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turbomanage/training-data-analyst
courses/machine_learning/deepdive2/launching_into_ml/solutions/explore_data.ipynb
1
231211
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Explore and create ML datasets\n", "\n", "In this notebook, we will explore data corresponding to taxi rides in New York City to build a Machine Learning model in support of a fare-estimation tool. The idea is to suggest a likely fare to taxi riders so that they are not surprised, and so that they can protest if the charge is much higher than expected.\n", "\n", "## Learning Objectives\n", "* Access and explore a public BigQuery dataset on NYC Taxi Cab rides\n", "* Visualize your dataset using the Seaborn library\n", "* Inspect and clean-up the dataset for future ML model training\n", "* Create a benchmark to judge future ML model performance off of\n", "\n", "Each learning objective will correspond to a __#TODO__ in the [student lab notebook](../labs/explore_data.ipynb) -- try to complete that notebook first before reviewing this solution notebook. \n", "\n", "Let's start off with the Python imports that we need." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from google.cloud import bigquery\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3> Extract sample data from BigQuery </h3>\n", "\n", "The dataset that we will use is <a href=\"https://bigquery.cloud.google.com/table/nyc-tlc:yellow.trips\">a BigQuery public dataset</a>. Click on the link, and look at the column names. Switch to the Details tab to verify that the number of records is one billion, and then switch to the Preview tab to look at a few rows.\n", "\n", "Let's write a SQL query to pick up interesting fields from the dataset. It's a good idea to get the timestamp in a predictable format." ] }, { "cell_type": "code", "execution_count": 2, "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>pickup_datetime</th>\n", " <th>pickup_longitude</th>\n", " <th>pickup_latitude</th>\n", " <th>dropoff_longitude</th>\n", " <th>dropoff_latitude</th>\n", " <th>passenger_count</th>\n", " <th>trip_distance</th>\n", " <th>tolls_amount</th>\n", " <th>fare_amount</th>\n", " <th>total_amount</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>2010-03-04 00:35:16 UTC</td>\n", " <td>-74.035201</td>\n", " <td>40.721548</td>\n", " <td>-74.035201</td>\n", " <td>40.721548</td>\n", " <td>1</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", " <td>1</td>\n", " <td>2010-03-15 17:18:34 UTC</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1</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", " <td>2</td>\n", " <td>2015-03-18 01:07:02 UTC</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>5</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", " <td>3</td>\n", " <td>2015-03-09 18:24:03 UTC</td>\n", " <td>-73.937248</td>\n", " <td>40.758202</td>\n", " <td>-73.937263</td>\n", " <td>40.758190</td>\n", " <td>1</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", " <td>4</td>\n", " <td>2010-03-06 06:33:41 UTC</td>\n", " <td>-73.785514</td>\n", " <td>40.645400</td>\n", " <td>-73.784564</td>\n", " <td>40.648681</td>\n", " <td>2</td>\n", " <td>4.1</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <td>5</td>\n", " <td>2013-08-07 00:42:45 UTC</td>\n", " <td>-74.025817</td>\n", " <td>40.763044</td>\n", " <td>-74.046752</td>\n", " <td>40.783240</td>\n", " <td>1</td>\n", " <td>4.8</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <td>6</td>\n", " <td>2015-04-26 02:56:37 UTC</td>\n", " <td>-73.987656</td>\n", " <td>40.771656</td>\n", " <td>-73.987556</td>\n", " <td>40.771751</td>\n", " <td>1</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", " <td>7</td>\n", " <td>2015-04-29 18:45:03 UTC</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1</td>\n", " <td>1.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <td>8</td>\n", " <td>2010-03-11 21:24:48 UTC</td>\n", " <td>-74.571511</td>\n", " <td>40.910800</td>\n", " <td>-74.628928</td>\n", " <td>40.964321</td>\n", " <td>1</td>\n", " <td>68.4</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <td>9</td>\n", " <td>2013-08-24 01:58:23 UTC</td>\n", " <td>-73.972171</td>\n", " <td>40.759439</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>4</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", "</div>" ], "text/plain": [ " pickup_datetime pickup_longitude pickup_latitude \\\n", "0 2010-03-04 00:35:16 UTC -74.035201 40.721548 \n", "1 2010-03-15 17:18:34 UTC 0.000000 0.000000 \n", "2 2015-03-18 01:07:02 UTC 0.000000 0.000000 \n", "3 2015-03-09 18:24:03 UTC -73.937248 40.758202 \n", "4 2010-03-06 06:33:41 UTC -73.785514 40.645400 \n", "5 2013-08-07 00:42:45 UTC -74.025817 40.763044 \n", "6 2015-04-26 02:56:37 UTC -73.987656 40.771656 \n", "7 2015-04-29 18:45:03 UTC 0.000000 0.000000 \n", "8 2010-03-11 21:24:48 UTC -74.571511 40.910800 \n", "9 2013-08-24 01:58:23 UTC -73.972171 40.759439 \n", "\n", " dropoff_longitude dropoff_latitude passenger_count trip_distance \\\n", "0 -74.035201 40.721548 1 0.0 \n", "1 0.000000 0.000000 1 0.0 \n", "2 0.000000 0.000000 5 0.0 \n", "3 -73.937263 40.758190 1 0.0 \n", "4 -73.784564 40.648681 2 4.1 \n", "5 -74.046752 40.783240 1 4.8 \n", "6 -73.987556 40.771751 1 0.0 \n", "7 0.000000 0.000000 1 1.0 \n", "8 -74.628928 40.964321 1 68.4 \n", "9 0.000000 0.000000 4 0.0 \n", "\n", " tolls_amount fare_amount total_amount \n", "0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "5 0.0 0.0 0.0 \n", "6 0.0 0.0 0.0 \n", "7 0.0 0.0 0.0 \n", "8 0.0 0.0 0.0 \n", "9 0.0 0.0 0.0 " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%%bigquery\n", "SELECT\n", " FORMAT_TIMESTAMP(\n", " \"%Y-%m-%d %H:%M:%S %Z\", pickup_datetime) AS pickup_datetime,\n", " pickup_longitude, pickup_latitude, dropoff_longitude,\n", " dropoff_latitude, passenger_count, trip_distance, tolls_amount, \n", " fare_amount, total_amount \n", "FROM\n", " `nyc-tlc.yellow.trips`\n", "LIMIT 10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's increase the number of records so that we can do some neat graphs. There is no guarantee about the order in which records are returned, and so no guarantee about which records get returned if we simply increase the LIMIT. To properly sample the dataset, let's use the HASH of the pickup time and return 1 in 100,000 records -- because there are 1 billion records in the data, we should get back approximately 10,000 records if we do this.\n", "\n", "We will also store the BigQuery result in a Pandas dataframe named \"trips\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "%%bigquery trips\n", "SELECT\n", " FORMAT_TIMESTAMP(\n", " \"%Y-%m-%d %H:%M:%S %Z\", pickup_datetime) AS pickup_datetime,\n", " pickup_longitude, pickup_latitude, \n", " dropoff_longitude, dropoff_latitude,\n", " passenger_count,\n", " trip_distance,\n", " tolls_amount,\n", " fare_amount,\n", " total_amount\n", "FROM\n", " `nyc-tlc.yellow.trips`\n", "WHERE\n", " ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 100000)) = 1" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10789\n" ] } ], "source": [ "print(len(trips))" ] }, { "cell_type": "code", "execution_count": 5, "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>pickup_datetime</th>\n", " <th>pickup_longitude</th>\n", " <th>pickup_latitude</th>\n", " <th>dropoff_longitude</th>\n", " <th>dropoff_latitude</th>\n", " <th>passenger_count</th>\n", " <th>trip_distance</th>\n", " <th>tolls_amount</th>\n", " <th>fare_amount</th>\n", " <th>total_amount</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>0</td>\n", " <td>2009-02-12 17:51:38 UTC</td>\n", " <td>-73.965325</td>\n", " <td>40.769670</td>\n", " <td>-73.980505</td>\n", " <td>40.748393</td>\n", " <td>1</td>\n", " <td>1.70</td>\n", " <td>0.0</td>\n", " <td>11.1</td>\n", " <td>11.60</td>\n", " </tr>\n", " <tr>\n", " <td>1</td>\n", " <td>2012-02-27 09:19:10 UTC</td>\n", " <td>-73.874431</td>\n", " <td>40.774011</td>\n", " <td>-73.983967</td>\n", " <td>40.744082</td>\n", " <td>1</td>\n", " <td>11.60</td>\n", " <td>4.8</td>\n", " <td>27.7</td>\n", " <td>38.00</td>\n", " </tr>\n", " <tr>\n", " <td>2</td>\n", " <td>2014-05-20 23:09:00 UTC</td>\n", " <td>-73.995203</td>\n", " <td>40.727307</td>\n", " <td>-73.948775</td>\n", " <td>40.813487</td>\n", " <td>1</td>\n", " <td>10.31</td>\n", " <td>0.0</td>\n", " <td>33.5</td>\n", " <td>38.00</td>\n", " </tr>\n", " <tr>\n", " <td>3</td>\n", " <td>2014-04-30 16:45:10 UTC</td>\n", " <td>-73.989434</td>\n", " <td>40.756601</td>\n", " <td>-73.949989</td>\n", " <td>40.826892</td>\n", " <td>1</td>\n", " <td>6.20</td>\n", " <td>0.0</td>\n", " <td>24.5</td>\n", " <td>31.20</td>\n", " </tr>\n", " <tr>\n", " <td>4</td>\n", " <td>2013-04-09 09:39:13 UTC</td>\n", " <td>-73.981443</td>\n", " <td>40.763466</td>\n", " <td>-74.010072</td>\n", " <td>40.704927</td>\n", " <td>1</td>\n", " <td>6.50</td>\n", " <td>0.0</td>\n", " <td>24.5</td>\n", " <td>30.00</td>\n", " </tr>\n", " <tr>\n", " <td>5</td>\n", " <td>2014-04-19 14:08:46 UTC</td>\n", " <td>-73.964716</td>\n", " <td>40.773071</td>\n", " <td>-73.997511</td>\n", " <td>40.697289</td>\n", " <td>1</td>\n", " <td>8.70</td>\n", " <td>0.0</td>\n", " <td>26.5</td>\n", " <td>33.75</td>\n", " </tr>\n", " <tr>\n", " <td>6</td>\n", " <td>2009-03-08 08:51:42 UTC</td>\n", " <td>-73.777129</td>\n", " <td>40.645050</td>\n", " <td>-73.944360</td>\n", " <td>40.662902</td>\n", " <td>1</td>\n", " <td>15.40</td>\n", " <td>0.0</td>\n", " <td>38.9</td>\n", " <td>38.90</td>\n", " </tr>\n", " <tr>\n", " <td>7</td>\n", " <td>2014-05-17 15:15:00 UTC</td>\n", " <td>-73.980682</td>\n", " <td>40.734032</td>\n", " <td>-73.961948</td>\n", " <td>40.755545</td>\n", " <td>1</td>\n", " <td>2.20</td>\n", " <td>0.0</td>\n", " <td>22.5</td>\n", " <td>23.00</td>\n", " </tr>\n", " <tr>\n", " <td>8</td>\n", " <td>2009-11-01 02:59:23 UTC</td>\n", " <td>-74.006934</td>\n", " <td>40.734067</td>\n", " <td>-73.895708</td>\n", " <td>40.851511</td>\n", " <td>4</td>\n", " <td>12.10</td>\n", " <td>0.0</td>\n", " <td>28.5</td>\n", " <td>29.50</td>\n", " </tr>\n", " <tr>\n", " <td>9</td>\n", " <td>2009-03-28 20:30:35 UTC</td>\n", " <td>-73.973926</td>\n", " <td>40.757725</td>\n", " <td>-73.981695</td>\n", " <td>40.761591</td>\n", " <td>1</td>\n", " <td>0.50</td>\n", " <td>0.0</td>\n", " <td>4.6</td>\n", " <td>4.60</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pickup_datetime pickup_longitude pickup_latitude \\\n", "0 2009-02-12 17:51:38 UTC -73.965325 40.769670 \n", "1 2012-02-27 09:19:10 UTC -73.874431 40.774011 \n", "2 2014-05-20 23:09:00 UTC -73.995203 40.727307 \n", "3 2014-04-30 16:45:10 UTC -73.989434 40.756601 \n", "4 2013-04-09 09:39:13 UTC -73.981443 40.763466 \n", "5 2014-04-19 14:08:46 UTC -73.964716 40.773071 \n", "6 2009-03-08 08:51:42 UTC -73.777129 40.645050 \n", "7 2014-05-17 15:15:00 UTC -73.980682 40.734032 \n", "8 2009-11-01 02:59:23 UTC -74.006934 40.734067 \n", "9 2009-03-28 20:30:35 UTC -73.973926 40.757725 \n", "\n", " dropoff_longitude dropoff_latitude passenger_count trip_distance \\\n", "0 -73.980505 40.748393 1 1.70 \n", "1 -73.983967 40.744082 1 11.60 \n", "2 -73.948775 40.813487 1 10.31 \n", "3 -73.949989 40.826892 1 6.20 \n", "4 -74.010072 40.704927 1 6.50 \n", "5 -73.997511 40.697289 1 8.70 \n", "6 -73.944360 40.662902 1 15.40 \n", "7 -73.961948 40.755545 1 2.20 \n", "8 -73.895708 40.851511 4 12.10 \n", "9 -73.981695 40.761591 1 0.50 \n", "\n", " tolls_amount fare_amount total_amount \n", "0 0.0 11.1 11.60 \n", "1 4.8 27.7 38.00 \n", "2 0.0 33.5 38.00 \n", "3 0.0 24.5 31.20 \n", "4 0.0 24.5 30.00 \n", "5 0.0 26.5 33.75 \n", "6 0.0 38.9 38.90 \n", "7 0.0 22.5 23.00 \n", "8 0.0 28.5 29.50 \n", "9 0.0 4.6 4.60 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# We can slice Pandas dataframes as if they were arrays\n", "trips[:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3> Exploring data </h3>\n", "\n", "Let's explore this dataset and clean it up as necessary. We'll use the Python Seaborn package to visualize graphs and Pandas to do the slicing and filtering." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = sns.regplot(\n", " x=\"trip_distance\", y=\"fare_amount\",\n", " fit_reg=False, ci=None, truncate=True, data=trips)\n", "ax.figure.set_size_inches(10, 8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hmm ... do you see something wrong with the data that needs addressing?\n", "\n", "It appears that we have a lot of invalid data that is being coded as zero distance and some fare amounts that are definitely illegitimate. Let's remove them from our analysis. We can do this by modifying the BigQuery query to keep only trips longer than zero miles and fare amounts that are at least the minimum cab fare ($2.50).\n", "\n", "Note the extra WHERE clauses." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "%%bigquery trips\n", "SELECT\n", " FORMAT_TIMESTAMP(\n", " \"%Y-%m-%d %H:%M:%S %Z\", pickup_datetime) AS pickup_datetime,\n", " pickup_longitude, pickup_latitude, \n", " dropoff_longitude, dropoff_latitude,\n", " passenger_count,\n", " trip_distance,\n", " tolls_amount,\n", " fare_amount,\n", " total_amount\n", "FROM\n", " `nyc-tlc.yellow.trips`\n", "WHERE\n", " ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 100000)) = 1\n", " AND trip_distance > 0\n", " AND fare_amount >= 2.5" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10716\n" ] } ], "source": [ "print(len(trips))" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ax = sns.regplot(\n", " x=\"trip_distance\", y=\"fare_amount\",\n", " fit_reg=False, ci=None, truncate=True, data=trips)\n", "ax.figure.set_size_inches(10, 8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What's up with the streaks around 45 dollars and 50 dollars? Those are fixed-amount rides from JFK and La Guardia airports into anywhere in Manhattan, i.e. to be expected. Let's list the data to make sure the values look reasonable.\n", "\n", "Let's also examine whether the toll amount is captured in the total amount." ] }, { "cell_type": "code", "execution_count": 10, "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>pickup_datetime</th>\n", " <th>pickup_longitude</th>\n", " <th>pickup_latitude</th>\n", " <th>dropoff_longitude</th>\n", " <th>dropoff_latitude</th>\n", " <th>passenger_count</th>\n", " <th>trip_distance</th>\n", " <th>tolls_amount</th>\n", " <th>fare_amount</th>\n", " <th>total_amount</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>2</td>\n", " <td>2012-02-27 09:19:10 UTC</td>\n", " <td>-73.874431</td>\n", " <td>40.774011</td>\n", " <td>-73.983967</td>\n", " <td>40.744082</td>\n", " <td>1</td>\n", " <td>11.6</td>\n", " <td>4.8</td>\n", " <td>27.7</td>\n", " <td>38.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pickup_datetime pickup_longitude pickup_latitude \\\n", "2 2012-02-27 09:19:10 UTC -73.874431 40.774011 \n", "\n", " dropoff_longitude dropoff_latitude passenger_count trip_distance \\\n", "2 -73.983967 40.744082 1 11.6 \n", "\n", " tolls_amount fare_amount total_amount \n", "2 4.8 27.7 38.0 " ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tollrides = trips[trips[\"tolls_amount\"] > 0]\n", "tollrides[tollrides[\"pickup_datetime\"] == \"2012-02-27 09:19:10 UTC\"]" ] }, { "cell_type": "code", "execution_count": 12, "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>pickup_datetime</th>\n", " <th>pickup_longitude</th>\n", " <th>pickup_latitude</th>\n", " <th>dropoff_longitude</th>\n", " <th>dropoff_latitude</th>\n", " <th>passenger_count</th>\n", " <th>trip_distance</th>\n", " <th>tolls_amount</th>\n", " <th>fare_amount</th>\n", " <th>total_amount</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>47</td>\n", " <td>2012-02-27 09:19:10 UTC</td>\n", " <td>-73.972311</td>\n", " <td>40.753067</td>\n", " <td>-73.957389</td>\n", " <td>40.817824</td>\n", " <td>1</td>\n", " <td>5.6</td>\n", " <td>0.0</td>\n", " <td>16.9</td>\n", " <td>22.62</td>\n", " </tr>\n", " <tr>\n", " <td>7750</td>\n", " <td>2012-02-27 09:19:10 UTC</td>\n", " <td>-73.987582</td>\n", " <td>40.725468</td>\n", " <td>-74.016628</td>\n", " <td>40.715534</td>\n", " <td>1</td>\n", " <td>2.8</td>\n", " <td>0.0</td>\n", " <td>12.1</td>\n", " <td>15.75</td>\n", " </tr>\n", " <tr>\n", " <td>10544</td>\n", " <td>2012-02-27 09:19:10 UTC</td>\n", " <td>-74.015483</td>\n", " <td>40.715279</td>\n", " <td>-73.998045</td>\n", " <td>40.756273</td>\n", " <td>1</td>\n", " <td>3.3</td>\n", " <td>0.0</td>\n", " <td>10.9</td>\n", " <td>13.40</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pickup_datetime pickup_longitude pickup_latitude \\\n", "47 2012-02-27 09:19:10 UTC -73.972311 40.753067 \n", "7750 2012-02-27 09:19:10 UTC -73.987582 40.725468 \n", "10544 2012-02-27 09:19:10 UTC -74.015483 40.715279 \n", "\n", " dropoff_longitude dropoff_latitude passenger_count trip_distance \\\n", "47 -73.957389 40.817824 1 5.6 \n", "7750 -74.016628 40.715534 1 2.8 \n", "10544 -73.998045 40.756273 1 3.3 \n", "\n", " tolls_amount fare_amount total_amount \n", "47 0.0 16.9 22.62 \n", "7750 0.0 12.1 15.75 \n", "10544 0.0 10.9 13.40 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "notollrides = trips[trips[\"tolls_amount\"] == 0]\n", "notollrides[notollrides[\"pickup_datetime\"] == \"2012-02-27 09:19:10 UTC\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at a few samples above, it should be clear that the total amount reflects fare amount, toll and tip somewhat arbitrarily -- this is because when customers pay cash, the tip is not known. So, we'll use the sum of fare_amount + tolls_amount as what needs to be predicted. Tips are discretionary and do not have to be included in our fare estimation tool.\n", "\n", "Let's also look at the distribution of values within the columns." ] }, { "cell_type": "code", "execution_count": 13, "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>pickup_longitude</th>\n", " <th>pickup_latitude</th>\n", " <th>dropoff_longitude</th>\n", " <th>dropoff_latitude</th>\n", " <th>passenger_count</th>\n", " <th>trip_distance</th>\n", " <th>tolls_amount</th>\n", " <th>fare_amount</th>\n", " <th>total_amount</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>count</td>\n", " <td>10716.000000</td>\n", " <td>10716.000000</td>\n", " <td>10716.000000</td>\n", " <td>10716.000000</td>\n", " <td>10716.000000</td>\n", " <td>10716.000000</td>\n", " <td>10716.000000</td>\n", " <td>10716.000000</td>\n", " <td>10716.000000</td>\n", " </tr>\n", " <tr>\n", " <td>mean</td>\n", " <td>-72.602192</td>\n", " <td>40.002372</td>\n", " <td>-72.594838</td>\n", " <td>40.002052</td>\n", " <td>1.650056</td>\n", " <td>2.856395</td>\n", " <td>0.226428</td>\n", " <td>11.109446</td>\n", " <td>13.217078</td>\n", " </tr>\n", " <tr>\n", " <td>std</td>\n", " <td>9.982373</td>\n", " <td>5.474670</td>\n", " <td>10.004324</td>\n", " <td>5.474648</td>\n", " <td>1.283577</td>\n", " <td>3.322024</td>\n", " <td>1.135934</td>\n", " <td>9.137710</td>\n", " <td>10.953156</td>\n", " </tr>\n", " <tr>\n", " <td>min</td>\n", " <td>-74.258183</td>\n", " <td>0.000000</td>\n", " <td>-74.260472</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.010000</td>\n", " <td>0.000000</td>\n", " <td>2.500000</td>\n", " <td>2.500000</td>\n", " </tr>\n", " <tr>\n", " <td>25%</td>\n", " <td>-73.992153</td>\n", " <td>40.735936</td>\n", " <td>-73.991566</td>\n", " <td>40.734310</td>\n", " <td>1.000000</td>\n", " <td>1.040000</td>\n", " <td>0.000000</td>\n", " <td>6.000000</td>\n", " <td>7.300000</td>\n", " </tr>\n", " <tr>\n", " <td>50%</td>\n", " <td>-73.981851</td>\n", " <td>40.753264</td>\n", " <td>-73.980373</td>\n", " <td>40.752956</td>\n", " <td>1.000000</td>\n", " <td>1.770000</td>\n", " <td>0.000000</td>\n", " <td>8.500000</td>\n", " <td>10.000000</td>\n", " </tr>\n", " <tr>\n", " <td>75%</td>\n", " <td>-73.967400</td>\n", " <td>40.767340</td>\n", " <td>-73.964142</td>\n", " <td>40.767510</td>\n", " <td>2.000000</td>\n", " <td>3.160000</td>\n", " <td>0.000000</td>\n", " <td>12.500000</td>\n", " <td>14.600000</td>\n", " </tr>\n", " <tr>\n", " <td>max</td>\n", " <td>0.000000</td>\n", " <td>41.366138</td>\n", " <td>0.000000</td>\n", " <td>41.366138</td>\n", " <td>6.000000</td>\n", " <td>42.800000</td>\n", " <td>16.000000</td>\n", " <td>179.000000</td>\n", " <td>179.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pickup_longitude pickup_latitude dropoff_longitude dropoff_latitude \\\n", "count 10716.000000 10716.000000 10716.000000 10716.000000 \n", "mean -72.602192 40.002372 -72.594838 40.002052 \n", "std 9.982373 5.474670 10.004324 5.474648 \n", "min -74.258183 0.000000 -74.260472 0.000000 \n", "25% -73.992153 40.735936 -73.991566 40.734310 \n", "50% -73.981851 40.753264 -73.980373 40.752956 \n", "75% -73.967400 40.767340 -73.964142 40.767510 \n", "max 0.000000 41.366138 0.000000 41.366138 \n", "\n", " passenger_count trip_distance tolls_amount fare_amount \\\n", "count 10716.000000 10716.000000 10716.000000 10716.000000 \n", "mean 1.650056 2.856395 0.226428 11.109446 \n", "std 1.283577 3.322024 1.135934 9.137710 \n", "min 0.000000 0.010000 0.000000 2.500000 \n", "25% 1.000000 1.040000 0.000000 6.000000 \n", "50% 1.000000 1.770000 0.000000 8.500000 \n", "75% 2.000000 3.160000 0.000000 12.500000 \n", "max 6.000000 42.800000 16.000000 179.000000 \n", "\n", " total_amount \n", "count 10716.000000 \n", "mean 13.217078 \n", "std 10.953156 \n", "min 2.500000 \n", "25% 7.300000 \n", "50% 10.000000 \n", "75% 14.600000 \n", "max 179.000000 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "trips.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hmm ... The min, max of longitude look strange.\n", "\n", "Finally, let's actually look at the start and end of a few of the trips." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def showrides(df, numlines):\n", " lats = []\n", " lons = []\n", " for iter, row in df[:numlines].iterrows():\n", " lons.append(row[\"pickup_longitude\"])\n", " lons.append(row[\"dropoff_longitude\"])\n", " lons.append(None)\n", " lats.append(row[\"pickup_latitude\"])\n", " lats.append(row[\"dropoff_latitude\"])\n", " lats.append(None)\n", "\n", " sns.set_style(\"darkgrid\")\n", " plt.figure(figsize=(10, 8))\n", " plt.plot(lons, lats)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "showrides(notollrides, 10)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x576 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "showrides(tollrides, 10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you'd expect, rides that involve a toll are longer than the typical ride." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3> Quality control and other preprocessing </h3>\n", "\n", "We need to do some clean-up of the data:\n", "<ol>\n", "<li>New York city longitudes are around -74 and latitudes are around 41.</li>\n", "<li>We shouldn't have zero passengers.</li>\n", "<li>Clean up the total_amount column to reflect only fare_amount and tolls_amount, and then remove those two columns.</li>\n", "<li>Before the ride starts, we'll know the pickup and dropoff locations, but not the trip distance (that depends on the route taken), so remove it from the ML dataset</li>\n", "<li>Discard the timestamp</li>\n", "</ol>\n", "\n", "We could do preprocessing in BigQuery, similar to how we removed the zero-distance rides, but just to show you another option, let's do this in Python. In production, we'll have to carry out the same preprocessing on the real-time input data. \n", "\n", "This sort of preprocessing of input data is quite common in ML, especially if the quality-control is dynamic." ] }, { "cell_type": "code", "execution_count": 17, "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>pickup_longitude</th>\n", " <th>pickup_latitude</th>\n", " <th>dropoff_longitude</th>\n", " <th>dropoff_latitude</th>\n", " <th>passenger_count</th>\n", " <th>fare_amount</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>count</td>\n", " <td>10476.000000</td>\n", " <td>10476.000000</td>\n", " <td>10476.000000</td>\n", " <td>10476.000000</td>\n", " <td>10476.000000</td>\n", " <td>10476.000000</td>\n", " </tr>\n", " <tr>\n", " <td>mean</td>\n", " <td>-73.975206</td>\n", " <td>40.751526</td>\n", " <td>-73.974373</td>\n", " <td>40.751199</td>\n", " <td>1.653303</td>\n", " <td>11.349003</td>\n", " </tr>\n", " <tr>\n", " <td>std</td>\n", " <td>0.038547</td>\n", " <td>0.029187</td>\n", " <td>0.039086</td>\n", " <td>0.033147</td>\n", " <td>1.278827</td>\n", " <td>9.878630</td>\n", " </tr>\n", " <tr>\n", " <td>min</td>\n", " <td>-74.258183</td>\n", " <td>40.452290</td>\n", " <td>-74.260472</td>\n", " <td>40.417750</td>\n", " <td>1.000000</td>\n", " <td>2.500000</td>\n", " </tr>\n", " <tr>\n", " <td>25%</td>\n", " <td>-73.992336</td>\n", " <td>40.737600</td>\n", " <td>-73.991739</td>\n", " <td>40.735904</td>\n", " <td>1.000000</td>\n", " <td>6.000000</td>\n", " </tr>\n", " <tr>\n", " <td>50%</td>\n", " <td>-73.982090</td>\n", " <td>40.754020</td>\n", " <td>-73.980780</td>\n", " <td>40.753597</td>\n", " <td>1.000000</td>\n", " <td>8.500000</td>\n", " </tr>\n", " <tr>\n", " <td>75%</td>\n", " <td>-73.968517</td>\n", " <td>40.767774</td>\n", " <td>-73.965851</td>\n", " <td>40.767921</td>\n", " <td>2.000000</td>\n", " <td>12.500000</td>\n", " </tr>\n", " <tr>\n", " <td>max</td>\n", " <td>-73.137393</td>\n", " <td>41.366138</td>\n", " <td>-73.137393</td>\n", " <td>41.366138</td>\n", " <td>6.000000</td>\n", " <td>179.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pickup_longitude pickup_latitude dropoff_longitude dropoff_latitude \\\n", "count 10476.000000 10476.000000 10476.000000 10476.000000 \n", "mean -73.975206 40.751526 -73.974373 40.751199 \n", "std 0.038547 0.029187 0.039086 0.033147 \n", "min -74.258183 40.452290 -74.260472 40.417750 \n", "25% -73.992336 40.737600 -73.991739 40.735904 \n", "50% -73.982090 40.754020 -73.980780 40.753597 \n", "75% -73.968517 40.767774 -73.965851 40.767921 \n", "max -73.137393 41.366138 -73.137393 41.366138 \n", "\n", " passenger_count fare_amount \n", "count 10476.000000 10476.000000 \n", "mean 1.653303 11.349003 \n", "std 1.278827 9.878630 \n", "min 1.000000 2.500000 \n", "25% 1.000000 6.000000 \n", "50% 1.000000 8.500000 \n", "75% 2.000000 12.500000 \n", "max 6.000000 179.000000 " ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def preprocess(trips_in):\n", " trips = trips_in.copy(deep=True)\n", " trips.fare_amount = trips.fare_amount + trips.tolls_amount\n", " del trips[\"tolls_amount\"]\n", " del trips[\"total_amount\"]\n", " del trips[\"trip_distance\"] # we won't know this in advance!\n", "\n", " qc = np.all([\n", " trips[\"pickup_longitude\"] > -78,\n", " trips[\"pickup_longitude\"] < -70,\n", " trips[\"dropoff_longitude\"] > -78,\n", " trips[\"dropoff_longitude\"] < -70,\n", " trips[\"pickup_latitude\"] > 37,\n", " trips[\"pickup_latitude\"] < 45,\n", " trips[\"dropoff_latitude\"] > 37,\n", " trips[\"dropoff_latitude\"] < 45,\n", " trips[\"passenger_count\"] > 0\n", " ], axis=0)\n", "\n", " return trips[qc]\n", "\n", "tripsqc = preprocess(trips)\n", "tripsqc.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The quality control has removed about 300 rows (11400 - 11101) or about 3% of the data. This seems reasonable.\n", "\n", "Let's move on to creating the ML datasets.\n", "\n", "<h3> Create ML datasets </h3>\n", "\n", "Let's split the QCed data randomly into training, validation and test sets.\n", "Note that this is not the entire data. We have 1 billion taxicab rides. This is just splitting the 10,000 rides to show you how it's done on smaller datasets. In reality, we'll have to do it on all 1 billion rides and this won't scale." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "shuffled = tripsqc.sample(frac=1)\n", "trainsize = int(len(shuffled[\"fare_amount\"]) * 0.70)\n", "validsize = int(len(shuffled[\"fare_amount\"]) * 0.15)\n", "\n", "df_train = shuffled.iloc[:trainsize, :]\n", "df_valid = shuffled.iloc[trainsize:(trainsize + validsize), :]\n", "df_test = shuffled.iloc[(trainsize + validsize):, :]" ] }, { "cell_type": "code", "execution_count": 19, "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>pickup_datetime</th>\n", " <th>pickup_longitude</th>\n", " <th>pickup_latitude</th>\n", " <th>dropoff_longitude</th>\n", " <th>dropoff_latitude</th>\n", " <th>passenger_count</th>\n", " <th>fare_amount</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>9718</td>\n", " <td>2011-07-27 09:45:56 UTC</td>\n", " <td>-73.98012</td>\n", " <td>40.730552</td>\n", " <td>-73.990246</td>\n", " <td>40.756076</td>\n", " <td>2</td>\n", " <td>11.3</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pickup_datetime pickup_longitude pickup_latitude \\\n", "9718 2011-07-27 09:45:56 UTC -73.98012 40.730552 \n", "\n", " dropoff_longitude dropoff_latitude passenger_count fare_amount \n", "9718 -73.990246 40.756076 2 11.3 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_train.head(n=1)" ] }, { "cell_type": "code", "execution_count": 20, "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>pickup_longitude</th>\n", " <th>pickup_latitude</th>\n", " <th>dropoff_longitude</th>\n", " <th>dropoff_latitude</th>\n", " <th>passenger_count</th>\n", " <th>fare_amount</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>count</td>\n", " <td>7333.000000</td>\n", " <td>7333.000000</td>\n", " <td>7333.000000</td>\n", " <td>7333.000000</td>\n", " <td>7333.000000</td>\n", " <td>7333.000000</td>\n", " </tr>\n", " <tr>\n", " <td>mean</td>\n", " <td>-73.975458</td>\n", " <td>40.751361</td>\n", " <td>-73.974390</td>\n", " <td>40.751321</td>\n", " <td>1.642029</td>\n", " <td>11.339677</td>\n", " </tr>\n", " <tr>\n", " <td>std</td>\n", " <td>0.035886</td>\n", " <td>0.027382</td>\n", " <td>0.038016</td>\n", " <td>0.032218</td>\n", " <td>1.258757</td>\n", " <td>9.643396</td>\n", " </tr>\n", " <tr>\n", " <td>min</td>\n", " <td>-74.258183</td>\n", " <td>40.608573</td>\n", " <td>-74.260472</td>\n", " <td>40.561076</td>\n", " <td>1.000000</td>\n", " <td>2.500000</td>\n", " </tr>\n", " <tr>\n", " <td>25%</td>\n", " <td>-73.992532</td>\n", " <td>40.737748</td>\n", " <td>-73.991604</td>\n", " <td>40.736398</td>\n", " <td>1.000000</td>\n", " <td>6.000000</td>\n", " </tr>\n", " <tr>\n", " <td>50%</td>\n", " <td>-73.982140</td>\n", " <td>40.754077</td>\n", " <td>-73.980835</td>\n", " <td>40.753983</td>\n", " <td>1.000000</td>\n", " <td>8.500000</td>\n", " </tr>\n", " <tr>\n", " <td>75%</td>\n", " <td>-73.968541</td>\n", " <td>40.767605</td>\n", " <td>-73.965786</td>\n", " <td>40.768035</td>\n", " <td>2.000000</td>\n", " <td>12.500000</td>\n", " </tr>\n", " <tr>\n", " <td>max</td>\n", " <td>-73.137393</td>\n", " <td>41.366138</td>\n", " <td>-73.137393</td>\n", " <td>41.366138</td>\n", " <td>6.000000</td>\n", " <td>179.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pickup_longitude pickup_latitude dropoff_longitude dropoff_latitude \\\n", "count 7333.000000 7333.000000 7333.000000 7333.000000 \n", "mean -73.975458 40.751361 -73.974390 40.751321 \n", "std 0.035886 0.027382 0.038016 0.032218 \n", "min -74.258183 40.608573 -74.260472 40.561076 \n", "25% -73.992532 40.737748 -73.991604 40.736398 \n", "50% -73.982140 40.754077 -73.980835 40.753983 \n", "75% -73.968541 40.767605 -73.965786 40.768035 \n", "max -73.137393 41.366138 -73.137393 41.366138 \n", "\n", " passenger_count fare_amount \n", "count 7333.000000 7333.000000 \n", "mean 1.642029 11.339677 \n", "std 1.258757 9.643396 \n", "min 1.000000 2.500000 \n", "25% 1.000000 6.000000 \n", "50% 1.000000 8.500000 \n", "75% 2.000000 12.500000 \n", "max 6.000000 179.000000 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_train.describe()" ] }, { "cell_type": "code", "execution_count": 21, "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>pickup_longitude</th>\n", " <th>pickup_latitude</th>\n", " <th>dropoff_longitude</th>\n", " <th>dropoff_latitude</th>\n", " <th>passenger_count</th>\n", " <th>fare_amount</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>count</td>\n", " <td>1571.000000</td>\n", " <td>1571.000000</td>\n", " <td>1571.000000</td>\n", " <td>1571.000000</td>\n", " <td>1571.000000</td>\n", " <td>1571.000000</td>\n", " </tr>\n", " <tr>\n", " <td>mean</td>\n", " <td>-73.973921</td>\n", " <td>40.752835</td>\n", " <td>-73.973232</td>\n", " <td>40.751910</td>\n", " <td>1.698281</td>\n", " <td>11.190777</td>\n", " </tr>\n", " <tr>\n", " <td>std</td>\n", " <td>0.047222</td>\n", " <td>0.034633</td>\n", " <td>0.045578</td>\n", " <td>0.039445</td>\n", " <td>1.330913</td>\n", " <td>10.243204</td>\n", " </tr>\n", " <tr>\n", " <td>min</td>\n", " <td>-74.043078</td>\n", " <td>40.452290</td>\n", " <td>-74.181608</td>\n", " <td>40.417750</td>\n", " <td>1.000000</td>\n", " <td>2.500000</td>\n", " </tr>\n", " <tr>\n", " <td>25%</td>\n", " <td>-73.991508</td>\n", " <td>40.738050</td>\n", " <td>-73.991885</td>\n", " <td>40.735402</td>\n", " <td>1.000000</td>\n", " <td>6.000000</td>\n", " </tr>\n", " <tr>\n", " <td>50%</td>\n", " <td>-73.981873</td>\n", " <td>40.754845</td>\n", " <td>-73.980273</td>\n", " <td>40.753456</td>\n", " <td>1.000000</td>\n", " <td>8.100000</td>\n", " </tr>\n", " <tr>\n", " <td>75%</td>\n", " <td>-73.968047</td>\n", " <td>40.768333</td>\n", " <td>-73.963515</td>\n", " <td>40.768279</td>\n", " <td>2.000000</td>\n", " <td>12.100000</td>\n", " </tr>\n", " <tr>\n", " <td>max</td>\n", " <td>-73.137393</td>\n", " <td>41.366138</td>\n", " <td>-73.137393</td>\n", " <td>41.366138</td>\n", " <td>6.000000</td>\n", " <td>144.800000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pickup_longitude pickup_latitude dropoff_longitude dropoff_latitude \\\n", "count 1571.000000 1571.000000 1571.000000 1571.000000 \n", "mean -73.973921 40.752835 -73.973232 40.751910 \n", "std 0.047222 0.034633 0.045578 0.039445 \n", "min -74.043078 40.452290 -74.181608 40.417750 \n", "25% -73.991508 40.738050 -73.991885 40.735402 \n", "50% -73.981873 40.754845 -73.980273 40.753456 \n", "75% -73.968047 40.768333 -73.963515 40.768279 \n", "max -73.137393 41.366138 -73.137393 41.366138 \n", "\n", " passenger_count fare_amount \n", "count 1571.000000 1571.000000 \n", "mean 1.698281 11.190777 \n", "std 1.330913 10.243204 \n", "min 1.000000 2.500000 \n", "25% 1.000000 6.000000 \n", "50% 1.000000 8.100000 \n", "75% 2.000000 12.100000 \n", "max 6.000000 144.800000 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_valid.describe()" ] }, { "cell_type": "code", "execution_count": 22, "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>pickup_longitude</th>\n", " <th>pickup_latitude</th>\n", " <th>dropoff_longitude</th>\n", " <th>dropoff_latitude</th>\n", " <th>passenger_count</th>\n", " <th>fare_amount</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <td>count</td>\n", " <td>1572.000000</td>\n", " <td>1572.000000</td>\n", " <td>1572.000000</td>\n", " <td>1572.000000</td>\n", " <td>1572.000000</td>\n", " <td>1572.000000</td>\n", " </tr>\n", " <tr>\n", " <td>mean</td>\n", " <td>-73.975317</td>\n", " <td>40.750986</td>\n", " <td>-73.975436</td>\n", " <td>40.749916</td>\n", " <td>1.660941</td>\n", " <td>11.550636</td>\n", " </tr>\n", " <tr>\n", " <td>std</td>\n", " <td>0.040827</td>\n", " <td>0.031306</td>\n", " <td>0.036921</td>\n", " <td>0.030409</td>\n", " <td>1.317821</td>\n", " <td>10.571027</td>\n", " </tr>\n", " <tr>\n", " <td>min</td>\n", " <td>-74.187541</td>\n", " <td>40.633522</td>\n", " <td>-74.187541</td>\n", " <td>40.590919</td>\n", " <td>1.000000</td>\n", " <td>2.900000</td>\n", " </tr>\n", " <tr>\n", " <td>25%</td>\n", " <td>-73.992529</td>\n", " <td>40.736388</td>\n", " <td>-73.992098</td>\n", " <td>40.734470</td>\n", " <td>1.000000</td>\n", " <td>6.000000</td>\n", " </tr>\n", " <tr>\n", " <td>50%</td>\n", " <td>-73.982109</td>\n", " <td>40.753436</td>\n", " <td>-73.981128</td>\n", " <td>40.752893</td>\n", " <td>1.000000</td>\n", " <td>8.500000</td>\n", " </tr>\n", " <tr>\n", " <td>75%</td>\n", " <td>-73.969370</td>\n", " <td>40.767797</td>\n", " <td>-73.967812</td>\n", " <td>40.766533</td>\n", " <td>2.000000</td>\n", " <td>12.500000</td>\n", " </tr>\n", " <tr>\n", " <td>max</td>\n", " <td>-73.137393</td>\n", " <td>41.366138</td>\n", " <td>-73.776288</td>\n", " <td>41.001380</td>\n", " <td>6.000000</td>\n", " <td>99.300000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " pickup_longitude pickup_latitude dropoff_longitude dropoff_latitude \\\n", "count 1572.000000 1572.000000 1572.000000 1572.000000 \n", "mean -73.975317 40.750986 -73.975436 40.749916 \n", "std 0.040827 0.031306 0.036921 0.030409 \n", "min -74.187541 40.633522 -74.187541 40.590919 \n", "25% -73.992529 40.736388 -73.992098 40.734470 \n", "50% -73.982109 40.753436 -73.981128 40.752893 \n", "75% -73.969370 40.767797 -73.967812 40.766533 \n", "max -73.137393 41.366138 -73.776288 41.001380 \n", "\n", " passenger_count fare_amount \n", "count 1572.000000 1572.000000 \n", "mean 1.660941 11.550636 \n", "std 1.317821 10.571027 \n", "min 1.000000 2.900000 \n", "25% 1.000000 6.000000 \n", "50% 1.000000 8.500000 \n", "75% 2.000000 12.500000 \n", "max 6.000000 99.300000 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_test.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's write out the three dataframes to appropriately named csv files. We can use these csv files for local training (recall that these files represent only 1/100,000 of the full dataset) just to verify our code works, before we run it on all the data." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['fare_amount', 'pickup_datetime', 'pickup_longitude', 'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude', 'passenger_count', 'key']\n", "['fare_amount', 'pickup_datetime', 'pickup_longitude', 'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude', 'passenger_count', 'key']\n", "['fare_amount', 'pickup_datetime', 'pickup_longitude', 'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude', 'passenger_count', 'key']\n" ] } ], "source": [ "def to_csv(df, filename):\n", " outdf = df.copy(deep=False)\n", " outdf.loc[:, \"key\"] = np.arange(0, len(outdf)) # rownumber as key\n", " # Reorder columns so that target is first column\n", " cols = outdf.columns.tolist()\n", " cols.remove(\"fare_amount\")\n", " cols.insert(0, \"fare_amount\")\n", " print (cols) # new order of columns\n", " outdf = outdf[cols]\n", " outdf.to_csv(filename, header=False, index_label=False, index=False)\n", "\n", "to_csv(df_train, \"taxi-train.csv\")\n", "to_csv(df_valid, \"taxi-valid.csv\")\n", "to_csv(df_test, \"taxi-test.csv\")" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6.0,2013-10-13 10:02:20 UTC,-73.950821,40.810753,-73.964465,40.807449,1,0\n", "120.0,2015-04-28 15:00:24 UTC,-73.86323547363281,40.76896286010742,-73.964599609375,40.9226188659668,1,1\n", "7.5,2012-12-31 21:07:30 UTC,-74.000094,40.738104,-73.985071,40.736013,2,2\n", "9.5,2014-11-16 09:00:22 UTC,-73.968167,40.75261,-73.958636,40.778393,2,3\n", "9.3,2012-02-03 19:07:41 UTC,-73.970027,40.789105,-73.987416,40.761225,1,4\n", "4.5,2015-04-21 23:19:43 UTC,-73.97529602050781,40.76133728027344,-73.96334838867188,40.75605392456055,1,5\n", "6.1,2011-11-22 07:39:09 UTC,-73.969197,40.764832,-73.980742,40.774061,2,6\n", "20.0,2013-04-02 21:15:20 UTC,-73.992788,40.749358,-73.963698,40.716271,1,7\n", "5.7,2012-04-13 22:23:16 UTC,-73.99782,40.745847,-73.993732,40.732632,1,8\n", "8.9,2009-07-18 01:19:34 UTC,-73.987535,40.749581,-73.991891,40.721913,1,9\n" ] } ], "source": [ "!head -10 taxi-valid.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3> Verify that datasets exist </h3>" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-rw-r--r-- 1 jupyter jupyter 123269 Nov 6 04:53 taxi-test.csv\n", "-rw-r--r-- 1 jupyter jupyter 579473 Nov 6 04:53 taxi-train.csv\n", "-rw-r--r-- 1 jupyter jupyter 123017 Nov 6 04:53 taxi-valid.csv\n" ] } ], "source": [ "!ls -l *.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have 3 .csv files corresponding to train, valid, test. The ratio of file-sizes correspond to our split of the data." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "11.3,2011-07-27 09:45:56 UTC,-73.98012,40.730552,-73.990246,40.756076,2,0\n", "10.0,2014-12-08 21:50:00 UTC,-73.97866,40.752247,-73.955233,40.777127,1,1\n", "11.0,2013-12-26 18:32:32 UTC,-73.873022,40.774008,-73.907011,40.779224,1,2\n", "8.5,2013-03-07 17:27:13 UTC,-73.951629,40.766381,-73.9672,40.763297,1,3\n", "17.5,2014-05-17 15:15:00 UTC,-73.973497,40.75226,-73.98016,40.783375,1,4\n", "4.5,2012-07-19 06:27:00 UTC,-73.986312,40.762285,-73.989482,40.7522,2,5\n", "6.9,2012-04-18 22:37:09 UTC,-73.955483,40.77361,-73.950013,40.775647,3,6\n", "8.1,2010-12-21 13:08:00 UTC,-73.96252,40.754513,-73.988832,40.755882,5,7\n", "7.3,2010-12-19 18:25:51 UTC,-73.967995,40.765737,-73.981012,40.7446,1,8\n", "7.5,2014-10-06 15:16:00 UTC,-73.99088,40.73448,-74.00699,40.72737,1,9\n" ] } ], "source": [ "%%bash\n", "head taxi-train.csv" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks good! We now have our ML datasets and are ready to train ML models, validate them and evaluate them." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h3> Benchmark </h3>\n", "\n", "Before we start building complex ML models, it is a good idea to come up with a very simple model and use that as a benchmark.\n", "\n", "My model is going to be to simply divide the mean fare_amount by the mean trip_distance to come up with a rate and use that to predict. Let's compute the RMSE of such a model." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rate = $2.5942426114682986/km\n", "Train RMSE = 7.037332431803795\n", "Valid RMSE = 7.5827177000074\n", "Test RMSE = 9.751687930029119\n" ] } ], "source": [ "def distance_between(lat1, lon1, lat2, lon2):\n", " # Haversine formula to compute distance \"as the crow flies\".\n", " lat1_r = np.radians(lat1)\n", " lat2_r = np.radians(lat2)\n", " lon_diff_r = np.radians(lon2 - lon1)\n", " sin_prod = np.sin(lat1_r) * np.sin(lat2_r)\n", " cos_prod = np.cos(lat1_r) * np.cos(lat2_r) * np.cos(lon_diff_r)\n", " minimum = np.minimum(1, sin_prod + cos_prod)\n", " dist = np.degrees(np.arccos(minimum)) * 60 * 1.515 * 1.609344\n", "\n", " return dist\n", "\n", "def estimate_distance(df):\n", " return distance_between(\n", " df[\"pickuplat\"], df[\"pickuplon\"], df[\"dropofflat\"], df[\"dropofflon\"])\n", "\n", "def compute_rmse(actual, predicted):\n", " return np.sqrt(np.mean((actual - predicted) ** 2))\n", "\n", "def print_rmse(df, rate, name):\n", " print (\"{1} RMSE = {0}\".format(\n", " compute_rmse(df[\"fare_amount\"], rate * estimate_distance(df)), name))\n", "\n", "FEATURES = [\"pickuplon\", \"pickuplat\", \"dropofflon\", \"dropofflat\", \"passengers\"]\n", "TARGET = \"fare_amount\"\n", "columns = list([TARGET])\n", "columns.append(\"pickup_datetime\")\n", "columns.extend(FEATURES) # in CSV, target is first column, after the features\n", "columns.append(\"key\")\n", "df_train = pd.read_csv(\"taxi-train.csv\", header=None, names=columns)\n", "df_valid = pd.read_csv(\"taxi-valid.csv\", header=None, names=columns)\n", "df_test = pd.read_csv(\"taxi-test.csv\", header=None, names=columns)\n", "rate = df_train[\"fare_amount\"].mean() / estimate_distance(df_train).mean()\n", "print (\"Rate = ${0}/km\".format(rate))\n", "print_rmse(df_train, rate, \"Train\")\n", "print_rmse(df_valid, rate, \"Valid\") \n", "print_rmse(df_test, rate, \"Test\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h2>Benchmark on same dataset</h2>\n", "\n", "The RMSE depends on the dataset, and for comparison, we have to evaluate on the same dataset each time. We'll use this query in later labs:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Final Validation Set RMSE = 8.135336354024895\n" ] } ], "source": [ "validation_query = \"\"\"\n", "SELECT\n", " (tolls_amount + fare_amount) AS fare_amount,\n", " pickup_datetime,\n", " pickup_longitude AS pickuplon,\n", " pickup_latitude AS pickuplat,\n", " dropoff_longitude AS dropofflon,\n", " dropoff_latitude AS dropofflat,\n", " passenger_count*1.0 AS passengers,\n", " \"unused\" AS key\n", "FROM\n", " `nyc-tlc.yellow.trips`\n", "WHERE\n", " ABS(MOD(FARM_FINGERPRINT(CAST(pickup_datetime AS STRING)), 10000)) = 2\n", " AND trip_distance > 0\n", " AND fare_amount >= 2.5\n", " AND pickup_longitude > -78\n", " AND pickup_longitude < -70\n", " AND dropoff_longitude > -78\n", " AND dropoff_longitude < -70\n", " AND pickup_latitude > 37\n", " AND pickup_latitude < 45\n", " AND dropoff_latitude > 37\n", " AND dropoff_latitude < 45\n", " AND passenger_count > 0\n", "\"\"\"\n", "\n", "client = bigquery.Client()\n", "df_valid = client.query(validation_query).to_dataframe()\n", "print_rmse(df_valid, 2.59988, \"Final Validation Set\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simple distance-based rule gives us a RMSE of <b>$8.14</b>. We have to beat this, of course, but you will find that simple rules of thumb like this can be surprisingly difficult to beat.\n", "\n", "Let's be ambitious, though, and make our goal to build ML models that have a RMSE of less than $6 on the test set." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2019 Google Inc.\n", "Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at\n", "http://www.apache.org/licenses/LICENSE-2.0\n", "Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License." ] } ], "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": "3.5.3" } }, "nbformat": 4, "nbformat_minor": 4 }
apache-2.0
psci2195/espresso-ffans
doc/tutorials/04-lattice_boltzmann/04-lattice_boltzmann_part1.ipynb
1
20657
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial 4 : The lattice-Boltzmann method in ESPResSo - Part 1\n", "\n", "#### Before you start:\n", "\n", "With this tutorial you can get started using the lattice-Boltzmann method\n", "for scientific applications. We give a brief introduction about the theory\n", "and how to use the method in **ESPResSo**. We have selected two interesting problems for\n", "which LB can be applied and which are well understood. You can start with any of them.\n", "\n", "The tutorial is relatively long and working through it carefully is work for at least a full day. You can however get a glimpse of different aspects by starting to work on the tasks.\n", "\n", "Note: LB cannot be used as a black box. It is unavoidable to spend time\n", "learning the theory and gaining practical experience." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1 Introduction\n", "\n", "In this tutorial, you will learn the basics of the lattice-Boltzmann method (LBM) with\n", "special focus on the application on soft matter simulations or more precisely on how to\n", "apply it in combination with molecular dynamics to take into account hydrodynamic\n", "solvent effects without the need to introduce thousands of solvent particles.\n", "\n", "The LBM – its theory as well as its applications – is still a very active field of research.\n", "After almost 20 years of development there are many cases in which the LBM has proven\n", "to be fruitful, in other cases the LBM is considered promising, and in some cases it has\n", "not been of any help. We encourage you to contribute to the scientific discussion of\n", "the LBM because there is still a lot that is unknown or only vaguely known about this\n", "fascinating method.\n", "\n", "### Tutorial Outline\n", "\n", "This tutorial should enable you to start a scientific project applying the LB method\n", "with **ESPResSo**. In the first part we summarize a few basic ideas behind LB and\n", "describe the interface. In the second part we suggest three different classic examples\n", "where hydrodynamics are important. These are\n", "\n", "* **Polymer diffusion.** We analyze the length dependence of the diffusion of polymers.\n", "\n", "* **Poiseuille flow.** We reproduce the flow profile between two walls.\n", "\n", "### Notes on the **ESPResSo** version you will need\n", "\n", "**ESPResSo** offers both CPU and GPU support for LB. We recommend using the GPU code,\n", "as it is much (100x) faster than the CPU code.\n", "\n", "For the tutorial you will have to compile in the following features:\n", "* <tt>LB_BOUNDARIES_GPU</tt>,\n", "* <tt>LENNARD_JONES</tt>." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2 The LBM in brief\n", "\n", "### Linearized Boltzmann equation\n", "\n", "Here we want to repeat a few very basic facts about the LBM. You will find much better\n", "introductions in various books and articles, e.g. [1, 2]. It will however help clarifying\n", "our choice of words and we will eventually say something about the implementation in\n", "**ESPResSo**. It is very loosely written, with the goal that the reader understands basic\n", "concepts and how they are implemented in **ESPResSo**.\n", "\n", "The LBM essentially consists of solving a fully discretized version of the linearized\n", "Boltzmann equation. The Boltzmann equation describes the time evolution of the one\n", "particle distribution function $f \\left(\\vec{x}, \\vec{p}, t\\right)$, which is the probability of finding a molecule in\n", "a phase space volume $d^3\\vec{x}\\,d^3\\vec{p}$ at time $t$.The function $f$ is normalized so that the integral\n", "over the whole phase space is the total mass of the particles:\n", "\n", "$$\\int \\int f \\left(\\vec{x}, \\vec{p}, t\\right) \\,d^3\\vec{x}\\,d^3\\vec{p} = N,$$\n", "\n", "where $N$ denotes the particle number. The quantity $f\\left(\\vec{x}, \\vec{p}, t\\right) \\,d^3\\vec{x}\\,d^3\\vec{p}$\n", "corresponds to the number of particles in this particular cell of the phase space, the\n", "population." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Discretization\n", "\n", "The LBM discretizes the Boltzmann equation not only in real space (the lattice!) and\n", "time, but also the velocity space is discretized. A surprisingly small number of velocities,\n", "in 3D usually 19, is sufficient to describe incompressible, viscous flow correctly. Mostly\n", "we will refer to the three-dimensional model with a discrete set of 19 velocities, which is\n", "conventionally called D3Q19. These velocities, $\\vec{c_i}$ , are chosen such that they correspond to\n", "the movement from one lattice node to another in one time step. A two step scheme is\n", "used to transport information through the system. In the streaming step the particles\n", "(in terms of populations) are transported to the cell where the corresponding velocity\n", "points to. In the collision step, the distribution functions in each cell are relaxed towards\n", "the local thermodynamic equilibrium. This will be described in more detail below.\n", "\n", "The hydrodynamic fields, the density $\\rho$, the fluid momentum density $\\vec{j}$ and the pressure tensor $\\Pi$ can be calculated straightforwardly from the populations. They correspond to the\n", "moments of the distribution function:\n", "\n", "\\begin{align}\n", " \\rho &= \\sum_i f_i \\\\\n", " \\vec{j} = \\rho \\vec{u} &= \\sum_i f_i \\vec{c_i} \\\\\n", " \\Pi^{\\alpha \\beta} &= \\sum_i f_i \\vec{c_i}^{\\alpha}\\vec{c_i}^{\\beta}\n", " \\label{eq:fields}\n", "\\end{align}\n", "\n", "Here the Greek indices denotes the cartesian axis and the\n", "Latin indices indicate the number in the discrete velocity set.\n", "Note that the pressure tensor is symmetric.\n", "It is easy to see that these equations are linear transformations\n", "of the $f_i$ and that they carry the most important information. They\n", "are 10 independent variables, but this is not enough to store the\n", "full information of 19 populations. Therefore 9 additional quantities\n", "are introduced. Together they form a different basis set of the\n", "19-dimensional population space, the modes space and the modes are denoted by \n", "$m_i$. The 9 extra modes are referred to as kinetic modes or\n", "ghost modes. It is possible to explicitly write down the \n", "base transformation matrix, and its inverse and in the **ESPResSo**\n", "LBM implementation this basis transformation is made for every\n", "cell in every LBM step.\n", "\n", "<figure>\n", "<img src='figures/latticeboltzmann-grid.png', style=\"width: 300px;\"/>\n", "<center>\n", "<figcaption>The 19 velocity vectors $\\vec{c_i}$ for a D3Q19 lattice. From the central grid point, the velocity vectors point towards all 18 nearest neighbours marked by filled circles. The 19th velocity vector is the rest mode (zero velocity).</figcaption>\n", "</center>\n", "</figure>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The second step: collision\n", "The second step is the collision part, where the actual physics happens. For the LBM it is assumed that the collision process linearly relaxes the populations to the local equilibrium, thus that it is a linear (=matrix) operator \n", "acting on the populations in each LB cell. It should conserve \n", "the particle number and the momentum. At this point it is clear\n", "why the mode space is helpful. A 19 dimensional matrix that\n", "conserves the first 4 modes (with the eigenvalue 1) is diagonal in the\n", "first four rows and columns.\n", "Some struggling with lattice symmetries shows that four independent\n", "variables are enough to characterize the linear relaxation\n", "process so that all symmetries of the lattice are obeyed. \n", "Two of them are closely related to \n", "the shear and bulk viscosity of the fluid, and two of them\n", "do not have a direct physical equivalent. They are just called\n", "relaxation rates of the kinetic modes.\n", "\n", "In mode space the equilibrium distribution is calculated from \n", "the local density and velocity.\n", "The kinetic modes 11-19 have the value 0 at equilibrium.\n", "The collision operator is diagonal in mode space\n", "and has the form\n", "\n", "\\begin{align*}\n", " m^\\star_i &= \\gamma_i \\left( m_i - m_i^\\text{eq} \\right) + m_i ^\\text{eq}.\n", "\\end{align*}\n", "\n", "Here $m^\\star_i$ is the $i$th mode after the collision.\n", "In other words: each mode is relaxed towards\n", "its equilibrium value with a relaxation rate $\\gamma_i$.\n", "The conserved modes are not relaxed, i.e. their relaxation rate is 1.\n", "\n", "By symmetry consideration one finds that only four independent\n", "relaxation rates are allowed. We summarize them here.\n", "\n", "\\begin{align*}\n", " m^\\star_i &= \\gamma_i m_i \\\\\n", " \\gamma_1=\\dots=\\gamma_4&=1 \\\\\n", " \\gamma_5&=\\gamma_\\text{b} \\\\\n", " \\gamma_6=\\dots=\\gamma_{10}&=\\gamma_\\text{s} \\\\\n", " \\gamma_{11}=\\dots=\\gamma_{16}&=\\gamma_\\text{odd} \\\\\n", " \\gamma_{17}=\\dots = \\gamma_{19}&=\\gamma_\\text{even} \\\\\n", "\\end{align*}\n", "\n", "To include hydrodynamic fluctuations of the fluid, \n", "random fluctuations are added to the nonconserved modes $5\\dots 19$ on every LB node so that\n", "the LB fluid temperature is well defined and the corresponding\n", "fluctuation formula holds, according to the fluctuation dissipation theorem.\n", "An extensive discussion of this topic is found in [3]." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Particle coupling\n", "\n", "In **ESPResSo** particles are coupled to the LB fluid via the so called the force coupling method:\n", "the fluid velocity $\\vec{u}$ at the position of a particle is calculated \n", "via trilinear interpolation and a force is applied on the particle\n", "that is proportional to the velocity difference between particle and fluid:\n", "\n", "\\begin{equation}\n", " \\vec{F}_H = - \\gamma \\left(\\vec{v}-\\vec{u}\\right)\n", "\\end{equation}\n", "\n", "The opposite force is distributed on the surrounding LB nodes. Additionally\n", "a random force is added to maintain a constant temperature, according\n", "to the fluctuation dissipation theorem. \n", "\n", "<figure>\n", "<img src='figures/latticeboltzmann-momentumexchange.png', style=\"width: 300px;\"/>\n", "<center>\n", "<figcaption>The coupling scheme between fluid and particles is based on the interpolation of the fluid velocity $\\vec{u}$ from the grid nodes. This is done by trilinear interpolation. The difference between the particle velocity $\\vec{v}(t)$ and the interpolated velocity $\\vec{u}(\\vec{r},t)$ is used in the momentum exchange of the equation $\\vec{F}_H$ above.</figcaption>\n", "</center>\n", "</figure>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3 The LB interface in ESPResSo\n", "\n", "**ESPResSo** features two virtually independent implementations of LB. One implementation uses CPUs and one uses a GPU to perform the computational work. For this, we provide two actor classes <tt>LBFluid</tt> and <tt>LBFluidGPU</tt> in the module <tt>espressomd.lb</tt>,\n", "as well as the optional <tt>LBBoundary</tt> class found in <tt>espressomd.lbboundaries</tt>.\n", "\n", "The LB lattice is a cubic lattice, with a lattice constant <tt>agrid</tt> that\n", "is the same in all spatial directions. The chosen box length must be an integer multiple\n", "of <tt>agrid</tt>. The LB lattice is shifted by 0.5 <tt>agrid</tt> in all directions: the node\n", "with integer coordinates $\\left(0,0,0\\right)$ is located at\n", "$\\left(0.5a,0.5a,0.5a\\right)$.\n", "The LB scheme and the MD scheme are not synchronized: in one\n", "LB time step, several MD steps may be performed. This allows to speed\n", "up the simulations and is adjusted with the parameter <tt>tau</tt>.\n", "The LB parameter <tt>tau</tt> must be an integer multiple of the MD timestep.\n", "\n", "Even if MD features are not used, the System parameters <tt>cell_system.skin</tt> and <tt>time_step</tt> must be set. LB steps are performed \n", "in regular intervals, such that the timestep $\\tau$ for LB is recovered.\n", "\n", "Important note: all commands of the LB interface use\n", "MD units. This is convenient, as e.g. a particular \n", "viscosity can be set and the LB time step can be changed without\n", "altering the viscosity. On the other hand this is a source\n", "of a plethora of mistakes: the LBM is only reliable in a certain \n", "range of parameters (in LB units) and the unit conversion\n", "may take some of them far out of this range. So remember to always\n", "make sure you are not messing with that!\n", "\n", "One brief example: a certain velocity may be 10 in MD units.\n", "If the LB time step is 0.1 in MD units, and the lattice constant\n", "is 1, then it corresponds to a velocity of $1\\ \\frac{a}{\\tau}$ in LB units.\n", "This is the maximum velocity of the discrete velocity set and therefore\n", "causes numerical instabilities like negative populations." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The <tt>LBFluid</tt> class\n", "\n", "The <tt>LBFluid</tt> class provides an interface to the LB-Method in the **ESPResSo** core. When initializing an object, one can pass the aforementioned parameters as keyword arguments. Parameters are given in MD units. The available keyword arguments are:\n", "\n", "+ <tt>dens</tt>: The density of the fluid.\n", "+ <tt>agrid</tt>: The lattice constant of the fluid. It is used to determine the number of LB nodes per direction from <tt>box_l</tt>. *They have to be compatible.*\n", "+ <tt>visc</tt>: The kinematic viscosity\n", "+ <tt>tau</tt>: The time step of LB. It has to be an integer multiple of <tt>System.time_step</tt>.\n", "+ <tt>ext_force_density</tt>: An external force density applied to every node. This is given as a list, tuple or array with three components.\n", "\n", "Using these arguments, one can initialize an <tt>LBFluid</tt> object. This object then needs to be added to the system's actor list. The code below provides a minimum example.\n", "\n", "```python\n", "import espressomd\n", "import espressomd.lb\n", "\n", "# initialize the System and set the necessary MD parameters for LB.\n", "system = espressomd.System(box_l=[31, 41, 59])\n", "system.time_step = 0.01\n", "system.cell_system.skin = 0.4\n", "\n", "# Initialize and LBFluid with the minimum set of valid parameters.\n", "lbf = lb.LBFluidGPU(agrid=1, dens=10, visc=.1, tau=0.01)\n", "# Activate the LB by adding it to the System's actor list.\n", "system.actors.add(lbf)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sampling data from a node\n", "\n", "The <tt>LBFluid</tt> class also provides a set of methods which can be used to sample data from\n", "the fluid nodes. For example <tt>lbf[X ,Y ,Z].quantity</tt> returns the quantity of the node\n", "with $(X, Y, Z)$ coordinates. Note that the indexing in every direction starts with 0.\n", "The possible properties are:\n", "\n", "+ <tt>velocity</tt>: the fluid velocity (list of three floats)\n", "+ <tt>stress</tt>: the pressure tensor (list of six floats: $\\Pi_{xx}$, $\\Pi_{xy}$, $\\Pi_{yy}$, $\\Pi_{xz}$, $\\Pi_{yz}$, and $\\Pi_{zz}$)\n", "+ <tt>stress_neq</tt>: the nonequilibrium part of the pressure tensor, components as above.\n", "+ <tt>population</tt>: the 19 populations of the D3Q19 lattice.\n", "+ <tt>boundary</tt>: the boundary flag.\n", "+ <tt>density</tt>: the local density." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The <tt>LBBoundary</tt> class\n", "\n", "The <tt>LBBoundary</tt> class represents a boundary on the <tt>LBFluid</tt>\n", "lattice. It depends on the classes of the module <tt>espressomd.shapes</tt> as it derives its geometry from them. For the initialization, the arguments <tt>shape</tt> and <tt>velocity</tt> are supported. The <tt>shape</tt> argument takes an object from the <tt>shapes</tt> module and the <tt>velocity</tt> argument expects a list, tuple or array containing 3 floats. Setting the <tt>velocity</tt> will results in a slip boundary condition.\n", "\n", "Note that the boundaries are not constructed through the periodic boundary. If, for example, one would set a sphere with its center in one of the corner of the boxes, a sphere fragment will be generated. To avoid this, make sure the sphere, or any other boundary, fits inside the central box.\n", "\n", "This part of the LB implementation is still experimental, so please tell us about your experience with it. In general even the simple case of no-slip\n", "boundary is still an important research topic in the LB community and in combination with point particle coupling not much experience exists. This means: do research on that topic, play around with parameters and figure out what happens.\n", "\n", "Boundaries are initialized by passing a shape object to the <tt>LBBoundary</tt> class. One way to initialize a wall is:\n", "\n", "```python\n", "import espressomd.lbboundaries\n", "import espressomd.shapes\n", "\n", "wall = espressomd.lbboundaries.LBBoundary(shape=espressomd.shapes.Wall(normal=[1, 0, 0], dist=1),\n", " velocity=[0, 0, 0.01])\n", "system.lbboundaries.add(wall)\n", "```\n", "\n", "Note that all used variables are inherited from previous examples. This will create a wall with a surface normal of $(1, 0, 0)$ at a distance of 1 from the origin of the coordinate system in direction of the normal vector. The wall exhibits a slip boundary condition with a velocity of $(0, 0, 0.01)$. For a no-slip boundary condition, leave out the velocity argument or set it to zero. Please refer to the user guide for a complete list of constraints.\n", "\n", "In **ESPResSo** the so-called *link bounce back* method is implemented, where the effective hydrodynamic boundary is located midway between boundary and fluid node." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "\n", "[1] S. Succi. *The lattice Boltzmann equation for fluid dynamics and beyond.* Clarendon Press, Oxford, 2001. \n", "[2] B. Dünweg and A. J. C. Ladd. *Advanced Computer Simulation Approaches for Soft Matter Sciences III*, chapter II, pages 89–166. Springer, 2009. \n", "[3] B. Dünweg, U. Schiller, and A.J.C. Ladd. Statistical mechanics of the fluctuating lattice-Boltzmann equation. *Phys. Rev. E*, 76:36704, 2007. \n", "[4] P. G. de Gennes. *Scaling Concepts in Polymer Physics*. Cornell University Press, Ithaca, NY, 1979. \n", "[5] M. Doi. *Introduction to Polymer Physics.* Clarendon Press, Oxford, 1996. \n", "[6] Michael Rubinstein and Ralph H. Colby. *Polymer Physics.* Oxford University Press, Oxford, UK, 2003. \n", "[7] Daan Frenkel and Berend Smit. *Understanding Molecular Simulation.* Academic Press, San Diego, second edition, 2002." ] } ], "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": 1 }
gpl-3.0
TimothyADavis/KinMSpy
kinms/docs/KinMS_example_notebook.ipynb
1
183623
