNN</a>を実行することに似ています。"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "knIzawEeojho"
},
"source": [
"### 2.1 モデル回路を構築する\n",
"\n",
"次の例では、このレイヤー化アプローチを説明しています。各レイヤーは同一ゲートの *n* 個のインスタンスを使用しており、各データキュービットは読み出しキュービットに影響を与えています。\n",
"\n",
"ゲートのレイヤーを回路に追加する簡単なクラスから始めましょう。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "-hjxxgU5ojho"
},
"outputs": [],
"source": [
"class CircuitLayerBuilder():\n",
" def __init__(self, data_qubits, readout):\n",
" self.data_qubits = data_qubits\n",
" self.readout = readout\n",
" \n",
" def add_layer(self, circuit, gate, prefix):\n",
" for i, qubit in enumerate(self.data_qubits):\n",
" symbol = sympy.Symbol(prefix + '-' + str(i))\n",
" circuit.append(gate(qubit, self.readout)**symbol)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Sjo5hANFojhr"
},
"source": [
"サンプル回路レイヤーを構築して、どのようになるかを確認します。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "SzXWOpUGojhs"
},
"outputs": [],
"source": [
"demo_builder = CircuitLayerBuilder(data_qubits = cirq.GridQubit.rect(4,1),\n",
" readout=cirq.GridQubit(-1,-1))\n",
"\n",
"circuit = cirq.Circuit()\n",
"demo_builder.add_layer(circuit, gate = cirq.XX, prefix='xx')\n",
"SVGCircuit(circuit)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T-QhPE1pojhu"
},
"source": [
"では、2 レイヤーモデルを構築しましょう。 データ回路サイズに一致するようにし、準備と読み出し演算を含めます。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "JiALbpwRGL69"
},
"outputs": [],
"source": [
"def create_quantum_model():\n",
" \"\"\"Create a QNN model circuit and readout operation to go along with it.\"\"\"\n",
" data_qubits = cirq.GridQubit.rect(4, 4) # a 4x4 grid.\n",
" readout = cirq.GridQubit(-1, -1) # a single qubit at [-1,-1]\n",
" circuit = cirq.Circuit()\n",
" \n",
" # Prepare the readout qubit.\n",
" circuit.append(cirq.X(readout))\n",
" circuit.append(cirq.H(readout))\n",
" \n",
" builder = CircuitLayerBuilder(\n",
" data_qubits = data_qubits,\n",
" readout=readout)\n",
"\n",
" # Then add layers (experiment by adding more).\n",
" builder.add_layer(circuit, cirq.XX, \"xx1\")\n",
" builder.add_layer(circuit, cirq.ZZ, \"zz1\")\n",
"\n",
" # Finally, prepare the readout qubit.\n",
" circuit.append(cirq.H(readout))\n",
"\n",
" return circuit, cirq.Z(readout)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "2QZvVh7vojhx"
},
"outputs": [],
"source": [
"model_circuit, model_readout = create_quantum_model()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LY7vbY6yfABE"
},
"source": [
"### 2.2 tfq-keras モデルでモデル回路をラップする\n",
"\n",
"量子コンポーネントで Keras モデルを構築します。このモデルには、古典的なデータをエンコードする「量子データ」が `x_train_circ` からフィードされます。*パラメータ化された量子回路*レイヤーの `tfq.layers.PQC` を使用して、量子データでモデル回路をトレーニングするモデルです。\n",
"\n",
"<a href=\"https://arxiv.org/pdf/1802.06002.pdf\" class=\"external\">Farhi et al.</a> は、画像を分類するには、パラメータ化された回路で読み出しキュービットの期待値を使用することを提案しています。期待値は、1 から -1 の値です。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ZYdf_KOxojh0"
},
"outputs": [],
"source": [
"# Build the Keras model.\n",
"model = tf.keras.Sequential([\n",