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img style=\"float:top,right\" src=\"Logo.png\">\n", "\n", "<br><br>\n", "\n", "# Welcome to the KinMS introduction \n", "\n", "<br><br>\n", "\n", "### Here you will learn how to import and use KinMS to generate mock interferometric data cubes and gain a better understanding of using the functionalities within the package.\n", "\n", "---\n", "\n", "Copyright (C) 2016, Timothy A. Davis\n", "E-mail: DavisT -at- cardiff.ac.uk, zabelnj -at- cardiff.ac.uk, dawsonj5 -at- cardiff.ac.uk\n", "\n", "---\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial aims at getting you up and running with KinMS! To start you will need to download the KinMSpy code and have it in your python path. \n", "\n", "The simplest way to do this is to call `pip install kinms`\n", "\n", "Once you have completed/understood this tutorial you may want to check out the tutorial on galaxy fitting with KinMS!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### HOUSEKEEPING" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Firstly, we want to import the KinMS package and instantiate the class so that we can freely use it throughout this example notebook" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from kinms import KinMS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Secondly we're going to need some more basic Python packages as well as the premade colourmap for viewing velocity maps found in $\\texttt{sauron-colormap}$" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy import interpolate\n", "from kinms.utils.sauron_colormap import sauron" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Example 1.\n", "\n", "### Lets try making a data cube by providing the class with the physical attributes necessary for describing a simple exponential disk. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First lets start by creating a surface brightness profile which decays radially" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "scalerad = 10 # arcseconds\n", "radius = np.arange(0, 1000, 0.1) # radius vector in arcseconds\n", "sbprof = np.exp(-radius / scalerad)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, lets make the velocity profile, assuming an arctan form." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "vel = (210) * (2/np.pi)*np.arctan(radius) # Scaling the maximum velocity to 210 km/s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Although not necessary, we may also wish to provide our class with the position angle and inclination angle of our galaxy. We do that here by defining $\\theta_\\texttt{pos}$ and $\\phi_\\texttt{inc}$ respectively" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "pos = 270 # degrees\n", "inc= 45 # degrees" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we need to define the properties of the data cube which we would like to return, including the physical dimensions, channel width, and beam size" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "xsize = 128 # arcsec\n", "ysize = 128 # arcsec\n", "vsize = 700 # km/s\n", "cellsize = 1 # arcsec/pixel\n", "dv = 10 # km/s/channel\n", "beamsize = [4, 4, 0] # arcsec, arcsec, degrees" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we provide all of the parameters defined above to the class which returns the modelled data cube. \n", "\n", "**Note**: If you wish, the user can use the \"verbose = True\" argument to see useful information and feedback on the input parameters while generating the cube. We show an example of this behaviour below" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", "*** Hello and welcome to KinMSpy ***\n", "_____________________________________\n", " \n", "Setting user defined variables to: \n", "\n", "xs = 128\n", "ys = 128\n", "vs = 700\n", "cellSize = 1\n", "dv = 10\n", "inc = [45]\n", "posAng = [90]\n", "sbProf = user defined array of length 10000\n", "_____________________________________\n", " \n", "Setting default values to: \n", "\n", "inClouds = []\n", "vLOS_clouds = []\n", "massDist = []\n", "seed = [100 101 102 103]\n", "intFlux = 0\n", "vSys = 0\n", "phaseCent = [0 0]\n", "vOffset = 0\n", "vPhaseCent = [0 0]\n", "restFreq = 230542000000.0\n", "nSamps = 500000\n", "vPosAng = []\n", "gasSigma = [0]\n", "diskThick = [0]\n", "sbRad = default array of length 10000\n", "velRad = []\n", "velProf = default array of length 10000\n", "_____________________________________\n", " \n", "Setting options to: \n", "\n", "skySampler = False\n", "fixSeed = False\n", "cleanOut = False\n", "returnClouds = False\n", "huge_beam = False\n", "verbose = True\n", "inClouds_given = False\n", "_____________________________________\n", "\n" ] } ], "source": [ "kin = KinMS(xsize, ysize, vsize, cellsize, dv, beamSize = beamsize, inc = inc, sbProf = sbprof,\n", " sbRad = radius, velProf = vel, posAng = pos, verbose = True) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can then generate the model cube using the following:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Generating cloudlets, Using a thin disc assumption.\n", "_____________________________________\n", "\n", " *** Cube successfully created ***\n" ] } ], "source": [ "model=kin.model_cube()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you do not want to see the printed information (for example during MCMC fitting routines), it is easy to switch off by either not using the verbose argument or setting it to False explicitly." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "kin.verbose=False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A similar behaviour exists for outputting plots of the generated cube, which can also be toggled on and off. Plots are created by passing the \"toplot = True\" argument to model_cube. We show this behaviour below" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cube = kin.model_cube(toplot=True) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example 2. \n", "\n", "Next we're going to demonstrate the use of $\\texttt{inclouds}$, which allows the user to pass specific cloudlet positions and their associated velocities to $\\texttt{KinMS}$. These particles could be generated by some other means (e.g. if you are making mock observations of a simulation), or be the output from some analytic function.\n", "\n", "As in the first example, we need to set up our cube parameters" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "xsize = 128 # arcsec\n", "ysize = 128 # arcsec\n", "vsize = 1400 # km/s\n", "cellsize = 1 # arcsec/pixel\n", "dv = 10 # km/s/channel\n", "beamsize = [4, 4, 0] # arcsec, arcsec, degrees\n", "inc = 35 # degrees\n", "intflux = 30 # Jy km/s\n", "posang = 90 # degrees" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can specify the x,y and z positions of the cloudlets we wish to pass to $\\texttt{KinMS}$ as an (n,3) vector. These should be specified in arcseconds around some central location." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "inclouds = np.array([[40, 0, 0], [39.5075, 6.25738, 0], [38.0423, 12.3607, 0.00000], [35.6403, 18.1596, 0],\n", " [32.3607, 23.5114, 0], [28.2843, 28.2843, 0], [23.5114, 32.3607, 0], [18.1596, 35.6403, 0],\n", " [12.3607, 38.0423, 0], [6.25737, 39.5075, 0], [0, 40, 0], [-6.25738, 39.5075, 0],\n", " [-12.3607, 38.0423, 0], [-18.1596, 35.6403, 0], [-23.5114, 32.3607, 0],\n", " [-28.2843, 28.2843, 0], [-32.3607, 23.5114, 0], [-35.6403, 18.1596, 0],\n", " [-38.0423, 12.3607, 0], [-39.5075, 6.25738, 0], [-40, 0, 0], [-39.5075, -6.25738, 0],\n", " [-38.0423,-12.3607, 0], [-35.6403, -18.1596, 0], [-32.3607, -23.5114, 0], [-28.2843, -28.2843, 0],\n", " [-23.5114, -32.3607, 0], [-18.1596, -35.6403, 0], [-12.3607,-38.0423, 0], [-6.25738, -39.5075, 0],\n", " [0, -40, 0], [6.25738, -39.5075, 0], [12.3607, -38.0423, 0], [18.1596, -35.6403, 0],\n", " [23.5114, -32.3607, 0], [28.2843, -28.2843, 0], [32.3607,-23.5114, 0], [35.6403, -18.1596, 0],\n", " [38.0423, -12.3607, 0], [39.5075, -6.25737, 0], [15, 15, 0], [-15, 15, 0],\n", " [-19.8504, -2.44189, 0], [-18.0194, -8.67768, 0], [-14.2856, -13.9972, 0],\n", " [-9.04344, -17.8386, 0], [-2.84630, -19.7964, 0], [3.65139, -19.6639, 0],\n", " [9.76353, -17.4549, 0], [14.8447, -13.4028, 0], [18.3583, -7.93546, 0],\n", " [19.9335, -1.63019, 0]])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have a choice to make. If you are generating mock observations from a hydrodynamic simulation, lets say, then you already have full 3D velocity information, and you will want to supply the line-of-sight velocity for every resolution element. In this case you can pass the velocity information as vLOS_clouds - but you should make sure your input cloudlets have already been projected to the desired inclination.\n", "\n", "Alternativly, perhaps you would like to input a circular velocity profile, and have KinMS handle the projection. Here we create a velocity profile with a few radial position anchors and linearly interpolate between them to get a full profile" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "x = np.arange(0, 100, 0.1)\n", "velfunc = interpolate.interp1d([0, 0.5, 1, 3, 500], [0, 50, 100, 210, 210], kind = 'linear')\n", "vel = velfunc(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again, lets make a cube with all the specified parameters above" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 720x720 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "cube = KinMS(xsize, ysize, vsize, cellsize, dv, beamsize, inc, intFlux = intflux, inClouds = inclouds,\n", " velProf = vel, velRad = x, posAng = posang).model_cube(toplot = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Example 3.\n", "\n", "$\\texttt{KinMS}$ can accomodate a variety of departures from simple orderly rotation. In this example we will demonstrate the creation of datacubes containing a galaxy with a non-zero thickness disk with a warp in the position angle across the radius of the disk.\n", "\n", "As in the other examples, we need to set up our cube parameters" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "xsize = 128\n", "ysize = 128\n", "vsize = 1400\n", "cellsize = 1\n", "dv = 10\n", "beamsize = 2\n", "intflux = 30\n", "fcent = 10\n", "scalerad = 20\n", "inc = 60\n", "discthick=1.\n", "\n", "# create an exponetial surface brightness profile and an arctan velocity curve\n", "radius = np.arange(0, 100, 0.1)\n", "sbprof = fcent * np.exp(-radius / scalerad)\n", "vel = (210) * (2/np.pi)*np.arctan(radius)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we need to create an array of position angle values" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "posangfunc = interpolate.interp1d([0, 15, 50, 500], [270, 270, 300, 300], kind='linear')\n", "posang = posangfunc(radius)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And lastly, we simply run KinMS to generate the final cube" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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csbUG5EpDsNREfawU4egrhn6NrICcDMXJ0eA4DK9bObqqEJz1QFzNdcBZ05wVMPpba/gdjtHfsbf9Bq/7XRqvmTo0G+bzpNVUOpdvJQ+ypcPvqidCGC0XWFc+1f/1pG2yj6kmPMfHVhKEc0Jw1vvCZpcoUiUhWFOgDYX6WKlZVvhdVuWIaNaIMQwMxVlheNzEwaPBeSE4LfkwXK7eaFvq+ynzvabDb3L0t9nDb3p+4U0TZwDP1eVezaSZArBI9dL1vRUsXNEXCtMhd8J2A/elg2ryfTLo5qnkmFKSAToZhPNGk0uMBqeVnC1iKKqdbi0tbzEOEWmYIkZ+IT8wJ4NxHIbTQThrNDhrurRSo8AVK7UCXAUzP5QqfWj28NtORnoAjv/0rW1oK6Qxigy/8b680dlKwuzy3tKfT5mcf3z6s+Q9s4Jw+n06CJcKwdHn5UJwzaPF6TKJWgKxgnCzUB/b5rq+AJ27DNy3Ygirn6WvBQODcRyG00G4XAguNQpcsxLfZ17pQ6lZHxR+m8tID8CHRtshDrXJiJNeJjcr/GY93JYVfNOhslTYLRdyV6wM285J1Z2b9VkcirOCcFpcohEr9/BcIgQnZc03POTR4rzV6Cqh2uBGUx/bhrq+MPB9LYF3xUPZYTfvWvGxcRjOCsLpkoi8EFzIKHBSHNAz2j4Sw2+7B9/YsAdgM+sCunI+PtbMZpW7BLAF8CrgYMCB1l+zT4KsOt/kHL4pg6YtS8oa7a02/K5YGcJuHHyT+6tRKjCng3BaHIzTdco11A1Xs3wzVDk6PNTyCKmI+lgZinT4TasmDJc7NhmQk8d27pIdhNOjwemH4/JUVAcch9y4/OGh1P6M0geF35GtESPAc4CzM/Yb8O4qr2XABuCCIbZJRoJk+C21ZDFlgi+UDr+lRmqzgm2FYXfd8jW5n+X2zZ2TBgfhrGCcDMGx9KhwlnLlEmVUPTpcbQiOR381BVo15qA+Vmqwz4lDK3dYdn/+Z1P3GLwvfe343vH+dBBOjwYnQ3DNpRC9GfsqLH2ItVr4teda/wE4aFwJRN5TxdU+bXw3cKa73zHE9shIkQ6/eaO+1c63Wyr8lgm4cbDtmDJ+wPtqZJ3TMWX8wJKKdNuy2lqqTCJPMgQnf25DnWM4TzIElyqPSJY+bFyuEFwd9bFSlX1OHPg+L/iWCrmlVBKOk8E3/X7ZgsGjwaVGgsvOC5xcfCM9+pveH7Ujq+631cLvmIULoU0W2mhEAL4EmJ94b8ANhF+znQXcXOb8TcBq4GF37y2+edJ0Ss3rm1J2BbR4hDRdTpAMkzmBNx10k/vSX6etX5GxJFHC2M7BY8Drlq/pv1deCE+XTqSnYSs15Vq5muEKR4drqhFO/8MlPTKsut+huAT1sVKF6fsPDqFJpcJrLfXB6VHmvOsng3DWaHAyBEP2rBBp45LBtzfxdZnSh3Lht5mnOisXfscsXNj39dSjjy78/s1q2AOwuz8CPJLcZ9Y3KHGvu3cPd5ukiWU97JaxelluyUOlpQ5V1OxWMsJbKvCuXhEeqJ/QOa7k8WM7OwaG4CxxDfLy3sEzSSRLI7LUqUQi+d+i4mBcLvyOnqI5gCukPlaqMX3/wfuyAulQZn6o9Fp9D8LdP3h/1mhwsi44OQpctgwia/QX6hZ+K13drdHht900yywQB0fbexvaCmke6VHf9INuySnNkgoIv+mA2zFlfMVlDXnBNw695fbFJnSOY/2KdX0hOM+AMgnIDsGxrDBcUAguO/I+FAq/RVAfK4MkR36h+uC7dtmzhbRj3NStBt2rc5f8IJwuiah2FBjIHv2F3PCb98BbEeG3USUP7Rx+oUkCsEYkZIDk/L6QOctD2fBbSkb4LRUyqyltSIbaCZ3jMkPuqufWD3g/ccuxg45ZvWJtXwiG/hKJ5P0GjRCnR7ErHRGuZNaIGh+Wq6g8Il3/m5z6TOG3EOpjJSk56ltp6K007JrdU3E73PfJvPa4qVsNHPG9PwT1OPwOKInYMzlFWvasEEd15CyDvICBpQ8LKDnqC/3hdySXPEB2+G2n8gdokgCcZGajgXcChwN7Ap3AGHefmTruZcCWwEp3v2/YGyr1kSx5yHnYrao5fWPpmt9EWKz2obVKR3mT79OhNynrs4lbjh1QKpF1z6x9fZXEWQ/NTZk8+OdQapENKF8nXGGNMOSUQ5R6+E3hty7Ux0qWakJvNSG3lPR10oE4GYSzxCUTyVKIUsatIpQ/9FJR+K3kQTeoLfw2U/CF9gu/0GQB2MzmAD8Ctk/uJjy8kXY0cB6wysy2dXetVNQKkuG32qnN8iRD3/Le6ufoTUiGzkpGd7P0rnyh5OeTJ23ed51kEE6La4iTo8R9I8LJ7zF+UC6vTjhWS4nEUJdfzpsWTeG3LtTHyvit8uffhezQW1ngzVnnuGJ7DrrP2mUhEMdBOD0aHEtOkQaj2WHSXjAGbqrktonwW03wzQq96WPyKPw2h6YJwGb2FuD/gFH0zz25Gpicc8pFwDmEpTrfDFxZ/1ZKXaVLHxJqmtcXBoffKmWNspYKvuXCLcAzawbPRDVtfH/+SF4j/nrypM0HHJ8eIU4a9OBc/KAcZI8Kx/LCcKV1wlBRrfCAEJwOv5t1KvzWifpYGb9V/9dDC76Vht1Kjtsz49g9B9w/DsKQ+AZS4nrgpIO2+jps/GT/jvTo72+i/VH4rXV2h6zj8hQdfocSfDe77z4mnXlmoe0ZSZoiAJvZVsBlwGhgJXAa8FPgMOCXWee4+zNmdjPweuANjMDO2cy2Bz4HHAFMBZYCVwHnuXvG2r4tLBl+s5Y1jlWymltsCOG30jKH5IhvHFazAm456XOSgTh5bcgeIU7XCyfLOvpGhMstz5wOw9WGYGla7drHgvrZmNmzmQ+bpYNvdugtF2SHMvo7OPim91UchKPTOzK6ur7pz5Lht8SobysEX8gOv5vdFyqa2jn8QpMEYODjhFGGF4DD3P1OGDB1T57bgIOAV9a1dXVgZjOBW4CtgV8B9wOvAT4BHGFmB7r7shKXaA2lVnejwLKHWI3lD6VmbIAQUNMh9umMIPzMemPaWGfrVMB9eo3xzPpw/LSxztNrbNAx/fpHhVc9tz6zXjg5r3BmWQQMDsRZJRJJyX9sFBWGtTzycGm7PhbUz8bMBo/ulg++pULtUMsdKr3unol9FQbhPWHcxFAGsYjUVGi9hPD7ELmjvkU93Jak8NucmiUAH0moQftZ3DFXKP6vOxJ/X/ptQqd8qrt/M95pZucDnwK+CJzSoLYNr2T4LTfTQznph7wqUM00Z0l5ZQ/J4BuH2qR4Xxxwk+E3/jwOwfFxya+fWWMDRojjEAyDg3ochDPnE06uMpel1DzCNY4Il5wRQuUP9dSOfSyonx0w8ltZmUNeuC0deot4MC5+CG7wPUsF4cEheIupA0eB94ZQ/hA/6Pab8uG31rl804oMv0MJvtAffiVolgAcP318Y5XnxUNaWxbYlrqLRiUOB5YA30p9fA4wFzjezE539+eHuXmNk7Gs8XCpNgSvem59Zr1vXvh9+oX8kba8kDxtbH9ArqQ9wIAgnDUaXKisEFyiFrjkLBAKv/XWVn0sqJ+FgSO/yfDbTKE373oDw3B+EM4KwVOjUeCZkw6BeFa0XsLob3R6MvzWI/hCMeG30tAbKzXqG9Pob9AsAXiLaPtclefFT/+UXme2+cST0l/n7puSH7j7qqju7nBgf+D64W7csImnPCtX+pCnVP1vHWXN9JAuf6g0/OZ9vvXmPiAE9x2bKI3oXfnCoIfjkqPBabmryuXVB1drwna5ZSoKvw3Xbn0stHk/O7Twmx18iw68pWSH4cFB2GzwSPCyBdG0aFG3Ni45+puo+03P8lBk+B2qegRfUPhNapYAvAzYBnhJleftGm2fKbY5dbd7tM1bhuVBQse8G6mOubu7u5K6Pdzz6kebRDr8lhr9rab2d9I2NZVBFKma8FuLeER42njPDMGlxCE4Hu0uucwyVLecci2LZWQteyz10G59LLRxPztq1A3AwHKC8sG3qNCbVcc7NGb3ZIwI7zng8zgEJ1e2q0ReyB3u8Ftt4I1VGnzbfdW3LM0SgO8jdM5zCDVblTqKUNd2Rx3aVE/xcFveE1nx/sn1b0oTKDXfby1KheDOSUOaB3i4PP2CsfXmTfKXa7kQXI2OKYP/sTNaD8ENg3brY6FN+9kQfgcaGGIrG/GtPPhWM0PE0MNwKcn5jYdqzMKFQw7BcajNKoWoNfDGFHyHrlkC8O8I0+wcZWZ7uXvZPxlmdjzwCkLn/Os6t69pdHV1MX/+/EY3Y2hy5vsdcvDNM2XywJkg4l/3lwjCYzs7cqdCm7jlWFY9t57JkzbPrAOeNtYz63rTskocyh2TPzNE9pLKafGIb9mR37S8EJxVA5wYBc6c81elD42gPrYKI7WfrT781hp86zULxGCDH4yD/iC9Z98x8YN+sXETwyTX5RQRdMsZatiNVfNwW9ax43/0o0La0SpGNboBke8DTwNjgN+Z2ctLHWxmHyJM0u7AYsJ8liNJnLzyCi/j/b31b8ow2rR48LRnQ3nwrdQsBOmwljW9V6ruNR0KK3l4LC4/SM/bG4fWrTf33JHc5DGVyAq/8f3T4Te9OEas7IN+peZLXvlU+fKSSkogNuvsD78yXNqtj4U262fT4dfsnkSYXcDA0Jp+nz4+ywKyzqtcdaO/7vtkhN89yQu/nbuEFeI6d+lfFGPRi+FnsnZidPgu4WVbrGLMffexWRQS47CYDpJjFi5s+Ahq3IZKw2/esQq/gzXFCLC7P29mJxFGGV4K3GVm1xMmLgHAzM4hLN95KLAjYSWj9cB70w84jAAPRNu8f3bGdXet87uLuOY3Vmqxi6Ry9b8Tp+c/DBeH4Di0xSE4PRqcGAmOQ3AcFOOR4Amd4wZMMRaPAqclpywbsH/zwQF50FzAiXrh+Pi+kJwO2In3pcJv3WaAyJIKvyVXfItp9HdYtGEfC+3Yz2ZKB9+Byo/4FjHaW1n4zR7tTZ/fH3yBQeG3c5d4GrSN8GLitMmEuVCiS9lDIQRDeBguHgmOA2Xygbg4UA5HXXAlgTtvOrP0uXHA3/zWW4fesBbUFAEYwN2vMbNjgR8Qptw5PP4o2p6dONwI/2o/1t3/MmyNLE48FdHhZjYq+ZeLmU0EDgTWECahH/lqDb+VKhWCYXBNcDoIZ9QFVzMtWlwKMS2aozcOwaXKG5KBNq+sIR1+06PMWQ+/5Y38FiavFrjUyG+y5CGm0odh12Z9LLRbP5spP/wOT/CFcuG3stA78H2y5CEdfl/6ulD+kHQfMDseBV4U7XyofyQ4rVwQjo8pSpGhF/qDLyj8ltI0ARjA3X9hZrcD/w4cB2QNGz0H/AT4krs3Zh6sIXL3RWZ2HeEvoI8C30x8fB5hyqKLWmJuymT4zQq+WQ9F1UPWg3HJ2uCMuuA4BFcyCtwfRgfXBFdi6/GDR4ST4vCbDr3J0d+aw28lU6BV8RBc5pRncfiNyx4UfhuiXfpYaK9+dtSobzEwMA5l1LfI+t788Ftr8IX+UV8YGH7j0oeOSRt5jIy628n0z4j9FmBB/0jwi3vvzWYLF/aNBsPAIAz5YTg+NkutJRTlFq0oF3xB4becpgrAAO7+OHAqcKqZ7Q3sRKjVWg08Adw9Qn8dl/avhCU6LzCzQwm9zn6EuSsXAq03Wd9mnYNDcCL82rgpgx+EKzG37ACl6oHj0eFkiEuWRWQ9IAewYuWAEJwUB+FkAE0+GJcerYWBcwXHo8XJ96VkjfZW8tBbWenwm1UrnRd+M37mJVd6iyn8NlQb9bHQBv1sCL+x0g+5DV/wherD7+Ayh/Tx6VHf+Os4/G4xdfDob2ztxGg+4IxmJcshYukgDOSGYShmtoVSobfU9dPBd8x992HPVTvld/tpugCc5O73EX570XKi0YnZwOeAI4A3AUuBbwDnuXvBdQINkDPbQ59KR36zfr1ezdzAWZL1wekQHIvCYXFVtANHhyspaYiVC7t5o7/pleCqnv0hGX7z/pER/fepqOZXmkor97HQJv1sn1rD78gKvpAfftNuevaTdCWnGo/DcU4IBgaMBsPAIAyVheFSKl2OuKKyiIzgCyj8VqipA3Crc/fHgJMa3Y5hU0Htb+YocJZKR4ah/INycQjOsrx3QHlE3ohwXCKRZXCZRAVNzgm86TKMeF9SYQ++1Tr3bzL8JksfNPIrDdB2/SwwMsJvdqlD+rhKwm/fsRP7yx/iGSBKmjl4V9ZoMPSvGlcuDNeqXOBNh90B5ybur/BbuREVgC0szbMrod2L3H3wY/jSHJLTnVUpHkksG4SLDMGQPcVX8oG5jBHh5GwRQNla4TjYJmeQqKSUIRlya631LTv6m/xHQDr8xqO/OQ+75db9KviOKOpjR5aB5Q+xSsJvc476Jo9NzuubrvdN7oPSpQ8Av14HdMDeE/vX9k6sphymRyN6v0v/aLA/P7HiMAx1qAOuMPTGFH6r0xQB2MzGAYdFb++O/sWePuY9wPnA1tGu1Wb2NXc/d3haKRVLlz5sWNE/92sVM0BUNBpcTXlEJbNFZEmOEGeMCMeyRoYh1AunQ26l8/ZWIhm+Y+np3PramffAW6nwW0JFNb/ScOpjW8/IDL/lH3CD0qO+A/anSh86Jm2stNEDb7+I/hCckBWEk+URsbxAXKlSQTcpK/TGFH6r1xQBGHgncAmwERg0XGRmbwQui99G24nAWWY20d1PH45GSpXi0d/41+Dp8FvBDBAVl0QklRoZjkcySwXhtHS9MAwMwjAgDCeDcKnSiGpUUtaQdcyAUd+s8FtJ8M0Y/R1U7xv/d9Qqb81KfWzL6w+2wxN8odHht++cxOhv5uwPafHxvQwOwQ/RPzocvbctskeEX4xqfysNsNXKC7xxe/osrWwxJRmoWQJwPDJxe9bIBPDf9HfKdwJLonMmAZ8wsx+7+9/q3kqpXLzSG1Q98ptWeAiGymaNSEtOpZY1lzDkBmHoH6HNqh9OKhV2K5mfOLPUoXNSfp1zXrlDjoqWNwaF3+aiPralNU/4LV/yMPB9JeE3LQ6/WQ++pX1+BZyVeBCubzYICNOi9UZfzyQ/BMOgIAzZAfXFKh6Ii5Ua2U0aFHxB4XcImiUA70WYjP2m9Adm9irgZdHnX3P3T0f7dwHuBsYDHwA+MWytlXzJeX/zRn6TKpwHuOK64KRaZ49IB8BkIE5PpZYMlTWUR0Blo7vJa5UKwYPCb3rUt1R5Q6ngmzXTQ5Iedmt26mNbyKhRH6M/RLZ++M2q++07LxrNraT84T6gL55OpH8dxDjsZoVgyA3CSaVCcbUyg26SQm8hmiUAT4u2D2R89sZo+yLwxXinuz9kZj8jPN37uvo2T2pW8KpvpWpOK549AqqbRi2vbCIeEU7PJJEuj6D0VGqVrjg34HrVTmc2ZXLx4VdTnY0k6mNbRAi/0Jh6XxiO8JuUV/pQyehvnsxR4KwQDPlBOP4sUja0DlV83z8p/BalWQJw/LhnVhV33PHekjFn4x2EznnnejVMhqDS8Dtcq8ElVRKE02UUWQ/RxaEyq04YBpdIJEWrzlUdZmMZSziXXdiiTHnDAHk1vxD+m6WXNgaN/jYv9bEtrBXCb1Je6cNQ/HodHJUeiYhHgSczOARDfhCGwWE4PmYoMv4BIPXTLAE4rj0b8LRQNCXPa8n51R3wbLSdUL+myUiRDmlVjQgXJb3kcrpWOClvRoYVK7PDbdZ51czqUGn4Tf1McsPv6MR+Bd9mpz62RbVK+K1m9Hco4jKIvlHgvBAM/aPBMDAIQ/bK0wqwI0qzBOBngOlAehK91xD+WDphOcu0eOhsXcZn0mjJEcJyo8G1/Dq9ghkk8lRcS5weKc4rh0juz1tyOUupYJwXbktdL1blg20lR3tjWQ+6gcLvyKA+tgU13zRnWcdnn58XftOzPmSJyx/K1f8etNXXuenZT+ZfKJYVgmHwaDAMDsKQ/S0W9Z9hT2Ceyh7qoVkCcA+wPfAeM/uKu8erCHw42r4I3JxxXvw379L6Nk8qknwALm2IM0FkigNZDeUTeTNL5M44kVUOAeWnUyu1yAaUD7KVqmQKs7xyjwnbVbaMcfJBt5jC70jRg/rYljJ8I7/Zagm/2efUXzwTRFwGMWgUGAaGYMgPwjB43uCs0oe8fzcsKPN5msJv3TRLAP458C+EP1Lzzexywp/PDxBGJn7n7s9nnLdftB2+/+slXxyGhjMEQ801xHkjnSVDcCw9IhzLm0Gi1CIb1ar2Yba43RnlHn0/g3Ij8JrlYaRTH9vymrfsIX1OudHfpCLLH5JKhmAYOEVasiwC8keFk/Lqgav5PhR+66pZAvBPgI8DsxOv2AvAuekTzGxL4GBC531b/ZsoZZUaAY5VUxZRjawQnB4hrmLEuOy0a+kwWfTqc5WqorQhqWzwTf53io2ZPXifjBTqY1tE9shvPVQTfkufW+3Ib6nyh6HKehiuZAiGwaPBsfSocNJQvod/V/AdDk0RgN19k5kdCXwfOIr+BzaeBE52979nnHYisDmhc/7jcLRTqjR6SvlAXIu8keS8MJfeX2QQjpWaVaJcSK1mVbpS10y3odYR33SNr4x46mNb3fDV/Q71+GpGf4uUXhADBs4LnBuCYfBocCwvDMPgQFwphd9hM6wB2MzifwY+7O4DJs1z92XA28xsGqHubA1wn7tvyrnc/YTpedzd76xXm2UIigi/WSORDZBXLjEoGNey+EZeQI6DcSWzNyTvW+6BtmpGfWMqexgR1MfK0BX70NtQ6n6zRoKrnf8360G45ChwyRAM2aPBsbwwDIMDcVo6IM8E3q7wO5yGewS4hzCa8Dbg1/FOMzs7+vIKd19IeGK5JHe/rh4NlAJUOvKbDlzpUd28QFaPOuIaVbRMc1btcPqzakeOa5m+rdyobyw9+qvwO5L0oD62zRQ5+jvU8FtaJaO/RZU/zBxzCItevGHAvuQocEUhGLJHg2FgEIbqZ4JIB2SF32HXFCUQhPozJ3TeCxvaEhmaoZQ91OshuTzVTr2WVWO8bnllITiWF1wLno+4olXbFHrbybmoj21BzRZ+K3vwrWjrVo4uuRRychS40hAMJUaDoT8Iw+AwHEv/iPL+cyn8NsRwB+BNhNqzMcN8XxkuQy17qKbkodQDdfWcdi1DqTmHYxWH5ApUcj+gsnIHTWvWStTHto2RE37T8kZ58/ZXMwPEDuzFY/yj8hMSkiEYckaDYWAQhoFhONabsS89mwQo/DbQqGG+X2+03XGY7yv1VskMEEXZrLN0UB7OuuEqRpErDq2J4/Neme3Ie9gvFv/cFH5bWW+0VR/bgkaPnkZIUI0Mv+Wlzys1+lvPh99mjjmk7+uDtvp639efT4yN/Dq1xMt9qWusndg/ItxnIv1hOM9ksoPxnoTQG7+kYYZ7BPg+4PXAx8zsT0CPu7+Y+Fx/Gkaq4Qq/eSoJxPGIcPJ9+rNaVDEPcbUhuOx9yoXemB5waxfqY6UKtYTf8qO/SenwW2mNbz2nQktLT40Wh+CSo8EwOASnP4eBM0gA7KX/BZvFcAfgKwid845E80qGpeiB8Gu7qxLvq+Hu3iz1zO0pOUfspsVhW0uwKnIkOR36yr0vdZ1KlnJOzzecVu1iHVnXqfRBtjyq9W116mNb2MaNZZ9drFgYTa7W0EofSqlX4E0+DJdXCwzZ8wOXLYlISz8wF1PobUrD3aFdBLwZeFPO5zX1zNJkhhKqRs0IrzhEZ4XhOMQlP8vaF++P96WPyXtgLxl441BZaQgu93kyJOct0FHtw3nJdsbyfh6g0Nva1MdKRcqF6VGjPpbaU93IL5Qf/a1X+UOldcC1hmAoEYTjUeGJCr3NblgDcDQZ+1uAowkd9A7AWKCL8Ku5fwDPDmebpEnFIa3asJYe4dy4fPC+rEUeah0drlYy3FZTvpAO5Gl5YV4jvm1FfawUZdOmC6MQXNsaxPWc9SFLuZkgYul5gbMWyEhLh2AoMxqs8DsiDPuvtNzdgV9GLwDMLJ6I/Ux3/3XmiSJpyTCXVXaxaXEIgHmhL/68XqvVQeW1xVkhN1mjnH5wLavNY2b3/xxAYbdNqY+VomzadOGQrzE+ysGlShzqXe+bNSdwnqxRYAgheHa6hL7cg3DS1IZ7FgiR+ohLJ8rty/q81LK/yc+SITRrxDhrloVKJENuqXuk25V8JUfMy33fIiLDZE0Nv28oIhDvwF65nyVnhICBs0LA4JkhICP8yojXLEshHxxts9ajF6mvOCxm1RTHX8efVRpQs47JGg2OPy9Xq5zVXpEU9bHSjNY8C9P3H7x/2f3l64CrmQN4KNKlEAq8ra8plkIm1KcBLAUaPJ+WtLU4bMYhM1lSEH+eVzJRrpyiVP1u+t5xeUayLSLl9aA+VprQE7cNDsFP3Ba2XV8Y/vaU8rkOhd920CzT2pyLlumURsorG8h6GC993KbF+Q/tpQN0XkDOKt8QKc65qI+VBosDb1r3Z4d+7c+sLP1595hPDNyx7SeyD5S2Mdw1wPGDGFqmU1pHJXXG8TFxze6Y2YPrd0WGTn2stKWvTMr/7Ie8fPgaIiPGcI8A9wKdaJlOaUca5ZX660V9rLSp8xid2qPgK/mGewT4PsJE7B8zs33NLD1KocIbEZHaqY8VEamAlkIWEWkd6mNFRCow3CPAFwFXEzri5CuW3l/NS0Sk3amPFRGpgJZCFhFpEepjRUQqo6WQRURaiPpYEZHytBSyiIiIiLSVZnmoIV6m896GtkJEpDWpjxURSWiKAOzu3Y1ug4hIq1IfKyIyUFMEYAAz2wN4A3AAMB2YGn20DHgCuBn4o7s/0JgWioiMXOpjRUT6NTwAm9n+hHXqDytz6Luj468DznH3v9S5aSIiI576WBGRwRr6EJyZnQncROiYK52L8nDgz2Z2RiPaLCIyUqiPFRHJ1rARYDP7f8Dp8VtgA3A9cCvwAGFNe4DJwG7Aawm/vtssen3ezCa7+78PX6tFREYG9bEiIvkaEoDN7Djg0/SvS38R8Dl3X1rmvG2As4BTCB366WbW4+6X17O9IiIjifpYEZHShr0Ewsw6gC9Hb18E3uHuHynXMQO4+1Pu/lHgmOhcA75sZmPr1mARkRFEfayISHmNqAF+B7A9YWTi0+7+yzLHD+LuvwJOi95OB95ZXPNEREY09bEiImU0IgAfGW0fcvcLh3CdbwMPRl+/aWhNEhFpGepjRUTKaEQAfjVhZOL/hnKRaL37/yP8iu5VBbRLRKQVqI8VESmjEQF462hbxGTr8TW2LnmUiEj7UB8rIlJGIwLwuGi7poBrxdcYV/IoEZH2oT5WRKSMRgTgZ6PttgVcK77GsyWPEhFpH+pjRUTKaEQAfiTaHlLAtQ6Oto8WcC0RkVagPlZEpIxGBOA/Ei23aWa71HqR6NwjCA97/KGgtomIjHTqY0VEymhEAL6S0KFuDlxiZlXXlkXnXBJdw4GfF9lAEZERTH2siEgZwx6A3f1e4KeEEYrXAteZ2UsrPd/MdgCuBQ4gdMw/c/f76tFWEZGRRn2siEh5jRgBhrDC0OPR1wcA95nZBWa2r5mNTh9sZqPNbLaZfQO4D3hd9NFjwKeGpcUiIiOH+lgRkRI2a8RN3f0pMzuSUKv2EmAL4KPRa72ZPQz0RodPBnYCOqL3Fm2fAt7k7v8cnlaLiIwM6mNFREprSAAGcPf7zOwVwI+Bw+nvdDuAPVKHW+r974ET3P3p+rZSRGRkUh8rIpKvUSUQALj70+5+BHAQ4SGL5dFHlnoRffZz4PXufqQ6ZhGR0tTHiohka9gIcJK7/xn4M4CZ7QFsD0yJPl4OPO7u9zeoeSIiI5r6WBGRgZoiACdFnbA6YhGROlAfKyLS4BIIEREREZHhpgAsIiIiIm1FAVhERERE2ooCsIiIiIi0FQVgEREREWkrCsAiIiIi0lYUgEVERESkrSgAi4iIiEhbUQAWERERkbaiACwiIiIibUUBWERERETaigKwiIiIiLQVBWARERERaSsKwCIiIiLSVhSARURERKStKACLiIiISFtRAB4iM9vJzLzE64oS555gZn8xs9VmttLM5pvZvwxn+0VEmpn6WBGph80a3YAW8jfgqoz992YdbGZfBU4HHge+B2wOHAv8xsw+7u4X1qmdIiIjkfpYESmMAnBxetz93EoONLMDCB3zImBfd18R7f9v4C7gq2b2W3dfUqe2ioiMNOpjRaQwKoFojFOi7Rfjjhkg6oy/BYwFTmpAu0REWoH6WBEpSQG4ONuZ2clmdka03afEsYdE22szPrsmdYyIiKiPFZECqQSiOIdFrz5mNh84wd0fTezbApgOrHb3pRnXeTDa7pZ1k+7ubsysbGPcvbJWi4iMDMPSx4L6WZF2oBHgoVsDfB54NdAZvbqAG4E5wPVRhxybFG1X5lwv3j+56IaKiIxA6mNFpHAKwICZLSkzzU76dVl8rrs/7e5nu/tf3b03et0EHA7cDuwCfKiotnZ1deHuZV8iIs1iJPWxoH5WpB2oBCJYBKyr4vgnyx3g7hvM7PvAfsBBwDeij+LRh0mZJ/bv762iPSIizUx9rIg0FQVgwN0PrdOln4m2fb+ec/fnzewJYLqZbZtRo7ZrtF1YpzaJiAwr9bEi0mxUAlFf+0fbxan9N0TbIzLOOTJ1jIiIZFMfKyI1UQAeIjN7lZkN+jma2aHAp6K3l6U+/m60PdPMOhPn7AR8FFgPXFx8a0VERhb1sSJSDyqBGLrzgV3N7BbCkpsA+9A/x+RZ7n5L8gR3v8XMzgdOA+4xsysJy3S+G5gCfFwrFImIAOpjRaQOFICH7lLgbcC+hF+tjQH+CfwMuNDd/5R1krufbmZ/J4xGzAU2AX8F/tvdfzscDRcRGQHUx4pI4RSAh8jdfwD8oMZzLwEuKbI9IiKtRH2siNSDaoBFREREpK0oAIuIiIhIW1EAFhEREZG2ogAsIiIiIm1FAVhERERE2ooCsIiIiIi0FQVgEREREWkrCsAyiJlhZo1uRsvTz3n46GctzUR/HoePftbDYyT+nBWARURERKStKACLiIiISFtRABYRERGRtqIALCIiIiJtRQFYRERERNqKArCIiIiItBUFYBERERFpKwrAIiIiItJWNmt0A6Qqu/T09DBnzpxhudlw3afd6ec8fOrxs+7p6QHYpfALS6MMWz+r//eHj37Ww6NeP+d69LPm7kVeT+rIzO4GpgEPNbotItJnF+AZd39loxsiQ6d+VqQpFd7PKgCLiIiISFtRDbCIiIiItBUFYBERERFpKwrAIiIiItJWFIAFADPbycy8xOuKEueeYGZ/MbPVZrbSzOab2b8MZ/tHEjPb3sx+aGZPmtl6M1tiZl83s85Gt22kiX52eX9mn8o55wAzu9rMlpvZWjO7x8w+aWajh7v90j7Uxw4v9bPFadV+VtOgSdrfgKsy9t+bdbCZfRU4HXgc+B6wOXAs8Bsz+7i7X1indo5IZjYTuAXYGvgVcD/wGuATwBFmdqC7L2tgE0eilcDXM/avTu8ws7cCvwDWAT8FlgNvAb4GHAi8s26tFAnUx9aZ+tm6aL1+1t310gtgJ8CBS6o454DonIeAztS1lhH+8O/U6O+tmV7A76Of2cdT+8+P9n+30W0cSS9gCbCkwmO3BJ4G1gOzE/s7CH9ZOnBso78nvVrzpT52WH/W6meL/Xm2ZD+rEggZilOi7RfdfUW8092XAN8CxgInNaBdTSkalTic0Jl8K/XxOcDzwPFmtsUwN61dvIMwv+sV7n5nvNPd1wGfjd5+pBENE8mhPrZK6mcbbsT0swrAkradmZ1sZmdE231KHHtItL0247NrUscIHBxtr3P3TckP3H0VcDMwHth/uBs2wo01s/dFf2Y/YWYH59SZlfrzehOwBjjAzMbWraUi6mPrTf1sfbRcP6saYEk7LHr1MbP5wAnu/mhi3xbAdGC1uy/NuM6D0Xa3OrVzJNo92i7M+fxBwsjFbsD1w9Ki1rANcGlq38NmdpK7dyf25f783X2DmT0M7A3MABbUpaUi6mPrTf1sfbRcP6sRYImtAT4PvBrojF5dwI3AHOD61K+MJkXblTnXi/dPLrqhI5h+ZsW7GDiU0DlvAbwcuIhQI3mNmb0icax+/tJI6mOHh35uxWvJflYBuIWUmaok63VZfK67P+3uZ7v7X929N3rdRPiX8u2Edbg/1KjvTSSLu5/n7je4+z/dfY273+vupxAedhkHnNvYFkorUR8r7ahV+1mVQLSWRYSngiv1ZLkDol9ZfB/YDzgI+Eb0UfyvuEmZJ/bv762iPa1OP7Ph813C1FEHJfbp5y9DpT62+ennNnxGdD+rANxC3P3QOl36mWjb9+s5d3/ezJ4AppvZthk1artG27w6rHb0QLTNq9nTz6w4g/7MEn7+swk//7uSB5vZZsDOwAZg8XA0UEYe9bEjgvrZ4TOi+1mVQEgl4qdl039gb4i2R2Scc2TqGAm1fgCHm9mA//fMbCJhgvA1wG3D3bAWlPVnttSf14MIT4bf4u7r69kwkQzqY4ujfnb4jOh+VgFYADCzV6U7i2j/ocCnoreXpT7+brQ9M7m8pJntBHyUMBH2xcW3dmRy90XAdYQHBz6a+vg8wr+iL3X354e5aSOSme2ZNZdn9OcvXh0r+Wf2SuBZ4Fgzm504vgP4QvT2O/VprbQ79bHDQ/1ssVq5n7VohQ5pc9E0PLsSVmp5PNq9D/1z+p3l7l/IOO9/gNOic64kLNP5bmAqYRUeLdOZkLFE5wJC7d/BhF/JHeBaorMiZnYuof7sJuARYBUwE3gzYdWhq4G3ufsLiXOOJvw5XQdcQVii8yjC1D1XAu9ydYpSB+pjh4/62eK0cj+rACwAmNkHgbcBLwO2AsYA/wRuBS509z+VOPdEwr+09wI2AX8F/tvdf1vnZo9IZrYD8DnCr4imAkuBXwLnJVd7ktLMrIuwUtYr6Z+epxfoIcxXeWlWJ2tmBwJnAq8ldOAPAT8ELnD3jcPRdmk/6mOHl/rZYrRyP6sALCIiIiJtRTXAIiIiItJWFIBFREREpK0oAIuIiIhIW1EAFhEREZG2ogAsIiIiIm1FAVhERERE2ooCsIiIiIi0FQVgEREREWkrCsAiUhMzm2pmz5vZ/WY2If2+0e0TERnJ1MfWlwKwiNTqFEIf8m53X53xXkREaqc+to60FLKIVM3MNgeWAJ939++k3zeybSIiI5362PrTCLCI1OI9wJ8THXH6vYiI1E59bJ0pAEvLM7PRZvYRM7vJzJaZ2UYz8+g1q9Zj24GZHR197+vMbHq8391/5O7vynufcZ33RdfpNbOt691uERk+6mNrpz62cTZrdANE0szs5cA7gDcAOwJbAWuBp4G7gGuAK919bQXXGgX8Cnhzkcc2mpntBJwYvZ3v7vPrcI8O4GvR23nu/sQQLve/wFnAbsB/AR8cYvNEpEbqY8tTH9v6VAMsTcPMtgX+BzgWsDKHPw78h7v/pMw1jwF+Eb19BLgw2r4Y7bvR3VdWe2yjmdkc4Mbo7Xnufm4d7nEa4b/HOmCmuz85xOsdD/wY2AS8zN0XDL2VIlIp9bGVUx/b+jQCLE3BzPYmjDrsEO16AbgOuAFYCowHdgeOAXYBtgcui35l9u+e/y+5NyW+PtbdbyvRjGqObWlmNg74j+jtJUPtmCOXA58njDidQ/hLWESGgfrY5qI+tvEUgKXhzOwlwB+BbaJdtwEnuvsDGcf+J/Ax4KvAGODTwHOE/+mz7JD4+u4yTanm2Fb3fmBa9PWPi7igu280s58AZwDvMLOXuvujRVxbRPKpj21K6mMbTA/BSTP4Ef0d863AG7I6ZgB33+TuFxD+ZRuPSJxjZgfkXHts4tz1ZdpRzbGt7iPRdpG731rgdeNfp44G5hZ4XRHJpz62+aiPbTAFYGkoM3sd8Mbo7Rrgve7+fLnz3P3/gHnR29HAuYlrzomfKga6Evs99Tq3mmMTn402s+PN7Ddm9lj09O7a6Ou/mtllZnaCmW1R5nufYGafNLM/mNmTZrbezJab2R1m9jkzm5ZxzpyorTcmdp+T0d6ai/ujB2ReEb29vNbrZHH3fwA90dv3mVm5OkQRGQL1sepji7x+K1EAlkY7NfH1Je7+cBXnfg7YEH19mJntVVyzspnZVoQRlB8D/0KokxsLdERfvxJ4L3AJcFiJ6xwJLCI8AfwGYFtgc6ATmE14mneRmR1Vp2+llKMTX9+Yd9AQxNfckf6/BESkPtTHqo+VDKoBloaJ/mV6aGJXVXVQ7v6kmV1P/+jGG4B/APcCb4v2fQHYO/r6bQOvwP3As1UcC/A9YN/o64cIU88sJEwhtCXhIZKDgP3y2m1mbwd+ShhVeRH4NTAf+Gd0jYOBdwETgV+a2WHufkN0evy9vYz+mryfAlfk3a8G8V8qm4A7C7xuLPngyxvpH60QkQKpj1Ufi/rYfO6ul14NeQF7EmrMnDANzJgarnFO4hpXZHw+P/68gmuVPBbYmtBhOXAHsEWJa+0I7JixfwdgZXSNR4CX55z/GqA3Ou6x9M8GmJP4vs8t8L/JaOD56Lr31um/+46Jtv9fo/8c6qVXq77Ux6qPVR+b/1IJhDTS9omvH3b3F3OPzJd8kGN67lHFmEH/3JmXe4k6Ond/xN0fyfjo3wgjEBuBt7r733PO/wtwWvR2e+CdNbe6OjMI0yHBwJ9tYaKfSzzB/j71uIeIAOpj1ceqj82lACyNNCXxdW+N10ieN7XmllRmTeLrvXOPyhH9OvK90dvr3b2nzCk/pb/+7vBq71ejHRNfL6/jfVZE2x30kIZI3aiPLU19bBtTDbBI5e4DngS2Az4YdSrfA/7i7psqOH9v+v9CWmVmR1dwzmpgMuFXmcMh+RdmPTvnZYSf4+bAFoTvU0Tam/rY4qiPLUMBWBop+T//5BqvkTxvWc0tqYCHScZPJizluTnwgejVa2a3An8Gfu/ud+VcYqfE12+PXpXqrL7FNRmb+HpVHe/zXOLrcahzFqkH9bGVUx/bZlQCIY30eOLrncxsTA3X2C3x9RNDbE9Z7v5bwsMTV9G/fv1k4Ejgi8CdZvZ3Mzsi4/RJQ7j15kM4txrJyem3rON9kj+LtblHichQqI+tnPrYNqMALI10P/0jFB2E+R2r9drE1zcPuUUVcPe/ufvbCPVwRxKmyummv7N+GXC1mb03dWryX+Cfc3er4rVTvb+vSHLEaEruUUMXX/sFwhPRIlI89bHqY9XH5lAAlobxMF/L9Yldx1dzvpltS5iXMvbHItpVKXdf5e7XuvvZ7j6HMNH61+LmAeeb2ejEKcnRk+TT2c1kSeLr4eicH43+HIhIwdTHNqUlia/VxzaQArA02gWJr08ysx1zjxzss/TXsf/B3RcU16zqufsydz+N/onNtwZ2TRxyN/11WYea2VD+/0s+EFLkE74P0z9asHuB1+1jZjsRRqMA7qnHPUSkj/rY2qiPbXEKwNJQ7v5n4PfR2y2Ay8xsfIlTADCztwIfid5uJEzW3iyWJL7ue9DU3TcCP4ne7gh8aAj3SP6qb4shXGeAqI3xAyZ7mFk9atSSKzjdXofri0hEfWzN1Me2OAVgaQYnAE9FX78O+IOZ7Zp1oJmNMrOPAj+n/1/l57n7rfVupJm90cw+YWa5D1qY2S70L3O5mrAWfdKX6J9X8wIze3+Ze25tZmeZWXoy84cTX7+qbOOr84doOwqYXfC1YWDn/Pvco0SkKOpj86+nPrZNmUpDpBmY2cuAqwnLWEIo3L8WuBFYSlg5Z3fgGAb+yut84NN5NU5mNh/oAnD3kr/GKnesmZ0IXBy17UbCv6wXEyZv34qwfv276B8t+IK7n5VxnTcS1qaPnzr+W/T+QcLTupMIT17vDxxIWDrz9dFITvI6f6X/oZaLCLV+fdPquPu1pb7fPNF/i3j1pM+7+9m1XKfE9e8GZgFL3H3nIq8tItnUx6qPlZR6rrOsl17VvAgPOFxB/1rwpV6PA8dXcM358TlDPZYwilKuXR61/+vAqBL32p8wclHJ9VaRsZ494enoDXnnDfG/xd3RdR4q+L/xnok2fr7Rf+b00qudXupj1cfq1f/SCLA0HTN7OWFd9sOAlxL+5b8WeBr4K2EU4+fuXnZuw4JHJ4wwAvEGQue6J+EvlA7Cr+IeJkzU/kN3v7uCtm0GvBs4KrrutOhazxFGPe4mPHX9O3fPnMbGzPYDTiVMVbQNYcJzKvl+y7Ttw8C86O2B7n5LrddKXfeLwBmEmsIZ7v5oEdcVkcqpj1UfKyqBEJEMZtYBPEJ4ynqeu59cwDVHAw8RVmv6qbsfO9RrioiMROpjG08PwYnIIO6+Dvhy9Pb9ZrZdAZd9D6Fj3gScV8D1RERGJPWxjacALCJ5vkP4lWMH8J9DuVA0MvHZ6O0l3uD5REVEmoD62AZSCYSI5DKzo4FfEtavn+nuT5Q+I/c67wMuBVYCu7n704U1UkRkhFIf2zgKwCIiIiLSVlQCISIiIiJtRQFYRERERNqKArCIiIiItBUFYBERERFpKwrAIiIiItJWFIBFREREpK0oAIuIiIhIW1EAFhEREZG2ogAsIiIiIm1FAVhERERE2ooCsIiIiIi0FQVgEREREWkrCsAiIiIi0lYUgEVERESkrSgAi4iIiEhbUQAWERERkbaiACwiIiIibUUBWERERETaigKwiIiIiLQVBWARERERaSsKwCIiIiLSVhSARURERKStKACLiIiISFtRABYRERGRtqIALCIiIiJtRQFYRERERNqKArCIiIiItBUFYBERERFpKwrAIiIiItJWFIBFREREpK0oAIuIiIhIW1EAFhEREZG2ogAsIiIiIm1FAVhERERE2ooCsIiIiIi0FQVgEREREWkrCsAiIiIi0lYUgEVERESkrSgAi4iIiEhbUQAWERERkbayWaMbIJUzs7uBacBDjW6LiPTZBXjG3V/Z6IbI0KmfFWlKhfezCsAjy7RJkyZNnzVr1vRGN0REgp6eHlauXNnoZkhx1M+KNJl69LMKwCPLQ7NmzZo+f/78RrdDRCJz5syhu7tbo4WtQ/2sSJOpRz+rGmARERERaSsKwCIiIiLSVhSARURERKSt1L0G2MzGA+OAte6+pt73ExEREREppdAAbGb7AG8E9gP2AXYANk98/gLwKHAPcDvwe3f/e5FtEBEREREpZcgB2My2B+YC7wN2TH+cej+WMJfbLsAxwFfMbAlwKfB9d398qO0RERERESml5hpgM9vdzC4HFgNnAjsRAm/8Wgc8CfwduBm4N3q/PnXczsBZwGIz+4mZ7V5rm0REREREyql6BNjMtgL+CziREKDjUd4e4EbgNuB2d3+0xDV2JJRJ7AccDMyK2nIs8C4zuxg4w92frbZ9IiIiIiKl1FICsRCYRAi+jwA/AX7i7gsqvYC7PxKd+zMAM9uDUEJxHGEk+YPA24GpNbRPRERERCRXLSUQk4EHgfcDM939s9WE3yzufr+7f5ZQG/z+6PqTh3JNERERkXqaN28ec+bMYd68eY1uilSplgB8PLCXu1/m7puKbIy7b3L3y4C9CEFYREREpCldfvnldHd3c/nllze6KVKlqksg3P0n9WhI6h6bCKUVIiIiIiKF0kpwIiIiItJWFIBFREREpK0oAIuIiIhIW1EAFhGRipnZ9mb2QzN70szWm9kSM/u6mXVWcY3DzOx/zOx6M1tmZm5mfy5x/HQz+7iZXRPdb3103h/M7Jicc+ZE1817fbmW71+GTjMnSDMY8lLIeczsA8D27v65et1DRESGj5nNBG4BtgZ+BdwPvAb4BHCEmR3o7ssquNRHgbcSVgx9CJhS5viPA58BHiYsuPQUsCNwDPAGM/uau5+Wc243MD9jf27glvqKZ04AmDt3boNbI+2qbgEY+DChY1QAFhFpDd8mhN9T3f2b8U4zOx/4FPBF4JQKrvMV4ExCgN6BEGxL+Qswx927kzvNbE/C6qOfMrOfuPtdGefOd/dzK2iTiLQRlUCIiEhZ0ejv4cAS4Fupj88BngeON7Mtyl3L3W919/vcfWMl93b3/0uH32j/AuCn0ds5lVxLRAQUgEVEpDIHR9vr0osgufsq4GZgPLD/MLfrxWi7IefzXczsY2Z2hpl9wMx2Ha6GiUjzKlsCYWYV/Qs961TAazxXRESay+7RdmHO5w8SRoh3A64fjgaZ2ZbA2wl/11yXc9h7o1fyvF8AH3b3FVkndHd3Y2Zl7++uv+JERqpKRoAtem2q8iUiIq1jUrRdmfN5vH9y/ZsCFhLq94GXAN+JyiGSngH+A3g5MBGYBhwJ3E0Izb8xM/0WVKRNVfI//5OEf11v5+5jKn0Bt9e36SIi0sb+B3gn8Cdg0AwQUY3xV9z9Xndf7e7Puvu1hFrhh4EDgbdkXbirqwt3L/sSkZGrkgAcB9nZ9WyIiIg0tXiEd1LO5/H+3no3xMz+H2HWiZuAN7n7+krPdffngMujtwfVoXkiMgJUEoD/QiiBeE2V1y5fQCUiIiPFA9F2t5zP44fL8mqEC2FmXwP+jTAf8JHuvrqGyzwTbcvOWCEiramSeYD/QHiqd02V1/4coeZKRERGvhuj7eFmNio5E4SZTSSUFKwhzMtbuKjm90LgXwl/L73V3dfWeLl4porFRbRNREaesgHY3f8KvK3aC7v71TW1SEREmo67LzKz6wgzPXwU+Gbi4/MIo6kXufvz8U4z2yM69/6h3DsKv/OADwHXAMe4+7oy58x29zsz9r8PeDfwAvCzobRLREaueq4EJyIireVfCUshX2BmhwILgP0IcwQvJKzulhTPzDCgJM7MXkcIswATou2uZnZJfIy7n5g45ezo+LVAD/AfGdOU9bj7VYn3V5rZBuBO4HGgA9iXUM63ATjZ3ZeU/nZFpFUpAGcwsyWEdeaz/NPdt8k45wDgs4RfrY0jzIn5Q+Cbeasdmdm/AJ8GXgmMBu4Dvu3uPxrq9yAiUrRoFHg2ocTtCOBNwFLgG8B5efPqZtgFOCG1b+vUvhMTX+8cbccB/5lzzR8BVyXefwd4A6E0YytCCH8CuAT4urv/rcK2ikgLGnIANrO3ApPc/ccFtKeZrAS+nrF/0AMX0c/gF8A6wrKcywnT63yN0Pm+M+OcjxF+hbgMuIzw67h3AJeY2cvd/dOFfBciIgVy98eAkyo8NvNhaHe/hBBEK73niQwMxJWc8xXgK9WcIyLto4gR4C8TngputQDc6+7nljsoWonoe8BGYE5cc2ZmZwE3AO8ws2Pd/YrEOTsBXyUE5dnxr+HM7HPAHcDpZvYLd7+10O9IRERERCqaBk1Kewdhtosrkg9cRA9ofDZ6+5HUOR8AxgIXJmvQol8ffil6e0q9GiwiIiLSzlQDnG9s9LTwS4HngXuAmzLqeQ+JttdmXOMmwrRAB5jZ2MRk7aXOuSZ1jIiIiIgUSAE43zbApal9D5vZSe7endi3e7QdNPm7u28ws4eBvYEZ9D8RXeqcpWb2PLC9mY139wHzL3d3d5Px9PMgWqZTREREJJtKILJdDBxKCMFbAC8HLgJ2Aq4xs1ckjo2X/1xJtnj/5BrOyVtyVERERERqpACcwd3Pc/cb3P2f7r7G3e9191OA8wnT8JzbqLZ1dXXh7mVfIiIiIpJNAbg63422ByX2lRutjff31nBO3gixiIiIiNRIAbg6z0TbLRL7Hoi2u6UPNrPNCBO4b2DgmvOlztk2uv7j6fpfERERERk6BeDq7B9tk2H2hmh7RMbxBwHjgVsSM0CUO+fI1DEiIiIiUiAF4BQz29PMtsjYvxNwYfT2ssRHVwLPAsdGS4TGx3cAX4jefid1uYuB9cDHouvG53QCZ0Rvv4uIiIiIFE7ToA32bsJKbDcBjwCrgJnAm4EO4GrCKm4AuPtzZvZhQhCeb2ZXEFZ4O4ow3dmVhOWRSZzzsJn9G3ABcKeZ/ZT+pZC3B/5Hq8CJiIiI1IcC8GA3EoLrK4EDCfW4vcCfCfMCX+qpaRbc/Soz6wLOBN5OCMoPAacBF6SPj875ppktAT4NvJ8wGv8P4LPu/qO6fGciIiIiUkgA/iuhBKAlRItcdJc9cPB5NwNvqvKc3wC/qfZeIiIiIlK7IQdgd39vEQ0RERERERkOeghORERERNqKArCIiIiItJVhfwjOzAzYNbr3otT8uCIiIiIidVXYCLCZjTOzo6LXDjnHvAd4ElgA/B142szOLaoNIiIiIiLlFDkC/E7gEmAjMCP9oZm9kf4FJCzaTgTOMrOJ7n56gW0REREREclUZA3wYdH2dnd/LOPz/yYEXwPuJCwQsTJ6/wkze0WBbRERERERyVRkAN4LcOCm9Adm9irgZdHn57v7a9z9XcC+wPOEEPyBAtsiIiIiIpKpyAA8Ldo+kPHZG6Pti8AX453u/hDwM0IAfl2BbRERERERyVRkAN4q2j6X8Vkcbm9x9xWpz+6ItjsX2BYRERERkUxFBuD4wbZxA3aGac9eS055BP3LKE8osC0iIiIiIpmKDMDPRNvdUvtfA0yOvr4l47zx0XZdgW0REREREclUZADuIYwCv8fMkqPAH462LwI3Z5wXT5m2tMC2iIiIiIhkKnIe4J8D/wLsAsw3s8uBvQmzOzjwO3d/PuO8/aLtggLbIiIiIiKSqcgA/BPg48DsxCv2AnBu+gQz2xI4mBCQbyuwLSIiIiIimQorgXD3TcCRwK+iXfGiF08C73D3v2ecdiKwefT1H4tqi4iIiIhIniJHgHH3ZcDbzGwaobZ3DXBfFI6z3A+cFE71O4tsi4iIiIhIlkIDcMzdn6F/VohSx11Xj/uLiIiIiOQpchYIERFpYWa2vZn90MyeNLP1ZrbEzL5uZp1VXOMwM/sfM7vezJaZmZvZnys4by8z+5mZPW1m68zsATM7LzXrUPqcA8zsajNbbmZrzeweM/ukmY2utL0i0prqMgIsIiKtxcxmEuZy35rwrMf9hHnePwEcYWYHRmVw5XwUeCth7veHgCkV3Hs/4AZgDHAl8BhwCHA2cKiZHeru61PnvBX4RXSfnwLLgbcAXwMOBN5ZQVtFpEVpBFhERCrxbUL4PdXdj3b3/3D3QwiBcnfgixVe5yvAywirf76l3MHRaO3FhEWT3uHux7n7ZwhTaP6CEGY/lTpnS+B7wEZgjrt/0N3/DZgF3Aq8w8yOrbC9ItKCFIBFRKSkaPT3cGAJ8K3Ux+cAzwPHm9kW5a7l7re6+33uvrHC23cBewI3ufuvE9fZBPx79PYUM7PEOe8ApgFXJB+wdvd1wGejtx+p8P4i0oIUgEVEpJyDo+116Vl93H0VYZXP8cD+dbj3IdH22vQH7r4YWAjsSP+qoiXPAW4izFB0gJmNLbCdIjKCKACLiEg5u0fbhTmfPxhtd2uSe+ee4+4bgIcJz8DMSH8O0N3djZmVfYnIyKUALCIi5UyKtitzPo/3T26SezeyvSIyAigAi4iIJHR1deHuZV8iMnIpAIuISDnxiOmknM/j/b1Ncu9GtldERgAFYBERKeeBaJtX47trtM2r0x3ue+eeY2abATsDG4DFRTRQREYeBWARESnnxmh7uJkN+HvDzCYS5uJdA9xWh3vfEG2PSH9gZjMIIfcRBobZ3HOAgwgzVtySXjxDRNqHArCIiJTk7ouA64CdCCu5JZ0HbAFc6u7PxzvNbA8z26OA23cDC4CDzOyoxPVHERbVAPiuDyzKvRJ4FjjWzGYnzukAvhC9/U4BbROREaouSyFHHc4bgb2ATqCjgtPc3Q+tR3tERGTI/pWwFPIFZnYoIZTuR5gjeCFwZur4BdF2wHxhZvY64EPR2wnRdlczuyQ+xt1PTHy90cxOIozqXmlmVwKPAocCswlzEH8teQ93f87MPkwIwvPN7ArCUshHEaZIu5KwPLKItKlCA3D066hLCL8Oq+pUQI/Uiog0KXdfFA1ufI5QWvAmYCnwDeA8d19R4aV2AU5I7ds6te/E1L1vN7N9CaPNhwMTCWUPnwO+nFXK4O5XmVkXIZi/nTAQ8xBwGnCBaxoHkbZWWAA2s5cAfwZeQupf/COJmU0F3ga8GXg5MB14Afg7YT36i5MrIZnZToRJ1fP81N0z15w3sxMIv07ci7Bm/d3AV939t0P/TkREiuXujwEnVXhs5t8D7n4JYaCk2nv/A3hnlefcTAjqIiIDFDkCfDawDWEk9+/AlwmB+J/u/kKB96m3dxJqw5YSHvx4lBDqjwG+DxxpZu/MGD34G3BVxvXuzbqJmX0VOB14HPgesDlwLPAbM/u4u1849G9FRERERNKKDMBvJoTfe4H93X1tgdceTgsJdWK/S430ngH8hfCrtGOAX6TO63H3cyu5gZkdQAi/i4B9418dmtl/A3cBXzWz37r7kqF9KyIiIiKSVuQsEC+JtvNGcPjF3W9w998kw2+0/yngu9HbOUO8zSnR9ovJurko8H4LGEuFv2YUERERkeoUGYCfibb/LPCazebFaLsh47PtzOxkMzsj2u5T4jqHRNtrMz67JnWMiIiIiBSoyBKIewgPjO1Y4DWbRrR60Pujt1nB9bDolTxnPnCCuz+a2LcF4ee02t2XZlznwWibt+qRiIiIiAxBkSPA3yHM/vDeAq/ZTL4MvAy42t1/n9i/Bvg88GrCnMedQBfhAbo5wPVR6I3Fa9CvJFu8f3LWh93d3ZhZ2ZeIiIiIZCssALv77whT28wys29aC6UwMzuV8NDa/cDxyc/c/Wl3P9vd/+ruvdHrJsJclbcT5rz80KCLioiIiEhDFL0S3FzgecKKQa8zs3mEmROWAZtKnQiQLBVoFmb2McJE7/8ADnX35ZWc5+4bzOz7hJWSDoquAf0jvJMyT+zf35v1YVdXF/Pnz6+kCSIiIiKSodAAHIW+bwCvBV4FVDOXrRfdnqEys08Slti8lxB+n67yEvGDgX0lEO7+vJk9AUw3s20z6oB3jbYLa2iyiIiIiJRRZA0wZnYiYaT0lYRAa1W+moaZfYYQfnuAg2sIvwD7R9vFqf03RNsjMs45MnWMiIiIiBSoyKWQDwB+QH+QXQXcSZgWbdA67c3MzM4irDF/F3B4qbIHM3sVYRGMTan9hwKfit5eljrtu4Ra4jPN7KrEQhg7EZZGXk9YdllEREREClZkycF/EMLvJuAs4H9G2BLIAJjZCYTwuxH4E3BqxvN8S6L17AHOB3Y1s1sIyxoD7EP/PL5nufstyZPd/RYzOx84DbjHzK4kLIX8bmAK8HGtAiciIiJSH0UG4FcTyh7+193/q8DrDredo+1o4JM5x3QTZrwAuBR4G7AvoXxhDGHU+2fAhe7+p6wLuPvpZvZ3wojvXMI/HP4K/Le7/3bI34WIiIiIZCoyAE+OtlmLRIwY7n4ucG4Vx/+AUPpRy70uoT9Ii4iIiMgwKPIhuCeibdnpzkREREREGqXIAPyHaPvqAq8pIiIiIlKoIgPw14F1wIfMbHqB1xURERFpGvPmzWPOnDn09PQ0uilSoyKXQn6QMLXXWOAGM9u3qGuLiIiINIvLL7+cnp4eZs2a1eimSI2KnAf47OjLPwD/AtxmZncBt1P5UsifK6o9IiIiIvUya9Ys5s+fz5w5cxrdFKlBkbNAnEuYBg36V4F7NdXVBCsAt6NNi2HjctiwYuD+zTrDdvSUsB01Y3jbJSIiIi2pyAAMg5czrmZ5Yy9/iLScF++EDSvwFQ/B6idh1RP9n02cDhO2w8ZFAbhjUWNDcRzU8yioi4hUrKenhzlz5nDccccxd+7cRjdH2kyRAfjgAq8l7WL0lIEjvyufYt0dC1jS8xQAE7ccy4TOcQBM2rkTpk6GKZNh0jZ9ARlIhOQpg0My1B5KNy0O2zj4blgB65bja5f3B/aVT8HyXljWG47ZdSfY/tXYDgfB2JkKxCIiGWbNmtX3EJkCsAy3wgKwu3cXdS1pI6NmwLgZ2Gad+OpfwYNL+MM1j3HjP0dHB6xl683XMG2ss/X4Z5g23pk8afO+YByHYodBwXhAKCYxehxLBuQsydALA4Pv43fB8l7WLVrKssUr+OsDq3l6jbH1eGfa+AfYYYdbmH7Y9XDA8di0fWHM7EJ+XCIiraCrq0v1s9JQRZdA1MzMJrj76ka3QxpkzGxs2iJ817t41e5PAiFQxkKwDFUyDy59EZa+CKyG2/pDMfSPGI/t7KBj8rjBI8axZECGKCSnrOsvdxgw4huLR3yBaeO9r73znxjNtGefZ+sH/sJBdz7MpMNfib32UxoNFhERaRJFzgLxCXf/Ro3nTgR+DxxQVHtkBOp8N/b6mUzf4w6m3/8b1t2xgPUr1rF6xVpWPbee3pUv8Mwa474Vo3j6hYHl5VtvvjEaJX6RaeNXAQwYKR7b2QEQQjGEUeM4GMPAcJwlLnWAgeUOkcmTNmfrNS/2te3pF4z7VgG3PcOrVtzCdAgheJwCsIiISKMVOQL8P2b2tLv/bzUnmdkE4DrgNQW2RUaqMbOx7WbDNm+kY4/f07H6SSalyg12fW59GAXOEI8SA/SufIHelS/AY6v6RohjE7ccCzA4HE+dHA6YMrn/4ETgXde7FoD1K9YxtrOD9SvW9dUoT1v5AtPWhPsnA/pfH1jN1DsW0LH9TdguGgUWERFptCID8CjgEjNb5u7XVXJCNPJ7LbAfmgVCkkbNwLb7SHgIbZeTYONyxq1exPS1y2HpHeyx8il4cAnreteybHGo0Y1HiQGeWTNwhPiZNanAHAXo9GgxPDkgFMfWr1gHwOoVa/v2Jb8G2GGHiUyeFML5tOj+960Ia828avEKpj9+F+xwkEaBRUREGqzIAPwwsDPwCzM71N3/UurgRPh9bbTrpwW2RVpFPFo6agZ0zsY6gWn7hgfTXrmcjmfuZfqqJ+Dh+2FZL+t617J+xTp2SJRNQH8gTtYVP7Pe+kZseXYDW4/vD8X9gbjfqufWD3gfXztZfzxxy7HsShgNvu/ZUUwb6zyzPrrng0vwPR7CVAssIiLSUEUG4MOBPwMvAX5nZq9z9weyDozC7zUMDL/vK7At0srGzIYxwDiwzneHuYT3CtOTdaxdTsfSO5iUHiF+bFVfCO4LpBlfTxsbAvEza15k2soX+sJtHHaz9CaOg/7yir0JNctbj3dWPbeedb1r6Vj9ZJhdQgFYCmBmm7t7/h/O8ufPcveeApskIjIiFDkN2iIzexNwIzAVuM7MDnD3J5LHRTW/19D/wNvPgPe5e9mlkkUyJQPxpMVhhHj1InyPEIanr3yK6Q8u4Ym/PjngYbqkeGR460QNcTjmhdT7gXXGwKD64gmd45jQOY6JK9bCY6HEYtsdJ/c/gLdhRWivyNDdYWbHuvuCak80s9OBLwDjim+WiEhzK3QaNHe/28zeBlwNbE8Iwa939+XQF36vpT/8/hx4r7tvLLId0sZGzegvl4jD8IYV+B4PMX32HYNKJeIZJqYlQvHTqXCclgzC6VKJdP3wDtG2b9+qAf8eFBmqlwN3mtmn3f07lZxgZtsAPwLeUMsNzWx7wrL1RxAGO5YCVwHnufuKEqemrzMFOBs4GtgWWEb4++Fsd388deyJwMVlLrnJ3eMJxDGznQileXl+6u7HVtpeEWkthc8D7O43mtn7CGUNexDKIQ4lPCSXDr/HKfxK3cRheAyh7rZzF5gRFrPoWHoHHSufYlI0w8PKh1eww4q1PPbYKqaN90EjxEnxCHBy5DeeCSIZftMP0vVZt1xjblKkDuBCM3sj8EF3X5Z3oJm9BfgBIbga8Ew1NzKzmcAtwNbAr4D7CTP4fAI4wswOLHX/xHWmRtfZDbgBuILw98VJwJvN7LXuvjhxSg9wXs7lXg8cQvjNYpa/EQJ62r3l2ikirasuC2G4+y/M7KPAdwid4y+BLegPv1ei8CvDKVpxLtQN018mEc0qMWnXp5j04BIglElMnkTmjBLp8Jsc/c0KvIP2rXwKX7s8jE6rDliG7p3APKATeAtwj5kd7+43JA8ys7HA14CT413AH4ATqrzftwnh91R3/2bi+ucDnwK+CJxSwXW+RAi/57v76YnrnAp8I7rPEfH+qE65J+tCZnZr9OW8nHv1uPu5FbRJRNpI3VaCc/eLzOwlwLmEX7XFKeL/gPco/EpDjZkNk6ZgE5aHkeF1y/Ht72X6rv1zDsclEpOjuuGsuYTjkd+0uN43njd4wBzDIgWJBhtuBy4DDiKUElxnZv8DnOnuG8xsH+ByYE9CP/wCcIa7n1/NvaLR38OBJcC3Uh+fA8wFjjez0939+RLXmQAcDzxP+Psh6ULgNOCNZjYjNQqcda2XA/sDTwC/q/ibEZG2V9elkN39c2Y2DfgoYZ7fXwLvVviVppAokWDsYmzCTNjhIHzFQ3Ts8yQd91zP2EVLmbBiXXigjVDqsDrxddaob9/DbqmvRerB3R83s4OB/yQEys2ATwMHm9lvgDOAzQnh937Cb996arjVwdH2uvRDy+6+ysxuJgTk/YHrS1xnf0IR0HXuvip1nU1m9ntCmD4YKBmAo+MAflDi75XtzOxkQtnHMuBWd7+nzHVFpMVVHYDN7OwqT1kGrARGAwuAM82y6yvd/XPVtkekEMl64c06oXMFPmE7OnaOaoUfXNJ36KSdO8OUZqlRXsgPvOt619IBYYU5TYUmBXN3B75kZn8EfgLMBF4dveIOdx7wKXdfm32VsnaPtgtzPn+QEIB3o3QAruQ6RNfJZWbjCNNnbgS+X+LQw6JX8tz5wAnu/mipe4hI66plBPhcal+17YwynysAS+NF06r1PTgXlUew6glY+RQAHct7w7HLeqsb5V3eG+ZHEakDd/+LmZ1B/8JCRuiv/8/dK6nNLWVStF2Z83m8f/IwXedd0TG/c/fHMj5fA3ye8ABcPJK8D+HvsIOB66N5kAeVa3R3d5M3UJMU/t0hIiNRrSUQ5XuG6qknkeYyagaMBTbrxDqmhCC8dnkYwd2eEIinhEBMHIizLCvxmUhBzGwM8P+AjxP602Q/fYyZ/Q44yd2fbkT76iAuf7go68Po+0z/xvImM4sXbdoP+BDhoTsRaTO1BOCDyx8i0iIG1Qkvh3GLAPAJ24VHjlY/CZP6R4f7RFOsxSUS63rX0jF1suYClsKZ2Z6EB932IQTfF4EzgQcI5QHTCLMq3GNmJ7l73pRhpcQjs5NyPo/399b7Oma2N2FWoccJ885XLHow8PuEAHwQGQG4q6uL+fPnV3NZERlhqg7A7t5dj4aINL04DE+aAhuX940KM25K/68v0iE4sn7FOgA6lvXCzmg1OCmMmZ0CfJXwYJkRamuPc/e/Rp/vA1xKmI1na+C3ZnYh8G9VLqMcL22fV5u7a7TNq+0t8jqVPPxWSjz/8RY1nCsiLWBUoxsgMuKMmhHqhMfOhAkzoWMKTNgOJk6HSduUPHXlwyvg4fvxFQ/BpnIPuIuUZmZXEaYkG08Ivz8EXhWHXwB3/6e7Hw78G2EKNAM+RlhGee8qbndjtD3czAb83WFmE4EDCXW3t5W5zm3AWuDA6LzkdUYRHqRL3o/UMR2EadQ2Ehb1qMX+0Vb/E4q0KQVgkVqNmgGjp4Qa4c5dBofgKZMz5/5dt2gpLL0D1i8a1uZKSzqKEGh7gXe5+4fcfU3Wge7+P4Tg90B0zsuBv1R6I3dfBFwH7ESY2jLpPMJo6qXJh8rMbA8z2yN1ndWEEektGDwP8Mei6/++xBzA7yQs/HFNzsNv8b1flQ7q0f5DCYt2QJg/WUTaUF3nARZpeYmpzKxzl8xyiI7J4/pKIAbQkshSjJuA40uFwZi795jZq4CvAx8mLKNcjX8lLGF8QRQkFxBqaQ8mlCycmTp+QbRNPzh9BjAHOM3MZhGC+J7AW4GnGRywk+Lyh7yV32LnA7ua2S2EWmEINdKHRF+f5e63lLmGiLSoWuYB3tbdl9ajMY24j8iQxbXBoxeH0WAGT2kyNm+u4E1aElmG5CzgS17FfFzRPMAnm9m1wPequZm7LzKz2YQpK48A3gQsJTxIdp67r6jwOsvM7LWEFeSOBl5PmDP+YuBsd38867zoYb/XUdnDb5cCbwP2BY4kVN3/E/gZcKG7/6mStopIa6plBHiRmV0EfMXds5/4GQIz24YwOvBB9ICCjCRxkJ3AwBC8PMwVnFwwg5VP4WuXY3nPwYtUwN2/OIRzfxkto1zteY8BJ1V4bO6Ume6+HPhE9Kr03guocBpOd/8BtdcIi0iLqyUAdwCnAnPN7GJCzVfVnWiame0PnAC8n+p/LSfSHLJC8JSnNBewNCV3f7LRbRARaYRaAvAxwNeAHYGPAB8xs0WEOShvBO7MWlknzcwmALMJdWDHEZbuhJAbHgZOq6FtIo0Xh+COFeHBuGjlt44Hl/SPAi/v1ZLIIiIiDVLLPMBXmdk1hIcUPkOYYH0XQi3aWcAmM1tAeNJ4efRaBWwJTIleuwN70D8LRfwrraeBLwPfcff1NX5PI4aZbU9/Ld1UQi3dVVRRSydNatQM2Gw5Fj8Utz2hFCJ5zKonNB+wiIhIA9Q0C0QUTs83s28TyhZOAV4RfTwa2Dt6lZKs4+oBvk0op2j54AtgZjMJT1NvDfwKuB94DaEe7ggzO9DdlzWwiTJUo6eEUeC1y8P7KZP7SyGW9YbSiHXLYbM7w7zCIlUys1oWgUhyd9dsQCLSdobU8bn7OsI67BeZ2csIT/MeBryaMDF7nueBu4A/AFe5+31DaccI9W1C+D3V3b8Z7zSz8wlzVH6R8A8LaUHretfS8eASfPt7w4pyGgWW2lT0QJiIiAxU2L/83f1e4F7gC9Hk4zMIdcJTgLHAekI5xBLgYXffVNS9R5po9Pdwws/iW6mPzyHMc3m8mZ1eST21NKlRM2AsWGc8I8RdQFgNbvWKtUydAR33XI9P2A4bO1O1wFKLmxg8617aKGArwtLDo6Pj7yaUpomItKW6/OorCrcPRS8Z7OBoe136HwLuvsrMbiYE5P2B64e7cVKgOASPW45P2obwb55g/Yp1dCzr1cNwUjN3n1PpsWY2mfDsxmcJv6F7d7S6m4hI29FSyI2xe7RdmPP5g9F2t/QH3d3dmFnZl4wg8cNwm/JWfhUZOnfvjeYNfjuhD/q1mZUqVRMRaVkKwI0RL3+wMufzeP/k+jdFhkXHlMzdAxbHEBkG7n418GvCTDyllhwWEWlZCsAjTFdXF+5e9iUjw4BlkTcub1xDpN1cQ3iA7t2Nboi0j3nz5jFnzhx6enoa3RQRBeAGiUd48xbCjff31r8pUnejZsBmnTBx+oDdYzs7YOrksCzyiodUBiHDKf7X1sySR4kU6PLLL6enp4dZs2Zx3HHHNbo5NZs3bx7d3d0D9vX09DBnzhzmzZvXoFZJtTT/Y2M8EG0H1fhGdo22eTXCMtKMmY3tsAKf/QSTuI2xnR0DR3+X3oETZoxgnB6Gk7qL/5BpAj4ZVrNmzWL+/PkD9nV3dzNv3jzmzp3bmEZV6fLLLwfoC/HxNh7ZHinfR7vTCHBj3BhtD4+mjOtjZhOBA4E1wG3D3TCpo7EzsRlvhF13omPyONb1rmXdoqXw4BJ4+P4wGwTAi3c2tJnS2qLZIE4hTIf2cGNbI+0uDo9xqBwpurq6+oLu3LlzmT9/PrNmzWpso6QqCsANEE09dB2wE4MfQjkP2IKwKp7mAG5h61esY/2KdeFBuGW98PhdKoWQujCzzczspWZ2InA7oe+BsPS6SMPMnTuXrq6uRjdD2pBKIBrnXwlLIV9gZocCC4D9CHMELwTObGDbpF46pkBqPuA+y3sHlkKMRXMDS0lDXAr5EeCrRbVFRGQkUQBuEHdfZGazgc8BRwBvApYC3wDOc/cVjWyf1MGoGcCi8DDc1MmMTUyBtq53LR0A3A/QH4I3Ww5jZg9/W2WkqHXS7z8BJ7h73lSMIiItTQG4gdz9MeCkRrdDhtFmnWE7ZTIdy3qz5wFe+RRMfBLGTYEOYPRijQRLnkqWQoawFH0v8A/gWnf/Sz0bJSLS7BSARYbT6CkwYTvgLoC+h+EgOQoMTHoijAJPe1l4v9nycK6CsCRUsxSyiIj0KywAm9n/Ahe5+/yirinSckbNwMZNwSdtA1N7w8NvCQNKIaY8ha96ArbdFxs3BSb0X0NERERqV+QI8LuBd5nZYuB7wCXu/nSB1xdpDRNmwrb7hlKHZb2sX7EOiBbGIDUSDMAd+MTpodizY4UejhMRERmioksgjDDB+n8BnzezXwPfd/ffF3wfkZFr9JQwCrw8pwY4lhodVkmEiIhIMYoMwK8G5gLvAbYkrDB0DHCMmT0K/AC42N2fKPCeIi0pDsYdEILw1N7+koiJ02HCdljnLgrCIiIiNSgsALv73cBHzOw04Fjgg8AB0cc7EhZ4ONvMriGUSFzt7puKur/IiBPNBJEWl0QM+PrhFdHyyUtg6mSYMjnUEW+7r4JwG4hKy+rB3X1mna4tItK0Cp8Fwt3XAhcDF5vZnoRR4fcBU6P7/Uv0WmpmPwR+4O6PFN0OkaYXPQjXAZmlEKtXDN63fsU6xvauDcF5ai+sfArf/tUaEW59OxFVwRR83UqmUBMRaTl1nQbN3RcAnzKzzxDKIT5EWOnMgO0Iq52dYWZ/BOYBv3b3DfVsk0jDjZoBHYvC11MmA+SG4Dx9D8ot6w0ryE2ZrCDc2h5FYVVEpDDDMg+wu78AXAFcYWY7E4LwicC2hDB8WPR6JhoV/k60SIRI65o4feD7KACP7exg/Yp1TOgcN2AUOP463k7ojGaP6F0Li5bS8eAS2HWn/tKIcVNggoJwK3D3nRrdBhGRVjLsC2G4+8NmdiOwB/A2Bv5ab2vgM8DpZnYx8B/u3jvcbRSpu806w4IYq/qfCU0uihGH4LRVz60f/PUjMHHLscAKJsS1wjPvx3feQzXCIiIiGYYtAJvZtoRlfz8A7Jz8CFgA/BE4GtiBMIPEh4E3mNlr3f2Z4WqnSMNMnZxbCpEMvkm9K1/o206etDmrnlvPxBVjmbBiHWMXLQ1BeMpkGFAe0akwLCIiba2uAdjMDHgzoeThTcBo+kd71wO/IKwe96fo+E8SHpA7C5hNCMpnAx+vZztFGmblU6GGd1lvmN2BgSPBkB9+Y8+ssWj7Ytix9EWmjV/F5EmbQ89TbLvjZMZ2LqBj5rYaFRaRptTT08OcOXM47rjjmDt3bqObI22gLgHYzHYkTIN2EuFhN+gPvg8RHni72N2XJc9zdwd+Y2a/A64h1AUfWY82ijSNeCq0ZdkLY0zccmzZEJwlHh3mkd6Bo8KTb8N33SnMQtEXhjUq3OzM7Pzoyx+4+30NbYxIgY477jgghGBAAViGRWEB2Mw2I5QwfBg4lBB449C7AfgV8F13v77ctdx9k5ldQgjALy2qjSJNI14NbtI2wJLM4Du2syP3gTjoHxmeNt77RoHj90m9K18IYfixVQBMnrQ5E//6JFNndPbXCk+cHlaZ61ikMNy8Pkl4ZmI+kBmAo4eIAS5w955haZXIEM2dO5e5c+cyZ86cRjdF2kiRI8BPAFtFX8d/Gz9CWPTiB+7+zyqvtzzaji6gbSLNJZ4KLT0TRIaxnR19X6dD8ORJm9O78oVBobeUOBCvem49ExevYELnw9EiG+MgOTI8bgp0TFEgHllOJITkq4Ceoi9uZtsDnwOOIMztvjS613nuvqKK60whlLcdTZgNaBlwLXC2uz+ecfwSwoJKWf7p7tvk3OcA4LPA/sA44EHgh8A33X1jpe0VAZg3bx6XX345PT09zJo1q9HNkSEqMgBPi7YbgauB7wLXRmUNtXgC+FERDRNpSpt1Dngbz/qQDLxpyZHgMPNDf4lE/D5ZLtFXBgEDRonD+xf76oWhf2R4Quc4Ju2cKpMYN0Wjw23OzGYCtxBm6/kVcD/wGuATwBFmdmC6rC3nOlOj6+wG3ECYInMPQsncm6MHn7NWvlsJfD1j/+qc+7yV8JzJOuCnhEGVtwBfAw4E3lmurSJJyfAbl23IyFX0CPAPgO+5+xPlDi7H3e8ldIgirWn0lDAVWiQ5upsVgpPlEOnjpiaOmxAF6dUr1qbqh0MYHhyEEw/RxQ/QPdLbF4bHdt44cHS4r1xiCoyNVtFVIG4H3yaE31Pd/Zvxzqg2+VPAF4FTKrjOlwjh93x3Pz1xnVOBb0T3OSLjvF53P7eShprZloTfPm4E5rj7ndH+swih+x1mdqy7X1HJ9URis2bNYv78+Y1uhhSgyAC8o7tvKvB6Ii3Pxk3Bp05m/cMrBj3oFofbjsn9gTf5NdA3c0S8ohyEVeVY3suk+OE6wtRq61esY/WKtUyO7tO78gWeWWM8nQrET68xeHYDW49/EVjNrtuOAUiMDnfiU6+HvNphUCBuMdHo7+HAEuBbqY/PISx5f7yZne7uz5e4zgTgeOB54NzUxxcCpwFvNLMZOaPAlXoH4beSP47DL4C7rzOzzwLXAx8hjD6LSBsqLAAr/IpUadSMsFLbznswdtFSJq4YXMLQNxIcB10YEHaZFJU+Tpw+YDQZgNVP9i200bHyKTqiUByH4YlRGJ6WKJPIkpxrmMfC6DDAxC3/EY0Qd9Axc9vQrqwRYoXhVnBwtL0u3de7+yozu5kQkPcnhMs8cS3ude6+KnWdTWb2e0KYPhhIB+CxZvY+woPRzwP3ADfl1PIeEm2vzfjsJmANcICZjXX36qdYEZERr8hZIDYBm4Bj3P3XVZz3RkLNsLv7sK9MJ9JQY2Zje72HDmDqH29k2eLwHNGq59YPKHUAwohrLH54bsJ2oT43Fj+0BtAZPZO0LjxP6mvDtmP1k3SseoJJMHAeYvoX4Vi2uH9EOh4pjiXnG4bVTBvv7LB4RX8YnjwujBDvcyg2bTlMQiF45Ns92i7M+fxBQgDejdIBuJLrEF0nbRvg0tS+h83sJHfvrvQ+7r7BzB4G9gZmEBZiGqC7u5swjX1ptT/iIiKNVnTgLN9jFHueyMg37jBsn046gOl33sbKh0NwHVQHvPKpvhXdgP5ZGqA/9CYfUBsTnTc2DKTZhGhilWnAhv5wHAdjCOGYVU8wfeVT8OCSvtHiHVaUX5hj6SO9iWWZn2TqoqV07Lsn7LE8zDWs0eCRbFK0XZnzebx/cp2uczHwJ8L0b6sIwfVjhNHia6IH5/5Wh/aKSIvSiKtIMxgzG9vnX/Ft92XS/b+BB5fkHjpgerJYqZkZ4v3JzxPh2Caljt+4PATkVy5nHNCxdjmT4nKKxIhxsq44OVocL8vM4hVMn7wEJt2BA9YJjEUheOgqGXZsqaFJdz8vtete4BQzWw2cTqgnfltR9+vq6tKDTiItrhkC8MRoO3glAJF2MmY2ts0UGDcF32N5qOFNmrDdwFXbYkMJlFnnjpoRAvI4YFMUkONQDH2jxn2lFKkyij5TJ/c9KMfqJ0MIHrc81D1rKrWhuKrMr+etgmNilZaexSOm6X8ukdrfO0zXiX2XEIAPqvN9RKTFNEMAPjTaPtXQVog0g1EzoHNGGC0F2LS4f3+j2hNv41HjcfS3D0IbN0ZlFBsy6o7jIB+H4I4pAwO81CIv3XoFx9TigWibVZsLsGu0zavtLfo6sWei7RYZ95kd3eeu5AfRqqU7E1YoHcpMEyIygtUUgM2sC+jK+fhYM5tV7hKEDutVhKd9Hbi1lraItLSRMEo6akbpuuP4YTzQQhpDVy7U1ut5ihuj7eFmNio5E4SZTSQsLLEGuK3MdW4j/LbvQDObmJwJwsxGER6kS96vnP2jbTrI3gC8lzCf8P+mPjsIGE+YQUIzQIi0qVpHgOcQlrFMM+DdVV7LCP8Sv6DGtohIs8kaOZYhcfdRDbz3IjO7jhBQPwp8M/HxeYQBjYuScwCb2R7RufcnrrPazC4lPLx2LqF8IfYxYCfg98k5gM1sT+DR9PzCZrYTYe5ggMtSTb4S+AphQOabiYUwOoAvRMd8p8JvX4ZRd3c38+bNY+7cuY1uirS4oXSolnrl7S/3uhs4yt3vGEJbRESkvv4VeBq4wMyuMrP/MrMbCKvALQTOTB2/gIwpxoAzouNPM7Pro+tcRVgF7mlCwE56N/CUmf3OzL5tZl8xsyuja+9CmEbzq8kT3P054MPAaGC+mX3fzP4f0AO8lhCQf1rLD0HqJ15e+PLLL29wSwabN28e3d3p2fZkJKt1BPgSYH7ivRF+5eTAWcDNZc7fRFi//WF3762xDSIiMkyiUeDZwOcIpQVvApYSgut57r6i1PmJ6ywzs9cSVpA7Gng9sIww1dnZ7v546pQbCfP6vpJQarEF4eG1PxPmBb7UMybkdferonK9M4G3ExZJfIiw2twFWedIY82dO7cpwy/0h/I4pMvIV1MAdvdHgEeS+xJPHN+bMSm5iIiMcO7+GHBShcfm1iO7+3LgE9Gr3HW6gZr+TnH3mwlBXWTIurq6VJrRQoqcBSJeKvPeAq8pIiIiIlKowgKwRn1FREREZCRo2FPFIiIiIiKNUPUIsJn9MPrS3f2DGftrNeB6jWBmuwLHAG8kTMj+EmAFYe7Kr7v7oLkpzexEwsMbeT7i7t/NOG8c8B/AscCOwHOEBwvPcfesJ6dFREREpAC1lECcSP9qQx/M2V+rhgZg4POEKXf+QZhaZznh6eOjgKPM7BPunjdf8a8IU+yk3ZneYWZjgT8Qnmi+k/AU9Q7AO4E3m9kh7n770L4VEREREclSaw2wkR12h7IKUTNMSXMt8BV3vzu5M5pK5w/Af5vZz919aca5V7n7JRXe5zRC+L0SeHe8qpKZ/RS4Cvihmb08udqSiIhIO+jp6WHOnDkcd9xxDZ91Yd68eVx++eX09PQwa9ashrZFilVLAN65yv0jRl6AdfduM5sPHAYcAPyi1ntYmC/ulOjtvydDrrv/ysz+RJgXs4vKlwMVEREZ8eJ5dnt6egAaHoCT4VdzALeWqgNwNAdwxftbyIvRdkPO57PM7JOEydafAG7MmNAdYCbwUmChuz+c8fk1hAB8CArAIiLSRubOncvcuXOZM2dOo5vSZ9asWcyfP7/RzZCCFTkPcMsysx2BQ4E1wE05h6UndN9oZt8HPunu6xL7d4+2C3Ou82C03S3rw+7u7uSiI7m0yJGIiIhINk2DVkb0wNpPgLHAuRnLfT4MfJwQbLcAtgPeBSwBTgbSs2NMirYrc24Z7588lHaLiIiMZN3d3cybN6/RzZAW1XIB2MyWmJlX8bqsxLVGE9aaPxD4KfDV9DHu3u3uF7r7Qndf4+5L3f3nhJXxVgDvMbNXFPX9dXV14e5lXyIiIiNVXG97+eWXN7gl0qoKK4Ews4nA1wkzQVzi7nmlAslzDiJMn7YRONXd1xbQlEXAurJH9Xsya2cUfi8jTE32M+B9XkWydPfHzOxq4L3AQcDfoo/iEd5JmSf27++t9F4iIiKtZO7cuQq/UldF1gAfC5wErAU+VeE5fyOUC4wD/gT8eKiNcPdDh3oNMxtDKHt4J3A58H5331jDpZ6Jtlsk9j0QbTNrfAkLcEB+jbCIiIjU2bx58+ju7qarq6vRTZE6KLIE4oho+3t3z6tvHSA67hrCqPGbC2xLzcxsc+DnhPD7Y+D4GsMvwH7RdnFi3yLgUWA3M8uaOu7IaHtDjfcUERGRGs2bN485c+Zw8sknA2j6sxZVZACeRVjM4pYqz7s12r6ywLbUJHrg7ZfAW4EfACeVW4zCzGZn7BtlZv8JvBZ4lrDABhDWewbipZH/n5mNSpz3VsIUaP8Auof23YiIiEi14rl/u7q6uOiiixo+F7HUR5ElENtG28eqPO+JaLtdgW2p1XeBNxFC6xPA2RlTjs139/mJ93eY2b2Eco4nCDW8BwIvI0yb9l53fy51jfOBfwHeAdxuZtcT5gZ+Z3TOB7QKnIiItLtGrQqnuX9bXz3mAa52OeR4BLQZ5iSOSxK2As4ucdz8xNdfBV5DWLhiCrCJUOLwLeB8d1+cPtnd15vZYcB/AO8h1Ew/R1gG+Rx3/8eQvgsREZERLi496O7upru7u++huGZYIllGviJD57PAdGCXKs+bGW2XF9iWmrj7nBrO+bca77WGELJLBW0REZG2FK8KN2/evL7w2yxLJMvIV2QN8N8Io79vr/K8dxBqh+8tsC0iIiLSAubOncv8+fOZP38+s2bNquu94pkfpPUVGYCvjrb7mNnHKjnBzD4O7BO9/V2BbREREZEWlFwhLp6xYc6cOYWsGpcss5DWVmQAvgT4Z/T118zs82a2RdaBZraFmX2B8DCYE8onvl9gW0RERKTFxMH05JNP7puqrKenh+7ubk4++eRBITgOyFmBOS80d3V1qcSiDRRWA+zua83sJOA3hGB9BvBxM7sRWACsBiYAexKWCZ5IKJnYSJhu7Pmi2iIiIiKtJw6ml19+eV+pwqxZszjuuOM4+eST+0Zw4218TPwQXfy+q6trwMN1q1evZsKECfT09NS9zEKaQ6EzL7j7tWb2XsIculsAWwJHRa+keKaI1cAH3f1qRERERMpIPxwXzwoRz9+bDLldXV194TbeFx8fn5+s+Y0/l9ZX+NRj7v4zM7sD+E/CMsdbZhz2HPC/wFfcfUnRbRAREZHWFgfhWDK4VjJVWl6QlvZQl7l33f1hYK6ZnUJ4yG17QhB+DngcuEcLPYiIiEhR0oG43ufJyFbXxSeikNsTvUREREREGq7IWSBERERERJpe3UaAzWxL4HBgP2BbwqwPq4Angb8Av3f3VfW6v4iIiIhIlsIDsJltDnweOIUw7Vme1Wb2HeAcd19fdDtERERERLIUWgJhZp3A7cCn6Z/nN+81Efg34NboPBERERGRuit6BPgXwCuir9cQpjq7DlhI/0IYuxJKI95DmCv4FcCVwKEFt0VEREREZJDCRoDN7GhgDmFp4zuBvdz9w+7+c3f/m7svirZXuvtcYC/gDsJo8Bwze2tRbRERkfows+3N7Idm9qSZrTezJWb29Wp/k2dmU6LzlkTXeTK67vYZx041sw+Z2S/N7CEzW2tmK83sz2b2QTMb9HeZme1kZl7idcVQfg4iMrIVOQL8nmj7T+Bwd+8tdbC7P2ZmRwD/ALYG3gv8qsD2iIhIgcxsJnALoc/+FXA/8BrgE8ARZnaguy+r4DpTo+vsBtwAXAHsAZwEvNnMXuvuixOnvBP4DrAUuBF4FHgJcAzwfeBIM3unu3vG7f4GXJWx/96y37CItKwiA/B+hNHfH5YLvzF3X2Fm3wfOjM4XEZHm9W1C+D3V3b8Z7zSz84FPAV8kPABdzpcI4fd8dz89cZ1TgW9E9zkicfxC4Cjgd8lFlMzsDMKsQm8nhOFfZNyrx93PreSbE5H2UeRDcFtH279XeV78r/BpBbZFREQKFI3+Hg4sAb6V+vgc4HngeDPbosx1JgDHR8efm/r4QuAR4I1mNiPe6e43uPtv0iuIuvtTwHejt3Oq+HZEpM0VGYBfiLbjqjwvPv6FkkeJiEgjHRxtr8sIoquAm4HxwP5lrrM/od+/OT0XfHTd36fuV86L0XZDzufbmdnJZnZGtN2nwuuKSAsrMgA/Fm0r7bRih6TOFxGR5rN7tF2Y8/mD0Xa3YboOZrYZ8P7o7bU5hx1GGCX+YrT9m5ndaGYvzbtud3c3Zlb2JSIjV5EB+HrCjA7vMbMDKznBzA4gPDzn0fkiItKcJkXblTmfx/snD9N1AL4MvAy42t1/n/psDWFRplcDndGri/AQ3Rzg+nLlGiLSuooMwN8FNgKjgWvMbG70r/NBzGy0mX0QuDo6fiP9dVwiIiIlRQ/MnU6YieL49Ofu/rS7n+3uf3X33uh1E6GO+XZgF+BDWdfu6urC3cu+RGTkKmwWCHe/38y+BJxFWODiO8AXzGw+4Vddz0f7dyX863srwoixA19y9/uLaouIiBQuHpmdlPN5vL+33tcxs48RZov4B3Couy8vc88+7r4hmn1oP+Cg6Doi0mYKXQnO3c8xs82BfyeE260I09NkMWAT8BVNUSMi0vQeiLZ5tbm7Rtu82t5CrmNmnwS+RphB6FB3f7rM/bI8E21VAiHSpoosgQDA3f8TeB1h4vH1hKCbfr0A/B9woLufWXQbRESkcDdG28PTK6+Z2UTgQELd7W1lrnMbsBY4MDoveZ1RhBKF5P2Sn3+GEH57gINrDL/QP1PF4pJHiUjLKjwAA7j7re5+DOFXWfsBRxNqtI6O3k9y93e4++31uL+IiBTL3RcB1wE7AR9NfXweYTT1Und/Pt5pZnuY2R6p66wGLo2OPzd1nY9F1/99aiU4zOwswkNvdxFGfp8t1V4ze1XOEsmHEhbtALis1DVEpHUVWgKR5u4vAHfU8x4iIjJs/pWwhPEFUZBcQBjUOJhQspD+jd6CaJueM+wMwrMgp5nZLMJqbnsCbwWeJhWwzewE4HOEB6b/BJyaMQ3ZEne/JPH+fGBXM7sFeDzatw/9U2+e5e63lPuGRaQ11TUAi4hI63D3RWY2mxBGjwDeBCwlPEh2nruvqPA6y8zstYQV5I4GXg8sAy4Gznb3x1On7BxtRwOfzLlsN3BJ4v2lwNuAfYEjgTHAP4GfARe6+58qaauItCYFYBERqZi7PwacVOGxuatFRDM3fCJ6lbvOuQwulyh3zg+AH1Rzjoi0j7rUAIuIiIiINKuqR4DN7If1aAjg7v7BOl1bRERERASorQTiRMLiFfWgACwiIiIidVVrDXBuXdcQaF1JEREREam7WgLwzuUPERERERFpTlUHYHd/pB4NaQZmthPwcIlDfurux+acewJh7sq9CHNV3g181d1/m3P8aOBUwtPUuxJWRroN+ILmphQRERGpH02Dlu1vhKWc0+7NOtjMvgqcTphs/XvA5sCxwG/M7OPufmHqeAOuAN4BPABcCEwB3g3cZGZvd/dfFfOtiIiIiEiSAnC2nmjeybLM7ABC+F0E7BtPBG9m/01YsvOrZvZbd1+SOO1YQvi9hbCk57ronO8Cfwa+Z2Y3uPuqgr4fEREREYnUZR5gMxttZsea2Q/N7FYzu9/MFmUc9zIzO8DM9q5HO4bJKdH2i8lVkKLA+y1gLIMnjf9ItP1sHH6jc+4AfgpMIwRkERERESlY4QHYzOYAi4GfACcArwF2A3bKOPxowrruN5vZuKLbMgTbmdnJZnZGtN2nxLHxuvLXZnx2TeoYzKwDOABYQ/jey54jIiIiIsUptATCzN4C/B8hWBuwAVgNTM455SLCWvATgTcDVxbZniE4LHr1MbP5wAnu/mhi3xbAdGC1uy/NuM6D0Xa3xL6ZhPXsF7v7hgrP6dPd3U0oIS7NXbPKiYiIiGQpbATYzLYCLiOEu+cIi1pMpsSa8e7+DHBz9PYNRbVlCNYAnwdeDXRGry7gRmAOcH0UemOTou3KnOvF+ycP8RwRERERKUiRJRAfJ4zkvgAc5u4Xu/uaCs67jTBa/MoiGmFmS8zMq3hdFp/r7k+7+9nu/ld3741eNwGHA7cDuwAfKqKdterq6sLdy75EREREJFuRJRBHElZz+5m731nFeQuj7YyC2rEIWFf2qH5PljvA3TeY2feB/YCDgG9EH8WjtZMyT+zf35vYV8s5IiIiIlKQIgPwzGh7Y5XnxYFwyyIa4e6HFnGdDM9E274SCHd/3syeAKab2bYZdcC7RtuFiX2LCAtlzDCzzTLqgLPOEREREZGCFFkCEQfD56o8L579oZpR20bYP9ouTu2/IdoekXHOkaljiKY9uwUYD7y+knNEREREpDhFBuBl0fYlVZ4Xj3g+U/KoYWBmrzKzQT8TMzsU+FT09rLUx9+NtmeaWWfinJ0ISyOvBy5OnfOdaPuFaFq0+Jx9CavBPQP8osZvQ0RERERKKLIE4j5gG8JsCd+u4ryjCLXDdxTYllqdD+xqZrcQljUG2If+OXnPcvdbkie4+y1mdj5wGnCPmV1JWAr53YTljT+eWgUOwjLIxxAWu7jbzH4DTI3OGQ182N2rHUkXERERkQoUGYB/R5jK7Cgz28vd/1HuBDM7HngFIQD/usC21OpS4G3AvoRShDHAP4GfARe6e9bCFbj76Wb2d8KI71xgE/BX4L/d/bcZx7uZvYdQCvEBwgwa64CbgC+kQ7aIiIiIFKfIAPx94D8Jy/j+zsyOcve/5x1sZh8CLiCE38WEJYAbyt1/APygxnMvAS6p4vgNwNeil4iIiIgMk8ICcDQjwkmEkdyXAneZ2fXAqvgYMzsH2B44FNiRMP/veuC97r6pqLaIiIiIiOQpdClkd7/GzI4ljKJuSVhAAsIoL8DZicONMNftse7+lyLbISIiIiKSp8hZIABw918ALwMuBFYQgm76tYowE8LL3f26otsgIiIiIpKn0BHgmLs/DpwKnGpmewM7EVY4Ww08AdytkgcRERERaYSaArCZbePuT1VyrLvfR5giTURERESk4WotgXjUzH5tZm8zs7qMIouIiIiI1EOtAXgz4M3AlcCTZvY1M3tFcc0SEREREamPWgPwKvofaJtKqPf9q5n91cw+ZmZTimqgiIiIiEiRag3A2wAnADdE7+Mw/ArgG4RR4Z+Z2ZvMrPCZJkREREREalVTOHX3te5+qbu/gTDDwznAIvqD8ObA24HfAI+Z2ZfMbLdimiwiIiIiUrshj866+2Pu/nl33xU4CLiYgSUS2wCfARaY2c1m9kEzmzjU+4qIiIiI1KLQ8gR3/7O7f5AQek8Ebow+isPw/sA8YKmZ/cjMDi7y/iIiUj9mtr2Z/dDMnjSz9Wa2xMy+bmadVV5nSnTekug6T0bX3b7Ie5vZXlE53tNmts7MHjCz88xsXDXtFZHWU5f63KhE4sfufiiwM3AuA0skxgPvA/5oZovN7Gwz27EebRERkaEzs5nAXcBJwF+ArwGLgU8At5rZ1AqvMxW4NTpvUXSdv0TXvcvMZhRxbzPbD7gDOBr4I+H5lOeAs4E/mNnYCr91EWlBdX9Azd0fdffPRSUSXcCPCCvCxWF4J0IN8UP1bouIiNTs28DWwKnufrS7/4e7H0IIo7sDX6zwOl8CdgPOd/dDo+scTQizW0f3GdK9zWw0oRxvPPAOdz/O3T8D7Af8AjgQ+FQV37uItJhhnaHB3f/k7icRSiROApYCTgjCmi1CRKQJRSOwhwNLgG+lPj4HeB443sy2KHOdCcDx0fHnpj6+EHgEeGNyFLjGe3cBewI3ufuv453uvgn49+jtKWZmpdorIq1r2EOnmb0UOA04kxCERUSkucXPa1wXhcg+7r4KuJkw2rp/mevsD4wDbo7OS15nE/D71P1qvfch0fbadAPcfTGwENgRGFRuISLtYViWMTaz8YRp0U4k/Ms8/ld3vF0N/Gw42iIiIlXb/f+3d+Zhc1VVvn5/iQoSGQUUZYiAICKYFpVJIDStoDiAIJNAABFHRLltX6VlUttur6KAqCg2RBkEm0a9eBkFAlcFwSEgVyaBgAwCBgiBJEDCun/sXamTSs11qur7vvq9z7OfM9Te66xz6py119ln77Xz8s4Gv99FaqXdBLiqRzlkOb0cu50ym+R0d+2P1157Le00Dh911FHMnj27ZT6TmD17NtOmTRu2Gn1l9uzZTJ8+fdhqjCumTZvGySefPPDj9tUBlrQjyendG6h8nqpYlQCuJfXTujAiFvRTF2OMMV2zal7Oa/B7Zf9qfZAzqDKmz0ybNo0DDjhg2Gr0jYl8bhOR0h1gSVNJs8QdTBrgBlWnF+B+0kC4mRFxb9nHN8YYY3php512YtasWcNWw4wzjjjiCI444ohhq2HapBQHOHdx2Ifk+O7A8l0cFgI/JbX2Xh0RUcZxTX+ofPrz39RffJ0Hh691z1RaTFdt8Htl/5N9kDOoMuMG38/t42vVPqN2rXpygPNEFjOA97N8FwdI8RrPBM6PiKd6OZYxxpihcUdeNprS/rV52ajPbS9yBlXGGDNCdBUFIs+kcy8puPhBwMuoxvV9BPg6sHlEbBMR37fza4wx45rKrJ7vkLRMvZGntt8eWADc0ELODaQvgtvnckU5k0iD2YrH6/bYV+flbrUK5BBrm5BCrt3TQl9jzASl2zBoxwLrU3V6nyd1cXgvsG5E/EtE3FaOisYYY4ZJRNwNXEEa1/GJmp9PJH0BPDsinqnslPQ6Sa+rkfM0cHbOf0KNnE9m+ZfnUGVdH5s0wPo2YEdJ7y3oNAn4at483d3xjBldeukCIeAWUr/ecyPi7+WoZIwxZgzyceA3wKmSdiE5mFuT4vTeSYrtXqTSCFIbT+wYYDpwtKRppK5ymwHvAx5leSe342NHxBJJh5Jagi+UdCFpAPYuwJtJsYO/2f6pG2MmGt22AJ8GbBUR0yLiFDu/xhgzscktsW8GZpKcz/8BbAScAmwTEXPblDMX2BY4Fdg4y9ma1JiyVT5Oz8eOiN8CbwF+Tupa8RnS4LcvAm+PiGfbO3NjzESkqxbgiPhU2YoYY4wZ20TEX0nT2LeTt+FMEhHxOHBUTqUfu1Dmz8AHOiljjBkNBj4VsjHGGGOMMcPEDrAxxhhjjBkp+joVsimdjQc5z7jnMx8Mvs6Dox/Xevbs2ZD6spqJwUDtbC+MBx3HCr5W7TMWr1U/7KwcBWb8IOmPwFrAX4atizFmKRsDj0XEPwxbEdM7trPGjElKt7N2gI0xxhhjzEjhPsDGGGOMMWaksANsjDHGGGNGCjvAxhhjjDFmpLADbACQNFVSNEnnNyk7Q9KNkp6WNE/SLEnvHqT+4wlJ60o6U9JDkp6VNEfSyZJWH7Zu44187Rrds39rUGY7SZdIelzSQkm3SPq0pMmD1t+YIoO0w5ImS/pMvv8X5ufhEknb9efsBsso2tlB2UNJ787317x8v/1W0oz+nVl/cBg0U8vNwM/q7L+1XmZJXydNS/oAcAbwEmA/4GJJR0bEaX3Sc1wiaSPgN8DapClabwfeSpoRazdJ27c7paxZyjzg5Dr7n67dIel9wH8Di4ALgMeB9wDfBLbHs4aZsUFf7bAkAecDewN3AKcBawD7AtdJ2isifl7OqQyeEbezfbWHkj4JfAuYC5wDPEe6j2ZK2iIi/rmUsxgEEeHkBDAVCGBmB2W2y2X+AqxeI2su6aGaOuxzG0sJuDxfsyNr9n8j7z992DqOpwTMAea0mXcV4FHgWeDNhf0rkirLAPYb9jk5jW4alB0G9s9lfg2sWNj/lvx8PAqsPOzr0cN1HEk72297mO+pRfm+mlrYv3q+/wLYdtjXod3kLhCmFz6al/8WEU9UdkbEHODbwArAoUPQa0ySWyXeQTJS3675+XjgGeAgSVMGrNqosDcpvuv5EfG7ys6IWAR8IW9+bBiKGdMD3djhyn3+hXz/V8rcRGoJXIv0vIw7bGfbpht7eBjpfjot31+VMk8AX8mbH2WcYAfY1PIqSR+RdExebtkk7z/m5WV1fru0Jo+BnfPyioh4ofhDRMwntcasBGwzaMXGOStIOjDfs0dJ2rlB/7Vm9+t1wAJgO0kr9E1TY9qjb3ZY0oqkVuMFwP9tp8w4Y9TtbD/t4YSq890H2NTy9pyWImkWMCMi7i/smwK8Gng6Ih6uI+euvNykT3qORzbNyzsb/H4XqeViE+CqgWg0MXglcHbNvnslHRoR1xb2Nbz+EbFY0r3A5sCGwG190dSY9uinHd4ImAzcExGL2ywznhh1O9tPe9iszMOSngHWlbRSRCzo5SQGgVuATYUFwJeArUj9eVYHdgKuAaYDV9V8Mlo1L+c1kFfZv1rZio5jfM3K5yxgF5LRnwJsAXyP1FftUklvLOT19TdjnUHY4Yn+HEz082tGv+1hu2VWbfD7mMIO8ASiRQiUeumcStmIeDQijouIP0TEkzldR3pT/i1pHu7Dh3VuxtQjIk6MiKsj4pGIWBARt0bER0mDXV4KnDBcDc2oYTtshoXtYWe4C8TE4m7SCM12eahVhvwp5AfA1sCOwCn5p1ZvepX9T3agz0TH12xwnE4KC7VjYZ+vvxkEY90OT/TnYKKfXzeUZQ/nAWvm3+qFkWvVQjymsAM8gYiIXfok+rG8XPrpLSKekfQg8GpJ69Tpf/bavGzUD2sUuSMvG/Wt8zUrj+XuWdL1fzPp+v++mFnSi4DXAIuBewahoJmYjAM7fDewBNhQ0ovq9AMe73bIdnZ5yrKHd5Ac4E2A62vKrJPlPzAe+v+Cu0CY9qiMlq11DK7Oy93qlHlnTR6T+vEBvEPSMs+epJVJgccXADcMWrEJSL17ttn9uiNpZPhvIuLZfipmTJeUYodzmKvfkO73HdopM86wnV2esuzhxKrzhx2I2GlsJOBNwKQ6+3chfc4LYLua3zwRRufXeSQDtPfpWm4GTKmzfypppHcAxxT2r0JqCfFEGE5jMg3KDtPeRBirDPt69HAdR87ODsIeklqFJ8xEGMrKmxEnh9h5LenGfyDv3pJqTL9jI+LLdcqdBBydy1xImoJzX+DlJOPjqZAL1Jmi8zZSv76dSZ/ktouJO0VnqUg6gdSv7TrgPmA+KcTT7iQjfgmwZ0Q8VyizB+k+XUSaCvZx4L2k8D4XAvuEjaIZEoOyw3kq5J+QJkO4Hbg4592X9OxMtKmQJ7ydHZQ9lHQkcCrJCb6A6lTI6wInhadCdhpvCfgQ8AvS7DlPk94K7yfd4Du0KHsIcBNphp35wLXAu4d9TmM1AeuRwtU8TDIe95Hmbl992LqNp0QKD/VjUgX+JPA8qUXjSuBgSC/4dcptT6oMngAWAn8CPgNMHvY5OY12GqQdJo0B+ky+/xfm5+ESalqYx2saNTs7SHsIvCffX/Pz/XYTKUb10K9DJ8ktwMYYY4wxZqTwIDhjjDHGGDNS2AE2xhhjjDEjhR1gY4wxxhgzUtgBNsYYY4wxI4UdYGOMMcYYM1LYATbGGGOMMSOFHWBjjDHGGDNS2AE2xhhjjDEjhR1gY0xXSHq5pGck3S7pZbXbw9bPGGPM2EfSVpI+J+kiSQ9KCkl9n6XtRf0+gDFmwvJR0kv0vhHxtKSjitvDVc0YY8w44VjgfYM+qB1gY0zHSHoJ8Ang6Ii4uXZ7uNoZY4wZR1wP3AzclNODwOR+H1QRfW9lNsZMMCTNAHaPiH3qbRtjjDHdIGkxMDki1M/juA+wmfBImizpY5KukzRX0pJKHyNJ07rNOwpI2iOf+yJJr67sj4gfFp3d2u06cg7Mcp6UtHa/9TZmPCFpasHOzBy2Pq0Yb/o2o5GNK/w+vXCuJwxBRdOEXuoWO8BmzCFpC0knSvq1pAeyYXpC0h2SzpN0kKSXtilrEvBz4DvADsAaNLjvO8k7bHIFdEJO0/t0jBWBb+bN70fEgz2I+zFwJ7Aq8O+96mZMJ0j6VsGJ+VIX5VeSNC+XXyzpVf3Qc6IwCPtUBiXbODMcuq5b3AfYjBkkrQOcBOwH1H76WAFYDdgE2B/4iqTPRcS5LcTuAeye1+8DTsvL5/O+e7vMO2ymAscXtmf14Rgfz8dZBPxHL4IiYomkLwM/Ag6R9PWIuK13FY1pi7OAT+b1gyUdF531/9sLWCWvXx4RD5Wq3cRjKv23T2VQmo0zw6GXusUOsBkTSNocuBRYL+96DrgCuBp4GFgJ2BR4P7AxsC5wTu6W8C9NKrN3Fdb3i4gbmqjRSd4JTW5h/1zenFlShX8e8CVgA1LluF8JMo1pSUT8QdItwJbA+sA/Ald1IOKQwvpZJao2LomIOSzfSDGu6JONM8Ohq7rFDrAZOpJeAfwSeGXedQNwSETcUSfv50ktOV8HXgz8M/AU6eavx3qF9T+2UKWTvBOdg4G18vqPyhCY39TPBY4B9pa0fkTcX4ZsY9rgLKqfu2fQpgMsaX1g57w5F/jf5atmhkDpNm4UyTZ9qw6LfT4iflqWDt3WLWOyf6MZOX5I1fm9Hvines4vQES8EBGnkt7wKq2+x0varoHsFQpln22hRyd5Jzofy8u7I+L6EuVWuqxMBo4oUa4xrTiXanem93cwWcsMqq2d50XEc6VrZoZBv2zcqLEB6etsJ2nVPujRcd1iB9gMFUlvA3bNmwuAD0bEM63KRcRFwPfz5mTghILMpaN2gZ0K+6MmndBJ3sJvk/NAvIsl/TUP0luY1/8g6RxJMyRNaXHuL5P0aUlXSnpI0rOSHpd0k6QvSlqrTpnpWddrCruPr6Nv1/ENJW0BvDFvntetnHpExJ+B2XnzQEnj+jOqGT9ExGPAL/LmFKDdkH0zCusNuz908zx3i6TNJJ0i6dY8OG+hpPsk/UTSnh3IWUnSxyX9ItuvhTndozQr1xGSVqlTrm4UiE7tU7alD+R9jynFE2+l85sKci5o91xrZJRu4yRtJOnurNcLkj5T+G25SBKSNpH0bUl3SVqQ75mLJW1fR/buhf9oUf6vvyPplbV5u9S96zotIt4WEeowzSxD7xo9Oq9bIsLJaWgJ+AmpJTeAb3dY9lWkFp1K+dfn/dML+5qlEzrJm2WvCdzYZpk9muj+TuCRFuWfAt5bU65dfaOH/+TYgpyd+/Cff6Mgf9qw70Gn0UnAewr33rVt5N+hkH92k3xdPc+F8lML+Wa20OlEYHGLY10DrNFCzm7A39qwJWe1q2839imfT2X/Pm38J98t5N+ly/ugbRtXc04nNMjzD4Vr+TxwYDMZpEGVzzS4Ni8Ah+ZyLwbObHIdHwY27vGZKKVOK/k5XVy8Rzoo11Hd4j7AZmjkN7RdCrs66ocVEQ9JuopqC/I/AX8GbgUqrSBfBjbP67UtI7cDf+8gL8AZwFvy+l+ohmBZSBolvimwI7B1I70l7QVcQGq5fp7Up3AWqQJdhdTfcB9gZeCnkt4eEVfn4pVzewPVfs8XAOc3Ol4XvD0vXwB+V6LcCsXBhbtSfWs3pt9cSnrOXgHsIGnDiLinSf5DCutn1cvQ4/PcEZL+nerArSWk5/5qkv3ZAjgsn9t04BpJW0fEojpy9iG1fFZm27oF+G+STQvSeIjtSM9nJ19purFPZwD/mnX5MKlRpC6SVgIOyJv3kM69G0qzcZJ2Bn5G+q8XAHtHxKVNimxF+g+fA07Ox59EeiHZn3S9z5D0K+BTwKGk/+ccUlSiV5A+8b+B1HVwJvC2Hk6h5zptDNFZ3TIIb97JqV4CNqP6trYIeHEXMo4vyDi/zu+zKr+3IatpXmBtksEM0nSNU5rI2gDYoM7+9YB5WcZ9wBYNyr8VeDLn+2vttaGNVoku/5PJVFsmbu3T/75BQfeLhn0fOo1WAr5WuP9ObJJvJVKrbZCclTXr5CnreZ5a0GlmAxnbFuzP08COdfKskW1TRdbX6uTZMJcPkhN9FHlW2Dp5Vwem19nfVN9O7RPppaHS+vmaJvkOLcj9fJf/f0c2rtm5kFpyF+Xf5gLbtiEjSI7m+nXyHVPI8/t8Pb4DTKpzb95SyPvWLq9Fz3VaPxLdtwB3VLe4D7AZJusW1u+NiOcb5mxMcbDccrP4lMyGLDsYpmFf5Yi4LyLuq/PTZ0lv1UuA90XEnxqUvxE4Om+uC3yga607Y0OScYVlr21p5OuyMG9u2Y9jGNOEYkvuwU36Cu5NarUFuDgi/l4nzyCf589StT+fjYjr6hzn8az3grzro5JWq8n2OVIfaICvRsQpkb2HOvKeiIhZXejaKafnpYAPNcl3eF4upvtwdKXYOEkfIbVWrwA8COwQ7Q+mOzDqRyk4CZif199EalE/MiJeKGaKiAUsG7d4V7qjjDqtZ3If5xsqifxlorhP0uEtxHRct9gBNsNkjcL6k13KKJZ7edeatMeCwvrmDXM1IFe0H8ybV0XE7BZFLiAZeoB3dHq8LtmgsP54H4/zRF6u54FwZpBEGixzY96cSmHwaw2HFNaXc7YG+TxLWoFqnPK5wH82ypudgB/nzZcVjyVpMrBv3pzP2JmV8TJSCzrAoVnPZZD0elK3DEgvJH/r8lg92zhJx5Gc9kkkJ3q7fF+1w++jQYz5SNGHil0yvhcRSxrI+VVh/fVtHruWnuq0ElmL1MWikioU961bp1w92q5b3AfYmPb5f8BDpMF3H8oP1xnAjbVv6A3YnKrTP1/SHm2UeZo0A95mHWvbHcWXkn46wHNJ1/ElpNaop/t4LGNqOZPULQGSozur+KOkDUifrSENbrqsjoxBPs9vpBqmcVa0DsV2BdWW1K2p9qvdkuqMdtdExPzagsMgIl6QdAZpHMarSDNy1sZb/nBh/YweDteLjZsk6TTgE3n7JuBdDb4ONOK3LX5/pLB+Y8Ncy+ZbvYPjF+m1TiuFSFEhZpYkru26xQ6wGSZF47NalzKK5eZ2rUkbRAq2/RHSYJGXkAacHAY8Kel60hv55RHx+wYiphbW98qpXbo1cJ2yQmG9n5XjU4X1l2IH2AyW80mTYryUFDj/kxFRvAdnUP00/KOIWFwrgME+z+sU1u9sI38xT7FssRVtrE1F/p+kCAkvInV1WOoA5xbwg/Lm/cDlPRynFxv3KaoxbH8J7Flz37RDq3qqGIO+Yd6IeLbQwLlihzpUZPRap41F2q5b3AXCDJMHCutTJb24CxmbFNYf7FGflkTEL0gtRz+jGlR/NVIYpH8DfifpT5J2q1N81Tr72qVlfMySKBrf5eJ/lkjxWixsmMuYPhAR84Cf5s0pFJzX3Ap2cCF7o76mg3yeVy6st4yTzrKVfrHsKg3yDJ3cpeHnefNdkopjOvak2sXtzB5bJ3uxccVGwyl0Nx10J7r3vRW2xzptLNJ23WIH2AyT26m2Aq9IiqXYKdsW1n/ds0ZtEBE3R0TFIL+TFO7nWqrG4w3AJZI+WFO0WOF8MToLHD613+eVKbbKr9EwV+9UZD9HexW6MWVTdGwPKazvAGyU12+IiNupzyCf52JLZdMJdjLFWe6KZZ9qkGesUBkMN5kU8aFCpfvDElL3lV7oxcadQvXFaVvgckkrN8k/LuihThuLtF232AE2QyOPPL6qsOugRnnrIWkdUuzfCr8sQ692iYj5EXFZRBwXEdNJnxq/WVEP+EbNYI5iC3W7HfoHzZzC+iAc4PsbjUA3ps9cTfqcDrCTpKl5veh4NYs0MMjn+eHC+mvbyF/M81BhvfjVbVDjCjrhKlKIMIDDlNiQFEsZ4LKI+GuPx5hTWO/Uxj1PGkR4Ud6eME4wdFWnjUXarlvsAJthc2ph/dA8+KRdvkD1k9SVETHUPm0RMTcijqY6indtlq2I/ki1BWYXSb08f8VPY2VGUbiX6lvzpiXKXUp2NCp91m7pxzGMaUX+jP7DvClSSLQppDBikD6fNptqt8znuRU3U/10P72N7mLFKBPFgVS3UNV55z46bl3Zp+ywVKa4fw2pgePwgoxeBr9V6MnG5XCd+5L6zULVCe5nl7Gh0EadNqbotG6xA2yGSkT8iuqAhinAOXm2n6ZIeh/wsby5hDQhxlhhTmF9aZ+xHM7m3Ly5AdWYlt1Q/PzazifRtsg6VgY8vK5PRr0Y5qbViGhj+slMUtB8SP1+P0C1a8BFua9wXUp+npuSw2P9n7y5Jst22VgGSeuRZhSDZCeWDhjLOldCpK0MfL5sXQvHrdCpfTqLqrP/Marn+jDVa9A1Zdi4PChyP+DCvGtb4LKJ6ARn5hTWx3LwhI7qFjvAZiwwgxRqCNKUjldKqvuWKWmSpE8A/0W1VeDEDgKQd42kXSUdJanh4BdJG1OdZvNp4O6aLF+hGrv4VEkH0wRJa0s6VlJtUO97C+tvaql8Z1yZl5OAN5csG5Y1Ur2M5jamJyJNg1yZUGIj0vNZoZ2JFsp6ntvha1RbVk+StH0d+auTnLKK03l6HSf+q1Qd1P+ZbVrdVlpJq0lqFCe5GV3bpxxSrNK6uifVKBZnNYjG0Q0927isy/4s6wSPq5bgkuq0sURHdctY9uTNiBARj0h6O3AJ1Tnob5V0GXAN6c1/JdLnqvez7CeYb5BiRw6CdUhzt/8vSdeQ3jDvIQUTX5M0n/o+VCufkyNimVGoEfGApP1IIX5WAH4o6ei8fRfps+uqpOgW2wDbkwaEXFMj5wlJfyQNHNxZ0umk/nPzC3nqxS5th5+RBkFAioV6dZdyGlHpzzcnIm4uWbYxnXIW1ckwKs7WfbRx35f1PLdDRNwg6aukVtuVgWsl/TjruZA0UOlw4BW5yC3AcXXk3CvpQ6SW4Ekkm3aYpAtJzs0LpFk1tyUNiPov0oCoTnTt1T59DzigKBL4QSc6tOBnlGDjImKxpP1J+n2A9B9fLmnXiHiqeekxQc912hijs7olBjCvs5NTO4n0MJ5PdW7yZukB4KA2ZM6qlOk1L6mlupVelfnsT6Zm/vYaWduQKpt25M0Htqgj453kOdPrpR7/iz9mOX8p+T/erKDjl4Z9zzk5kSr3+TXPz4kdyujpeSbFFK78PrPFsb7Y7LnPaRbw8hZy3g081oa+Z9Yp21LfXu0T8OdC/iv68L+3ZeNIDnJFjxMa5HkRqb94Jd8NwCqdyCjknVnIO7VF3qX/d5fXoLQ6bdipm7rFXSDMmCEiHo6I/UizHn2JZEQeIoUzmUdqUbmA9NC+NiLOHrCKPyJ9YvlX4GLSaOVnSH2Q5wGzgdOArSLi09EkVmWkqTA3BQ4kzdJ0L+nz0mJSmJ7fkQZ87Au8MiL+VEfGpaQWpfNy+TLfzL+TlxtJ2q5pzs44MC+XUM6AFmN6IiKeoTpTGmSnrkMZPT/PHRzrONKMbt8iOYnzSX1mHyB1HdgrIqZHRNMJFyLFf90QOJrUOvsIKcrBQpIzfyEpIsaRXerZq30qRvXph60ozcZF6g5xANVBk1sDV4yD7hCl1WljgI7rFmXP2RhjliJpRdJn4LWB70fER0qQOZlkYKcCF+SXHWOMWYYcUWMOqUvcY8C60Xr6506PUbqNM8Oh27rFLcDGmOWIiEXAf+TNgyW9qgSx+5MM1AvAiSXIM8ZMTHYnOb+QBr+V6vxC32ycGQ5d1S12gI0xjfgu6dPlivQYLim/oX8hb86MIcdsNsaMTbKtqAzeW0y1q0I/KM3GmeHQS93iLhDGmIZI2oM09eezwEYR8WDzEg3lHAicTepXtklEPFqaksaYcY2kLUiRJ9Ygxf2thN36QUR8uFG5ko69ByXYODMceqlb7AAbY4wxZmhImkka3FxkDmnw1eMDV8iMBO4CYYwxxpixwBJSl4TvAtvY+TX9xC3AxhhjjDFmpHALsDHGGGOMGSnsABtjjDHGmJHCDrAxxhhjjBkp7AAbY4wxxpiRwg6wMcYYY4wZKewAG2OMMcaYkcIOsDHGGGOMGSnsABtjjDHGmJHCDrAxxhhjjBkp7AAbY4wxxpiR4v8D/v3HP/u6Y+0AAAAASUVORK5CYII=\n", "text/plain": [ "<Figure size 720x720 with 4 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "kin = KinMS(xsize, ysize, vsize, cellsize, dv, beamsize, inc, sbProf=sbprof, sbRad=radius, velProf=vel, intFlux=intflux,\n", " posAng=posang,diskThick=discthick)\n", "cube=kin.model_cube(toplot=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## Final notes\n", "\n", "For a more in-depth exploration of the capabilities of $\\texttt{KinMS}$, please check out the $\\texttt{KinMS}$ testsuite in the GitHub repository!\n", "\n", "You may also want to check out the [tutorial on galaxy fitting with KinMS](https://github.com/TimothyADavis/KinMSpy/blob/master/kinms/docs/KinMSpy_tutorial.ipynb)!" ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
chse-ohsu/PublicUseData
HealthCareProviderTaxonomy/HealthCareProviderTaxonomy.ipynb
2
73884
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Health Care Provider Taxonomy\n", "\n", "[Source](http://www.nucc.org/index.php?option=com_content&view=article&id=107&Itemid=132)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Grab current version from source." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "url <- \"http://www.nucc.org/images/stories/CSV/nucc_taxonomy_160.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Read the CSV file." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Code</th><th scope=col>Grouping</th><th scope=col>Classification</th><th scope=col>Specialization</th><th scope=col>Definition</th><th scope=col>Notes</th></tr></thead>\n", "<tbody>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|llllll}\n", " & Code & Grouping & Classification & Specialization & Definition & Notes\\\\\n", "\\hline\n", "\\end{tabular}\n" ], "text/plain": [ "Empty data.table (0 rows) of 6 cols: Code,Grouping,Classification,Specialization,Definition,Notes\n", "NULL" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "library(data.table)\n", "varnames <- fread(url, nrows=0)\n", "varnames" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Classes 'data.table' and 'data.frame':\t845 obs. of 6 variables:\n", " $ Code : chr \"101Y00000X\" \"101YA0400X\" \"101YM0800X\" \"101YP1600X\" ...\n", " $ Grouping : chr \"Behavioral Health & Social Service Providers\" \"Behavioral Health & Social Service Providers\" \"Behavioral Health & Social Service Providers\" \"Behavioral Health & Social Service Providers\" ...\n", " $ Classification: chr \"Counselor\" \"Counselor\" \"Counselor\" \"Counselor\" ...\n", " $ Specialization: chr \"\" \"Addiction (Substance Use Disorder)\" \"Mental Health\" \"Pastoral\" ...\n", " $ Definition : chr \"A provider who is trained and educated in the performance of behavior health services through interpersonal communications and \"| __truncated__ \"Definition to come...\" \"Definition to come...\" \"Definition to come...\" ...\n", " $ Notes : chr \"Sources: Abridged from definitions provided by the National Board of Certified Counselors and the American Association of Pasto\"| __truncated__ \"\" \"\" \"\" ...\n", " - attr(*, \".internal.selfref\")=<externalptr> \n" ] } ], "source": [ "D <- fread(url)\n", "str(D)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table>\n", "<thead><tr><th></th><th scope=col>Grouping</th><th scope=col>N</th></tr></thead>\n", "<tbody>\n", "\t<tr><th scope=row>1</th><td>Agencies</td><td>15</td></tr>\n", "\t<tr><th scope=row>2</th><td>Allopathic & Osteopathic Physicians</td><td>218</td></tr>\n", "\t<tr><th scope=row>3</th><td>Ambulatory Health Care Facilities</td><td>63</td></tr>\n", "\t<tr><th scope=row>4</th><td>Behavioral Health & Social Service Providers</td><td>37</td></tr>\n", "\t<tr><th scope=row>5</th><td>Chiropractic Providers</td><td>12</td></tr>\n", "\t<tr><th scope=row>6</th><td>Dental Providers</td><td>19</td></tr>\n", "\t<tr><th scope=row>7</th><td>Dietary & Nutritional Service Providers</td><td>8</td></tr>\n", "\t<tr><th scope=row>8</th><td>Emergency Medical Service Providers</td><td>4</td></tr>\n", "\t<tr><th scope=row>9</th><td>Eye and Vision Services Providers</td><td>17</td></tr>\n", "\t<tr><th scope=row>10</th><td>Group</td><td>2</td></tr>\n", "\t<tr><th scope=row>11</th><td>Hospital Units</td><td>5</td></tr>\n", "\t<tr><th scope=row>12</th><td>Hospitals</td><td>18</td></tr>\n", "\t<tr><th scope=row>13</th><td>Laboratories</td><td>4</td></tr>\n", "\t<tr><th scope=row>14</th><td>Managed Care Organizations</td><td>4</td></tr>\n", "\t<tr><th scope=row>15</th><td>Nursing & Custodial Care Facilities</td><td>13</td></tr>\n", "\t<tr><th scope=row>16</th><td>Nursing Service Providers</td><td>59</td></tr>\n", "\t<tr><th scope=row>17</th><td>Nursing Service Related Providers</td><td>13</td></tr>\n", "\t<tr><th scope=row>18</th><td>Other Service Providers</td><td>37</td></tr>\n", "\t<tr><th scope=row>19</th><td>Pharmacy Service Providers</td><td>13</td></tr>\n", "\t<tr><th scope=row>20</th><td>Physician Assistants & Advanced Practice Nursing Providers</td><td>58</td></tr>\n", "\t<tr><th scope=row>21</th><td>Podiatric Medicine & Surgery Service Providers</td><td>9</td></tr>\n", "\t<tr><th scope=row>22</th><td>Residential Treatment Facilities</td><td>8</td></tr>\n", "\t<tr><th scope=row>23</th><td>Respiratory, Developmental, Rehabilitative and Restorative Service Providers</td><td>82</td></tr>\n", "\t<tr><th scope=row>24</th><td>Respite Care Facility</td><td>5</td></tr>\n", "\t<tr><th scope=row>25</th><td>Speech, Language and Hearing Service Providers</td><td>9</td></tr>\n", "\t<tr><th scope=row>26</th><td>Student, Health Care</td><td>1</td></tr>\n", "\t<tr><th scope=row>27</th><td>Suppliers</td><td>31</td></tr>\n", "\t<tr><th scope=row>28</th><td>Technologists, Technicians & Other Technical Service Providers</td><td>65</td></tr>\n", "\t<tr><th scope=row>29</th><td>Transportation Services</td><td>16</td></tr>\n", "</tbody>\n", "</table>\n" ], "text/latex": [ "\\begin{tabular}{r|ll}\n", " & Grouping & N\\\\\n", "\\hline\n", "\t1 & Agencies & 15\\\\\n", "\t2 & Allopathic & Osteopathic Physicians & 218\\\\\n", "\t3 & Ambulatory Health Care Facilities & 63\\\\\n", "\t4 & Behavioral Health & Social Service Providers & 37\\\\\n", "\t5 & Chiropractic Providers & 12\\\\\n", "\t6 & Dental Providers & 19\\\\\n", "\t7 & Dietary & Nutritional Service 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Therapist\"</li>\n", "\t<li>\"Developmental Therapist\"</li>\n", "\t<li>\"Orthotist\"</li>\n", "\t<li>\"Mastectomy Fitter\"</li>\n", "\t<li>\"Pedorthist\"</li>\n", "\t<li>\"Prosthetist\"</li>\n", "\t<li>\"Clinical Exercise Physiologist\"</li>\n", "\t<li>\"Occupational Therapy Assistant\"</li>\n", "\t<li>\"Orthotic Fitter\"</li>\n", "\t<li>\"Physical Therapist\"</li>\n", "\t<li>\"Physical Therapy Assistant\"</li>\n", "\t<li>\"Rehabilitation Practitioner\"</li>\n", "\t<li>\"Specialist/Technologist\"</li>\n", "\t<li>\"Dance Therapist\"</li>\n", "\t<li>\"Massage Therapist\"</li>\n", "\t<li>\"Recreation Therapist\"</li>\n", "\t<li>\"Music Therapist\"</li>\n", "\t<li>\"Pulmonary Function Technologist\"</li>\n", "\t<li>\"Rehabilitation Counselor\"</li>\n", "\t<li>\"Occupational Therapist\"</li>\n", "\t<li>\"Recreational Therapist Assistant\"</li>\n", "\t<li>\"Kinesiotherapist\"</li>\n", "\t<li>\"Respiratory Therapist, Certified\"</li>\n", "\t<li>\"Respiratory Therapist, Registered\"</li>\n", "\t<li>\"Anaplastologist\"</li>\n", "\t<li>\"Audiologist\"</li>\n", "\t<li>\"Speech-Language Pathologist\"</li>\n", "\t<li>\"Audiologist-Hearing Aid Fitter\"</li>\n", "\t<li>\"Hearing Instrument Specialist\"</li>\n", "\t<li>\"Perfusionist\"</li>\n", "\t<li>\"Radiology Practitioner Assistant\"</li>\n", "\t<li>\"Spec/Tech, Pathology\"</li>\n", "\t<li>\"Technician, Pathology\"</li>\n", "\t<li>\"Technician, Cardiology\"</li>\n", "\t<li>\"Spec/Tech, Cardiovascular\"</li>\n", "\t<li>\"Spec/Tech, Health Info\"</li>\n", "\t<li>\"Specialist/Technologist, Other\"</li>\n", "\t<li>\"Technician, Health Information\"</li>\n", "\t<li>\"Radiologic Technologist\"</li>\n", "\t<li>\"Technician, Other\"</li>\n", "\t<li>\"Local Education Agency (LEA)\"</li>\n", "\t<li>\"Case Management\"</li>\n", "\t<li>\"Day Training, Developmentally Disabled Services\"</li>\n", "\t<li>\"Home Health\"</li>\n", "\t<li>\"Home Infusion\"</li>\n", "\t<li>\"Hospice Care, Community Based\"</li>\n", "\t<li>\"Nursing Care\"</li>\n", "\t<li>\"Public Health or Welfare\"</li>\n", "\t<li>\"Community/Behavioral Health\"</li>\n", "\t<li>\"PACE Provider Organization\"</li>\n", "\t<li>\"Voluntary or Charitable\"</li>\n", "\t<li>\"Supports Brokerage\"</li>\n", "\t<li>\"Early Intervention Provider Agency\"</li>\n", "\t<li>\"Foster Care Agency\"</li>\n", "\t<li>\"In Home Supportive Care\"</li>\n", "\t<li>\"Clinic/Center\"</li>\n", "\t<li>\"Epilepsy Unit\"</li>\n", "\t<li>\"Psychiatric Unit\"</li>\n", "\t<li>\"Rehabilitation Unit\"</li>\n", "\t<li>\"Medicare Defined Swing Bed Unit\"</li>\n", "\t<li>\"Rehabilitation, Substance Use Disorder Unit\"</li>\n", "\t<li>\"Chronic Disease Hospital\"</li>\n", "\t<li>\"Long Term Care Hospital\"</li>\n", "\t<li>\"Religious Nonmedical Health Care Institution\"</li>\n", "\t<li>\"General Acute Care Hospital\"</li>\n", "\t<li>\"Psychiatric Hospital\"</li>\n", "\t<li>\"Rehabilitation Hospital\"</li>\n", "\t<li>\"Special Hospital\"</li>\n", "\t<li>\"Military Hospital\"</li>\n", "\t<li>\"Christian Science Sanitorium\"</li>\n", "\t<li>\"Military Clinical Medical Laboratory\"</li>\n", "\t<li>\"Clinical Medical Laboratory\"</li>\n", "\t<li>\"Dental Laboratory\"</li>\n", "\t<li>\"Physiological Laboratory\"</li>\n", "\t<li>\"Exclusive Provider Organization\"</li>\n", "\t<li>\"Health Maintenance Organization\"</li>\n", "\t<li>\"Preferred Provider Organization\"</li>\n", "\t<li>\"Point of Service\"</li>\n", "\t<li>\"Assisted Living Facility\"</li>\n", "\t<li>\"Intermediate Care Facility, Mental Illness\"</li>\n", "\t<li>\"Alzheimer Center (Dementia Center)\"</li>\n", "\t<li>\"Custodial Care Facility\"</li>\n", "\t<li>\"Nursing Facility/Intermediate Care Facility\"</li>\n", "\t<li>\"Skilled Nursing Facility\"</li>\n", "\t<li>\"Hospice, Inpatient\"</li>\n", "\t<li>\"Intermediate Care Facility, Mentally Retarded\"</li>\n", "\t<li>\"Christian Science Facility\"</li>\n", "\t<li>\"Residential Treatment Facility, Mental Retardation and/or Developmental Disabilities\"</li>\n", "\t<li>\"Residential Treatment Facility, Physical Disabilities\"</li>\n", "\t<li>\"Community Based Residential Treatment Facility, Mental Illness\"</li>\n", "\t<li>\"Community Based Residential Treatment, Mental Retardation and/or Developmental Disabilities\"</li>\n", "\t<li>\"Residential Treatment Facility, Emotionally Disturbed Children\"</li>\n", "\t<li>\"Psychiatric Residential Treatment Facility\"</li>\n", "\t<li>\"Substance Abuse Rehabilitation Facility\"</li>\n", "\t<li>\"Blood Bank\"</li>\n", "\t<li>\"Military/U.S. Coast Guard Pharmacy\"</li>\n", "\t<li>\"Department of Veterans Affairs (VA) Pharmacy\"</li>\n", "\t<li>\"Indian Health Service/Tribal/Urban Indian Health (I/T/U) Pharmacy\"</li>\n", "\t<li>\"Non-Pharmacy Dispensing Site\"</li>\n", "\t<li>\"Durable Medical Equipment & Medical Supplies\"</li>\n", "\t<li>\"Eye Bank\"</li>\n", "\t<li>\"Eyewear Supplier (Equipment, not the service)\"</li>\n", "\t<li>\"Hearing Aid Equipment\"</li>\n", "\t<li>\"Home Delivered Meals\"</li>\n", "\t<li>\"Emergency Response System Companies\"</li>\n", "\t<li>\"Pharmacy\"</li>\n", "\t<li>\"Prosthetic/Orthotic Supplier\"</li>\n", "\t<li>\"Medical Foods Supplier\"</li>\n", "\t<li>\"Organ Procurement Organization\"</li>\n", "\t<li>\"Portable X-ray and/or Other Portable Diagnostic Imaging Supplier\"</li>\n", "\t<li>\"Ambulance\"</li>\n", "\t<li>\"Military/U.S. Coast Guard Transport\"</li>\n", "\t<li>\"Secured Medical Transport (VAN)\"</li>\n", "\t<li>\"Non-emergency Medical Transport (VAN)\"</li>\n", "\t<li>\"Taxi\"</li>\n", "\t<li>\"Air Carrier\"</li>\n", "\t<li>\"Bus\"</li>\n", "\t<li>\"Private Vehicle\"</li>\n", "\t<li>\"Train\"</li>\n", "\t<li>\"Transportation Broker\"</li>\n", "\t<li>\"Physician Assistant\"</li>\n", "\t<li>\"Nurse Practitioner\"</li>\n", "\t<li>\"Clinical Nurse Specialist\"</li>\n", "\t<li>\"Nurse Anesthetist, Certified Registered\"</li>\n", "\t<li>\"Advanced Practice Midwife\"</li>\n", "\t<li>\"Anesthesiologist Assistant\"</li>\n", "\t<li>\"Chore Provider\"</li>\n", "\t<li>\"Adult Companion\"</li>\n", "\t<li>\"Day Training/Habilitation Specialist\"</li>\n", "\t<li>\"Technician\"</li>\n", "\t<li>\"Doula\"</li>\n", "\t<li>\"Religious Nonmedical Practitioner\"</li>\n", "\t<li>\"Religious Nonmedical Nursing Personnel\"</li>\n", "\t<li>\"Home Health Aide\"</li>\n", "\t<li>\"Nursing Home Administrator\"</li>\n", "\t<li>\"Homemaker\"</li>\n", "\t<li>\"Nurse's Aide\"</li>\n", "\t<li>\"Respite Care\"</li>\n", "\t<li>\"Student in an Organized Health Care Education/Training Program\"</li>\n", "\t<li>\"Prevention Professional\"</li>\n", "</ol>\n" ], "text/latex": [ "\\begin{enumerate*}\n", "\\item \"Counselor\"\n", "\\item \"Psychoanalyst\"\n", "\\item \"Poetry Therapist\"\n", "\\item \"Clinical Neuropsychologist\"\n", "\\item \"Behavioral Analyst\"\n", "\\item \"Psychologist\"\n", "\\item \"Social Worker\"\n", "\\item \"Marriage & Family Therapist\"\n", "\\item \"Chiropractor\"\n", "\\item \"Dentist\"\n", "\\item \"Denturist\"\n", "\\item \"Dental Hygienist\"\n", "\\item \"Dental Therapist\"\n", "\\item \"Advanced Practice Dental Therapist\"\n", "\\item \"Oral Medicinist\"\n", "\\item \"Dental Assistant\"\n", "\\item \"Dental Laboratory Technician\"\n", "\\item \"Dietary Manager\"\n", "\\item \"Nutritionist\"\n", "\\item \"Dietitian, Registered\"\n", "\\item \"Dietetic Technician, Registered\"\n", "\\item \"Personal Emergency Response Attendant\"\n", "\\item \"Emergency Medical Technician, Paramedic\"\n", "\\item \"Emergency Medical Technician, Intermediate\"\n", "\\item \"Emergency Medical Technician, Basic\"\n", "\\item \"Optometrist\"\n", "\\item \"Technician/Technologist\"\n", "\\item \"Registered Nurse\"\n", "\\item \"Licensed Practical Nurse\"\n", "\\item \"Licensed Vocational Nurse\"\n", "\\item \"Licensed Psychiatric Technician\"\n", "\\item \"Medical Genetics, Ph.D. Medical Genetics\"\n", "\\item \"Genetic Counselor, MS\"\n", "\\item \"Military Health Care Provider\"\n", "\\item \"Acupuncturist\"\n", "\\item \"Case Manager/Care Coordinator\"\n", "\\item \"Interpreter\"\n", "\\item \"Contractor\"\n", "\\item \"Driver\"\n", "\\item \"Mechanotherapist\"\n", "\\item \"Naprapath\"\n", "\\item \"Community Health Worker\"\n", "\\item \"Legal Medicine\"\n", "\\item \"Reflexologist\"\n", "\\item \"Sleep Specialist, PhD\"\n", "\\item \"Meals\"\n", "\\item \"Specialist\"\n", "\\item \"Health Educator\"\n", "\\item \"Veterinarian\"\n", "\\item \"Lactation Consultant, Non-RN\"\n", "\\item \"Clinical Ethicist\"\n", "\\item \"Naturopath\"\n", "\\item \"Homeopath\"\n", "\\item \"Midwife, Lay\"\n", "\\item \"Peer Specialist\"\n", "\\item \"Midwife\"\n", "\\item \"Funeral Director\"\n", "\\item \"Lodging\"\n", "\\item \"Pharmacist\"\n", "\\item \"Pharmacy Technician\"\n", "\\item \"Multi-Specialty\"\n", "\\item \"Single Specialty\"\n", "\\item \"Independent Medical Examiner\"\n", "\\item \"Phlebology\"\n", "\\item \"Neuromusculoskeletal Medicine, Sports Medicine\"\n", "\\item \"Neuromusculoskeletal Medicine & OMM\"\n", "\\item \"Oral & Maxillofacial Surgery\"\n", "\\item \"Transplant Surgery\"\n", "\\item \"Electrodiagnostic Medicine\"\n", "\\item \"Allergy & Immunology\"\n", "\\item \"Anesthesiology\"\n", "\\item \"Dermatology\"\n", "\\item \"Emergency Medicine\"\n", "\\item \"Family Medicine\"\n", "\\item \"Internal Medicine\"\n", "\\item \"Medical Genetics\"\n", "\\item \"Neurological Surgery\"\n", "\\item \"Nuclear Medicine\"\n", "\\item \"Obstetrics & Gynecology\"\n", "\\item \"Ophthalmology\"\n", "\\item \"Orthopaedic Surgery\"\n", "\\item \"Otolaryngology\"\n", "\\item \"Pathology\"\n", "\\item \"Pediatrics\"\n", "\\item \"Physical Medicine & Rehabilitation\"\n", "\\item \"Plastic Surgery\"\n", "\\item \"Preventive Medicine\"\n", "\\item \"Psychiatry & Neurology\"\n", "\\item \"Radiology\"\n", "\\item \"Surgery\"\n", "\\item \"Urology\"\n", "\\item \"Colon & Rectal Surgery\"\n", "\\item \"General Practice\"\n", "\\item \"Thoracic Surgery (Cardiothoracic Vascular Surgery)\"\n", "\\item \"Hospitalist\"\n", "\\item \"Clinical Pharmacology\"\n", "\\item \"Pain Medicine\"\n", "\\item \"Assistant, Podiatric\"\n", "\\item \"Podiatrist\"\n", "\\item \"Art Therapist\"\n", "\\item \"Developmental Therapist\"\n", "\\item \"Orthotist\"\n", "\\item \"Mastectomy Fitter\"\n", "\\item \"Pedorthist\"\n", "\\item \"Prosthetist\"\n", "\\item \"Clinical Exercise Physiologist\"\n", "\\item \"Occupational Therapy Assistant\"\n", "\\item \"Orthotic Fitter\"\n", "\\item \"Physical Therapist\"\n", "\\item \"Physical Therapy Assistant\"\n", "\\item \"Rehabilitation Practitioner\"\n", "\\item \"Specialist/Technologist\"\n", "\\item \"Dance Therapist\"\n", "\\item \"Massage Therapist\"\n", "\\item \"Recreation Therapist\"\n", "\\item \"Music Therapist\"\n", "\\item \"Pulmonary Function Technologist\"\n", "\\item \"Rehabilitation Counselor\"\n", "\\item \"Occupational Therapist\"\n", "\\item \"Recreational Therapist Assistant\"\n", "\\item \"Kinesiotherapist\"\n", "\\item \"Respiratory Therapist, Certified\"\n", "\\item \"Respiratory Therapist, Registered\"\n", "\\item \"Anaplastologist\"\n", "\\item \"Audiologist\"\n", "\\item \"Speech-Language Pathologist\"\n", "\\item \"Audiologist-Hearing Aid Fitter\"\n", "\\item \"Hearing Instrument Specialist\"\n", "\\item \"Perfusionist\"\n", "\\item \"Radiology Practitioner Assistant\"\n", "\\item \"Spec/Tech, Pathology\"\n", "\\item \"Technician, Pathology\"\n", "\\item \"Technician, Cardiology\"\n", "\\item \"Spec/Tech, Cardiovascular\"\n", "\\item \"Spec/Tech, Health Info\"\n", "\\item \"Specialist/Technologist, Other\"\n", "\\item \"Technician, Health Information\"\n", "\\item \"Radiologic Technologist\"\n", "\\item \"Technician, Other\"\n", "\\item \"Local Education Agency (LEA)\"\n", "\\item \"Case Management\"\n", "\\item \"Day Training, Developmentally Disabled Services\"\n", "\\item \"Home Health\"\n", "\\item \"Home Infusion\"\n", "\\item \"Hospice Care, Community Based\"\n", "\\item \"Nursing Care\"\n", "\\item \"Public Health or Welfare\"\n", "\\item \"Community/Behavioral Health\"\n", "\\item \"PACE Provider Organization\"\n", "\\item \"Voluntary or Charitable\"\n", "\\item \"Supports Brokerage\"\n", "\\item \"Early Intervention Provider Agency\"\n", "\\item \"Foster Care Agency\"\n", "\\item \"In Home Supportive Care\"\n", "\\item \"Clinic/Center\"\n", "\\item \"Epilepsy Unit\"\n", "\\item \"Psychiatric Unit\"\n", "\\item \"Rehabilitation Unit\"\n", "\\item \"Medicare Defined Swing Bed Unit\"\n", "\\item \"Rehabilitation, Substance Use Disorder Unit\"\n", "\\item \"Chronic Disease Hospital\"\n", "\\item \"Long Term Care Hospital\"\n", "\\item \"Religious Nonmedical Health Care Institution\"\n", "\\item \"General Acute Care Hospital\"\n", "\\item \"Psychiatric Hospital\"\n", "\\item \"Rehabilitation Hospital\"\n", "\\item \"Special Hospital\"\n", "\\item \"Military Hospital\"\n", "\\item \"Christian Science Sanitorium\"\n", "\\item \"Military Clinical Medical Laboratory\"\n", "\\item \"Clinical Medical Laboratory\"\n", "\\item \"Dental Laboratory\"\n", "\\item \"Physiological Laboratory\"\n", "\\item \"Exclusive Provider Organization\"\n", "\\item \"Health Maintenance Organization\"\n", "\\item \"Preferred Provider Organization\"\n", "\\item \"Point of Service\"\n", "\\item \"Assisted Living Facility\"\n", "\\item \"Intermediate Care Facility, Mental Illness\"\n", "\\item \"Alzheimer Center (Dementia Center)\"\n", "\\item \"Custodial Care Facility\"\n", "\\item \"Nursing Facility/Intermediate Care Facility\"\n", "\\item \"Skilled Nursing Facility\"\n", "\\item \"Hospice, Inpatient\"\n", "\\item \"Intermediate Care Facility, Mentally Retarded\"\n", "\\item \"Christian Science Facility\"\n", "\\item \"Residential Treatment Facility, Mental Retardation and/or Developmental Disabilities\"\n", "\\item \"Residential Treatment Facility, Physical Disabilities\"\n", "\\item \"Community Based Residential Treatment Facility, Mental Illness\"\n", "\\item \"Community Based Residential Treatment, Mental Retardation and/or Developmental Disabilities\"\n", "\\item \"Residential Treatment Facility, Emotionally Disturbed Children\"\n", "\\item \"Psychiatric Residential Treatment Facility\"\n", "\\item \"Substance Abuse Rehabilitation Facility\"\n", "\\item \"Blood Bank\"\n", "\\item \"Military/U.S. Coast Guard Pharmacy\"\n", "\\item \"Department of Veterans Affairs (VA) Pharmacy\"\n", "\\item \"Indian Health Service/Tribal/Urban Indian Health (I/T/U) Pharmacy\"\n", "\\item \"Non-Pharmacy Dispensing Site\"\n", "\\item \"Durable Medical Equipment & Medical Supplies\"\n", "\\item \"Eye Bank\"\n", "\\item \"Eyewear Supplier (Equipment, not the service)\"\n", "\\item \"Hearing Aid Equipment\"\n", "\\item \"Home Delivered Meals\"\n", "\\item \"Emergency Response System Companies\"\n", "\\item \"Pharmacy\"\n", "\\item \"Prosthetic/Orthotic Supplier\"\n", "\\item \"Medical Foods Supplier\"\n", "\\item \"Organ Procurement Organization\"\n", "\\item \"Portable X-ray and/or Other Portable Diagnostic Imaging Supplier\"\n", "\\item \"Ambulance\"\n", "\\item \"Military/U.S. Coast Guard Transport\"\n", "\\item \"Secured Medical Transport (VAN)\"\n", "\\item \"Non-emergency Medical Transport (VAN)\"\n", "\\item \"Taxi\"\n", "\\item \"Air Carrier\"\n", "\\item \"Bus\"\n", "\\item \"Private Vehicle\"\n", "\\item \"Train\"\n", "\\item \"Transportation Broker\"\n", "\\item \"Physician Assistant\"\n", "\\item \"Nurse Practitioner\"\n", "\\item \"Clinical Nurse Specialist\"\n", "\\item \"Nurse Anesthetist, Certified Registered\"\n", "\\item \"Advanced Practice Midwife\"\n", "\\item \"Anesthesiologist Assistant\"\n", "\\item \"Chore Provider\"\n", "\\item \"Adult Companion\"\n", "\\item \"Day Training/Habilitation Specialist\"\n", "\\item \"Technician\"\n", "\\item \"Doula\"\n", "\\item \"Religious Nonmedical Practitioner\"\n", "\\item \"Religious Nonmedical Nursing Personnel\"\n", "\\item \"Home Health Aide\"\n", "\\item \"Nursing Home Administrator\"\n", "\\item \"Homemaker\"\n", "\\item \"Nurse's Aide\"\n", "\\item \"Respite Care\"\n", "\\item \"Student in an Organized Health Care Education/Training Program\"\n", "\\item \"Prevention Professional\"\n", "\\end{enumerate*}\n" ], "text/markdown": [ "1. \"Counselor\"\n", "2. \"Psychoanalyst\"\n", "3. \"Poetry Therapist\"\n", "4. \"Clinical Neuropsychologist\"\n", "5. \"Behavioral Analyst\"\n", "6. \"Psychologist\"\n", "7. \"Social Worker\"\n", "8. \"Marriage & Family Therapist\"\n", "9. \"Chiropractor\"\n", "10. \"Dentist\"\n", "11. \"Denturist\"\n", "12. \"Dental Hygienist\"\n", "13. \"Dental Therapist\"\n", "14. \"Advanced Practice Dental Therapist\"\n", "15. \"Oral Medicinist\"\n", "16. \"Dental Assistant\"\n", "17. \"Dental Laboratory Technician\"\n", "18. \"Dietary Manager\"\n", "19. \"Nutritionist\"\n", "20. \"Dietitian, Registered\"\n", "21. \"Dietetic Technician, Registered\"\n", "22. \"Personal Emergency Response Attendant\"\n", "23. \"Emergency Medical Technician, Paramedic\"\n", "24. \"Emergency Medical Technician, Intermediate\"\n", "25. \"Emergency Medical Technician, Basic\"\n", "26. \"Optometrist\"\n", "27. \"Technician/Technologist\"\n", "28. \"Registered Nurse\"\n", "29. \"Licensed Practical Nurse\"\n", "30. \"Licensed Vocational Nurse\"\n", "31. \"Licensed Psychiatric Technician\"\n", "32. \"Medical Genetics, Ph.D. Medical Genetics\"\n", "33. \"Genetic Counselor, MS\"\n", "34. \"Military Health Care Provider\"\n", "35. \"Acupuncturist\"\n", "36. \"Case Manager/Care Coordinator\"\n", "37. \"Interpreter\"\n", "38. \"Contractor\"\n", "39. \"Driver\"\n", "40. \"Mechanotherapist\"\n", "41. \"Naprapath\"\n", "42. \"Community Health Worker\"\n", "43. \"Legal Medicine\"\n", "44. \"Reflexologist\"\n", "45. \"Sleep Specialist, PhD\"\n", "46. \"Meals\"\n", "47. \"Specialist\"\n", "48. \"Health Educator\"\n", "49. \"Veterinarian\"\n", "50. \"Lactation Consultant, Non-RN\"\n", "51. \"Clinical Ethicist\"\n", "52. \"Naturopath\"\n", "53. \"Homeopath\"\n", "54. \"Midwife, Lay\"\n", "55. \"Peer Specialist\"\n", "56. \"Midwife\"\n", "57. \"Funeral Director\"\n", "58. \"Lodging\"\n", "59. \"Pharmacist\"\n", "60. \"Pharmacy Technician\"\n", "61. \"Multi-Specialty\"\n", "62. \"Single Specialty\"\n", "63. \"Independent Medical Examiner\"\n", "64. \"Phlebology\"\n", "65. \"Neuromusculoskeletal Medicine, Sports Medicine\"\n", "66. \"Neuromusculoskeletal Medicine & OMM\"\n", "67. \"Oral & Maxillofacial Surgery\"\n", "68. \"Transplant Surgery\"\n", "69. \"Electrodiagnostic Medicine\"\n", "70. \"Allergy & Immunology\"\n", "71. \"Anesthesiology\"\n", "72. \"Dermatology\"\n", "73. \"Emergency Medicine\"\n", "74. \"Family Medicine\"\n", "75. \"Internal Medicine\"\n", "76. \"Medical Genetics\"\n", "77. \"Neurological Surgery\"\n", "78. \"Nuclear Medicine\"\n", "79. \"Obstetrics & Gynecology\"\n", "80. \"Ophthalmology\"\n", "81. \"Orthopaedic Surgery\"\n", "82. \"Otolaryngology\"\n", "83. \"Pathology\"\n", "84. \"Pediatrics\"\n", "85. \"Physical Medicine & Rehabilitation\"\n", "86. \"Plastic Surgery\"\n", "87. \"Preventive Medicine\"\n", "88. \"Psychiatry & Neurology\"\n", "89. \"Radiology\"\n", "90. \"Surgery\"\n", "91. \"Urology\"\n", "92. \"Colon & Rectal Surgery\"\n", "93. \"General Practice\"\n", "94. \"Thoracic Surgery (Cardiothoracic Vascular Surgery)\"\n", "95. \"Hospitalist\"\n", "96. \"Clinical Pharmacology\"\n", "97. \"Pain Medicine\"\n", "98. \"Assistant, Podiatric\"\n", "99. \"Podiatrist\"\n", "100. \"Art Therapist\"\n", "101. \"Developmental Therapist\"\n", "102. \"Orthotist\"\n", "103. \"Mastectomy Fitter\"\n", "104. \"Pedorthist\"\n", "105. \"Prosthetist\"\n", "106. \"Clinical Exercise Physiologist\"\n", "107. \"Occupational Therapy Assistant\"\n", "108. \"Orthotic Fitter\"\n", "109. \"Physical Therapist\"\n", "110. \"Physical Therapy Assistant\"\n", "111. \"Rehabilitation Practitioner\"\n", "112. \"Specialist/Technologist\"\n", "113. \"Dance Therapist\"\n", "114. \"Massage Therapist\"\n", "115. \"Recreation Therapist\"\n", "116. \"Music Therapist\"\n", "117. \"Pulmonary Function Technologist\"\n", "118. \"Rehabilitation Counselor\"\n", "119. \"Occupational Therapist\"\n", "120. \"Recreational Therapist Assistant\"\n", "121. \"Kinesiotherapist\"\n", "122. \"Respiratory Therapist, Certified\"\n", "123. \"Respiratory Therapist, Registered\"\n", "124. \"Anaplastologist\"\n", "125. \"Audiologist\"\n", "126. \"Speech-Language Pathologist\"\n", "127. \"Audiologist-Hearing Aid Fitter\"\n", "128. \"Hearing Instrument Specialist\"\n", "129. \"Perfusionist\"\n", "130. \"Radiology Practitioner Assistant\"\n", "131. \"Spec/Tech, Pathology\"\n", "132. \"Technician, Pathology\"\n", "133. \"Technician, Cardiology\"\n", "134. \"Spec/Tech, Cardiovascular\"\n", "135. \"Spec/Tech, Health Info\"\n", "136. \"Specialist/Technologist, Other\"\n", "137. \"Technician, Health Information\"\n", "138. \"Radiologic Technologist\"\n", "139. \"Technician, Other\"\n", "140. \"Local Education Agency (LEA)\"\n", "141. \"Case Management\"\n", "142. \"Day Training, Developmentally Disabled Services\"\n", "143. \"Home Health\"\n", "144. \"Home Infusion\"\n", "145. \"Hospice Care, Community Based\"\n", "146. \"Nursing Care\"\n", "147. \"Public Health or Welfare\"\n", "148. \"Community/Behavioral Health\"\n", "149. \"PACE Provider Organization\"\n", "150. \"Voluntary or Charitable\"\n", "151. \"Supports Brokerage\"\n", "152. \"Early Intervention Provider Agency\"\n", "153. \"Foster Care Agency\"\n", "154. \"In Home Supportive Care\"\n", "155. \"Clinic/Center\"\n", "156. \"Epilepsy Unit\"\n", "157. \"Psychiatric Unit\"\n", "158. \"Rehabilitation Unit\"\n", "159. \"Medicare Defined Swing Bed Unit\"\n", "160. \"Rehabilitation, Substance Use Disorder Unit\"\n", "161. \"Chronic Disease Hospital\"\n", "162. \"Long Term Care Hospital\"\n", "163. \"Religious Nonmedical Health Care Institution\"\n", "164. \"General Acute Care Hospital\"\n", "165. \"Psychiatric Hospital\"\n", "166. \"Rehabilitation Hospital\"\n", "167. \"Special Hospital\"\n", "168. \"Military Hospital\"\n", "169. \"Christian Science Sanitorium\"\n", "170. \"Military Clinical Medical Laboratory\"\n", "171. \"Clinical Medical Laboratory\"\n", "172. \"Dental Laboratory\"\n", "173. \"Physiological Laboratory\"\n", "174. \"Exclusive Provider Organization\"\n", "175. \"Health Maintenance Organization\"\n", "176. \"Preferred Provider Organization\"\n", "177. \"Point of Service\"\n", "178. \"Assisted Living Facility\"\n", "179. \"Intermediate Care Facility, Mental Illness\"\n", "180. \"Alzheimer Center (Dementia Center)\"\n", "181. \"Custodial Care Facility\"\n", "182. \"Nursing Facility/Intermediate Care Facility\"\n", "183. \"Skilled Nursing Facility\"\n", "184. \"Hospice, Inpatient\"\n", "185. \"Intermediate Care Facility, Mentally Retarded\"\n", "186. \"Christian Science Facility\"\n", "187. \"Residential Treatment Facility, Mental Retardation and/or Developmental Disabilities\"\n", "188. \"Residential Treatment Facility, Physical Disabilities\"\n", "189. \"Community Based Residential Treatment Facility, Mental Illness\"\n", "190. \"Community Based Residential Treatment, Mental Retardation and/or Developmental Disabilities\"\n", "191. \"Residential Treatment Facility, Emotionally Disturbed Children\"\n", "192. \"Psychiatric Residential Treatment Facility\"\n", "193. \"Substance Abuse Rehabilitation Facility\"\n", "194. \"Blood Bank\"\n", "195. \"Military/U.S. Coast Guard Pharmacy\"\n", "196. \"Department of Veterans Affairs (VA) Pharmacy\"\n", "197. \"Indian Health Service/Tribal/Urban Indian Health (I/T/U) Pharmacy\"\n", "198. \"Non-Pharmacy Dispensing Site\"\n", "199. \"Durable Medical Equipment & Medical Supplies\"\n", "200. \"Eye Bank\"\n", "201. \"Eyewear Supplier (Equipment, not the service)\"\n", "202. \"Hearing Aid Equipment\"\n", "203. \"Home Delivered Meals\"\n", "204. \"Emergency Response System Companies\"\n", "205. \"Pharmacy\"\n", "206. \"Prosthetic/Orthotic Supplier\"\n", "207. \"Medical Foods Supplier\"\n", "208. \"Organ Procurement Organization\"\n", "209. \"Portable X-ray and/or Other Portable Diagnostic Imaging Supplier\"\n", "210. \"Ambulance\"\n", "211. \"Military/U.S. Coast Guard Transport\"\n", "212. \"Secured Medical Transport (VAN)\"\n", "213. \"Non-emergency Medical Transport (VAN)\"\n", "214. \"Taxi\"\n", "215. \"Air Carrier\"\n", "216. \"Bus\"\n", "217. \"Private Vehicle\"\n", "218. \"Train\"\n", "219. \"Transportation Broker\"\n", "220. \"Physician Assistant\"\n", "221. \"Nurse Practitioner\"\n", "222. \"Clinical Nurse Specialist\"\n", "223. \"Nurse Anesthetist, Certified Registered\"\n", "224. \"Advanced Practice Midwife\"\n", "225. \"Anesthesiologist Assistant\"\n", "226. \"Chore Provider\"\n", "227. \"Adult Companion\"\n", "228. \"Day Training/Habilitation Specialist\"\n", "229. \"Technician\"\n", "230. \"Doula\"\n", "231. \"Religious Nonmedical Practitioner\"\n", "232. \"Religious Nonmedical Nursing Personnel\"\n", "233. \"Home Health Aide\"\n", "234. \"Nursing Home Administrator\"\n", "235. \"Homemaker\"\n", "236. \"Nurse's Aide\"\n", "237. \"Respite Care\"\n", "238. \"Student in an Organized Health Care Education/Training Program\"\n", "239. \"Prevention Professional\"\n", "\n", "\n" ], "text/plain": [ " [1] \"Counselor\" \n", " [2] \"Psychoanalyst\" \n", " [3] \"Poetry Therapist\" \n", " [4] \"Clinical Neuropsychologist\" \n", " [5] \"Behavioral Analyst\" \n", " [6] \"Psychologist\" \n", " [7] \"Social Worker\" \n", " [8] \"Marriage & Family Therapist\" \n", " [9] \"Chiropractor\" \n", " [10] \"Dentist\" \n", " [11] \"Denturist\" \n", " [12] \"Dental Hygienist\" \n", " [13] \"Dental Therapist\" \n", " [14] \"Advanced Practice Dental Therapist\" \n", " [15] \"Oral Medicinist\" \n", " [16] \"Dental Assistant\" \n", " [17] \"Dental Laboratory Technician\" \n", " [18] \"Dietary Manager\" \n", " [19] \"Nutritionist\" \n", " [20] \"Dietitian, Registered\" \n", " [21] \"Dietetic Technician, Registered\" \n", " [22] \"Personal Emergency Response Attendant\" \n", " [23] \"Emergency Medical Technician, Paramedic\" \n", " [24] \"Emergency Medical Technician, Intermediate\" \n", " [25] \"Emergency Medical Technician, Basic\" \n", " [26] \"Optometrist\" \n", " [27] \"Technician/Technologist\" \n", " [28] \"Registered Nurse\" \n", " [29] \"Licensed Practical Nurse\" \n", " [30] \"Licensed Vocational Nurse\" \n", " [31] \"Licensed Psychiatric Technician\" \n", " [32] \"Medical Genetics, Ph.D. Medical Genetics\" \n", " [33] \"Genetic Counselor, MS\" \n", " [34] \"Military Health Care Provider\" \n", " [35] \"Acupuncturist\" \n", " [36] \"Case Manager/Care Coordinator\" \n", " [37] \"Interpreter\" \n", " [38] \"Contractor\" \n", " [39] \"Driver\" \n", " [40] \"Mechanotherapist\" \n", " [41] \"Naprapath\" \n", " [42] \"Community Health Worker\" \n", " [43] \"Legal Medicine\" \n", " [44] \"Reflexologist\" \n", " [45] \"Sleep Specialist, PhD\" \n", " [46] \"Meals\" \n", " [47] \"Specialist\" \n", " [48] \"Health Educator\" \n", " [49] \"Veterinarian\" \n", " [50] \"Lactation Consultant, Non-RN\" \n", " [51] \"Clinical Ethicist\" \n", " [52] \"Naturopath\" \n", " [53] \"Homeopath\" \n", " [54] \"Midwife, Lay\" \n", " [55] \"Peer Specialist\" \n", " [56] \"Midwife\" \n", " [57] \"Funeral Director\" \n", " [58] \"Lodging\" \n", " [59] \"Pharmacist\" \n", " [60] \"Pharmacy Technician\" \n", " [61] \"Multi-Specialty\" \n", " [62] \"Single Specialty\" \n", " [63] \"Independent Medical Examiner\" \n", " [64] \"Phlebology\" \n", " [65] \"Neuromusculoskeletal Medicine, Sports Medicine\" \n", " [66] \"Neuromusculoskeletal Medicine & OMM\" \n", " [67] \"Oral & Maxillofacial Surgery\" \n", " [68] \"Transplant Surgery\" \n", " [69] \"Electrodiagnostic Medicine\" \n", " [70] \"Allergy & Immunology\" \n", " [71] \"Anesthesiology\" \n", " [72] \"Dermatology\" \n", " [73] \"Emergency Medicine\" \n", " [74] \"Family Medicine\" \n", " [75] \"Internal Medicine\" \n", " [76] \"Medical Genetics\" \n", " [77] \"Neurological Surgery\" \n", " [78] \"Nuclear Medicine\" \n", " [79] \"Obstetrics & Gynecology\" \n", " [80] \"Ophthalmology\" \n", " [81] \"Orthopaedic Surgery\" \n", " [82] \"Otolaryngology\" \n", " [83] \"Pathology\" \n", " [84] \"Pediatrics\" \n", " [85] \"Physical Medicine & Rehabilitation\" \n", " [86] \"Plastic Surgery\" \n", " [87] \"Preventive Medicine\" \n", " [88] \"Psychiatry & Neurology\" \n", " [89] \"Radiology\" \n", " [90] \"Surgery\" \n", " [91] \"Urology\" \n", " [92] \"Colon & Rectal Surgery\" \n", " [93] \"General Practice\" \n", " [94] \"Thoracic Surgery (Cardiothoracic Vascular Surgery)\" \n", " [95] \"Hospitalist\" \n", " [96] \"Clinical Pharmacology\" \n", " [97] \"Pain Medicine\" \n", " [98] \"Assistant, Podiatric\" \n", " [99] \"Podiatrist\" \n", "[100] \"Art Therapist\" \n", "[101] \"Developmental Therapist\" \n", "[102] \"Orthotist\" \n", "[103] \"Mastectomy Fitter\" \n", "[104] \"Pedorthist\" \n", "[105] \"Prosthetist\" \n", "[106] \"Clinical Exercise Physiologist\" \n", "[107] \"Occupational Therapy Assistant\" \n", "[108] \"Orthotic Fitter\" \n", "[109] \"Physical Therapist\" \n", "[110] \"Physical Therapy Assistant\" \n", "[111] \"Rehabilitation Practitioner\" \n", "[112] \"Specialist/Technologist\" \n", "[113] \"Dance Therapist\" \n", "[114] \"Massage Therapist\" \n", "[115] \"Recreation Therapist\" \n", "[116] \"Music Therapist\" \n", "[117] \"Pulmonary Function Technologist\" \n", "[118] \"Rehabilitation Counselor\" \n", "[119] \"Occupational Therapist\" \n", "[120] \"Recreational Therapist Assistant\" \n", "[121] \"Kinesiotherapist\" \n", "[122] \"Respiratory Therapist, Certified\" \n", "[123] \"Respiratory Therapist, Registered\" \n", "[124] \"Anaplastologist\" \n", "[125] \"Audiologist\" \n", "[126] \"Speech-Language Pathologist\" \n", "[127] \"Audiologist-Hearing Aid Fitter\" \n", "[128] \"Hearing Instrument Specialist\" \n", "[129] \"Perfusionist\" \n", "[130] \"Radiology Practitioner Assistant\" \n", "[131] \"Spec/Tech, Pathology\" \n", "[132] \"Technician, Pathology\" \n", "[133] \"Technician, Cardiology\" \n", "[134] \"Spec/Tech, Cardiovascular\" \n", "[135] \"Spec/Tech, Health Info\" \n", "[136] \"Specialist/Technologist, Other\" \n", "[137] \"Technician, Health Information\" \n", "[138] \"Radiologic Technologist\" \n", "[139] \"Technician, Other\" \n", "[140] \"Local Education Agency (LEA)\" \n", "[141] \"Case Management\" \n", "[142] \"Day Training, Developmentally Disabled Services\" \n", "[143] \"Home Health\" \n", "[144] \"Home Infusion\" \n", "[145] \"Hospice Care, Community Based\" \n", "[146] \"Nursing Care\" \n", "[147] \"Public Health or Welfare\" \n", "[148] \"Community/Behavioral Health\" \n", "[149] \"PACE Provider Organization\" \n", "[150] \"Voluntary or Charitable\" \n", "[151] \"Supports Brokerage\" \n", "[152] \"Early Intervention Provider Agency\" \n", "[153] \"Foster Care Agency\" \n", "[154] \"In Home Supportive Care\" \n", "[155] \"Clinic/Center\" \n", "[156] \"Epilepsy Unit\" \n", "[157] \"Psychiatric Unit\" \n", "[158] \"Rehabilitation Unit\" \n", "[159] \"Medicare Defined Swing Bed Unit\" \n", "[160] \"Rehabilitation, Substance Use Disorder Unit\" \n", "[161] \"Chronic Disease Hospital\" \n", "[162] \"Long Term Care Hospital\" \n", "[163] \"Religious Nonmedical Health Care Institution\" \n", "[164] \"General Acute Care Hospital\" \n", "[165] \"Psychiatric Hospital\" \n", "[166] \"Rehabilitation Hospital\" \n", "[167] \"Special Hospital\" \n", "[168] \"Military Hospital\" \n", "[169] \"Christian Science Sanitorium\" \n", "[170] \"Military Clinical Medical Laboratory\" \n", "[171] \"Clinical Medical Laboratory\" \n", "[172] \"Dental Laboratory\" \n", "[173] \"Physiological Laboratory\" \n", "[174] \"Exclusive Provider Organization\" \n", "[175] \"Health Maintenance Organization\" \n", "[176] \"Preferred Provider Organization\" \n", "[177] \"Point of Service\" \n", "[178] \"Assisted Living Facility\" \n", "[179] \"Intermediate Care Facility, Mental Illness\" \n", "[180] \"Alzheimer Center (Dementia Center)\" \n", "[181] \"Custodial Care Facility\" \n", "[182] \"Nursing Facility/Intermediate Care Facility\" \n", "[183] \"Skilled Nursing Facility\" \n", "[184] \"Hospice, Inpatient\" \n", "[185] \"Intermediate Care Facility, Mentally Retarded\" \n", "[186] \"Christian Science Facility\" \n", "[187] \"Residential Treatment Facility, Mental Retardation and/or Developmental Disabilities\" \n", "[188] \"Residential Treatment Facility, Physical Disabilities\" \n", "[189] \"Community Based Residential Treatment Facility, Mental Illness\" \n", "[190] \"Community Based Residential Treatment, Mental Retardation and/or Developmental Disabilities\"\n", "[191] \"Residential Treatment Facility, Emotionally Disturbed Children\" \n", "[192] \"Psychiatric Residential Treatment Facility\" \n", "[193] \"Substance Abuse Rehabilitation Facility\" \n", "[194] \"Blood Bank\" \n", "[195] \"Military/U.S. Coast Guard Pharmacy\" \n", "[196] \"Department of Veterans Affairs (VA) Pharmacy\" \n", "[197] \"Indian Health Service/Tribal/Urban Indian Health (I/T/U) Pharmacy\" \n", "[198] \"Non-Pharmacy Dispensing Site\" \n", "[199] \"Durable Medical Equipment & Medical Supplies\" \n", "[200] \"Eye Bank\" \n", "[201] \"Eyewear Supplier (Equipment, not the service)\" \n", "[202] \"Hearing Aid Equipment\" \n", "[203] \"Home Delivered Meals\" \n", "[204] \"Emergency Response System Companies\" \n", "[205] \"Pharmacy\" \n", "[206] \"Prosthetic/Orthotic Supplier\" \n", "[207] \"Medical Foods Supplier\" \n", "[208] \"Organ Procurement Organization\" \n", "[209] \"Portable X-ray and/or Other Portable Diagnostic Imaging Supplier\" \n", "[210] \"Ambulance\" \n", "[211] \"Military/U.S. Coast Guard Transport\" \n", "[212] \"Secured Medical Transport (VAN)\" \n", "[213] \"Non-emergency Medical Transport (VAN)\" \n", "[214] \"Taxi\" \n", "[215] \"Air Carrier\" \n", "[216] \"Bus\" \n", "[217] \"Private Vehicle\" \n", "[218] \"Train\" \n", "[219] \"Transportation Broker\" \n", "[220] \"Physician Assistant\" \n", "[221] \"Nurse Practitioner\" \n", "[222] \"Clinical Nurse Specialist\" \n", "[223] \"Nurse Anesthetist, Certified Registered\" \n", "[224] \"Advanced Practice Midwife\" \n", "[225] \"Anesthesiologist Assistant\" \n", "[226] \"Chore Provider\" \n", "[227] \"Adult Companion\" \n", "[228] \"Day Training/Habilitation Specialist\" \n", "[229] \"Technician\" \n", "[230] \"Doula\" \n", "[231] \"Religious Nonmedical Practitioner\" \n", "[232] \"Religious Nonmedical Nursing Personnel\" \n", "[233] \"Home Health Aide\" \n", "[234] \"Nursing Home Administrator\" \n", "[235] \"Homemaker\" \n", "[236] \"Nurse's Aide\" \n", "[237] \"Respite Care\" \n", "[238] \"Student in an Organized Health Care Education/Training Program\" \n", "[239] \"Prevention Professional\" " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "unique(D[, Classification])" ] } ], "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", "version": "3.2.2" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
sgratzl/ipython-tutorial-VA2015
03_Plotting_solution.ipynb
1
403314
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# (Interactive) Plotting using Matplotlib and Seaborn\n", "\n", "[Matplotlib](http://matplotlib.org) is basic plotting library for Python inspired by Matlab. [Seaborn](http://stanford.edu/~mwaskom/software/seaborn) is built on top of it with integrated analysis and specialized plots + pretty good integration with Pandas\n", "\n", "Also see [the full gallery of Seaborn](http://stanford.edu/~mwaskom/software/seaborn/examples/index.html) or [Matplotlib](http://matplotlib.org/gallery.html).\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#disable some annoying warning\n", "import warnings\n", "warnings.filterwarnings('ignore', category=FutureWarning)\n", "\n", "#plots the figures in place instead of a new window\n", "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 46, "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>car</th>\n", " <th>mpg</th>\n", " <th>cyl</th>\n", " <th>disp</th>\n", " <th>hp</th>\n", " <th>drat</th>\n", " <th>wt</th>\n", " <th>qsec</th>\n", " <th>vs</th>\n", " <th>am</th>\n", " <th>gear</th>\n", " <th>carb</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Mazda RX4</td>\n", " <td>21.0</td>\n", " <td>6</td>\n", " <td>160</td>\n", " <td>110</td>\n", " <td>3.90</td>\n", " <td>2.620</td>\n", " <td>16.46</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Mazda RX4 Wag</td>\n", " <td>21.0</td>\n", " <td>6</td>\n", " <td>160</td>\n", " <td>110</td>\n", " <td>3.90</td>\n", " <td>2.875</td>\n", " <td>17.02</td>\n", " <td>0</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>4</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Datsun 710</td>\n", " <td>22.8</td>\n", " <td>4</td>\n", " <td>108</td>\n", " <td>93</td>\n", " <td>3.85</td>\n", " <td>2.320</td>\n", " <td>18.61</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>4</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Hornet 4 Drive</td>\n", " <td>21.4</td>\n", " <td>6</td>\n", " <td>258</td>\n", " <td>110</td>\n", " <td>3.08</td>\n", " <td>3.215</td>\n", " <td>19.44</td>\n", " <td>1</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Hornet Sportabout</td>\n", " <td>18.7</td>\n", " <td>8</td>\n", " <td>360</td>\n", " <td>175</td>\n", " <td>3.15</td>\n", " <td>3.440</td>\n", " <td>17.02</td>\n", " <td>0</td>\n", " <td>0</td>\n", " <td>3</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " car mpg cyl disp hp drat wt qsec vs am gear \\\n", "0 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 \n", "1 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 \n", "2 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 \n", "3 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 \n", "4 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 \n", "\n", " carb \n", "0 4 \n", "1 4 \n", "2 1 \n", "3 1 \n", "4 2 " ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#use a standard dataset of heterogenous data\n", "cars = pd.read_csv('data/mtcars.csv')\n", "cars.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Scatterplot" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10154d68>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(x=cars['mpg'],y=cars['wt'])\n", "plt.xlabel('miles per gallon')\n", "plt.ylabel('weight')\n", "plt.title('MPG vs WT')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1012bdd8>" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10199d30>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#integrated in pandas, too\n", "cars.plot(x='mpg',y='wt',kind='scatter')" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10111198>" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xed66780>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cars.plot(kind='scatter', x='mpg',y='wt',c='hp',s=cars['cyl']*20,alpha=0.5)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xedb7e48>" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CzUMvQJIk9Ho9t932U2699Qds3PhbEokk5513EXfeeQ/z5y/k0UcfqvY2NXLMZtm/gs5m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\nJRTVePqfDmCZNvc8vHNNRMRWikujNqxipqbRPdc1xrkzY6zfUrcm3dP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"text/plain": [ "<matplotlib.figure.Figure at 0xed70470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#what if we plot everything?\n", "cars.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Histogram" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10d0da20>" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10cba470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cars['mpg'].hist(bins=5)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x10d9aa58>" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10d36278>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(cars['mpg'],bins=5)\n", "plt.title('miles per gallon')" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10ded940>" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10d363c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#seaborn not just a histogram but also an kernel density enstimation and better default settings\n", "sns.distplot(cars['mpg'],bins=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Box Plots" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10e7ed30>" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x10e84f60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#box plots\n", "cars['mpg'].plot(kind='box')" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'boxes': [<matplotlib.lines.Line2D at 0x1004de10>],\n", " 'caps': [<matplotlib.lines.Line2D at 0xeffa668>,\n", " <matplotlib.lines.Line2D at 0x100ab5c0>],\n", " 'fliers': [<matplotlib.lines.Line2D at 0x101b4160>],\n", " 'means': [],\n", " 'medians': [<matplotlib.lines.Line2D at 0x100e3240>],\n", " 'whiskers': [<matplotlib.lines.Line2D at 0xefea4e0>,\n", " <matplotlib.lines.Line2D at 0xefea208>]}" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x110f4b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cars.boxplot('mpg')" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0xed90048>" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0xf044240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#group by gear\n", "cars.boxplot('mpg', by='gear')" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index(['continent', 'year', 'lifeExp', 'pop', 'gdpPercap'], dtype='object')" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load gapminder again and select 2007\n", "gap = pd.read_csv('data/gapminder-unfiltered.tsv',index_col=0, sep='\\t')\n", "gap2007 = gap[gap.year == 2007]\n", "gap2007.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Log Scale" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1012e128>" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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VmmsSI80zSXKpNM492jDTVKpfNIlcv7rmhkDdw4KDr62qquTTbavo2u8iAFoe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\nos/POJ5YPZGFLOt7H7fqKn8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"text/plain": [ "<matplotlib.figure.Figure at 0x100923c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gap2007.plot(kind='scatter', x='lifeExp',y='gdpPercap')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "unbalanced with outliers what about log scale?" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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E/uWSuPb6mmySy+mK7U0ZtZ+qqmpPr8+kEInrTPx0Ol0Q/zazkc8Cf28Cd/Tw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\niyflHwGOx+nXLTijiVD0D8A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"text/plain": [ "<matplotlib.figure.Figure at 0x1018a160>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gap2007.plot(kind='scatter', x='lifeExp',y='gdpPercap')\n", "plt.yscale('log')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Grouping / Coloring Plots\n", "\n", "grouped by color?" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAV0AAABGCAYAAABv7kdbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAAYZJREFUeJzt2DEuRVEYRtH7PAyIUq+QiELCCBTqF61CK2qFEZAoRKLQ\nKxkQkWsCorv7Cmu1f/NVOydnMY7jAEBjbe4BAP+J6AKERBcgJLoAIdEFCIkuQGj9p+P7x+e4ubGs\ntgD8CSdPb8PN3tbiu9uP0d3cWA77q4dpVv0Cj1cHw9Ht6dwzJnN3fD28HBzOPWMyOw/3w8Xqce4Z\nkzi/2h9en8/mnjGZ7d3L4eTpbe4Zs/C9ABASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ\n6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKE\nRBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcg\nJLoAIdEFCIkuQEh0AUKiCxASXYCQ6AKERBcgJLoAIdEFCC3GcZx7A8C/4aULEBJdgJDoAoREFyAk\nugAh0QUIfQHQuR6AY7gr5QAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1115b908>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#create a color palette\n", "colors = sns.color_palette()\n", "sns.palplot(colors)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x123193c8>" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PRdVRsits3hTMMvsxjp84wJtvvsFbby2hvNzO0qVLuO++B3njjUUcPHiAoUPz8S0Nk5KS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\nk0yx2DfV76131PyC8I8EehH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"text/plain": [ "<matplotlib.figure.Figure at 0xa5ce7f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#for each group create an own plot an overlay them\n", "for (name, group),color in zip(gap2007.groupby('continent'),colors):\n", " plt.scatter(x=group['lifeExp'],y=group['gdpPercap'],label=name, c=color,s=30)\n", "plt.yscale('log')\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x125c7ba8>" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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S2TFevVrDDwAAbAOBHwAAAACAEXq98+GeYedglc6O8TLSCwAAtoPA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\nANh8R1OEfRINP20Z2/BTSvneJD+S5D9O8meT/E+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16a/3fI5r25o/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\n4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zdWrZZj+87JCq8xOLM7bh5I4GfTjpsFDVm3xsADP943rYfADwAAAACMj+mIOHzB6dOd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\nq5gVo5D5rS/ASjV6B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"text/plain": [ "<matplotlib.figure.Figure at 0x12663470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#playing with categories ... seaborn is pretty good with it\n", "plt.figure(figsize=(40,20))\n", "plt.subplot(121)\n", "sns.boxplot(x='continent',y='gdpPercap',data=gap)\n", "\n", "plt.subplot(122)\n", "sns.violinplot(x='continent',y='gdpPercap',data=gap2007)" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.axisgrid.FacetGrid at 0x12663710>" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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pUSySiaiphJRYTpeQKTjce0xEXUFIgVUrDVe4UBXOe62KRTIRNY3luEjnXAwfUrmSQkRd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06QPjy69ES8FTDGECkUcBLre+C9iEg735/rGnduE96cWrXB5YvjXDw3tmBkdP9ACnd0hJ17C/c1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\nRKTN/wcNPUEMlCSryQAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x12663dd8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# or with linear regression\n", "\n", "anscombe = sns.load_dataset(\"anscombe\")\n", "sns.lmplot('x','y',col='dataset',hue='dataset', data=anscombe, col_wrap=2)\n", "#g = sns.FacetGrid(anscombe, col=\"dataset\", size=4, aspect=1)\n", "#g.map(sns.regplot, \"x\", \"y\")" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<seaborn.matrix.ClusterGrid at 0x126672b0>" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x124f1550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# or with structured heatmaps\n", "\n", "#compute the correlations and take a look at them\n", "corrmat = gap.corr()\n", "\n", "# draw a clustered heatmap using seaborn\n", "sns.clustermap(corrmat, square=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TASK\n", "> create a scatterplot where \n", "> * x = lifeExp \n", "> * y = gdpPerCap\n", "> * color = continent\n", "> * size = pop \n", ">\n", "> label the axis appropiately and use a log scale for gdp" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x112927b8>" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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eZ0jvoTnOn/3dfBLOBDJ92o+A/aHye0s/ZG/QcWbumsO+w/uyzj24/zBxkRqHD0m8vb0r\nVYAoDjXcpChKlRUYWI3HRzyBxWLhu2/m0vvWbsTUb0dAQAAAmqYRHx+Pn783e48cIi4uFoDExMuY\nfCy4Aq61PDh89CAnzkRSPSSYu4YNJNO2gB69upVjy8qOChKKolQ58XFxHN69k/TUFAKrh1G3QUMO\nHzhFzVqh9B/YF7D3Fl6f8D46SzUyzEnUbujN4JEjAQgJCaWpSwPkwRPYTlg5Fh5L4s2C9CNnqebn\nxz33jMi6l8lk4s/FC8m4eBHPsBr0umsobm5u5dLu0qCChKIoVcaurf8g1/6O4eRx6gP+LnoSMzNZ\n7eNNlyZN6djp6tDQ/73xIbWDuqDX2z8G9x9czwsvdgDsD5HHPzqelJRk3p30PcePnif45tz3u3wp\ngYUTx9MxLR13g4H0vXv44e+N3PvuB1kzmCo79UxCUZQqYdnMGSTM+II2587S0tWIj6sRo15PsLs7\nHTItNN+7lzUTx3Nk3x4ALkYmZgUIAFe9V47sr+fPn2fN6nWE1jXy0LD+dM6IYWTj2jS6qUHWOWtn\nz6KHyYy7wV6Oh8FAj4wM1s75oWwaXQZUkFAUpdJJS0vDZDJlvV4153uCNv9NHVdjvte46HS0t9nY\n9fmnnDt9Cg93T06csQeFDFMaaRnJxMban0ns27ufbz9fwcXjfqTE+HA5IZG77riDTq3b5CjTFhWV\nazqri06HNfKCs5pa7lSQUBSl0om7lEBisn0abkpKMknr/yQkn9Tf12qraWyeO5t6DcLw9w3hyIlt\nnIs8TGhYAL1u6QnApvX/YcCPhMtReLj5s/XvfVkL5rJngdU8PPK8x86oSA4fPljidlYEKkgoilJp\nzJhqz3tUp2YtQoKCAFi34GdauhTto0x//Bh3jxxAYM0MQmv4UPsmX0Y+cidubm5omsaB/QexWMzE\nxp3j2KmduBLE5x9/g6ZpObLANup9K6csOVOOn7Jkcu+Yp2jSpJlzGl3O1INrRVEqjVHjXsx1LPXQ\nQQxFDBLN9QZ2/r6ap54dneu9/fsPUDOoFUGBtbiUGE3zxvaVzFGRko0bN+TIAjtz5hymblzP139v\nRGe1Euznz0Njnua39Wsxu+ho3rwF7777FqmpKcTFxTJkyHAGDx5WvMaXExUkFEWpNAyGPD6yipH9\nVK/TYUmxD1d98dl3pCSm8vz4JzAajVgyLVk5law2KxZrJga9K5oujQ0b1uXKAivPnOKdT7+iceMm\nLFv2C+373MqGndsAOH/+HH363E6PHr2Ii4tl7NjHVZBQFEUpjuTkJC7GXKR+vZtyJb4rkEvx9lBw\n0dnvEXk2Ac1qJCUlmcDAarRqfTML/NaQlGKkRmgDDhz/jRYtm3PnkE68+dZEkpOTHFlgU1i8eCET\nJrzO/PlziYy8QEREixzJ/gICAlm48Gc2bVqPp6c3Vqslv+pUWCpIKIpSIRw+dpyWzZoVLUAA+PhC\nfHyRLjFZrcSkpQHw+DP3kJqaQmBgNcC+RuLFV59m27btZKSnM6rZBEJCQvjll/m5ssAOHz4ILy8v\nXnppAkajkRdeeIYDB66m8Jg/fx4REc0ZPHgYu3btYOvWzUVrWwWgHlwrilIhtG/dulgrlQNvbkW6\npWjf0PfpdATWt+8BUaduHZo0bZr13tq/t7Do99/p2LEDp/9cw4KXxwH2LLB33NEv6zw3N3d69LiF\nwMBqPP30KJ577kkCAwNp2jQCsAebLl26sWTJIsaOHcOiRT9jMBiwFLGu5U1lga2gKkOmzZKoyu2r\nym2Ditc+k8nEgqfH0JHCDTtpmsaG4GCeeG9qnu+v2rSBY2ejGHf/fezatJG4yPPcNmKkM6tcroqa\nBVYNNymKUm5W/b6CO28fUKIy3NzcqD90OKcWzKdeXg+2r7HuwnncTSbOnT5N7fDwXO8/PHRgVhBs\n3b1HiepWFajhJkVRyk3fW+8s0fXr/viLD9/5itpNmuM2ZBh7Ms357hKXmpnJmnNnaR0UTFc3N/Zu\nqXzPB8qDChKKopQJuX8vi95/mw1Lfsk6VpiH1Bs3bwDsw0Q/vv8OUefPZb13cN8xXEw12Lt3Pz0H\nDqbHe1PZ16Il/+r1HElL42RqCnvT0/ivWiC/GV3pVj2Mau7uHLFaaNy6tfMbWQWp4SZFUcrElhlf\n0sVk4uSB/RxpJGgc0bxQ13XrfHXIx2oxY7Vas14/PGo4/23fSe8+twAQGlaD4c+9gNVqJS4ulvT0\ndPz9/fH3DyAzM5PlM79CS0wivEs3GjS2P6zeuWMXonFDvL19nNjaqkMFCUVRyobjcakVcNEXfhDj\nSm9Dp9Px8MQ3crzn5+dPn1t757pGr9cTGlodsG8gtG7tn/S+9RaGOqavZlerdg3c3NwLXZ8bjRpu\nUhSlTHR7+jn2Nm6M9/ARNCrDvEbfz1zA3n9SWb5sZZ7vh4ZWx7WQyQFvRKonoSiK00yb+im33d6b\niOYRud5r0KQpDZo0zeOq0tW4SX327jpGRPM7ALDZbEDhnodcsWvXDiZPnkC9evWzjvn7B/DWW+85\nt7IVkAoSiqI4zdPP2PMflZSmaWiaVvTV13m4485buePOW7Nev/HVG3gYPZgwekKhy9DpdLRt2543\n3ni7xPWpbFSQUBTFaYqyYjo9PR03N7c8A8GsmXM5cugkH37yerHqERUVxcwvFpBp0lG7nh9jnno4\n671hvYZgNBZtZfeVoHWtsWPH8MorE6lTpy7Llv1CQkIC/foN4JVXxuHn50+nTl1o27Y9n3wyFRcX\nF4xGN8aPn4jNZmPSpFcJCgoiJiaGjh07M2bMU1y8GM2HH76DyWTCzc2NV16ZSEhIaLF+Bs6igoSi\nKIVyPuo8x88cY/iAgSUu68yZM3w5bT4hNXx4cfxTud5v2aoZXt6exS5/+eK1BHvfDN4Qc/Y8hw4e\npGkz+3OQFk3z2Ky6EHbt2sEzzzye9bpTp67X7Ep39c8JCQnMmjUPg8HAY489wIQJk2nQoCGbN2/k\n888/ZuzYcURHR/Hxx1/g5eXFU0+N4ujRI8ydO5thw0bQsWNnduzYzowZ05k8+a1i1ddZVJBQFKVQ\nPNw88PH0dUpZ7u5u6F3B0zPvWUVt27Wmbbvir2NwMeiwaDZcdC5YbWY8PPPeQa4oWrduy5Qp7+Q4\nlj1hX/aeRlhYjay05vHxcTRo0BCAFi1aMWPGdAAaNGiIj4992m3Tps04e/YMJ0+e4Mcfv2fevNlo\nmlYhHqirIKEoSqFUC6xGNUem1JI4eeIoderW552p451Qq9y27fyXvv17sWDuCswZLjRrUz3HA2dn\nMhrdiIuLpU6duhw9eoTg4BAg50PxoKBgTpw4zk03NWDPnl3Url0XgDNnTmMyZWAwuHLo0EH69RtI\n3bp1uffeB4iIaMHZs6fZvXtXqdS7KFSQUBSlTKxdvYjLly5y6MAWuvYaQZ9bSz5slZc2Ldui1+t5\n8dXcw1jFpdPpcg03Adx33wNMm/Y+ISHVCQ4Ozhp+yj4MNX78RD7++AM0TcNgMPDqq5Mcf3Zl0qRX\nSUhIoFev3jRo0JCnnx7H1KnvYTabMJlMjBv3stPaUFwqC2wFVdEybTpbVW5fVW4bFL99ly4lkJaa\njLePH76+fteM5xcsPj6eWV8vwJrpQrsujendp2eR719YZfH7i4qK5I03JvL119+X6n3yUtQssGox\nnaIoZSIgIJCateri5+dfpAABsGDer3jpmuDn1pj1q3dXuj0Z8lLUn0F5UcNNilLJxcRcZMPaOdjM\n0aC5oPeozcAhY3B3r5qpJjSNfDO9VhZhYTWYMWNWeVejUFRPQlEqMZPJxMpfPqBj0zS6tPKjS2sf\n2jaM5+fZ75Z31Zxq6D13kmw9xNEzm4iMPl4hZv3cKFRPQlEqsb/WL6dLK48cQxcGg56baiRx6NBe\nmjZtWY61c57Q0FDG/+9pzp8/z8H9h8u7OjcU1ZNQlEosIzUOD/fcaTDCa/ly6sTBcqhR6apVqxa3\n9731+ifm4/TpU8TGxjqxRlWf6kkoCnD8xClOnr3Abb26lndVisTdK4j0jAu5AsXpc0nUa1B2mVZL\n0++b1vD7wXUYMfLEnWMIrxNerHK27PiHb3bPRpdo47PHp+Hj45yFgVWdChKKAhiNBjzcSp6Yrqz1\n6j2IuTO30ruTa9aQk8Vi5USUF90HVq6hpouRF9jww3e4BQQy+Imn0el0WK1WFu1ailsLP0xYmLN2\nDk8MeoJvl39LlDkGDxd3ejTqyp29+l+3/LT0NAw+RqwZpiLPjoqKiuShh+5FiMZZx9q0aUdoaHXW\nrFmFpmlkZmby6KOjadeuI9999zXVqgUxePDQrPPHjHmYN998j+rVqxfp3uVNBQml3N3/6Bi++PgD\n/Pz8y60OdWrXpk7t2uV2/+IyGo30Hz6ev9b+iNUcDbjg6lGHex8aVWZ1uHD2DIf37KJBRAvC699U\n7HL+/PZr2p49S1KmZEPt2tzSfxAAGldnMlk1jXfmvou5hQGdzkgqNpZFr8H9Hw96d7m6+ZDZbGbN\nqnnYLKnUqN2M9h170afbrbj960ZQ6yACAgKLXL969erz+edfZ71OTU3hscceYO7cRRgMBuLi4hgz\n5iEWL16Z5/TWyjLl9VoqSCjlbtaM6bnSS3/1zUyeHDO6nGpUuYSEhHL3/S+Vy723/fkH53/8gUY6\nF/YuXsTpIcPoOWhIkcv5/qfF/LvnMBG+bsSiUcOR+VSv1zMwoh9/HNqAUedKRO3GrPHfhLvu6uwm\nY6gnm+U/WUHCZrMx59s36dnGBaPRwOkLv7FhXSK9+gymW8fuzmk4YDC4kpmZydKlv9C5c1dq1qzF\nwoW/VtpgkJ8KFSSEEKHASillu/Kui1J28tp/4InRZfdNWCm+o8uX0cFg/8BuotezddVKKEKQuHgx\nmo8WfUzM5Xh8RBPOtxRUrxNOy/Yds84Z1Gcwg/oMBmDtX2vQp+T+2LJwdd/rc+fOUi80GaPRnmcq\nvKYP2w/uBQYXp4lZTp8+mSMtx+uv/x+ffTaDhQt/4sUXn8ViyeT++x9i8OBh+ZZRGeNHhQkSQggd\n8DJwupyrolQAVe3bWFXlYrUU+PqKK4vfrv29Lt2wjJQmNrx0gdQ6502/ESMLvF/PzrewZMYKiLi6\nH4QlLZNGgc2zXhuNRsyWnIvtrLaS/30KD8853BQXF0dGRgbPP/8KYA9OL774DC1a3IybmxuZmZk5\nrk9PT6uUe2lXpCmwTwBzgYzyroiiKIXj0SyCBMeHYbIlE5dGItc5hw8dYdIr05g8/iN+/GkBFyIj\nsx4cR9zUjIwTSaSdS+Km0HrXvZ/RaOThbg+gP2gm5ewlMmQSIr4WIwdeDS5hYTVIyKjN5cR0APYf\nTaJBk17OaG4O8fFxvPXWZNLS0gD7Xtl+fv64uhpp1Kgxmzdvwmq193AuXDiP2ZyJv3/5PXcrrjLp\nSQghOgDvSSl7CSFcgC+BFoAJGCWlPAH0cRxrL4QYKqVcXBZ1U5QbRUpKMvv37aR9h27o9XqnlHnX\nE2NZFxTMhTOn8agexn0jH8x6z2KxEHnhPP9t303Nam3QNI2Tx7azd9sRAgP9eO31cXRt143aobVJ\nN2XQuGHjAu50VcdWHelwcwfOnz/H8k3L6diiU67d7e578CU2bviNM2fiaNK6HQ0alnxv7Wt7QUI0\nZtiwuxk7djRubm5YrTYGDryL2rXrULt2Hfbt28Njjz2Al5cXmqYxadKbJa5DeSj1LLBCiFeA+4EU\nKWVnIcQQoL+U8lFH8JggpRyc7fw5UsoH8ysvG5UFthKryu2rqG2bPfN1WjbI4GRMOEPuebLAc0+d\nOs5fq6fjF9SIIXfnTLld2PatmPcju5f+wqMff8EPM5eATmP0U/fw65Lf8fP34Z57i/6A+1qfzfuM\ntk3a0rl15xKXdUVF/f05S1GzwJZFT+I4MAT40fG6K7AGQEq5TQjRNvvJhQwQiqIUkU5nIOFyBgbX\n668HSUtNwsMtk/TUxGLfr+8995LuqefbtbNIrpmCj8Gb/cf2M+bJh3Kdm5qaym8r13DngL54eua9\nbanVasVms+XI2/TsyGeLXT+lcMpkPwkhRDjws5SykxBiJrBYSrnG8d4ZoJ6U0lbEYit3GsgbxOkz\nZ5mz6He2/fX3AAAgAElEQVQ83Aw8M/reKpuZtDIwm82cOnWSRo1EoSYGXLp0CV9f32IPTX3x41es\nS9qGa9DVrUMz4zPo49OBpx94Ise5n037lrizgQSHX+KZcY/lWd4bn/4fCUmX+GzSR8WqzxV/b9vM\noVNHeHzEDTuDrsL1JK6VBPhke+1SjAABUNW7hJW+fVarlRemfEdmQGts1kzOTP6M11+2D3NUhfbl\npyK3LTCwJnFxKYU820BCQlquo4VpX2xcLL+f/Qf3Bj45jrtWc2ft8c30OnwbwUHBWcdbtm7O2ov/\n0LJV13zLvq/fA5hMphL/bA8dO86hs8fyLaci//6cITjY5/onZVMes5v+AfoBCCE6AvvKoQ5KGUhK\nSiTJ6g2Ai96V+NRyrpBSKJcvX+LixegSlbH0jyW4VM97WMtY35tVm1bmONa0WVPGvTSaJk2bAPDJ\nx5/kui7AP5DqoWHFqs+uA7uY/tN0UlNTGXL7UP43+n/FKudGVJZB4srw0FIgQwjxD/AR8HwZ1kEp\nQ/7+AVT3TMFmzSQzPZFGtYr2DUYpH76+fgRl+5ZfVLNmzkVuTsTnzzrELY9Hs+UcKNC56LDYrPlc\nbTfu+XHFvn9eFm9ZyqGQ0/yyZpFTy70RlMlwk5TyNNDZ8WcNKHhqhVIl6HQ63pv4DEuW/0aAvx+3\n9ynZilelbFw7nbQo/t74N3HnPGhQuz0AdTObsXXLQqp1Dco6J+N8Ml06Om82UmHcfvOtbD20lf4D\nB5TpfauC6wYJIUQA8AZwC2ABfgP+T0qZXrpVU6oCNzc37h1+V3lXQykjUZEx+HhWy3ptdHXHkHJ1\n2MlqtlAnrTpNGpV83UJRdG/fne7tS5a3KTLyAl988QlJSUlYLBYaNGjEk08+k+9srJJ6/fXXmDTp\nTQyG8k2MUZivDHOBTOA+4BHAG/i2NCulKErFlpqa9wOmlq0iiE88kfU67vJ5NFM6qUcvYTuYRkR8\nPSaOmlhW1XQakymDCRNe5P77H+bzz7/mq6++o2nTZrzxRum1ZcqUd8o9QEDhhpvqSinvzPb6OSHE\ngdKqkKIope/fP34n5sxpBjw2psh5siwWC8vXLCNj1z5cY2Kw+PrQ66lnqXtTA0RjQc++8Wz9ez82\nG0S0q8Frg2eRlpaKl5d3iYayispms5GSkoyvr1+Jy9qyZTOtWrWhSZOrGzn17dufZcsWc/78Od57\n7y0sFgtubu5MmfIOJlMGH374DiaTCTc3N155ZSIhIaHMmDEdKQ+TmJhIgwYNee211/nuu6+Jjo7i\n0qUEoqOjefbZF2jfviPDhg3g55+XcO7cGaZP/wSr1UZi4mVeeulVIiJalLhNhVWYIHFMCNFdSrkJ\nQAjREvsCOUVRKqkja1ejRUaSMfJBPDw8rn9BNgaDAe3oKTpFR+Oi00FiEhumf0rXZ5/lk+XTsWDh\n3j5306NDj6xrirMLXGpqKmdOn6Fps7yHpk6fOUVyShoHDknuHZ579fbHc+dx3KzjwVaN6dSmbR4l\nFF5UVCQ1atTMdbx69TBGjXqAN998j/btO7J58yaOHTvCypW/MmzYCDp27MyOHduZMWM6L730Kr6+\nvnz88RfYbDYefPAe4uJi0el0GI1Gpk79jP/+28b8+fNo374jOp0OTdM4deoUY8eOo379BvzxxxpW\nrVpR4YJEA+AvIcRR7M8kBJAghDgFaFLK+qVZQUVRnO+OcS8RGx1d5ABxhUviZXuAyPZ69b9r0Jq7\noceNvw5vyhEkMjIymP7jZ9QMrUV8WgK3tL6Fptd5LjH/p1/Yv/sEn3yZd86j5NQUmjdrTvNmeW/T\n6ufliTElAR9Pr2K0MKegoBAOH869Z7g9cZ+ZiAh7FtquXe3PPT79dBo//vg98+bNRtM0XF1dMRrd\nSEhI4I03JuLh4UlaWlpWosOGDe2JEUNCQjGbTVnl63Q6goKC+eGH73Bzc8vqkZWlwgSJ7NMBNOyr\n9a78X1GUSqh6zVpUr1mr2Nfrw2pgungRN70eTdOwBofQtE4Tdh89BB4u1PHNmaxP0zT2yL0cDjiL\ne4gn+/6YztuBU7IW1L0weSqp6SY+f/vlrP1FRj5wD6e6niItLS3Ph8PNmzbPdSy7UUOHFvh+UXTr\n1oM5c2Zx+PDBrCGnFSuW4e/vT6dOXTh06CBt27Zn7drVJCcnER4ezogR9xMR0YKzZ0+ze/cu/v13\nC7GxF5ky5V0uXbrE339v4HoZLzRN49NPp/L66/9H3brhWUNTZakwQSIa++I3L+yBQY89jcbk0qyY\noiil79+tG4iNuUCXbn0JDKx2/Qsc7nr6WZZM/wRbZCRWb28Gjx1HQGAgtUJqkZSSTLtWOfcN8/Dw\nQEQ0ITowCQBbLQMHjuynV9dbAEjLMJORacOWbU2FxWLho+UfY3Ox8cEj7xEUFER58fDw4P33p/H5\n59NITEzEarXSoEFDpkx5h8uXL/PBB+8we/Z3eHh4MGnSW3Tq1JWpU9/DbDZhMpkYN+5lwsLCmD37\nW8aOHQNAjRq1iIuLBXJmmL36Z/v/b7+9L5MmjcfHx5fg4BCSkoqfT6s4rpu7SQjxG+ABNAQ2Ad2B\nrVLK4aVfvQKpLLCVWFVuX2Vp2+qVc6lm3E9okCfr/73M8AffxNv7+gsei9O+5OQkPv/0XU5d3I+7\nqwFrfCa3dBtI21v6EF7/JjIzM7FarTlye12+fIlxs15CM8A7w6ZQs0bxez5FUVl+f8VVGllgBfbn\nEp8Bs4CXALXXg6JUcqmXTtAswj5e37aZO3v3bKdL195OvcfxI4dZ8OEHhCVfpp+nJ56GIEgHXMG6\ndQsHN6xnc1gYtbr1oMfAnIst/f0DeHP4ZCxWa5kFCCW3wsxHu+hYJX0EaCGljATcrnONoigVnAVv\nLBZ7eowT5zKof1PhNv0pDE3TWPH9t/w7+X8M12l09fXD0+Ca4xy9TkdDDw/aX76Mcckifpg4nvi4\nuBzn1Kldl/rham5MeSpMkDgohPgc2ACME0JMoALtja0oSvHcdfdY9pwM5b8jntRtOpywsNxTPItD\n0zR+mvoeIRv/or1v4fJ1Bbka6XzxIr9OmkDsxYtOqYfiHIX5sH8S6CSlPCSEeB3ojX31taIolZib\nmxtD7nnq+icWQlpaGl9+v4DzcRnYzuxhpDWZgEJsbpSdTqeji9nMsnff4tGPPi3UPhY/fPcTmk3j\nkdEjr3uuUjwF9iQceZsCpJR/Ow4lAW9LKdWKa0VRsnz45RwOJNYiNt2PFkmXihwgrtDpdLS7fJkV\ns2YW6vymzRrSrHmjYt1LKZx8g4QQohVwGGiT7fBtwG7HqmtFURQATkan4+Kix/f0P3T3L1nCO0+D\ngZRt/2I2m697bvuO7Wjfsd11z1OKr6CexEfAiCvbjAJIKV8DHnW8pyhKFRN1MZqY2NgiX+fppsdq\nMdPI7JznCc0tFjYsU5MoK4KCgkSAlPKvaw9KKX8Hir8jiaIoFVZocAghwQX/81791+88/tlTfLf4\nu6xjt3YSJB/fRAfP4u2HfS0Pg4HkU6ecUpZSMgUFCYMQItf7jmOueZyvKEolt2rJ9XduO3bmBLZw\nV87Fnc86NqT/7fRv4kOQuwd7Ey8X6l6RGRkFvp8ZV/QejeJ8Bc1u2gS87vgvu0nAjlKrkVIia9dv\nwqZp3NG7x/VPViqsP36eiyk1lf6jHi/T+w4Yds91z3n6/scJXBxE515dchyv5pju2tLPH4DtpniS\njDb66PLumXhfJ224ZjIV+L5SNgoKEhOA34QQ9wPbsfc6WgMxwMAyqJtSRAkJ8Xy/ah+go93NEVSr\nVvhcPJXJ86+9yeC+venRrcv1T66kUhISyEgpemqItfPn0axzV2rWqVsKtbLT6/Xc3qtvruPaNVNW\n6+s8STRZwD3XqQD4GgueAaWrABvuKAUECSllkhCiO9ALaAVYgenZpsMqFYyfnz+NQvWgafj5lXyj\nlYrqjfHPFmt/gtISdf4cm+bOwRgQwKDRTzilzLuefrZY12WkpGBOL5+dhQPq1CHln814u9pHo4OM\nHlxJyZdps3ExLY1a3oVPc+1aRb/kVDYFhmoppQ340/GfUspenPA/3n/rjWJvWajX65nyypNOrlXF\n4+cYzqgo1s34gg7R0aRYLKyrFsTIJx8rt7oMLOPhqSuio6No2bEza+f9yM15PLLcHB2FC+DlaiDA\nLZ+uRTZWTcO1elgp1FQpqrLbS1C5rrcmvVYh9rRVikZzcUHTNMyahut1hlCqovT0dObPGs/mTaux\n1Ms7z1LzwEBc9S74G/NP+3bZZOLQpQQ0TeOA1UL3IcNKq8pKEVw3SAgh1KdWGclrYxWl4hsw7iX2\nN2/BpdvuoOeAQeVdnTLn4eFB+x4P0av3IJr07c+ZzNyL4ILcPegcGpbvftoWm42tF6Op5u7OpuhI\nzKIxAQGBpV11pRAK05P4r9RroSiVWEBgIMPGvUjfkQ/k+yFYmaSnp/PW1A+KdE3nLr3x8vKiVafO\nxDRphiXb5kGnk5P4OyqSYwVMjbVqGt6urgS5exCpwcCxzxW7/opzFWpnOscD7G1SSjUnTVGqOA8P\nDya+8FKB55w5dZJtSxZhi4kBVwPRRi/i3EOpFeLLQ8+9wOxXX6J7cjIGFxdOJSfTq0ZNNkReoGE+\nz5Pc9Hqqe3qyJCqSvq9NrFATE250hQkSbYG/AIQQV45pUkrnLK1UFMUp5i6fy+aTW3nq9jG0aFKy\n9GouBaxh2LR6NYenf0VLvSGr55RusTDr0mH+jriLsA2beOi9qcx/500anTlDps3KuZQUMiyWfMu0\n2GzEVQti8PiJNGurcjFVJNcNElJKlYJDqRI0TasSw0H5MWWaMNsyMZszS+0eGRkZ7PzmW9pfs4GQ\nh8HAE0F6Pjy2gYw2ffHw8OCRt95lw69L8F67hrioKPrUqp2rvEybjX2aDZpGMOypZ/Dy8iq1uivF\nc90gIYRww75lqQCeAZ4D3pNSXj9Fo6JUEAuWrGDNX9v5/rO3yrsqpeaxoY/xqPZovoFw74E9tIy4\nuUT3+OvXJVhi4yGPNQx6nY4IayRDBlxdaNdr0BC0gXex7a/1HD2wD3NsLJhMoHfBEBCIe1hNBgwe\ngncR1k8oZasww01fALHYU4ZbgIbAd8ADpVgvRXGqW3t2oVpA1R/nLqindPzM8RIHiYz4ODoXsMit\npoc7ZrMZV9erPQ2dTkfHXr2hl3P3z1bKRmGCRBspZSshxB1SylQhxIOA2nRIqVQCAwPp0+vGzmc1\n9M6SrztwrxZEhsWCez7reWI0jSXv/R8usTHgH0Cr4SNo2qZtie+rlJ/CTIG1CSGyrxAKAmz5nawo\nStXVc9AQ9hvzTgJtsdlISUujQ1QU7SxW2sXFseOLT0lJSSnjWirOVJiexKfAOqC6EOJT4C5gSqnW\nSlGUXDIzM1n/6xLidv4Hly9jM5txcXUFbx98mkXQ++57S/3Br7u7O63HPMaB6V/RLNvspgyLhRXY\naO/hkeP8Nuj4e9UK+t5zb6nWSyk9hZndNEcIsRN7oj8XoL+Ucl+p10xRFABsNhtLZ3xB2p5dNDeb\nqXdlZpHOBSxWuHwZ86aN/Pr3RnSNmzBo7Lhcq/flgX1s/HkeY95+v8T16d63Lx6BYWxf+gtabAya\nwRXfps0Y2bkreyeOz3FumtWCVz7JJm02G8nJSRUuF5eSU75BQgihB57G/qD6Hynl9DKrlaIoAJhM\nJua8PpF20dF4GgxgyHuox6jX0xawHDnCvPEvMmTym1TLtsNcSI1a1GnhvK3pw+vfRPiL43Md39Cg\nIaazZ3HT67FpGjt9fBl96+15lrFizg8c/mMNr85b6LR6Kc6n0zQtzzeEEF8DTYCtwB3AEillRRpm\n0mJji55vv7IIDvZBta9yclbbbDYbsyZNoHNUFIbrbNCTZrGwNz4OvU5Hi8BqbPP14f73p+FxzfCP\nMxTUvszMTNbMm0PG+XO4BFbjtgceynf1dEpKCiePHKJF2/ZOr2NJVOW/mwDBwT5FWixU0HBTD6Cp\nlNImhPgA2IB6FqEoZWbNz/NofeHCdTMDb4u5iE3TaBMUjE3T2BEXi9elBH798nNGvPhKGdXWztXV\nlQEP558qXdM0EhISqFatGt7e3hUuQCi5FfT1JN2xnwRSynjUjCZFYeXvf/LV9ws4fvJUqd8rfsd2\nvK8TIA5fSiDM05NOodUx6vW4Gwx0rR6Gt6srUdv/xVTBtgCds3Q2Y79+jpiYmPKuilJIRdlPIu9x\nKUW5QcxfspL5mxP472Iwb3+1jEuXEkrtXrv/3ULtuLjrnheTkU4db59cxxv7B+CRlMifi4s33r9r\n524uXnT+B/ktHXvTp2EvgoKCcr2naRonTxzn4sVop99XKb6CvqbUFULMAq6MX9XJ9lqTUj5a6rVT\nlArk5Pl4jF72/ENm97ocOiLp0qlTqdzr2F/rudkt/w16rtAXsMLaqNdzed9euK/oyREWzltF+E1h\nPPG0c3fZq12jNo8Nz11m7MWL/PrOm9SOiSHNxYXkxo0Z+dpk9HqVR7S8FRQkXiBn72Gj47UO1atQ\nbkAtGtXi8N+RGLzD8DSdpGXzW0vtXrq0tEKdZ7Xl/U9R0zT7e6mpxbr/K/97Eu88eiilZd13X9Mt\nJQWdY51H6vHjrFuyiNuHjyizOih5yzdISCl/KMN6KEqF1/+O3lQL/I/jJ89yx6iHS/VD1JaZf1rt\n7Or5+rI/IZ7mgTnzKW2PjaFZYCCnMouXETYwMP/8TKUiNjZH3ikvV1dSz50t2zooeVJbkypKEXRq\n345O7Utnv4M/FvxMwr491O99Gy5uhdsru463D0cuX+KvyAuEeHhg0zTiMjKo4+1NkLsHpyvLnttB\nQZB8ddppmiUTzxq1yrFCyhUqSChKKfvnj99JvnyJOwoYOrHZbFxYtZzORje2L1uMW736aOfOFWr/\ni8b+ATT2DyAuIx0XdERk6wXosu0TbbPZ+HnOhxi0SwTVbEvv20qe8M9Zbnl0NMvfeYvw+DjSdS4k\nNGzIA8PvKe9qKVwnSAghBmJfcb1FSrm1bKqkVERbtm1n847D2GwaHVo2oFf3ruVdpUqj4y19sNkK\nnkHu4uJCRrVqHI+Khvr16TRkGDt37aCJm3uh7xPknnPhXJzZTN3u3bNe79r1L6JmHCFBPmze+Q9Q\ncYJEaFgNRn32JUePHMLbx5eaeWxQpJSPgtJyvAWMAHYCLwoh/k9K+WWZ1UypMGb+uJCNR6wYfez/\ncA/8EcneQz8w7omHS1TuldX+VXm3OAC9Xl+oWTqPffAxkZEXqFOnLjqdjo11wqEE00GP+frw4C1X\nH67XqXMT63ab8fXJJMNS8Tb50el0iCbNyrsayjUKWicxHGgppRwBdAJGlU2VlIokLj6ev/fHY/QJ\nzTpm9Apix2krp06fLna5R4+f5LGXP2T0y+8TG3v99QDFYbVa+X7Oj6VSdmlwdXWlbt3wrKDZ8Lbb\nOWsp3oPnS5ZMQjp1yRGAQ0JC6dnvRc4mtuW+hyc6pc5K1Xe9FddpAFLKM4CasHwD2rBpMy7+DXId\nd/ULZ+OW/4pd7o5d+7D4RZDu3oDD8mhJqpgvvV7PIw9W3g0U2/foRWrX7sRlFm2n4DSrlSMNGnFH\nHusjatSsQ5/bB+HuXvhhLOXGVpQV1yotxw2ods0aWNIv5TpuMaUSGhSYxxWFc8/QAXSocYmejax0\n61I6C9KqgkGjHieuRy/OFHIqa1xmJgcaN+aB1yZV+WE8pWwU9OC6uhBiMldXXGd/rUkp3yz12inl\nrkO7tvy04m+StWo557FnHOW23s8Xu1y9Xs/Tj410RhWrvIGPjmaXaMzOP37H7dRJmhlccwWAYyYT\nl2rWpFbX7tzff2A51VSpigoKEl9zNUDosr1WX09uIDqdjtfGjuTz7xdzJs6KpumoHajj8SeGVdmU\nCf/74iuqBwYw9t7yX+1rNpv5bf2fDL6jL627dGP+gkXsvXgOl+RkrCYzeqMreHvTut8A6jcS5V1d\npQoqaMX1G2VYD6UCqx4aytuvPkVaWho2mw1v74o3M8aZaocEExIQUOTrjhzYT1BodYKDS7YSW9M0\noqOjCAurgaurK51atQbsezUs+2MLk14cQ7MmTUp0D0UprHw3HQIQQjyKfXe6xkA6cBD4QkpZEbaS\nUpsOVWJX2jdtxmyOn4nlzZceIahaGaeCcKKt69dx6buZnPZw5/Vfl5Tod/fJjDlsPZZOv7bBPHTv\nECfW0jlulL+bVVVRNx3K98G1EOJlYBzwHtAO6AZ8CbwmhHiyJJVUlCuiYi4Rn+bC5cuJ5V2VEvHw\n8iJRBzYnpMEwGl1x0cy4upZOQoSEhHiio6NKpWyl6ilo+9JDQHcpZdw1x8OA36SUrcqgfgVRPYkK\nSNO0Qs2qudK+9PR0kpKSCA0Nve41FV1CQjyenl7Urh1cot+dpmmkpCTnu+1nQTIzM3F1zXsfbIAj\n+/awc+r7uFttVL//Qbr0vbPI96isfzcL6wZon3N6EoD52gABIKWMQqUKr5B27tnLtC+/L5d722w2\nXpg8lYde/Ig/N/5T6Os8PDyqRIAAe+ZUZ6w/0Ol0xQoQFouFXXv25Dp+YOcOvn30Af76dSlH9+ym\nlcGV5h4eRB0+VOK6KlVfQf3ZggKBmuFUATW6qR6WQqaYdra0tDSiU4y4BTdj3+ET9O7RpczrMGfB\nMuTpOER4EA/eM7jM719aTCYTK7/7hozjx0CzYaxXn36PPY6XY++FKwwGAx3a5c5Q6+ntjcHHF29/\nP9rfeju/nDyBzmymt0qgpxRCQcNNUcBX5B0QnpBShpVmxQpBDTdVMIt+Xc2JM1GMum8QQUEFP4R2\ndvv+27mbT5Ycws0nDHNSFM8Pb0abVjcX+vp9Bw4SE5dAn57dSlwXZ7ZN0zRmvvw8XRMSMLjYO/5W\nTWOTtxePffQZhuvsgV0aKuPfzaK4AdpXpC/5hV0nkZ0OmFGUmyg3huGD+pbbvTMy0tG5OB4au7iS\nnp5epOtnLVpPbKKJnl07lcsHb362/vkHzWNiMGR7IK7X6WibmMjGlb/Se/DQcqydciNQ6ySUKqFr\n507sPjiXE5GSm+r40KVTxyJdP+yOjsTGJ5Q4QKSmprJ95zYimjTH09OzRGUBxMjDNM1jxpSPwZUz\nJ0+UuHxFuZ7r7SfxFBAlpVwqhNgOBAMWoK+U8nhZVFBRCkOn0/HsmOIn8+vayTm7zb327gziDQ0J\nWb6FaVOKn7bkCoO3DxabLWuo6QqbpqHz9MrnKkVxnoLWSUwAhgBXpkC4Az2BT4EJpV4zpUhWr/uL\nhUtXlXc1bnjJJh1Gd2+STc6Z29Fz6HB25FHUbquFbkPvdso9FKUgBU2BfQi4S0opHa+tjpThX2Hf\nX0KpIGw2G3NX7eCXfyI5VwU3jzebzRw/cZyCsgNUFPfd2Z7GfpGMvLODU8rz9vah9ZNj+cfDnTNp\nqZxLS2WL0YgY9ThBwcFOuYeiFKSg4SarlDL7I/63AaSUViGEqXSrpRSFi4sLXVvUIjk1lRo1apZ3\ndZxuykffcDzBi35t9vHQiIqXpiK7Pj27cu/wvjlmx0RGX2DhH4u4v9/9BFULKnKZEW3b06xNO44e\nOYTVauOWZhEqDbhSZgoKEjohhK+UMglASvkLgBDCj1JYTCeEaAOMxT576hUpZYyz71GVPflI+Wcs\nLTUaoHNBs1X8nkRelm9YwZHgs6zcsIKHhz1SrDLU1p5KeSlouGkeMNsRFAAQQvgA3zveczY37Lmi\nVqGGs5RsJr84mvZ1TBw8FceH02dhtVrLu0pF8uCgB+lhasu9A+4r1vU2m42EhPhKMdymVD0FBYn3\ngVggUgjxn2N2UxRwEZjm7IpIKbcATYGXgNy5BZQbhs1mw2y+umXn6TNn2XXelXh9Qw4khLJw6cpy\nrF3ReXp6cu+g+3BzcyvytVarlZmvPM9fY59k5qsvYbOpDSKVslXQOgkLMEYI8SbQ3nF4p+PhdZEI\nIToA70kpewkhXLBnk20BmIBRUsoTQoi2wA6gL/A68FxR76NUfidOneLdLxeRYXVlYHfB3YP7YTJl\nYMOetE6nN2AyF7xQTtM0EhMv4+9f9D0himPX3v0cOHSUEUMHYHRCFtjsoqIiqXUhkoZeXhw8f46E\nhASCgor+XENRiuu6K4eklOeB88W9gRDiFeB+IMVxaDBglFJ2dgSPjxzHfIFZgBn7am/lBrT2r3+x\nVWuLEdh24Ch3D4YWzZvT5aa9HDx7mGpeGvcOHV1gGd/N/YU/d1/k9rZhPHxf6a5ITk1N5eM563AJ\naErCrPmMe+JBp5ZfvXoYK0NCcIm5yMXqYQQGFn9fcUUpjrLIP3Ac+3qLHx2vuwJrAKSU2xw9CKSU\n64H1ZVCfG47NZmP1Tz+SkZTIbQ88XKwMo2Wl3c1N2fLT31j0frRtlPU4jLGj7y90GZqmoaErkzF8\nvV6PXsvEkpmKh3v+KbqLy2Aw8NjUT4iKiuTWGjVxcSlohFhRnK/AnemcRQgRDvwspewkhJgJLJZS\nrnG8dwaoJ6Us6mCreopXSAu//ArfVavx0hvYW68OYz/9pLyrVKDo6GhiYuNp0bx4s3k0TSM+Pr7M\nhmXOnD3PgUNH6Hd77yJNTd24ahUHliwDg4FeTzxO0yIkJMzu6LETHD56kkF33lqs65UbjtMS/JWW\nJCD7JsAuxQgQAFU9U6PT2hd/LpKaBvu33MxLSaXyc8vMzCQhIaHQe0MU1D693ouw6l4lrKdbgddP\n//YnEi4nMfH50ej1+hLcBzw9/GjfpgNxcfYR1cL87lJSktn1xQzau9qfYax8/0OCPy9e3kwvT3+a\nNGxUZv8eboAsqVW+fUVRHn3Xf4B+AEKIjsC+cqjDDaXbiJFs8fPjXw8Pmt1VOmP0H37xA8++u4A9\neyvHr3PfsUiOnE8jLS21XO6fnJyMrzkz67U+PaPYZRmNRgIC1LMKpXSUZU/iyvDQUuBWIcSV7cuK\ntzWwNFYAAA1iSURBVLpIKbTQGjV5+KNPS/UeN4XXJO7yccKqV45d5l576m6Skou2RajZbGbewqUM\nH9wPb++ifRu7VvXqYcQ1aEDsqVOkouHTtXuJylOU0lImzyRKidp06Brzl/xGTPwlnh090il12H/w\nEN/M/4O+3VvS79aeTinzisrYpd++Yyfvzd7Eg7c3ZnD//PfOKGzbbDYbu/7dgqe3D01btHRmVUtV\nZfzdFcUN0L4K/0xCcaK5i5Zz4FgU/Xu15vT5KKJjEpxWdmxcAnEpNqIv5trq/IbUvm0bpgUFUrdu\nuFPKc3FxoW3nrk4pS1FKiwoSldzabScwBLVk5YZdvPfa404t+5YeXenQtpVTNs+pKsLD65V3FRSl\nTKlJ15Vcg+ruWGL2ENGgdLYc9/LyUhlHFeUGpnoSldzkl54s7yoA9rUJr739GSnpFqa89AiBaraN\nolQJqiehOIXZbOZcgpV4WyinTpfexkdWq5Vdu/ewc/fuCpMN9sTJk4weW/KtShWlIlI9iRuY1Wol\nJSUZPz//Epfl5ubGUyO6c+nSZdoUc+Xw9ZhMJl579wsizTUACFu6nndeexp3d/dSuV9h3VS/PjM+\nnVqudVCU0qJ6Ejewr7//iWcnOS9FR+f2bbnz9j5OK+9acxYsI961GR6+IXj4hpDgFsHs+UtL7X5F\nUdJV24pSUamexA3s7sH9aNnseHlXo9BS0zNx0V9NoueidyU1o2IMOSlKVaV6EjewoKBqdOnUobyr\nUWjNG4djSYnKep2ZcpEWjeuWyb2zb4KkKDcSFSSUSqN3j64MaOv//+3de3AV5RnH8W8wEEEkCI0R\nvGCl9VE6XkbwhnITtVWr0qrFCxVE8I6jTnW8tNBaRkWLU52qRVCgjo5tbcURB6qDN2qtCnIRGR6q\ngrWFqWBVKiIQkv7xvsED5g1EkmzO8vvMZJLds2f3fZKT85z33d3npXyDU77eOb1nB07s36fR9j/7\nlb+zxJd+Zf1zL7zE+aPG8sG/Pmi0Y4kUCw03SVE5Z9BpnDOo8fc7f8FCHnh6CW02fMjk8ddvcW/I\nUT0PZ/k/V9Blr6a5F0WkJVOSEAG67FVJ+5pVdCxv/ZWbB8s7lDNy6HkZtUwkW0oSUq+qqioef2Ia\nQ849O+umNKnKykoeHHd91s0QaXF0TkK2SWU5RHZeShJSr9LSUi4Y3DQTFYlIy6ckISIiSUoSIiKS\npCQhIiJJShI5996yZTwz8zmKeJpaEcmQLoHNufGTprGmtButWj3PKScPzLo5IlJk1JPIue57d6J8\n0/scbN/KuikiUoTUk8i5664YmnUTAPj3ihUsWfoOA/v3zbopItIAShLSLO6b+hTvrm7Fgd33Z999\n98u6OSKynTTcJFtYuXIll1z7M9atW9eo+z2lXy/69mhP1657N+p+RaRpqSchW+jUqRO9ex1CWVlZ\no+63T+8j6dP7yEbdp4g0PSUJ2UJZWRnDLvhR1s0QkRZCw00iIpKkJCEiIklKEiIikqQkISIiSUoS\nIiKSpCQhIiJJShIiIpKkJCEiIkm6mU7q9fqcebz8xiJKS2oYfv6ZdOhQnnWTRKQZqSchSW8ueIv7\nnpjD4k/3ZsHHXbl53ESqqqqybpaINCMlCUma/fpCdunYHYCSklas2ljB8veXZ9soEWlWShKStFtZ\na6o3bdy8XFq9lj067pFhi0SkuSlJSNKF5w5iz6rFrPvoHdavWsSpx+xD586ds26WiDQjnbiWpDZt\n2jBu9DWsXr2atm3b0r59+6ybJCLNTElC6lVSUkJFRUXWzRCRjGi4SUREkpQkREQkSUlCRESSlCRE\nRCRJSUJERJKUJEREJElJQkREkpQkREQkSUlCRESSlCRERCRJSUJERJKUJEREJElJQkREkpQkREQk\nSUlCRESSlCRERCRJSUJERJKUJEREJElJQkREkpQkREQkSUlCRESSlCRERCSpNOsG1DKzgcBgoB1w\np7svzLhJIiI7vZbUk2jr7pcAvwJOzroxIiLSgpKEu083s92Aq4EpGTdHRERopuEmMzsauMPdB5hZ\nK+B+4FBgPTDC3d81s28AdwKj3X11c7RLRETq1+Q9CTO7AZgIlMVVg4A27t4buBEYH9ePByqB283s\nrKZul4iIbFtz9CTeAX4IPBKXjwdmArj7a2bWK/48tBnaIiIiDdDkPQl3/zNQVbBqd2BNwfKmOAQl\nIiItTBaXwK4hJIpardy9+mvsp6SiYvdtb1XEFF/xynNsoPh2Jll8gn8FOBXAzI4BdD+EiEgL1Zw9\niZr4/UngJDN7JS5f1IxtEBGRBiipqanZ9lYiIrJT0gljERFJUpIQEZEkJQkREUlqMVVg62NmuxDu\n2j6QcAL8MkJJjylANbAIuNLdi/YEi5ntCcwFBhJ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"text/plain": [ "<matplotlib.figure.Figure at 0x123bcf28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#for each group create an own plot an overlay them\n", "pop_max = gap2007['pop'].max()\n", "for (name, group),color in zip(gap2007.groupby('continent'),colors):\n", " plt.scatter(x=group['lifeExp'],y=group['gdpPercap'],label=name, c=color,s=(group['pop']/pop_max)*400)\n", "plt.yscale('log')\n", "plt.title('Life Expectancy vs GDP')\n", "plt.xlabel('Life Expectancy')\n", "plt.ylabel('GDP Per Cap')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interactive plots\n", "\n", "simple interaction is possible with IPython by default. That means whenever the user changes some parameter the visualization is recreated on the server side and send to the client. " ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.html.widgets import interact, interact_manual" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello 50 True red\n" ] } ], "source": [ "@interact(text='Hello', slider=(0,10),check=True,categories=['red','green','blue'])\n", "def react(text, slider,check,categories):\n", " print(text,slider*10,check,categories)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [], "source": [ "@interact_manual(text='Hello', slider=(0,10),check=True,categories=['red','green','blue'])\n", "def react(text, slider,check,categories):\n", " print(text,slider*10,check,categories)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11299ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "@interact(bins=(5, 25, 5),color=['red','green','orange','blue'])\n", "def show_distplot(bins,color):\n", " cars['mpg'].hist(bins=bins, color=color)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# custom build widgets:\n", "\n", "http://nbviewer.ipython.org/github/ipython/ipython/blob/3.x/examples/Interactive%20Widgets/Widget%20List.ipynb " ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['Accordion',\n", " 'BoundedFloatText',\n", " 'BoundedIntText',\n", " 'Box',\n", " 'Button',\n", " 'CallbackDispatcher',\n", " 'Checkbox',\n", " 'Color',\n", " 'Dropdown',\n", " 'FlexBox',\n", " 'FloatProgress',\n", " 'FloatRangeSlider',\n", " 'FloatSlider',\n", " 'FloatText',\n", " 'HBox',\n", " 'HTML',\n", " 'Image',\n", " 'IntProgress',\n", " 'IntRangeSlider',\n", " 'IntSlider',\n", " 'IntText',\n", " 'Latex',\n", " 'Output',\n", " 'RadioButtons',\n", " 'Select',\n", " 'SelectMultiple',\n", " 'Tab',\n", " 'Text',\n", " 'Textarea',\n", " 'ToggleButton',\n", " 'ToggleButtons',\n", " 'VBox']" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#hard core\n", "\n", "from IPython.html import widgets\n", "\n", "[widget for widget in dir(widgets) if not widget.endswith('Widget') and widget[0] == widget[0].upper() and widget[0] != '_']" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x12ae7f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "@interact(bins=widgets.FloatTextWidget(value=5))\n", "def show_distplot(bins):\n", " cars['mpg'].hist(bins=bins)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello 50.0 True red\n" ] } ], "source": [ "text_widget = widgets.Textarea(value='Hello', description='text area')\n", "slider_widget = widgets.BoundedFloatText(5,min=0,max=10, description='slider area')\n", "check_widget = widgets.Checkbox(True,description=\"CheckboxWidget\")\n", "toggle = widgets.RadioButtons(options=['red','green','blue'], description=\"RadioButtonsWidget\")\n", "\n", "@interact(text=text_widget, slider=slider_widget,check=check_widget,categories=toggle)\n", "def react(text, slider,check,categories):\n", " print(text,slider*10,check,categories)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false }, "outputs": [], "source": [ "b = widgets.Button(description=\"Update\")\n", "checkbox = widgets.Checkbox(description=\"CheckboxWidget\")\n", "\n", "tab1_children = [b,\n", " checkbox,\n", " widgets.Dropdown(options=['A','B'], description=\"DropdownWidget\"),\n", " widgets.RadioButtons(options=['A','B'], description=\"RadioButtonsWidget\"),\n", " widgets.Select(options=['A','B'], description=\"SelectWidget\"),\n", " widgets.Text(description=\"TextWidget\"),\n", " widgets.Textarea(description=\"TextareaWidget\"),\n", " widgets.ToggleButton(description=\"ToggleButtonWidget\"),\n", " widgets.ToggleButtons(options=[\"Value 1\", \"Value2\"], description=\"ToggleButtonsWidget\"),\n", " ]\n", "\n", "tab2_children = [widgets.BoundedFloatText(description=\"BoundedFloatTextWidget\"),\n", " widgets.BoundedIntText(description=\"BoundedIntTextWidget\"),\n", " widgets.FloatSlider(description=\"FloatSliderWidget\"),\n", " widgets.FloatText(description=\"FloatTextWidget\"),\n", " widgets.IntSlider(description=\"IntSliderWidget\"),\n", " widgets.IntText(description=\"IntTextWidget\"),\n", " ]\n", "\n", "tab1 = widgets.Box(children=tab1_children)\n", "tab2 = widgets.Box(children=tab2_children)\n", "\n", "\n", "i = widgets.Accordion(children=[tab1, tab2])\n", "\n", "i.set_title(0,\"Basic Widgets\")\n", "i.set_title(1,\"Numbers Input\")\n", "\n", "from IPython.display import display\n", "\n", "def button_clicked(bb):\n", " print(checkbox.value)\n", " #TODO update plot\n", "\n", "b.on_click(button_clicked)\n", "\n", "display(i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TASK\n", "> make the plot from before interactive, such that you can slide the year" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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5hYHEu0j4B4KecB93Xw1gZs2BJcW8p9hholBrgkdl7rfV+sKonxMJ\nPry3dbz86GOYWTuCKbU3LdcjeGrXWOBd4EtgSNT+N0a1bdN+tt5nB3efQzCUdRrBUNX1JbRL/uQ0\nTCTxLvqhPG8DFwKY2Z4EPYK6xWxfrPBD+lGCZwrnmNn5US8PDLfpAjQAvt7G8dIJphzetH07gnH8\nIoIASSGYhbOQ4JnM7xI++nA77Yze5yEE50YgmNHyYKBZVV8NJbWbegZSm0WAg8xsXdS6Z4D/4/fe\nQHTP4CLgUTP7kuBD/3R3zy5mn8eY2edbrR8NdCMYapllZkOAj83stfD13cxsVvj+U8KhnuKOt97M\nrieYgvgLggA4PdzHZGAKwYf/FwTToucA7wG7lPA7gKDn8LiZXUDwdKxBAO6+MTzn8eU23i8CaApr\nkQoLnwZ3fdRzaWsMM6sPfAj0dfel1V2P1FwaJhKJU2bWFfgJeERBINujnoGIiKhnICIiCgMREUFh\nICIiKAxERASFgYiIoDAQERHg/wHekaADZO/UwgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x12bfa518>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pop_max = gap['pop'].max()\n", "\n", "@interact(year=(gap.year.min(), gap.year.max()))\n", "def plot_gapminder(year):\n", " gapyear = gap[gap.year == year]\n", " for (name, group),color in zip(gapyear.groupby('continent'),colors):\n", " plt.scatter(x=group['lifeExp'],y=group['gdpPercap'],label=name, c=color,s=(group['pop']/pop_max)*400)\n", " plt.yscale('log')\n", " plt.title('Life Expectancy vs GDP')\n", " plt.xlabel('Life Expectancy')\n", " plt.ylabel('GDP Per Cap')\n", " plt.xlim(gap.gdpPercap.min(),gap.gdpPercap.max())\n", " plt.xlim(gap.lifeExp.min(),gap.lifeExp.max())\n", " plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Next\n", "\n", "[Machine Learning using Scikit Learn](04_MachineLearning.ipynb)" ] } ], "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 }
cc0-1.0
sanger-pathogens/pathogen-informatics-training
Notebooks/ROARY/prepare_data.ipynb
1
6239
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Preparing the data\n", "\n", "In this tutorial we have included three assemblies of _Streptococcus pneumoniae_. The assemblies are available for download from the ENA and the accession numbers are included below. If you have access to the the clustre at the Wellcome Sanger Institute the lane ids are also listed below. \n", "\n", "|Name |Accession |Sanger Lane ID|\n", "|--------|--------- |--------------|\n", "|sample1 |GCA_900194945.1|13681_1#18 |\n", "|sample2 |GCA_900194155.1|13682_2#34 |\n", "|sample3 |GCA_900194195.1|13682_2#39 |\n", "\n", "If you are using the cluster at the Wellcome Sanger Institute and want to create a symlink to one of the samples in your working directory, you can use the command below. However, note that this is not neccessary for the sake of this tutorial.\n", "\n", " pf assembly -t lane -i 13681_1#18 -l .\n", "\n", "## Roary input files\n", "Roary takes annotated assemblies in GFF3 format as input. The files must include the nucleotide sequence at the end of the file, and to make it easier for you to identify where genes came from, each input file should have a unique locus tag for the gene IDs.\n", "\n", "All GFF3 files created by Prokka are valid with Roary and this is therefor the recommended way of generating the input files. We are now going to look closer at how you can use Prokka to annotate your genomes.\n", "\n", "## Annotation\n", "Prokka is a tool that performs whole genome annotation. It is easy to install and use and as mentioned the GFF files that it outputs are compatible with Roary.\n", "\n", "Our three assembled *S. pneumoniae* genomes are located in a directory called \"assemblies\"." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ls assemblies" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To run Prokka on a single file using the default settings, you can use the following command:\n", "\n", " prokka sample1.fasta\n", "\n", "If you have a lot of assemblies that you want to analyse, running this for each sample will soon become tedious. Instead, we will use a for-loop to run Prokka on all the fasta files in the assemblies directory. We will also use the following options for Prokka:\n", "\n", "|Option |Description |\n", "|------ |----------- |\n", "|--locustag|Specifying a locus tag prefix |\n", "|--outdir |Specifying a directory to put the output in|\n", "|--prefix |Specifying a prefix for the output files |\n", "\n", "By specifying a unique locus tag we make it easier to identify which sample different genes came from when we look at the results from Roary. The outdir and prefix options will make it easier for us to keep track of our files. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for F in assemblies/*.fasta; do FILE=${F##*/}; PREFIX=${FILE/.fasta/}; \\\n", " prokka --locustag $PREFIX --outdir annotated_$PREFIX \\\n", " --prefix $PREFIX $F; done" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is going to take around 5 minutes to run, so be patient.\n", "\n", "Once this has finished, you should have three new directories called annotated_sample1, annotated_sample2 and annotated_sample3. Have a look to see that it worked:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ls -l" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ls -l annotated_sample1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, for sample1 we now have a number of annotation files. There is more information about the different output files, along with information about other usage options, on the [Prokka GitHub page](https://github.com/tseemann/prokka). For now, we are only interrested in the GFF files that were generated as this is what we are going to use as input for Roary.\n", "\n", "__Note:__ If you are working on the Sanger Institute cluster, Prokka is automatically run as part of the annotation pipeline. To create a symlink to the GFF file, you can use the command below (though this is not neccessary for the sake of this tutorial):\n", "\n", " pf annotation -t lane -i 13681_1#18 -l .\n", "\n", "Also for Sanger users, to run Prokka independently of the automated pipeline, you can use the script called *annotate_bacteria*. Run the below command for more information:\n", "\n", " annotate_bacteria -h\n", "\n", "## Check your understanding\n", "**Q3: Why do we need to run Prokka?** \n", "a) It will perform QC on our data \n", "b) It will annotate our data \n", "c) We don't, Roary can handle fasta files as input \n", " \n", "**Q4: Why do we use the --locustag option when we run Prokka?** \n", "a) To make it easier to keep track of the output files \n", "b) Because Roary won't work without it \n", "c) To make the Roary results easier to interpret \n", "\n", "The answers to these questions can be found [here](answers.ipynb). \n", "\n", "Now continue to the next section of the tutorial: [Performing QC on your data](qc.ipynb). \n", "You can also revisit the [previous section](pan_genome.ipynb) or return to the [index page](index.ipynb)" ] } ], "metadata": { "kernelspec": { "display_name": "Bash", "language": "bash", "name": "bash" }, "language_info": { "codemirror_mode": "shell", "file_extension": ".sh", "mimetype": "text/x-sh", "name": "bash" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
ljchang/psyc63
Notebooks/7_Introduction_to_Scraping.ipynb
1
8909