" # The input is the data-circuit, encoded as a tf.string\n",
" tf.keras.layers.Input(shape=(), dtype=tf.string),\n",
" # The PQC layer returns the expected value of the readout gate, range [-1,1].\n",
" tfq.layers.PQC(model_circuit, model_readout),\n",
"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "jz-FbVc9ojh3"
},
"source": [
"次に、`compile` メソッドを使用して、モデルにトレーニング手順を指定します。\n",
"\n",
"期待される読み出しは `[-1,1]` の範囲であるため、ヒンジ損失を最適化すると、ある程度自然な適合となります。\n",
"\n",
"注意: もう 1 つの有効なアプローチとして、出力範囲を `[0,1]` にシフトし、モデルがクラス `3` に割りてる確率として扱う方法があります。これは、標準的な`tf.losses.BinaryCrossentropy` 損失で使用することができます。\n",
"\n",
"ここでヒンジ損失を使用するには、小さな調整を 2 つ行う必要があります。まず、ラベル `y_train_nocon` をブール型からヒンジ損失が期待する `[-1,1]` に変換することです。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CgMNkC1Fojh5"
},
"outputs": [],
"source": [
"y_train_hinge = 2.0*y_train_nocon-1.0\n",
"y_test_hinge = 2.0*y_test-1.0"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5nwnveDiojh7"
},
"source": [
"次に、`[-1, 1]` を `y_true` ラベル引数として正しく処理するカスタムの `hinge_accuracy` メトリックを使用します。`tf.losses.BinaryAccuracy(threshold=0.0)` は `y_true` がブール型であることを期待するため、ヒンジ損失とは使用できません。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "3XKtZ_TEojh8"
},
"outputs": [],
"source": [
"def hinge_accuracy(y_true, y_pred):\n",
" y_true = tf.squeeze(y_true) > 0.0\n",
" y_pred = tf.squeeze(y_pred) > 0.0\n",
" result = tf.cast(y_true == y_pred, tf.float32)\n",
"\n",
" return tf.reduce_mean(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "FlpETlLRojiA"
},
"outputs": [],
"source": [
"model.compile(\n",
" loss=tf.keras.losses.Hinge(),\n",
" optimizer=tf.keras.optimizers.Adam(),\n",
" metrics=[hinge_accuracy])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "jkHq2RstojiC"
},
"outputs": [],
"source": [
"print(model.summary())"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lsuOzDYblA9s"
},
"source": [
"### 量子モデルをトレーニングする\n",
"\n",
"では、モデルをトレーニングしましょう。これには約 45 分かかりますが、その時間を待てない方は、小規模なデータのサブセット（以下の`NUM_EXAMPLES=500` セット）を使用するとよいでしょう。トレーニング時のモデルの進捗にあまり影響はありません（パラメータは 32 しかなく、これらを制約する上であまりデータは必要ありません）。サンプル数を減らすことでトレーニングを早めに（5 分程度）終わらせることができますが、検証ログに進捗状況を示すには十分な長さです。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "n8vuQpSLlBV2"
},
"outputs": [],
"source": [
"EPOCHS = 3\n",
"BATCH_SIZE = 32\n",
"\n",
"NUM_EXAMPLES = len(x_train_tfcirc)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "qJnNG-3JojiI"
},
"outputs": [],
"source": [
"x_train_tfcirc_sub = x_train_tfcirc[:NUM_EXAMPLES]\n",
"y_train_hinge_sub = y_train_hinge[:NUM_EXAMPLES]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QMSdgGC1GL7D"
},
"source": [
"このモデルを収束までトレーニングすると、テストセットにおいて 85% を超える精度が達成されます。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Ya9qP3KkojiM"
},
"outputs": [],
"source": [
"qnn_history = model.fit(\n",
" x_train_tfcirc_sub, y_train_hinge_sub,\n",
" batch_size=32,\n",
" epochs=EPOCHS,\n",
" verbose=1,\n",
" validation_data=(x_test_tfcirc, y_test_hinge))\n",
"\n",