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Introduction to Web Scraping\n", "Often we are interested in getting data from a website. Modern websites are often built using a [REST](https://en.wikipedia.org/wiki/Representational_state_transfer) framework that has an Application Programming Interface ([API](https://en.wikipedia.org/wiki/Application_programming_interface)) to make [HTTP](https://www.tutorialspoint.com/http/http_requests.htm) requests to retrieve structured data in the form of [JSON](https://en.wikipedia.org/wiki/JSON) or XML.\n", "\n", "However, when there is not a clear API, we might need to perform web scraping by directly grabbing the data ourselves" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-03-06T13:26:29.942689Z", "start_time": "2017-03-06T08:26:29.777081-05:00" }, "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "try:\n", " import requests\n", "except:\n", " !pip install requests\n", "try:\n", " from bs4 import BeautifulSoup\n", "except:\n", " !pip install bs4" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Getting data using requests\n", "[Requests](http://docs.python-requests.org/en/master/) is an excellent library for performing HTTP requests. \n", "\n", "In this simple example, we will scrape data from the PBS faculty webpage." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-03-06T13:26:32.826356Z", "start_time": "2017-03-06T08:26:32.720782-05:00" }, "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "page = requests.get(\"http://pbs.dartmouth.edu/people\")\n", "print(page)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Here the response '200' indicates that the get request was successful. Now let's look at the actual text that was downloaded from the webpage." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-03-06T05:28:31.476250Z", "start_time": "2017-03-06T00:28:31.463432-05:00" }, "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "print(page.content)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Here you can see that we have downloaded all of the data from the PBS faculty page and that it is in the form of HTML. \n", "HTML is a markup language that tells a browser how to layout content. HTML consists of elements called tags. Each tag indicates a beginning and end. Here are a few examples: \n", "\n", " - `<a></a>` - indicates hyperlink\n", " - `<p></p>` - indicates paragraph\n", " - `<div></div>` - indicates a division, or area, of the page.\n", " - `<b></b>` - bolds any text inside.\n", " - `<i></i>` - italicizes any text inside.\n", " - `<h1></h1>` - indicates a header\n", " - `<table></table>` - creates a table.\n", " - `<ol></ol>` - ordered list\n", " - `<ul></ul>` - unordered list\n", " - `<li></li>` - list item\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Parsing HTML using Beautiful Soup\n", "There are many libraries that can be helpful for quickly parsing structured text such as HTML. We will be using Beautiful Soup as an example." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-03-06T13:26:39.183625Z", "start_time": "2017-03-06T08:26:39.071917-05:00" }, "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "soup = BeautifulSoup(page.content, 'html.parser')\n", "print(soup.prettify())" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Here we are going to find the unordered list tagged with the id 'faculty-container'. We are then going to look for any nested tag that use the 'h4' header tag. This should give us all of the lines with the faculty names as a list." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-03-06T13:26:41.798326Z", "start_time": "2017-03-06T08:26:41.769082-05:00" }, "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "names_html = soup.find_all('ul',id='faculty-container')[0].find_all('h4')\n", "names = [x.text for x in names_html]\n", "print(names)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "What if we wanted to get all of the faculty email addresses?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-03-06T13:26:45.729051Z", "start_time": "2017-03-06T08:26:45.696816-05:00" }, "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "email_html = soup.find_all('ul',id='faculty-container')[0].find_all('span',{'class' : 'contact'})\n", "email = [x.text for x in email_html]\n", "print(email)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Parsing string data\n", "What if we wanted to grab the name from the list of email addresses?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-03-06T06:16:47.161190Z", "start_time": "2017-03-06T01:16:47.154047-05:00" }, "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "print([x.split('@')[0] for x in email])" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "One thing we might do with this data is create a dictionary with names and emails of all of the professors in the department. This could be useful if we wanted to send a bulk email to them. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-03-06T13:37:49.514990Z", "start_time": "2017-03-06T08:37:49.509404-05:00" }, "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "email_dict = dict([(x.split('@')[0],x) for x in email])\n", "print(email_dict)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that every name also includes an initial. Let's try to just pull out the first and last name." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "ExecuteTime": { "end_time": "2017-03-06T13:37:51.752857Z", "start_time": "2017-03-06T08:37:51.747826-05:00" }, "collapsed": false }, "outputs": [], "source": [ "for x in email_dict.keys():\n", " old = x.split('.')\n", " email_dict[\" \".join([i for i in old if len(i) > 2])] = email_dict[x]\n", " del email_dict[x]\n", "\n", "print(email_dict)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Interacting with web page using Selenium\n", "Sometimes we need to directly interact with aspects of the webpage. Maybe there is a form that needs to be submitted or a javascript button that needs to be pressed. [Selenium](http://selenium-python.readthedocs.io/) is a useful tool for these types of tasks." ] }, { "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
rdhyee/webtech-learning
notebooks/simple_events.ipynb
1
5301
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from nbfiddle import Fiddle" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%html\n", "\n", "<div id=\"c1\">\n", " <select>\n", " <option value=\"volvo\">Volvo</option>\n", " <option value=\"saab\">Saab</option>\n", " <option value=\"mercedes\">Mercedes</option>\n", " <option value=\"audi\">Audi</option>\n", " </select>\n", "</div>\n", "\n", "<script type=\"text/javascript\">\n", "\n", "\n", " $(document).ready(function()\n", " {\n", " var element = $('#c1');\n", " var sel_elem = element.find('select');\n", " \n", " sel_elem.change(function(ev)\n", " {\n", " var sel_option = sel_elem.find(\"option:selected\").val();\n", " \n", " console.log('Selection changed: ' + sel_option);\n", " });\n", " });\n", "\n", "\n", "</script>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%html\n", "\n", "<!-- http://my.safaribooksonline.com/book/programming/javascript/9781457189821/14dot-handling-events/propagation_html -->\n", "<p class=\"e1\">A paragraph with a <button>button</button>.</p>\n", "\n", "<script>\n", " var para = document.querySelector(\"p.e1\");\n", " var button = document.querySelector(\"p.e1 button\");\n", " \n", " para.addEventListener(\"mousedown\", function() {\n", " console.log(\"Handler for paragraph.\");\n", " });\n", " button.addEventListener(\"mousedown\", function(event) {\n", " console.log(\"Handler for button.\");\n", " if (event.which == 3)\n", " event.stopPropagation();\n", " });\n", "</script>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Fiddle(\n", "\n", " html = \"\"\"\n", "<button>A</button>\n", "<button>B</button>\n", "<button>C</button>\n", "\"\"\", \n", " \n", " js = \"\"\"\n", "document.body.addEventListener(\"click\", function(event) {\n", "if (event.target.nodeName == \"BUTTON\")\n", " console.log(\"Clicked\", event.target.textContent);\n", "});\n", "\"\"\"\n", "\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# turning off defaultEvent\n", "\n", "Fiddle(\n", "\n", " html = \"\"\"\n", "<p>Links:</p>\n", "<ul>\n", "<li><a href=\"https://www.wikidata.org/wiki/Wikidata:Main_Page\">Can't follow this link</a></li>\n", "<li><a href=\"https://www.wikidata.org/wiki/Wikidata:Main_Page\">Can't follow this link (2)</a></li>\n", "</ul>\n", "\"\"\",\n", "\n", " js = \"\"\"\n", " element.find('a').each(function(){\n", " this.addEventListener('click', function (event){\n", " console.log(event);\n", " event.preventDefault();\n", " })\n", " })\n", " \n", " \"\"\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "# http://my.safaribooksonline.com/book/programming/javascript/9781457189821/iidot-browser/ch13_html#X2ludGVybmFsX0h0bWxWaWV3P3htbGlkPTk3ODE0NTcxODk4MjElMkZtb3VzZV9jbGlja3NfaHRtbCZxdWVyeT0=\n", "\n", "Fiddle(\n", " \n", " css = \"\"\"\n", ".dot {\n", " height: 8px; width: 8px;\n", " border-radius: 4px; /* rounds corners */\n", " background: blue;\n", " position: absolute;\n", "} \n", " \"\"\",\n", " div_css = \"\"\"\n", " \n", "height: 200px;\n", "background: beige; \n", " \n", " \"\"\",\n", "\n", " js = \"\"\"\n", "addEventListener(\"click\", function(event) {\n", " var dot = document.createElement(\"div\");\n", " dot.className = \"dot\";\n", " dot.style.left = (event.pageX - 4) + \"px\";\n", " dot.style.top = (event.pageY - 4) + \"px\";\n", " document.body.appendChild(dot); \n", "})\n", " \"\"\" \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": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
Leguark/pygeomod
notebooks_GeoPyMC/PyMC for Geology Tutorial/PyMC geomod-3.ipynb
1
692165
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## PyMC geomod 3\n", "\n", "\n", "**Importing**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from IPython.core.display import Image" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Couldn't import dot_parser, loading of dot files will not be possible.\n" ] } ], "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": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<module 'geomodeller_xml_obj' from 'C:\\Users\\Miguel\\workspace\\pygeomod\\pygeomod\\geomodeller_xml_obj.pyc'>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Coping our Model in a new folder" ] }, { "cell_type": "code", "execution_count": 4, "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/Simple_Graben_3', 'Temp_Graben/')\n", "except:\n", " print \"The folder is already created\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Complex case: Graben\n", "#### Loading pre-made Geomodeller model " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " You have to be very careful with the path, and all the bars to the RIGHT" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Temp_graben/Simple_Graben_3.xml\n" ] } ], "source": [ "graben = 'Temp_graben/Simple_Graben_3.xml'#C:\\Users\\Miguel\\workspace\\Thesis\\Thesis\\Temp3\n", "print graben" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#%%timeit\n", "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(graben)\n", "\n", "nx = 400\n", "ny = 2\n", "nz = 400\n", "G1.define_regular_grid(nx,ny,nz)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#%%timeit\n", "G1.update_from_geomodeller_project(graben)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "Tha axis here represent the number of cells not the real values of geomodeller" ] }, { "cell_type": "code", "execution_count": 8, "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": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAWIAAAFhCAYAAABKyKDUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XuYXXV97/H3ZybclEtEbIAQhYOgQGuCtYhX0EEJHgv0\n", "sSrxWkT6HClKa08l0Kd6rE8VOOYUj1T7HEQavAEi9VIbIQx4eeQmlglqEiBqlBATINwCoiSZ7/lj\n", "rZ3sTCbJzJ49+7d+8/u8nmeerPVbl/3ZZPHNmu9eey1FBGZmlk5f6gBmZqVzITYzS8yF2MwsMRdi\n", "M7PEXIjNzBJzITYzS2zKFWJJcyUtl3SvpHNT5zEz2xlNpeuIJfUDdwMnAPcDPwLmRcSypMHMzHZg\n", "qp0RHwOsiIiVEbEBuBI4JXEmM7MdmmqFeCZwX9v8qnrMzKyxplohnjp9FjMrxrTUAbrsfmBW2/ws\n", "qrPizRYsWBBz5szpaahuGRoawtl7K9fckHd2gIGBAaXO0CtTrRDfARwm6WBgNfBWYF77CkuWLOG8\n", "L/6s98m6YNNv7qT/AGfvpVxzQ97ZF33y7akj9NSUak1ExEbgbOA6YClw1cgrJtasWZMiWlfE0+tT\n", "R+hYrtlzzQ15Zy/NVDsjJiIWAYtS5zAzG6spdUY8FieeeGLqCB3r2/ew1BE6lmv2XHND3tlLU1wh\n", "zvnDi769DkgdoWO5Zs81N+SdvTTFFeKhoaHUETo2vP43qSN0LNfsueaGvLOXprhCbGbWNMUVYrcm\n", "0sg1e665Ie/spSmuEJuZNU1xhdg94jRyzZ5rbsg7e2mKK8RmZk1TXCF2jziNXLPnmhvyzl6a4gqx\n", 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-300, 0.5)\n", "\n", "#Thickness of the layers\n", "thickness_layer1 = pm.Normal(\"thickness_layer1\", -150, 0.005) # a lot of uncertainty so the constrains are necessary\n", "thickness_layer2 = pm.Normal(\"thickness_layer2\", -150, 0.005)\n", "\n", "# Offset\n", "alpha_offset = pm.Normal(\"alpha_offset\",-550,0.5)\n", "\n", "\n", "@pm.deterministic\n", "def alpha(alpha_l = alpha_l, alpha_r = alpha_r, alpha_offset = alpha_offset):\n", " return [alpha_offset, alpha_l, alpha_r ]\n", " \n", "@pm.deterministic\n", "def beta(alpha = alpha, thickness_layer1 = thickness_layer1):\n", " return alpha + thickness_layer1\n", "\n", "@pm.deterministic\n", "def gamma(beta = beta, thickness_layer2 = thickness_layer2):\n", " return beta + thickness_layer2\n", "\n", "\n", "@pm.deterministic\n", "def section(Form3 = alpha, Form2 = beta , Form1 = gamma):\n", " # Create the array we will use to modify the xml. We have to check the order of the formations\n", " #print alpha, alpha.value\n", " print Form3\n", " samples = zip(Form3,Form2, Form1)\n", " samples = np.reshape(samples, -1 )\n", " # print samples\n", " print samples\n", " # Load the xml to be modify\n", " org_xml = 'Temp_graben\\Simple_Graben_3.xml'\n", " \n", " #Create the instance to modify the xml\n", " # Loading stuff\n", " reload(gxml)\n", " gmod_obj = gxml.GeomodellerClass()\n", " gmod_obj.load_geomodeller_file(org_xml)\n", " \n", " # Create a dictionary so we can acces the section through the name\n", " section_dict = gmod_obj.create_sections_dict()\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'])\n", " \n", " # Check the position of points you want to change\n", " points_changed = gmod_obj.get_point_coordinates(contact_points)\n", " # print \"Points coordinates\", points_changed\n", " # print len(contact_points[:-4])\n", " \n", " #Perform the position Change\n", " for i, point in enumerate(contact_points[:-4]):\n", " gmod_obj.change_formation_point_pos(point, y_coord = [samples[i],samples[i]])#, print_points = True)\n", " \n", " # Check the new position of points\n", " #points_changed = gmod_obj.get_point_coordinates(contact_points)\n", " #print \"Points coordinates\", points_changed\n", " \n", " # Write the new xml\n", " gmod_obj.write_xml(\"Temp_graben/new.xml\")\n", " \n", " # Read the new xml\n", " new_xml = 'Temp_graben/new.xml'\n", " G1 = geogrid.GeoGrid()\n", " \n", " # Getting dimensions and definning grid\n", " \n", " G1.get_dimensions_from_geomodeller_xml_project(new_xml)\n", " \n", " # Resolution!\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(new_xml)\n", " # G1.plot_section('y',cell_pos=1,colorbar = True, cmap='RdBu', figsize=(6,6),interpolation= 'nearest' ,ve = 1, geomod_coord= True, contour = True)\n", " #print \"I am here\"\n", " return G1\n", "\n", "print alpha, type(alpha)\n", "\n", "\n", "\n", "#MODEL!!\n", "model = pm.Model([alpha_l,alpha_r,alpha_offset,thickness_layer1, thickness_layer2,alpha,beta,gamma,section])" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(-250.0)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alpha_l.value" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "alpha [array(-551.0386682225075), array(-351.59078440209237), array(-299.7497891768681)]\n", " [--- 10% ] 2 of 20 complete in 3.5 secalpha [array(-551.79219753635), array(-348.8644479736964), array(-299.6013621420937)]\n", " [----- 15% ] 3 of 20 complete in 6.8 secalpha [array(-551.8412524764105), array(-349.1357656484327), array(-300.1205168346977)]\n", " [------- 20% ] 4 of 20 complete in 10.1 secalpha [array(-549.8661730939244), array(-351.54612983814826), array(-299.34800882559847)]\n", " [--------- 25% ] 5 of 20 complete in 13.3 secalpha [array(-550.7952568391308), array(-351.6191676981028), array(-298.32746775448794)]\n", " [----------- 30% ] 6 of 20 complete in 16.6 secalpha [array(-549.4954961764308), array(-350.0644980532205), array(-300.72358619511044)]\n", " [------------- 35% ] 7 of 20 complete in 19.8 secalpha [array(-551.137397906036), array(-349.5299350311435), array(-299.64260203010883)]\n", " [--------------- 40% ] 8 of 20 complete in 23.0 secalpha [array(-552.172083296413), array(-350.11577571427875), array(-300.4449968713463)]\n", " [-----------------45% ] 9 of 20 complete in 26.3 secalpha [array(-550.1078383127943), array(-346.4249008161172), array(-299.134798584948)]\n", " [-----------------50% ] 10 of 20 complete in 29.5 secalpha [array(-549.6480430850306), array(-350.46717724892335), array(-300.38257878793263)]\n", " [-----------------55% ] 11 of 20 complete in 32.9 secalpha [array(-548.7975543946536), array(-349.8130801121436), array(-301.0106140156918)]\n", " [-----------------60%-- ] 12 of 20 complete in 36.1 secalpha [array(-549.3852994268623), array(-347.0487895825302), array(-298.84869578045067)]\n", " [-----------------65%---- ] 13 of 20 complete in 39.4 secalpha [array(-549.2654409240544), array(-351.4965736137939), array(-297.23103251996093)]\n", " [-----------------70%------ ] 14 of 20 complete in 42.7 secalpha [array(-550.1891307316378), array(-349.8287316322119), array(-299.470545355909)]\n", " [-----------------75%-------- ] 15 of 20 complete in 45.9 secalpha [array(-549.6698716251364), array(-350.57200278379406), array(-299.7986209630088)]\n", " [-----------------80%---------- ] 16 of 20 complete in 49.2 secalpha [array(-550.1651070225315), array(-352.5789588434093), array(-298.80338145676245)]\n", " [-----------------85%------------ ] 17 of 20 complete in 52.4 secalpha [array(-547.9266266010097), array(-350.38947036203535), array(-301.3112821991627)]\n", " [-----------------90%-------------- ] 18 of 20 complete in 55.7 secalpha [array(-549.3017267409268), array(-350.47045573681726), array(-302.4260177158403)]\n", " [-----------------95%---------------- ] 19 of 20 complete in 59.1 secalpha [array(-550.1857777300322), array(-350.62164329394835), array(-300.8707128786505)]\n", " [-----------------100%-----------------] 20 of 20 complete in 62.5 secalpha [array(-551.9341312993527), array(-348.8052052121724), array(-300.90854043449525)]\n", " [-----------------105%------------------] 21 of 20 complete in 66.0 sec" ] } ], "source": [ "M = pm.MCMC(model)\n", "M.sample(iter=20)" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = gr.dag(M)\n", "a.write_png(\"Geomodeller_no_const.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Extracting Posterior Traces to Arrays **" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "n_samples = 9\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", "section_samples, section_samples_all = M.trace('section')[-n_samples:], M.trace('section')[:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Plotting the results **" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": [ 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"ax[0].hist(beta_samples_all, histtype='stepfilled', bins=30, alpha=1,\n", " label=\"Middle layer\", normed=True)#, color = \"g\")\n", "\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 i in section_samples:\n", " i.plot_section('y',cell_pos=1,colorbar = True, ax = ax[1], alpha = 0.3, figsize=(6,6),interpolation= 'nearest' ,ve = 1, geomod_coord= True, contour = True)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAkoAAAJACAYAAAByuEn/AAAABmJLR0QA/wD/AP+gvaeTAAAgAElE\n", "QVR4nOzdeVwT5/Y/8E8g7KvKJirgDoooiiuCilrX2OIObdVqtWrtrV2/rcWu1ttr6XJrtRZra9UK\n", 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"\n", "for i, g in enumerate(section_samples):\n", " g.plot_section('y',cell_pos=1,colorbar = True, ax = axs[i- 3*(i/3),i/3], alpha = 1, figsize=(6,6),interpolation= 'nearest' ,ve = 1, geomod_coord= True, contour = True)\n", " " ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Plotting gamma_0\n", "Plotting gamma_1\n", "Plotting gamma_2\n", "Plotting thickness_layer1\n", "Plotting thickness_layer2\n", "Plotting alpha_offset\n", "Plotting alpha_r\n", "Plotting alpha_l\n", "Plotting beta_0\n", "Plotting beta_1\n", "Plotting beta_2\n" ] }, { "data": { "image/png": [ "iVBORw0KGgoAAAANSUhEUgAAAk8AAAFwCAYAAAChA5+LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXmYVMX1v98DiIACouICIqOAuw4o7RaXcQaBaOISl4hf\n", "RUxijPlpXMigJiqjRo004JoYY6KIJm64L4gy7aC4DsKAG8oqgoIalhFBQOb8/qhq5tJ0T3fP0rdm\n", 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"<matplotlib.figure.Figure at 0x3ee1c588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(M)" ] } ], "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
bicepjai/Deep-Survey-Text-Classification
deep_models/paper_07_mgnccnn/models.ipynb
1
69950
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:18:56.153391Z", "start_time": "2017-10-27T07:18:55.027650Z" }, "collapsed": true }, "outputs": [], "source": [ "import sys\n", "import os\n", "\n", "import re\n", "import collections\n", "import itertools\n", "import bcolz\n", "import pickle\n", "sys.path.append('../../lib')\n", "sys.path.append('../')\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import gc\n", "import random\n", "import smart_open\n", "import h5py\n", "import csv\n", "import json\n", "import functools\n", "import time\n", "import string\n", "\n", "import datetime as dt\n", "from tqdm import tqdm_notebook as tqdm\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "import global_utils\n", "\n", "random_state_number = 967898" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:18:56.595980Z", "start_time": "2017-10-27T07:18:56.154879Z" } }, "outputs": [ { "data": { "text/plain": [ "['/gpu:0', '/gpu:1']" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import tensorflow as tf\n", "from tensorflow.python.client import device_lib\n", "def get_available_gpus():\n", " local_device_protos = device_lib.list_local_devices()\n", " return [x.name for x in local_device_protos if x.device_type == 'GPU']\n", "\n", "config = tf.ConfigProto()\n", "config.gpu_options.allow_growth=True\n", "sess = tf.Session(config=config)\n", "get_available_gpus()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:18:56.682112Z", "start_time": "2017-10-27T07:18:56.597309Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using matplotlib backend: TkAgg\n", "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/bicepjai/Programs/anaconda3/envs/dsotc-c3/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['random']\n", "`%matplotlib` prevents importing * from pylab and numpy\n", " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" ] } ], "source": [ "%pylab\n", "%matplotlib inline\n", "%load_ext line_profiler\n", "%load_ext memory_profiler\n", "%load_ext autoreload" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:18:56.686432Z", "start_time": "2017-10-27T07:18:56.683461Z" }, "collapsed": true }, "outputs": [], "source": [ "pd.options.mode.chained_assignment = None\n", "pd.options.display.max_columns = 999\n", "color = sns.color_palette()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Data" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:19:13.775631Z", "start_time": "2017-10-27T07:18:56.687459Z" }, "collapsed": true }, "outputs": [], "source": [ "store = pd.HDFStore('../../data_prep/processed/stage1/data_frames.h5')\n", "train_df = store['train_df']\n", "test_df = store['test_df']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:19:14.119942Z", "start_time": "2017-10-27T07:19:13.776797Z" } }, "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>ID</th>\n", " <th>Gene</th>\n", " <th>Variation</th>\n", " <th>Class</th>\n", " <th>Sentences</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>[fam58a]</td>\n", " <td>[truncating, mutations]</td>\n", " <td>1</td>\n", " <td>[[cyclin-dependent, kinases, , cdks, , regulat...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>[cbl]</td>\n", " <td>[w802*]</td>\n", " <td>2</td>\n", " <td>[[abstract, background, non-small, cell, lung,...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>[cbl]</td>\n", " <td>[q249e]</td>\n", " <td>2</td>\n", " <td>[[abstract, background, non-small, cell, lung,...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>[cbl]</td>\n", " <td>[n454d]</td>\n", " <td>3</td>\n", " <td>[[recent, evidence, has, demonstrated, that, a...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>[cbl]</td>\n", " <td>[l399v]</td>\n", " <td>4</td>\n", " <td>[[oncogenic, mutations, in, the, monomeric, ca...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ID Gene Variation Class \\\n", "0 0 [fam58a] [truncating, mutations] 1 \n", "1 1 [cbl] [w802*] 2 \n", "2 2 [cbl] [q249e] 2 \n", "3 3 [cbl] [n454d] 3 \n", "4 4 [cbl] [l399v] 4 \n", "\n", " Sentences \n", "0 [[cyclin-dependent, kinases, , cdks, , regulat... \n", "1 [[abstract, background, non-small, cell, lung,... \n", "2 [[abstract, background, non-small, cell, lung,... \n", "3 [[recent, evidence, has, demonstrated, that, a... \n", "4 [[oncogenic, mutations, in, the, monomeric, ca... " ] }, "metadata": {}, "output_type": "display_data" }, { "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>ID</th>\n", " <th>Gene</th>\n", " <th>Variation</th>\n", " <th>Sentences</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>0</td>\n", " <td>[acsl4]</td>\n", " <td>[r570s]</td>\n", " <td>[[2, this, mutation, resulted, in, a, myelopro...</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1</td>\n", " <td>[naglu]</td>\n", " <td>[p521l]</td>\n", " <td>[[abstract, the, large, tumor, suppressor, 1, ...</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>2</td>\n", " <td>[pah]</td>\n", " <td>[l333f]</td>\n", " <td>[[vascular, endothelial, growth, factor, recep...</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>3</td>\n", " <td>[ing1]</td>\n", " <td>[a148d]</td>\n", " <td>[[inflammatory, myofibroblastic, tumor, , imt,...</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>4</td>\n", " <td>[tmem216]</td>\n", " <td>[g77a]</td>\n", " <td>[[abstract, retinoblastoma, is, a, pediatric, ...</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " ID Gene Variation Sentences\n", "0 0 [acsl4] [r570s] [[2, this, mutation, resulted, in, a, myelopro...\n", "1 1 [naglu] [p521l] [[abstract, the, large, tumor, suppressor, 1, ...\n", "2 2 [pah] [l333f] [[vascular, endothelial, growth, factor, recep...\n", "3 3 [ing1] [a148d] [[inflammatory, myofibroblastic, tumor, , imt,...\n", "4 4 [tmem216] [g77a] [[abstract, retinoblastoma, is, a, pediatric, ..." ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display(train_df.head())\n", "display(test_df.head())" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:19:14.241312Z", "start_time": "2017-10-27T07:19:14.121426Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "352220 352220\n" ] } ], "source": [ "corpus_vocab_list, corpus_vocab_wordidx = None, None\n", "with open('../../data_prep/processed/stage1/vocab_words_wordidx.pkl', 'rb') as f:\n", " (corpus_vocab_list, corpus_wordidx) = pickle.load(f)\n", "print(len(corpus_vocab_list), len(corpus_wordidx))" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-08-14T08:20:17.449244Z", "start_time": "2017-08-14T08:20:15.593136Z" }, "collapsed": true }, "source": [ "# Data Prep" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To control the vocabulary pass in updated corpus_wordidx" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:19:14.282120Z", "start_time": "2017-10-27T07:19:14.242713Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 5)\n", "(333, 5)\n" ] } ], "source": [ "from sklearn.model_selection import train_test_split\n", "x_train_df, x_val_df = train_test_split(train_df,\n", " test_size=0.10, random_state=random_state_number,\n", " stratify=train_df.Class)\n", "\n", "print(x_train_df.shape)\n", "print(x_val_df.shape)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:19:21.512895Z", "start_time": "2017-10-27T07:19:14.283392Z" } }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "from tensorflow.contrib.keras.python.keras.utils import np_utils\n", "from keras.preprocessing.sequence import pad_sequences\n", "from keras.utils.np_utils import to_categorical" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:19:21.516230Z", "start_time": "2017-10-27T07:19:21.514135Z" }, "collapsed": true }, "outputs": [], "source": [ "vocab_size=len(corpus_vocab_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## T:sent_words" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### generate data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:19:27.761473Z", "start_time": "2017-10-27T07:19:27.755637Z" }, "collapsed": true }, "outputs": [], "source": [ "custom_unit_dict = {\n", " \"gene_unit\" : \"words\",\n", " \"variation_unit\" : \"words\",\n", " # text transformed to sentences attribute\n", " \"doc_unit\" : \"words\",\n", " \"doc_form\" : \"sentences\",\n", " \"divide_document\": \"multiple_unit\"\n", " }" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:20:33.023633Z", "start_time": "2017-10-27T07:19:28.002516Z" }, "collapsed": true }, "outputs": [], "source": [ "%autoreload\n", "import global_utils\n", "gen_data = global_utils.GenerateDataset(x_train_df, corpus_wordidx)\n", "x_train_21_T, x_train_21_G, x_train_21_V, x_train_21_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:20:34.892504Z", "start_time": "2017-10-27T07:20:33.024778Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train data\n", "(1086419,) [352216, 252037, 202038, 70974, 86431, 164788, 109857, 338562, 123191, 209585, 221967, 49123, 331220, 140212, 209585, 229015, 140770, 182848, 111721, 8208, 0, 352217]\n", "(1086419, 3) [352216, 164788, 352217]\n", "(1086419,) [352216, 86196, 352217]\n", "(1086419,) 4\n" ] } ], "source": [ "print(\"Train data\")\n", "print(np.array(x_train_21_T).shape, x_train_21_T[0])\n", "print(np.array(x_train_21_G).shape, x_train_21_G[0])\n", "print(np.array(x_train_21_V).shape, x_train_21_V[0])\n", "print(np.array(x_train_21_C).shape, x_train_21_C[0])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:20:41.535812Z", "start_time": "2017-10-27T07:20:34.893653Z" }, "collapsed": true }, "outputs": [], "source": [ "gen_data = global_utils.GenerateDataset(x_val_df, corpus_wordidx)\n", "x_val_21_T, x_val_21_G, x_val_21_V, x_val_21_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:20:41.761602Z", "start_time": "2017-10-27T07:20:41.536933Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Val data\n", "text (128341,)\n", "gene (128341, 3) [352216, 217983, 352217]\n", "variation (128341,) [352216, 41934, 352217]\n", "classes (128341,) 4\n" ] } ], "source": [ "print(\"Val data\")\n", "print(\"text\",np.array(x_val_21_T).shape)\n", "print(\"gene\",np.array(x_val_21_G).shape, x_val_21_G[0])\n", "print(\"variation\",np.array(x_val_21_V).shape, x_val_21_V[0])\n", "print(\"classes\",np.array(x_val_21_C).shape, x_val_21_C[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### format data" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:20:41.765073Z", "start_time": "2017-10-27T07:20:41.762718Z" }, "collapsed": true }, "outputs": [], "source": [ "word_unknown_tag_idx = corpus_wordidx[\"<UNK>\"]\n", "char_unknown_tag_idx = global_utils.char_unknown_tag_idx" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:20:41.783330Z", "start_time": "2017-10-27T07:20:41.766429Z" }, "collapsed": true }, "outputs": [], "source": [ "MAX_SENT_LEN = 60" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:20:47.951791Z", "start_time": "2017-10-27T07:20:41.784595Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1086419, 60) (128341, 60)\n" ] } ], "source": [ "x_train_21_T = pad_sequences(x_train_21_T, maxlen=MAX_SENT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "x_val_21_T = pad_sequences(x_val_21_T, maxlen=MAX_SENT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "print(x_train_21_T.shape, x_val_21_T.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "keras np_utils.to_categorical expects zero index categorical variables\n", "\n", "https://github.com/fchollet/keras/issues/570" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:20:48.049420Z", "start_time": "2017-10-27T07:20:47.953027Z" }, "collapsed": true }, "outputs": [], "source": [ "x_train_21_C = np.array(x_train_21_C) - 1\n", "x_val_21_C = np.array(x_val_21_C) - 1" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:20:48.076634Z", "start_time": "2017-10-27T07:20:48.050685Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1086419, 9) (128341, 9)\n" ] } ], "source": [ "x_train_21_C = np_utils.to_categorical(np.array(x_train_21_C), 9)\n", "x_val_21_C = np_utils.to_categorical(np.array(x_val_21_C), 9)\n", "print(x_train_21_C.shape, x_val_21_C.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## T:text_words" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### generate data" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:46:54.083902Z", "start_time": "2017-10-27T02:46:54.078362Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "custom_unit_dict = {\n", " \"gene_unit\" : \"words\",\n", " \"variation_unit\" : \"words\",\n", " # text transformed to sentences attribute\n", " \"doc_unit\" : \"words\",\n", " \"doc_form\" : \"text\",\n", " \"divide_document\": \"single_unit\"\n", " }" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:47:05.636131Z", "start_time": "2017-10-27T02:46:55.087119Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "%autoreload\n", "import global_utils\n", "gen_data = global_utils.GenerateDataset(x_train_df, corpus_wordidx)\n", "x_train_22_T, x_train_22_G, x_train_22_V, x_train_22_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:47:06.673362Z", "start_time": "2017-10-27T02:47:05.637277Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train data\n", "text (2988,)\n", "gene (2988, 3) [352216, 164788, 352217]\n", "variation (2988,) [352216, 86196, 352217]\n", "classes (2988,) 4\n" ] } ], "source": [ "print(\"Train data\")\n", "print(\"text\",np.array(x_train_22_T).shape)\n", "print(\"gene\",np.array(x_train_22_G).shape, x_train_22_G[0])\n", "print(\"variation\",np.array(x_train_22_V).shape, x_train_22_V[0])\n", "print(\"classes\",np.array(x_train_22_C).shape, x_train_22_C[0])" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:47:07.863793Z", "start_time": "2017-10-27T02:47:06.675212Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "gen_data = global_utils.GenerateDataset(x_val_df, corpus_wordidx)\n", "x_val_22_T, x_val_22_G, x_val_22_V, x_val_22_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:47:07.991209Z", "start_time": "2017-10-27T02:47:07.865046Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Val data\n", "text (333,)\n", "gene (333, 3) [352216, 217983, 352217]\n", "variation (333,) [352216, 41934, 352217]\n", "classes (333,) 4\n" ] } ], "source": [ "print(\"Val data\")\n", "print(\"text\",np.array(x_val_22_T).shape)\n", "print(\"gene\",np.array(x_val_22_G).shape, x_val_22_G[0])\n", "print(\"variation\",np.array(x_val_22_V).shape, x_val_22_V[0])\n", "print(\"classes\",np.array(x_val_22_C).shape, x_val_22_C[0])" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### format data" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:47:08.015565Z", "start_time": "2017-10-27T02:47:07.992509Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "word_unknown_tag_idx = corpus_wordidx[\"<UNK>\"]\n", "char_unknown_tag_idx = global_utils.char_unknown_tag_idx" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:47:08.036767Z", "start_time": "2017-10-27T02:47:08.016770Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "MAX_TEXT_LEN = 5000" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:47:09.113995Z", "start_time": "2017-10-27T02:47:08.037908Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 5000) (333, 5000)\n" ] } ], "source": [ "x_train_22_T = pad_sequences(x_train_22_T, maxlen=MAX_TEXT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "x_val_22_T = pad_sequences(x_val_22_T, maxlen=MAX_TEXT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "print(x_train_22_T.shape, x_val_22_T.shape)" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:47:09.151860Z", "start_time": "2017-10-27T02:47:09.115126Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 1) (2988, 4)\n", "(333, 1) (333, 4)\n" ] } ], "source": [ "MAX_GENE_LEN = 1\n", "MAX_VAR_LEN = 4\n", "x_train_22_G = pad_sequences(x_train_22_G, maxlen=MAX_GENE_LEN, value=word_unknown_tag_idx)\n", "x_train_22_V = pad_sequences(x_train_22_V, maxlen=MAX_VAR_LEN, value=word_unknown_tag_idx)\n", "\n", "x_val_22_G = pad_sequences(x_val_22_G, maxlen=MAX_GENE_LEN, value=word_unknown_tag_idx)\n", "x_val_22_V = pad_sequences(x_val_22_V, maxlen=MAX_VAR_LEN, value=word_unknown_tag_idx)\n", "\n", "print(x_train_22_G.shape, x_train_22_V.shape)\n", "print(x_val_22_G.shape, x_val_22_V.shape)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "keras np_utils.to_categorical expects zero index categorical variables\n", "\n", "https://github.com/fchollet/keras/issues/570" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:47:09.155774Z", "start_time": "2017-10-27T02:47:09.152976Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "x_train_22_C = np.array(x_train_22_C) - 1\n", "x_val_22_C = np.array(x_val_22_C) - 1" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:47:09.188518Z", "start_time": "2017-10-27T02:47:09.157136Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 9) (333, 9)\n" ] } ], "source": [ "x_train_22_C = np_utils.to_categorical(np.array(x_train_22_C), 9)\n", "x_val_22_C = np_utils.to_categorical(np.array(x_val_22_C), 9)\n", "print(x_train_22_C.shape, x_val_22_C.shape)" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### test Data setup" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-09-26T03:43:32.887420Z", "start_time": "2017-09-26T03:43:29.372697Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "gen_data = global_utils.GenerateDataset(test_df, corpus_wordidx)\n", "x_test_22_T, x_test_22_G, x_test_22_V, _ = gen_data.generate_data(custom_unit_dict, \n", " has_class=False,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2017-09-26T03:43:33.178763Z", "start_time": "2017-09-26T03:43:32.888877Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test data\n", "text (986,)\n", "gene (986, 3) [364606, 188717, 364607]\n", "variation (986,) [364606, 317947, 364607]\n" ] } ], "source": [ "print(\"Test data\")\n", "print(\"text\",np.array(x_test_22_T).shape)\n", "print(\"gene\",np.array(x_test_22_G).shape, x_test_22_G[0])\n", "print(\"variation\",np.array(x_test_22_V).shape, x_test_22_V[0])" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2017-09-26T03:43:33.546461Z", "start_time": "2017-09-26T03:43:33.181689Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(986, 5000)\n" ] } ], "source": [ "x_test_22_T = pad_sequences(x_test_22_T, maxlen=MAX_TEXT_LEN, value=word_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "print(x_test_22_T.shape)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2017-09-26T03:43:33.575305Z", "start_time": "2017-09-26T03:43:33.548386Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(986, 1) (986, 4)\n" ] } ], "source": [ "MAX_GENE_LEN = 1\n", "MAX_VAR_LEN = 4\n", "x_test_22_G = pad_sequences(x_test_22_G, maxlen=MAX_GENE_LEN, value=word_unknown_tag_idx)\n", "x_test_22_V = pad_sequences(x_test_22_V, maxlen=MAX_VAR_LEN, value=word_unknown_tag_idx)\n", "\n", "print(x_test_22_G.shape, x_test_22_V.shape)" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## T:text_chars" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true, "hidden": true }, "source": [ "### generate data" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T23:07:49.588270Z", "start_time": "2017-10-26T23:07:49.582681Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "custom_unit_dict = {\n", " \"gene_unit\" : \"raw_chars\",\n", " \"variation_unit\" : \"raw_chars\",\n", " # text transformed to sentences attribute\n", " \"doc_unit\" : \"raw_chars\",\n", " \"doc_form\" : \"text\",\n", " \"divide_document\" : \"multiple_unit\"\n", " }" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T23:09:42.095904Z", "start_time": "2017-10-26T23:07:50.005896Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "%autoreload\n", "import global_utils\n", "gen_data = global_utils.GenerateDataset(x_train_df, corpus_wordidx)\n", "x_train_33_T, x_train_33_G, x_train_33_V, x_train_33_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T23:09:48.474776Z", "start_time": "2017-10-26T23:09:42.097144Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train data\n", "text (1086419,) [74, 71, 19, 7, 4, 72, 71, 19, 20, 12, 14, 17, 72, 71, 18, 20, 15, 15, 17, 4, 18, 18, 14, 17, 72, 71, 6, 4, 13, 4, 72, 71, 15, 19, 4, 13, 72, 71, 8, 18, 72, 71, 5, 17, 4, 16, 20, 4, 13, 19, 11, 24, 72, 71, 12, 20, 19, 0, 19, 4, 3, 72, 71, 8, 13, 72, 71, 3, 8, 21, 4, 17, 18, 4, 72, 71, 7, 20, 12, 0, 13, 72, 71, 2, 0, 13, 2, 4, 17, 18, 72, 71, 0, 13, 3, 72, 71, 8, 13, 72, 71, 0, 20, 19, 14, 18, 14, 12, 0, 11, 72, 71, 3, 14, 12, 8, 13, 0, 13, 19, 72, 71, 2, 0, 13, 2, 4, 17, 72, 71, 15, 17, 4, 3, 8, 18, 15, 14, 18, 8, 19, 8, 14, 13, 72, 71, 3, 8, 18, 14, 17, 3, 4, 17, 18, 72, 71, 72, 75]\n", "gene (1086419,) [74, 71, 15, 19, 4, 13, 72, 75]\n", "variation (1086419,) [74, 71, 24, 27, 32, 2, 72, 75]\n", "classes (1086419,) 4\n" ] } ], "source": [ "print(\"Train data\")\n", "print(\"text\",np.array(x_train_33_T).shape, x_train_33_T[0])\n", "print(\"gene\",np.array(x_train_33_G).shape, x_train_33_G[0])\n", "print(\"variation\",np.array(x_train_33_V).shape, x_train_33_V[0])\n", "print(\"classes\",np.array(x_train_33_C).shape, x_train_33_C[0])" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T23:10:05.745670Z", "start_time": "2017-10-26T23:09:48.475925Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "%autoreload\n", "import global_utils\n", "gen_data = global_utils.GenerateDataset(x_val_df, corpus_wordidx)\n", "x_val_33_T, x_val_33_G, x_val_33_V, x_val_33_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T23:10:06.510202Z", "start_time": "2017-10-26T23:10:05.746898Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Val data\n", "text (128341,) [74, 71, 0, 19, 72, 71, 19, 7, 8, 18, 72, 71, 19, 8, 12, 4, 72, 71, 15, 14, 8, 13, 19, 72, 71, 72, 71, 19, 7, 4, 72, 71, 4, 23, 15, 17, 4, 18, 18, 8, 14, 13, 72, 71, 14, 5, 72, 71, 22, 8, 11, 3, 36, 19, 24, 15, 4, 72, 71, 15, 27, 32, 8, 13, 10, 30, 0, 72, 71, 8, 13, 72, 71, 20, 28, 14, 18, 72, 71, 2, 4, 11, 11, 18, 72, 71, 8, 13, 3, 20, 2, 4, 3, 72, 71, 15, 14, 19, 4, 13, 19, 72, 71, 2, 4, 11, 11, 72, 71, 2, 24, 2, 11, 4, 72, 71, 0, 17, 17, 4, 18, 19, 72, 71, 0, 19, 72, 71, 1, 14, 19, 7, 72, 71, 19, 4, 12, 15, 4, 17, 0, 19, 20, 17, 4, 18, 72, 71, 72, 71, 15, 27, 32, 8, 13, 10, 30, 0, 72, 71, 8, 13, 3, 20, 2, 4, 3, 72, 71, 18, 36, 15, 7, 0, 18, 4, 72, 71, 8, 13, 7, 8, 1, 8, 19, 8, 14, 13, 72, 71, 14, 5, 72, 71, 30, 28, 33, 29, 72, 71, 72, 71, 0, 13, 3, 72, 71, 30, 35, 33, 29, 72, 71, 72, 71, 0, 19, 72, 71, 29, 33, 27, 2, 72, 71, 0, 13, 3, 72, 71, 30, 26, 27, 2, 72, 71, 72, 71, 17, 4, 18, 15, 4, 2, 19, 8, 21, 4, 11, 24, 72, 71, 72, 75]\n", "gene (128341,) [74, 71, 2, 3, 10, 13, 28, 0, 72, 75]\n", "variation (128341,) [74, 71, 0, 32, 26, 21, 72, 75]\n", "classes (128341,) 4\n" ] } ], "source": [ "print(\"Val data\")\n", "print(\"text\",np.array(x_val_33_T).shape, x_val_33_T[98])\n", "print(\"gene\",np.array(x_val_33_G).shape, x_val_33_G[0])\n", "print(\"variation\",np.array(x_val_33_V).shape, x_val_33_V[0])\n", "print(\"classes\",np.array(x_val_33_C).shape, x_val_33_C[0])\n" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "### format data" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T23:10:06.513430Z", "start_time": "2017-10-26T23:10:06.511325Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "word_unknown_tag_idx = corpus_wordidx[\"<UNK>\"]\n", "char_unknown_tag_idx = global_utils.char_unknown_tag_idx" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T23:10:06.527659Z", "start_time": "2017-10-26T23:10:06.514422Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "MAX_CHAR_IN_SENT_LEN = 150" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T23:10:18.903599Z", "start_time": "2017-10-26T23:10:06.529246Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1086419, 150) (128341, 150)\n" ] } ], "source": [ "x_train_33_T = pad_sequences(x_train_33_T, maxlen=MAX_CHAR_IN_SENT_LEN, value=char_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "x_val_33_T = pad_sequences(x_val_33_T, maxlen=MAX_CHAR_IN_SENT_LEN, value=char_unknown_tag_idx,\n", " padding=\"post\",truncating=\"post\")\n", "print(x_train_33_T.shape, x_val_33_T.shape)" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T23:10:27.876369Z", "start_time": "2017-10-26T23:10:18.905017Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1086419, 150) (1086419, 150)\n", "(128341, 150) (128341, 150)\n" ] } ], "source": [ "x_train_33_G = pad_sequences(x_train_33_G, maxlen=MAX_CHAR_IN_SENT_LEN, value=char_unknown_tag_idx)\n", "x_train_33_V = pad_sequences(x_train_33_V, maxlen=MAX_CHAR_IN_SENT_LEN, value=char_unknown_tag_idx)\n", "\n", "x_val_33_G = pad_sequences(x_val_33_G, maxlen=MAX_CHAR_IN_SENT_LEN, value=char_unknown_tag_idx)\n", "x_val_33_V = pad_sequences(x_val_33_V, maxlen=MAX_CHAR_IN_SENT_LEN, value=char_unknown_tag_idx)\n", "\n", "print(x_train_33_G.shape, x_train_33_V.shape)\n", "print(x_val_33_G.shape, x_val_33_V.shape)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "keras np_utils.to_categorical expects zero index categorical variables\n", "\n", "https://github.com/fchollet/keras/issues/570" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T23:10:27.985411Z", "start_time": "2017-10-26T23:10:27.877544Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "x_train_33_C = np.array(x_train_33_C) - 1\n", "x_val_33_C = np.array(x_val_33_C) - 1" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T23:10:28.016098Z", "start_time": "2017-10-26T23:10:27.986732Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1086419, 9) (128341, 9)\n" ] } ], "source": [ "x_train_33_C = np_utils.to_categorical(np.array(x_train_33_C), 9)\n", "x_val_33_C = np_utils.to_categorical(np.array(x_val_33_C), 9)\n", "print(x_train_33_C.shape, x_val_33_C.shape)" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## T:text_sent_words" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "### generate data" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:20:00.685040Z", "start_time": "2017-10-27T02:20:00.670683Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "custom_unit_dict = {\n", " \"gene_unit\" : \"words\",\n", " \"variation_unit\" : \"words\",\n", " # text transformed to sentences attribute\n", " \"doc_unit\" : \"word_list\",\n", " \"doc_form\" : \"text\",\n", " \"divide_document\" : \"single_unit\"\n", " }" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:20:15.952551Z", "start_time": "2017-10-27T02:20:00.686423Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "%autoreload\n", "import global_utils\n", "gen_data = global_utils.GenerateDataset(x_train_df, corpus_wordidx)\n", "x_train_34_T, x_train_34_G, x_train_34_V, x_train_34_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:20:17.054217Z", "start_time": "2017-10-27T02:20:15.953722Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train data\n", "text (2988,) [[352216, 252037, 202038, 70974, 86431, 164788, 109857, 338562, 123191, 209585, 221967, 49123, 331220, 140212, 209585, 229015, 140770, 182848, 111721, 8208, 0, 352217]]\n", "gene (2988, 3) [352216, 164788, 352217]\n", "variation (2988,) [352216, 86196, 352217]\n", "classes (2988,) 4\n" ] } ], "source": [ "print(\"Train data\")\n", "print(\"text\",np.array(x_train_34_T).shape, x_train_34_T[0][:1])\n", "print(\"gene\",np.array(x_train_34_G).shape, x_train_34_G[0])\n", "print(\"variation\",np.array(x_train_34_V).shape, x_train_34_V[0])\n", "print(\"classes\",np.array(x_train_34_C).shape, x_train_34_C[0])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:20:18.329383Z", "start_time": "2017-10-27T02:20:17.055319Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "%autoreload\n", "import global_utils\n", "gen_data = global_utils.GenerateDataset(x_val_df, corpus_wordidx)\n", "x_val_34_T, x_val_34_G, x_val_34_V, x_val_34_C = gen_data.generate_data(custom_unit_dict, \n", " has_class=True,\n", " add_start_end_tag=True)\n", "del gen_data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:20:18.467889Z", "start_time": "2017-10-27T02:20:18.330665Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Val data\n", "text (333,) [[352216, 252037, 156537, 91785, 67201, 109857, 123191, 209585, 213751, 5638, 0, 126280, 49123, 331220, 0, 352217]]\n", "gene (333, 3) [352216, 217983, 352217]\n", "variation (333,) [352216, 41934, 352217]\n", "classes (333,) 4\n" ] } ], "source": [ "print(\"Val data\")\n", "print(\"text\",np.array(x_val_34_T).shape, x_val_34_T[98][:1])\n", "print(\"gene\",np.array(x_val_34_G).shape, x_val_34_G[0])\n", "print(\"variation\",np.array(x_val_34_V).shape, x_val_34_V[0])\n", "print(\"classes\",np.array(x_val_34_C).shape, x_val_34_C[0])\n" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "### format data" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:20:18.479648Z", "start_time": "2017-10-27T02:20:18.468992Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "word_unknown_tag_idx = corpus_wordidx[\"<UNK>\"]\n", "char_unknown_tag_idx = global_utils.char_unknown_tag_idx" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:20:18.502495Z", "start_time": "2017-10-27T02:20:18.481643Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "MAX_DOC_LEN = 500 # no of sentences in a document\n", "MAX_SENT_LEN = 80 # no of words in a sentence" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:20:28.645457Z", "start_time": "2017-10-27T02:20:18.504233Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "for doc_i, doc in enumerate(x_train_34_T):\n", " x_train_34_T[doc_i] = x_train_34_T[doc_i][:MAX_DOC_LEN]\n", " # padding sentences\n", " if len(x_train_34_T[doc_i]) < MAX_DOC_LEN:\n", " for not_used_i in range(0,MAX_DOC_LEN - len(x_train_34_T[doc_i])):\n", " x_train_34_T[doc_i].append([word_unknown_tag_idx]*MAX_SENT_LEN)\n", " # padding words\n", " x_train_34_T[doc_i] = pad_sequences(x_train_34_T[doc_i], maxlen=MAX_SENT_LEN, value=word_unknown_tag_idx)\n", " \n", "for doc_i, doc in enumerate(x_val_34_T):\n", " x_val_34_T[doc_i] = x_val_34_T[doc_i][:MAX_DOC_LEN]\n", " # padding sentences\n", " if len(x_val_34_T[doc_i]) < MAX_DOC_LEN:\n", " for not_used_i in range(0,MAX_DOC_LEN - len(x_val_34_T[doc_i])):\n", " x_val_34_T[doc_i].append([word_unknown_tag_idx]*MAX_SENT_LEN)\n", " # padding words\n", " x_val_34_T[doc_i] = pad_sequences(x_val_34_T[doc_i], maxlen=MAX_SENT_LEN, value=word_unknown_tag_idx)\n", " \n", "x_train_34_T = np.array(x_train_34_T)\n", "x_val_34_T = np.array(x_val_34_T)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:20:28.650166Z", "start_time": "2017-10-27T02:20:28.646889Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(333, 500, 80) (2988, 500, 80)\n" ] } ], "source": [ "print(x_val_34_T.shape, x_train_34_T.shape)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:20:28.722888Z", "start_time": "2017-10-27T02:20:28.651790Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 80) (2988, 80)\n", "(333, 80) (333, 80)\n" ] } ], "source": [ "x_train_34_G = pad_sequences(x_train_34_G, maxlen=MAX_SENT_LEN, value=word_unknown_tag_idx)\n", "x_train_34_V = pad_sequences(x_train_34_V, maxlen=MAX_SENT_LEN, value=word_unknown_tag_idx)\n", "\n", "x_val_34_G = pad_sequences(x_val_34_G, maxlen=MAX_SENT_LEN, value=word_unknown_tag_idx)\n", "x_val_34_V = pad_sequences(x_val_34_V, maxlen=MAX_SENT_LEN, value=word_unknown_tag_idx)\n", "\n", "print(x_train_34_G.shape, x_train_34_V.shape)\n", "print(x_val_34_G.shape, x_val_34_V.shape)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "keras np_utils.to_categorical expects zero index categorical variables\n", "\n", "https://github.com/fchollet/keras/issues/570" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:20:28.728851Z", "start_time": "2017-10-27T02:20:28.724555Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "x_train_34_C = np.array(x_train_34_C) - 1\n", "x_val_34_C = np.array(x_val_34_C) - 1" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:20:28.742924Z", "start_time": "2017-10-27T02:20:28.730480Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 9) (333, 9)\n" ] } ], "source": [ "x_train_34_C = np_utils.to_categorical(np.array(x_train_34_C), 9)\n", "x_val_34_C = np_utils.to_categorical(np.array(x_val_34_C), 9)\n", "print(x_train_34_C.shape, x_val_34_C.shape)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "hidden": true }, "source": [ "Need to form 3 dimensional target data for rationale model training" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T02:20:28.795678Z", "start_time": "2017-10-27T02:20:28.744106Z" }, "hidden": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2988, 500, 9) (333, 500, 9)\n" ] } ], "source": [ "temp = (x_train_34_C.shape[0],1,x_train_34_C.shape[1])\n", "x_train_34_C_sent = np.repeat(x_train_34_C.reshape(temp[0],temp[1],temp[2]), MAX_DOC_LEN, axis=1)\n", "\n", "#sentence test targets\n", "temp = (x_val_34_C.shape[0],1,x_val_34_C.shape[1])\n", "x_val_34_C_sent = np.repeat(x_val_34_C.reshape(temp[0],temp[1],temp[2]), MAX_DOC_LEN, axis=1)\n", "\n", "print(x_train_34_C_sent.shape, x_val_34_C_sent.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Embedding layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### for words" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:20:48.081162Z", "start_time": "2017-10-27T07:20:48.078301Z" }, "collapsed": true }, "outputs": [], "source": [ "WORD_EMB_SIZE1 = 300\n", "WORD_EMB_SIZE2 = 200\n", "WORD_EMB_SIZE3 = 100" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:21:58.524326Z", "start_time": "2017-10-27T07:20:48.082931Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(352220, 300)\n", "(352220, 200)\n", "(352220, 100)\n" ] } ], "source": [ "%autoreload\n", "import global_utils\n", "ft_file_path = \"/home/bicepjai/Projects/Deep-Survey-Text-Classification/data_prep/processed/stage1/pretrained_word_vectors/ft_sg_300d_50e.vec\"\n", "trained_embeddings1 = global_utils.get_embeddings_from_ft(ft_file_path, WORD_EMB_SIZE1, corpus_vocab_list)\n", "\n", "ft_file_path = \"/home/bicepjai/Projects/Deep-Survey-Text-Classification/data_prep/processed/stage1/pretrained_word_vectors/ft_sg_200d_50e.vec\"\n", "trained_embeddings2 = global_utils.get_embeddings_from_ft(ft_file_path, WORD_EMB_SIZE2, corpus_vocab_list)\n", "\n", "ft_file_path = \"/home/bicepjai/Projects/Deep-Survey-Text-Classification/data_prep/processed/stage1/pretrained_word_vectors/ft_sg_100d_20e.vec\"\n", "trained_embeddings3 = global_utils.get_embeddings_from_ft(ft_file_path, WORD_EMB_SIZE3, corpus_vocab_list)\n", "\n", "print (trained_embeddings1.shape)\n", "print (trained_embeddings2.shape)\n", "print (trained_embeddings3.shape)" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "### for characters" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T23:10:28.019172Z", "start_time": "2017-10-26T23:10:28.017168Z" }, "collapsed": true, "hidden": true }, "outputs": [], "source": [ "CHAR_EMB_SIZE = 64" ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "ExecuteTime": { "end_time": "2017-10-26T23:10:28.051353Z", "start_time": "2017-10-26T23:10:28.020527Z" }, "hidden": true }, "outputs": [ { "data": { "text/plain": [ "(75, 64)" ] }, "execution_count": 95, "metadata": {}, "output_type": "execute_result" } ], "source": [ "char_embeddings = np.random.randn(global_utils.CHAR_ALPHABETS_LEN, CHAR_EMB_SIZE)\n", "char_embeddings.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Models" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## prep" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:21:58.563159Z", "start_time": "2017-10-27T07:21:58.525455Z" }, "collapsed": true }, "outputs": [], "source": [ "%autoreload\n", "import tensorflow.contrib.keras as keras\n", "import tensorflow as tf\n", "\n", "from keras import backend as K\n", "\n", "from keras.engine import Layer, InputSpec, InputLayer\n", "\n", "from keras.models import Model, Sequential\n", "\n", "from keras.layers import Dropout, Embedding, concatenate\n", "from keras.layers import Conv1D, MaxPool1D, Conv2D, MaxPool2D, ZeroPadding1D, GlobalMaxPool1D\n", "from keras.layers import Dense, Input, Flatten, BatchNormalization\n", "from keras.layers import Concatenate, Dot, Merge, Multiply, RepeatVector\n", "from keras.layers import Bidirectional, TimeDistributed\n", "from keras.layers import SimpleRNN, LSTM, GRU, Lambda, Permute\n", "\n", "from keras.layers.core import Reshape, Activation\n", "from keras.optimizers import Adam\n", "from keras.callbacks import ModelCheckpoint,EarlyStopping,TensorBoard\n", "from keras.constraints import maxnorm\n", "from keras.regularizers import l2\n", "\n", "from paper_2_cnn_modelling_sentences.utils import KMaxPooling, Folding" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-08-24T06:58:17.661183Z", "start_time": "2017-08-24T06:58:17.655020Z" } }, "source": [ "## model_1: paper" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-09-19T04:27:34.478402Z", "start_time": "2017-09-19T04:27:34.475759Z" } }, "source": [ "refer https://github.com/bwallace/rationale-CNN" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:21:59.560304Z", "start_time": "2017-10-27T07:21:58.564711Z" }, "collapsed": true }, "outputs": [], "source": [ "text_seq_input = Input(shape=(MAX_SENT_LEN,), dtype='int32')\n", "text_embedding1 = Embedding(vocab_size, WORD_EMB_SIZE1, input_length=MAX_SENT_LEN,\n", " weights=[trained_embeddings1], trainable=True)(text_seq_input)\n", "text_embedding2 = Embedding(vocab_size, WORD_EMB_SIZE2, input_length=MAX_SENT_LEN,\n", " weights=[trained_embeddings2], trainable=True)(text_seq_input)\n", "text_embedding3 = Embedding(vocab_size, WORD_EMB_SIZE3, input_length=MAX_SENT_LEN,\n", " weights=[trained_embeddings3], trainable=True)(text_seq_input)\n", "\n", "k_top = 4\n", "filter_sizes = [3,5]\n", "\n", "conv_pools = []\n", "for text_embedding in [text_embedding1, text_embedding2, text_embedding3]:\n", " for filter_size in filter_sizes:\n", " l_zero = ZeroPadding1D((filter_size-1,filter_size-1))(text_embedding)\n", " l_conv = Conv1D(filters=16, kernel_size=filter_size, padding='same', activation='tanh')(l_zero)\n", " l_pool = GlobalMaxPool1D()(l_conv)\n", " conv_pools.append(l_pool)\n", " \n", "l_merge = Concatenate(axis=1)(conv_pools)\n", "l_dense = Dense(128, activation='relu', kernel_regularizer=l2(0.01))(l_merge)\n", "l_out = Dense(9, activation='softmax')(l_dense)\n", "model_1 = Model(inputs=[text_seq_input], outputs=l_out)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### training" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:21:59.588948Z", "start_time": "2017-10-27T07:21:59.561399Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "____________________________________________________________________________________________________\n", "Layer (type) Output Shape Param # Connected to \n", "====================================================================================================\n", "input_1 (InputLayer) (None, 60) 0 \n", "____________________________________________________________________________________________________\n", "embedding_1 (Embedding) (None, 60, 300) 105666000 input_1[0][0] \n", "____________________________________________________________________________________________________\n", "embedding_2 (Embedding) (None, 60, 200) 70444000 input_1[0][0] \n", "____________________________________________________________________________________________________\n", "embedding_3 (Embedding) (None, 60, 100) 35222000 input_1[0][0] \n", "____________________________________________________________________________________________________\n", "zero_padding1d_1 (ZeroPadding1D) (None, 64, 300) 0 embedding_1[0][0] \n", "____________________________________________________________________________________________________\n", "zero_padding1d_2 (ZeroPadding1D) (None, 68, 300) 0 embedding_1[0][0] \n", "____________________________________________________________________________________________________\n", "zero_padding1d_3 (ZeroPadding1D) (None, 64, 200) 0 embedding_2[0][0] \n", "____________________________________________________________________________________________________\n", "zero_padding1d_4 (ZeroPadding1D) (None, 68, 200) 0 embedding_2[0][0] \n", "____________________________________________________________________________________________________\n", "zero_padding1d_5 (ZeroPadding1D) (None, 64, 100) 0 embedding_3[0][0] \n", "____________________________________________________________________________________________________\n", "zero_padding1d_6 (ZeroPadding1D) (None, 68, 100) 0 embedding_3[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_1 (Conv1D) (None, 64, 16) 14416 zero_padding1d_1[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_2 (Conv1D) (None, 68, 16) 24016 zero_padding1d_2[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_3 (Conv1D) (None, 64, 16) 9616 zero_padding1d_3[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_4 (Conv1D) (None, 68, 16) 16016 zero_padding1d_4[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_5 (Conv1D) (None, 64, 16) 4816 zero_padding1d_5[0][0] \n", "____________________________________________________________________________________________________\n", "conv1d_6 (Conv1D) (None, 68, 16) 8016 zero_padding1d_6[0][0] \n", "____________________________________________________________________________________________________\n", "global_max_pooling1d_1 (GlobalMa (None, 16) 0 conv1d_1[0][0] \n", "____________________________________________________________________________________________________\n", "global_max_pooling1d_2 (GlobalMa (None, 16) 0 conv1d_2[0][0] \n", "____________________________________________________________________________________________________\n", "global_max_pooling1d_3 (GlobalMa (None, 16) 0 conv1d_3[0][0] \n", "____________________________________________________________________________________________________\n", "global_max_pooling1d_4 (GlobalMa (None, 16) 0 conv1d_4[0][0] \n", "____________________________________________________________________________________________________\n", "global_max_pooling1d_5 (GlobalMa (None, 16) 0 conv1d_5[0][0] \n", "____________________________________________________________________________________________________\n", "global_max_pooling1d_6 (GlobalMa (None, 16) 0 conv1d_6[0][0] \n", "____________________________________________________________________________________________________\n", "concatenate_1 (Concatenate) (None, 96) 0 global_max_pooling1d_1[0][0] \n", " global_max_pooling1d_2[0][0] \n", " global_max_pooling1d_3[0][0] \n", " global_max_pooling1d_4[0][0] \n", " global_max_pooling1d_5[0][0] \n", " global_max_pooling1d_6[0][0] \n", "____________________________________________________________________________________________________\n", "dense_1 (Dense) (None, 128) 12416 concatenate_1[0][0] \n", "____________________________________________________________________________________________________\n", "dense_2 (Dense) (None, 9) 1161 dense_1[0][0] \n", "====================================================================================================\n", "Total params: 211,422,473\n", "Trainable params: 211,422,473\n", "Non-trainable params: 0\n", "____________________________________________________________________________________________________\n" ] } ], "source": [ "model_1.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['categorical_accuracy'])\n", "model_1.summary()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:21:59.913418Z", "start_time": "2017-10-27T07:21:59.590055Z" }, "collapsed": true }, "outputs": [], "source": [ "%rm -rf ./tb_graphs/*" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:21:59.917955Z", "start_time": "2017-10-27T07:21:59.914883Z" }, "collapsed": true }, "outputs": [], "source": [ "tb_callback = keras.callbacks.TensorBoard(log_dir='./tb_graphs', histogram_freq=0, write_graph=True, write_images=True)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:21:59.939998Z", "start_time": "2017-10-27T07:21:59.919237Z" }, "collapsed": true }, "outputs": [], "source": [ "checkpointer = ModelCheckpoint(filepath=\"model_1_weights.hdf5\", \n", " verbose=1,\n", " monitor=\"val_categorical_accuracy\",\n", " save_best_only=True,\n", " mode=\"max\")" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "ExecuteTime": { "end_time": "2017-10-27T07:33:03.939043Z", "start_time": "2017-10-27T07:21:59.941284Z" }, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "no checkpoints available !\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/bicepjai/Programs/anaconda3/envs/dsotc-c3/lib/python3.6/site-packages/tensorflow/python/ops/gradients_impl.py:92: UserWarning: Converting sparse IndexedSlices to a dense Tensor with 105666000 elements. This may consume a large amount of memory.\n", " \"This may consume a large amount of memory.\" % num_elements)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Train on 1086419 samples, validate on 128341 samples\n", "Epoch 1/5\n", "1085440/1086419 [============================>.] - ETA: 0s - loss: 1.2243 - categorical_accuracy: 0.5866Epoch 00000: val_categorical_accuracy improved from -inf to 0.57361, saving model to model_1_weights.hdf5\n", "1086419/1086419 [==============================] - 131s - loss: 1.2241 - categorical_accuracy: 0.5866 - val_loss: 1.2274 - val_categorical_accuracy: 0.5736\n", "Epoch 2/5\n", "1085440/1086419 [============================>.] - ETA: 0s - loss: 0.8676 - categorical_accuracy: 0.6769Epoch 00001: val_categorical_accuracy improved from 0.57361 to 0.59294, saving model to model_1_weights.hdf5\n", "1086419/1086419 [==============================] - 134s - loss: 0.8676 - categorical_accuracy: 0.6769 - val_loss: 1.2030 - val_categorical_accuracy: 0.5929\n", "Epoch 3/5\n", "1085440/1086419 [============================>.] - ETA: 0s - loss: 0.7659 - categorical_accuracy: 0.7059Epoch 00002: val_categorical_accuracy improved from 0.59294 to 0.59564, saving model to model_1_weights.hdf5\n", "1086419/1086419 [==============================] - 134s - loss: 0.7659 - categorical_accuracy: 0.7059 - val_loss: 1.2434 - val_categorical_accuracy: 0.5956\n", "Epoch 4/5\n", "1085440/1086419 [============================>.] - ETA: 0s - loss: 0.7078 - categorical_accuracy: 0.7211Epoch 00003: val_categorical_accuracy did not improve\n", "1086419/1086419 [==============================] - 127s - loss: 0.7078 - categorical_accuracy: 0.7211 - val_loss: 1.2774 - val_categorical_accuracy: 0.5933\n", "Epoch 5/5\n", "1085440/1086419 [============================>.] - ETA: 0s - loss: 0.6691 - categorical_accuracy: 0.7320Epoch 00004: val_categorical_accuracy improved from 0.59564 to 0.60195, saving model to model_1_weights.hdf5\n", "1086419/1086419 [==============================] - 134s - loss: 0.6691 - categorical_accuracy: 0.7320 - val_loss: 1.3011 - val_categorical_accuracy: 0.6020\n" ] } ], "source": [ "with tf.Session() as sess:\n", " # model = keras.models.load_model('current_model.h5')\n", " sess.run(tf.global_variables_initializer())\n", " try:\n", " model_1.load_weights(\"model_1_weights.hdf5\")\n", " except IOError as ioe:\n", " print(\"no checkpoints available !\")\n", " model_1.fit(x_train_21_T, x_train_21_C, \n", " validation_data=(x_val_21_T, x_val_21_C),\n", " epochs=5, batch_size=1024, shuffle=True,\n", " callbacks=[tb_callback,checkpointer])\n", " #model.save('current_sent_model.h5')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.6.3" }, "toc": { "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "toc_cell": false, "toc_position": { "height": "833px", "left": "0px", "right": "1192px", "top": "52px", "width": "300px" }, "toc_section_display": "block", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }
mit
basnijholt/holoviews
doc/Tutorials/Exporting.ipynb
1
25741