"qnn_results = model.evaluate(x_test_tfcirc, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3ER7B7aaojiP"
},
"source": [
"注意: トレーニング精度はエポックの平均値を示します。検証精度はエポックの終了ごとに評価されます。"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8952YvuWGL7J"
},
"source": [
"## 3. 従来のニューラルネットワーク\n",
"\n",
"量子ニューラルネットワークは、この単純化された MNIST 問題で機能するものの、このタスクでは、従来のニューラルネットワークの性能が QNN をはるかに上回ります。1 つのエポックが終了した時点で、従来のニューラルネットワークは縮小したセットで 98% を超える精度を達成することができます。\n",
"\n",
"次の例では、画像をサブサンプリングする代わりに 28x28 の画像を使用した 3 と 6 の分類問題に従来のニューラルネットワークを使用しています。これはほぼ 100% 精度のテストセットに難なく収束します。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "pZofEHhLGL7L"
},
"outputs": [],
"source": [
"def create_classical_model():\n",
" # A simple model based off LeNet from https://keras.io/examples/mnist_cnn/\n",
" model = tf.keras.Sequential()\n",
" model.add(tf.keras.layers.Conv2D(32, [3, 3], activation='relu', input_shape=(28,28,1)))\n",
" model.add(tf.keras.layers.Conv2D(64, [3, 3], activation='relu'))\n",
" model.add(tf.keras.layers.MaxPooling2D(pool_size=(2, 2)))\n",
" model.add(tf.keras.layers.Dropout(0.25))\n",
" model.add(tf.keras.layers.Flatten())\n",
" model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
" model.add(tf.keras.layers.Dropout(0.5))\n",
" model.add(tf.keras.layers.Dense(1))\n",
" return model\n",
"\n",
"\n",
"model = create_classical_model()\n",
"model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
" optimizer=tf.keras.optimizers.Adam(),\n",
" metrics=['accuracy'])\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "CiAJl7sZojiU"
},
"outputs": [],
"source": [
"model.fit(x_train,\n",
" y_train,\n",
" batch_size=128,\n",
" epochs=1,\n",
" verbose=1,\n",
" validation_data=(x_test, y_test))\n",
"\n",
"cnn_results = model.evaluate(x_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "X5-5BVJaojiZ"
},
"source": [
"上記のモデルには約 120 万個のパラメータがあります。より公正な比較を行うために、サブサンプリングした画像で 37 個のパラメータモデルを使用してみましょう。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "70TOM6r-ojiZ"
},
"outputs": [],
"source": [
"def create_fair_classical_model():\n",
" # A simple model based off LeNet from https://keras.io/examples/mnist_cnn/\n",
" model = tf.keras.Sequential()\n",
" model.add(tf.keras.layers.Flatten(input_shape=(4,4,1)))\n",
" model.add(tf.keras.layers.Dense(2, activation='relu'))\n",
" model.add(tf.keras.layers.Dense(1))\n",
" return model\n",
"\n",
"\n",
"model = create_fair_classical_model()\n",
"model.compile(loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
" optimizer=tf.keras.optimizers.Adam(),\n",
" metrics=['accuracy'])\n",
"\n",
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "lA_Fx-8gojid"
},
"outputs": [],
"source": [
"model.fit(x_train_bin,\n",
" y_train_nocon,\n",
" batch_size=128,\n",
" epochs=20,\n",
" verbose=2,\n",
" validation_data=(x_test_bin, y_test))\n",
"\n",
"fair_nn_results = model.evaluate(x_test_bin, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RH3mam7EGL7N"
},
"source": [
"## 4. 比較\n",
"\n",