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Most of the other tutorials show you how to use HoloViews for interactive, exploratory visualization of your data, while the [Options](Options.ipynb) tutorial shows how to use HoloViews completely non-interactively, generating and rendering images directly to disk. In this notebook, we show how HoloViews works together with the IPython/Jupyter Notebook to establish a fully interactive yet *also* fully reproducible scientific or engineering workflow for generating reports or publications. That is, as you interactively explore your data and build visualizations in the notebook, you can automatically generate and export them as figures that will feed directly into your papers or web pages, along with records of how those figures were generated and even storing the actual data involved so that it can be re-analyzed later. \n", "\n", "## Reproducible research\n", "\n", "To understand why this capability is important, let's consider the process by which scientific results are typically generated and published without HoloViews. Scientists and engineers use a wide variety of data-analysis tools, ranging from GUI-based programs like Excel spreadsheets, mixed GUI/command-line programs like Matlab, or purely scriptable tools like matplotlib or bokeh. The process by which figures are created in any of these tools typically involves copying data from its original source, selecting it, transforming it, choosing portions of it to put into a figure, choosing the various plot options for a subfigure, combining different subfigures into a complete figure, generating a publishable figure file with the full figure, and then inserting that into a report or publication. \n", "\n", "If using GUI tools, often the final figure is the only record of that process, and even just a few weeks or months later a researcher will often be completely unable to say precisely how a given figure was generated. Moreover, this process needs to be repeated whenever new data is collected, which is an error-prone and time-consuming process. The lack of records is a serious problem for building on past work and revisiting the assumptions involved, which greatly slows progress both for individual researchers and for the field as a whole. Graphical environments for capturing and replaying a user's GUI-based workflow have been developed, but these have greatly restricted the process of exploration, because they only support a few of the many analyses required, and thus they have rarely been successful in practice. With GUI tools it is also very difficult to \"curate\" the sequence of steps involved, i.e., eliminating dead ends, speculative work, and unnecessary steps, with a goal of showing the clear path from incoming data to a final figure.\n", "\n", "In principle, using scriptable or command-line tools offers the promise of capturing the steps involved, in a form that can be curated. In practice, however, the situation is often no better than with GUI tools, because the data is typically taken through many manual steps that culminate in a published figure, and without a laboriously manually created record of what steps are involved, the provenance of a given figure remains unknown. Where reproducible workflows are created in this way, they tend to be \"after the fact\", as an explicit exercise to accompany a publication, and thus (a) they are rarely done, (b) they are very difficult to do if any of the steps were not recorded originally. \n", "\n", "An IPython/Jupyter notebook helps significantly to make the scriptable-tools approach viable, by recording both code and the resulting output, and can thus in principle act as a record for establishing the full provenance of a figure. But because typical plotting libraries require so much plotting-specific code before any plot is visible, the notebook quickly becomes unreadable. To make notebooks readable, researchers then typically move the plotting code for a specific figure to some external file, which then drifts out of sync with the notebook so that the notebook no longer acts as a record of the link between the original data and the resulting figure. \n", "\n", "HoloViews provides the final missing piece in this approach, by allowing researchers to work directly with their data interactively in a notebook, using small amounts of code that focus on the data and analyses rather than plotting code, yet showing the results directly alongside the specification for generating them. This tutorial will describe how use a Jupyter notebook with HoloViews to export your results in a way that preserves the information about how those results were generated, providing a clear chain of provenance and making reproducible research practical at last." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import holoviews as hv\n", "from holoviews.operation import contours\n", "hv.notebook_extension()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exporting specific files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "During interactive exploration in the IPython Notebook, your results are always visible within the notebook itself, but you can explicitly request that any IPython cell is also exported to an external file on disk:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%output filename=\"macaw_plot\" fig=\"png\" holomap=\"gif\"\n", "parrot = hv.RGB.load_image('../assets/macaw.png')\n", "parrot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This mechanism can be used to provide a clear link between the steps for generating the figure, and the file on disk. You can now load the exported plot back into HoloViews, if you like, though the result would be a bit confusing due to the additional set of axes applied to the new plot:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hv.RGB.load_image('macaw_plot.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``fig=\"png\"`` part of the ``%%output`` magic above specified that the file should be saved in PNG format, which is useful for posting on web pages or editing in raster-based graphics programs. It also specified that if the object contained a ``HoloMap`` (which this particular one does not), it would be saved in GIF format, which supports animation. Because of the need for animation, objects containing a ``HoloMap`` are handled specially, as animation is not supported by the common PNG or SVG formats.\n", "\n", "For a publication, you will usually want to select SVG format, using ``fig=\"svg\"``, because this vector format preserves the full resolution of all text and drawing elements. SVG files can be be used in some document preparation programs directly (e.g. [LibreOffice](http://www.libreoffice.org/)), and can easily be converted using e.g. [Inkscape](https://inkscape.org) to PDF for use with PDFLaTeX or to EMF for use with Microsoft Word. They can also be edited using Inkscape or other vector drawing programs to move graphical elements around, add arbitrary text, etc., if you need to make final tweaks before using the figures in a document. You can also embed them within other SVG figures in such a drawing program, e.g. by creating a larger figure as a template that automatically incorporates multiple SVG files you have exported separately." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exporting notebooks" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``%%output`` magic is useful when you want specific plots saved into specific files. Often, however, a notebook will contain an entire suite of results contained in multiple different cells, and manually specifying these cells and their filenames is error-prone, with a high likelihood of accidentally creating multiple files with the same name or using different names in different notebooks for the same objects.\n", "\n", "To make the exporting process easier for large numbers of outputs, as well as more predictable, HoloViews also offers a powerful automatic notebook exporting facility, creating an archive of all your results. Automatic export is very useful in the common case of having a notebook that contains a series of figures to be used in a report or publication, particularly if you are repeatedly re-running the notebook as you finalize your results, and want the full set of current outputs to be available to an external document preparation system." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To turn on automatic adding of your files to the export archive, run ``hv.archive.auto()``:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hv.archive.auto()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This object's behavior can be customized extensively; try pressing shift-[tab] twice within the parentheses for a list of options, which are described more fully below.\n", "\n", "By default, the output will go into a directory with the same name as your notebook, and the names for each object will be generated from the groups and labels used by HoloViews. Objects that contain HoloMaps are not exported by default, since those are usually rendered as animations that are not suitable for inclusion in publications, but you can change it to ``.auto(holomap='gif')`` if you want those as well. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding files to an archive" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To see how the auto-exporting works, let's define a few HoloViews objects:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "parrot[:,:,'R'].relabel(\"Red\") + parrot[:,:,'G'].relabel(\"Green\") + parrot[:,:,'B'].relabel(\"Blue\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "parrot * hv.Arrow(-0.1, 0.2, 'Polly', '>')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%opts Contours (linewidth=1.3) Image (cmap=\"gray\")\n", "cs = contours(parrot[:,:,'R'], levels=[0.10,0.80])\n", "cs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now list what has been captured, along with the names that have been generated:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hv.archive.contents()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here each object has resulted in two files, one in SVG format and one in Python \"pickle\" format (which appears as a ``zip`` file with extension ``.hvz`` in the listing). We'll ignore the pickle files for now, focusing on the SVG images.\n", "\n", "The name generation code for these files is heavily customizable, but by default it consists of a list of dimension values and objects:\n", "\n", " ``{dimension},{dimension},...{group}-{label},{group}-{label},...``. \n", "\n", "The ``{dimension}`` shows what dimension values are included anywhere in this object, if it contains any high-level ``Dimensioned`` objects like ``HoloMap``, ``NdOverlay``, and ``GridLayout``. In the last SVG image in the contents list above, which is for the ``contours`` object, there is one dimension ``Levels``, and the name shows that dimension values included in this object range from 0.1 to 0.8 (as is visible in the contours specification above.) Of course, nearly all HoloViews objects have dimensions, such as ``x`` and ``y`` in this case, but those dimensions are not used in the filenames because they are explicitly shown in the plots; only the top-level dimensions are used (those that determine which plot this is, not those that are shown in the plot itself.)\n", "\n", "The ``{group}-{label}`` information lists the names HoloViews uses for default titles and for attribute access for the various objects that make up a given displayed object. E.g. the first SVG image in the list is a ``Layout`` of the three given ``Image`` objects, and the second one is an ``Overlay`` of an ``RGB`` object and an ``Arrow`` object. This information usually helps distinguish one plot from another, because they will typically be plots of objects that have different labels. \n", "\n", "If the generated names are not unique, a numerical suffix will be added to make them unique. A maximum filename length is enforced, which can be set with ``hv.archive.max_filename=``_num_.\n", "\n", "If you prefer a fixed-width filename, you can use a hash for each name instead (or in addition), where ``:.8`` specifies how many characters to keep from the hash:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hv.archive.filename_formatter=\"{SHA:.8}\"\n", "cs" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hv.archive.contents()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that the newest files added have the shorter, fixed-width format, though the names are no longer meaningful. If the ``filename_formatter`` had been set from the start, all filenames would have been of this type, which has both practical advantages (short names, all the same length) and disadvantages (no semantic clue about the contents)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generated indexes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the files that were added to the archive for each of the cell outputs above, the archive exporter also adds an ``index.html`` file with a static copy of the notebook, with each cell labelled with the filename used to save it. This HTML file acts as a definitive index to your results, showing how they were generated and where they were exported on disk. \n", "\n", "The exporter will also add a cleared, runnable copy of the notebook ``index.ipynb`` (with output deleted), so that you can later regenerate all of the output, with changes if necessary. \n", "\n", "The exported archive will thus be a complete set of your results, along with a record of how they were generated, plus a recipe for regenerating them -- i.e., fully reproducible research! This HTML file and .ipynb file can the be submitted as supplemental materials for a paper, allowing any reader to build on your results, or it can just be kept privately so that future collaborators can start where this research left off." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adding your own data to the archive" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course, your results may depend on a lot of external packages, libraries, code files, and so on, which will not automatically be included or listed in the exported archive.\n", "\n", "Luckily, the archive support is very general, and you can add any object to it that you want to be exported along with your output. For instance, you can store arbitrary metadata of your choosing, such as version control information, here as a JSON-format text file: " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import json\n", "hv.archive.add(filename='metadata.json', \n", " data=json.dumps({'repository':'[email protected]:ioam/holoviews.git',\n", " 'commit':'437e8d69'}), info={'mime_type':'text/json'})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The new file can now be seen in the contents listing:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hv.archive.contents()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this way, you should be able to automatically generate output files, with customizable filenames, storing any data or metadata you like along with them so that you can keep track of all the important information for reproducing these results later." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Controlling the behavior of ``hv.archive``" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ``hv.archive`` object provides numerous parameters that can be changed. You can e.g.:\n", "\n", "- output the whole directory to a single compressed ZIP or tar archive file (e.g. ``hv.archive.set_param(pack=False, archive_format='zip')`` or ``archive_format='tar'``)\n", "\n", "- generate a new directory or archive every time the notebook is run (``hv.archive.uniq_name=True``); otherwise the old output directory is erased each time \n", "\n", "- choose your own name for the output directory or archive (e.g. ``hv.archive.export_name=\"{timestamp}\"``)\n", "\n", "- change the format of the optional timestamp (e.g. to retain snapshots hourly, ``archive.set_param(export_name=\"{timestamp}\", timestamp_format=\"%Y_%m_%d-%H\")``)\n", "\n", "- select PNG output, at a specified rendering resolution:\n", "```hv.archive.exporters=[hv.Store.renderers['matplotlib'].instance(size=50, fig='png', dpi=144)])\n", "```\n", "\n", "These options and any others listed above can all be set in the ``hv.archive.auto()`` call at the start, for convenience and to ensure that they apply to all of the files that are added." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Writing the archive to disk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To actually write the files you have stored in the archive to disk, you need to call ``export()`` after any cell that might contain computation-intensive code. Usually it's best to do so as the last or nearly last cell in your notebook, though here we do it earlier because we wanted to show how to use the exported files." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hv.archive.export()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Shortly after the ``export()`` command has been executed, the output should be available as a directory on disk, by default in the same directory as the notebook file, named with the name of the notebook: " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.getcwd()\n", "if os.path.exists(\"Exporting\"):\n", " print(sorted(os.listdir(\"Exporting\")))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For technical reasons to do with how the IPython Notebook interacts with JavaScript, if you use the IPython command ``Run all``, the ``hv.archive.export()`` command is not actually executed when the cell with that call is encountered during the run. Instead, the ``export()`` is queued until after the final cell in the notebook has been executed. This asynchronous execution has several awkward but not serious consequences:\n", "\n", "- It is not possible for the ``export()`` cell to show whether any errors were encountered during exporting, because these will not occur until after the notebook has completed processing. To see any errors, you can run ``hv.archive.last_export_status()`` separately, *after* the ``Run all`` has completed. E.g. just press shift-[Enter] in the following cell, which will tell you whether the previous export was successful.\n", "\n", "- If you use ``Run all``, the directory listing ``os.listdir()`` above will show the results from the *previous* time this notebook was run, since it executes before the export. Again, you can use shift-[Enter] to update the data once complete.\n", "\n", "- The ``Export name:`` in the output of ``hv.archive.export()`` will not always show the actual name of the directory or archive that will be created. In particular, it may say ``{notebook}``, which when saving will actually expand to the name of your IPython Notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "hv.archive.last_export_status()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Accessing your saved data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, HoloViews saves not only your rendered plots (PNG, SVG, etc.), but also the actual HoloViews objects that the plots visualize, which contain all your actual data. The objects are stored in compressed Python pickle files (``.hvz``), which are visible in the directory listings above but have been ignored until now. The plots are what you need for writing a document, but the raw data is is a crucial record to keep as well. For instance, you now can load in the HoloViews object, and manipulate it just as you could when it was originally defined. E.g. we can re-load our ``Levels`` ``Overlay`` file, which has the contours overlaid on top of the image, and easily pull out the underlying ``Image`` object:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "from holoviews.core.io import Unpickler\n", "c, a = None,None\n", "path = \"Exporting/Overlay,Image,Level.hvz\"\n", "\n", "if os.path.isfile(path):\n", " o = Unpickler.load(open(path,\"rb\"))\n", " c = o.Image\n", "print(c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given the ``Image``, you can also access the underlying array data, because HoloViews objects are simply containers for your data and associated metadata. This means that years from now, as long as you can still run HoloViews, you can now easily re-load and explore your data, plotting it entirely different ways or running different analyses, even if you no longer have any of the original code you used to generate the data. All you need is HoloViews, which is permanently archived on GitHub and is fully open source and thus should always remain available. Because the data is stored conveniently in the archive alongside the figure that was published, you can see immediately which file corresponds to the data underlying any given plot in your paper, and immediately start working with the data, rather than laboriously trying to reconstruct the data from a saved figure.\n", "\n", "If you do not want the pickle files, you can of course turn them off if you prefer, by changing ``hv.archive.auto()`` to:\n", "\n", "```python\n", "hv.archive.auto(exporters=[hv.Store.renderers['matplotlib'].instance(holomap=None)])\n", "```\n", "\n", "Here, the exporters list has been updated to include the usual default exporters *without* the `Pickler` exporter that would usually be included." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using HoloViews (and Lancet) to do reproducible research" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The export options from HoloViews help you establish a feasible workflow for doing reproducible research: starting from interactive exploration, either export specific files with ``%%output``, or enable ``hv.archive.auto()``, which will store a copy of your notebook and its output ready for inclusion in a document but retaining the complete recipe for reproducing the results later. \n", "\n", "HoloViews also works very well with the [Lancet](http://ioam.github.io/lancet) tool for exploring large parameter spaces, and Lancet provides an interface to HoloViews that makes Lancet output directly available for use in HoloViews. Lancet, when used with IPython Notebook and HoloViews, makes it feasible to work with large numbers of computation-intensive processes that generate heterogeneous data that needs to be collated, analyzed, and visualized. For more background and a suggested workflow, see our [2013 paper on using Lancet](http://dx.doi.org/10.3389/fninf.2013.00044) with IPython Notebook. Because that paper was written before the release of HoloViews, it does not discuss how HoloViews helps in this process, but that aspect is covered in our [2015 paper on using HoloViews for reproducible research](http://conference.scipy.org/proceedings/scipy2015/pdfs/jean-luc_stevens.pdf)." ] } ], "metadata": { "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
phenology/infrastructure
applications/notebooks/romulo/plot_svd.ipynb
1
129762
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Plot SVD\n", "\n", "With this example the user can load GeoTiffs from HDFS and then explore all the features of Python packages such as [rasterio](https://github.com/mapbox/rasterio)." ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Dependencies" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "#Add all dependencies to PYTHON_PATH\n", "import sys\n", "sys.path.append(\"/usr/lib/spark/python\")\n", "sys.path.append(\"/usr/lib/spark/python/lib/py4j-0.10.4-src.zip\")\n", "sys.path.append(\"/usr/lib/python3/dist-packages\")\n", "\n", "#Define environment variables\n", "import os\n", "os.environ[\"HADOOP_CONF_DIR\"] = \"/etc/hadoop/conf\"\n", "os.environ[\"PYSPARK_PYTHON\"] = \"python3\"\n", "os.environ[\"PYSPARK_DRIVER_PYTHON\"] = \"ipython\"\n", "\n", "#Load PySpark to connect to a Spark cluster\n", "from pyspark import SparkConf, SparkContext\n", "\n", "from osgeo import gdal\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from pylab import *\n", "#To read GeoTiffs as a ByteArray\n", "from io import BytesIO\n", "from rasterio.io import MemoryFile" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Connect to Spark" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "appName = \"plot_GeoTiff\"\n", "masterURL=\"spark://pheno0.phenovari-utwente.surf-hosted.nl:7077\"\n", "\n", "#A context needs to be created if it does not already exist\n", "try:\n", " sc.stop()\n", "except NameError:\n", " print(\"A new Spark Context will be created.\")\n", " \n", "sc = SparkContext(conf = SparkConf().setAppName(appName).setMaster(masterURL))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Support functions" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def diff(first, second):\n", " second = set(second)\n", " return [item for item in first if item not in second]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read data" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "#EigenFile = open('/media/emma/emma/eScience/Results/SVD/SVD_results/V.txt', 'r')\n", "resultDirectory = \"hdfs:///user/pheno/svd/spark/BloomGridmetLeafGridmet/\"\n", "file_path = \"V.csv\"\n", "f = sc.textFile(resultDirectory + file_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Retrieve index from mask" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Read mask" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "mask_path = \"hdfs:///user/hadoop/usa_mask_gridmet.tif\"\n", "mask_data = sc.binaryFiles(mask_path).take(1)\n", "mask_byteArray = bytearray(mask_data[0][1])\n", "mask_memfile = MemoryFile(mask_byteArray)\n", "mask_dataset = mask_memfile.open()\n", "mask_data = np.array(mask_dataset.read()[0])\n", "mask_memfile.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Retrieve Index" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "xsize = mask_data.shape[1]\n", "ysize = mask_data.shape[0]\n", "\n", "index_sel = np.nonzero(mask_data)\n", "inx_tuple = [(index_sel[1][i],index_sel[0][i]) for i in range(0,len(index_sel[0]))]\n", "\n", "idx = [np.ravel_multi_index(inx_tuple[i], (xsize,ysize)) for i in range(0,len(inx_tuple))]\n", "mask_data1 = np.reshape(mask_data, mask_data.shape[0]*mask_data.shape[1], 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plot images" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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4vbNrjMG2d7h9fcP4yQvB2+67n5Lno7VmulohUYrj45P83YVzPG1+H2u9HWbL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\nHy8u4QuXiHRIx7wT9emlKRXf557NLaq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VK1DQ4w1b40QHH0gFxhlELiBzCWbusUVKBWnE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\n9K/r108/Lm88QGqr3PrBuddr8MXbd4fYSByd8dSMoyg9eKoH7eFWK6s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"text/plain": [ "<matplotlib.figure.Figure at 0x7f99a55465f8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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SHkM7jili05Ajmu2aH7jiZDVM1pg7RY90gkrN4Wc5hD4qypI4z9HKFLOlq1aoO+kb2I0f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\n7oJC33q28tt3FYwNl+4T+tEb57CoG/iiveCY5h4LLBkTYr1zoX2HShz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"text/plain": [ "<matplotlib.figure.Figure at 0x7f995a397240>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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vvYCXdw7wqFxikqS4Wsxwf302eC2br01JgSv5BJlK8d+9/nX8xZdfRmMNFm2L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\nTbEQAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f99304edc18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for pc in range(0,3):\n", " list2 = f.map(lambda line: (line.split(',')[pc])).collect()\n", " mask_data_tmp = mask_data1\n", "\n", " # 1684x840 \n", " for i in range(len(idx)):\n", " mask_data_tmp[idx[i]]=list2[i]\n", "\n", " mask_data_tmp = np.reshape(mask_data_tmp,(xsize,ysize))\n", " #plt.figure(1)\n", " #plt.imshow(mask_data1.T, interpolation=\"None\")\n", " #plt.show()\n", "\n", " plt.figure(1)\n", " cmap = cm.get_cmap('YlGn')\n", " img = plt.imshow(mask_data_tmp.T, cmap='YlGn')\n", " plt.colorbar(orientation='horizontal')\n", " plt.clim(float(np.min(mask_data_tmp.T)),float(np.max(mask_data_tmp.T)))\n", " plt.axis('off')\n", " plt.show()" ] }, { "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
Wahlque/Wahlque-Complete
zh-cn/questions/00001-stable-haibitable-orbit.ipynb
1
1278
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 确定一条稳定的宜居轨道" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 问题描述" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "给瓦克星游戏确定一条稳定宜居的轨道。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 问题状态" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "部分解决，需要验证和进一步优化" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 相关问题" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* [龙格-库塔法里的能量漂移](10001-energy-drift-of-rk-method.ipynb)" ] } ], "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 }
cc0-1.0
herruzojm/udacity-deep-learning
gan_mnist/Intro_to_GANs_Exercises.ipynb
1
223222
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Generative Adversarial Network\n", "\n", "In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\n", "\n", "GANs were [first reported on](https://arxiv.org/abs/1406.2661) in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out:\n", "\n", "* [Pix2Pix](https://affinelayer.com/pixsrv/) \n", "* [CycleGAN](https://github.com/junyanz/CycleGAN)\n", "* [A whole list](https://github.com/wiseodd/generative-models)\n", "\n", "The idea behind GANs is that you have two networks, a generator $G$ and a discriminator $D$, competing against each other. The generator makes fake data to pass to the discriminator. The discriminator also sees real data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks _as close as possible_ to real data. And the discriminator is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistiguishable from real data to the discriminator.\n", "\n", "![GAN diagram](assets/gan_diagram.png)\n", "\n", "The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector the generator uses to contruct it's fake images. As the generator learns through training, it figures out how to map these random vectors to recognizable images that can fool the discriminator.\n", "\n", "The output of the discriminator is a sigmoid function, where 0 indicates a fake image and 1 indicates an real image. If you're interested only in generating new images, you can throw out the discriminator after training. Now, let's see how we build this thing in TensorFlow." ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pickle as pkl\n", "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets('MNIST_data')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Model Inputs\n", "\n", "First we need to create the inputs for our graph. We need two inputs, one for the discriminator and one for the generator. Here we'll call the discriminator input `inputs_real` and the generator input `inputs_z`. We'll assign them the appropriate sizes for each of the networks.\n", "\n", ">**Exercise:** Finish the `model_inputs` function below. Create the placeholders for `inputs_real` and `inputs_z` using the input sizes `real_dim` and `z_dim` respectively." ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def model_inputs(real_dim, z_dim):\n", " inputs_real = tf.placeholder(tf.float32, (None, real_dim), name='input_real') \n", " inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')\n", " \n", " return inputs_real, inputs_z\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Generator network\n", "\n", "![GAN Network](assets/gan_network.png)\n", "\n", "Here we'll build the generator network. To make this network a universal function approximator, we'll need at least one hidden layer. We should use a leaky ReLU to allow gradients to flow backwards through the layer unimpeded. A leaky ReLU is like a normal ReLU, except that there is a small non-zero output for negative input values.\n", "\n", "#### Variable Scope\n", "Here we need to use `tf.variable_scope` for two reasons. Firstly, we're going to make sure all the variable names start with `generator`. Similarly, we'll prepend `discriminator` to the discriminator variables. This will help out later when we're training the separate networks.\n", "\n", "We could just use `tf.name_scope` to set the names, but we also want to reuse these networks with different inputs. For the generator, we're going to train it, but also _sample from it_ as we're training and after training. The discriminator will need to share variables between the fake and real input images. So, we can use the `reuse` keyword for `tf.variable_scope` to tell TensorFlow to reuse the variables instead of creating new ones if we build the graph again.\n", "\n", "To use `tf.variable_scope`, you use a `with` statement:\n", "```python\n", "with tf.variable_scope('scope_name', reuse=False):\n", " # code here\n", "```\n", "\n", "Here's more from [the TensorFlow documentation](https://www.tensorflow.org/programmers_guide/variable_scope#the_problem) to get another look at using `tf.variable_scope`.\n", "\n", "#### Leaky ReLU\n", "TensorFlow doesn't provide an operation for leaky ReLUs, so we'll need to make one . For this you can just take the outputs from a linear fully connected layer and pass them to `tf.maximum`. Typically, a parameter `alpha` sets the magnitude of the output for negative values. So, the output for negative input (`x`) values is `alpha*x`, and the output for positive `x` is `x`:\n", "$$\n", "f(x) = max(\\alpha * x, x)\n", "$$\n", "\n", "#### Tanh Output\n", "The generator has been found to perform the best with $tanh$ for the generator output. This means that we'll have to rescale the MNIST images to be between -1 and 1, instead of 0 and 1.\n", "\n", ">**Exercise:** Implement the generator network in the function below. You'll need to return the tanh output. Make sure to wrap your code in a variable scope, with 'generator' as the scope name, and pass the `reuse` keyword argument from the function to `tf.variable_scope`." ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def generator(z, out_dim, n_units=128, reuse=False, alpha=0.01):\n", " ''' Build the generator network.\n", " \n", " Arguments\n", " ---------\n", " z : Input tensor for the generator\n", " out_dim : Shape of the generator output\n", " n_units : Number of units in hidden layer\n", " reuse : Reuse the variables with tf.variable_scope\n", " alpha : leak parameter for leaky ReLU\n", " \n", " Returns\n", " -------\n", " out, logits: \n", " '''\n", " with tf.variable_scope('generator', reuse=reuse):\n", " # Hidden layer\n", " h1 = tf.layers.dense(z, n_units, activation=None)\n", " # Leaky ReLU\n", " h1 = tf.maximum(alpha * h1, h1) \n", " \n", " # Logits and tanh output\n", " logits = tf.layers.dense(h1, out_dim, activation=None)\n", " out = tf.tanh(logits)\n", " \n", " return out" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Discriminator\n", "\n", "The discriminator network is almost exactly the same as the generator network, except that we're using a sigmoid output layer.\n", "\n", ">**Exercise:** Implement the discriminator network in the function below. Same as above, you'll need to return both the logits and the sigmoid output. Make sure to wrap your code in a variable scope, with 'discriminator' as the scope name, and pass the `reuse` keyword argument from the function arguments to `tf.variable_scope`." ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def discriminator(x, n_units=128, reuse=False, alpha=0.01):\n", " ''' Build the discriminator network.\n", " \n", " Arguments\n", " ---------\n", " x : Input tensor for the discriminator\n", " n_units: Number of units in hidden layer\n", " reuse : Reuse the variables with tf.variable_scope\n", " alpha : leak parameter for leaky ReLU\n", " \n", " Returns\n", " -------\n", " out, logits: \n", " '''\n", " with tf.variable_scope('discriminator', reuse=reuse):\n", " # Hidden layer\n", " h1 = tf.layers.dense(x, n_units, activation=None)\n", " # Leaky ReLU\n", " h1 = tf.maximum(h1 * alpha, h1) \n", " \n", " logits = tf.layers.dense(h1, 1, activation=None)\n", " out = tf.sigmoid(logits)\n", " \n", " return out, logits" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Hyperparameters" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Size of input image to discriminator\n", "input_size = 784 # 28x28 MNIST images flattened\n", "# Size of latent vector to generator\n", "z_size = 100\n", "# Sizes of hidden layers in generator and discriminator\n", "g_hidden_size = 128\n", "d_hidden_size = 128\n", "# Leak factor for leaky ReLU\n", "alpha = 0.01\n", "# Label smoothing \n", "smooth = 0.1" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Build network\n", "\n", "Now we're building the network from the functions defined above.\n", "\n", "First is to get our inputs, `input_real, input_z` from `model_inputs` using the sizes of the input and z.\n", "\n", "Then, we'll create the generator, `generator(input_z, input_size)`. This builds the generator with the appropriate input and output sizes.\n", "\n", "Then the discriminators. We'll build two of them, one for real data and one for fake data. Since we want the weights to be the same for both real and fake data, we need to reuse the variables. For the fake data, we're getting it from the generator as `g_model`. So the real data discriminator is `discriminator(input_real)` while the fake discriminator is `discriminator(g_model, reuse=True)`.\n", "\n", ">**Exercise:** Build the network from the functions you defined earlier." ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "tf.reset_default_graph()\n", "# Create our input placeholders\n", "input_real, input_z = model_inputs(input_size, z_size)\n", "\n", "# Generator network here\n", "g_model = generator(input_z, input_size)\n", "# g_model is the generator output\n", "\n", "# Disriminator network here\n", "d_model_real, d_logits_real = discriminator(input_real)\n", "d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Discriminator and Generator Losses\n", "\n", "Now we need to calculate the losses, which is a little tricky. For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_loss_real + d_loss_fake`. The losses will by sigmoid cross-entropies, which we can get with `tf.nn.sigmoid_cross_entropy_with_logits`. We'll also wrap that in `tf.reduce_mean` to get the mean for all the images in the batch. So the losses will look something like \n", "\n", "```python\n", "tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))\n", "```\n", "\n", "For the real image logits, we'll use `d_logits_real` which we got from the discriminator in the cell above. For the labels, we want them to be all ones, since these are all real images. To help the discriminator generalize better, the labels are reduced a bit from 1.0 to 0.9, for example, using the parameter `smooth`. This is known as label smoothing, typically used with classifiers to improve performance. In TensorFlow, it looks something like `labels = tf.ones_like(tensor) * (1 - smooth)`\n", "\n", "The discriminator loss for the fake data is similar. The logits are `d_logits_fake`, which we got from passing the generator output to the discriminator. These fake logits are used with labels of all zeros. Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.\n", "\n", "Finally, the generator losses are using `d_logits_fake`, the fake image logits. But, now the labels are all ones. The generator is trying to fool the discriminator, so it wants to discriminator to output ones for fake images.\n", "\n", ">**Exercise:** Calculate the losses for the discriminator and the generator. There are two discriminator losses, one for real images and one for fake images. For the real image loss, use the real logits and (smoothed) labels of ones. For the fake image loss, use the fake logits with labels of all zeros. The total discriminator loss is the sum of those two losses. Finally, the generator loss again uses the fake logits from the discriminator, but this time the labels are all ones because the generator wants to fool the discriminator." ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Calculate losses\n", "d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, \n", " labels=tf.ones_like(d_logits_real) * (1 - smooth)))\n", "\n", "\n", "d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, \n", " labels=tf.zeros_like(d_logits_real)))\n", "\n", "d_loss = d_loss_real + d_loss_fake\n", "\n", "g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_logits_fake)))\n" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Optimizers\n", "\n", "We want to update the generator and discriminator variables separately. So we need to get the variables for each part and build optimizers for the two parts. To get all the trainable variables, we use `tf.trainable_variables()`. This creates a list of all the variables we've defined in our graph.\n", "\n", "For the generator optimizer, we only want to generator variables. Our past selves were nice and used a variable scope to start all of our generator variable names with `generator`. So, we just need to iterate through the list from `tf.trainable_variables()` and keep variables that start with `generator`. Each variable object has an attribute `name` which holds the name of the variable as a string (`var.name == 'weights_0'` for instance). \n", "\n", "We can do something similar with the discriminator. All the variables in the discriminator start with `discriminator`.\n", "\n", "Then, in the optimizer we pass the variable lists to the `var_list` keyword argument of the `minimize` method. This tells the optimizer to only update the listed variables. Something like `tf.train.AdamOptimizer().minimize(loss, var_list=var_list)` will only train the variables in `var_list`.\n", "\n", ">**Exercise: ** Below, implement the optimizers for the generator and discriminator. First you'll need to get a list of trainable variables, then split that list into two lists, one for the generator variables and another for the discriminator variables. Finally, using `AdamOptimizer`, create an optimizer for each network that update the network variables separately." ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [], "source": [ "# Optimizers\n", "learning_rate = 0.002\n", "\n", "# Get the trainable_variables, split into G and D parts\n", "t_vars = tf.trainable_variables()\n", "g_vars = [var for var in t_vars if var.name.startswith('generator')]\n", "d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n", "\n", "d_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list = d_vars)\n", "g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list = g_vars)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/150... Discriminator Loss: 0.3543... Generator Loss: 4.6863\n", "Epoch 2/150... Discriminator Loss: 0.5163... Generator Loss: 2.0940\n", "Epoch 3/150... Discriminator Loss: 0.5112... Generator Loss: 3.3978\n", "Epoch 4/150... Discriminator Loss: 0.7559... Generator Loss: 6.5077\n", "Epoch 5/150... Discriminator Loss: 1.0489... Generator Loss: 2.8354\n", "Epoch 6/150... Discriminator Loss: 1.5431... Generator Loss: 1.2256\n", "Epoch 7/150... Discriminator Loss: 1.7298... Generator Loss: 1.4167\n", "Epoch 8/150... Discriminator Loss: 1.4588... Generator Loss: 0.8193\n", "Epoch 9/150... Discriminator Loss: 0.9729... Generator Loss: 2.2115\n", "Epoch 10/150... Discriminator Loss: 1.2601... Generator Loss: 1.6875\n", "Epoch 11/150... Discriminator Loss: 0.8551... Generator Loss: 1.8181\n", "Epoch 12/150... Discriminator Loss: 1.8856... Generator Loss: 1.1084\n", "Epoch 13/150... Discriminator Loss: 1.3166... Generator Loss: 1.2830\n", "Epoch 14/150... Discriminator Loss: 1.4743... Generator Loss: 2.7563\n", "Epoch 15/150... Discriminator Loss: 1.4235... Generator Loss: 1.8310\n", "Epoch 16/150... Discriminator Loss: 2.2431... Generator Loss: 3.3765\n", "Epoch 17/150... Discriminator Loss: 1.2496... Generator Loss: 3.0080\n", "Epoch 18/150... Discriminator Loss: 1.5099... Generator Loss: 1.6924\n", "Epoch 19/150... Discriminator Loss: 1.7642... Generator Loss: 1.2679\n", "Epoch 20/150... Discriminator Loss: 1.0638... Generator Loss: 1.7834\n", "Epoch 21/150... Discriminator Loss: 1.0206... Generator Loss: 1.7475\n", "Epoch 22/150... Discriminator Loss: 0.9131... Generator Loss: 2.7771\n", "Epoch 23/150... Discriminator Loss: 0.8432... Generator Loss: 2.3240\n", "Epoch 24/150... Discriminator Loss: 0.8119... Generator Loss: 1.8505\n", "Epoch 25/150... Discriminator Loss: 1.3236... Generator Loss: 1.6487\n", "Epoch 26/150... Discriminator Loss: 1.1219... Generator Loss: 1.4922\n", "Epoch 27/150... Discriminator Loss: 0.7254... Generator Loss: 2.2417\n", "Epoch 28/150... Discriminator Loss: 0.8725... Generator Loss: 1.7679\n", "Epoch 29/150... Discriminator Loss: 0.8435... Generator Loss: 2.3163\n", "Epoch 30/150... Discriminator Loss: 1.1063... Generator Loss: 1.7187\n", "Epoch 31/150... Discriminator Loss: 0.8981... Generator Loss: 2.9401\n", "Epoch 32/150... Discriminator Loss: 0.9447... Generator Loss: 2.1921\n", "Epoch 33/150... Discriminator Loss: 1.4340... Generator Loss: 1.5198\n", "Epoch 34/150... Discriminator Loss: 1.0670... Generator Loss: 1.4552\n", "Epoch 35/150... Discriminator Loss: 1.1534... Generator Loss: 2.0281\n", "Epoch 36/150... Discriminator Loss: 0.8602... Generator Loss: 2.1037\n", "Epoch 37/150... Discriminator Loss: 0.7665... Generator Loss: 2.9363\n", "Epoch 38/150... Discriminator Loss: 0.9091... Generator Loss: 2.3846\n", "Epoch 39/150... Discriminator Loss: 1.1473... Generator Loss: 1.6169\n", "Epoch 40/150... Discriminator Loss: 0.9984... Generator Loss: 1.8649\n", "Epoch 41/150... Discriminator Loss: 0.9414... Generator Loss: 2.0641\n", "Epoch 42/150... Discriminator Loss: 0.9030... Generator Loss: 2.5454\n", "Epoch 43/150... Discriminator Loss: 1.0518... Generator Loss: 1.2813\n", "Epoch 44/150... Discriminator Loss: 0.9489... Generator Loss: 1.5453\n", "Epoch 45/150... Discriminator Loss: 0.9563... Generator Loss: 1.7254\n", "Epoch 46/150... Discriminator Loss: 1.1360... Generator Loss: 1.7041\n", "Epoch 47/150... Discriminator Loss: 0.9167... Generator Loss: 1.5364\n", "Epoch 48/150... Discriminator Loss: 1.1863... Generator Loss: 1.7189\n", "Epoch 49/150... Discriminator Loss: 0.9131... Generator Loss: 2.1160\n", "Epoch 50/150... Discriminator Loss: 0.8979... Generator Loss: 2.0546\n", "Epoch 51/150... Discriminator Loss: 0.8489... Generator Loss: 2.8572\n", "Epoch 52/150... Discriminator Loss: 1.0901... Generator Loss: 1.7684\n", "Epoch 53/150... Discriminator Loss: 1.0024... Generator Loss: 1.5383\n", "Epoch 54/150... Discriminator Loss: 0.9194... Generator Loss: 2.0673\n", "Epoch 55/150... Discriminator Loss: 0.8950... Generator Loss: 2.1577\n", "Epoch 56/150... Discriminator Loss: 0.9468... Generator Loss: 1.9880\n", "Epoch 57/150... Discriminator Loss: 0.8224... Generator Loss: 2.1215\n", "Epoch 58/150... Discriminator Loss: 0.9871... Generator Loss: 1.9954\n", "Epoch 59/150... Discriminator Loss: 1.0662... Generator Loss: 1.5353\n", "Epoch 60/150... Discriminator Loss: 1.0441... Generator Loss: 1.7999\n", "Epoch 61/150... Discriminator Loss: 0.8516... Generator Loss: 1.8130\n", "Epoch 62/150... Discriminator Loss: 1.0291... Generator Loss: 1.8489\n", "Epoch 63/150... Discriminator Loss: 0.9007... Generator Loss: 1.8095\n", "Epoch 64/150... Discriminator Loss: 0.9906... Generator Loss: 1.8590\n", "Epoch 65/150... Discriminator Loss: 0.9189... Generator Loss: 1.9991\n", "Epoch 66/150... Discriminator Loss: 0.8102... Generator Loss: 1.8992\n", "Epoch 67/150... Discriminator Loss: 0.9236... Generator Loss: 1.9212\n", "Epoch 68/150... Discriminator Loss: 1.0045... Generator Loss: 1.8808\n", "Epoch 69/150... Discriminator Loss: 0.9995... Generator Loss: 1.4038\n", "Epoch 70/150... Discriminator Loss: 1.0351... Generator Loss: 2.1540\n", "Epoch 71/150... Discriminator Loss: 0.9049... Generator Loss: 1.9552\n", "Epoch 72/150... Discriminator Loss: 0.9659... Generator Loss: 1.7944\n", "Epoch 73/150... Discriminator Loss: 0.9535... Generator Loss: 1.5596\n", "Epoch 74/150... Discriminator Loss: 1.0065... Generator Loss: 2.0591\n", "Epoch 75/150... Discriminator Loss: 1.1370... Generator Loss: 1.7054\n", "Epoch 76/150... Discriminator Loss: 0.9244... Generator Loss: 2.1410\n", "Epoch 77/150... Discriminator Loss: 0.9148... Generator Loss: 1.8572\n", "Epoch 78/150... Discriminator Loss: 1.0300... Generator Loss: 1.5755\n", "Epoch 79/150... Discriminator Loss: 1.2083... 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Generator Loss: 1.7664\n", "Epoch 106/150... Discriminator Loss: 0.9345... Generator Loss: 1.9073\n", "Epoch 107/150... Discriminator Loss: 0.8294... Generator Loss: 2.0644\n", "Epoch 108/150... Discriminator Loss: 1.1754... Generator Loss: 2.5481\n", "Epoch 109/150... Discriminator Loss: 0.9169... Generator Loss: 1.9539\n", "Epoch 110/150... Discriminator Loss: 0.9473... Generator Loss: 1.8705\n", "Epoch 111/150... Discriminator Loss: 0.8682... Generator Loss: 2.3611\n", "Epoch 112/150... Discriminator Loss: 1.0016... Generator Loss: 1.4836\n", "Epoch 113/150... Discriminator Loss: 0.9418... Generator Loss: 2.0223\n", "Epoch 114/150... Discriminator Loss: 0.9428... Generator Loss: 1.9810\n", "Epoch 115/150... Discriminator Loss: 0.9744... Generator Loss: 2.0240\n", "Epoch 116/150... Discriminator Loss: 0.9232... Generator Loss: 1.5851\n", "Epoch 117/150... Discriminator Loss: 0.9533... Generator Loss: 1.6223\n", "Epoch 118/150... Discriminator Loss: 0.9906... Generator Loss: 1.6456\n", "Epoch 119/150... Discriminator Loss: 0.7927... Generator Loss: 2.2820\n", "Epoch 120/150... Discriminator Loss: 0.8673... Generator Loss: 2.3215\n", "Epoch 121/150... Discriminator Loss: 0.9608... Generator Loss: 2.0732\n", "Epoch 122/150... Discriminator Loss: 1.1055... Generator Loss: 1.8185\n", "Epoch 123/150... Discriminator Loss: 1.0077... Generator Loss: 2.2996\n", "Epoch 124/150... Discriminator Loss: 0.9047... Generator Loss: 2.2648\n", "Epoch 125/150... Discriminator Loss: 0.9644... Generator Loss: 1.7584\n", "Epoch 126/150... Discriminator Loss: 0.9740... Generator Loss: 1.9809\n", "Epoch 127/150... Discriminator Loss: 0.9530... Generator Loss: 2.2543\n", "Epoch 128/150... Discriminator Loss: 0.9361... Generator Loss: 1.7793\n", "Epoch 129/150... Discriminator Loss: 0.8385... Generator Loss: 1.9185\n", "Epoch 130/150... Discriminator Loss: 1.0161... Generator Loss: 1.5820\n", "Epoch 131/150... Discriminator Loss: 0.9076... Generator Loss: 1.9545\n", "Epoch 132/150... Discriminator Loss: 0.9576... Generator Loss: 1.9898\n", "Epoch 133/150... Discriminator Loss: 1.0372... Generator Loss: 1.6318\n", "Epoch 134/150... Discriminator Loss: 0.8798... Generator Loss: 2.2058\n", "Epoch 135/150... Discriminator Loss: 0.9783... Generator Loss: 1.8801\n", "Epoch 136/150... Discriminator Loss: 0.8222... Generator Loss: 1.8983\n", "Epoch 137/150... Discriminator Loss: 0.9741... Generator Loss: 1.9390\n", "Epoch 138/150... Discriminator Loss: 0.8223... Generator Loss: 2.3069\n", "Epoch 139/150... Discriminator Loss: 0.8527... Generator Loss: 1.7928\n", "Epoch 140/150... Discriminator Loss: 0.8006... Generator Loss: 2.2071\n", "Epoch 141/150... Discriminator Loss: 0.8472... Generator Loss: 1.6071\n", "Epoch 142/150... Discriminator Loss: 0.8663... Generator Loss: 2.1888\n", "Epoch 143/150... Discriminator Loss: 0.8407... Generator Loss: 2.2853\n", "Epoch 144/150... Discriminator Loss: 0.9330... Generator Loss: 1.6930\n", "Epoch 145/150... Discriminator Loss: 0.9400... Generator Loss: 2.4600\n", "Epoch 146/150... Discriminator Loss: 1.0330... Generator Loss: 1.5598\n", "Epoch 147/150... Discriminator Loss: 0.8942... Generator Loss: 2.5549\n", "Epoch 148/150... Discriminator Loss: 0.9114... Generator Loss: 2.0592\n", "Epoch 149/150... Discriminator Loss: 0.9802... Generator Loss: 1.7939\n", "Epoch 150/150... Discriminator Loss: 0.9495... Generator Loss: 2.0127\n" ] } ], "source": [ "batch_size = 100\n", "epochs = 150\n", "samples = []\n", "losses = []\n", "saver = tf.train.Saver(var_list = g_vars)\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for e in range(epochs):\n", " for ii in range(mnist.train.num_examples//batch_size):\n", " batch = mnist.train.next_batch(batch_size)\n", " \n", " # Get images, reshape and rescale to pass to D\n", " batch_images = batch[0].reshape((batch_size, 784))\n", " batch_images = batch_images*2 - 1\n", " \n", " # Sample random noise for G\n", " batch_z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n", " \n", " # Run optimizers\n", " _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})\n", " _ = sess.run(g_train_opt, feed_dict={input_z: batch_z})\n", " \n", " # At the end of each epoch, get the losses and print them out\n", " train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})\n", " train_loss_g = g_loss.eval({input_z: batch_z})\n", " \n", " print(\"Epoch {}/{}...\".format(e+1, epochs),\n", " \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n", " \"Generator Loss: {:.4f}\".format(train_loss_g)) \n", " # Save losses to view after training\n", " losses.append((train_loss_d, train_loss_g))\n", " \n", " # Sample from generator as we're training for viewing afterwards\n", " sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n", " gen_samples = sess.run(\n", " generator(input_z, input_size, reuse=True),\n", " feed_dict={input_z: sample_z})\n", " samples.append(gen_samples)\n", " saver.save(sess, './checkpoints/generator.ckpt')\n", "\n", "# Save training generator samples\n", "with open('train_samples.pkl', 'wb') as f:\n", " pkl.dump(samples, f)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Training loss\n", "\n", "Here we'll check out the training losses for the generator and discriminator." ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f5254d9b4a8>" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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wRomewl2+hrJNqjb1fR+PNbqaqM9LCbfxd+SI24AS7ugE3XdWDEQ6oNOp+9pO\nGzWBfFolwUbcCK2DcuUDwOpH6P/udko/VGIclMedTv8nZGqv+fhMSwWdbED/Im6VfhifAcQmHx8R\ntzpeoUTcPZ3Ark+AzPH0uU0v0uu1O+nmnjRsYKyS9hoKElT20YZn6S8UVEssaxLQ5iHc6mbV31rv\nuz4GHp0JfP0P7/daDwONB/q3/r4gJbD+P0BHQ+/LGlHXpLVZs0kMndYccRtQwu3pbwMDkw6oBu8k\nj6DH1qrQPG6HjTzy2BQgMZtuMKFE3NvepDxaQLNKkgBzFFkggSJup5OabErk4zXh9nUiqosT6GfE\nra07IVOLuI8D4e5up/TH9prgswb2fk7nxPkPAKMXkKXRepiEMHsynSv9jbh7Oun427uA9iMkRPV7\ng6/jrlBiM+oEyuM2pqCqCLI/2RIf3QG8cTVdEzve8X7/vZuBx2YD798CtB3p+3ZCpbaUcq+3vxV4\nOacDWPwzYMt/6bkKjKSTjr/6HeMz+m6RDgCRJ9yqOqCnvw3owt3TD+FWn03RhLutGo6oOLxVUgEp\nDRUJ/Qm3ijpjUyi7Iy0/cMRdsQGo30f/S0kWhhr40NOmf8/eRk9am+nkiVMRtybgviJudXGmjhq4\niDsm6diPuKWk3yRnKj0P1i7Z8Q6QOAzIXwCc+w9qET45H2jYR8Ido7VW+pP3224QuYb99NuotLRQ\nroemMgoSsidRi9M4MladN8oWCBUpgc2vAlO+D5z5J6Bhr/cgn/o9FDRtewt49+fe69i+GFhyS9+2\nHwiVltmb2K79P2DHYmo1AO7XZFeTLtzDptO6+lt+o49EnnAHjLh76Zxs2A8cXBl4/SqjREXcditq\nrSbcuXgbdh5u7d0qUU2l2BR6TC8I3PR770Y9lUilH3bWUyaKskqA3oVbXUwq4o5NpcjQ12eaymhk\nZeb4/k3yq24Kyio51iNuWyfdHPPm0PO6IITb2gLs+YLEymSmAmA3raSbpnQCw6bRuSId/Wspthk6\noRsPkAAqgikJrGgqo2AjKVdb72H39wB9oFeodDXRfLEj5wFjTqfXylbp79u6KJNl1kJg5k98D7vf\n+CKw5TXA3hPatqUEDm/x/74v4a7e6t4ird8HLPsb/a8yboyt4K4mPb8/dzpVzPSXHBBmIk+4TVoe\nt2cON0CiLkz+Pe5vHgQW/zTw+lV0ooQbQCdo0E9zp82QVeInUu02RNwACXfTIf933rYaXdiNA2ba\njuhZJQAb/fvZAAAgAElEQVTZER0+OhpdO6miXy3iFoIE1Ze90lRGwhGT3M+IW3VOZuhR47GMuoiz\nJtD5VxvA57Z3A1/+GXh0Fl3A06/Q38scB/zsS+An7wATL6SbHtA/u8QYcTcecC9aFewkHADZIamj\nDVbhYff3gL5H3ErsEnMoIo1JAcq+MaxfE820fDo/OxvchdNhozogfcnUOrgSeOZU/yUeVOtJrbe7\nDfjPmcD6Z/RlPr6DBgCOOVM/LsYWiTHiVoXejlIHZQQKt4kiSV8RtxDa9GV+hLu7hU7iQB2LKupJ\nHu56qcNJUX6r1UY3B1OUf29Y/XBKuNMK6ML17OgBtKnROvThsW7CXa1nlQCUchfoAjRmeCgSstxP\nLIWKqmIS++9xx6TQoKhAEffGl8jXPRqseji0DJCuZv+jItVNLiYZyJ4YuINy25vA6oeBkXOBa5YA\nI2a7vx9lAcaeRedzjBLuftz4VMQdl07C3WAU7iAjbqeDhCstH0jWIm4lUNZWXbD76nErzzopl1of\no09yF24V0aeN1kfjGgX6yDb9+jT20wRDozb/a+UG3+97Rtz1e8kqUttx2IGy1cDs6ykLqL2GXvNl\nlQiTbqcdpQ7KyBNugCoE+vK4gcDzTqpoujlAfQ4l6il5rpfaNeFu6bJpEwYHqBCohFtdjIFSAtWP\nbuskUTYKd3M5Dc5Q60nMoRPD32AeJdBxBuGOz/BvlaTlU9TYlxnry1bR9+ls0G8UMSm0v55N2KqN\nwMeLgJX/0kWgamPo2Q59obuNbKiV/wr+M4t/Crx9nf/1AXTuZU0MfENo2E83+Stf1W0Bf8Sm0mN/\nI25TFN0gGg9Ss16dO8FG3G3VZGWk5dP5Jsz6b2aMHPsccSvh1lJVC06mm4wa4esS7nyK+gF34TZG\ny6GWklDf48g27/d6tOApNoWsTmsr9T8ABkukluystNEU1Emn1kFdT+c+oAl3Mx131/6zcOuYo31H\n3EDgyRSUSAUqrKREPyHLVTyp1UH2TGuXmn8yKUDnpI+IG/B9ohm9x8aDQGslXFUFlUeprJLEbHr0\nlZcNAIc306PyuAGyVzz9TTXAQkXcvRXM8kRK4PWrgf+cQdtUaYequW/0zHs6gHdvpM4u6aS8YimB\nD24HPv611uwNI+qmteeL4EevHtlG+cpqrlEj6je3JAI5U+jY+vs9msqoXozJ3Ps2XceuPxH3EeoA\nzRire9wj59J7wQq3ig7TRtN+J+bowqXeS87ru3ArOydxGD3mn0yPKupuKqPAKyHLd8RdvoZmd4qK\nCz3iVjeH6q3e7ynLa+z3tGUrdKtJdZ4q4U8eASRprfG2ajrHMsfScxVxx6YA0bH0PSNVuIUQI4UQ\nXwshSoUQO4UQvwz7Xs27CZj8fd/vBYy4NeEO5I+5Jk5IcI1CbLGTcLe4hDvAZAqewp0ykm4AgSJu\ngE7Elkq6aKLjdeFWLYtAA2q2vQVs+A8w61pdBABKCfRMBzQ2Ry0JAGRoUXfrYbKcupooKlE3ChXd\nGQfhLP8nRZ6XPw+MOpFSqPZ+oVUmFPR+OFHHt6cNOLDC9zLffaBHctZWEjm7VdtHD4wRd84U+t/X\ncgBdsKoUQG+oc6U/EXfbEYpk0wvJfmvcTz5rdHzwVonqa1H7nZzrHXEPL+qfVRKbog9oy5lKrQ2X\ncGvHTAgS76g4fbtSAhXrgNEn0rkbqnCr3PP6Pd7nu7JJxp9Lj83l+vWnLE71+eThuo3aWkXHNimX\nbuZKuOO0FlTa6Ii2SuwAfi2lnATgBAC3CCEmh3WvTv89MM5Pac1AEbcthIg7Ot7VhG2yaZG3VRPu\nQDO9K49LRcrmKBJvXxG3MRJSwp0ykk6EOiXcWiesal569lIf3gy8fyvlB5/vMdw5IZOE1Bhtqqya\n3KLg6q54Ur+bHi95gqI75eXF+vBpy9dSClzBKUDR1eS7fnQHRU2n/55EvLIk+G2HivH4ln7ge5lP\n7wK+1jIFlA8KUJqmJ6o/wJIEZCvh9jPhQFMZXbjBEBNi52R7rXeKX3sNRXiq4BcAZIzTOrWDjLh3\nfUQBQooW7SYP14W7qYy+d8YYbbSg1krbu1TvE7B3A9ve9p+p1FatR9sA+fv5C4CDhohb3TSEoKhb\nBVlNB+k7jpynpdiWBfedFK2H6XyXTu/frLaUbhIFWguguVy3SjobAJvVPeJ2CXc1tboSMsmiVFkl\n6kacOY62pRIT2o4MXFXJXuhVuKWU1VLKTdr/bQBKAYwI/KkwEqhz0hVxB/K4tQvCEHE39mjC3WXX\n3/PbOdlKF6JxVGd6oe+UQHVBxaZqwl1F3nrycF1EVPZMoh/h3r6YTvIrX9Fz3BWu0ZOGqHvXR9Sj\nnzZavymE0kGpbihjzwJu2QCc+Ud6HuOjud9SoXt9ky+li6O1Cph/O3DC/9DJ3pdOS2sLFatv6yXV\nStlEI08Adn+iDzVX2LtJTGp2khA1aMdcmH13YqmJLWKSKE8+KVcXAYdN/7xVa5GkBincoVglNivl\ngD99ivt57Iq4C/TXMsdpaaRBCHdzBd1IZ15DwQZAloDRKkkbTb+Zo5sCnCPbgdcuA56YR6MOnzkd\nePcGYNPLvrfRVkOjRI3kn0xRddMhd+EG3IVbzRA06kSyH3urd93VRJ2JAC3XWgWMPZOee9oltTup\nszkxh0ZMNx0i4VZ1f9qq6fNRsfRafAb1X7RWaqOVMynKNlolal+7GvXofcX9wCPTvc/DMBCSxy2E\nyAcwE4BXzo0Q4kYhRIkQoqSuLoT0pFCJjtMja0+C8rgNs7prP1xDD/mUulXSS8StfjhFegHQWOZ9\noqnh9DlTKBptO0zCnZRLnUSA+wAcwFu4WzWxN2aTKFyjJzUBaztCzc1JF+vfA9AFKRjqd+ujQo03\nJ8+I295D20sdqb8/7TISg5k/IfGbfR2wfxmJUSisexpY9SB1egZCCVbx9XTzKveYTqq5AoCk99pr\nNeEVwJgzfAu3usGp3yR7sm6VfPso8H8n0oXs8orzg/s+0fFkpwUTce/6iDrKmsuB584G6nbTse5q\npGg2dRTdeABqEQUr3JtepvNz9kL9teThdDPpbtOtHyVmXU26/WeKolGHnfU0Utjf/JltR7yFW0W5\nO9+ja89LuLVjeehbOu+yJtIyPe2+LSCnA1jzf8AjRcCL51OgYdWqfObNoWui2iOfW41gVVH+odVk\nlykPvq2aIu7k4bSMEHSN1pZSh2VCJh0X1TmpOptHnajt+2o6tnu/oJaxujGGkaCFWwiRCOAdAL+S\nUnqFDlLKZ6SUxVLK4qysrIHcR3f8WSVSBh9xCxPla2peVYvdkA4IBJ7p3dri7jMDFCEoX9hIh9bM\nSsunwQHSqUXcufoyKio2R9Od3lO4W6rcUhfd8KxXsusjepx0obZuTYBCjbgzJ9DJa8Qz4m6tBCDJ\n+lFc8CDwi2/1Ea7Zk2iZUDpwbFYS7pgU+j7faRZIR73vG6MlEZh0EUX7nkOsjdut2UFRVkoeiUnT\nQaDdQ/DUb66Km+VMIeF02Gg/HN1UgEmtN1irRIjg8+A3vUzi8vOvaHtf3KOfE0k5dJ6kjqL00bhU\nzSrpxeN22Gi9476ndwoa93/P53SjSB2tBwidjXoW1M+XAT98GfjFWiBnsrvlpJCSOic9hTtrEkXx\nm1/Vtpmvv5c6ShPDVqqqWHAqBQtqGRV1GyPYrW8An/9OX+bINr1jMnkE+f7GiLuyhI5f9iR9m+r9\nwlPpsfWwJtwGIyF5BFCtZajEG4XbELilF9LNtHwNpY62VNAxHgSCEm4hRDRItF+TUr4b3l3qBX+d\nk7YuAJLeb6v2P/LK1kkXphCu6EINwGkNtnNS3XEV/lICO+roR08roJxRgHrtjSeIcV7NxGHenZOt\nh+kzvvCsV1L6EUVhWRPd1x2qx5013vt1VwebqkWs3RwNaZV0M0zTnys/NpSiQltfpxvRD1+kUYcf\nLwKeOAH41xjgpYt0uwIgwYrPoN9ryqVkKxlvUm7CvZMEJ71QHxlZ5eG/qwFRqqWRM5VaRmXf6FFc\nVUnoETcQXL2SxoPAwRXAzGvpu0+9nPxh9T2UfzxyHtUaAfSI23hTk9J9QNjuT0lUiz0Gp024gI7F\n+7fSdZE22j3ibqnU6sVnA5MvIVFPH+P+GyjUqMmkXPfXlc+t8s6N9pK6iez5nFqjSvSMwr3mceCh\nyXoWUNVGuqn/7AtqCdTs1P3plDwS7tpSEt3P/0CtlsQcYOIF2jIj4SrLWqAJt7JKjAFScq4+IE5F\n3O01dJzU9S8EdaYe+pa+AwCMO9v72ISBYLJKBIDnAJRKKR8M/y71gr+IW0XbmeMBSLcKZx9uPYyz\nH1oBh1O6l3HVTlKrtCAz0YIWo8cdilXiLyWws54uLOMFrqwShYq4AbpAjBG300EnVW8Rd0cdRUhl\n31D0qaJlte5gs0o6G2ldmRO833P55Zpwq2gsdaT3soo0Pzc0fziddKHmFgGFpwMXP0YtpLhUYP6v\n6GJ88iS9w7OjTreYZi0kS2jne/r6mstpJG7iMD3izhhL6zdFedslxpGsgJ5Z8o122sem0rabykg8\njDep3gimZMDmV6k1WHQ1PR97FhWVUt9JRbPff4oiYIC+v+e0dyvuAx4t0n/3tU9Sh+RYj2gwygJc\n/gKltgEkqmqcQFcjRZApee6tr4wxdG15dp4aR016UnCK/r8x4lciXvKc/n0BvSVQ+x0NsGqvoZYP\noNkekyhIyByvCbchIyR3Bh2Pp0+mc2nmj4Fb1ulBhNp+TAqdC9HxWsRd7X5dGq85JdzqxhtnCNxG\nnUTb3/QS3ehTBqf7L5iIez6AawCcIYTYov2dH+b98o8lwY9wa5GWahIZfO4dVS3YU9OO+vZuOplV\nzRPtztmJWIzOSDBklSTRndXX7DLdrT6EO58eVU+1osOXcI9wPyncIu4c9w659hry2PwJd2wq+Z2d\n9eQlO+00xNpz3cHWK1GdLFk+hNscTcdNnbwtFQCE/9YAoNfx9hVx27uB1Y8CL18C7P6MLJIPb6Nj\neNJtJBbDZwJ/OAz89DPge38BbllLn1OTFajjC1AEmjmBLiBF0yESnmHTaFCRtYWExxJPF1nFevd9\nMo5kBUgYTFF0Q0zLJwuqaqPWyTYKIdGbVSIltTbGnqVf/PnzaTCaqiaphFv5sIChlrtml9i7aRh3\n8yGynCo3AuXfUmexL+81dSRw2bMkbMOLPCLuCncrDNAF0DNIMY6a9ER5yYnD3GvfKxEtXwPkTNPP\n8+g4Ws+GZ3Ub8Mh2Oka13+nXeM4Uet5aRTe8xGF0/ObeBFz4EPCrHXTzN95g1TYzx+pedvU2ahEb\nW8JJhmtOWSUK4/U/WvO5Gw8Mmk0CBJdVskpKKaSU06WURdrfJ4Oxcz6JjtOKAXn4nco+UaLTXAHs\nXAIs/ilarRRJH27uouXU4J60fDhFFBpkMkZnxKPH7oTV5jBUCPQRqfqKuC3xdOLt/1p/TUotIszQ\nrZTYFLopqJPbHOOeKaIibvXdjE1AX5hM+ujJgyspihg+07BfvVQ69ERFNZk+rBLAvbRrcwUJiWem\nixEhtFou2kVu7wa+ex/48k/AE3OBL/9IF+TrVwIPT6OI8+TfAFMv870+lWOrUsXU8VXbmr2QomiV\nCdKsZUrkTNGjsvQx9Jg3h/xq483ZWPQLoO+mWh8TzqfPdDVSBkQoNgnQu1XSeID2cYIhJrIk0LBx\nlYKa4KPvyHMSjl0fU2dsyigakr/873RezLrG/7bHngXcvpl+TyVQyuP2PPcytOPnaZd4jpo0kjVB\nC2A8+gQSMqlvAvBO/03Lp3Mtt4iWObKNrg1rsy7c2ZPp5lJbSqJtjqIb7/n3ky3kqzWohDtjHD0m\nD9cHt7lZJUbhzvAQbkPErcr2AsC4c7y3FyYic+RkIKLjqJPP4eFhu6ySCQAERW6f/x7Y8Q46O+m9\n6hare8Q9/hwsXvAx6pCKggwSudYum5twW20OEpz3bwW2vkknU4xH5yRA0VjFOt2j7m6lfUzI0nzY\nRD16ScyhC9FzWH9iDnVIuaJazY7wF3ED2ujJBhLu/PnuI/lcEXeQwl2/h1KiUv1Ek8bJFFrKvaMx\nX6QZqieuexp461rKCkgcBvzkXWDRLuDMP1Pz86o3KP3Qs2PUbX35eqdVZ727mM24itK4VC3l5nL6\nLioXHaDmMUAibOtwH9be0+7eAgJ0u2T8ucCIYm25tuBTARWxKYGtEpURozIVFCrFLSHL9yhNT+He\n9BKJ9o9epfNo31LKujHekAIRHav3E3XU+Yi4NeH27KD0HDVpRAiqVX7qnd6vq3PN08ZRN8YFv6Lf\n4Mh2fSCNK+LWftcDKwJfI77Wq/pxknINFUN9CHdMCt3A/UXcJjPZJbGpet/JIDAEhdtPaVcVVcal\nUeRQ8pwryhLaSV3dYtUibm0dQqDSmQ4hgJHp9Fqr1ebKrS4/UoPp936B0i2rgc2vUIlWwDviBjSL\nQlI+MaA3XROy6ATNmaJHsuYoEmlPkVBNYSX+xkEB/ojPoF7ypoPuXiJAEXl0gI5WT+p2k7D5G8Yd\nl65HVs0V/lsCRtILSUAddrIcMsYBv68CfvY5iVKUBTh5EXDrBmDCeb2vTwm3tZmsIaNwx6cDo+eT\nWPV0kPCkjtbFV5j1qG+kdpFVGuyS7jZvgRt/DuXFjz6JBCNab62FRG8Rd/kaOnc9WzvK9/XlHQPu\nwt14EDiwnKLr3BnAtCvoRjbvptD2NS4dOKKlQXr+xrHJtE1fEbdx1KQnUy7Vv4uRtNEUCKnh+4oJ\n59HNctLFZHUd2QbUKOHWxv/laI89bcF7y4nZwFVvAsU/o+duYm3MKtFeV/1I/oQbAM67D7jm3UFJ\nA1QMQeHW5528Z8l23PKaNj+ecWBNyki6SKKo08XcRSJa3dxFyxnmsmzq6EFKXDTSEgyFprSIe/O+\nKvQ4nGis1Lzrk39DVsSoed77lTOFLuZSLSXPJdzaD/+j18l3UyTlekfurnolmji2VlEzMVAnWEKm\n7ucrL9FITKJ3fZEXzgfeuYGa1cbsg/rd/m0SgHzkqhKKulurAndMKtILSGCbD2kjLedTx1JfScun\nDATVGvG0D8acQalZFdpQg9TRNFDFpKXRmbWywWkFdNMzjuz07JwEgGmXAzd/Q58zmXUrKlThViV2\n/Q3OKF9LA4k8p+vLmkjns7/WjXHau40vaJ2bP6bXLnwYuOmb4KNRRVyaPmmwr9/YV2aJqqUSKqfd\nDfzgP/rvoph8CXD1m3qNc2sL5UnHZ+rXVPIIXUQDBTeeTDhX72BUx8YU5X4uJeYAEPq2jOMojJ2T\nAN18PKtDhpkhKNx0R+/saMPijZX48rsasjOMNUjUyXbSbQCAGKsm3C1WahYZClg1dvYgPd6C5FjD\n6EnNwthXqfWUq9Fd838J3Ljc948kBEXdB1foNTEAPWUvIcP9B59xFQ1YMeJZr0SlKAWyDtT64zP0\nSMSIZ0767k9pwMDuz2iKqdUP0+sN++l7BjoBx55JIrzjHbKBgrFKVGfWro/IKhg9v/fPBEIJpipg\nZSy6BeiV+jZqnZRpo0kUhhcBwwyWiRBA3lz3DkrPzklf5Gl2SchWiUcefE8nTeG18UXKJ2/Yp3d0\nGRECuPot4Fw/I1DNURQh1+yg0Y1Tvq9HnzGJNGIwVOLTdPvAV6sqY4y3VeJr8E0wjJhNQhqIYdPp\n8eBK3SYB6Nio0gSh3pwUqr8pabj7TdMcTddjfBAR91FgCAo3RdzrdlfAanOix+HE9qoW98puky8h\nYZz5EwBAXA/lOVe3aBG3oTnX3NmDtAQLkuO0CoFWmytyaKwuo022V9IP5znwxpOJF5Kg7ftS7w33\n1aEEAPNuBBbc4f6aK+LWMktaD/feBFQRQf4C72gN8K7Jvf1tik7uPEAR+sYXKOrevhiAoAvfHyNP\noNbKxhfpuT8v3IhKCdz8Gj16erihooRbpfJ5Ht/sKfSamnpKCeyPXqcMAyN5xZRfrIoqdfvwuD2Z\neQ0w72a9ky5Y1MWuhHvvFyTWX/4Z2PMpvebv2ORMDhzhJ2QBpR/SuXf6H0LbL1+4SgcL9+wKRXoh\nnaOqJed00rnaF+EOhuzJ1JKA9A5OlF0SSsRtRAm+L+E/68+6zaQ6JM0WV0v+aDJ0hXtPFdI1e2ND\nWaMh4o4n4f7+U64INsFGF6bL4zZYJY0dNqTFW5ASZ6gQmJYPKczIsZEFEd9ZFVx0OXIubXPb23rE\nrYQ1GGJTKdNECXdLVe8npFq/p7+tMNbk7mgg/3fqZeQtz1pIUfahVSToo+cHvlFEWWjUoRqMEozH\nnZRL36l+N3WaBWOvBMIl3JrF4SncJhPlgDttdIGpm2Filrfl5BqIs4kGbDm6fXc8G8kcS55mMOVc\njXhOprDjHer4srYAn/2O9lXNqhIqrlz2a0O/ofhCHSd/WUNqG9VbgWX/D3h4Ko2kNRbAGkgs8XoW\niGcLQvVf9FW4VcTtS7iLrtZHV6pO29jUwC3gQWIICjdFy7vKa3Bp0QiMzU5ESVmTe9U/17JxkJZE\npMlmWMwm1LR2QRoH4IA87vSEaCS5rBIbEGVBW9wIFIojGJEah5SeI8FFlyYzRfl7P6ch7jHJofm5\nQui53K7BN72ckNmT6aL31fEDaJUOtcjouyVkdUzTptmaeAHt4xd/pMjT07rxxZgz9P+DuZmZTHo6\npC8rIFQSsujGq1IXPa0SQLdLUkcFvshGzKJIrnK9YfabXiLuvmIs7WptpYh7xo9ogEhPO1kGffX+\nk4ZRX8ipdw3Mvio/19/vqzJzXr6UJrHImUKlfRf0UlumPwybRo+eEfeU7wOn3Em/ZV9IzKEoOpjr\nOy4tImwSYAgLd5TTigtn5GJOfhpKyhohu9u1Yj7ukZAjPguZogVjshMRLW0Qalg8ACklGjWrJCbK\njNhokyvnuxy5GB9Vg6K8FGTaa4L7YQGKYqWkpnoo0bZC5XK31wYefKMYfRLwu0r/TWljpcPti6mz\nS10Elnhg6g8ogjZFUYW/3hijpafFpvRuHSmUXdJfmwQgIU7LByBpH3xFhIWn0WNvv1lMEglB5Qa9\n2d+bVdJXjB737k+pyNHUy4DT76GbZ2Evs+gE4ow/AAs/GDirQkXc/lpU6WNomeEzqY7Jj9/WW3Hh\nYtQJdHMyetxqX8/4g3fnZrCYo4BrP3D1hwUkgoR78PJXBgpNdIcnSMwcmYoDdR14fX0FmluakBbt\nnYpki81EJloxcVgSjlRrmQha52RnjwM9difS4umES4mLRkunDVJKbOvKxGXYitFxXYhDd/DCnTaa\nRlDt/cK/vx2IxBxKd3MN4w2iCRjopFWdk63VNILu9Hvco9Cin5BnPeYM3xUIPckYo09EHCyqCd3f\njklFWj6V6vR3fJOHUxpZ/oLe15U3B9jxru49hzvi3vgiRd0pI2nbJhPwy62hHU9P0gsH1qZQHrc/\n4bbEA7/erU3ePUi2wezrqYUYDuEMtiU49iz6zhHAEBRu8rjHp5khhMCcfK2mdnMz0nxMd2aNyUCm\nOIzxOUlYD23CAU3gGztoEE+6JtzJsdFotdqwv64dO3tycHW0FVMcNECjJzEPQf9kxT/VU5dCJS2f\nPrtF68zrb+2DmCSKuA9ptYs9R6jlFQMn/5oKDgWDEMB594c2Hdr0K+hzmeOC/0wgVOsi0I3xyleC\nW1feHOqgVaPnLEEOVAmV1HzghF9QtoutgyI81ZkczA1zMHFF3AGssP6kdPYFc1TfM0cGiu/95ehu\n38CQE25nVBxMALLjKP94VHo8spJi0N7aAsR7R0udlnRkihZMGJaIOEHCXd4G/PWlDTh/GnVMqBzu\n5LhotHTZsPZAIw5Iem98B2UvNFly4GcIhDfjzqZ86L6kYp3yG0opLHmenve100VhSSShKFtFopQz\nzf19IYAz/xTaOoMZKGNk+Ez3ofj9RQm3L387VFQH5f5l9BiuiNtkAs79B+Ut7/l80KrI9QlluYSr\ns5HpN0NOuGusArkAMmOoxoQQArNHpaHnYCuQ6m2VtJvTMUK0Iy85GmlRVERqyXfNWHqoFktLKV86\nPYGshpS4aNS2WbH2QAPaE0YDNmBEA83MUWsKQbhNZuDm1X3y3fZ3WHD4xGdx8rc/paySUCrQ+UIJ\n0d4vaeDQII7uChvBRNzBkjGWMgUOLKfnwQ4N7yuxKcD0H4Z3G/1l+EzgmveAgtOO9p4wfhhynZMV\nrdRET7c4qFjRh79CfmYCTPYuyGhvq6TFTPmXabIFeYn02bUVXbhidh5m5JFflp1EeZnJsVFo6bJh\n3cFGFBaOA6LjEd9+CK0yDjU9IeZuRvXN/3vi63248Z1DcF7/GXDDl/33EFVnW2vlwHQORgIDKdwm\nE9lFahKMcHVODiWENkuQr3EBTEQw5MKviiYrpkkL0kQbsP5tID4do05ahHhYYTXFIc5j+UaQcCc5\nmjAiXgJdQIeMwW1njEN2cgxKq1tddUqS46JR2dQFKYF5hZlAyxjgyHZUySzUtXdjMKho7ESXzYHy\njijk+yqvGirGCHKgOgePNmn5ZHGoyQT6S95cym8Hwh9xM8wAMORuqRVNneiCBcllX5B321aNkWkx\niIcV7U7vDpN6UFQd092AnDiyVyaPzsWojHjERpsxc5RuRaTERbv63OYVprvyVStlJurbBke4yxsp\nH313TQjzRAbCNRt9TN9zXSONKAtww1K9cl5/UcPYAY64mSHB0BPuxi70iFiINq1yntOO/NhOxAsr\nWh3ennKtk9KsREcdsjVf/NyZvjtdkmPp81lJMSjMTHCN1qo1Z9MkDEFwzXPr8PKashC+kY7V5kBN\nK21nz5GBEm7NPsorHvxMgKGCqs9itoQ3F5lhBoihJ9xNnbCbNUNEG646zNSEeHSjye590R22a03f\n9lpMyyFhPmmS75zs5DhyjuYVpEMI4Yq4W2KGo77dzxyWBqw2B77ZW49X1oQwOa6BqmZ9Zp9dAxVx\nq+sJMaYAACAASURBVM7J0ScNzPqOReJSaWAS2yTMEGHICXdlYyekKu065wYAQHR7NRJEN+q7SXjf\n3FCOm16hWhb1PdGwIgboqMPwtu1AbCqiE3znzap6JfMKtTQzrYBNS/xo1AVhlSjh3VvbjgN1IUzQ\nq6FskowEy8BF3OljqFPS36wyDKFSOBlmCDCkhLvH7kR1qxXCEk+ju6ZfSW9ocz3WWGm4+3ubq/D5\nzhp0dNvR2mVDqzmVZmEp/YiGePtJiZs6IgUz8lJw1iStMNGwacCNy1GZeXJQVkllkx4xf76zJsCS\nfj6vCfcZE7NxsL4D3XYfc16GSmwyzdnoOVSYcaPt5Htw5Afv9b4gw0QAQ0q4DzdTxkf5uIU0ei95\nOM1qUr8XAFDdZYbd4cS2Sppp5EBdB1qtdnREpdOgB3sXlXv1Q15aPN6/dQFyUwy5KcNnIispNqis\nksomEt5hybH44rsjIX+/iqYuWKJMWDAuE3anxIG6IGdnHyDe31KFfbWhtxSOBe5+dyeuenbd0d6N\nQcHplKhptR7t3WD6wZAS7gpNGE1TLqFh1CYzjfJyRdxR2FrZgs4eilT317WjzWpDpyWDCjalF/Zp\nXrjMRAvarHaasCEAlU1diDYLXDV3FDaXN4d8cZQ3dGJkWhwmDqMO1T01baho7MQHWw/7/YzTKbGj\nKsCUWEHSarXhjje34LFle/u9rqFGS6cNX35Xg4P1HWjv9jNDzTHEh9sOY8F9y2jybGZIMrSEu5FO\nNJV3DYA6KDXh7kQMPthS5Xprf107Wrvs6InVPOvpV/ZpQEtmImVj9GaXVDZ1YXhqHM6bRkOGv/gu\nNLukoqkTI9PjUZCZgGizwObyZlz/4gbc/vpmNHf67hxdsqUKFz62Cmv2N4S0LavNgUseX4XPd1LL\nYNOhJjglqETuMcgtr23C3z76zud7n+yoRo+DSijsPw5aHCVlTbA5JL4N8ZxhIoehJdxNnYgyCQxL\nNoxiTM51TVrQiVh8tK0amYkxKMhMQGl1G7psDvTEaYPV+zjUOCtJCXfgzJKqpk7kpcVhXHYiRqTG\nYe0B/xfGXz7ciUVvbnF7raKxEyPT4mGJMqEwMxEvrylzWRdbKpp9rmfFHvruL6w+GOzXAQAs21WL\nrZUteLuEJotQgl3V3EUzBflgU3kTtvrZj0hm/cFGfLy9Gm+WVMDmcHq9v2RzlWvqur3a8a5s6sQr\na8qwZn8D2qy2fu+D0ynxyfbqAVlXbzR39mDXEf8zypdW03vrApyfA81v396KP72/Y9C2d6zTq3AL\nIZ4XQtQKIY76Ua9o7MTw1DiYTYao2VCEqUPGoqGjBzNHpWJMViK2VJAYHSy8imov9LFojoq4e8sU\nqWzqwojUOAghMGFYkt/oTUqJ97ccxvtbD6Olky7klk4bWq12jNJaE+OHJcEpgevn58MkgM3l3oIp\npcTqffWINgssLa1xeeyelNV3QHpU81uymVomq/c1wGpzYH1Zo2syCX9R96/e2IKfPLsO5Q2+t+OP\n9Qcb8eu3tuKzHdW92k3h4LFleyEE0Ga102xJBqqau7DuYCOum18Ai9nkulE++tVe/PH9nbjqP2tx\n0j+XYVN5cC2RQw0dOO+Rb7DXI51zyZYq/OK1Tfj7J6V+P7tyT51bSmhf+ccnu3DpE6tpNicPnE6J\nXVrG0rqDjV7vh4PS6la8vbESL685hC9DbIUyvgkm4n4RQC+zeQ4Ohxo6XcLmQk09BKocCACzRqVh\nTHaCK0KOScl2n7klRMZqEfSdi7fh4aV7YPcRtVltDtS2dSMvLd71mQP1HXA4vcuf7qttR2NHDxxO\nieV7qNCV8u9HptN3uHx2Hq6aOxJ/OH8Sxuck+Yy4dx1pQ317D249fRyEEHhlrXf++KbyJpz2wHI8\nvfKA67WWThuW767DhJwkdNkcWLW3HlsrmnHZrDzEW8woKfO+oMsbOlHe2Im2bjtufX1T0Bkv5Q2d\nuPGVEry7uRI3v7oJZ/57hctHbrXa8Nq6Qz6PUTAcqGvH79/bjs0BRHVLRTO+2VuP204fC4vZhGVa\nYTHFB1uo/+CyWSNQkJmAfbUkaiVlTVgwNhMvXD8H6QkWLHxufVDi/c7GSpRWt+LRZftcr3V023Hf\nZ7tgNgm8VVKJg/Xenc7bKpux8IX1+Oenu1yv7a1pQ22I/SRSSizbXQurzYmPtnn3jVQ2daG9245x\n2Ykob+z027oaSJ5fdRBx0WaMzU7En97f0ad+BCkleuze111pdSt+/dZWnzepgebT7dVYGiE3nl6F\nW0q5EsDg3JoDYLU5sOtIK6aM8Cg4b6jRm5JCdUlmaRG3Iimmj7NjaCTEROHj2xfgwum5eHjpXjz6\nlXcHnuroyUsj4R2blYgeuxMVjd7RqYp04qLNrgqFajkl/KeOz8I/fjAdUWYTZo5KxZaKZq+oefU+\nmpD4iuI8nDMlB29uqPBqir+wugwA8PDSPa5tfKp5uv976VTERJnw2LK96LY7cUJhBmaOSkXJIW+B\nWr2ftvXbcyZgW2UL7vt0d2+HDZ09dtz4SgmkBL5adCoeuGIGqpq7sGwXfefnVx3EH97bgfe1fgkp\nJfbVtnt9T0V7tx13v7MNt/x3E257fTPOfmgl/ruuHH96f6fPz3T1OPD3T0qRGh+NG08dgxPGZOCr\nXbpwd/bY8cLqg5hbkI7RGQkYm52IfbXtaGjvxoH6Dswfm4nTJ2TjjRtPQHoiiffOw/47gqWU+Hh7\nNYQAPt52GGWaQD+9Yj9qWrvxfz+eBYvZhIe+3OP2OYdT4p4lOyAlsHx3LWwOJ3rsTlz5zFrc8t9N\nvR5nI6XVbahr64ZJAIs3Vnq9/51mkyw8KR8AsO6A96Xd0W3Hg1/uCSn6L6vvQFeP9828vr0b7285\njMtmj8D9l0/HkVYrHvi893PHk9fXV2D23750a1U2dvTghpdK8M6mSry1oSLkdYbC1opm3Pr6Ztzx\n1paI6MAeMI9bCHGjEKJECFFSV1c3UKt18V11K2wOmvXGDUPEnZGeDrNJYFpeiptwqxnc+0NqvAUP\n/2gmLikajqdWHsChBveoSeVwK+Edk03b95Vet/5gI7KTYnDRjFws311LAq+dkKMyvEvTFo1MRUuX\nzStSW7WvHoVZCRieGoefn1yINqsdV/9nnWuwUE2rFZ9ur8aF03NhEgL3frATHd12vLu5CoWZCZiT\nn4aTxmRgq5Y+WZyfhtmj01Fa3ep1cq7eV4/spBj84rQxuO6kfDy/+iC+2Bk45fGRr/ZiT00bHr1q\nJgqzEvGDmSOQkxyDT7ZVu+wiAHh82T44nBL/+eYAznpwhZewASSKv317K94qqUDp4Vas2V+PK4pH\n4s5zJ2B7VQuWa17/V6U1eHdTJUrKGnHF099iQ1kjfn/+JCTGROGsSZQfryyvZ785iNq2btx1LhXz\nGqtFoarTTk3SkZsSh9d/fgKSYqOw8PkNXr+9Yk9NO/bXdeC208ciymzCUyv2491NlXh65QFcPGM4\nzpkyDD9dkI8Pth7G9S+sx7kPr8Sdi7fi/s92YVtlCy6Ynuuyc1buqUNjRw82lDVh4yF3cX1l7SE8\nt8p3n4Zqwd1wciE2lzd7nX+l1a0QArh05ggkxUZh3UFvn/uF1Qfx6Fd7ccWT37qOlZQS3+ytw/+8\nuhFflepRZ02rFXe8uQWnPbAc9322y2tdr649hB6HE9fPL8CsUWm49oTReGlNmasFuftIGx78cg9u\n/e8m/PKNzfh4WzU6e7yF8fX15Wiz2vGPT2gbdocTt72+CXXt3SjITMB/15drN/42XPLEatd+t3fb\nsejNLT5bOcFitTmw6K0tSIqNQpvVjjfWl7veczol/rhkB85/5Bss2VzlszUeDgZMuKWUz0gpi6WU\nxVlZA1Bu0wPl8RqLQgFwi7jPnTkGN51SiHhLFMa6CffAFUH83XmTEGUS+F+PDAUl3CNUxK2E28MX\nl1Ji/cFGzC1Ix1mTctBmtePb/fVYd6ARKXHRrnopRtR3Nvrc3XYH1h1oxMljM13L/Ofa2dhb24bL\nnvwWGw814bW1h+CQEneeMxF3nDUeX+2qxZQ/f471BxtxSdEICCFwxkQabFSYmYDMxBjMyU+DU8LN\nfnA6Jdbsb8CCsZkQQuB350/EtBEp+M3bW10RkN3hxD8+LcWiN7dASgmbw4nFJZX43uQcnDqezgeT\nSeC8qbn4enct1uxvwMH6Dpw1KRsH6jtw/2e78K/PdyMtPhqPLtuH/67TLw4AeGblAXy64wjuPm8i\nlv3mNJTc8z384wfTcMOCQoxIjcNjX+3Fg1/uwc9eKsGit7bi8qfWoKy+E88tLMYPi2kmF/VdP99Z\ng9o2K55asR/nThmG2aPTXb+ZUwJvlVTAYjZh6gh9mqzhqXF4+WdzYXc6ce3z69Hqo5PxEy3a/smJ\no/HD4jy8saECi97aiinDk3HPBTQA6saTx2DisCRUt1iRlRSDj7ZV4+mVB3BiYQbuv2w6LGYTviqt\nxXtbqpCeYEFqfDSeXqHbXDaHE//+Yjce/GK3z/6CFbvrMDk3GT8/uRBmk8Br6w7h6121+FBLKS2t\nbkVBRgISY6IwJz8dq/c14N9f7MZFj63Cnpo2dPbY8fzqMszIS0G33YnLn1qDa55bh/Me+QbXPLce\nX35Xg5+9VIJ/f7Eb936wE6f+62t8vK0aI9Pj8PH2ajgNtteRFiueX3UQZ0zMdgVSvzlnAnKSYnH3\nO9uwck8dvv9/q/H4sr3YVtmCVXvrcct/N6HoL1/iqmfW4s0NdA7sq23D9qoWFGYl4OPt1fh0ezV+\n9lIJVu9rwP+7dCpuO2MsDtZ3YNW+evx28TZsrWh2tTTf2ViJdzdX4Ymv9yFUNh5qwsNL9+CGl0qw\nv64Dj181C/MK0vH8qoOwOZyQUuLvn5TilbWH0NjRg1+9uQVnP7RyUPpxhkxZ1y0VzchNiUVOskdd\nbINwnz2jAGdrNYRT4qORmRiD+vZun2LYV4alxOL2M8fhn5/uwqfbq3GeNotOVTNlvORoGSgpcdHI\nSorxingqGrtwpNWKeQXpWDAuEzFRJtz86kZYbU7ccvoYn9sck5WIxJgobKloxmWzaR7ATYea0WVz\nYP5YfXq0Mybm4PWfn4BfvLYJlz35LWKiTDhzYjZGZcTjuvn56LY7YDIJjEiNw9mTKWXxtAnZAHZi\nTj6J18xRaTAJ4KEv96CsoRPnThmGurZuNHT0uLYVE2XG41fPxIWPrsIVT63BVXNHYe2BBlekev60\nXNidEg0dPbhyjvv0V+dNHYYXvy3D3e9uhyXKhH9fUYQfPr0GT688gOykGHx8+8m4c/FW3LNkO8bn\nJKI4Px07D7fgvs924YJpufj5ye4dzJYoE24+bQz+uGQHNpU344rZebjh5ELsrmnDjLwUjM7Qa7Tn\npcVjUm4y7vtsFx76cg+cUuKu8/RZitTNdtW+eswalYbYaPeJp8dmJ+E/1xbjyqfX4B+f7MI/fuA+\nm9An26sxJz8d2Umx+J/TxmLn4f/f3p3HR1mdCxz/Pckkk30jCVkISQhhCxAgYXdBEUFFUIoUELGF\nqr3VulaRcsWi94r1CtdqrQhqUa4LFRQhChQRpVVBAiFsgbCFJGwJEAwQQrZz/3jfDEmYQDSBeUfP\n9/OZD3mXmXk475znnfecd84p5faesUzoG4+H2aEe7OfFioevcTznzLkq1uYWk5YQir/dRv+kVizf\nepjjZyoY2zuOYF8vXlmzhz1Fp2kfGcD6fSc4aXZor80t5saU8xMEnyqvZOOBEu65ph0RgXYGdYjg\n71/nOZJYgN1GzpFSuscaV619E8P4YmcRf12zB18vT+59J5ORPWI5caaCuXelEervzZ+Wbuf0uSqC\nfb149raujEiN4U9Lt/PKF3uweQgje8Ty4OD2bC44yUMfbCaroIS0+DCUUkxZvIXKasX04ednZg/0\n8eKZkSncu2AjE9/6jg6tA1gwuS+tg3yorlGs33+cNTuL+HJXMVMWbyUy0IeNB0rwEHj7130YN28d\n//HuJrw9PXju9m7ckR5HeWU1M5bt4OEPNnP8TAVtw/z4OOsgU27q5Oj3WZZ9iGk3d3bMdnUpuUdP\nMW7eOiqqaogItPPokA5clRxORXU1k+ZnMvOznRw7fY6l2Ye4u388T9+awqqco+QcLr3gc3M5uE3i\nzsovoWfbkAs3ePkaM5hUV14w8HtShL+RuFugqaSuSQMTWb71MA8v3ExEoJ30hDAKS84SHeKDzfN8\nDO0jAhyJe0PeCU6XVzmaMfoktsLP28YNnVuzNreYF8enMry78zn1PD2E1LhgsgrOfwv+MLMAP29P\n+ifVn76rZ9tQPn/0Wl7+YjfvrcvnvmuNk4GXpwcPXH/hnI9xYX48OzKF/klGUg6w2/jttUks3lTI\nU0u28dKqXMd71D1JxLfy581f9ealz3OZvSoXb08Pnh/Vjde+2svsVbm0DrLTOsjONcn1r77SE8KI\nCLSTf6KMm7tFEeznxeNDO/LgB1nMGpNKRKCdv47vxZDZXzH9k+0s+/1V/FdGDsG+Xjw3qpsx+FcD\nY9LbsHhjIb0TQpl6U2c8PIy7epx5dXxP1uwqZl/xabq3CSYx/HxiTwz3x0OgRkF6vPOZh3onhPGb\nq9sxd+0+hnePdpRJZt4JdhedZsaIFABiQ3z5+HeXHv/c325znPwBbugc6bjF87aescSF+fH62n28\n9uVeZo1JZfm2w/h6eeJt82DFtiPcmBLF88t3sjrnKCkxQVTVKAaZVzhPDOtEl5gg0hPCeGbZdv5z\nyTYOnjzLL80rkDHpcZRVVHNrajQnyyoZN28df1m9mz6JYaSbJ/IFk/teEPPsMamM6BFDh9aBxIYY\nV5hh/t54exoxpcWH8f53BXyVW8wzI1NICK8/wcmNKVGMSW9D/okyXrszzZFMPT2EAUnhDEgK5/Gh\nNQx/5V/88eOteIgwsH04cWF+zBzVjdmrcpk+vIvjStTHy5PRaW1489/7GdQxggeua8/oOd8y9aOt\n7Ck6zb3XGMfrH5kFjvrQUHWNIudwKVHBPgT5ePHIws0E2m0sn3K1Y6IVgEEdIunYOpC3vt5PoI+N\nyVclMu1m4zM3NCWKoXVOpJfTJRO3iLwPDALCRaQQeFop9eblDqyu4lPnKCw5y8T+8c53CIp13Mtd\nV1JkAJkHSvD3btkzoLfNg7d+1Zs75nzLpPkbmDMhjcKSs7RpMHVa+8gAlmQdpKyiisnzN1BaXoWX\npxDi50Wy+e3uxTtSqVaKAPvFD0XPuFBe+2ov+cfL8LZ5sDT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"text/plain": [ "<matplotlib.figure.Figure at 0x7f52489f6c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "losses = np.array(losses)\n", "plt.plot(losses.T[0], label='Discriminator')\n", "plt.plot(losses.T[1], label='Generator')\n", "plt.title(\"Training Losses\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Generator samples from training\n", "\n", "Here we can view samples of images from the generator. First we'll look at images taken while training." ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def view_samples(epoch, samples):\n", " fig, axes = plt.subplots(figsize=(7,7), nrows=4, ncols=4, sharey=True, sharex=True)\n", " for ax, img in zip(axes.flatten(), samples[epoch]):\n", " ax.xaxis.set_visible(False)\n", " ax.yaxis.set_visible(False)\n", " im = ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n", " \n", " return fig, axes" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Load samples from generator taken while training\n", "with open('train_samples.pkl', 'rb') as f:\n", " samples = pkl.load(f)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "These are samples from the final training epoch. You can see the generator is able to reproduce numbers like 5, 7, 3, 0, 9. Since this is just a sample, it isn't representative of the full range of images this generator can make." ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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ugPYad3bldhI+fJGBviipJJFvIWCUZhR16ZxzW2yxhWlfW/k5R44caZpJoHHP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\nIUQQNOEIIYQIgiy1GkiSBC8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PP3bO5SZ+MsGTkZ6lgjZhWrTCEUIIEQRNOEIIIYIQ1FKj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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f5248987940>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = view_samples(-1, samples)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Below I'm showing the generated images as the network was training, every 10 epochs. With bonus optical illusion!" ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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Oi2658JpVWe2Oi0650D8uF5deemlHXGy44YZDnZGruLD+wawi/y4XWjCeq2h3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\nNt//85//DBRvwumnnw7AxhtvDBRLJ9YPNfybFk4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aqItYi6b1p7UgrKrXKjA25H33OJ3u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FAAAg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\n9/ya8fj/85//BNI1l9dteR4bUzQGlNPEWhjP3GSTTYCkyn/yk58A6Xo37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"text/plain": [ "<matplotlib.figure.Figure at 0x7f5254cecf28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rows, cols = 10, 6\n", "fig, axes = plt.subplots(figsize=(7,12), nrows=rows, ncols=cols, sharex=True, sharey=True)\n", "\n", "for sample, ax_row in zip(samples[::int(len(samples)/rows)], axes):\n", " for img, ax in zip(sample[::int(len(sample)/cols)], ax_row):\n", " ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n", " ax.xaxis.set_visible(False)\n", " ax.yaxis.set_visible(False)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "It starts out as all noise. Then it learns to make only the center white and the rest black. You can start to see some number like structures appear out of the noise. Looks like 1, 9, and 8 show up first. Then, it learns 5 and 3." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Sampling from the generator\n", "\n", "We can also get completely new images from the generator by using the checkpoint we saved after training. We just need to pass in a new latent vector $z$ and we'll get new samples!" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "(<matplotlib.figure.Figure at 0x7f524963ee48>,\n", " array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f525511c390>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f524f6412b0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f5252985be0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f5248da80b8>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5251876780>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f5251967eb8>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f5251bd8e48>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f5251600208>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5251f6eef0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f5251ff3e10>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f5251e6e240>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f5252150908>],\n", " [<matplotlib.axes._subplots.AxesSubplot object at 0x7f5252227a20>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f5251ccca58>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f52522e0be0>,\n", " <matplotlib.axes._subplots.AxesSubplot object at 0x7f5252313710>]], dtype=object))" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TORO7+c0337RltGCTWmshhPDzn//cNJ+vXr16meY4ehFofO64nNYcow2bav00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\nnLr37dvXNKf6nLImJfG9KLKuXbuaPv/881OPsbK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"text/plain": [ "<matplotlib.figure.Figure at 0x7f524963ee48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "saver = tf.train.Saver(var_list=g_vars)\n", "with tf.Session() as sess:\n", " saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n", " sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n", " gen_samples = sess.run(\n", " generator(input_z, input_size, reuse=True),\n", " feed_dict={input_z: sample_z})\n", "view_samples(0, [gen_samples])" ] } ], "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", <<<<<<< HEAD "version": "3.5.2" }, "widgets": { "state": {}, "version": "1.1.2" ======= "version": "3.6.1" >>>>>>> refs/remotes/udacity/master } }, "nbformat": 4, "nbformat_minor": 2 }