"解像度の高い入力とより強力なモデルの場合、CNN はこの問題を簡単に解決できますが、似たようなパワー（最大 32 個のパラメータ）を持つ古典的モデルはわずかな時間で似たような精度までトレーニングすることができます。いずれにせよ、従来のニューラルネットワークは量子ニューラルネットワークの性能を簡単に上回ります。古典的なデータでは、従来のニューラルネットワークを上回るのは困難といえます。"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "NOMeN7pMGL7P"
},
"outputs": [],
"source": [
"qnn_accuracy = qnn_results[1]\n",
"cnn_accuracy = cnn_results[1]\n",
"fair_nn_accuracy = fair_nn_results[1]\n",
"\n",
"sns.barplot([\"Quantum\", \"Classical, full\", \"Classical, fair\"],\n",
" [qnn_accuracy, cnn_accuracy, fair_nn_accuracy])"
]
}
],
"metadata": {
"colab": {
"collapsed_sections": [],
"name": "mnist.ipynb",
"toc_visible": true
},
"kernelspec": {
"display_name": "Python 3",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
| apache-2.0 |
a-slide/iPython-Notebook | Notebooks/VCF_analysis.ipynb | 1 | 17769 | {
"metadata": {
"name": "VCF analysis"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Analysis of VCF file containing variants along rAAV genome from datasets generated by SSV-Seq"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Create classes to handle Sample during VCF parsing"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"class Sample:\n",
"\n",
" def __init__ (self, id, min_var_freq, ref_seq):\n",
" self.id = id\n",
" self.min_var_freq = min_var_freq\n",
" self.position_list = []\n",
" \n",
" # iterate other bases of the reference sequence\n",
" for base in ref_seq:\n",
" \n",
" # Create a dict for all 4 DNA base where the reference base is identified \n",
" self.position_list.append( {\n",
" \"A\": {\"ref_base\": True, \"freq\" : 1} if base == \"A\" else {\"ref_base\": False, \"freq\" : 0},\n",
" \"C\": {\"ref_base\": True, \"freq\" : 1} if base == \"C\" else {\"ref_base\": False, \"freq\" : 0},\n",
" \"G\": {\"ref_base\": True, \"freq\" : 1} if base == \"G\" else {\"ref_base\": False, \"freq\" : 0},\n",
" \"T\": {\"ref_base\": True, \"freq\" : 1} if base == \"T\" else {\"ref_base\": False, \"freq\" : 0}})\n",
" \n",
" def add_variant (self, pos, base, freq):\n",
" if freq > self.min_var_freq: \n",
" self.position_list[pos-1][base][\"freq\"] = freq"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Test Sample and Position Classes"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"a = Sample(\"test\", 0.01, \"ATATCGATC\")\n",
"a.add_variant(3, 'T', 0.1)\n",
"a.add_variant(3, 'C', 0.1)\n",
"a.add_variant(3, 'A', 0.8)\n",
"a.__dict__"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 2,
"text": [
"{'id': 'test',\n",
" 'min_var_freq': 0.01,\n",
" 'position_list': [{'A': {'freq': 1, 'ref_base': True},\n",
" 'C': {'freq': 0, 'ref_base': False},\n",
" 'G': {'freq': 0, 'ref_base': False},\n",
" 'T': {'freq': 0, 'ref_base': False}},\n",
" {'A': {'freq': 0, 'ref_base': False},\n",
" 'C': {'freq': 0, 'ref_base': False},\n",
" 'G': {'freq': 0, 'ref_base': False},\n",
" 'T': {'freq': 1, 'ref_base': True}},\n",
" {'A': {'freq': 0.8, 'ref_base': True},\n",
" 'C': {'freq': 0.1, 'ref_base': False},\n",
" 'G': {'freq': 0, 'ref_base': False},\n",