mit
mlovci/tmpnb_PAG2015
examples/PAG_Demo.ipynb
1
6520419
null
mit
PythonBootCampIAG-USP/NASA_PBC2015
Day_01/03_Advanced_iPython/03_Advanced_iPython.ipynb
2
42072
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Note: This is basically a grab-bag of things... \n", "\n", "# Advanced iPython" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "iPython: interactive Python\n", "\n", "Many different ways to work with Python:\n", "\n", "* type 'python' from the command line\n", "* run a python script/program from the command line ('python my_prog.py')\n", "\n", "iPython adds functionallity and interactivity to python that makes it more useful in your day-to-day life.\n", "\n", "It is an interactive shell for the Python programming language that offers *enhanced introspection, additional shell syntax, tab completion and rich history*.\n", "\n", " Fernando Pérez, Brian E. Granger, IPython: A System for Interactive Scientific Computing, Computing in Science and Engineering, vol. 9, no. 3, pp. 21-29, May/June 2007, doi:10.1109/MCSE.2007.53. URL: http://ipython.org" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## python shell\n", "\n", "Go out of the notebook and play with the python shell. Show some of the limitations." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "[user@machine ~]$ python\n", "Python 2.7.8 (default, Nov 18 2014, 11:55:58)\n", "[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.54)] on darwin\n", "Type \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n", ">>>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## run a python script\n", "\n", "Go out of the notebook and create a python script to run." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "hello.py:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting hello.py\n" ] } ], "source": [ "%%writefile hello.py\n", "#!/usr/bin/env python\n", "def printHello():\n", " print \"Hello World\"\n", " \n", "print \"File Loaded\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ipython shell" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "[user@machine ~]$ ipython\n", "Python 2.7.8 (default, Nov 18 2014, 11:55:58)\n", "Type \"copyright\", \"credits\" or \"license\" for more information.\n", "\n", "IPython 3.1.0 -- An enhanced Interactive Python.\n", "? -> Introduction and overview of IPython's features.\n", "%quickref -> Quick reference.\n", "help -> Python's own help system.\n", "object? -> Details about 'object', use 'object??' for extra details.\n", "\n", "In [1]:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Tab Completion and History Search is Great\n", "\n", "* Start typing and use the 'tab' key for auto complete. Can use this on python functions, modules, variables, files, and more...\n", "* iPython stores history. You can search it (ctrl-r) or you can use the up and down arrows.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run a file" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "In [1]: %run hello.py\n", "File Loaded" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run it and get access to the functions and modules inside" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "In [2]: %run -i hello.py\n", "File Loaded\n", "\n", "In [3]: prin\n", "print printHello\n", "\n", "In [3]: printHello()\n", "Hello World" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(notice I used 'tab' completion). Try using the 'up' arrow and running it again.\n", "\n", "Here's something else useful:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "%notebook -e mystuff.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(what did this do...)\n", "\n", "Note that this is a 'magic command' - I'll talk about this in a bit." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The four most helpful commands\n", "\n", "<table>\n", "<thead valign=\"bottom\">\n", "<tr class=\"row-odd\"><th class=\"head\">command</th>\n", "<th class=\"head\">description</th>\n", "</tr>\n", "</thead>\n", "<tbody valign=\"top\">\n", "<tr class=\"row-even\"><td>?</td>\n", "<td>Introduction and overview of IPython&#8217;s features.</td>\n", "</tr>\n", "<tr class=\"row-odd\"><td>%quickref</td>\n", "<td>Quick reference.</td>\n", "</tr>\n", "<tr class=\"row-even\"><td>help</td>\n", "<td>Python&#8217;s own help system.</td>\n", "</tr>\n", "<tr class=\"row-odd\"><td>object?</td>\n", "<td>Details about &#8216;object&#8217;, use &#8216;object??&#8217; for extra details.</td>\n", "</tr>\n", "</tbody>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The notebook (ipython notebook or jupyter)\n", "\n", "How does this work... Let's look at what it says about itself (from http://ipython.org/ipython-doc/3/notebook/notebook.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Introduction\n", "The notebook extends the console-based approach to interactive computing in a qualitatively new direction, providing a web-based application suitable for capturing the whole computation process: developing, documenting, and executing code, as well as communicating the results. The IPython notebook combines two components:\n", "\n", "**A web application**: a browser-based tool for interactive authoring of documents which combine explanatory text, mathematics, computations and their rich media output.\n", "\n", "**Notebook documents**: a representation of all content visible in the web application, including inputs and outputs of the computations, explanatory text, mathematics, images, and rich media representations of objects." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Main features of the web application\n", "* In-browser editing for code, with automatic syntax highlighting, indentation, and tab completion/introspection.\n", "* The ability to execute code from the browser, with the results of computations attached to the code which generated them.\n", "* Displaying the result of computation using rich media representations, such as HTML, LaTeX, PNG, SVG, etc. For example, publication-quality figures rendered by the matplotlib library, \n", "can be included inline.\n", "* In-browser editing for rich text using the Markdown markup language, which can provide commentary for the code, is not limited to plain text.\n", "* The ability to easily include mathematical notation within markdown cells using LaTeX, and rendered natively by MathJax.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What is a notebook:\n", "\n", "It's just a JSON formatted text file. Let's look at the really simple one we just created from the iPython interpreter." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{\r\n", " \"cells\": [\r\n", " {\r\n", " \"cell_type\": \"code\",\r\n", " \"execution_count\": 1,\r\n", " \"metadata\": {},\r\n", " \"outputs\": [],\r\n", " \"source\": [\r\n", " \"%run hello.py\"\r\n", " ]\r\n", " },\r\n", " {\r\n", " \"cell_type\": \"code\",\r\n", " \"execution_count\": 2,\r\n", " \"metadata\": {},\r\n", " \"outputs\": [],\r\n", " \"source\": [\r\n", " \"%run -i hello.py\"\r\n", " ]\r\n", " },\r\n", " {\r\n", " \"cell_type\": \"code\",\r\n", " \"execution_count\": 3,\r\n", " \"metadata\": {},\r\n", " \"outputs\": [],\r\n", " \"source\": [\r\n", " \"printHello()\"\r\n", " ]\r\n", " },\r\n", " {\r\n", " \"cell_type\": \"code\",\r\n", " \"execution_count\": 4,\r\n", " \"metadata\": {},\r\n", " \"outputs\": [],\r\n", " \"source\": [\r\n", " \"import numpy as np\"\r\n", " ]\r\n", " },\r\n", " {\r\n", " \"cell_type\": \"code\",\r\n", " \"execution_count\": 5,\r\n", " \"metadata\": {},\r\n", " \"outputs\": [],\r\n", " \"source\": [\r\n", " \"np.linspace()\"\r\n", " ]\r\n", " }\r\n", " ],\r\n", " \"metadata\": {},\r\n", " \"nbformat\": 4,\r\n", " \"nbformat_minor\": 0\r\n", "}\r\n" ] } ], "source": [ "cat mystuff.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Ok, backup, how do I interact with the notebook and what is it?\n", "\n", "First step, the notebook is made up of cells. These cells can be of different types. If you create a cell (click the '+' sign on the menu bar) then you can use the pull down to make it either \n", "\n", "* code: actual python code you want to execute\n", "* markdown: notes in markdown format\n", "* raw: raw text (like code you want to display like the json code above)\n", "* heading: you can make a cell a heading\n", "\n", "Let's play with the four types below:\n", "\n", "#### Code:\n" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2+4 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, you exectued this by hitting the 'play' button in the tool bar or you used 'shift-enter'. Some other ways: \n", "\n", "* Shift-enter: run cell, go to next cell\n", "* Ctrl-enter: run cell in place\n", "* Alt-enter: run cell, insert below" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Markdown" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Fancy Markdown Cell\n", "\n", " code code code\n", "* Bullet 1\n", "* Bullet 2\n", "1. numbered\n", "2. numbered\n", "\n", "Some verbose words\n", "\n", "[Markdown Reference](http://daringfireball.net/projects/markdown/syntax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Raw text" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "# Fancy Markdown Cell\n", "\n", " code code code\n", "* Bullet 1\n", "* Bullet 2\n", "1. numbered\n", "2. numbered\n", "\n", "Some verbose words\n", "\n", "[Mark Down Reference](http://daringfireball.net/projects/markdown/syntax)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Can I do this quickly?\n", "\n", "Yep - take a look at the 'Keyboard Shortcuts' menu. \n", "\n", "First of all, there are two 'modes': 'command' and 'edit'. When you're in a cell, you're in 'edit mode' and when you're out of the cell you're in 'command mode' You go into 'command mode' by hitting 'esc' and into edit by hitting 'return'. Try it a couple times. Move up and down through cells with the arrow keys when in 'command mode'.\n", "\n", "*go through the keyboard shortcuts*\n", "\n", "My Favs:\n", "* r,m,y in command mode\n", "* ⌘Z : undo\n", "* d: delete\n", "* (also, cntl-e and cntl-a work in a cell, if you live in unix, you'll understand why this is awesome)\n", "\n", "Once you start getting the shortcuts down, you're crazy productive." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise: Copy and Paste is Neat\n", "\n", "Try copy and pasting our for loop code from the iPython console into a cell (only use the keyboard):" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The menubar and toolbar\n", "\n", "Let's go over all of these functions and talk about what they do..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## It's like Magic (functions)\n", "\n", "IPython has a set of predefined ‘magic functions’ that you can call with a command line style syntax. There are two kinds of magics, line-oriented and cell-oriented. Line magics are prefixed with the % character and work much like OS command-line calls: they get as an argument the rest of the line, where arguments are passed without parentheses or quotes. Cell magics are prefixed with a double %%, and they are functions that get as an argument not only the rest of the line, but also the lines below it in a separate argument.\n", "\n", "### Examples:\n", "\n", "You've already seen a few magic functions above (%run and %notebook). Here's some others." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100000 loops, best of 3: 6.5 µs per loop\n" ] } ], "source": [ "%timeit range(1000) " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1000 loops, best of 3: 235 µs per loop\n" ] } ], "source": [ "%%timeit x = range(10000)\n", "max(x)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/json": { "cell": { "!": "OSMagics", "HTML": "Other", "SVG": "Other", "bash": "Other", "capture": "ExecutionMagics", "debug": "ExecutionMagics", "file": "Other", "html": "DisplayMagics", "javascript": "DisplayMagics", "latex": "DisplayMagics", "perl": "Other", "prun": "ExecutionMagics", "pypy": "Other", "python": "Other", "python2": "Other", "python3": "Other", "ruby": "Other", "script": "ScriptMagics", "sh": "Other", "svg": "DisplayMagics", "sx": "OSMagics", "system": "OSMagics", "time": "ExecutionMagics", "timeit": "ExecutionMagics", "writefile": "OSMagics" }, "line": { "alias": "OSMagics", "alias_magic": "BasicMagics", "autocall": "AutoMagics", "automagic": "AutoMagics", "autosave": "KernelMagics", "bookmark": "OSMagics", "cat": "Other", "cd": "OSMagics", "clear": "KernelMagics", "colors": "BasicMagics", "config": "ConfigMagics", "connect_info": "KernelMagics", "cp": "Other", "debug": "ExecutionMagics", "dhist": "OSMagics", "dirs": "OSMagics", "doctest_mode": "BasicMagics", "ed": "Other", "edit": "KernelMagics", "env": "OSMagics", "gui": "BasicMagics", "hist": "Other", "history": "HistoryMagics", "install_default_config": "DeprecatedMagics", "install_ext": "ExtensionMagics", "install_profiles": "DeprecatedMagics", "killbgscripts": "ScriptMagics", "ldir": "Other", "less": "KernelMagics", "lf": "Other", "lk": "Other", "ll": "Other", "load": "CodeMagics", "load_ext": "ExtensionMagics", "loadpy": "CodeMagics", "logoff": "LoggingMagics", "logon": "LoggingMagics", "logstart": "LoggingMagics", "logstate": "LoggingMagics", "logstop": "LoggingMagics", "ls": "Other", "lsmagic": "BasicMagics", "lx": "Other", "macro": "ExecutionMagics", "magic": "BasicMagics", "man": "KernelMagics", "matplotlib": "PylabMagics", "mkdir": "Other", "more": "KernelMagics", "mv": "Other", "notebook": "BasicMagics", "page": "BasicMagics", "pastebin": "CodeMagics", "pdb": "ExecutionMagics", "pdef": "NamespaceMagics", "pdoc": "NamespaceMagics", "pfile": "NamespaceMagics", "pinfo": "NamespaceMagics", "pinfo2": "NamespaceMagics", "popd": "OSMagics", "pprint": "BasicMagics", "precision": "BasicMagics", "profile": "BasicMagics", "prun": "ExecutionMagics", "psearch": "NamespaceMagics", "psource": "NamespaceMagics", "pushd": "OSMagics", "pwd": "OSMagics", "pycat": "OSMagics", "pylab": "PylabMagics", "qtconsole": "KernelMagics", "quickref": "BasicMagics", "recall": "HistoryMagics", "rehashx": "OSMagics", "reload_ext": "ExtensionMagics", "rep": "Other", "rerun": "HistoryMagics", "reset": "NamespaceMagics", "reset_selective": "NamespaceMagics", "rm": "Other", "rmdir": "Other", "run": "ExecutionMagics", "save": "CodeMagics", "sc": "OSMagics", "set_env": "OSMagics", "store": "StoreMagics", "sx": "OSMagics", "system": "OSMagics", "tb": "ExecutionMagics", "time": "ExecutionMagics", "timeit": "ExecutionMagics", "unalias": "OSMagics", "unload_ext": "ExtensionMagics", "who": "NamespaceMagics", "who_ls": "NamespaceMagics", "whos": "NamespaceMagics", "xdel": "NamespaceMagics", "xmode": "BasicMagics" } }, "text/plain": [ "Available line magics:\n", "%alias %alias_magic %autocall %automagic %autosave %bookmark %cat %cd %clear %colors %config %connect_info %cp %debug %dhist %dirs %doctest_mode %ed %edit %env %gui %hist %history %install_default_config %install_ext %install_profiles %killbgscripts %ldir %less %lf %lk %ll %load %load_ext %loadpy %logoff %logon %logstart %logstate %logstop %ls %lsmagic %lx %macro %magic %man %matplotlib %mkdir %more %mv %notebook %page %pastebin %pdb %pdef %pdoc %pfile %pinfo %pinfo2 %popd %pprint %precision %profile %prun %psearch %psource %pushd %pwd %pycat %pylab %qtconsole %quickref %recall %rehashx %reload_ext %rep %rerun %reset %reset_selective %rm %rmdir %run %save %sc %set_env %store %sx %system %tb %time %timeit %unalias %unload_ext %who %who_ls %whos %xdel %xmode\n", "\n", "Available cell magics:\n", "%%! %%HTML %%SVG %%bash %%capture %%debug %%file %%html %%javascript %%latex %%perl %%prun %%pypy %%python %%python2 %%python3 %%ruby %%script %%sh %%svg %%sx %%system %%time %%timeit %%writefile\n", "\n", "Automagic is ON, % prefix IS NOT needed for line magics." ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%lsmagic" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "My favs..." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "03_Advanced_iPython.ipynb hello.py\r\n", "Running Code.ipynb mystuff.ipynb\r\n" ] } ], "source": [ "ls" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "MUCHS INFOS: https://ipython.org/ipython-doc/dev/interactive/magics.html" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some others to try: %edit, %capture" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%capture capt\n", "from __future__ import print_function\n", "import sys\n", "print('Hello stdout')\n", "print('and stderr', file=sys.stderr)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "('Hello stdout\\n', 'and stderr\\n')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "capt.stdout, capt.stderr" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello stdout\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "and stderr\n" ] } ], "source": [ "capt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Magics for running code under other interpreters\n", "IPython has a %%script cell magic, which lets you run a cell in a subprocess of any interpreter on your system, such as: bash, ruby, perl, zsh, R, etc.\n", "\n", "It can even be a script of your own, which expects input on stdin.\n", "\n", "To use it, simply pass a path or shell command to the program you want to run on the %%script line, and the rest of the cell will be run by that script, and stdout/err from the subprocess are captured and displayed." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello from Python 2.7.8 (default, Nov 18 2014, 11:55:58) \n", "[GCC 4.2.1 Compatible Apple LLVM 6.0 (clang-600.0.54)]\n" ] } ], "source": [ "%%script python\n", "import sys\n", "print 'hello from Python %s' % sys.version" ] }, { "cell_type": "code", "execution_count": 58, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello from /bin/bash\n" ] } ], "source": [ "%%bash\n", "echo \"hello from $BASH\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Exercise: write your own script that numbers input lines\n", "Write a file, called lnum.py, such that the following cell works as shown (hint: don't forget about the executable bit!):" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 : my first line\n", "2 : my second\n", "3 : more\n", "\n", "---- END ---\n" ] } ], "source": [ "%%script ./lnum.py\n", "my first line\n", "my second\n", "more" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Hints:\n", "\n", "* Useful function: sys.stdin.readlines()\n", "* Another useful function: enumerate()\n", "\n", "You could use the notebook to query what these do..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Out and In\n", "\n", "You can access the input and output of previous cells." ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a = 3" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": true }, "outputs": [], "source": [ "b = 4" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a + b" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "12" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a*b" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-1" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a - b" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-1" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "12" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "___" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_49" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Out[62]" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'Out[62]'" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_i" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "u'a*b'" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "In[50]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Running Shell Commands\n", "\n", "There are some magics for some shell commands (like ls and pwd - this is relaly great on a windows system by the way) but you can also run arbitrary system commands with a '!'." ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python 2.7.8\r\n" ] } ], "source": [ "!python --version" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PING www.google.com (64.233.169.105): 56 data bytes\n", "64 bytes from 64.233.169.105: icmp_seq=0 ttl=47 time=48.425 ms\n", "64 bytes from 64.233.169.105: icmp_seq=1 ttl=47 time=45.380 ms\n", "64 bytes from 64.233.169.105: icmp_seq=2 ttl=47 time=44.583 ms\n", "64 bytes from 64.233.169.105: icmp_seq=3 ttl=47 time=48.418 ms\n", "64 bytes from 64.233.169.105: icmp_seq=4 ttl=47 time=46.023 ms\n", "64 bytes from 64.233.169.105: icmp_seq=5 ttl=47 time=46.956 ms\n", "64 bytes from 64.233.169.105: icmp_seq=6 ttl=47 time=46.265 ms\n", "64 bytes from 64.233.169.105: icmp_seq=7 ttl=47 time=49.450 ms\n", "64 bytes from 64.233.169.105: icmp_seq=8 ttl=47 time=46.727 ms\n", "^C\n", "--- www.google.com ping statistics ---\n", "9 packets transmitted, 9 packets received, 0.0% packet loss\n", "round-trip min/avg/max/stddev = 44.583/46.914/49.450/1.491 ms\n", "\n" ] } ], "source": [ "!ping www.google.com" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Managing the IPython Kernel\n", "\n", "Code is run in a separate process called the IPython Kernel. The Kernel can be interrupted or restarted. Try running the following cell and then hit the \"Stop\" button in the toolbar above.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time\n", "time.sleep(10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the Kernel dies you will be prompted to restart it. Here we call the low-level system libc.time routine with the wrong argument via\n", "ctypes to segfault the Python interpreter:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "from ctypes import CDLL\n", "# This will crash a Linux or Mac system; equivalent calls can be made on Windows\n", "dll = 'dylib' if sys.platform == 'darwin' else 'so.6'\n", "libc = CDLL(\"libc.%s\" % dll) \n", "libc.time(-1) # BOOM!!" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### Side note on versions\n", "\n", "There can be (and probably are) different python versions and ipython versions. This is normal. Don't Panic. Everybody got their towel?" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python 2.7.8\r\n" ] } ], "source": [ "!python --version" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.1.0\r\n" ] } ], "source": [ "!ipython --version" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## I promised you latex!\n", "\n", "You just need to surround equations in markdown with '$' signs." ] }, { "cell_type": "raw", "metadata": {}, "source": [ "$y = x^{2}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$y = x^{2}$" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "$\\frac{dN}{dE} = \\frac{N_{\\text peak}}{E_{\\text peak}} (E/E_{\\text peak})^{\\gamma} (e^{1 - E/E_{\\text peak}})^{\\gamma+2}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$\\frac{dN}{dE} = \\frac{N_{\\text peak}}{E_{\\text peak}} (E/E_{\\text peak})^{\\gamma} (e^{1 - E/E_{\\text peak}})^{\\gamma+2}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that it's using [MathJax](http://www.mathjax.org) for the rendering so if you're offline, you might not get latex." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Running a remote file!\n", "\n", "Note: do this after the plotting bit." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%load http://matplotlib.sourceforge.net/mpl_examples/pylab_examples/integral_demo.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Debugging\n", "\n", "iPython has a powerful debugger. Let's see how it works a bit:" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "In [1]: %run divide_by_zero.py\n", "---------------------------------------------------------------------------\n", "ZeroDivisionError Traceback (most recent call last)\n", "/Users/jsperki1/Desktop/gsfcpyboot/Day_01/03_Advanced_iPython/divide_by_zero.py in <module>()\n", " 5\n", " 6 if __name__ == '__main__':\n", "----> 7 divide_one_by(0)\n", "\n", "/Users/jsperki1/Desktop/gsfcpyboot/Day_01/03_Advanced_iPython/divide_by_zero.py in divide_one_by(divisor)\n", " 2\n", " 3 def divide_one_by(divisor):\n", "----> 4 return 1/divisor\n", " 5\n", " 6 if __name__ == '__main__':\n", "\n", "ZeroDivisionError: integer division or modulo by zero\n", "\n", "In [2]: %debug\n", "> /Users/jsperki1/Desktop/gsfcpyboot/Day_01/03_Advanced_iPython/divide_by_zero.py(4)divide_one_by()\n", " 3 def divide_one_by(divisor):\n", "----> 4 return 1/divisor\n", " 5\n", "\n", "ipdb> print divisor\n", "0\n", "ipdb> quit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When iPython encounters an exception (in this case a ZeroDivisionError) it'll drop us into a pdb session if we use the %debug magic. Commands:\n", "\n", "* ? for \"help\"\n", "* ? s for \"help for command s\"\n", "* l for \"some more context\"\n", "* s for \"step into\"\n", "* n for \"step over\"\n", "* c for \"continue to next breakpoint\"\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also turn on automatic debugging." ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Automatic pdb calling has been turned ON\n" ] } ], "source": [ "%pdb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lots of useful features in the python debugger (and could probably do with a seperate lecture). We'll save that for another day..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## nbconvert\n", "\n", "You can convert notebooks into lots of different formats with the nbconvert command (type 'ipython nbconvert' for all of the options). Example:" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[NbConvertApp] Converting notebook mystuff.ipynb to pdf\n", "[NbConvertApp] Writing 12451 bytes to notebook.tex\n", "[NbConvertApp] Building PDF\n", "[NbConvertApp] Running pdflatex 3 times: [u'pdflatex', u'notebook.tex']\n", "[NbConvertApp] PDF successfully created\n", "[NbConvertApp] Writing 39310 bytes to mystuff.pdf\n" ] } ], "source": [ "!ipython nbconvert mystuff.ipynb --to pdf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The %cython magic\n", "\n", "Probably the most important magic is the %cython magic. The %%cython magic uses manages everything using temporary files in the ~/.ipython/cython/ directory. All of the symbols in the Cython module are imported automatically by the magic.\n", "\n", "cython is a way of running c code inside iPython. Sometimes a c function can be much faster than the equivalent function in python." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The Cython extension is already loaded. To reload it, use:\n", " %reload_ext Cython\n" ] } ], "source": [ "#Note that I had to install cython to get this to work.\n", "# try doing 'conda update cython' if you get an error\n", "\n", "%load_ext Cython" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%cython\n", "cimport numpy\n", "\n", "cpdef cysum(numpy.ndarray[double] A):\n", " \"\"\"Compute the sum of an array\"\"\"\n", " cdef double a=0\n", " for i in range(A.shape[0]):\n", " a += A[i]\n", " return a" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def pysum(A):\n", " \"\"\"Compute the sum of an array\"\"\"\n", " a = 0\n", " for i in range(A.shape[0]):\n", " a += A[i]\n", " return a" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "==>Python 100 10000 loops, best of 3: 18.6 µs per loop\n", "==>np.sum 100 The slowest run took 8.76 times longer than the fastest. This could mean that an intermediate result is being cached \n", "100000 loops, best of 3: 3.19 µs per loop\n", "==>Cython 100 The slowest run took 7.01 times longer than the fastest. This could mean that an intermediate result is being cached \n", "1000000 loops, best of 3: 987 ns per loop\n", "==>Python 1000 10000 loops, best of 3: 171 µs per loop\n", "==>np.sum 1000 The slowest run took 7.38 times longer than the fastest. This could mean that an intermediate result is being cached \n", "100000 loops, best of 3: 3.65 µs per loop\n", "==>Cython 1000 The slowest run took 4.59 times longer than the fastest. This could mean that an intermediate result is being cached \n", "1000000 loops, best of 3: 1.77 µs per loop\n", "==>Python 10000 1000 loops, best of 3: 1.71 ms per loop\n", "==>np.sum 10000 The slowest run took 4.86 times longer than the fastest. This could mean that an intermediate result is being cached \n", "100000 loops, best of 3: 7.6 µs per loop\n", "==>Cython 10000 100000 loops, best of 3: 9.74 µs per loop\n" ] } ], "source": [ "for sz in (100, 1000, 10000):\n", " A = np.random.random(sz)\n", " print(\"==>Python %i\" % sz, end=' ')\n", " %timeit pysum(A)\n", " print(\"==>np.sum %i\" % sz, end=' ')\n", " %timeit A.sum()\n", " print(\"==>Cython %i\" % sz, end=' ')\n", " %timeit cysum(A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sharing notebooks\n", "\n", "You could send the raw notebook to a colleage, you could send them a PDF. You could also use the nbviewer: http://nbviewer.ipython.org" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### More Information\n", "\n", "This is just the tip of the iceberg. \n", "\n", "Good place to start: http://ipython.org\n", "Gallery: https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks\n", "Examples: http://nbviewer.ipython.org/github/ipython/ipython/blob/master/examples/Index.ipynb\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Breakout\n", "\n", "Go to the [Notebook Gallery](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks) and pick out one that looks interesting. Download it into your working directory and run through it. This might not work right away - you might need to install a package, you might need to debug it, and it might never work. These notebooks will give you ideas and show some of the other features avaialble in the notebook (and other packages).\n", "\n", "We'll be wandering around to help you out.\n", "\n", "(if you can't decide, this is a good one: http://www.astro.washington.edu/users/vanderplas/Astr599/notebooks/21_IPythonParallel)" ] }, { "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": "2.7.8" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
elivre/arfe
e2018/010-rede2018_candidaturas.ipynb
2
41464
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 010-candidaturas" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ano_eleicao = '2018'\n", "dbschema = f'rede{ano_eleicao}'\n", "table_candidaturas = f\"{dbschema}.candidaturas_{ano_eleicao}\"\n", "table_consulta_cand = f\"tse{ano_eleicao}.consulta_cand_{ano_eleicao}\"\n", "table_despesas_candidatos = f\"tse{ano_eleicao}.despesas_contratadas_candidatos_{ano_eleicao}\"\n", "table_receitas_candidatos = f\"tse{ano_eleicao}.receitas_candidatos_{ano_eleicao}\"\n", "table_votacao_candidato_munzona = f\"tse{ano_eleicao}.votacao_candidato_munzona_{ano_eleicao}\"" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "sys.path.append('../')\n", "import mod_tse as mtse\n", "home = os.environ[\"HOME\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TABELA CANDIDATURAS" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "query_create_table_candidaturas = F\"\"\"\n", "drop table if exists {table_candidaturas} cascade;\n", "\n", "-- Atributos obtidos da tabela do TSE consulta_cand\n", "create table {table_candidaturas}\n", "(\n", " ano_eleicao varchar,\n", " cd_tipo_eleicao varchar,\n", " cd_eleicao varchar,\n", " nr_turno varchar,\n", " tp_abrangencia varchar,\n", " sg_uf varchar,\n", " sg_ue varchar,\n", " nm_ue varchar,\n", " --------------------------------------\n", " ds_cargo varchar,\n", " sq_candidato varchar,\n", " nr_candidato varchar,\n", " nm_candidato varchar,\n", " nm_urna_candidato varchar,\n", " nr_cpf_candidato varchar,\n", " ds_situacao_candidatura varchar,\n", " ds_detalhe_situacao_cand varchar,\n", " tp_agremiacao varchar,\n", " nr_partido varchar,\n", " sg_partido varchar,\n", " nm_partido varchar,\n", " nm_coligacao varchar,\n", " ds_composicao_coligacao varchar,\n", " ds_nacionalidade varchar,\n", " sg_uf_nascimento varchar,\n", " nm_municipio_nascimento varchar,\n", " dt_nascimento varchar,\n", " nr_idade_data_posse varchar,\n", " ds_genero varchar,\n", " ds_grau_instrucao varchar,\n", " ds_estado_civil varchar,\n", " ds_cor_raca varchar,\n", " cd_ocupacao varchar,\n", " ds_ocupacao varchar,\n", " nr_despesa_max_campanha numeric(18,2),\n", " ds_sit_tot_turno varchar,\n", " st_reeleicao varchar,\n", " st_declarar_bens varchar,\n", " ---------------------------------------------\n", " candidato_id varchar,\n", " candidato_label varchar,\n", " candidato_titular_apto varchar,\n", " candidatura_id varchar,\n", " candidatura_nome varchar,\n", " candidatura_label varchar,\n", " ---------------------------------------------\n", " total_votos_turno_1 numeric,\n", " total_votos_turno_2 numeric,\n", " total_votos numeric,\n", " --------------------------------------------- \n", " nr_cnpj_prestador_conta varchar,\n", " declarou_receita varchar,\n", " receita_total numeric(18,2),\n", " declarou_despesa varchar,\n", " despesa_total numeric(18,2),\n", " custo_voto numeric(18,2),\n", " tse_id varchar\n", " );\n", "\n", "CREATE INDEX ON {table_candidaturas} (candidato_id);\n", "\n", "CREATE INDEX ON {table_candidaturas} (candidatura_id);\n", "\n", "CREATE INDEX ON {table_candidaturas} (nm_candidato);\n", "\n", "CREATE INDEX ON {table_candidaturas} (candidato_label);\n", "\n", "CREATE INDEX ON {table_candidaturas} (candidatura_label);\n", "\n", "CREATE INDEX ON {table_candidaturas} (nm_urna_candidato);\n", "\n", "CREATE INDEX IF NOT EXISTS sq_candidato_idx ON {table_candidaturas} ( sq_candidato );\n", "\"\"\"\n", "\n", "\n", "mtse.execute_query(query_create_table_candidaturas)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Insere os dados de consulta_cand " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def query_insert_candidaturas(cd_tipo_eleicao, nr_turno):\n", " query = f\"\"\"\n", " INSERT INTO {table_candidaturas} \n", " (SELECT\n", " ano_eleicao as ano_eleicao, \n", " cd_tipo_eleicao as cd_tipo_eleicao,\n", " cd_eleicao as cd_eleicao,\n", " nr_turno as nr_turno, \n", " tp_abrangencia as tp_abrangencia, \n", " sg_uf as sg_uf, \n", " sg_ue as sg_ue, \n", " nm_ue as nm_ue, \n", " ds_cargo as ds_cargo, \n", " sq_candidato as sq_candidato, \n", " nr_candidato as nr_candidato, \n", " upper(nm_candidato) as nm_candidato, \n", " nm_urna_candidato as nm_urna_candidato, \n", " nr_cpf_candidato as nr_cpf_candidato, \n", " ds_situacao_candidatura as ds_situacao_candidatura, \n", " ds_detalhe_situacao_cand as ds_detalhe_situacao_cand, \n", " tp_agremiacao as tp_agremiacao, \n", " nr_partido as nr_partido, \n", " sg_partido as sg_partido, \n", " nm_partido as nm_partido, \n", " nm_coligacao as nm_coligacao, \n", " ds_composicao_coligacao as ds_composicao_coligacao, \n", " ds_nacionalidade as ds_nacionalidade, \n", " sg_uf_nascimento as sg_uf_nascimento, \n", " nm_municipio_nascimento as nm_municipio_nascimento, \n", " dt_nascimento as dt_nascimento, \n", " nr_idade_data_posse as nr_idade_data_posse, \n", " ds_genero as ds_genero, \n", " ds_grau_instrucao as ds_grau_instrucao, \n", " ds_estado_civil as ds_estado_civil, \n", " ds_cor_raca as ds_cor_raca, \n", " cd_ocupacao as cd_ocupacao,\n", " ds_ocupacao as ds_ocupacao, \n", " nr_despesa_max_campanha::numeric(18,2) as nr_despesa_max_campanha, \n", " ds_sit_tot_turno as ds_sit_tot_turno, \n", " st_reeleicao as st_reeleicao, \n", " st_declarar_bens as st_declarar_bens, \n", " ---------------------------------------------\n", " get_candidato_id(nr_cpf_candidato) as candidato_id,\n", " get_candidato_label(nm_urna_candidato,ds_cargo,sg_uf,sg_partido) as candidato_label, \n", " public.eh_candidato_titular_apto(ds_cargo,ds_situacao_candidatura) as candidato_titular_apto,\n", " get_candidatura_id(sg_uf,nr_candidato) as candidatura_id,\n", " '' as candidatura_nome,\n", " '' as candidatura_label, \n", " --------------------------------------------\n", " 0 as total_votos_turno_1,\n", " 0 as total_votos_turno_2,\n", " 0 as total_votos, \n", " --------------------------------------------\n", " '' as nr_cnpj_prestador_conta,\n", " 'N' as declarou_receita,\n", " 0 as receita_total,\n", " 'N' as declarou_despesa,\n", " 0 as despesa_total,\n", " 0 as custo_voto,\n", " get_tse_id(sq_candidato) as tse_id\n", " from\n", " {table_consulta_cand} as c\n", " where\n", " c.cd_tipo_eleicao = '{cd_tipo_eleicao}'\n", " and c.nr_turno = '{nr_turno}' \n", " and get_candidato_id(c.nr_cpf_candidato)||ds_cargo not in (select candidato_id||ds_cargo from {table_candidaturas})\n", " )\n", " ;\n", " \"\"\"\n", "\n", " mtse.execute_query(query)\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "query_insert_candidaturas('2','2')\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "query_insert_candidaturas('2','1')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ATUALIZA DADOS 2. TURNO" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "q = f\"\"\"\n", "update {table_candidaturas} c\n", " set ds_sit_tot_turno = cc.ds_sit_tot_turno,\n", " nr_turno = '2'\n", "from (\n", " select nr_turno,sq_candidato, ds_sit_tot_turno from {table_consulta_cand}\n", " where nr_turno = '2'\n", " ) as cc\n", "where\n", " c.sq_candidato = cc.sq_candidato\n", ";\n", "\n", "update {table_candidaturas} c\n", " set ds_sit_tot_turno = 'NÃO ELEITO'\n", "where\n", " ds_sit_tot_turno = '#NULO#'\n", "\n", "\"\"\"\n", "mtse.execute_query(q)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### GERA TOTAL RECEITAS A PARTIR DA DECLARAÇÃO DE RECEITAS" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "query_update_cnpj_a_partir_receitas = f\"\"\"\n", " with receitas_candidatos as\n", " (\n", " SELECT \n", " sq_candidato, \n", " nr_cnpj_prestador_conta,\n", " round(sum(vr_receita),2) as receita_total, \n", " get_tse_id(sq_candidato) as tse_id \n", " FROM \n", " {table_receitas_candidatos}\n", " group by\n", " sq_candidato, \n", " nr_cnpj_prestador_conta,\n", " tse_id \n", " )\n", " update {table_candidaturas} as c\n", " set nr_cnpj_prestador_conta = r.nr_cnpj_prestador_conta,\n", " declarou_receita = 'S',\n", " receita_total = r.receita_total\n", " from \n", " receitas_candidatos as r\n", " where\n", " c.tse_id = r.tse_id \n", " ; \n", "\"\"\"\n", "\n", "mtse.execute_query(query_update_cnpj_a_partir_receitas)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### GERA TOTAL DESPESAS A PARTIR DA DECLARAÇÃO DE DESPESAS" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "query_update_cnpj_a_partir_despesas = f\"\"\"\n", " with despesas_candidatos as\n", " (\n", " SELECT \n", " sq_candidato, \n", " nr_cnpj_prestador_conta,\n", " round(sum(vr_despesa_contratada),2) as despesa_total,\n", " get_tse_id(sq_candidato) as tse_id \n", " FROM \n", " {table_despesas_candidatos}\n", " group by\n", " sq_candidato, \n", " nr_cnpj_prestador_conta,\n", " tse_id\n", " )\n", " update {table_candidaturas} as c\n", " set nr_cnpj_prestador_conta = d.nr_cnpj_prestador_conta,\n", " declarou_despesa = 'S',\n", " despesa_total = d.despesa_total\n", " from \n", " despesas_candidatos as d\n", " where\n", " c.tse_id = d.tse_id\n", " ;\n", "\"\"\"\n", "\n", "mtse.execute_query(query_update_cnpj_a_partir_despesas)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gera total votos turno 1" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "query_atualiza_total_votos_turno_1 = f\"\"\"\n", " with votos_turno_1 as \n", " (\n", " select\n", " get_tse_id(sq_candidato) as tse_id,\n", " sum(qt_votos_nominais::numeric) as total_votos\n", " from \n", " {table_votacao_candidato_munzona} \n", " where \n", " nr_turno = '1'\n", " group by \n", " tse_id\n", " )\n", " update {table_candidaturas} as c\n", " set \n", " total_votos_turno_1 = v1.total_votos,\n", " total_votos = v1.total_votos \n", " from \n", " votos_turno_1 as v1\n", " where \n", " c.tse_id = v1.tse_id\n", " ;\n", " \"\"\"\n", "\n", "mtse.execute_query(query_atualiza_total_votos_turno_1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Gera total votos turno 2" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "query_atualiza_total_votos_turno_2 = f\"\"\"\n", " with votos_turno_2 as \n", " (\n", " select\n", " get_tse_id(sq_candidato) as tse_id,\n", " sum(qt_votos_nominais::numeric) as total_votos\n", " from \n", " {table_votacao_candidato_munzona} as v2\n", " where \n", " nr_turno = '2'\n", " group by \n", " tse_id\n", " )\n", " update {table_candidaturas} as c\n", " set \n", " total_votos_turno_2 = v2.total_votos,\n", " total_votos = v2.total_votos\n", " from \n", " votos_turno_2 as v2\n", " where \n", " c.tse_id = v2.tse_id\n", " ;\n", "\"\"\"\n", " \n", "mtse.execute_query(query_atualiza_total_votos_turno_2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cálculo Custo do Voto" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "query_calcula_custo_voto = f\"\"\"\n", " update {table_candidaturas}\n", " set custo_voto = case when total_votos > 0 then round(receita_total / total_votos,2) else 0 end\n", "\"\"\"\n", "mtse.execute_query(query_calcula_custo_voto)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Verifica Candidatos com mais de um registro" ] }, { "cell_type": "code", "execution_count": 13, "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>candidato_id</th>\n", " <th>q</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>CD62849441520</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>CD62686895268</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>CD91920639268</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>CD00467065349</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>CD22361847191</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>113</th>\n", " <td>CD12048895115</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>114</th>\n", " <td>CD42717647600</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>115</th>\n", " <td>CD85700584115</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>116</th>\n", " <td>CD73259110682</td>\n", " <td>2</td>\n", " </tr>\n", " <tr>\n", " <th>117</th>\n", " <td>CD04805885890</td>\n", " <td>2</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>118 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " candidato_id q\n", "0 CD62849441520 2\n", "1 CD62686895268 2\n", "2 CD91920639268 2\n", "3 CD00467065349 2\n", "4 CD22361847191 2\n", ".. ... ..\n", "113 CD12048895115 2\n", "114 CD42717647600 2\n", "115 CD85700584115 2\n", "116 CD73259110682 2\n", "117 CD04805885890 2\n", "\n", "[118 rows x 2 columns]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mtse.pandas_query(f\"\"\"\n", " select candidato_id, q\n", " from (select candidato_id, count(*) as q from {table_candidaturas} \n", " group by candidato_id) t\n", " where q>1 \n", " order by q desc\n", " ;\n", " \"\"\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Muda o id do registro mais antigo de candidato com mais de um registro " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### exclui candidato_id mais antigo quando dois registros para o mesmo candidato \n", "def exclui_duplo_id():\n", " p=mtse.pandas_query(f\"\"\" \n", " select candidato_id, tse_id from {table_candidaturas} \n", " where candidato_id in(\n", " select candidato_id\n", " from (select candidato_id, count(*) as q from {table_candidaturas} \n", " group by candidato_id) t\n", " where q>1\n", " ) \n", " order by candidato_id, tse_id\n", " ;\n", " \"\"\"\n", " )\n", " p2=p[['candidato_id','tse_id']]\n", " n = p2['candidato_id'].size\n", " l=[]\n", " for i in range(0,n,2):\n", " l.append(p2.iloc[i]['tse_id'])\n", " l= \"'\"+\"', '\".join(l)+\"'\"\n", "\n", " mtse.execute_query(f\"\"\" \n", " update {table_candidaturas} \n", " set candidato_id = \n", " case \n", " when candidato_id = 'CD000000000-4' then 'CD'||tse_id\n", " else candidato_id||'-I'\n", " end\n", " where tse_id in ({l}) \n", " \"\"\"\n", " )\n", " return(n)\n", "\n", "while True:\n", " n=exclui_duplo_id()\n", " if n==0:\n", " break" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Verifica o resultado" ] }, { "cell_type": "code", "execution_count": 14, "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>candidato_id</th>\n", " <th>tse_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>CD00427876885</td>\n", " <td>50000622970</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>CD00427876885</td>\n", " <td>50000629870</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>CD00467065349</td>\n", " <td>180000628533</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>CD00467065349</td>\n", " <td>180000630075</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>CD00656424303</td>\n", " <td>60000611013</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>231</th>\n", " <td>CD93831382549</td>\n", " <td>50000628466</td>\n", " </tr>\n", " <tr>\n", " <th>232</th>\n", " <td>CD93865570020</td>\n", " <td>210000629226</td>\n", " </tr>\n", " <tr>\n", " <th>233</th>\n", " <td>CD93865570020</td>\n", " <td>210000629736</td>\n", " </tr>\n", " <tr>\n", " <th>234</th>\n", " <td>CD99502860772</td>\n", " <td>130000620831</td>\n", " </tr>\n", " <tr>\n", " <th>235</th>\n", " <td>CD99502860772</td>\n", " <td>130000629457</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>236 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " candidato_id tse_id\n", "0 CD00427876885 50000622970\n", "1 CD00427876885 50000629870\n", "2 CD00467065349 180000628533\n", "3 CD00467065349 180000630075\n", "4 CD00656424303 60000611013\n", ".. ... ...\n", "231 CD93831382549 50000628466\n", "232 CD93865570020 210000629226\n", "233 CD93865570020 210000629736\n", "234 CD99502860772 130000620831\n", "235 CD99502860772 130000629457\n", "\n", "[236 rows x 2 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mtse.pandas_query(f\"\"\" \n", " select candidato_id, tse_id from {table_candidaturas} \n", " where candidato_id in(\n", " select candidato_id\n", " from (select candidato_id, count(*) as q from {table_candidaturas} \n", " group by candidato_id) t\n", " where q>1\n", " ) \n", " order by candidato_id, tse_id\n", " ;\n", "\"\"\"\n", ")" ] }, { "cell_type": "code", "execution_count": 15, "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>nr_cpf_candidato</th>\n", " <th>candidato_id</th>\n", " <th>tse_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>00427876885</td>\n", " <td>CD00427876885</td>\n", " <td>50000622970</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>00427876885</td>\n", " <td>CD00427876885</td>\n", " <td>50000629870</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>00467065349</td>\n", " <td>CD00467065349</td>\n", " <td>180000628533</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>00467065349</td>\n", " <td>CD00467065349</td>\n", " <td>180000630075</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>00656424303</td>\n", " <td>CD00656424303</td>\n", " <td>60000611013</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>231</th>\n", " <td>93831382549</td>\n", " <td>CD93831382549</td>\n", " <td>50000628466</td>\n", " </tr>\n", " <tr>\n", " <th>232</th>\n", " <td>93865570020</td>\n", " <td>CD93865570020</td>\n", " <td>210000629226</td>\n", " </tr>\n", " <tr>\n", " <th>233</th>\n", " <td>93865570020</td>\n", " <td>CD93865570020</td>\n", " <td>210000629736</td>\n", " </tr>\n", " <tr>\n", " <th>234</th>\n", " <td>99502860772</td>\n", " <td>CD99502860772</td>\n", " <td>130000620831</td>\n", " </tr>\n", " <tr>\n", " <th>235</th>\n", " <td>99502860772</td>\n", " <td>CD99502860772</td>\n", " <td>130000629457</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>236 rows × 3 columns</p>\n", "</div>" ], "text/plain": [ " nr_cpf_candidato candidato_id tse_id\n", "0 00427876885 CD00427876885 50000622970\n", "1 00427876885 CD00427876885 50000629870\n", "2 00467065349 CD00467065349 180000628533\n", "3 00467065349 CD00467065349 180000630075\n", "4 00656424303 CD00656424303 60000611013\n", ".. ... ... ...\n", "231 93831382549 CD93831382549 50000628466\n", "232 93865570020 CD93865570020 210000629226\n", "233 93865570020 CD93865570020 210000629736\n", "234 99502860772 CD99502860772 130000620831\n", "235 99502860772 CD99502860772 130000629457\n", "\n", "[236 rows x 3 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mtse.pandas_query(f\"\"\" \n", " select nr_cpf_candidato, candidato_id, tse_id from {table_candidaturas} \n", " where nr_cpf_candidato in(\n", " select nr_cpf_candidato\n", " from (select nr_cpf_candidato, count(*) as q from {table_candidaturas} \n", " group by nr_cpf_candidato) t\n", " where q>1\n", " ) \n", " order by candidato_id, tse_id\n", " ;\n", "\"\"\"\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ESTABELECE NOME E LABEL PARA TODOS OS CANDIDATOS DA MESMA CANDIDATURA (candidatura_id)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "query_update_candidatura_nome_label = f\"\"\"\n", "with titulares as\n", " (\n", " select * from {table_candidaturas} c \n", " where eh_candidato_titular(ds_cargo) = 'S'\n", " )\n", " update {table_candidaturas} as c\n", " set candidatura_label = get_candidatura_label(t.nm_urna_candidato , t.ds_cargo , t.sg_uf , t.sg_partido ),\n", " candidatura_nome = get_candidatura_nome(t.nm_candidato, t.ds_cargo, t.sg_uf, t.sg_partido)\n", " from titulares as t\n", " where \n", " c.candidatura_id = t.candidatura_id\n", " ;\n", "\"\"\"\n", "mtse.execute_query(query_update_candidatura_nome_label)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 17, "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>c</th>\n", " <th>q</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "Empty DataFrame\n", "Columns: [c, q]\n", "Index: []" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mtse.pandas_query(f\"\"\" \n", " --select candidato_titular_apto, candidatura_id, q from {table_candidaturas} \n", " --where candidato_titular_apto||candidatura_id in(\n", " select c, q \n", " from (select candidato_titular_apto||candidatura_id c , count(*) as q from {table_candidaturas} \n", " where candidato_titular_apto = 'S'\n", " group by candidato_titular_apto||candidatura_id ) t\n", " where q>1\n", " -- ) \n", " -- order by candidato_id, tse_id\n", " ;\n", "\"\"\"\n", ")" ] }, { "cell_type": "code", "execution_count": 18, "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>candidatura_id</th>\n", " <th>tse_id</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>CAAC11</td>\n", " <td>10000603056</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>CAAC11</td>\n", " <td>10000603057</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>CAAC13</td>\n", " <td>10000615663</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>CAAC13</td>\n", " <td>10000615664</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>CAAC131</td>\n", " <td>10000615657</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>1920</th>\n", " <td>CATO555</td>\n", " <td>270000626089</td>\n", " </tr>\n", " <tr>\n", " <th>1921</th>\n", " <td>CATO555</td>\n", " <td>270000630091</td>\n", " </tr>\n", " <tr>\n", " <th>1922</th>\n", " <td>CATO777</td>\n", " <td>270000618818</td>\n", " </tr>\n", " <tr>\n", " <th>1923</th>\n", " <td>CATO777</td>\n", " <td>270000629454</td>\n", " </tr>\n", " <tr>\n", " <th>1924</th>\n", " <td>CATO777</td>\n", " <td>270000629455</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>1925 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " candidatura_id tse_id\n", "0 CAAC11 10000603056\n", "1 CAAC11 10000603057\n", "2 CAAC13 10000615663\n", "3 CAAC13 10000615664\n", "4 CAAC131 10000615657\n", "... ... ...\n", "1920 CATO555 270000626089\n", "1921 CATO555 270000630091\n", "1922 CATO777 270000618818\n", "1923 CATO777 270000629454\n", "1924 CATO777 270000629455\n", "\n", "[1925 rows x 2 columns]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mtse.pandas_query(f\"\"\" \n", " select candidatura_id, tse_id from {table_candidaturas} \n", " where candidatura_id in(\n", " select candidatura_id\n", " from (\n", " select candidatura_id, count(*) as q from {table_candidaturas} \n", " --where candidato_titular_apto = 'S'\n", " group by candidatura_id\n", " ) t\n", " where q>1 \n", " ) \n", " order by candidatura_id, tse_id\n", " ;\n", "\"\"\"\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "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>sg_uf</th>\n", " <th>ds_cargo</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>AC</td>\n", " <td>1º SUPLENTE</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>AC</td>\n", " <td>2º SUPLENTE</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>AC</td>\n", " <td>DEPUTADO ESTADUAL</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>AC</td>\n", " <td>DEPUTADO FEDERAL</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>AC</td>\n", " <td>GOVERNADOR</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>186</th>\n", " <td>TO</td>\n", " <td>DEPUTADO ESTADUAL</td>\n", " </tr>\n", " <tr>\n", " <th>187</th>\n", " <td>TO</td>\n", " <td>DEPUTADO FEDERAL</td>\n", " </tr>\n", " <tr>\n", " <th>188</th>\n", " <td>TO</td>\n", " <td>GOVERNADOR</td>\n", " </tr>\n", " <tr>\n", " <th>189</th>\n", " <td>TO</td>\n", " <td>SENADOR</td>\n", " </tr>\n", " <tr>\n", " <th>190</th>\n", " <td>TO</td>\n", " <td>VICE-GOVERNADOR</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>191 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " sg_uf ds_cargo\n", "0 AC 1º SUPLENTE\n", "1 AC 2º SUPLENTE\n", "2 AC DEPUTADO ESTADUAL\n", "3 AC DEPUTADO FEDERAL\n", "4 AC GOVERNADOR\n", ".. ... ...\n", "186 TO DEPUTADO ESTADUAL\n", "187 TO DEPUTADO FEDERAL\n", "188 TO GOVERNADOR\n", "189 TO SENADOR\n", "190 TO VICE-GOVERNADOR\n", "\n", "[191 rows x 2 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd \n", "df_candidaturas_2018=mtse.pandas_query(f\"\"\" select sg_uf, ds_cargo, count(*) as qtd from {table_candidaturas} \n", " group by sg_uf, ds_cargo\n", " order by sg_uf, ds_cargo \n", " \"\"\")\n", "#df_candidaturas_2018.to_excel('df_candidaturas_2018.xlsx')\n", "df_candidaturas_2018[['sg_uf','ds_cargo']]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2020-10-20 19:22:37.903070\n" ] } ], "source": [ "import datetime\n", "print(datetime.datetime.now())" ] }, { "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
allentran/reinforcement-learning
TD/SARSA.ipynb
1
97476
{ "cells": [ { "cell_type": "code", <<<<<<< HEAD "execution_count": 1, "metadata": { "collapsed": false }, ======= "execution_count": 11, "metadata": {}, >>>>>>> f45bcbf23daddbb7cfd9d08103609053ba4f92c6 "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import gym\n", "import itertools\n", "import matplotlib\n", "import numpy as np\n", "import pandas as pd\n", "import sys\n", "\n", "if \"../\" not in sys.path:\n", " sys.path.append(\"../\") \n", "\n", "from collections import defaultdict\n", "from lib.envs.windy_gridworld import WindyGridworldEnv\n", "from lib import plotting\n", "\n", "matplotlib.style.use('ggplot')" ] }, { "cell_type": "code", <<<<<<< HEAD "execution_count": 2, "metadata": { "collapsed": false }, ======= "execution_count": 12, "metadata": {}, >>>>>>> f45bcbf23daddbb7cfd9d08103609053ba4f92c6 "outputs": [], "source": [ "env = WindyGridworldEnv()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def make_epsilon_greedy_policy(Q, epsilon, nA):\n", " \"\"\"\n", " Creates an epsilon-greedy policy based on a given Q-function and epsilon.\n", " \n", " Args:\n", " Q: A dictionary that maps from state -> action-values.\n", " Each value is a numpy array of length nA (see below)\n", " epsilon: The probability to select a random action . float between 0 and 1.\n", " nA: Number of actions in the environment.\n", " \n", " Returns:\n", " A function that takes the observation as an argument and returns\n", " the probabilities for each action in the form of a numpy array of length nA.\n", " \n", " \"\"\"\n", " def policy_fn(observation):\n", " A = np.ones(nA, dtype=float) * epsilon / nA\n", " best_action = np.argmax(Q[observation])\n", " A[best_action] += (1.0 - epsilon)\n", " return A\n", " return policy_fn" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "def sarsa(env, num_episodes, discount_factor=1.0, alpha=0.5, epsilon=0.1):\n", " \"\"\"\n", " SARSA algorithm: On-policy TD control. Finds the optimal epsilon-greedy policy.\n", " \n", " Args:\n", " env: OpenAI environment.\n", " num_episodes: Number of episodes to run for.\n", " discount_factor: Gamma discount factor.\n", " alpha: TD learning rate.\n", " epsilon: Chance the sample a random action. Float betwen 0 and 1.\n", " \n", " Returns:\n", " A tuple (Q, stats).\n", " Q is the optimal action-value function, a dictionary mapping state -> action values.\n", " stats is an EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\n", " \"\"\"\n", " \n", " # The final action-value function.\n", " # A nested dictionary that maps state -> (action -> action-value).\n", " Q = defaultdict(lambda: np.zeros(env.action_space.n))\n", " \n", " # Keeps track of useful statistics\n", " stats = plotting.EpisodeStats(\n", " episode_lengths=np.zeros(num_episodes),\n", " episode_rewards=np.zeros(num_episodes))\n", "\n", " # The policy we're following\n", " policy = make_epsilon_greedy_policy(Q, epsilon, env.action_space.n)\n", "\n", " for i_episode in range(num_episodes):\n", " \n", " current_state = env.reset()\n", " probs = policy(current_state)\n", " action = np.random.choice(np.arange(len(probs)), p=probs)\n", " \n", " while True:\n", " next_state, reward, done, _ = env.step(action)\n", " if done:\n", " break\n", " next_probs = policy(next_state)\n", " next_action = np.random.choice(np.arange(len(probs)), p=next_probs)\n", " Q[current_state][action] += alpha * (reward + discount_factor * Q[next_action][next_action] - Q[current_state][action])\n", " current_state = next_state\n", " action = next_action\n", " \n", " \n", " return Q, stats" ] }, { "cell_type": "code", <<<<<<< HEAD "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], ======= "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Episode 200/200." ] } ], >>>>>>> f45bcbf23daddbb7cfd9d08103609053ba4f92c6 "source": [ "Q, stats = sarsa(env, 200)" ] }, { "cell_type": "code", <<<<<<< HEAD "execution_count": 16, "metadata": { "collapsed": false }, ======= "execution_count": 17, "metadata": {}, >>>>>>> f45bcbf23daddbb7cfd9d08103609053ba4f92c6 "outputs": [ { "data": { "image/png": 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q166dRo8erXr16l2vUwIAAACAUrkp7tht3LhRCxcu1IgRIzR9+nSFhoZq2rRp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\ngLGhsLBQ//rXv5SXl6fAwECnuqGcB8XGxurcuXN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"text/plain": [ "<matplotlib.figure.Figure at 0x1123d8f10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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//RbffvutwccwgXvvXKpUKowbN053B+R+RUVFFfox6GHc3d3RpEkT7N27F3v2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\ntM9Bu3btUnFxsaZOnaqYmBjXuPrggw9UW1vb5FoIdq2gpqZGBQUF6t+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"text/plain": [ "<matplotlib.figure.Figure at 0x1126cee90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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mit
kingzone/kaggle
KnnRegressor.ipynb
1
8785
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Boston House Prices dataset\n", "===========================\n", "\n", "Notes\n", "------\n", "Data Set Characteristics: \n", "\n", " :Number of Instances: 506 \n", "\n", " :Number of Attributes: 13 numeric/categorical predictive\n", " \n", " :Median Value (attribute 14) is usually the target\n", "\n", " :Attribute Information (in order):\n", " - CRIM per capita crime rate by town\n", " - ZN proportion of residential land zoned for lots over 25,000 sq.ft.\n", " - INDUS proportion of non-retail business acres per town\n", " - CHAS Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n", " - NOX nitric oxides concentration (parts per 10 million)\n", " - RM average number of rooms per dwelling\n", " - AGE proportion of owner-occupied units built prior to 1940\n", " - DIS weighted distances to five Boston employment centres\n", " - RAD index of accessibility to radial highways\n", " - TAX full-value property-tax rate per $10,000\n", " - PTRATIO pupil-teacher ratio by town\n", " - B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n", " - LSTAT % lower status of the population\n", " - MEDV Median value of owner-occupied homes in $1000's\n", "\n", " :Missing Attribute Values: None\n", "\n", " :Creator: Harrison, D. and Rubinfeld, D.L.\n", "\n", "This is a copy of UCI ML housing dataset.\n", "http://archive.ics.uci.edu/ml/datasets/Housing\n", "\n", "\n", "This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University.\n", "\n", "The Boston house-price data of Harrison, D. and Rubinfeld, D.L. 'Hedonic\n", "prices and the demand for clean air', J. Environ. Economics & Management,\n", "vol.5, 81-102, 1978. Used in Belsley, Kuh & Welsch, 'Regression diagnostics\n", "...', Wiley, 1980. N.B. Various transformations are used in the table on\n", "pages 244-261 of the latter.\n", "\n", "The Boston house-price data has been used in many machine learning papers that address regression\n", "problems. \n", " \n", "**References**\n", "\n", " - Belsley, Kuh & Welsch, 'Regression diagnostics: Identifying Influential Data and Sources of Collinearity', Wiley, 1980. 244-261.\n", " - Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann.\n", " - many more! (see http://archive.ics.uci.edu/ml/datasets/Housing)\n", "\n", "max target value is 50.0\n", "min target value is 5.0\n", "avg target value is 22.5328063241\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/ifeng/anaconda2/lib/python2.7/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", "/Users/ifeng/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:586: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n", "/Users/ifeng/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n", "/Users/ifeng/anaconda2/lib/python2.7/site-packages/sklearn/preprocessing/data.py:649: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and will raise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.\n", " warnings.warn(DEPRECATION_MSG_1D, DeprecationWarning)\n" ] } ], "source": [ "from sklearn.datasets import load_boston\n", "boston=load_boston()\n", "print boston.DESCR\n", "from sklearn.cross_validation import train_test_split\n", "import numpy as np\n", "X=boston.data\n", "y=boston.target\n", "X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=33,test_size=0.25)\n", "print 'max target value is ', np.max(boston.target)\n", "print 'min target value is ', np.min(boston.target)\n", "print 'avg target value is ', np.mean(boston.target)\n", "from sklearn.preprocessing import StandardScaler\n", "ss_X=StandardScaler()\n", "ss_y=StandardScaler()\n", "X_train=ss_X.fit_transform(X_train)\n", "X_test=ss_X.transform(X_test)\n", "y_train=ss_y.fit_transform(y_train)\n", "y_test=ss_y.transform(y_test)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsRegressor\n", "#预测方式为平均回归：weights='uniform'\n", "uni_knr=KNeighborsRegressor(weights='uniform')\n", "uni_knr.fit(X_train,y_train)\n", "uni_knr_y_predict=uni_knr.predict(X_test)\n", "\n", "# 预测方式为根据距离加权回归\n", "dis_knr=KNeighborsRegressor(weights='distance')\n", "dis_knr.fit(X_train,y_train)\n", "dis_knr_y_predict=dis_knr.predict(X_test)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " R-squared value of uniform-weighted KneighborRegression : 0.690345456461\n", "MSE of uniform-weighted KNeighborRegression : 24.0110141732\n", "MAE of uniform-weighted KNeighborRegression : 2.96803149606\n" ] } ], "source": [ "from sklearn.metrics import r2_score,mean_absolute_error,mean_squared_error\n", "print 'R-squared value of uniform-weighted KneighborRegression : ',uni_knr.score(X_test,y_test)\n", "print 'MSE of uniform-weighted KNeighborRegression : ',mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(uni_knr_y_predict))\n", "print 'MAE of uniform-weighted KNeighborRegression : ',mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(uni_knr_y_predict))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R-squared value of distance-weighted KneighborRegression : 0.719758997016\n", "MSE of distance-weighted KNeighborRegression : 21.7302501609\n", "MAE of distance-weighted KNeighborRegression : 2.80505687851\n" ] } ], "source": [ "print 'R-squared value of distance-weighted KneighborRegression : ',dis_knr.score(X_test,y_test)\n", "print 'MSE of distance-weighted KNeighborRegression : ',mean_squared_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(dis_knr_y_predict))\n", "print 'MAE of distance-weighted KNeighborRegression : ',mean_absolute_error(ss_y.inverse_transform(y_test),ss_y.inverse_transform(dis_knr_y_predict))" ] } ], "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 }
apache-2.0
nagordon/mechpy
tutorials/testing.ipynb
1
341108