" 'T': {'freq': 0.1, 'ref_base': False}},\n",
" {'A': {'freq': 0, 'ref_base': False},\n",
" 'C': {'freq': 0, 'ref_base': False},\n",
" 'G': {'freq': 0, 'ref_base': False},\n",
" 'T': {'freq': 1, 'ref_base': True}},\n",
" {'A': {'freq': 0, 'ref_base': False},\n",
" 'C': {'freq': 1, 'ref_base': True},\n",
" 'G': {'freq': 0, 'ref_base': False},\n",
" 'T': {'freq': 0, 'ref_base': False}},\n",
" {'A': {'freq': 0, 'ref_base': False},\n",
" 'C': {'freq': 0, 'ref_base': False},\n",
" 'G': {'freq': 1, 'ref_base': True},\n",
" 'T': {'freq': 0, 'ref_base': False}},\n",
" {'A': {'freq': 1, 'ref_base': True},\n",
" 'C': {'freq': 0, 'ref_base': False},\n",
" 'G': {'freq': 0, 'ref_base': False},\n",
" 'T': {'freq': 0, 'ref_base': False}},\n",
" {'A': {'freq': 0, 'ref_base': False},\n",
" 'C': {'freq': 0, 'ref_base': False},\n",
" 'G': {'freq': 0, 'ref_base': False},\n",
" 'T': {'freq': 1, 'ref_base': True}},\n",
" {'A': {'freq': 0, 'ref_base': False},\n",
" 'C': {'freq': 1, 'ref_base': True},\n",
" 'G': {'freq': 0, 'ref_base': False},\n",
" 'T': {'freq': 0, 'ref_base': False}}]}"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parse the reference sequence"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from Bio import SeqIO\n",
"ref_seq = str(SeqIO.read(open(\"./Cassette-AAV-CMV-GFP-hTK.fa\"), \"fasta\").seq)\n",
"ref_seq"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "pyout",
"prompt_number": 3,
"text": [
"'CTGCGCGCTCGCTCGCTCACTGAGGCCGCCCGGGCAAAGCCCGGGCGTCGGGCGACCTTTGGTCGCCCGGCCTCAGTGAGCGAGCGAGCGCGCAGAGAGGGAGTGGCCAACTCCATCACTAGGGGTTCCTTGTAGTTAATGATTAACCCGCCATGCTACTTATCTACGTAGCCATGCTCTAGGAAGATCTCGACGCGTTGACATTGATTATTGACTAGTTATTAATAGTAATCAATTACGGGGTCATTAGTTCATAGCCCATATATGGAGTTCCGCGTTACATAACTTACGGTAAATGGCCCGCCTGGCTGACCGCCCAACGACCCCCGCCCATTGACGTCAATAATGACGTATGTTCCCATAGTAACGCCAATAGGGACTTTCCATTGACGTCAATGGGTGGAGTATTTACGGTAAACTGCCCACTTGGCAGTACATCAAGTGTATCATATGCCAAGTACGCCCCCTATTGACGTCAATGACGGTAAATGGCCCGCCTGGCATTATGCCCAGTACATGACCTTATGGGACTTTCCTACTTGGCAGTACATCTACGTATTAGTCATCGCTATTACCATGGTGATGCGGTTTTGGCAGTACATCAATGGGCGTGGATAGCGGTTTGACTCACGGGGATTTCCAAGTCTCCACCCCATTGACGTCAATGGGAGTTTGTTTTGGCACCAAAATCAACGGGACTTTCCAAAATGTCGTAACAACTCCGCCCCATTGACGCAAATGGGCGGTAGGCGTGTACGGTGGGAGGTCTATATAAGCAGAGCTCTCTGGCTAACTAGAGAACCCACTGCTTACTGGCTTATCGAAATTAATACGACTCACTATAGGGAGACCCAAGCTGGCTAGCGACCATGGTGAGCAAGGGCGAGGAGCTGTTCACCGGGGTGGTGCCCATCCTGGTCGAGCTGGACGGCGACGTAAACGGCCACAAGTTCAGCGTGTCCGGCGAGGGCGAGGGCGATGCCACCTACGGCAAGCTGACCCTGAAGTTCATCTGCACCACCGGCAAGCTGCCCGTGCCCTGGCCCACCCTCGTGACCACCCTGACCTACGGCGTGCAGTGCTTCAGCCGCTACCCCGACCACATGAAGCAGCACGACTTCTTCAAGTCCGCCATGCCCGAAGGCTACGTCCAGGAGCGCACCATCTTCTTCAAGGACGACGGCAACTACAAGACCCGCGCCGAGGTGAAGTTCGAGGGCGACACCCTGGTGAACCGCATCGAGCTGAAGGGCATCGACTTCAAGGAGGACGGCAACATCCTGGGGCACAAGCTGGAGTACAACTACAACAGCCACAACGTCTATATCATGGCCGACAAGCAGAAGAACGGCATCAAGGTGAACTTCAAGATCCGCCACAACATCGAGGACGGCAGCGTGCAGCTCGCCGACCACTACCAGCAGAACACCCCCATCGGCGACGGCCCCGTGCTGCTGCCCGACAACCACTACCTGAGCACCCAGTCCGCCCTGAGCAAAGACCCCAACGAGAAGCGCGATCACATGGTCCTGCTGGAGTTCGTGACCGCCGCCGGGATCACTCTCGGCATGGACGAGCTGTACAAGTAAGTCGACCCCGCGAAATTAATACGACTCACTATAGGGAGACCACAACGGTTTCCCTCTAGCGGGATCAATTCCGCCCCCCCCCTAACGTTACTGGCCGAAGCCGCTTGGAATAAGGCCGGTGTGCGTTTGTCTATATGTTATTTTCCACCATATTGCCGTCTTTTGGCAATGTGAGGGCCCGGAAACCTGGCCCTGTCTTCTTGACGAGCATTCCTAGGGGTCTTTCCCCTCTCGCCAAAGGAATGCAAGGTCTGTTGAATGTCGTGAAGGAAGCAGTTCCTCTGGAAGCTTCTTGAAGACAAACAACGTCTGTAGCGACCCTTTGCAGGCAGCGGAACCCCCCACCTGGCGACAGGTGCCTCTGCGGCCAAAAGCCACGTGTATAAGATACACCTGCAAAGGCGGCACAACCCCAGTGCCACGTTGTGAGTTGGATAGTTGTGGAAAGAGTCAAATGGCTCTCCTCAAGCGTATTCAACAAGGGGCTGAAGGATGCCCAGAAGGTACCCCATTGTATGGGATCTGATCTGGGGCCTCGGTGCACATGCTTTACATGTGTTTAGTCGAGGTTAAAAAAACGTCTAGGCCCCCCGAACCACGGGGACGTGGTTTTCCTTTGAAAAACACGATGATATCATGAAAAAGCCTGAACTCACCGCGACGTCTGTCGAGAAGTTTCTGATCGAAAAGTTCGACAGCGTCTCCGACCTGATGCAGCTCTCGGAGGGCGAAGAATCTCGTGCTTTCAGCTTCGATGTAGGAGGGCGTGGATATGTCCTGCGGGTAAATAGCTGCGCCGATGGTTTCTACAAAGATCGTTATGTTTATCGGCACTTTGCATCGGCCGCGCTCCCGATTCCGGAAGTGCTTGACATTGGGGAATTCAGCGAGAGCCTGACCTATTGCATCTCCCGCCGTGCACGGGGTGTCACGTTGCAAGACCTGCCTGAAACCGAACTGCCCGCTGTTCTGCAGCCGGTCGCGGAGGCCATGGATGCGATCGCTGCGGCCGATCTTAGCCAGACGAGCGGGTTCGGCCCATTCGGACCGCAAGGAATCGGTCAATACACTACATGGCGTGATTTCATATGCGCGATTGCTGATCCCCATGTGTATCACTGGCAAACTGTGATGGACGACACCGTCAGTGCGTCCGTCGCGCAGGCTCTCGATGAGCTGATGCTTTGGGCCGAGGACTGCCCCGAAGTCCGGCACCTCGTGCACGCGGATTTCGGCTCCAACAATGTCCTGACGGACAATGGCCGCATAACAGCGGTCATTGACTGGAGCGAGGCGATGTTCGGGGATTCCCAATACGAGGTCGCCAACATCTTCTTCTGGAGGCCGTGGTTGGCTTGTATGGAGCAGCAGACGCGCTACTTCGAGCGGAGGCATCCGGAGCTTGCAGGATCGCCGCGGCTCCGGGCGTATATGCTCCGCATTGGTCTTGACCAACTCTATCAGAGCTTGGTTGACGGCAATTTCGATGATGCAGCTTGGGCGCAGGGTCGATGCGACGCAATCGTCCGATCCGGAGCCGGGACTGTCGGGCGTACACAAATCGCCCGCAGAAGCGCGGCCGTCTGGACCGATGGCTGTGTAGAAGTCGCGTCTGCGTTCGACCAGGCTGCGCGTTCTCGCGGCCATAGCAACCGACGTACGGCGTTGCGCCCTCGCCGGCAGCAAGAAGCCACGGAAGTCCGCCCGGAGCAGAAAATGCCCACGCTACTGCGGGTTTATATAGACGGTCCCCACGGGATGGGGAAAACCACCACCACGCAACTGCTGGTGGCCCTGGGTTCGCGCGACGATATCGTCTACGTACCCGAGCCGATGACTTACTGGCGGGTGCTGGGGGCTTCCGAGACAATCGCGAACATCTACACCACACAACACCGCCTCGACCAGGGTGAGATATCGGCCGGGGACGCGGCGGTGGTAATGACAAGCGCCCAGATAACAATGGGCATGCCTTATGCCGTGACCGACGCCGTTCTGGCTCCTCATATCGGGGGGGAGGCTGGGAGCTCACATGCCCCGCCCCCGGCCCTCACCCTCATCTTCGACCGCCATCCCATCGCCGCCCTCCTGTGCTACCCGGCCGCGCGGTACCTTATGGGCAGCATGACCCCCCAGGCCGTGCTGGCGTTCGTGGCCCTCATCCCGCCGACCTTGCCCGGCACCAACATCGTGCTTGGGGCCCTTCCGGAGGACAGACACATCGACCGCCTGGCCAAACGCCAGCGCCCCGGCGAGCGGCTGGACCTGGCTATGCTGGCTGCGATTCGCCGCGTTTACGGGCTACTTGCCAATACGGTGCGGTATCTGCAGTGCGGCGGGTCGTGGCGGGAGGACTGGGGACAGCTTTCGGGGACGGCCGTGCCGCCCCAGGGTGCCGAGCCCCAGAGCAACGCGGGCCCACGACCCCATATCGGGGACACGTTATTTACCCTGTTTCGGGCCCCCGAGTTGCTGGCCCCCAACGGCGACCTGTATAACGTGTTTGCCTGGGCCTTGGACGTCTTGGCCAAACGCCTCCGTTCCATGCACGTCTTTATCCTGGATTACGACCAATCGCCCGCCGGCTGCCGGGACGCCCTGCTGCAACTTACCTCCGGGATGGTCCAGACCCACGTCACCACCCCCGGCTCCATACCGACGATATGCGACCTGGCGCGCACGTTTGCCCGGGAGATGGGGGAGGCTAACTGAAAATCGATGGATCCACTAGTTCTAGAGGGCCCTATTCTATAGTGTCACCTAAATGCTAGAGCTCGCTGATCAGCCTCGACTGTGCCTTCTAGTTGCCAGCCATCTGTTGTTTGCCCCTCCCCCGTGCCTTCCTTGACCCTGGAAGGTGCCACTCCCACTGTCCTTTCCTAATAAAATGAGGAAATTGCATCGCATTGTCTGAGTAGGTGTCATTCTATTCTGGGGGGTGGGGTGGGGCAGGACAGCAAGGGGGAGGATTGGGAAGACAATAGCAGGCATGCTGGGGATGCGGTGGGCTCTATGGCTTCTGAGGCGGAAAGAACCAGGTAGATAAGTAGCATGGCGGGTTAATCATTAACTACAAGGAACCCCTAGTGATGGAGTTGGCCACTCCCTCTCTGCGCGCTCGCTCGCTCACTGAGGCCGGGCGACCAAAGGTCGCCCGACGCCCGGGCTTTGCCCGGGCGGCCTCAGTGAGCGAGCGAGCGCGCAG'"