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "- - - -\n", "# Mechpy Tutorials\n", "a mechanical engineering toolbox\n", "\n", "source code - https://github.com/nagordon/mechpy \n", "documentation - https://nagordon.github.io/mechpy/web/ \n", "\n", "- - - -\n", "\n", "Neal Gordon \n", "2017-02-20 \n", "\n", "- - - -\n", "\n", "## material testing analysis \n", "\n", "This quick tutorial shows some simple scripts for analyzing material test data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Python Initilaization with module imports" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using matplotlib backend: Qt4Agg\n" ] } ], "source": [ "# setup \n", "import numpy as np\n", "import sympy as sp\n", "import pandas as pd\n", "import scipy\n", "from pprint import pprint\n", "sp.init_printing(use_latex='mathjax')\n", "\n", "import matplotlib.pyplot as plt\n", "plt.rcParams['figure.figsize'] = (12, 8) # (width, height)\n", "plt.rcParams['font.size'] = 14\n", "plt.rcParams['legend.fontsize'] = 16\n", "from matplotlib import patches\n", "\n", "get_ipython().magic('matplotlib') # seperate window\n", "get_ipython().magic('matplotlib inline') # inline plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading raw test data example 1\n", "\n", "This example shows how to read multiple csv files and plot them together" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import glob as gb\n", "from matplotlib.pyplot import *\n", "%matplotlib inline\n", "\n", "csvdir='./examples/'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "e=[]\n", "y=[]\n", "\n", "for s in specimen:\n", " files = gb.glob(csvdir + '*.csv') # select all csv files\n", " fig, ax = subplots()\n", " title(s)\n", " Pult = []\n", " \n", " for f in files:\n", " d1 = pd.read_csv(f, skiprows=1)\n", " d1 = d1[1:] # remove first row of string\n", " d1.columns = ['t', 'load', 'ext'] # rename columns\n", " d1.head()\n", " # remove commas in data\n", " for d in d1.columns:\n", " #d1.dtypes\n", " d1[d] = d1[d].map(lambda x: float(str(x).replace(',','')))\n", " Pult.append(np.max(d1.load))\n", " plot(d1.ext, d1.load) \n", " ylabel('Pult, lbs')\n", " xlabel('extension, in')\n", " \n", " \n", " e.append(np.std(Pult))\n", " y.append(np.average(Pult) )\n", " show()\n", "\n", "\n", "# bar chart \n", "barwidth = 0.35 # the width of the bars\n", "\n", "fig, ax = subplots()\n", "x = np.arange(len(specimen))\n", "ax.bar(x, y, width=barwidth, yerr=e)\n", "\n", "#ax.set_xticks(x)\n", "xticks(x+barwidth/2, specimen, rotation='vertical')\n", "title('Pult with sample average and stdev of n=3')\n", "ylabel('Pult, lbs')\n", "margins(0.05)\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Reading test data - example 2\n", "\n", "This example shows how to read a different format of data and plot" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f36a6ee6438>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f36ac71cf60>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f = 'Aluminum_loops.txt'\n", "d1 = pd.read_csv(f, skiprows=4,delimiter='\\t')\n", "d1 = d1[1:] # remove first row of string\n", "d1.columns = ['time', 'load', 'cross','ext','strain','stress'] # rename columns\n", "d1.head()\n", "# remove commas in data\n", "for d in d1.columns:\n", " #d1.dtypes\n", " d1[d] = d1[d].map(lambda x: float(str(x).replace(',','')))\n", "plot(d1.ext, d1.load) \n", "ylabel('stress')\n", "xlabel('strain')" ] }, { "cell_type": "code", "execution_count": 10, "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>time</th>\n", " <th>load</th>\n", " <th>cross</th>\n", " <th>ext</th>\n", " <th>strain</th>\n", " <th>stress</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1</th>\n", " <td>0.124</td>\n", " <td>-0.009699</td>\n", " <td>0.001906</td>\n", " <td>0.000047</td>\n", " <td>0.000002</td>\n", " <td>-0.000157</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>0.224</td>\n", " <td>0.063070</td>\n", " <td>0.006730</td>\n", " <td>0.000826</td>\n", " <td>0.000032</td>\n", " <td>0.001023</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>0.324</td>\n", " <td>0.141036</td>\n", " <td>0.011673</td>\n", " <td>0.001650</td>\n", " <td>0.000065</td>\n", " <td>0.002288</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0.424</td>\n", " <td>0.222520</td>\n", " <td>0.016736</td>\n", " <td>0.002506</td>\n", " <td>0.000098</td>\n", " <td>0.003611</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>0.524</td>\n", " <td>0.302994</td>\n", " <td>0.021679</td>\n", " <td>0.003338</td>\n", " <td>0.000131</td>\n", " <td>0.004916</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " time load cross ext strain stress\n", "1 0.124 -0.009699 0.001906 0.000047 0.000002 -0.000157\n", "2 0.224 0.063070 0.006730 0.000826 0.000032 0.001023\n", "3 0.324 0.141036 0.011673 0.001650 0.000065 0.002288\n", "4 0.424 0.222520 0.016736 0.002506 0.000098 0.003611\n", "5 0.524 0.302994 0.021679 0.003338 0.000131 0.004916" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d1.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## another example of plotting data" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7fef15faaf60>" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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1UcaMamveuFFV3Pr1FZ4jLdJTp8a/14UX6qzwJZec/Ht+/lnB+dNP9TuPHFHV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\nAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fef11706080>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f = 'al_MTS_test.csv'\n", "d1 = pd.read_csv(f, skiprows=3,delimiter=',')\n", "d1 = d1[1:] # remove first row of string\n", "d1 = d1[['Time','Axial Force', 'Axial Fine Displacement', 'Axial Length']]\n", "d1.columns = ['time', 'load', 'strain','cross'] # rename columns\n", "# remove commas in data\n", "for d in d1.columns:\n", " #d1.dtypes\n", " d1[d] = d1[d].map(lambda x: float(str(x).replace(',','')))\n", "plot(d1.strain, d1.load) \n", "ylabel('stress')\n", "xlabel('strain')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Finding the \"first\" peak and delta-10 threshhold limit on force-displacement data of an aluminum coupon\n", "\n", " http://nbviewer.jupyter.org/github/demotu/BMC/blob/master/notebooks/DataFiltering.ipynb" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of data points = 42124\n" ] }, { "data": { "image/png": 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z4Y9/hOWWg/vug8GD81rjtdYqujJJkpoPw7EkSc3YVVdBy5Zw2mlw0UU5KG+x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\nwViSJEmqJXaOVbjLLsvB+NlnYZNNmv7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"text/plain": [ "<matplotlib.figure.Figure at 0x7f36d453f710>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "%matplotlib inline\n", "from scipy import signal\n", "from pylab import plot, xlabel, ylabel, title, rcParams, figure\n", "import numpy as np\n", "pltwidth = 16\n", "pltheight = 8\n", "rcParams['figure.figsize'] = (pltwidth, pltheight)\n", "\n", "csv = np.genfromtxt('./stress_strain1.csv', delimiter=\",\")\n", "disp = csv[:,0]\n", "force = csv[:,1]\n", "print('number of data points = %i' % len(disp))\n", "\n", "def moving_average(x, window):\n", " \"\"\"Moving average of 'x' with window size 'window'.\"\"\"\n", " y = np.empty(len(x)-window+1)\n", " for i in range(len(y)):\n", " y[i] = np.sum(x[i:i+window])/window\n", " return y\n", "\n", "plt1 = plot(disp, force);\n", "xlabel('displacement');\n", "ylabel('force');\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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74W9/gw02KLsySZ3Zt7+dt0D60Y/grbfKrkbzao7hN6X0KDBxlmMPpJQatnt+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\nHY46CjbYAK67DtZfv+4vKUmSJEk9Xj3Dr6s9ryA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"text/plain": [ "<matplotlib.figure.Figure at 0x7f36a68197f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure()\n", "mywindow = 1000 # the larger the filter window, the more agressive the filtering\n", "force2 = moving_average(force, mywindow)\n", "x2 = range(len(force2))\n", "plot(x2, force2);\n", "title('Force smoothed with moving average filter');" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f369adf9080>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Xrzc3Syec4GfEfuutcOsKWnogff55afJk6Qc/8EOVsxk/3v8Ze/zx1LaHHvIT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\nvj6Wzm08EW4BAAAAREZNjbRyZdf7TZi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"text/plain": [ "<matplotlib.figure.Figure at 0x7f369ac0f550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "\n", "# Find f' using diff to find the first intersection of the 0\n", "\n", "# mvavgforce = mvavgforce[:len(mvavgforce)/2]\n", "force2p = np.diff(force2)\n", "x2p = range(len(force2p))\n", "plot(x2p, force2p);\n", "title('Slope of the smoothed curve')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "i = np.argmax(force2p<0) \n", "### or\n", "# i = where(force2p<0)[0][0]\n", "#### or\n", "# for i, f in enumerate(force2p):\n", "# if f < 0:\n", "# break" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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vlHRjR5bJo4C6t0cekQ4+WLrrLunII6XPfS7uiAAAAAAkWWLLRmktuXs76CBp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\nwOgJtwAAAIyecAsAAMDo/T/npM3nm9wM3AAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f369aded668>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x2p, force2p, i,force2p[i],'o', markersize=15);\n", "title('find the point at which the slope goes negative, indicating a switch in the slope direction');" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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hhz+Exx7LPTJULsNvwfArSZI6ug8/hCFD4I9/zGN4jz8eNt4Yok0+yklqDYMH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\nJEnqnGz5ldSgZ5/NE1otvDA8/DCstVb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"text/plain": [ "<matplotlib.figure.Figure at 0x7f369af98198>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot(x2, force2, i,force2[i],'o',markersize=15);\n", "title('using that index, plot on the force-displacement curve');" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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uvDGd1XvKKWlzqyZNsq5KkqS675sNzcsuu6zG3mtdf18d+HpH9nHgxNz9E4DH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\nVtgib3L2Pr+19/l+ZvactX9Ze59nryf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"text/plain": [ "<matplotlib.figure.Figure at 0x7f369ac15978>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#Now, we need to find the next point from here that is 10 less.\n", "delta = 1\n", "\n", "i2 = np.argmax(force2[i]-delta > force2[i:])\n", "\n", "# If that point does not exist on the immediate downward sloping path, \n", "#then just choose the max point. In this case, 10 would exist very \n", "#far away from the point and not be desireable\n", "if i2 > i:\n", " i2=0\n", "plot(x2, force2, i,force2[i],'o', i2+i, force2[i2+i] ,'*', markersize=15);" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.00000000e+00, 9.99999000e-03, 1.99999810e-02, ...,\n", " 4.21189994e+02, 4.21200003e+02, 4.21210013e+02])" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "disp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modulus" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# remove nan \n", "disp = disp[~np.isnan(force)]\n", "force = force[~np.isnan(force)]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": true }, "outputs": [], "source": [ "A = 0.1 # area\n", "stress = force/A / 1e3\n", "strain = disp/25.4 * 1e-3" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x7f369adeab38>]" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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wIVx+eQqvW20Fn3yShgwbXiVJKkwGWElSUbroImjSBB58EMrLYcIE2GCDrKuS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\nrkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7f36a6844390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(strain, stress)" ] }, { "cell_type": "code", "execution_count": 115, "metadata": { "collapsed": true }, "outputs": [], "source": [ "stress_range = np.array([5, 15])\n", "PL = 0.0005\n", "E_tan = stress/strain" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert(len(stress)==len(strain))" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [], "source": [ "i = (stress > stress_range[0]) & (stress < stress_range[1])\n", "\n", "stress_mod = stress[i]\n", "strain_mod = strain[i]" ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "poly1d([ 1.97261266e+04, -3.22210726e+00])" ] }, "execution_count": 118, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fit = np.polyfit(strain_mod,stress_mod,1)\n", "fit_fn = np.poly1d(fit) \n", "fit_fn" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6095" ] }, "execution_count": 119, "metadata": {}, "output_type": "execute_result" } ], "source": [ "PLi = np.argmax( (stress - (fit_fn(strain-PL)) < 0) ) \n", "PLi" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/latex": [ "$$\\left ( 0, \\quad 140.7839\\right )$$" ], "text/plain": [ "(0, 140.7839)" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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39wZJ0qhRL6pnz8scJwIA/Bur5gJAB7NgwQJt3LhRmZmZlFCEJZ9vm5YsOTDl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\n5MCBz7Vu3Q+UmrqSEAoAAAAATcTyLc10/Ph2rV9/tfr0eUx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"text/plain": [ "<matplotlib.figure.Figure at 0x7f369a8d8470>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# fit_fn is now a function which takes in x and returns an estimate for y\n", "#plt.text(4,4,fit_fn)\n", "plt.plot(strain ,stress, 'y')\n", "plot(strain, fit_fn(strain-PL) , '--k', strain[PLi], stress[PLi],'o')\n", "plt.xlim(0, np.max(strain))\n", "plt.ylim(0, np.max(stress))" ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ultimate stress 140.783900\n", "ultimate strain 0.016583\n", "strain proportion limit 0.002400\n", "stress proportion limit 30.838765\n" ] } ], "source": [ "print('ultimate stress %f' % np.max(stress))\n", "print('ultimate strain %f' % np.max(strain))\n", "\n", "print('strain proportion limit %f' % strain[PLi])\n", "print('stress proportion limit %f' % stress[PLi])" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x7f369abc72b0>" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dd1wL0yeemBx+eHu+GQCgzzzTC9CRsb8oW7iwLdf08Idv/9wPf3jb7StXtgm/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\nGHZDE3pLKU8opVxZSrmqlPKq+zr3lFO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"text/plain": [ "<matplotlib.figure.Figure at 0x7f369aa023c8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "E_tan = E_tan[~np.isinf(E_tan)]\n", "strainE = strain[1:]\n", "plot(strainE, E_tan,'b', strainE[PLi], E_tan[PLi],'o')\n", "plt.ylim([0,25000])\n", "plt.title('Tangent Modulus')" ] }, { "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": 0 }
mit
csiu/100daysofcode
misc/day52_voronoi.ipynb
1
167787
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "plt.rcParams[\"figure.figsize\"] = [15,7]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dataset\n", "\n", "Before I do something, I need data" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "sklearn.datasets.base.Bunch" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.datasets import load_iris\n", "\n", "iris = load_iris()\n", "type(iris)" ] }, { "cell_type": "code", "execution_count": 5, "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>sepal length (cm)</th>\n", " <th>sepal width (cm)</th>\n", " <th>petal length (cm)</th>\n", " <th>petal width (cm)</th>\n", " <th>target</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", "0 5.1 3.5 1.4 0.2 \n", "1 4.9 3.0 1.4 0.2 \n", "2 4.7 3.2 1.3 0.2 \n", "3 4.6 3.1 1.5 0.2 \n", "4 5.0 3.6 1.4 0.2 \n", "\n", " target \n", "0 0.0 \n", "1 0.0 \n", "2 0.0 \n", "3 0.0 \n", "4 0.0 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert data type to pandas dataframe\n", "\n", "# http://stackoverflow.com/questions/38105539/how-to-convert-a-scikit-learn-dataset-to-a-pandas-dataset\n", "data = pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n", " columns= iris['feature_names'] + ['target'])\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "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>sepal_length</th>\n", " <th>sepal_width</th>\n", " <th>pedal_length</th>\n", " <th>pedal_width</th>\n", " <th>target</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " <td>1.3</td>\n", " <td>0.2</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " <td>1.5</td>\n", " <td>0.2</td>\n", " <td>0.0</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " <td>1.4</td>\n", " <td>0.2</td>\n", " <td>0.0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal_length sepal_width pedal_length pedal_width target\n", "0 5.1 3.5 1.4 0.2 0.0\n", "1 4.9 3.0 1.4 0.2 0.0\n", "2 4.7 3.2 1.3 0.2 0.0\n", "3 4.6 3.1 1.5 0.2 0.0\n", "4 5.0 3.6 1.4 0.2 0.0" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Rename columns\n", "data.columns = [\"sepal_length\", \"sepal_width\", \"pedal_length\", \"pedal_width\", \"target\"]\n", "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select 2 dimensions for analysis" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "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>sepal_length</th>\n", " <th>sepal_width</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>5.1</td>\n", " <td>3.5</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>4.9</td>\n", " <td>3.0</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>4.7</td>\n", " <td>3.2</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>4.6</td>\n", " <td>3.1</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>5.0</td>\n", " <td>3.6</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " sepal_length sepal_width\n", "0 5.1 3.5\n", "1 4.9 3.0\n", "2 4.7 3.2\n", "3 4.6 3.1\n", "4 5.0 3.6" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "points = data.iloc[:,[0,1]]\n", "points.head()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array(['setosa', 'versicolor', 'virginica'], \n", " dtype='<U10')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "iris.target_names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Creating a simple plot:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IiEg1NmQIJCaWbktMdNp91a5dO/bt20eTJk1o1KjRYfe9/PLLSU1N5cQTT2TgwIF07tyZ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\nSNFQVI+e+Kj277N/0PHy0s+e/pn+/MqfVZOskZNTabRUPxj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"text/plain": [ "<matplotlib.figure.Figure at 0x10dc44ba8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib\n", "\n", "my_colors = [\"red\", \"green\", \"blue\"]\n", "\n", "p = plt.scatter(x=data[\"sepal_length\"],\n", " y=data[\"sepal_width\"],\n", " c=data[\"target\"],\n", " cmap=matplotlib.colors.ListedColormap(my_colors))\n", "plt.xlabel(\"sepal_length\")\n", "plt.ylabel(\"sepal_width\")\n", "\n", "## Fix my legend\n", "plt.legend((p,p,p), (iris.target_names))\n", "ax = plt.gca()\n", "legend = ax.get_legend()\n", "legend.legendHandles[0].set_color(my_colors[0])\n", "legend.legendHandles[1].set_color(my_colors[1])\n", "legend.legendHandles[2].set_color(my_colors[2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Voronoi plot\n", "\n", "https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.Voronoi.html" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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69ev1jiOEWZMePpGuoqOj6d27N8eOHWPTpk2UKFFC70jCjAUEBDBo0CCCg4Mz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fj4eA4dOpRlypTh/v37SWq+NCgUCh44cMDM2pmX5ORk/vrrr+zZsyddXFzYpk0b/vzzz1qXxohF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\n8fP2229To0YNevfuTV5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"text/plain": [ "<matplotlib.figure.Figure at 0x110f5a630>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from scipy.spatial import Voronoi, voronoi_plot_2d\n", "\n", "vor = Voronoi(points)\n", "\n", "## Draw boundaries\n", "voronoi_plot_2d(vor, show_vertices=False, show_points=False)\n", "\n", "## Add color to points\n", "plt.scatter(x=data[\"sepal_length\"],\n", " y=data[\"sepal_width\"],\n", " c=data[\"target\"],\n", " cmap=matplotlib.colors.ListedColormap(my_colors))\n", "\n", "plt.xlabel(points.columns[0])\n", "plt.ylabel(points.columns[1])\n", "plt.title(\"Iris dataset\")\n", "\n", "## Fix my legend\n", "plt.legend((p,p,p), (iris.target_names))\n", "ax = plt.gca()\n", "legend = ax.get_legend()\n", "legend.legendHandles[0].set_color(my_colors[0])\n", "legend.legendHandles[1].set_color(my_colors[1])\n", "legend.legendHandles[2].set_color(my_colors[2])\n", "plt.show()" ] } ], "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
intellimath/pyaxon
examples/axon_fat_free_xml.ipynb
1
15076
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this post we will consider one advantage of [AXON](http://intellimath.bitbucket.org/axon) (see also early [posts](http://intellimath.bitbucket.org/blog/categories/axon.html)). It allows to resolve problems that arises when someone will try to translate `XML` to `JSON`. The root of this problem is in incompatibility of data models of `XML` and `JSON`. `XML` reprsents *attributed trees with tagged nodes*, but `JSON` represents compositions of *arrays* and *associative arrays*. This makes convertion difficult. You have to translate `XML` to fatty `JSON` (with convensions) in order to save initial structure of `XML` or have to reorganize initial structure in order to produce more optimal `JSON`. In last case inverse transformation is not possible without of losses." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well knowing disadvantage of `XML` is it's verbosity. `JSON` usually is considered as fat free alternative to `XML`. let's consider example:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``` xml\n", "<person>\n", " <name>John Smith</name>\n", " <age>25</age>\n", " <address type=\"home\">\n", " <street>21 2nd Street</street>\n", " <city>New York</city>\n", " <state>NY</state>\n", " </address>\n", " <address type=\"current\">\n", " <street>1410 NE Campus Parkway</street>\n", " <city>Seattle</city>\n", " <state>WA</state>\n", " </address>\n", " <phone type=\"home\">212-555-1234</phone>\n", " <phone type=\"fax\">646-555-4567</phone>\n", "</person>\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is `JSON` alternative:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``` javascript\n", "{\"person\": {\n", " \"name\": \"John Smith\",\n", " \"age\": 25,\n", " \"address\": [\n", " {\"type\": \"home\",\n", " \"street\": \"21 2nd Street\",\n", " \"city\": \"New York\",\n", " \"state\": \"NY\"\n", " },\n", " {\"type\": \"current\",\n", " \"street\": \"1410 NE Campus Parkway\",\n", " \"city\": \"Seattle\",\n", " \"state\": \"WA\"\n", " }\n", " ],\n", " \"phone\": [ \n", " {\"type\": \"home\", \"number\": \"212-555-1234\"},\n", " {\"type\": \"fax\", \"number\": \"646-555-4567\"}\n", " ]\n", "}}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`AXON` allows direct translation of XML that saves it's *element*/*attribute* structure:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "``` javascript\n", "person {\n", " name {\"John Smith\"}\n", " age {25}\n", " address {\n", " type: \"home\"\n", " street {\"21 2nd Street\"}\n", " city {\"New York\"}\n", " state {\"NY\"}\n", " }\n", " address { \n", " type: \"current\"\n", " street {\"1410 NE Campus Parkway\"}\n", " city {\"Seattle\"}\n", " state {\"WA\"}\n", " }\n", " phone {type:\"home\" \"212-555-1234\"}\n", " phone {type:\"fax\" \"646-555-4567\"}\n", "}\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`AXON` representation can be built from `XML` one in 4 steps:\n", "\n", "1. Replace **&lt;tag&gt;** with **tag {**\n", "2. Replace **&lt;/tag&gt;** with **}**\n", "3. Replace **attr=value** with **attr: value**\n", "4. Remove character **,** or replace it with one space character\n", "\n", "The result of such transformation is equivalent to original `XML`. One can also consider it as *fat free form* of `XML`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's now illustrate this feature of `AXON` using `pyaxon` package." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import unicode_literals, print_function\n", "from axon.api import loads, dumps\n", "from axon.objects import node, attribute, Attribute, Node\n", "from axon.objects import Builder, register_builder\n", "from axon import dump_as_str, as_unicode, factory, reduce\n", "from xml.etree import ElementTree\n", "import json\n", "from io import StringIO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are reduce functions for `ElementTree.Element` and `ElementTree.ElementTree` types from `xml.etree` package. These functions will used for dumping `ElementTree` into `AXON` text." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "@reduce(ElementTree.Element)\n", "def element_reduce(elem):\n", " children = elem.getchildren()\n", " children = children[:]\n", " if elem.text and elem.text.strip():\n", " children.append(elem.text)\n", " return node(elem.tag, elem.attrib, children)\n", " \n", "@reduce(ElementTree.ElementTree)\n", "def etree_reduce(element):\n", " return element_reduce(element.getroot())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is the class `ElementTreeBuilder` for construction of `ElementTree` from `AXON` text." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "class ElementTreeBuilder(Builder):\n", " def node(self, name, attrs, vals):\n", " str_type = type(u'')\n", " if type(vals[-1]) is str_type:\n", " text = vals.pop(-1)\n", " else:\n", " text = None\n", " attribs = {}\n", " children = []\n", " if attrs:\n", " for name, val in attrs.items():\n", " attribs[name] = val\n", " if vals:\n", " for val in vals:\n", " children.append(val)\n", " e = ElementTree.Element(name, attribs)\n", " if children:\n", " e.extend(children)\n", " if text:\n", " e.text = text\n", " return e " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's register `ElementTree` builder with name `etree`. This is new value for `mode` parameter in `load`/`loads` functions." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "register_builder('etree', ElementTreeBuilder()) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's consider `XML` text:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xml_text = u\"\"\"\n", "<person>\n", " <name>John Smith</name>\n", " <age>25</age>\n", " <address type=\"home\">\n", " <street>21 2nd Street</street>\n", " <city>New York</city>\n", " <state>NY</state>\n", " </address>\n", " <address type=\"current\">\n", " <street>1410 NE Campus Parkway</street>\n", " <city>Seattle</city>\n", " <state>WA</state>\n", " </address>\n", " <phone type=\"home\">212-555-1234</phone>\n", " <phone type=\"fax\">646-555-4567</phone>\n", "</person>\n", "\"\"\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's parse it into `ElementTree` that represents `XML` document." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<person>\n", " <name>John Smith</name>\n", " <age>25</age>\n", " <address type=\"home\">\n", " <street>21 2nd Street</street>\n", " <city>New York</city>\n", " <state>NY</state>\n", " </address>\n", " <address type=\"current\">\n", " <street>1410 NE Campus Parkway</street>\n", " <city>Seattle</city>\n", " <state>WA</state>\n", " </address>\n", " <phone type=\"home\">212-555-1234</phone>\n", " <phone type=\"fax\">646-555-4567</phone>\n", "</person>\n" ] } ], "source": [ "tree = ElementTree.parse(StringIO(xml_text))\n", "ElementTree.dump(tree)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<person>\n", " <name>John Smith</name>\n", " <age>25</age>\n", " <address type=\"home\">\n", " <street>21 2nd Street</street>\n", " <city>New York</city>\n", " <state>NY</state>\n", " </address>\n", " <address type=\"current\">\n", " <street>1410 NE Campus Parkway</street>\n", " <city>Seattle</city>\n", " <state>WA</state>\n", " </address>\n", " <phone type=\"home\">212-555-1234</phone>\n", " <phone type=\"fax\">646-555-4567</phone>\n", "</person>\n" ] } ], "source": [ "ElementTree.dump(tree)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we dumping `ElementTree` object into `AXON` text." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "person {\n", " name {\"John Smith\"}\n", " age {\"25\"}\n", " address {\n", " type: \"home\"\n", " street {\"21 2nd Street\"}\n", " city {\"New York\"}\n", " state {\"NY\"}}\n", " address {\n", " type: \"current\"\n", " street {\"1410 NE Campus Parkway\"}\n", " city {\"Seattle\"}\n", " state {\"WA\"}}\n", " phone {\n", " type: \"home\"\n", " \"212-555-1234\"}\n", " phone {\n", " type: \"fax\"\n", " \"646-555-4567\"}}\n" ] } ], "source": [ "axon_text = dumps([tree], pretty=1, braces=1)\n", "print(axon_text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And load again from `AXON` text into `ElementTree` object:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "xml_tree = loads(axon_text, mode='etree')[0]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<Element 'person' at 0x104e0a598>\n", "<person><name>John Smith</name><age>25</age><type type=\"home\"><street>21 2nd Street</street><city>New York</city><state>NY</state></type><type type=\"current\"><street>1410 NE Campus Parkway</street><city>Seattle</city><state>WA</state></type><type type=\"home\">212-555-1234</type><type type=\"fax\">646-555-4567</type></person>\n" ] } ], "source": [ "print(xml_tree)\n", "ElementTree.dump(xml_tree)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is `AXON` compact representation for comparison:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "person{name{\"John Smith\"} age{\"25\"} type{type:\"home\" street{\"21 2nd Street\"} city{\"New York\"} state{\"NY\"}} type{type:\"current\" street{\"1410 NE Campus Parkway\"} city{\"Seattle\"} state{\"WA\"}} type{type:\"home\" \"212-555-1234\"} type{type:\"fax\" \"646-555-4567\"}}\n" ] } ], "source": [ "axon_compact_text = dumps([xml_tree], braces=1)\n", "print(axon_compact_text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is `JSON` representation for comparison too:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'{\"person\": {\"phone\": [{\"number\": \"212-555-1234\", \"type\": \"home\"}, {\"number\": \"646-555-4567\", \"type\": \"fax\"}], \"name\": \"John Smith\", \"address\": [{\"street\": \"21 2nd Street\", \"state\": \"NY\", \"type\": \"home\", \"city\": \"New York\"}, {\"street\": \"1410 NE Campus Parkway\", \"state\": \"WA\", \"type\": \"current\", \"city\": \"Seattle\"}], \"age\": 25}}'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "json_text = u\"\"\"\n", "{\"person\": {\n", " \"name\": \"John Smith\",\n", " \"age\": 25,\n", " \"address\": [\n", " {\"type\": \"home\",\n", " \"street\": \"21 2nd Street\",\n", " \"city\": \"New York\",\n", " \"state\": \"NY\"\n", " },\n", " {\"type\": \"current\",\n", " \"street\": \"1410 NE Campus Parkway\",\n", " \"city\": \"Seattle\",\n", " \"state\": \"WA\"\n", " }\n", " ],\n", " \"phone\": [ \n", " {\"type\": \"home\", \"number\": \"212-555-1234\"},\n", " {\"type\": \"fax\", \"number\": \"646-555-4567\"}\n", " ]\n", "}}\n", "\"\"\"\n", "json.dumps(json.loads(json_text))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "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
stijnvanhoey/course_gis_scripting
notebooks/02-scientific-python-introduction.ipynb
1
55258
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<p><font size=\"6\"><b>Scientific Python essentials</b></font></p>\n", "\n", "> *Introduction to GIS scripting* \n", "> *May, 2017*\n", "\n", "> *© 2017, Stijn Van Hoey (<mailto:[email protected]>). Licensed under [CC BY 4.0 Creative Commons](http://creativecommons.org/licenses/by/4.0/)*\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "source": [ "There is a large variety of packages available in Python to support research. Importing a package is like getting a piece of lab equipment out of a storage locker and setting it up on the bench for use in a project. Once a library is set up (imported), it can be used or called to perform many tasks.\n", "\n", "In this notebook, we will focus on two fundamental packages within most scientific applications:\n", "\n", "1. Numpy\n", "1. Pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Furthermore, if plotting is required, this will be done with matplotlib package (we only use `plot` and `imshow` in this tutorial):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.style.use('seaborn-white')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Numpy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NumPy is the fundamental package for **scientific computing** with Python." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Information for the *freaks*:\n", " \n", "* a powerful N-dimensional array/vector/matrix object\n", "* sophisticated (broadcasting) functions\n", "* function implementation in C/Fortran assuring good performance if vectorized\n", "* tools for integrating C/C++ and Fortran code\n", "* useful linear algebra, Fourier transform, and random number capabilities\n", "\n", "*In short*: Numpy is the Python package to do **fast** calculations!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It is a community agreement to import the numpy package with the prefix `np` to identify the usage of numpy functions. Use the `CTRL` + `SHIFT` option to check the available functions of numpy:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# np. # explore the namespace" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Numpy provides many mathematical functions, which operate element-wise on a so-called **`numpy.ndarray`** data type (in short: `array`)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\">\n", " <b>REMEMBER</b>: \n", " <ul>\n", " <li> There is a lot of functionality in Numpy. Knowing **how to find a specific function** is more important than knowing all functions...\n", " </ul>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You were looking for some function to derive quantiles of an array..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.lookfor(\"quantile\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Different methods do read the manual:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "#?np.percentile" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# help(np.percentile) " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# use SHIFT + TAB" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Showcases" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* You like to play boardgames, but you want to better know you're chances of rolling a certain combination (sum) with 2 dices:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "throws = 1000 # number of rolls with the dices\n", "\n", "stone1 = np.random.uniform(1, 6, throws) # outcome of throws with dice 1\n", "stone2 = np.random.uniform(1, 6, throws) # outcome of throws with dice 2\n", "total = stone1 + stone2 # sum of each outcome\n", "histogram = plt.hist(total, bins=20) # plot as histogram" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Consider a random 10x2 matrix representing cartesian coordinates (between 0 and 1), how to convert them to polar coordinates?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# random numbers (X, Y in 2 columns)\n", "Z = np.random.random((10,2))\n", "X, Y = Z[:,0], Z[:,1]\n", "\n", "# Distance \n", "R = np.sqrt(X**2 + Y**2)\n", "# Angle\n", "T = np.arctan2(Y, X) # Array of angles in radians\n", "Tdegree = T*180/(np.pi) # If you like degrees more\n", "\n", "# NEXT PART (purely for illustration)\n", "# plot the cartesian coordinates\n", "plt.figure(figsize=(14, 6))\n", "ax1 = plt.subplot(121)\n", "ax1.plot(Z[:,0], Z[:,1], 'o')\n", "ax1.set_title(\"Cartesian\")\n", "#plot the polar coorsidnates\n", "ax2 = plt.subplot(122, polar=True)\n", "ax2.plot(T, R, 'o')\n", "ax2.set_title(\"Polar\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Rescale the values of a given array to values in the range [0-1] and mark zero values are Nan:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "nete_bodem = np.load(\"../data/nete_bodem.npy\")\n", "plt.imshow(nete_bodem)\n", "plt.colorbar(shrink=0.6)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "nete_bodem_rescaled = (nete_bodem - nete_bodem.min())/(nete_bodem.max() - nete_bodem.min()) # rescale\n", "nete_bodem_rescaled[nete_bodem_rescaled == 0.0] = np.nan # assign Nan values to zero values" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "plt.imshow(nete_bodem_rescaled)\n", "plt.colorbar(shrink=0.6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "(**Remark:** There is no GIS-component in the previous manipulation, these are pure element-wise operations on an array!)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating numpy array" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.array([1, 1.5, 2, 2.5]) #np.array(anylist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-warning\">\n", " <b>R comparison:</b><br>\n", " <p>One could compare the numpy array to the R vector. It contains a single data type (character, float, integer) and operations are element-wise.</p>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Provide a range of values, with a begin, end and stepsize:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.arange(5, 12, 2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Provide a range of values, with a begin, end and number of values in between:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.linspace(2, 13, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create empty arrays or arrays filled with ones:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.zeros((5, 2)), np.ones(5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Request the `shape` or the `size` of the arrays:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.zeros((5, 2)).shape, np.zeros((5, 2)).size" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And creating random numbers:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.random.rand(5,5) # check with np.random. + TAB for sampling from other distributions!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reading in from binary file:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "nete_bodem = np.load(\"../data/nete_bodem.npy\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "plt.imshow(nete_bodem)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reading in from a **text**-file:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "nete_bodem_subset = np.loadtxt(\"../data/nete_bodem_subset.out\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "plt.imshow(nete_bodem_subset)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Slicing (accessing values in arrays)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This i equivalent to the slicing of a `list`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array = np.random.randint(2, 10, 10)\n", "my_array" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array[:5], my_array[4:], my_array[-2:]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array[0:7:2]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "sequence = np.arange(0, 11, 1)\n", "sequence, sequence[::2], sequence[1::3], " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Assign new values to items" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array[:2] = 10\n", "my_array" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array = my_array.reshape(5, 2)\n", "my_array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With multiple dimensions, we get the option of slice amongst these dimensions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array[0, :]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aggregation calculations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array = np.random.randint(2, 10, 10)\n", "my_array" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "print('Mean value is', np.mean(my_array))\n", "print('Median value is', np.median(my_array))\n", "print('Std is', np.std(my_array))\n", "print('Variance is', np.var(my_array))\n", "print('Min is', my_array.min())\n", "print('Element of minimum value is', my_array.argmin())\n", "print('Max is', my_array.max())\n", "print('Sum is', np.sum(my_array))\n", "print('Prod', np.prod(my_array))\n", "print('Unique values in this array are:', np.unique(my_array))\n", "print('85% Percentile value is: ', np.percentile(my_array, 85))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_other_array = np.random.randint(2, 10, 10).reshape(2, 5)\n", "my_other_array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "use the argument `axis` to define the ax to calculate a specific statistic:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_other_array.max(), my_other_array.max(axis=1), my_other_array.max(axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Element-wise operations" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array = np.random.randint(2, 10, 10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "print('Cumsum is', np.cumsum(my_array))\n", "print('CumProd is', np.cumprod(my_array))\n", "print('CumProd of 5 first elements is', np.cumprod(my_array)[4])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.exp(my_array), np.sin(my_array)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array%3 # == 0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the numpy available function from the library or using the object method?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.cumsum(my_array) == my_array.cumsum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array.dtype" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>: \n", "<ul>\n", " <li>Check the documentation of both `np.cumsum()` and `my_array.cumsum()`. What is the difference?</li>\n", " <li>Why do we use brackets () to run `cumsum` and we do not use brackets when asking for the `dtype`?</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\">\n", " <b>REMEMBER</b>: \n", " <ul>\n", " <li> `np.cumsum` operates a <b>method/function</b> from the numpy library with input an array, e.g. `my_array`\n", " <li> `my_array.cumsum()` is a <b>method/function</b> available to the object `my_array`\n", " <li> `dtype` is an attribute/characteristic of the object `my_array`\n", " </ul>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-danger\">\n", "<ul>\n", " <li>It is all about calling a **method/function()** on an **object** to perform an action. The available methods are provided by the packages (or any function you write and import).\n", " <li>Objects also have **attributes**, defining the characteristics of the object (these are not actions)\n", "</ul>\n", " \n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array.cumsum()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array.max(axis=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "my_array * my_array # element-wise" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\">\n", " <b>REMEMBER</b>: \n", " <ul>\n", " <li> The operations do work on all elements of the array at the same time, you don't need a <strike>`for` loop<strike>\n", " </ul>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the added value of the numpy implementation compared to 'basic' python?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a_list = range(1000)\n", "%timeit [i**2 for i in a_list]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "an_array = np.arange(1000)\n", "%timeit an_array**2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Boolean indexing and filtering (!)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a fancy term for making selections based on a **condition**!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's start with an array that contains random values:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "row_array = np.random.randint(1, 20, 10)\n", "row_array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Conditions can be checked (*element-wise*):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "row_array > 5" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "boolean_mask = row_array > 5\n", "boolean_mask" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use this as a filter to select elements of an array:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "row_array[boolean_mask]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or, also to change the values in the array corresponding to these conditions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "row_array[boolean_mask] = 20\n", "row_array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "in short - making the values equal to 20 now -20:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "row_array[row_array == 20] = -20\n", "row_array" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-warning\">\n", " <b>R comparison:</b><br>\n", " <p>This is similar to conditional filtering in R on vectors...</p>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-danger\">\n", " Understanding conditional selections and assignments is CRUCIAL!\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This requires some practice..." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "AR = np.random.randint(0, 20, 15)\n", "AR" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>: \n", "<ul>\n", " <li>Count the number of values in AR that are larger than 10 (note: you can count with True = 1 and False = 0)</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "clear_cell": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# %load ../notebooks/_solutions/02-scientific-python-introduction52.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>: \n", "<ul>\n", " <li>Change all even numbers of `AR` into zero-values.</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "clear_cell": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# %load ../notebooks/_solutions/02-scientific-python-introduction53.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>: \n", "<ul>\n", " <li>Change all even positions of matrix AR into 30 values</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "clear_cell": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# %load ../notebooks/_solutions/02-scientific-python-introduction54.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>: \n", "<ul>\n", " <li>Select all values above the 75th `percentile` of the following array AR2 ad take the square root of these values</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "AR2 = np.random.random(10)\n", "AR2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "clear_cell": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# %load ../notebooks/_solutions/02-scientific-python-introduction56.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>: \n", "<ul>\n", " <li>Convert all values -99. of the array AR3 into Nan-values (Note that Nan values can be provided in float arrays as `np.nan`)</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "AR3 = np.array([-99., 2., 3., 6., 8, -99., 7., 5., 6., -99.])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "clear_cell": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# %load ../notebooks/_solutions/02-scientific-python-introduction58.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>: \n", "<ul>\n", " <li>Get an overview of the unique values present in the array `nete_bodem_subset`</li>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "nete_bodem_subset = np.loadtxt(\"../data/nete_bodem_subset.out\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "clear_cell": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# %load ../notebooks/_solutions/02-scientific-python-introduction60.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>: \n", "<ul>\n", " <li>Reclassify the values of the array `nete_bodem_subset` (binary filter):</li>\n", " <ul>\n", " <li>values lower than or equal to 100000 should be 0</li>\n", " <li>values higher than 100000 should be 1</li>\n", " </ul>\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "nete_bodem_subset = np.loadtxt(\"../data/nete_bodem_subset.out\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "clear_cell": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# %load ../notebooks/_solutions/02-scientific-python-introduction62.py" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\">\n", " <b>REMEMBER</b>: \n", " <ul>\n", " <li> No need to retain everything, but have the reflex to search in the documentation (online docs, SHIFT-TAB, help(), lookfor())!!\n", " <li> Conditional selections (boolean indexing) is crucial!\n", " </ul>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is just touching the surface of Numpy in order to proceed to the next phase (Pandas and GeoPandas)... \n", "\n", "More extended material on Numpy is available online:\n", "\n", "* http://www.scipy-lectures.org/intro/numpy/index.html (great resource to start with scientifi python!)\n", "* https://github.com/stijnvanhoey/course_python_introduction/blob/master/scientific/numpy.ipynb (more extended version of the material covered in this tutorial)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pandas: data analysis in Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For data-intensive work in Python, the Pandas library has become essential. Pandas originally meant **Pan**el **Da**ta, though many users probably don't know that.\n", "\n", "What is pandas?\n", "\n", "* Pandas can be thought of as **NumPy arrays with labels for rows and columns**, and better support for heterogeneous data types, but it's also much, much more than that.\n", "* Pandas can also be thought of as **R's data.frame** in Python.\n", "* Powerful for working with missing data, working with **time series** data, for reading and writing your data, for reshaping, grouping, merging your data,...\n", "\n", "Pandas documentation is available on: http://pandas.pydata.org/pandas-docs/stable/" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# community agreement: import as pd\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data exploration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reading in data to DataFrame" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "surveys_df = pd.read_csv(\"../data/surveys.csv\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "surveys_df.head() # Try also tail()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "surveys_df.shape" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "surveys_df.columns" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "surveys_df.info()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "surveys_df.dtypes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "surveys_df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-warning\">\n", " <b>R comparison:</b><br>\n", " <p>See the similarities and differences with the R `data.frame` - e.g. you would use `summary(df)` instead of `df.describe()` :-)</p>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "surveys_df[\"weight\"].hist(bins=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Series and DataFrames" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "source": [ "A Pandas **Series** is a basic holder for one-dimensional labeled data. It can be created much as a NumPy array is created:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a_series = pd.Series([0.1, 0.2, 0.3, 0.4])\n", "a_series" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a_series.index, a_series.values" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "source": [ "Series do have an index and values (*a numpy array*!) and you can give the series a name (amongst other things)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a_series.name = \"example_series\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a_series" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a_series[2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike the NumPy array, though, this index can be something other than integers:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "a_series2 = pd.Series(np.arange(4), index=['a', 'b', 'c', 'd'])\n", "a_series2['c']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A DataFrame is a tabular data structure (multi-dimensional object to hold labeled data) comprised of rows and columns, akin to a spreadsheet, database table, or R's data.frame object. You can think of it as multiple Series object which share the same index.\n", "<img src=\"../img/schema-dataframe.svg\" width=50%><br>\n", "Note that in the IPython notebook, the dataframe will display in a rich HTML view:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "surveys_df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "surveys_df[\"species_id\"].head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you selecte a single column of a DataFrame, you end up with... a Series:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "type(surveys_df), type(surveys_df[\"species_id\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Aggregation and element-wise calculations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Completely similar to Numpy, aggregation statistics are available:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "print('Mean weight is', surveys_df[\"weight\"].mean())\n", "print('Median weight is', surveys_df[\"weight\"].median())\n", "print('Std of weight is', surveys_df[\"weight\"].std())\n", "print('Variance of weight is', surveys_df[\"weight\"].var())\n", "print('Min is', surveys_df[\"weight\"].min())\n", "print('Element of minimum value is', surveys_df[\"weight\"].argmin())\n", "print('Max is', surveys_df[\"weight\"].max())\n", "print('Sum is', surveys_df[\"weight\"].sum())\n", "print('85% Percentile value is: ', surveys_df[\"weight\"].quantile(0.85))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculations are **element-wise**, e.g. adding the normalized weight (relative to its mean) as an additional column:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "surveys_df['weight_normalised'] = surveys_df[\"weight\"]/surveys_df[\"weight\"].mean()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas and Numpy collaborate well (Numpy methods can be applied on the DataFrame values, as these are actually numpy arrays):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "np.sqrt(surveys_df[\"hindfoot_length\"]).head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Groupby** provides the functionality to do an aggregation or calculation for each group:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "surveys_df.groupby('sex')[['hindfoot_length', 'weight']].mean() # Try yourself with min, max,..." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "source": [ "<div class=\"alert alert-warning\">\n", " <b>R comparison:</b><br>\n", " <p>Similar with groupby in R, i.e. working with factors</p>\n", "</div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Slicing" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "source": [ "<div class=\"alert alert-info\">\n", " <b>ATTENTION!:</b><br><br>\n", " One of pandas' basic features is the labeling of rows and columns, but this makes indexing also a bit more complex compared to numpy. <br><br>We now have to distuinguish between:\n", " <ul>\n", " <li> selection by **label**: loc\n", " <li> selection by **position** iloc\n", " </ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "# example dataframe from scratch\n", "data = {'country': ['Belgium', 'France', 'Germany', 'Netherlands', 'United Kingdom'],\n", " 'population': [11.3, 64.3, 81.3, 16.9, 64.9],\n", " 'area': [30510, 671308, 357050, 41526, 244820],\n", " 'capital': ['Brussels', 'Paris', 'Berlin', 'Amsterdam', 'London']}\n", "countries = pd.DataFrame(data)\n", "countries = countries.set_index('country')\n", "countries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### The shortcut []" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "countries['area'] # single []" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "countries[['area', 'population']] # double [[]]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "countries['France':'Netherlands']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Systematic indexing with loc and iloc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When using [] like above, you can only select from one axis at once (rows or columns, not both). For more advanced indexing, you have some extra attributes:\n", "\n", "* `loc`: selection by label\n", "* `iloc`: selection by position" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "countries.loc['Germany', 'area']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "countries.loc['France':'Germany', ['area', 'population']]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Selecting by position with iloc works **similar as indexing numpy arrays**:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "countries.iloc[0:2,1:3]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Boolean indexing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In short, similar to Numpy:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "countries['area'] > 100000" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Selecting by conditions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "countries[countries['area'] > 100000]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "countries['size'] = np.nan # create an exmpty new column\n", "countries" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "countries.loc[countries['area'] > 100000, \"size\"] = 'LARGE'\n", "countries.loc[countries['area'] <= 100000, \"size\"] = 'SMALL'" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "countries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Combining DataFrames (!)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An important way to combine `DataFrames` is to use columns in each dataset that contain common values (a common unique id) as is done in databases. Combining `DataFrames` using a common field is called *joining*. Joining DataFrames in this way is often useful when one `DataFrames` is a “lookup table” containing additional data that we want to include in the other.\n", "\n", "As an example, consider the availability of the species information in a separate lookup-table:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "species_df = pd.read_csv(\"../data/species.csv\", delimiter=\";\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-success\">\n", " <b>EXERCISE</b>: \n", "<ul>\n", " <li>Check the other `read_` functions that are available in the Pandas package yourself.\n", "</ul>\n", "</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "species_df.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "surveys_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that both tables do have a common identifier column (`species_id`), which we ca use to join the two tables together with the command `merge`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "merged_left = pd.merge(surveys_df, species_df, how=\"left\", on=\"species_id\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "merged_left.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optional section: Pandas is great with time series" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "flowdata = pd.read_csv(\"../data/vmm_flowdata.csv\", index_col=0, \n", " parse_dates=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "flowdata.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"alert alert-info\">\n", " <b>REMEMBER</b>: \n", " <ul>\n", " <li> `pd.read_csv` provides a lot of built-in functionality to support this kind of transactions when reading in a file! Check the **help** of the read_csv function...\n", " </ul>\n", "</div>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The index provides many attributes to work with:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "flowdata.index.year, flowdata.index.dayofweek, flowdata.index.dayofyear #,..." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Subselecting periods can be done by the string representation of dates:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "flowdata[\"2012-01-01 09:00\":\"2012-01-04 19:00\"].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or shorter when possible:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "flowdata[\"2009\"].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Combinations with other selection criteria is possible, e.g. to get all months with 30 days in the year 2009:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "flowdata.loc[(flowdata.index.days_in_month == 30) & (flowdata.index.year == 2009), \"L06_347\"].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select all 'daytime' data (between 8h and 20h) for all days, station \"L06_347\":" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "flowdata[(flowdata.index.hour > 8) & (flowdata.index.hour < 20)].head()\n", "# OR USE flowdata.between_time('08:00', '20:00')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A **very powerful method** is `resample`: converting the frequency of the time series (e.g. from hourly to daily data)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "flowdata.resample('A').mean().plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A practical example is: Plot the monthly minimum and maximum of the daily average values of the `LS06_348` column" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "daily = flowdata['LS06_348'].resample('D').mean() # calculate the daily average value\n", "daily.resample('M').agg(['min', 'max']).plot() # calculate the monthly minimum and maximum values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Other plots are supported as well, e.g. a bar plot of the mean of the stations in year 2013" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [ "flowdata['2013'].mean().plot(kind='barh')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Acknowledgments and Material\n", "\n", "* J.R. Johansson ([email protected]) http://dml.riken.jp/~rob/\n", "* http://scipy-lectures.github.io/intro/numpy/index.html\n", "* http://www.labri.fr/perso/nrougier/teaching/numpy.100/index.html" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "run_control": { "frozen": false, "read_only": false } }, "outputs": [], "source": [] } ], "metadata": { "celltoolbar": "Nbtutor - export exercises", "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": "3.5.3" }, "nav_menu": {}, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 1 }
bsd-3-clause
mtmarsh2/vislab
image_style_experiments/ava_style_aesth_results.ipynb
4
137802
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%load_ext autoreload\n", "%autoreload 2\n", "import re\n", "import os\n", "import aphrodite.results\n", "import pandas as pd\n", "\n", "import vislab\n", "import vislab.datasets\n", "import vislab.results" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "c = vislab.util.get_mongodb_client()['predict']['ava_style_aesth_oct29']\n", "if c.find({'features': 'noise'}).count() > 0:\n", " c.remove({'features': 'noise'})\n", "pd.DataFrame([x for x in c.find()])" ], "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>_id</th>\n", " <th>data</th>\n", " <th>features</th>\n", " <th>num_test</th>\n", " <th>num_train</th>\n", " <th>num_val</th>\n", " <th>quadratic</th>\n", " <th>results_name</th>\n", " <th>score_test</th>\n", " <th>score_val</th>\n", " <th>task</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0 </th>\n", " <td> 526f86549622958641aaee6f</td>\n", " <td> ava_style_rating_std_bin</td>\n", " <td> [lab_hist]</td>\n", " <td> 2809</td>\n", " <td> 8984</td>\n", " <td> 2286</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_std_bin_features_['lab_h...</td>\n", " <td> 0.535583</td>\n", " <td> 0.551982</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>1 </th>\n", " <td> 526f86689622958641aaee70</td>\n", " <td> ava_style_rating_std_cn_bin</td>\n", " <td> [lab_hist]</td>\n", " <td> 2809</td>\n", " <td> 9170</td>\n", " <td> 2100</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_std_cn_bin_features_['la...</td>\n", " <td> 0.543307</td>\n", " <td> 0.569578</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>2 </th>\n", " <td> 526f868b9622958641aaee71</td>\n", " <td> ava_style_rating_mean_bin</td>\n", " <td> [lab_hist]</td>\n", " <td> 2809</td>\n", " <td> 9060</td>\n", " <td> 2210</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_mean_bin_features_['lab_...</td>\n", " <td> 0.543319</td>\n", " <td> 0.583865</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>3 </th>\n", " <td> 526f873a9622958641aaee72</td>\n", " <td> ava_style_rating_mean_cn_bin</td>\n", " <td> [lab_hist]</td>\n", " <td> 2809</td>\n", " <td> 8998</td>\n", " <td> 2272</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_mean_cn_bin_features_['l...</td>\n", " <td> 0.549347</td>\n", " <td> 0.548444</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>4 </th>\n", " <td> 526f87f19622958641aaee73</td>\n", " <td> ava_style_rating_std_bin</td>\n", " <td> [gbvs_saliency]</td>\n", " <td> 2809</td>\n", " <td> 8984</td>\n", " <td> 2286</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_std_bin_features_['gbvs_...</td>\n", " <td> 0.511687</td>\n", " <td> 0.542105</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>5 </th>\n", " <td> 526f8d349622958641aaee75</td>\n", " <td> ava_style_rating_std_cn_bin</td>\n", " <td> [decaf_fc6]</td>\n", " <td> 2809</td>\n", " <td> 9170</td>\n", " <td> 2100</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_std_cn_bin_features_['de...</td>\n", " <td> 0.563672</td>\n", " <td> 0.601905</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>6 </th>\n", " <td> 526f8d499622958641aaee76</td>\n", " <td> ava_style_rating_std_bin</td>\n", " <td> [decaf_fc6]</td>\n", " <td> 2809</td>\n", " <td> 8984</td>\n", " <td> 2286</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_std_bin_features_['decaf...</td>\n", " <td> 0.554665</td>\n", " <td> 0.566054</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>7 </th>\n", " <td> 526f8df79622958641aaee77</td>\n", " <td> ava_style_rating_mean_cn_bin</td>\n", " <td> [decaf_fc6]</td>\n", " <td> 2809</td>\n", " <td> 8998</td>\n", " <td> 2272</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_mean_cn_bin_features_['d...</td>\n", " <td> 0.621670</td>\n", " <td> 0.626320</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>8 </th>\n", " <td> 526f92cb9622958641aaee78</td>\n", " <td> ava_style_rating_std_cn_bin</td>\n", " <td> [decaf_fc6_flatten]</td>\n", " <td> 2809</td>\n", " <td> 9170</td>\n", " <td> 2100</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_std_cn_bin_features_['de...</td>\n", " <td> 0.542578</td>\n", " <td> 0.671279</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>9 </th>\n", " <td> 526f93819622958641aaee79</td>\n", " <td> ava_style_rating_mean_bin</td>\n", " <td> [decaf_fc6_flatten]</td>\n", " <td> 2809</td>\n", " <td> 9060</td>\n", " <td> 2210</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_mean_bin_features_['deca...</td>\n", " <td> 0.586433</td>\n", " <td> 0.673303</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td> 527014449622958641aaee7a</td>\n", " <td> ava_style_rating_mean_bin</td>\n", " <td> [gbvs_saliency]</td>\n", " <td> 2809</td>\n", " <td> 9060</td>\n", " <td> 2210</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_mean_bin_features_['gbvs...</td>\n", " <td> 0.508035</td>\n", " <td> 0.551410</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>11</th>\n", " <td> 5270145a9622958641aaee7b</td>\n", " <td> ava_style_rating_std_cn_bin</td>\n", " <td> [gbvs_saliency]</td>\n", " <td> 2809</td>\n", " <td> 9170</td>\n", " <td> 2100</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_std_cn_bin_features_['gb...</td>\n", " <td> 0.526254</td>\n", " <td> 0.546367</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>12</th>\n", " <td> 527014989622958641aaee7c</td>\n", " <td> ava_style_rating_mean_cn_bin</td>\n", " <td> [gbvs_saliency]</td>\n", " <td> 2809</td>\n", " <td> 8998</td>\n", " <td> 2272</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_mean_cn_bin_features_['g...</td>\n", " <td> 0.507942</td>\n", " <td> 0.522546</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>13</th>\n", " <td> 52701bdb9622958641aaee7d</td>\n", " <td> ava_style_rating_mean_bin</td>\n", " <td> [decaf_fc6]</td>\n", " <td> 2809</td>\n", " <td> 9060</td>\n", " <td> 2210</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_mean_bin_features_['deca...</td>\n", " <td> 0.630831</td>\n", " <td> 0.612217</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>14</th>\n", " <td> 527020209622958641aaee7e</td>\n", " <td> ava_style_rating_std_bin</td>\n", " <td> [decaf_fc6_flatten]</td>\n", " <td> 2809</td>\n", " <td> 8984</td>\n", " <td> 2286</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_std_bin_features_['decaf...</td>\n", " <td> 0.535350</td>\n", " <td> 0.587204</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td> 527022309622958641aaee7f</td>\n", " <td> ava_style_rating_mean_cn_bin</td>\n", " <td> [decaf_fc6_flatten]</td>\n", " <td> 2809</td>\n", " <td> 8998</td>\n", " <td> 2272</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_mean_cn_bin_features_['d...</td>\n", " <td> 0.585735</td>\n", " <td> 0.593034</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td> 52702b579622958641aaee80</td>\n", " <td> ava_style_rating_std_bin</td>\n", " <td> [gist_256]</td>\n", " <td> 2809</td>\n", " <td> 8984</td>\n", " <td> 2286</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_std_bin_features_['gist_...</td>\n", " <td> 0.526603</td>\n", " <td> 0.566054</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td> 52702b5e9622958641aaee81</td>\n", " <td> ava_style_rating_mean_bin</td>\n", " <td> [gist_256]</td>\n", " <td> 2809</td>\n", " <td> 9060</td>\n", " <td> 2210</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_mean_bin_features_['gist...</td>\n", " <td> 0.523324</td>\n", " <td> 0.535294</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td> 52702baa9622958641aaee82</td>\n", " <td> ava_style_rating_mean_cn_bin</td>\n", " <td> [gist_256]</td>\n", " <td> 2809</td>\n", " <td> 8998</td>\n", " <td> 2272</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_mean_cn_bin_features_['g...</td>\n", " <td> 0.534917</td>\n", " <td> 0.573504</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td> 52702bb19622958641aaee83</td>\n", " <td> ava_style_rating_std_cn_bin</td>\n", " <td> [gist_256]</td>\n", " <td> 2809</td>\n", " <td> 9170</td>\n", " <td> 2100</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_std_cn_bin_features_['gi...</td>\n", " <td> 0.533203</td>\n", " <td> 0.536667</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td> 52702cab9622958641aaee84</td>\n", " <td> ava_style_rating_std_bin</td>\n", " <td> [mc_bit]</td>\n", " <td> 2809</td>\n", " <td> 8984</td>\n", " <td> 2286</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_std_bin_features_['mc_bi...</td>\n", " <td> 0.530448</td>\n", " <td> 0.580318</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td> 52702d2c9622958641aaee85</td>\n", " <td> ava_style_rating_std_cn_bin</td>\n", " <td> [mc_bit]</td>\n", " <td> 2809</td>\n", " <td> 9170</td>\n", " <td> 2100</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_std_cn_bin_features_['mc...</td>\n", " <td> 0.565200</td>\n", " <td> 0.570881</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td> 52702e439622958641aaee86</td>\n", " <td> ava_style_rating_mean_bin</td>\n", " <td> [mc_bit]</td>\n", " <td> 2809</td>\n", " <td> 9060</td>\n", " <td> 2210</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_mean_bin_features_['mc_b...</td>\n", " <td> 0.627473</td>\n", " <td> 0.725733</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td> 52702e479622958641aaee87</td>\n", " <td> ava_style_rating_mean_cn_bin</td>\n", " <td> [mc_bit]</td>\n", " <td> 2809</td>\n", " <td> 8998</td>\n", " <td> 2272</td>\n", " <td> False</td>\n", " <td> data_ava_style_rating_mean_cn_bin_features_['m...</td>\n", " <td> 0.620927</td>\n", " <td> 0.653404</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td> 5272510b8dc9cfbe92ca2345</td>\n", " <td> ava_style_rating_mean_bin</td>\n", " <td> [fusion_ava_style_oct22]</td>\n", " <td> 2809</td>\n", " <td> 9060</td>\n", " <td> 2210</td>\n", " <td> None</td>\n", " <td> data_ava_style_rating_mean_bin_features_['fusi...</td>\n", " <td> 0.559131</td>\n", " <td> 0.574208</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td> 527251378dc9cfbe92ca2346</td>\n", " <td> ava_style_rating_std_bin</td>\n", " <td> [fusion_ava_style_oct22]</td>\n", " <td> 2809</td>\n", " <td> 8984</td>\n", " <td> 2286</td>\n", " <td> None</td>\n", " <td> data_ava_style_rating_std_bin_features_['fusio...</td>\n", " <td> 0.545741</td>\n", " <td> 0.571741</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td> 527250e49622958641af684d</td>\n", " <td> ava_style_rating_std_bin</td>\n", " <td> [fusion_ava_style_oct22]</td>\n", " <td> 2809</td>\n", " <td> 8984</td>\n", " <td> 2286</td>\n", " <td> None</td>\n", " <td> data_ava_style_rating_std_bin_features_['fusio...</td>\n", " <td> 0.562303</td>\n", " <td> 0.582677</td>\n", " <td> clf</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td> 527319678dc9cfbe92ca2347</td>\n", " <td> ava_style_rating_mean_bin</td>\n", " <td> [fusion_ava_style_oct22, pascal_mc_for_fusion_...</td>\n", " <td> 2809</td>\n", " <td> 9060</td>\n", " <td> 2210</td>\n", " <td> fp</td>\n", " <td> data_ava_style_rating_mean_bin_features_['fusi...</td>\n", " <td> 0.558327</td>\n", " <td> 0.567873</td>\n", " <td> clf</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 4, "text": [ " _id data \\\n", "0 526f86549622958641aaee6f ava_style_rating_std_bin \n", "1 526f86689622958641aaee70 ava_style_rating_std_cn_bin \n", "2 526f868b9622958641aaee71 ava_style_rating_mean_bin \n", "3 526f873a9622958641aaee72 ava_style_rating_mean_cn_bin \n", "4 526f87f19622958641aaee73 ava_style_rating_std_bin \n", "5 526f8d349622958641aaee75 ava_style_rating_std_cn_bin \n", "6 526f8d499622958641aaee76 ava_style_rating_std_bin \n", "7 526f8df79622958641aaee77 ava_style_rating_mean_cn_bin \n", "8 526f92cb9622958641aaee78 ava_style_rating_std_cn_bin \n", "9 526f93819622958641aaee79 ava_style_rating_mean_bin \n", "10 527014449622958641aaee7a ava_style_rating_mean_bin \n", "11 5270145a9622958641aaee7b ava_style_rating_std_cn_bin \n", "12 527014989622958641aaee7c ava_style_rating_mean_cn_bin \n", "13 52701bdb9622958641aaee7d ava_style_rating_mean_bin \n", "14 527020209622958641aaee7e ava_style_rating_std_bin \n", "15 527022309622958641aaee7f ava_style_rating_mean_cn_bin \n", "16 52702b579622958641aaee80 ava_style_rating_std_bin \n", "17 52702b5e9622958641aaee81 ava_style_rating_mean_bin \n", "18 52702baa9622958641aaee82 ava_style_rating_mean_cn_bin \n", "19 52702bb19622958641aaee83 ava_style_rating_std_cn_bin \n", "20 52702cab9622958641aaee84 ava_style_rating_std_bin \n", "21 52702d2c9622958641aaee85 ava_style_rating_std_cn_bin \n", "22 52702e439622958641aaee86 ava_style_rating_mean_bin \n", "23 52702e479622958641aaee87 ava_style_rating_mean_cn_bin \n", "24 5272510b8dc9cfbe92ca2345 ava_style_rating_mean_bin \n", "25 527251378dc9cfbe92ca2346 ava_style_rating_std_bin \n", "26 527250e49622958641af684d ava_style_rating_std_bin \n", "27 527319678dc9cfbe92ca2347 ava_style_rating_mean_bin \n", "\n", " features num_test num_train \\\n", "0 [lab_hist] 2809 8984 \n", "1 [lab_hist] 2809 9170 \n", "2 [lab_hist] 2809 9060 \n", "3 [lab_hist] 2809 8998 \n", "4 [gbvs_saliency] 2809 8984 \n", "5 [decaf_fc6] 2809 9170 \n", "6 [decaf_fc6] 2809 8984 \n", "7 [decaf_fc6] 2809 8998 \n", "8 [decaf_fc6_flatten] 2809 9170 \n", "9 [decaf_fc6_flatten] 2809 9060 \n", "10 [gbvs_saliency] 2809 9060 \n", "11 [gbvs_saliency] 2809 9170 \n", "12 [gbvs_saliency] 2809 8998 \n", "13 [decaf_fc6] 2809 9060 \n", "14 [decaf_fc6_flatten] 2809 8984 \n", "15 [decaf_fc6_flatten] 2809 8998 \n", "16 [gist_256] 2809 8984 \n", "17 [gist_256] 2809 9060 \n", "18 [gist_256] 2809 8998 \n", "19 [gist_256] 2809 9170 \n", "20 [mc_bit] 2809 8984 \n", "21 [mc_bit] 2809 9170 \n", "22 [mc_bit] 2809 9060 \n", "23 [mc_bit] 2809 8998 \n", "24 [fusion_ava_style_oct22] 2809 9060 \n", "25 [fusion_ava_style_oct22] 2809 8984 \n", "26 [fusion_ava_style_oct22] 2809 8984 \n", "27 [fusion_ava_style_oct22, pascal_mc_for_fusion_... 