]
}
],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the min freq"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"min_freq = float(1)/1000\n",
"print min_freq"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"0.001\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Define the Sample in VCF file"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sample_dict = {\n",
" 'C1_AAV': Sample('C1_AAV', min_freq, ref_seq),\n",
" 'RUN1_S7_AAV': Sample('RUN1_S7_AAV', min_freq, ref_seq),\n",
" 'RUN2_S8_AAV': Sample('RUN2_S8_AAV', min_freq, ref_seq),\n",
" 'RUN1_S2_AAV': Sample('RUN1_S2_AAV', min_freq, ref_seq),\n",
" 'RUN2_S1_AAV': Sample('RUN2_S1_AAV', min_freq, ref_seq),\n",
" 'RUN1_S4_AAV': Sample('RUN1_S4_AAV', min_freq, ref_seq),\n",
" 'RUN2_S2_AAV': Sample('RUN2_S2_AAV', min_freq, ref_seq),\n",
" 'RUN1_S6_AAV': Sample('RUN1_S6_AAV', min_freq, ref_seq),\n",
" 'RUN2_S3_AAV': Sample('RUN2_S3_AAV', min_freq, ref_seq),\n",
" 'RUN1_S8_AAV': Sample('RUN1_S8_AAV', min_freq, ref_seq),\n",
" 'RUN2_S4_AAV': Sample('RUN2_S4_AAV', min_freq, ref_seq),\n",
" 'RUN1_S3_AAV': Sample('RUN1_S3_AAV', min_freq, ref_seq),\n",
" 'RUN2_S5_AAV': Sample('RUN2_S5_AAV', min_freq, ref_seq),\n",
" 'RUN1_S5_AAV': Sample('RUN1_S5_AAV', min_freq, ref_seq),\n",
" 'RUN2_S6_AAV': Sample('RUN2_S6_AAV', min_freq, ref_seq)}\n",
" \n",
"# 'RUN2_S7_AAV': \"Negative Control 2\", # Negative control have not interest for this analysis\n",
"# 'RUN1_S1_AAV': \"Negative Control 1\", # Negative control have not interest for this analysis\n",
"# 'RUN2_S9_AAV': Sample('', \"Internal Normalizer 3\", min_freq, ref_seq),"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Open vcf file and parse general metadata"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import HTSeq\n",
"vcfr = HTSeq.VCF_Reader( \"./NoIndel.vcf\" ) \n",
"vcfr.parse_meta() \n",
"vcfr.make_info_dict() "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Parse VCF variants and add to Sample objects if an alternative base if found"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for variant in vcfr:\n",
" # Extract information for samples in sample dict\n",
" for name, sample in sample_dict.items():\n",
" \n",
" # Extract genotypes from the INFO field for the current sample\n",
" GT = [int(i) for i in variant.samples[name]['GT'].split('/')]\n",
" #print variant.samples[name]\n",
" # Extract more informations only if needed\n",
" if len(GT) > 1:\n",
" DPG = [int(i) for i in variant.samples[name]['DPG'].split(',')]\n",
" DP = int(variant.samples[name]['DP'])\n",
" base = [variant.ref]+variant.alt\n",
" \n",
" # Modifify sample for each alternative genotype\n",
" for i in range (len (GT)):\n",
" sample.add_variant(\n",
" pos = variant.pos.pos,\n",
" base = base[GT[i]],\n",
" freq = float(DPG[i])/DP)\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sample_group = {\n",
" \"Internal Normalizer\" : ['RUN1_S7_AAV', 'RUN2_S8_AAV'],}\n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sample_group = {\n",
" \"IEX\" : ['RUN1_S3_AAV', 'RUN2_S5_AAV', 'RUN1_S5_AAV', 'RUN2_S6_AAV']}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sample_group = {\n",
" \"Internal Normalizer\" : ['RUN1_S7_AAV', 'RUN2_S8_AAV'],\n",
" \"CsCl-\" : ['RUN1_S2_AAV', 'RUN2_S1_AAV'],\n",
" \"CsCl+\" : ['RUN1_S4_AAV', 'RUN2_S2_AAV'],\n",
" \"AVB-\" : ['RUN1_S6_AAV', 'RUN2_S3_AAV'],\n",
" \"AVB+\" : ['RUN1_S8_AAV', 'RUN2_S4_AAV'],\n",
" \"IEX-\" : ['RUN1_S3_AAV', 'RUN2_S5_AAV'],\n",
" \"IEX+\" : ['RUN1_S5_AAV', 'RUN2_S6_AAV'],\n",
" \"CsCl\" : ['RUN1_S2_AAV', 'RUN2_S1_AAV', 'RUN1_S4_AAV', 'RUN2_S2_AAV'],\n",
" \"AVB\" : ['RUN1_S6_AAV', 'RUN2_S3_AAV', 'RUN1_S8_AAV', 'RUN2_S4_AAV'],\n",
" \"IEX\" : ['RUN1_S3_AAV', 'RUN2_S5_AAV', 'RUN1_S5_AAV', 'RUN2_S6_AAV'],\n",
" \"ALL_AAV\" : ['RUN1_S2_AAV', 'RUN2_S1_AAV', 'RUN1_S4_AAV', 'RUN2_S2_AAV','RUN1_S6_AAV', 'RUN2_S3_AAV', 'RUN1_S8_AAV', 'RUN2_S4_AAV', 'RUN1_S3_AAV', 'RUN2_S5_AAV', 'RUN1_S5_AAV', 'RUN2_S6_AAV']}"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"for name, group in sample_group.items():\n",
" sample = Sample('name', min_freq, ref_seq)\n",
" \n",
" # For each position along the length of reference the sequence\n",
" for i in range (len(ref_seq)):\n",
" for base in [\"A\", \"C\", \"G\", \"T\"]:\n",
" \n",
" # Not interested by the frequency of the reference base\n",
" if sample.position_list[i][base]['ref_base']:\n",
" sample.position_list[i][base][\"freq\"] = 0.0\n",
" \n",
" # Extract the list of frequency for this base in all samples\n",
" else: \n",
" var_freq_list = [sample_dict[var_name].position_list[i][base][\"freq\"] for var_name in group]\n",
" #print (var_freq_list)\n",
" \n",
" # At least half of the samples in the list must have the variant with a frequency > 0 else it is considered null\n",
" if len([j for j in var_freq_list if j != 0]) >= len(var_freq_list)/2:\n",
" sample.position_list[i][base][\"freq\"] = sum(var_freq_list)/len(var_freq_list)\n",
" #print (\"TRUE\")\n",
" else:\n",
" sample.position_list[i][base][\"freq\"] = 0.0\n",
" \n",
" # Write 1 report per sample\n",
" with open (name+\"_report.csv\", \"wb\") as report:\n",
" report.write(\"Pos\\tAlt_A\\tAlt_C\\tAlt_G\\tAlt_T\\tsum\\n\")\n",
" for n, p in enumerate(sample.position_list):\n",
" report.write (\"{}\\t{}\\t{}\\t{}\\t{}\\t{}\\n\".format(\n",
" n, p[\"A\"]['freq'], p[\"C\"]['freq'], p[\"G\"]['freq'], p[\"T\"]['freq'],\n",
" p[\"A\"]['freq'] + p[\"C\"]['freq'] + p[\"G\"]['freq'] + p[\"T\"]['freq']))\n",
" \n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
}
],
"metadata": {}
}
]
} | gpl-2.0 |
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