2809 9060 \n", "\n", " num_val quadratic results_name \\\n", "0 2286 False data_ava_style_rating_std_bin_features_['lab_h... \n", "1 2100 False data_ava_style_rating_std_cn_bin_features_['la... \n", "2 2210 False data_ava_style_rating_mean_bin_features_['lab_... \n", "3 2272 False data_ava_style_rating_mean_cn_bin_features_['l... \n", "4 2286 False data_ava_style_rating_std_bin_features_['gbvs_... \n", "5 2100 False data_ava_style_rating_std_cn_bin_features_['de... \n", "6 2286 False data_ava_style_rating_std_bin_features_['decaf... \n", "7 2272 False data_ava_style_rating_mean_cn_bin_features_['d... \n", "8 2100 False data_ava_style_rating_std_cn_bin_features_['de... \n", "9 2210 False data_ava_style_rating_mean_bin_features_['deca... \n", "10 2210 False data_ava_style_rating_mean_bin_features_['gbvs... \n", "11 2100 False data_ava_style_rating_std_cn_bin_features_['gb... \n", "12 2272 False data_ava_style_rating_mean_cn_bin_features_['g... \n", "13 2210 False data_ava_style_rating_mean_bin_features_['deca... \n", "14 2286 False data_ava_style_rating_std_bin_features_['decaf... \n", "15 2272 False data_ava_style_rating_mean_cn_bin_features_['d... \n", "16 2286 False data_ava_style_rating_std_bin_features_['gist_... \n", "17 2210 False data_ava_style_rating_mean_bin_features_['gist... \n", "18 2272 False data_ava_style_rating_mean_cn_bin_features_['g... \n", "19 2100 False data_ava_style_rating_std_cn_bin_features_['gi... \n", "20 2286 False data_ava_style_rating_std_bin_features_['mc_bi... \n", "21 2100 False data_ava_style_rating_std_cn_bin_features_['mc... \n", "22 2210 False data_ava_style_rating_mean_bin_features_['mc_b... \n", "23 2272 False data_ava_style_rating_mean_cn_bin_features_['m... \n", "24 2210 None data_ava_style_rating_mean_bin_features_['fusi... \n", "25 2286 None data_ava_style_rating_std_bin_features_['fusio... \n", "26 2286 None data_ava_style_rating_std_bin_features_['fusio... \n", "27 2210 fp data_ava_style_rating_mean_bin_features_['fusi... \n", "\n", " score_test score_val task \n", "0 0.535583 0.551982 clf \n", "1 0.543307 0.569578 clf \n", "2 0.543319 0.583865 clf \n", "3 0.549347 0.548444 clf \n", "4 0.511687 0.542105 clf \n", "5 0.563672 0.601905 clf \n", "6 0.554665 0.566054 clf \n", "7 0.621670 0.626320 clf \n", "8 0.542578 0.671279 clf \n", "9 0.586433 0.673303 clf \n", "10 0.508035 0.551410 clf \n", "11 0.526254 0.546367 clf \n", "12 0.507942 0.522546 clf \n", "13 0.630831 0.612217 clf \n", "14 0.535350 0.587204 clf \n", "15 0.585735 0.593034 clf \n", "16 0.526603 0.566054 clf \n", "17 0.523324 0.535294 clf \n", "18 0.534917 0.573504 clf \n", "19 0.533203 0.536667 clf \n", "20 0.530448 0.580318 clf \n", "21 0.565200 0.570881 clf \n", "22 0.627473 0.725733 clf \n", "23 0.620927 0.653404 clf \n", "24 0.559131 0.574208 clf \n", "25 0.545741 0.571741 clf \n", "26 0.562303 0.582677 clf \n", "27 0.558327 0.567873 clf " ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "results_df, preds_panel = aphrodite.results.load_pred_results(\n", " 'ava_style_aesth_oct29', os.path.expanduser('~/work/aphrodite/data/results2'),\n", " multiclass=False, force=True)\n", "pred_prefix = 'ava_style'\n", "print preds_panel.minor_axis\n", "preds_panel" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Results in collection ava_style_aesth_oct29: 28\n", "Index([u'decaf_fc6 False vw', u'decaf_fc6_flatten False vw', u'fusion_ava_style_oct22 None vw', u'fusion_ava_style_oct22,pascal_mc_for_fusion_ava_style_oct22 fp vw', u'gbvs_saliency False vw', u'gist_256 False vw', u'lab_hist False vw', u'label', u'mc_bit False vw', u'split'], dtype='object')" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] }, { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "<class 'pandas.core.panel.Panel'>\n", "Dimensions: 4 (items) x 14079 (major_axis) x 10 (minor_axis)\n", "Items axis: clf ava_style_rating_mean_bin to clf ava_style_rating_std_cn_bin\n", "Major_axis axis: 1187 to 97009\n", "Minor_axis axis: decaf_fc6 False vw to split" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "label_df = vislab.datasets.ava.get_ratings_df()\n", "label_df.iloc[: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>rating_mean</th>\n", " <th>rating_std</th>\n", " <th>rating_mean_bin</th>\n", " <th>rating_std_bin</th>\n", " <th>rating_mean_cn_bin</th>\n", " <th>rating_std_cn_bin</th>\n", " </tr>\n", " <tr>\n", " <th>image_id</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>953619</th>\n", " <td> 5.6371</td>\n", " <td> 1.4218</td>\n", " <td> True</td>\n", " <td> False</td>\n", " <td> False</td>\n", " <td> True</td>\n", " </tr>\n", " <tr>\n", " <th>953958</th>\n", " <td> 4.6984</td>\n", " <td> 1.9851</td>\n", " <td> False</td>\n", " <td> True</td>\n", " <td> False</td>\n", " <td> True</td>\n", " </tr>\n", " <tr>\n", " <th>954184</th>\n", " <td> 5.6746</td>\n", " <td> 1.1043</td>\n", " <td> True</td>\n", " <td> False</td>\n", " <td> False</td>\n", " <td> False</td>\n", " </tr>\n", " <tr>\n", " <th>954113</th>\n", " <td> 5.7734</td>\n", " <td> 1.2822</td>\n", " <td> True</td>\n", " <td> False</td>\n", " <td> True</td>\n", " <td> False</td>\n", " </tr>\n", " <tr>\n", " <th>953980</th>\n", " <td> 5.2093</td>\n", " <td> 1.1592</td>\n", " <td> False</td>\n", " <td> False</td>\n", " <td> False</td>\n", " <td> False</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 6, "text": [ " rating_mean rating_std rating_mean_bin rating_std_bin \\\n", "image_id \n", "953619 5.6371 1.4218 True False \n", "953958 4.6984 1.9851 False True \n", "954184 5.6746 1.1043 True False \n", "954113 5.7734 1.2822 True False \n", "953980 5.2093 1.1592 False False \n", "\n", " rating_mean_cn_bin rating_std_cn_bin \n", "image_id \n", "953619 False True \n", "953958 False True \n", "954184 False False \n", "954113 True False \n", "953980 False False " ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "task_name = 'clf ava_style_rating_mean_bin'\n", "\n", "task_names = preds_panel.items\n", "task_metrics = {}\n", "for task_name in task_names:\n", " pred_df = preds_panel[task_name]\n", " feature_names = [x for x in pred_df.columns if x not in ['label', 'split']]\n", " feat_metrics = {}\n", " for feature_name in feature_names:\n", " pdf = pred_df[[feature_name, 'label']]\n", " pdf.columns = ['pred', 'label']\n", " feat_metrics[feature_name] = vislab.results.binary_metrics(\n", " pdf, task_name + ' ' + feature_name, balanced=True,\n", " with_plot=False, with_print=True)\n", " task_metrics[task_name] = feat_metrics\n", " \n", "acc_df = pd.DataFrame([\n", " pd.Series(\n", " [\n", " task_metrics[task_name][key]['accuracy']\n", " for key in sorted(task_metrics[task_name].keys())\n", " ],\n", " sorted(task_metrics[task_name].keys()),\n", " name=task_name\n", " )\n", " for task_name in task_names\n", "])" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "------------------------------------------------------------\n", "Classification metrics on clf ava_style_rating_mean_bin decaf_fc6 False vw balanced\n", "ap_sklearn: 0.749066184304\n", "mcc: 0.371219456864\n", "ap: 0.749851428549\n", " precision recall f1-score support\n", "False 0.681406 0.697014 0.689121 6898\n", "True 0.689911 0.674108 0.681918 6898\n", "accuracy: 0.685561032183\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_bin decaf_fc6_flatten False vw balanced\n", "ap_sklearn: 0.869449111107\n", "mcc: 0.558725093791\n", "ap: 0.869543392057\n", " precision recall f1-score support\n", "False 0.777506 0.782691 0.780090 6898\n", "True 0.781232 0.776022 0.778618 6898\n", "accuracy: 0.77935633517\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_bin fusion_ava_style_oct22 None vw balanced\n", "ap_sklearn: 0.548160089853\n", "mcc: 0.169300167358\n", "ap: 0.616493919884\n", " precision recall f1-score support\n", "False 0.599896 0.501450 0.546273 6898\n", "True 0.571731 0.665555 0.615086 6898\n", "accuracy: 0.583502464482\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_bin fusion_ava_style_oct22,pascal_mc_for_fusion_ava_style_oct22 fp vw balanced\n", "ap_sklearn: 0.541793205005\n", "mcc: 0.194151409817\n", "ap: 0.645018790104\n", " precision recall f1-score support\n", "False 0.627494 0.460568 0.531226 6898\n", "True 0.573915 0.726587 0.641290 6898\n", "accuracy: 0.593577848652\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_bin gbvs_saliency False vw balanced\n", "ap_sklearn: 0.744156022126\n", "mcc: 0.0776281722926\n", "ap: 0.746245131622\n", " precision recall f1-score support\n", "False 0.542743 0.490577 0.515343 6898\n", "True 0.535247 0.586692 0.559790 6898\n", "accuracy: 0.538634386779\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_bin gist_256 False vw balanced\n", "ap_sklearn: 0.576194597093\n", "mcc: 0.115442377921\n", "ap: 0.57761317677\n", " precision recall f1-score support\n", "False 0.556104 0.571905 0.563894 6898\n", "True 0.559385 0.543491 0.551324 6898\n", "accuracy: 0.557697883444\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_bin lab_hist False vw balanced\n", "ap_sklearn: 0.665550326596\n", "mcc: 0.147378632246\n", "ap: 0.70478931719\n", " precision recall f1-score support\n", "False 0.577969 0.545375 0.561199 6898\n", "True 0.569645 0.601769 0.585266 6898\n", "accuracy: 0.57357204987\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_bin mc_bit False vw balanced\n", "ap_sklearn: 0.911798128465\n", "mcc: 0.685629238165\n", "ap: 0.928899991326\n", " precision recall f1-score support\n", "False 0.834371 0.855175 0.844645 6898\n", "True 0.851472 0.830241 0.840722 6898\n", "accuracy: 0.842708031313\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_cn_bin decaf_fc6 False vw balanced\n", "ap_sklearn: 0.738716445192\n", "mcc: 0.37358109213\n", "ap: 0.739409295433\n", " precision recall f1-score support\n", "False 0.683436 0.69582 0.689572 7009\n", "True 0.690206 0.67770 0.683896 7009\n", "accuracy: 0.686759880154\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_cn_bin decaf_fc6_flatten False vw balanced\n", "ap_sklearn: 0.805215518639\n", "mcc: 0.576988960153\n", "ap: 0.816463034094\n", " precision recall f1-score support\n", "False 0.786321 0.792267 0.789283 7009\n", "True 0.790684 0.784705 0.787683 7009\n", "accuracy: 0.788486231987\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_cn_bin fusion_ava_style_oct22 None vw balanced\n", "ap_sklearn: 0.500600375675\n", "mcc: 0.0\n", "ap: 0.504011380946\n", " precision recall f1-score support\n", "False 0.5 1 0.666667 7009\n", "True 0.0 0 0.000000 7009\n", "accuracy: 0.5\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_cn_bin fusion_ava_style_oct22,pascal_mc_for_fusion_ava_style_oct22 fp vw balanced\n", "ap_sklearn: 0.50159498011\n", "mcc: 0.0\n", "ap: 0.504924701141\n", " precision recall f1-score support\n", "False 0.5 1 0.666667 7009\n", "True 0.0 0 0.000000 7009\n", "accuracy: 0.5\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_cn_bin gbvs_saliency False vw balanced\n", "ap_sklearn: 0.541418562595\n", "mcc: 0.0905304254399\n", "ap: 0.601004300733\n", " precision recall f1-score support\n", "False 0.549499 0.500499 0.523856 7009\n", "True 0.541394 0.589670 0.564502 7009\n", "accuracy: 0.545084890855\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_cn_bin gist_256 False vw balanced\n", "ap_sklearn: 0.560323827475\n", "mcc: 0.115371794222\n", "ap: 0.562965225231\n", " precision recall f1-score support\n", "False 0.555434 0.577543 0.566273 7009\n", "True 0.560030 0.537737 0.548657 7009\n", "accuracy: 0.557640176915\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_cn_bin lab_hist False vw balanced\n", "ap_sklearn: 0.401516016035\n", "mcc: 0.150538406449\n", "ap: 0.513465852355\n", " precision recall f1-score support\n", "False 0.560112 0.683978 0.615879 7009\n", "True 0.594248 0.462833 0.520372 7009\n", "accuracy: 0.573405621344\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_mean_cn_bin mc_bit False vw balanced\n", "ap_sklearn: 0.480236181123\n", "mcc: 0.682088791949\n", "ap: 0.632661665457\n", " precision recall f1-score support\n", "False 0.835015 0.849907 0.842396 7009\n", "True 0.847182 0.832073 0.839559 7009\n", "accuracy: 0.840990155514\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_bin decaf_fc6 False vw balanced\n", "ap_sklearn: 0.686333285139\n", "mcc: 0.300033392604\n", "ap: 0.687235733168\n", " precision recall f1-score support\n", "False 0.650842 0.647271 0.649052 6926\n", "True 0.649196 0.652758 0.650972 6926\n", "accuracy: 0.650014438348\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_bin decaf_fc6_flatten False vw balanced\n", "ap_sklearn: 0.52244284987\n", "mcc: 0.543916532442\n", "ap: 0.638878707758\n", " precision recall f1-score support\n", "False 0.774523 0.767254 0.770871 6926\n", "True 0.769418 0.776639 0.773011 6926\n", "accuracy: 0.771946289344\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_bin fusion_ava_style_oct22 None vw balanced\n", "ap_sklearn: 0.400488534806\n", "mcc: 0.167329024066\n", "ap: 0.510982096323\n", " precision recall f1-score support\n", "False 0.560053 0.739243 0.637292 6926\n", "True 0.616561 0.419290 0.499141 6926\n", "accuracy: 0.579266531909\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_bin fusion_ava_style_oct22,pascal_mc_for_fusion_ava_style_oct22 fp vw balanced\n", "ap_sklearn: 0.475335258902\n", "mcc: 0.0\n", "ap: 0.514164896125\n", " precision recall f1-score support\n", "False 0.5 1 0.666667 6926\n", "True 0.0 0 0.000000 6926\n", "accuracy: 0.5\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_bin gbvs_saliency False vw balanced\n", "ap_sklearn: 0.384435488416\n", "mcc: 0.0867147640917\n", "ap: 0.507921037342\n", " precision recall f1-score support\n", "False 0.541482 0.565406 0.553185 6926\n", "True 0.545317 0.521224 0.532999 6926\n", "accuracy: 0.543315044759\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_bin gist_256 False vw balanced\n", "ap_sklearn: 0.566175708313\n", "mcc: 0.109157109023\n", "ap: 0.567837817106\n", " precision recall f1-score support\n", "False 0.546335 0.634999 0.587340 6926\n", "True 0.564288 0.472712 0.514456 6926\n", "accuracy: 0.553855038984\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_bin lab_hist False vw balanced\n", "ap_sklearn: 0.408496404846\n", "mcc: 0.145113431153\n", "ap: 0.506130411241\n", " precision recall f1-score support\n", "False 0.558729 0.675137 0.611442 6926\n", "True 0.589641 0.466792 0.521073 6926\n", "accuracy: 0.570964481663\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_bin mc_bit False vw balanced\n", "ap_sklearn: 0.4565498512\n", "mcc: 0.564808691604\n", "ap: 0.54545255991\n", " precision recall f1-score support\n", "False 0.737166 0.864568 0.795800 6926\n", "True 0.836272 0.691741 0.757171 6926\n", "accuracy: 0.778154779093\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_cn_bin decaf_fc6 False vw balanced\n", "ap_sklearn: 0.704879712741\n", "mcc: 0.333746648688\n", "ap: 0.705943661324\n", " precision recall f1-score support\n", "False 0.665956 0.669626 0.667786 6532\n", "True 0.667796 0.664115 0.665950 6532\n", "accuracy: 0.666870789957\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_cn_bin decaf_fc6_flatten False vw balanced\n", "ap_sklearn: 0.8171375431\n", "mcc: 0.553561939834\n", "ap: 0.822665905195\n", " precision recall f1-score support\n", "False 0.782908 0.765769 0.774243 6532\n", "True 0.770787 0.787661 0.779132 6532\n", "accuracy: 0.77671463564\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_cn_bin fusion_ava_style_oct22 None vw balanced\n", "ap_sklearn: 0.496307551719\n", "mcc: 0.0\n", "ap: 0.502507332852\n", " precision recall f1-score support\n", "False 0.5 1 0.666667 6532\n", "True 0.0 0 0.000000 6532\n", "accuracy: 0.5\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_cn_bin fusion_ava_style_oct22,pascal_mc_for_fusion_ava_style_oct22 fp vw balanced\n", "ap_sklearn: 0.497322445025\n", "mcc: 0.0\n", "ap: 0.503390743785\n", " precision recall f1-score support\n", "False 0.5 1 0.666667 6532\n", "True 0.0 0 0.000000 6532\n", "accuracy: 0.5\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_cn_bin gbvs_saliency False vw balanced\n", "ap_sklearn: 0.640363678587\n", "mcc: 0.0955299865268\n", "ap: 0.656249719747\n", " precision recall f1-score support\n", "False 0.547882 0.54654 0.547210 6532\n", "True 0.547648 0.54899 0.548318 6532\n", "accuracy: 0.547764849969\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_cn_bin gist_256 False vw balanced\n", "ap_sklearn: 0.577441455089\n", "mcc: 0.108475855516\n", "ap: 0.578585733381\n", " precision recall f1-score support\n", "False 0.547830 0.616350 0.580073 6532\n", "True 0.561505 0.491274 0.524047 6532\n", "accuracy: 0.553812002449\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_cn_bin lab_hist False vw balanced\n", "ap_sklearn: 0.713886018826\n", "mcc: 0.159221119206\n", "ap: 0.734740774005\n", " precision recall f1-score support\n", "False 0.569747 0.644672 0.604898 6532\n", "True 0.590869 0.513166 0.549283 6532\n", "accuracy: 0.578919167177\n", "\n", "------------------------------------------------------------" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Classification metrics on clf ava_style_rating_std_cn_bin mc_bit False vw balanced\n", "ap_sklearn: 0.817488711759\n", "mcc: 0.637019692825\n", "ap: 0.863165007963\n", " precision recall f1-score support\n", "False 0.787311 0.868187 0.825774 6532\n", "True 0.853097 0.765462 0.806907 6532\n", "accuracy: 0.816824862217\n", "\n" ] }, { "output_type": "stream", "stream": "stderr", "text": [ "/Users/sergeyk/work/vislab/vislab/results.py:72: UserWarning: The sum of true positives and false positives are equal to zero for some labels. Precision is ill defined for those labels [ True]. The precision and recall are equal to zero for some labels. fbeta_score is ill defined for those labels [ True]. \n", " pred_df['label'], pred_df['pred_bin'])\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "acc_df" ], "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>decaf_fc6 False vw</th>\n", " <th>decaf_fc6_flatten False vw</th>\n", " <th>fusion_ava_style_oct22 None vw</th>\n", " <th>fusion_ava_style_oct22,pascal_mc_for_fusion_ava_style_oct22 fp vw</th>\n", " <th>gbvs_saliency False vw</th>\n", " <th>gist_256 False vw</th>\n", " <th>lab_hist False vw</th>\n", " <th>mc_bit False vw</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>clf ava_style_rating_mean_bin</th>\n", " <td> 0.685561</td>\n", " <td> 0.779356</td>\n", " <td> 0.583502</td>\n", " <td> 0.593578</td>\n", " <td> 0.538634</td>\n", " <td> 0.557698</td>\n", " <td> 0.573572</td>\n", " <td> 0.842708</td>\n", " </tr>\n", " <tr>\n", " <th>clf ava_style_rating_mean_cn_bin</th>\n", " <td> 0.686760</td>\n", " <td> 0.788486</td>\n", " <td> 0.500000</td>\n", " <td> 0.500000</td>\n", " <td> 0.545085</td>\n", " <td> 0.557640</td>\n", " <td> 0.573406</td>\n", " <td> 0.840990</td>\n", " </tr>\n", " <tr>\n", " <th>clf ava_style_rating_std_bin</th>\n", " <td> 0.650014</td>\n", " <td> 0.771946</td>\n", " <td> 0.579267</td>\n", " <td> 0.500000</td>\n", " <td> 0.543315</td>\n", " <td> 0.553855</td>\n", " <td> 0.570964</td>\n", " <td> 0.778155</td>\n", " </tr>\n", " <tr>\n", " <th>clf ava_style_rating_std_cn_bin</th>\n", " <td> 0.666871</td>\n", " <td> 0.776715</td>\n", " <td> 0.500000</td>\n", " <td> 0.500000</td>\n", " <td> 0.547765</td>\n", " <td> 0.553812</td>\n", " <td> 0.578919</td>\n", " <td> 0.816825</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ " decaf_fc6 False vw \\\n", "clf ava_style_rating_mean_bin 0.685561 \n", "clf ava_style_rating_mean_cn_bin 0.686760 \n", "clf ava_style_rating_std_bin 0.650014 \n", "clf ava_style_rating_std_cn_bin 0.666871 \n", "\n", " decaf_fc6_flatten False vw \\\n", "clf ava_style_rating_mean_bin 0.779356 \n", "clf ava_style_rating_mean_cn_bin 0.788486 \n", "clf ava_style_rating_std_bin 0.771946 \n", "clf ava_style_rating_std_cn_bin 0.776715 \n", "\n", " fusion_ava_style_oct22 None vw \\\n", "clf ava_style_rating_mean_bin 0.583502 \n", "clf ava_style_rating_mean_cn_bin 0.500000 \n", "clf ava_style_rating_std_bin 0.579267 \n", "clf ava_style_rating_std_cn_bin 0.500000 \n", "\n", " fusion_ava_style_oct22,pascal_mc_for_fusion_ava_style_oct22 fp vw \\\n", "clf ava_style_rating_mean_bin 0.593578 \n", "clf ava_style_rating_mean_cn_bin 0.500000 \n", "clf ava_style_rating_std_bin 0.500000 \n", "clf ava_style_rating_std_cn_bin 0.500000 \n", "\n", " gbvs_saliency False vw gist_256 False vw \\\n", "clf ava_style_rating_mean_bin 0.538634 0.557698 \n", "clf ava_style_rating_mean_cn_bin 0.545085 0.557640 \n", "clf ava_style_rating_std_bin 0.543315 0.553855 \n", "clf ava_style_rating_std_cn_bin 0.547765 0.553812 \n", "\n", " lab_hist False vw mc_bit False vw \n", "clf ava_style_rating_mean_bin 0.573572 0.842708 \n", "clf ava_style_rating_mean_cn_bin 0.573406 0.840990 \n", "clf ava_style_rating_std_bin 0.570964 0.778155 \n", "clf ava_style_rating_std_cn_bin 0.578919 0.816825 " ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "fig = vislab.results_viz.plot_df_bar(acc_df)\n", "# TODO: why are these all so similar?" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "display_data", "png": 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6hoaGlJWVkZWVRWlpKa1ataJHjx4avxMZGcmUKVPo3bs3enp6eHt7o1Ao1M7lrl27Mnny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JXIni30msg3NTVhb28vZ0XMyMjgiy++4LnnnuOvf/0rtbW13HPPPYwZM4bLly/j5OTEV199xbFj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\n8SIhTdmCIhLq/rGgubKykvLycpYuXWqck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"text": [ "<matplotlib.figure.Figure at 0x1182eac90>" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "nice_feat_names = {\n", " 'decaf_fc6 False vw': 'DeCAF_6',\n", " 'decaf_fc6_flatten False vw': 'DeCAF_5',\n", " 'decaf_tuned_fc6 False vw': 'Fine-tuned DeCAF_6',\n", " 'fusion_ava_style_oct22,pascal_mc_for_fusion_ava_style_oct22 fp vw': 'Late-fusion x Content',\n", " 'mc_bit False vw': 'MC-bit',\n", " 'gbvs_saliency False vw': 'Graph-based Saliency',\n", " 'gist_256 False vw': 'GIST',\n", " 'lab_hist False vw': 'L*a*b* Histogram'\n", "}\n", "\n", "df = acc_df.ix[['clf ava_style_rating_mean_bin']]\n", "df.columns = [\n", " nice_feat_names[x] if x in nice_feat_names else x\n", " for x in df.columns\n", "]\n", "del df['fusion_ava_style_oct22 None vw']\n", "df.index = ['AVA Rating']\n", "df.to_csv('/Users/sergeyk/work/aphrodite-writeup/results/ava_style_aesthetics_acc_df.csv')\n", "df" ], "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>DeCAF_6</th>\n", " <th>DeCAF_5</th>\n", " <th>Late-fusion x Content</th>\n", " <th>Graph-based Saliency</th>\n", " <th>GIST</th>\n", " <th>L*a*b* Histogram</th>\n", " <th>MC-bit</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>AVA Rating</th>\n", " <td> 0.685561</td>\n", " <td> 0.779356</td>\n", " <td> 0.593578</td>\n", " <td> 0.538634</td>\n", " <td> 0.557698</td>\n", " <td> 0.573572</td>\n", " <td> 0.842708</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 19, "text": [ " DeCAF_6 DeCAF_5 Late-fusion x Content Graph-based Saliency \\\n", "AVA Rating 0.685561 0.779356 0.593578 0.538634 \n", "\n", " GIST L*a*b* Histogram MC-bit \n", "AVA Rating 0.557698 0.573572 0.842708 " ] } ], "prompt_number": 19 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 16 } ], "metadata": {} } ] }
bsd-2-clause
nanni3939/ml_lab_ecsc_306
labwork/lab1/lab_notes_1.ipynb
16
7875
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Machine Learning - Tutorial Lab\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Lab 1 : Introduction to Tools And Usage\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "\n", "* Prequisites - Git & Github Usage, Python\n", "\n", "* IDE - Jupyter notebook from Anaconda3 \n", "\n", "* Programming Language - Python 3.6\n", "\n", "* Framework - Tensorflow & TensorBoard " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Why Git\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "source": [ "#### Source Code Management Tool" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "fragment" } }, "source": [ "### Other SCM Tools\n", "\n", "\n", "1. SCCS - Source Code Control Systems\n", "2. Apache Subversion - SVN \n", "3. Bitbucket\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Basic Git Commands\n", "\n", "\n", "### To fetch/copy project\n", "\n", "\n", "\n", "git clone projectFile\n", "\n", "Ex. \n", "\n", "git clone https://github.com/SOCS-2017-18/ml_lab_ecsc_306.git\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## To update repository \n", "\n", "### git pull" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "## Tensorflow and TensorBoard\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Other frameworks used for Machine Learning\n", "\n", "1. Theano \n", "2. Torch\n", "3. Caffe\n", "4. SciKit Learn" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### Why TensorFlow" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## TensorFlow\n", "\n", "* Open source software library for numerical computation using data flow graphs\n", "\n", "* Originally developed by Google Brain Team to conduct machine learning and deep neural networks research" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Features\n", "\n", "* Python API\n", "\n", "* Portability: deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API\n", "\n", "* Flexibility: from Raspberry Pi, Android, Windows, iOS, Linux to server farms\n", "\n", "* Visualization (TensorBoard)\n", "\n", "* Checkpoints (for managing experiments)\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "## Companies using Tensorflow\n", "\n", "\n", "* Google\n", "\n", "* OpenAI\n", "\n", "* DeepMind\n", "\n", "* Snapchat\n", "\n", "* Uber\n", "\n", "* Airbus\n", "\n", "* Dropbox\n" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "\n", "\n", " Everything in TensorFlow is based on creating a computational graph\n", "\n", "It is a network of nodes, with each node known as an operation, running some function that can be as simple as addition or subtraction to as complex as some multi variate equation.\n", "\n", "Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "Computational graphs are a nice way to think about mathematical expressions. For example, consider the expression \n", "\n", "\n", "e=(a+b)∗(b+1). \n", "\n", "\n", "There are three operations: two additions and one multiplication. \n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ " let’s introduce two intermediary variables, c and d\n", "\n", " so that every function’s output has a variable. We now have:\n", "\n", " c=a+b\n", "\n", "d=b+1\n", "\n", " e=c∗d\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ " To create a computational graph, we make each of these operations, along with the input variables, into nodes. When one node’s value is the input to another node, an arrow goes from one to another.\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, "source": [ "![alt text](tree-def.png \"Title\")" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "## Extra Slides" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "slideshow": { "slide_type": "slide" } }, "source": [ "### Github Account\n", "\n", "### git commands\n", "* git add\n", "* git commit \n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "\n", "\n", "\n", "\n", "### Add file to staging index \n", "\n", "git add fileName\n", "\n", "\n", "\n", "git add example1.ipynb\n", "\n", "\n", "\n", "### To check status \n", "\n", "git status\n", "\n", "\n", "\n", "### To Commit file to repository\n", "\n", "\n", "git commit -m \"Message For Commit\"\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "### To undo changes\n", "\n", "#### From working directory\n", "\n", "\n", "git checkout -- filename\n", "\n", "Ex. \n", "\n", "git checkout file1.ipynb\n", "\n", "\n", "\n", "#### From staging index\n", "\n", "git reset HEAD filename\n", "\n", "Ex. \n", "\n", "git reset HEAD filename.html\n", "\n", "\n" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "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 }
apache-2.0
vipmunot/Data-Science-Course
Data Visualization/Lab 10/w10_Vipul_Munot.ipynb
1
1407562
null
mit
ledeprogram/algorithms
class5/homework/zhao_shengying_5_1.ipynb
1
6883
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import statsmodels.formula.api as smf #package used for linear regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the data from heights_weights_genders.csv to create a simple predictor that takes in a person's height and guesses their weight based on a model using all the data, regardless of gender. To do this, find the parameters (lm.params) and use those in your function (i.e. don't generate a model each time)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df=pd.read_csv(\"heights_weights_genders.csv\")" ] }, { "cell_type": "code", "execution_count": 5, "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>Gender</th>\n", " <th>Height</th>\n", " <th>Weight</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Male</td>\n", " <td>73.847017</td>\n", " <td>241.893563</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Male</td>\n", " <td>68.781904</td>\n", " <td>162.310473</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Male</td>\n", " <td>74.110105</td>\n", " <td>212.740856</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Male</td>\n", " <td>71.730978</td>\n", " <td>220.042470</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Male</td>\n", " <td>69.881796</td>\n", " <td>206.349801</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Gender Height Weight\n", "0 Male 73.847017 241.893563\n", "1 Male 68.781904 162.310473\n", "2 Male 74.110105 212.740856\n", "3 Male 71.730978 220.042470\n", "4 Male 69.881796 206.349801" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Intercept -350.737192\n", "Height 7.717288\n", "dtype: float64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lm = smf.ols(formula=\"Weight~Height\",data=df).fit() # formula regresses weight on Height (Weight~Height)\n", "lm.params" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def predict_weight(height):\n", " m=7.717288\n", " b=-350.737192\n", " y=b + m*float(height)\n", " return y" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['Weight_Predictor']=df['Height'].apply(predict_weight)" ] }, { "cell_type": "code", "execution_count": 13, "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>Gender</th>\n", " <th>Height</th>\n", " <th>Weight</th>\n", " <th>weight_predictor</th>\n", " <th>Weight_Predictor</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Male</td>\n", " <td>73.847017</td>\n", " <td>241.893563</td>\n", " <td>219.161506</td>\n", " <td>219.161506</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Male</td>\n", " <td>68.781904</td>\n", " <td>162.310473</td>\n", " <td>180.072571</td>\n", " <td>180.072571</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Male</td>\n", " <td>74.110105</td>\n", " <td>212.740856</td>\n", " <td>221.191835</td>\n", " <td>221.191835</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Male</td>\n", " <td>71.730978</td>\n", " <td>220.042470</td>\n", " <td>202.831427</td>\n", " <td>202.831427</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Male</td>\n", " <td>69.881796</td>\n", " <td>206.349801</td>\n", " <td>188.560753</td>\n", " <td>188.560753</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " Gender Height Weight weight_predictor Weight_Predictor\n", "0 Male 73.847017 241.893563 219.161506 219.161506\n", "1 Male 68.781904 162.310473 180.072571 180.072571\n", "2 Male 74.110105 212.740856 221.191835 221.191835\n", "3 Male 71.730978 220.042470 202.831427 202.831427\n", "4 Male 69.881796 206.349801 188.560753 188.560753" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "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 }
gpl-3.0
probml/pyprobml
notebooks/book1/21/vqDemo.ipynb
1
2062
{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "16c8f781", "metadata": {}, "outputs": [], "source": [ "# Vector Quantization Demo\n", "# Author: Animesh Gupta\n", "\n", "# Use racoon face image\n", "# https://docs.scipy.org/doc/scipy/reference/generated/scipy.misc.face.html\n", "\n", "\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", "\n", "try:\n", " from sklearn import cluster\n", "except ModuleNotFoundError:\n", " %pip install -qq scikit-learn\n", " from sklearn import cluster\n", "try:\n", " import probml_utils as pml\n", "except ModuleNotFoundError:\n", " %pip install -qq git+https://github.com/probml/probml-utils.git\n", " import probml_utils as pml\n", "\n", "try: # SciPy >= 0.16 have face in misc\n", " from scipy.misc import face\n", "\n", " face = face(gray=True)\n", "except ImportError:\n", " face = sp.face(gray=True)\n", "\n", "n_clusters = [2, 4]\n", "np.random.seed(0)\n", "\n", "X = face.reshape((-1, 1)) # We need an (n_sample, n_feature) array\n", "for n_cluster in n_clusters:\n", " k_means = cluster.KMeans(n_clusters=n_cluster, n_init=4)\n", " k_means.fit(X)\n", " values = k_means.cluster_centers_.squeeze()\n", " labels = k_means.labels_\n", "\n", " # create an array from labels and values\n", " face_compressed = np.choose(labels, values)\n", " face_compressed.shape = face.shape\n", "\n", " vmin = face.min()\n", " vmax = face.max()\n", "\n", " # compressed face\n", " plt.figure(figsize=(4, 4))\n", " plt.title(f\"K = {n_cluster}\")\n", " plt.imshow(face_compressed, cmap=plt.cm.gray, vmin=vmin, vmax=vmax)\n", " pml.savefig(f\"vectorQuantization_{n_cluster}.pdf\", dpi=300)\n", " plt.show()" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }
mit
ContinuumIO/pydata-strata-2014-sj
03-blaze.ipynb
1
12165
{ "metadata": { "name": "", "signature": "sha256:7fcffa0117b792c1fb2753bcb05d998cdd7d8c011263db7c47548dad91f56690" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/continuum_analytics_logo.png\" \n", " alt=\"Continuum Logo\",\n", " align=\"right\",\n", " width=\"30%\">,\n", "\n", "Blaze\n", "=====\n", "\n", "Blaze translates a subset of numpy/pandas syntax into database queries. It hides away the database.\n", "\n", "On simple datasets, like CSV files, Blaze acts like Pandas with slightly different syntax. In this case Blaze is just using Pandas." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pandas example" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "\n", "df = pd.read_csv('iris.csv')\n", "df.head(5)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "df.species.drop_duplicates()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Blaze example" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import blaze as bz\n", "\n", "d = bz.Data('iris.csv')\n", "d.head(5)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "d.species.distinct()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Foreign Data\n", "------------\n", "\n", "Blaze does different things under-the-hood on different kinds of data\n", "\n", "* CSV files: Pandas DataFrames (or iterators of DataFrames)\n", "* SQL tables: [SQLAlchemy](http://sqlalchemy.org).\n", "* Mongo collections: [PyMongo](http://api.mongodb.org/python/current/)\n", "* ...\n", "\n", "SQL\n", "---\n", "\n", "We'll play with SQL a lot during this tutorial. Blaze translates your query to SQLAlchemy. SQLAlchemy then translates to the SQL dialect of your database, your database then executes that query intelligently.\n", "\n", "* Blaze $\\rightarrow$ SQLAlchemy $\\rightarrow$ SQL $\\rightarrow$ Database computation\n", "\n", "This translation process lets analysts interact with a familiar interface while leveraging a potentially powerful database.\n", "\n", "To keep things local we'll use SQLite, but this works with any database with a SQLAlchemy dialect. Examples in this section use the iris dataset. Exercises use the Lahman Baseball statistics database, year 2013.\n", "\n", "If you have not downloaded this dataset you could do so here - https://github.com/jknecht/baseball-archive-sqlite/raw/master/lahman2013.sqlite. \n", "\n", "\n", "### PostgreSQL\n", "\n", "Alternatively we have set up a PostgreSQL instance on EC2 with this same data.\n", "\n", " postgresql://postgres:[email protected]\n", " \n", "If you want to use the Postgres instance you will have to install `psycopg2`. If you are on Mac/Linux you can probably just\n", "\n", " conda install psycopg2\n", " \n", "If you are on Windows you should download an installer here http://www.stickpeople.com/projects/python/win-psycopg/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Examples\n", "\n", "Lets dive into Blaze Syntax. For simple queries it looks and feels similar to Pandas" ] }, { "cell_type": "code", "collapsed": false, "input": [ "db = bz.Data('sqlite:///my.db')\n", "db.iris" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "db.iris.species.distinct()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "db.iris[db.iris.species == 'Iris-versicolor'][['species', 'sepal_length']]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Work happens on the database\n", "\n", "If we were using pandas we would read the table into pandas, then use pandas' fast in-memory algorithms for computation. Here we translate your query into SQL and then send that query to the database to do the work.\n", "\n", "* Pandas $\\leftarrow_\\textrm{data}$ SQL, then Pandas computes\n", "* Blaze $\\rightarrow_\\textrm{query}$ SQL, then database computes\n", "\n", "If we want to dive into the internal API we can inspect the query that Blaze transmits." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Inspect SQL query\n", "query = db.iris[db.iris.species == 'Iris-versicolor'][['species', 'sepal_length']]\n", "print bz.compute(query)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "query = bz.by(db.iris.species, longest=db.iris.petal_length.max(),\n", " shortest=db.iris.petal_length.min())\n", "print bz.compute(query)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercises\n", "\n", "Now we load the Lahman baseball database and perform similar queries" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# db = bz.Data('postgresql://postgres:[email protected]') # Use Postgres if you don't have the sqlite file\n", "db = bz.Data('sqlite:///lahman2013.sqlite')\n", "db.dshape" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# View the Salaries table\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# What are the distinct teamIDs in the Salaries table?\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# What is the minimum and maximum yearID in the Sarlaries table? \n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# For the Oakland Athletics (teamID OAK), pick out the playerID, salary, and yearID columns\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# Sort that result by salary. \n", "# Use the ascending=False keyword argument to the sort function to find the highest paid players\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example: Split-apply-combine\n", "\n", "In Pandas we perform computations on a *per-group* basis with the `groupby` operator. In Blaze our syntax is slightly different, using instead the `by` function." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "iris = pd.read_csv('iris.csv')\n", "iris.groupby('species').petal_length.min()" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "iris = bz.Data('sqlite:///my.db::iris')\n", "bz.by(iris.species, largest=iris.petal_length.max(), \n", " smallest=iris.petal_length.min())" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise: Split-apply-combine\n", "\n" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Find the average and maximum salary by team\n", "# If you like, also find the ratio between the highest and lowest paid players, sort by that ratio\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "# Track the average and maximum salary over time\n" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Store Results\n", "-------------\n", "\n", "By default Blaze only shows us the first ten lines of a result. This provides a more interactive feel and stops us from accidentally crushing our system. Sometimes we do want to compute all of the results and store them someplace.\n", "\n", "Blaze expressions are valid sources for `into`. So we can store our results in any format." ] }, { "cell_type": "code", "collapsed": false, "input": [ "iris = bz.Data('sqlite:///my.db::iris')\n", "query = bz.by(iris.species, largest=iris.petal_length.max(), # A lazily evaluated result\n", " smallest=iris.petal_length.min()) \n", "\n", "bz.into(list, query) # A concrete result" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "bz.into('iris-min-max.json', result)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "!head iris-min-max.json" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise: Storage\n", "\n", "The solution to the first split-apply-combine problem is below. Store that result in a list, a CSV file, and in a new SQL table in our database (use a uri like `sqlite://...` to specify the SQL table.)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "result = bz.by(db.Salaries.teamID, avg=db.Salaries.salary.mean(), \n", " max=db.Salaries.salary.max(), \n", " ratio=db.Salaries.salary.max() / db.Salaries.salary.min()\n", " ).sort('ratio', ascending=False)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [], "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": {} } ] }
bsd-2-clause
benbovy/tp_geomod_nb
1_Intro_Python_Numpy.ipynb
1
1452
{ "metadata": { "name": "", "signature": "sha256:f6ec47c2e19f96db05326b4ef7121e2fc6cb75a47a69484768d81f5368ea4923" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "TP Mod\u00e9lisation: Introduction \u00e0 Python et Numpy (r\u00e9f\u00e9rences externes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Deux notebooks proposant une bonne introduction \u00e0 Python et/ou Numpy:\n", "\n", "- [A Crash Course in Python for Scientists (R. Muller)](http://nbviewer.ipython.org/gist/rpmuller/5920182)\n", "- [An introduction to numpy (SciTools)](http://nbviewer.ipython.org/github/SciTools/courses/blob/master/course_content/numpy_intro.ipynb)\n", "\n", "Les liens ci-dessus sont des notebooks \"statiques\", il n'est pas possible de modifier et d'\u00e9x\u00e9cuter le code pr\u00e9sent dans les cellules. Pour les utiliser en version dynamique :\n", "\n", "- T\u00e9l\u00e9charger le notebook (ic\u00f4ne en haut \u00e0 droite)\n", "- Eventuellement renommer le fichier avec l'extension \".ipynb\"\n", "- Placer le fichier dans le m\u00eame r\u00e9pertoire o\u00f9 se trouvent les notebooks de ces TPs\n", "- Le notebook peut \u00eatre ensuite ouvert depuis le dashboard." ] } ], "metadata": {} } ] }
gpl-3.0
tensorflow/docs-l10n
site/zh-cn/hub/tutorials/tf_hub_generative_image_module.ipynb
1
15122
{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "N6ZDpd9XzFeN" }, "source": [ "##### Copyright 2018 The TensorFlow Hub Authors.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "both", "id": "KUu4vOt5zI9d" }, "outputs": [], "source": [ "# Copyright 2018 The TensorFlow Hub 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": { "id": "CxmDMK4yupqg" }, "source": [ "# 使用 CelebA 渐进式 GAN 模型生成人工面部\n" ] }, { "cell_type": "markdown", "metadata": { "id": "MfBg1C5NB3X0" }, "source": [ "<table class=\"tfo-notebook-buttons\" align=\"left\">\n", " <td><a target=\"_blank\" href=\"https://tensorflow.google.cn/hub/tutorials/tf_hub_generative_image_module\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">View 在 TensorFlow.org 上查看</a></td>\n", " <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/hub/tutorials/tf_hub_generative_image_module.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 中运行 </a></td>\n", " <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/hub/tutorials/tf_hub_generative_image_module.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 GitHub 上查看源代码</a></td>\n", " <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/hub/tutorials/tf_hub_generative_image_module.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载笔记本</a></td>\n", " <td><a href=\"https://tfhub.dev/google/progan-128/1\"><img src=\"https://tensorflow.google.cn/images/hub_logo_32px.png\">查看 TF Hub 模型</a></td>\n", "</table>" ] }, { "cell_type": "markdown", "metadata": { "id": "Sy553YSVmYiK" }, "source": [ "本 Colab 演示了如何使用基于生成对抗网络 (GAN) 的 TF-Hub 模块。该模块从 N 维向量（称为隐空间）映射到 RGB 图像。\n", "\n", "本文提供了两个示例：\n", "\n", "- 从隐空间**映射**到图像，以及\n", "- 提供一个目标图像，**利用梯度下降法找到**生成与目标图像相似的图像的隐向量。" ] }, { "cell_type": "markdown", "metadata": { "id": "v4XGxDrCkeip" }, "source": [ "## 可选前提条件\n", "\n", "- 熟悉[低级 Tensorflow 概念](https://tensorflow.google.cn/guide/low_level_intro)。\n", "- 维基百科上的[生成对抗网络](https://en.wikipedia.org/wiki/Generative_adversarial_network)。\n", "- 关于渐进式 GAN 的论文：[Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://arxiv.org/abs/1710.10196)。" ] }, { "cell_type": "markdown", "metadata": { "id": "HK3Q2vIaVw56" }, "source": [ "### 更多模型\n", "\n", "[这里](https://tfhub.dev/s?module-type=image-generator)可以找到 [tfhub.dev](tfhub.dev) 上当前托管的所有模型，您可以使用这些模型生成图像。" ] }, { "cell_type": "markdown", "metadata": { "id": "Q4DN769E2O_R" }, "source": [ "## 设置" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KNM3kA0arrUu" }, "outputs": [], "source": [ "# Install imageio for creating animations. \n", "!pip -q install imageio\n", "!pip -q install scikit-image\n", "!pip install git+https://github.com/tensorflow/docs" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "both", "id": "6cPY9Ou4sWs_" }, "outputs": [], "source": [ "#@title Imports and function definitions\n", "from absl import logging\n", "\n", "import imageio\n", "import PIL.Image\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "import tensorflow as tf\n", "tf.random.set_seed(0)\n", "\n", "import tensorflow_hub as hub\n", "from tensorflow_docs.vis import embed\n", "import time\n", "\n", "try:\n", " from google.colab import files\n", "except ImportError:\n", " pass\n", "\n", "from IPython import display\n", "from skimage import transform\n", "\n", "# We could retrieve this value from module.get_input_shapes() if we didn't know\n", "# beforehand which module we will be using.\n", "latent_dim = 512\n", "\n", "\n", "# Interpolates between two vectors that are non-zero and don't both lie on a\n", "# line going through origin. First normalizes v2 to have the same norm as v1. \n", "# Then interpolates between the two vectors on the hypersphere.\n", "def interpolate_hypersphere(v1, v2, num_steps):\n", " v1_norm = tf.norm(v1)\n", " v2_norm = tf.norm(v2)\n", " v2_normalized = v2 * (v1_norm / v2_norm)\n", "\n", " vectors = []\n", " for step in range(num_steps):\n", " interpolated = v1 + (v2_normalized - v1) * step / (num_steps - 1)\n", " interpolated_norm = tf.norm(interpolated)\n", " interpolated_normalized = interpolated * (v1_norm / interpolated_norm)\n", " vectors.append(interpolated_normalized)\n", " return tf.stack(vectors)\n", "\n", "# Simple way to display an image.\n", "def display_image(image):\n", " image = tf.constant(image)\n", " image = tf.image.convert_image_dtype(image, tf.uint8)\n", " return PIL.Image.fromarray(image.numpy())\n", "\n", "# Given a set of images, show an animation.\n", "def animate(images):\n", " images = np.array(images)\n", " converted_images = np.clip(images * 255, 0, 255).astype(np.uint8)\n", " imageio.mimsave('./animation.gif', converted_images)\n", " return embed.embed_file('./animation.gif')\n", "\n", "logging.set_verbosity(logging.ERROR)" ] }, { "cell_type": "markdown", "metadata": { "id": "f5EESfBvukYI" }, "source": [ "## 隐空间插值法" ] }, { "cell_type": "markdown", "metadata": { "id": "nJb9gFmRvynZ" }, "source": [ "### 随机向量\n", "\n", "两个随机初始化向量之间的隐空间插值。我们将使用包含预训练渐进式 GAN 的 TF-Hub 模块 [progan-128](https://tfhub.dev/google/progan-128/1)。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "8StEe9x9wGma" }, "outputs": [], "source": [ "progan = hub.load(\"https://tfhub.dev/google/progan-128/1\").signatures['default']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "fZ0O5_5Jhwio" }, "outputs": [], "source": [ "def interpolate_between_vectors():\n", " v1 = tf.random.normal([latent_dim])\n", " v2 = tf.random.normal([latent_dim])\n", " \n", " # Creates a tensor with 25 steps of interpolation between v1 and v2.\n", " vectors = interpolate_hypersphere(v1, v2, 50)\n", "\n", " # Uses module to generate images from the latent space.\n", " interpolated_images = progan(vectors)['default']\n", "\n", " return interpolated_images\n", "\n", "interpolated_images = interpolate_between_vectors()\n", "animate(interpolated_images)" ] }, { "cell_type": "markdown", "metadata": { "id": "L9-uXoTHuXQC" }, "source": [ "## 查找隐空间中的最近向量\n", "\n", "确定目标图像。例如，使用从模块生成的图像或上传自己的图像。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "both", "id": "phT4W66pMmko" }, "outputs": [], "source": [ "image_from_module_space = True # @param { isTemplate:true, type:\"boolean\" }\n", "\n", "def get_module_space_image():\n", " vector = tf.random.normal([1, latent_dim])\n", " images = progan(vector)['default'][0]\n", " return images\n", "\n", "def upload_image():\n", " uploaded = files.upload()\n", " image = imageio.imread(uploaded[list(uploaded.keys())[0]])\n", " return transform.resize(image, [128, 128])\n", "\n", "if image_from_module_space:\n", " target_image = get_module_space_image()\n", "else:\n", " target_image = upload_image()\n", "\n", "display_image(target_image)" ] }, { "cell_type": "markdown", "metadata": { "id": "rBIt3Q4qvhuq" }, "source": [ "定义目标图像与隐空间变量生成的图像之后，我们可以利用梯度下降法找到最大限度减少损失的变量值。" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "cUGakLdbML2Q" }, "outputs": [], "source": [ "tf.random.set_seed(42)\n", "initial_vector = tf.random.normal([1, latent_dim])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "u7MGzDE5MU20" }, "outputs": [], "source": [ "display_image(progan(initial_vector)['default'][0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "q_4Z7tnyg-ZY" }, "outputs": [], "source": [ "def find_closest_latent_vector(initial_vector, num_optimization_steps,\n", " steps_per_image):\n", " images = []\n", " losses = []\n", "\n", " vector = tf.Variable(initial_vector) \n", " optimizer = tf.optimizers.Adam(learning_rate=0.01)\n", " loss_fn = tf.losses.MeanAbsoluteError(reduction=\"sum\")\n", "\n", " for step in range(num_optimization_steps):\n", " if (step % 100)==0:\n", " print()\n", " print('.', end='')\n", " with tf.GradientTape() as tape:\n", " image = progan(vector.read_value())['default'][0]\n", " if (step % steps_per_image) == 0:\n", " images.append(image.numpy())\n", " target_image_difference = loss_fn(image, target_image[:,:,:3])\n", " # The latent vectors were sampled from a normal distribution. We can get\n", " # more realistic images if we regularize the length of the latent vector to \n", " # the average length of vector from this distribution.\n", " regularizer = tf.abs(tf.norm(vector) - np.sqrt(latent_dim))\n", " \n", " loss = target_image_difference + regularizer\n", " losses.append(loss.numpy())\n", " grads = tape.gradient(loss, [vector])\n", " optimizer.apply_gradients(zip(grads, [vector]))\n", " \n", " return images, losses\n", "\n", "\n", "num_optimization_steps=200\n", "steps_per_image=5\n", "images, loss = find_closest_latent_vector(initial_vector, num_optimization_steps, steps_per_image)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pRbeF2oSAcOB" }, "outputs": [], "source": [ "plt.plot(loss)\n", "plt.ylim([0,max(plt.ylim())])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "KnZkDy2FEsTt" }, "outputs": [], "source": [ "animate(np.stack(images))" ] }, { "cell_type": "markdown", "metadata": { "id": "GGKfuCdfPQKH" }, "source": [ "将结果与目标进行对比：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "TK1P5z3bNuIl" }, "outputs": [], "source": [ "display_image(np.concatenate([images[-1], target_image], axis=1))" ] }, { "cell_type": "markdown", "metadata": { "id": "tDt15dLsJwMy" }, "source": [ "### 试运行上述示例\n", "\n", "如果图像来自模块空间，则下降很快且会收敛到合理的样本。如果尝试下降到**不是来自模块空间**的图像，则只有当图像相当接近训练图像的空间时，下降才会收敛。\n", "\n", "如何使其更快速地下降并变成更真实的图像？您可以尝试：\n", "\n", "- 对图像差异使用不同的损失，例如二次方程，\n", "- 对隐向量使用不同的正则化器，\n", "- 在多次运行中从随机向量初始化，\n", "- 等等。\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [ "N6ZDpd9XzFeN" ], "name": "tf_hub_generative_image_module.ipynb", "toc_visible": true }, "kernelspec": { "display_name": "Python 3", "name": "python3" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
simpeg/simpegExamples
SciPy2016/notebooks/Visualize.ipynb
2
231113
{ "cells": [ { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "from SimPEG import Mesh, Survey\n", "import SimPEG.EM.Static.DC as DC\n", "%pylab inline\n", "import matplotlib\n", "matplotlib.rcParams['font.size'] = 16\n" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pickle\n", "fname = open(\"./PF/Magresults\", \"rb\")\n", "mag_results = pickle.load(fname)\n", "fname.close()\n", "fname = open(\"./PF/Gravresults\", \"rb\")\n", "grav_results = pickle.load(fname)\n", "fname.close()\n", "fname = open(\"./DC/DCresults\", \"rb\")\n", "DC_results = pickle.load(fname)\n", "fname.close()" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mesh = DC_results[\"mesh\"]\n", "x = mesh.vectorCCx[np.logical_and(mesh.vectorCCx>-300., mesh.vectorCCx<300.)]\n", "y = mesh.vectorCCy[np.logical_and(mesh.vectorCCy>-300., mesh.vectorCCy<300.)]\n", "Mx = Utils.ndgrid(x[:-1], y, np.r_[-12.5/2.])\n", "Nx = Utils.ndgrid(x[1:], y, np.r_[-12.5/2.])\n", "My = Utils.ndgrid(x, y[:-1], np.r_[-12.5/2.])\n", "Ny = Utils.ndgrid(x, y[1:], np.r_[-12.5/2.])\n", "Xx = 0.5*(Mx[:,0]+Nx[:,0]).reshape((23, 24), order=\"F\")\n", "Yx = Mx[:,1].reshape((23, 24), order=\"F\")\n", "Xy = My[:,0].reshape((24, 23), order=\"F\")\n", "Yy = 0.5*(My[:,1]+Ny[:,1]).reshape((24, 23), order=\"F\")\n", "rx_x = DC.Rx.Dipole(Mx, Nx)\n", "rx_y = DC.Rx.Dipole(My, Ny)\n", "Aloc1_x = np.r_[-600., 0, 0.]\n", "Bloc1_x = np.r_[600., 0, 0.]\n", "Aloc2_x = np.r_[-350., 0, 0.]\n", "Bloc2_x = np.r_[350., 0, 0.]\n", "Aloc1_y = np.r_[0, -600., 0.]\n", "Bloc1_y = np.r_[0, 600. , 0.]\n", "Aloc2_y = np.r_[0, -350., 0.]\n", "Bloc2_y = np.r_[0, 350. , 0.]\n", "src1 = DC.Src.Dipole([rx_x, rx_y], Aloc1_x, Bloc1_x)\n", "src2 = DC.Src.Dipole([rx_x, rx_y], Aloc2_x, Bloc2_x)\n", "src3 = DC.Src.Dipole([rx_x, rx_y], Aloc1_y, Bloc1_y)\n", "src4 = DC.Src.Dipole([rx_x, rx_y], Aloc2_y, Bloc2_y)\n", "survey = DC.Survey([src1, src2, src3, src4])" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dcdata = Survey.Data(survey, v=DC_results['Obs'])\n", "magdata = mag_results['Obs']\n", "gravdata = grav_results['Obs']" ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true }, "outputs": [], "source": [ "meshPF = mag_results[\"mesh\"]\n", "xc = 300+5.57e5\n", "yc = 600+7.133e6\n", "X_PF, Y_PF = np.meshgrid(meshPF.vectorCCx[::2], meshPF.vectorCCy[::2])\n", "X_PF, Y_PF = X_PF-xc, Y_PF-yc" ] }, { "cell_type": "code", "execution_count": 220, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def vizDCdata(data, src, rx, rxcomponent=\"X\", clim=None, contour=None, ls=\"-\", color=\"crimson\"):\n", " figsize(5,5)\n", " temp = data[src, rx]\n", " if rxcomponent==\"X\":\n", " X = Xx.copy()\n", " Y = Yx.copy() \n", " else:\n", " X = Xy.copy()\n", " Y = Yy.copy() \n", " temp = temp.reshape(X.shape, order=\"F\")\n", " if clim is not None:\n", " vmin, vmax = clim[0], clim[1]\n", " dat = plt.contourf(X, Y, temp, 20, clim=clim, vmin=vmin, vmax=vmax)\n", " else:\n", " dat = plt.contourf(X, Y, temp, 20)\n", " if contour is not None:\n", " CB = plt.contour(X, Y, temp, levels=contour, colors=color, linewidths = (3,), linestyles=ls)\n", "# plt.plot(X,Y,'k.', ms=2)\n", " plt.xlabel(\"Easting (m)\")\n", " plt.ylabel(\"Northing (m)\") \n", " plt.xlim(-280., 280.)\n", " plt.ylim(-280., 280.) \n", " \n", "# plt.colorbar(dat)" ] }, { "cell_type": "code", "execution_count": 201, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def vizPFdata(data, clim=None, color=\"brown\", contour=None, ls=\"-\"):\n", " figsize(5,5)\n", " temp = data.copy()\n", " X = X_PF.copy()\n", " Y = Y_PF.copy() \n", " temp = temp.reshape(X.shape)\n", " if clim is not None:\n", " vmin, vmax = clim[0], clim[1]\n", " dat = plt.contourf(X, Y, temp, 40, clim=clim, vmin=vmin, vmax=vmax)\n", " else:\n", " dat = plt.contourf(X, Y, temp, 40)\n", " if contour is not None:\n", " CB = plt.contour(X, Y, temp, levels=contour, colors=color, linewidths = (3,), linestyles=ls)\n", "# plt.plot(X,Y,'k.', ms=2)\n", " plt.xlabel(\"Easting (m)\")\n", " plt.ylabel(\"Northing (m)\") \n", " plt.xlim(-280., 280.)\n", " plt.ylim(-280., 280.) \n", "# plt.colorbar(dat)" ] }, { "cell_type": "code", "execution_count": 202, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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HbjmoPGalVKzYzf2hK5egK0tVVdpciGGMHzX+h5Gx9QB8kmrgpq5BOg2+Clil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\nxljtrEfV8HzvhCLvyINbtzzIHLJf+FE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Z1bjuco4KucrYx3A+edh83fOiz5rKS0+coAeOuEEjjmoWBuZAGTZfp7/UNLJJ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\nRUQ2eMvc3yAiw6vKjRGRO0RkiYhsEpH5IvJXEek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QzRhX4lgT7/zapjJykhYx/xdvaOH7wB+VUrHvSH9ix8eAL+BN0jheKXVxXNsy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\nLchRkdKNk3IMfVXK0GQxnwqcimfG6A1MVUp9KNL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6XE4IcRbGRkLDMGyC7CUMR+NE046J3CnGFoZqBUbne+iF8oA973H586dlT6yW\nCB4bfStvDP5MR6BXKHtpd7oWaIasA7R+FGLm7MPeWFR3KWesupbK9iYG7VrI4J1vslEcFamd4UWu\nMmYhxHXA4xg7sdUA7TkfbemPried2XIAz+B1yXVYZXFO9/AC5cE73uSqZ2ZmodwmKvnXuPv8QTmo\nLNlJvqordGW/KnKRObtZjGL2M1L09Zsq+rJkYMdxU0e62NgoaHnJqN1aGdcAfwKGSClPkFLOKvg4\n2c+DdglFtMLPq68cl1plL1AeueUFPvnsLGoObgWguawHf5v4KItrP9IR6AXKZvK7ss+NbeEbxlED\nOVcunkdXSZ2VLH4HuRsbHb76b1S0HsgrnVPdO8NJXlbLFsotmHsD/0jvwpYIgi97c3Mfjb6yX0UF\n5THrH+OyOWfSrXUvAAcq+nL34c+wot8ZHYE6oGwllSy5KLNjJ0XoO1vENvQ5iR3VhwJQ3bKbCWsf\nsOyic/N8HXIL5qcwVv0l8qIgTyWJia9sfh9/tcqdxrHcjOjevM2I9lYO4i+TX2Rd7+M7AoOCskqW\n7AnIQWbHDQofdtIMZ9VJQCF4q/6q7MupK/XZGUFPALqd/LsGeNBYS8KTmOzOJqVcqeWJikFhnDji\ndowCRW1h6FpAYgXlY97/LWe/8eVs287qUdw96Wl2dj+0I9ANgMPMkj1nx37U4LOfn/ukbK67mBQE\n54lBM/mYBFxQ/0lOXn09ZbQzassc+u1dwc51h4Y2CVhYNue2ntltxiyBvRgLMv6LsZ9E7scyj89b\nvCohC6OooLx2ACe9c2MelDf3mMSfJ79sD2WzDDesLNl1huw3O27AXSarW0730+Q7g/Ik4N5uQ1jW\n/+xs89SVf84LC9pn9iu3GfNfMJZd/xJj46LmwJ6oFFQkFoabuKih3LhuIEK2c+abX+G493+TbV/b\n6zj+NvFx+30vVO0Mp2vKWbKf7LjBR58g1JD+nLKJ8VlSZ1dG5yNrfqv+U4zd8RgAU1bdyZzDbzTt\nbrU8ewv1eSdohyG3YD4ZuEZK+ZcgHyb2ikO2bCEdpXFuFORSa7Pspay9hfNfvYojGu7Jti3vezr3\nT3iQlvIaoyEIKKtO7lkq7laFVzUQCJxz5XeZdvra+/3PobGynp4tm+l9YAOjN/4fG0b4mzLL3VFu\nOwMZQDCWiFsrYxvxOsQmvgrwNGs/FoaZgtgxLn987wtIzKDc1FDDR1++KA/Kiwd+mL9PfCSeULa1\nLorJqvDAPqRjAAAgAElEQVSqBofrPmwNjSsD28sqWVh/efb11JV3aD8P0G4C0M8BrW4z5t8AXxBC\nPCmlbHfZp7QU1oSfx35x85V1Qbl5VTcue+FMUltfzLa9MehqHht9i/fNiHRYGqFlyQ0eYnX2daOU\nw73tritOCipubvRW/VWcsO5mAMasf4yaA5sB4320eX899T0ytoXaHhe65BbMfYHDgSVCiKcxr8r4\nns4H65KygrYmX7lYoNy4dgCXzPtQHpRfGvYtnk392Pu+F0FDOXIg++3n914ph+vYxHg8VtOv12wS\nt63HONb0PoERe+ZSLluZvPpe3jnsctuuUfrMbsGcewzAGIuY0gVzGNlyCL6ymzidJ1u7WUBSqMZ1\nAzlyxW15eyk/nfopc4df1xEUFpQDz5IbXMQE0VdFmfumHGLsrttkz3aes0cYF+qd2ksZsWcuAPU7\nF/KOi6HMlOszByVXYHaz5WaigKRpwlFleWihglpA0rhuIP33LuPMN7+SbXt18DXxg7JjxUWQUPbb\nr8TlAtp7qzr+mKpbdkW+R7OdEuCGJc0VHVFmy4XSVRZX1t7ChfMuo6rNOJ5xa/fxxradGZUElBso\nDSg3KF53afNoLDloquib/bq6eZe+gQOQZcZsclipKwkhukspD6g9VpEpqBOtI86W/frKTve3Kuo/\ncfGPGLb9NcDYJe7BcffQWt7deuCihLIf+e2XKFd5YG6JN5jtMuYGIcS1Qoi+NjFZCSFOEEI8Cnxd\nz6OVkIo0W/Yjv77ysG3zOXHxD7Ntz4/8Pht7HtkR5GUTm1wlUA5QDYrXLX5mOlYFmsQ1pQ9pBX0Z\nc1CHs9p5zF8AbgJ+LIT4D/ASsBDYChwE+gGHAMcC5wIjMFYI/imQJ42rgsqWNSnMbNmvr1zV0siF\n8y6jLL154ereM5k77BsdQToqK5yUQDkiOZTRaVRuxty9Ob+wLLdkLg6yBLOU8kEhxCPA+cCnMfbJ\nqDYJXQ3cD/ypS21kFLQ81i3rLo/zE+N0fysL44y3rqV/4woAmsp789DYu8OtVY4dlP30iUoNqFVo\neJTbygwTHSzvlf26qrUREeMlGbZVGVLKVuAB4AEhRDdgCgYyqoHtwLtSyjWBP2UxK6xl3BqlY8LP\nrYUxdt0jHLXitmzbE4f+jl3VKeNFmLXKZoqkHM5Pn7irAaXFJ5okRTnNZT2oat+PQFLZuj/we/qV\n2zpmpJQHgVcDfJbiU8iTfnHJlnVZGD0PbOK81zpOmVg08CO8XXeZ9SBhTvaFDmWv8XFSA1qz4kJ5\nOVU7I4vMurm8F1XtBpCrWvcClYoPF4yScrkgVaLZspNcWRhSct5rn6LmoFFHuqdqKI+PvsV6ZV8C\n5SJXg8P1gp+r50Np3SnXzujWsjeYm2hQAuao5BHaxZItu7Uwjl7+R8ZseCLb9u8xd9pv4VmoBMox\nU0PUD+DqfdNc3vF3VNXaGODDqCkBs18FcPqIUj8NKsyWg7IwBux5jzPeujbbNm/IV1jZ71TrQfyC\n2kqxgHKDx/hSUIPDdR9bonqcN2jOmwA0z5h1rpL1qwTMRSyd2XJYFkZmdV9lm7EGaUuPiTw76qaO\nAL8Whpm8VGCEDuUg1EC0wI/qvu5VLFaG68m/RDmKyaSfX0VpYZy86HqG7ngdgFZRxQNj76W1LF2F\nGYavbKbQoOw2zqvMxvVzr5TSU7hTg/p9FErm8jLmBMxdTCHYESrZcqGCWL1kZmEM3/oKM5f8ONv2\nXOpHbO55hPEiSl/ZUnGHsu4xncZLab6fmXJK53J3mvNTmWGiYvGYXYFZCPEXjANZzdQO7AbeBB7w\ns79Gl5KmST+/CiNbNrMwqlr28qH5n6AsXdS/qs/JzBt6bae4rHRmz+DDwtC5cMRLbBTjeblvKqSY\nYJRnZVh4zHGQlzP/+qQ/WjGOmqrFOAJgNwa0vwrcKISYJaVUWRwbb8Vg0i/IbDkoC+O8Nz9F/0Zj\nYWhTeR8eGnsXUqSnOJwgnEA5gLG6porFynA7+fcxYBdwEdBdSplZ/fdhDDCfBxyTbvtJAM9ZGiqC\nbFn5HiYWxvi1D3JkzrHxj42+hT3dhhsv3EC4UIFWYMQRyg0ax1JVQ4gxGpV+z5SUlQH8EviZlPKh\nTIOUsg1jqXYd8P+klMcIIX4M3BDAcxaHuni2bL66byMffO0z2bZ3ai9lUd2ljs+WlUoFhpWKBso6\nxujCMpkkLBYrw23GPBlYbnFtJcZ5gABLMXadK02pTOrFLFv2OuHnx8JASi6YfyU9mrcDsLvbcB4/\n9Pcd16OwMIoCyg0axghSDQHHuJh0XefiaxM1V5SWlbEZw7Yw08Xp6wC9MTmoNZEe6cyWO/fxvgF+\nXn+TbPmYZb9n9KYnAZAIHhpzF02V6X+3o/KVTRUXKDco9i9B5f5DquE0k2LJmN1aGb8C/p8QYgjw\nT2ALUAd8BDgLY+IPYCZGdUbXk9+DVk2uhZ0tB2FhDNy9lNMWdOyp/MrQr9HQ92RvD56rwJdbW6kh\noFgd/aJUA8VYodFcluMxtzh7zLknZYcpt4ex/koI0YjhH5+dc2kdcLWU8o70698BpXmsVMQbEgWZ\nLetWeVszF837OJVtRuXkpprJPJfqOJ3Ec7Yc2WRfg4cbe4lV6ZNIRXlWRowzZtdLsqWUtwMjMf55\nOz79OZUDZaSUDVLK+BwDEJY0TvqZZctBLr0OIluetWg2g3e+BUCr6MaDY++lraybEaALylqWW0cF\n5QYffeKohhBj9KhwSXbjumCOhlKVp70ypJTtUso1UspX05/jewSAjYQQw4UQ/xJC7BJC7BZCPCCE\nGG7ZIcRJvzjJD5Rrdy9hxpKOislnRv2ELTWTjBe6qtsDr8Bo8HADr7Fe4ruSGkK5S0tZj+zXcc6Y\nXS/JFkL0wbAxhmNyxJSU8vsanyswCSF6AM9hWC6Xp5t/CDwvhJgspfR2rIFG8MYtW/ajqSvuQKQX\nia7qczKvDvmydbCuCUDQONnX4GGcoGKLRSlNMV7i1DR2+yPZr/d3q6XnsG228U7+8gDs+/uV2yXZ\nJwCPYaz8s1JRgBm4GhgFjMmcUSiEeBtYBnwWo2Zbj/xOCMZAfrLlsvZWJq++N9v28vBvul/dpyKt\nk32JnJXSGOcQMzjn69y9MoZ5/7qivYnp63+ebXpjdEd9fe5BrHVs6fQYdTmArje5nqtCmBeO52Yy\n0a2V8StgFTANY+VfWeGHy3HioPOAebkHx0opG4C5GAfPdgkFkS0fuvFJejYZb7o9VUNY2ddmj+VC\n6dzu07cagr5BCSjlMsYpzi5G8/l/aUAfu/439D1oHFG6r9tAls0417abl2oMJ1h7lVsrYzzwUSnl\nG1rvHo0mAg+ZtC/BqMkuOoW137KVMhMoU1bdlW17u+6yjpOug5T2Jddu1eAxPqXYP2ylAuxjF5cD\nZV3ZMlDTvIUT13ZUBr0w6QZaK3tQKLNsOVe5AA7KxgD3YF4LdAvsKcJVP8wXwewgZqsW3frLQcvN\n7nHVzTsZu/7h7OuFdVd0XNRhY5Tctlgph+sNMXgG3WM5xbjIlH1YGAAnr/4e3dqMyb6tvcfx7kkX\nZUNybYxsG7nWhpfMWd3GAPdgvhH4phDiWSnlbpd9Sl8xXlTiRX6OjCrUpNX3U9HeDMD6nkeztWaC\n+85B+s+u1RDFTW2UsrnWoHEsVbkZ2ynGBMhW2bIP1e17hyM33ZZ9/dTUX9Be3vl0bKdsOVe52bJu\nGwPcg/kcoB5YKYSYh8n/B6WUl3fqFU/txDwz7o/F/3NndySCzBoLs8YF8lza5LUaQ0UZG+OIHBtj\nYf0VVuH+FNp2nsWiVNQPgL4JQA9Q9pMtS8kZK79GGUZl7/JBp7Nx2rRsiJds2SuAC0G/fM567prT\n5qqvWzDPxNhzeS8wifxN8wXWm+jHUYsxvodCTcDwmTtpdkymBKNY6efGxhiw5z2Gb58PQJuo5J3a\nnN3jdK7qC0wNAceXmlIaYjRP8FnosJ1PcOiupwFoF2U8NfUXIESnOC/ZspWcbIoTZlVwwqwO5N58\nY4tlrNsl2Sl3j1YUegT4uRBilJRyFYAQIgVMB74Z+N1dlsmFtbRah41xxKq7s1+/3/9cDlQO8P9A\nbkGdlMhFoJSmGBsoa8yWy9pbOGPl17JNbx56NfsndQxqli3nyipbDtrGgK55SvZtGCnPw0KI84QQ\n5wEPA2uAP7oeRWONsk5/OWwbQ7S3cURDB5gX1NtM+vmRtoy6VGyMsJXCXflbJtZO/QkLygBHb7yV\ngQfeA6CpsjfPH26+1CI3W9a1YZHfSb+MLDNmIcQIYJOUsjn9ta2klGs83TkiSSn3CyFOwVhI8lcM\nK+YZ4CueV/1FrDjYGKO2PE+f/QY991UMZHm/s6wH1GVjaNj+sUMNOgcrAaUC6ONgWwy2v+xH1S07\nmbVmdvb1SxO+gxjdkYc6ZctBa+BW+53t7KyMBuA44DWc370S4/y/opCUci1FVrMcRpmcPxujY9Lv\nnbqP0VZW5f8BVLPjxMbwqVSA/XxAWUO2fNLaH9Cj1fhf0s6aUcwf+z90x35vDDeTflY2hu6tQe3A\nfBXG6SSZrxOpKmB/2cmm0Lnar3HdQKpa9jJ+7YPZtoVdwsZo0DhWlEoF3F9zljzMOSR75wPLOGbD\n77Kvn57yM7qP7ICy0/JrVanaGGADZinlnWZfJ3JQSHtg+DnTz4vc2BgT1v6LqjbD/dncYxIba6Za\nDxi0jZFkyy6UCmkMBShbZctYtJt8ffqqb1AujYqH1bUzWTL8Inqy3faRvGbLKnKyMcDD7nKJchTw\njnJRyE+WPqWwdtmkDMm1kkUlASkV4jiagAy+ll0DpHY9z7jtHQsPnpz6/+g5vAPKurJltzaGX4vD\ny7afs4BL6bztpwCklPIUX0/QVaQAczf+stdqC1Ubo2/jKlJbXgCgnTLervt4R0BQS7B9Tfp1xWqM\nVMhjuahJ9gJlPxoGQrZx5sqvZpsWpj7BniNSjl39Lr82kxPs3WTL4H7bz88Ct2C8y98Hml2Nnsiz\ndFVa6CyTM7Mxjmj4a/brFf3OoLHKg2moOztObAz0rwZ0O57GLDkjnxN+UzbfyaB9CwFoKe/Os0f8\nOG9Yr9ly2LXLuXKbMX8N+DtwpZQygbKVYuQvq4znmKFLmbeoZIHqEmw3oNZaIgelc0ZfKqKxAsqS\nPdgWuapq3csHGr6TfT13/HW0j+l0nkcnqVZTBGFjgHswDwU+n0BZr9z4y3G0MYZvm0v/xhUANJX3\n4b0BOWvWw95JzjZbLmUbIxXhWAFkyeC+8sIE2DPW/YSeLem9wLsPYe74b9At51xoN9myyr4YduNm\n5NbGAPdgfhM4BHjW9cilKj9ZccinlQRtY0xafV/268W1H6G1zDkzySoWe2MUs1IRjqVYAucFyh4y\n5z5Nq5m+7hfZ188ecRPdUuZQ9qOwbQxwD+YvAX8TQrwvpXwhyAfqynJjUYS92q9Qor2NCWv/lX1t\nu2GRG0ViYxSjUhGOFeDknof6ZKu+pzb8LxXyIAAb+h/F26nLqLH435LV8ms/k35B2RhgvyR7LcaK\nvkwNVG+MA0v3YWydKXKuSyml47LtklZEZ/ip2hheQN+4biAjt71AryaDlI2Vdazuc6J1h9jaGA0e\nbhIHpSIaRxHI4A/KHrLlYXvmc/jWv2df/9/UX1IzvON37ydb9lO7rNPGAPuM2YttUUzbfoargMvk\nChW0jTFxzT+zXy8deJG346P8gDo22XJDBPdMRTSOyy05g8iS3U74Zfda7iiPWzL8ItbUzaSnBUxV\ns2W3NoaO5dl2K/8+qTx6qSnihSVxszEWD/xIx8XkuCiNSkUwjof9kYPIkr1cS389aet9DN9r7APe\nWlbF01N+Rs9hOX6wYrZsHxfsJkiutv0UQnxPCGGKJSHEYCHE9/Q+VteTH+iqbuHp1cYYvn1e9hTs\nxsp6VveZad0hsTF8KIWePSy8jOOwFWehnLJkuzI4L1B28J5Tu+Zw7vLPZ1+/OuZ/aBnX2zLez9ae\ngdoYG+zHdDv5Nxv4P4vhhqavm2922hUUkb9cKKe9MbyUyZnZGP32rsh+varvyV3IxoB80DUEOHZY\nY3g8QSRo28Kq3QTYk7b8nQve/yQV6erdxup63jj9c3lhVuVxnQ9LdVciF/QS7ELp2CujL3BQwziJ\nchTFadhO6t7ccbj4/gr/GyRFqxTqYE3lfK0yVsoxQv8YxWdZZDVUMn3dzzl91XXZpr3Vg3joI/fS\n3M3IlgvtC79Qdru9p1Mm7idbBvuqjJOBk+moyvisEOLcgrDuwLkY5+glipm8WB1u/iGobukAc1NF\nznm2QXnFg/CZNVueqxuAUjlfN/joo+veTioiIBe+Tu+DcdaK/+GYjb/PNm/tPZ6HLrmXPX2GA/6h\nXCi3dctBbBmakV3GfBLw3ZzXV5rENGMcYPplnQ9VMoqJxaFLuRnzgcpwDtO01GBiuEdGquB1g4sY\nHfexkka7AsIBcmHbMKhs289F730sb9e4htoTefSSOzhY3RewX9nnBGW3e2LkV3I477nsN1sG+6qM\n2RjeMUKIduB4KeWr7oZNVIrqfrAjCz2QmzEXnVKEMwmYimi8IgVyYfsw6NGyjUsXfzBbfQGwaMRH\nePqiX9BWYaw4DQLKhQoTyuDCYxZCVAG/AdrdD5tIt8I6NdtOllZGLBWmnRGGUi5iSgTI6df9Dqzg\nssVnMeDAsmzzK+O+xivnXgfCKCgLCspBHhvlRo5gTh/G+hngQafYRO4Uh83x/YA+z8qoiNjK6DJK\nuYjR6B+Du53fvF53C+R025C9/+Xji8+hpmUrABLBc6f8kLeO+kw2zG3lhXHdGsqFcjvZZ3Uv1WwZ\n3FdlLAAOB170NnyiUlDjOqMCozoPzHHPmJ2UIv41zW6kYZVeRkGWvXnoM2b7Y1z87kepajeOLWsp\nr+aJc29h2ZiO2gOdUPY72ecayj7kaT9mIcQa4DEpZbIEO2KpLi7xo1yPORZWRiwnAFWV8hCrYS8L\nUN/PQse1dPtRG//IOcu/QFnaOd1f1Z9/X/xXNgw9BvBWeWFct7chdE72WcosW9a0wOQfQB/gYaBZ\nCLE13Z5sYlQkUtmDGQAp6d5SbBlzsfnMKZdxpQfkmubNnLL6eo7adFv20s6aUTx4yd/Y2X80oF4O\nZ5cpd471fjagawtDpY65QE4bGiUZdMRSORHbjSrb9lPebpw83FJWTWt590DvF45SxMPOSHmItYGy\n29O9nM7Ycyph03Et3V7Vupfp63/B9HU/p6p9X/by+v5H8/Ald7G/xvifocoknxFjD2Uvk31BWhgZ\nuQJzsqFR15LZcuzciT9HG8PvgpNhCn2LUimP8YpQ9gNkP+0u+pS1t3DUpts4ac2N9GzJh+a7Q8/n\nyYt/TUtVDaAfynbX/Uz2Wcpntgx6lmQnUlQQpXC6PejirWGOo52R8hhfOkBGSiZse4APrP52Xhkc\nwOY+h/P0lJ+yadrRIIwFx0FAWffKPh1VGIVyDWYhxGTgBowVgf0w3u1zgO9LKd9Re4xEcVdsa5iV\nJwBThGtnpDzGxwjKipUZI3e/yGmrrmPY3vx1art7DOP5w3/AihlnIcs6NsZSqbww4rxB2clXVrYw\ndC4wARBCTANeAA4AjwCbMX7lHwTOFkKcJKV83f1tE8VJbvbJSGqYVZXy0UcBymFlyS7aa/ct5tSG\nbzF2x2N5IU2VfXhpwrdZfPKltFbmz1n42R2uI8YeyGbSCmUFCyMjtxnzTcAi4ANSyr2ZRiFEL+CZ\n9PXTvN06kZXiuLNcadUwFyoV9QMUKAIgW11TAHKvg+s5efUNTNn8l2z5Gxib2r825ku8NOHblB/a\neUFxGFBWneyzlKKFkZFbMB8HXJ4LZQAp5V4hxE+Bu/U8ToxVYhsSeZVlDXNRTNbF0We2kk8oOwEZ\n9NgWLtq7te5mxrqfctz6X1HZ3nFatUTwduoynpv8A9rG1lBesMuD7nI48A5lJV/ZSj5g7RbMTuVw\nXbdcLgRg654c9LUcO6oaZt9bfxajAoKyDnvCRWx5+0GmbbyFE9f8kB6t2/PClg86nWem/JTGwzsP\nZHYEVBhQtrun1b0hWAsjI7dgfhX4XyHEM1LKPZlGIURP4JvAfMueXVUllmFXx9ljLvoVgCED2eqa\nT1AL2c6krfdxyurv0q9pVV7Yxn5TeXrKz9hy9JROQzgBGZwPTVUBsh9f2VJeAaxp5d+3MSb/GoQQ\nj2H8GQwGzgZ6ALM8PlaXlY4NjCJfjl1ZjB5zXO2M4s6SR+18ltMarmNI45t5ITtrUjw3+UesOuHU\n7E5wuYo7lK0UtIWRkdsFJq8JIY4FvgecSUe53HPAD5JyuWil86w/K3Uv6cm/qBQAlHVkyS6AXN+4\nkNMavsnonU/mheyvGsCLE7/L0lkfpq2iW6chvALZuB4clK2kbGHYSeOSbKSUbwMXe3yEREWu7M5y\nca1jLlpZQDmqQ09dtvVpWs0pq69n8pZ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OkiGBsqtrXuM0T/YlYFZQWGCG+GXNru/vAOge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\nLIAcZKyfeNV+qn1zFRKUIQGzkhzBDMUFZ81j+gU0RFdxYQb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"text/plain": [ "<matplotlib.figure.Figure at 0x11087dc50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "vizPFdata(gravdata, contour=[-1.0], color=\"olive\", ls=\"-\")" ] }, { "cell_type": "code", "execution_count": 217, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-280.0, 280.0)" ] }, "execution_count": 217, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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adWVLKu3KUMoP+fln+Prr3/Pmm71KJeXY2CQGDPgto0Y9F7SknJUVzccfl4xzHjMm/CeT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\n/Jhnh6OQjRs/YMuWz/C8MHrhhbdz+eU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"text/plain": [ "<matplotlib.figure.Figure at 0x10fdd9b10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figsize(5,5)\n", "temp = gravdata.reshape(Y_PF.shape)\n", "plt.contourf(X_PF, Y_PF, temp, levels=[-10,-1.0], colors=\"olive\", linewidths = (3,), linestyles=\"-\", alpha=0.3)\n", "plt.contour(X_PF, Y_PF, temp, levels=[-10,-1.0], colors=\"olive\", linewidths = (3,), linestyles=\"-\")\n", "temp = magdata.reshape(Y_PF.shape)\n", "plt.contourf(X_PF, Y_PF, temp, levels=[100., 1000.], colors=\"blue\", linewidths = (3,), linestyles=\"-\", alpha=0.3)\n", "plt.contour(X_PF, Y_PF, temp, levels=[100.,1000.], colors=\"blue\", linewidths = (3,), linestyles=\"-\")\n", "\n", "# temp = dcdata[src1,rx_x].reshape(Xx.shape,order=\"F\")\n", "# plt.contourf(Xx, Yx, temp, levels=[0., 0.025], colors=\"crimson\", linewidths = (3,), linestyles=\"-\", alpha=0.3)\n", "# plt.contour(Xx, Yx, temp, levels=[0., 0.025], colors=\"crimson\", linewidths = (3,), linestyles=\"-\")\n", "# temp = dcdata[src3,rx_y].reshape(Xy.shape,order=\"F\")\n", "# plt.contourf(Xy, Yy, temp, levels=[0., 0.025], colors=\"crimson\", linewidths = (3,), linestyles=\"--\", alpha=0.3)\n", "# plt.contour(Xy, Yy, temp, levels=[0., 0.025], colors=\"crimson\", linewidths = (3,), linestyles=\"--\")\n", "plt.xlabel(\"Easting (m)\")\n", "plt.ylabel(\"Northing (m)\") \n", "plt.xlim(-280., 280.)\n", "plt.ylim(-280., 280.) \n" ] }, { "cell_type": "code", "execution_count": 223, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-280.0, 280.0)" ] }, "execution_count": 223, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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TMdgS2Vi5kzPWXs86xzb93MVZ47k/5xqyLbXt5auIBdzJ1bHippKGCzPA+J7j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\nOZczPlzOOedyxhOzc87ljCdm55zLGU/MzjmXM56YnXMuZzw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"text/plain": [ "<matplotlib.figure.Figure at 0x1110d1e50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figsize(5,5)\n", "# temp = gravdata.reshape(Y_PF.shape)\n", "# plt.contourf(X_PF, Y_PF, temp, levels=[-10,-1.0], colors=\"olive\", linewidths = (3,), linestyles=\"-\", alpha=0.3)\n", "# plt.contour(X_PF, Y_PF, temp, levels=[-10,-1.0], colors=\"olive\", linewidths = (3,), linestyles=\"-\")\n", "# temp = magdata.reshape(Y_PF.shape)\n", "# plt.contourf(X_PF, Y_PF, temp, levels=[100., 1000.], colors=\"blue\", linewidths = (3,), linestyles=\"-\", alpha=0.3)\n", "# plt.contour(X_PF, Y_PF, temp, levels=[100.,1000.], colors=\"blue\", linewidths = (3,), linestyles=\"-\")\n", "\n", "temp = dcdata[src1,rx_x].reshape(Xx.shape,order=\"F\")\n", "plt.contourf(Xx, Yx, temp, levels=[0., 0.025], colors=\"crimson\", linewidths = (3,), linestyles=\"-\", alpha=0.3)\n", "plt.contour(Xx, Yx, temp, levels=[0., 0.025], colors=\"crimson\", linewidths = (3,), linestyles=\"-\")\n", "temp = dcdata[src3,rx_y].reshape(Xy.shape,order=\"F\")\n", "plt.contourf(Xy, Yy, temp, levels=[0., 0.025], colors=\"green\", linewidths = (3,), linestyles=\"-\", alpha=0.3)\n", "plt.contour(Xy, Yy, temp, levels=[0., 0.025], colors=\"green\", linewidths = (3,), linestyles=\"-\")\n", "plt.xlabel(\"Easting (m)\")\n", "plt.ylabel(\"Northing (m)\") \n", "plt.xlim(-280., 280.)\n", "plt.ylim(-280., 280.) \n" ] }, { "cell_type": "code", "execution_count": 225, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(-280.0, 280.0)" ] }, "execution_count": 225, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3o/D6nKojehfQN6W8XdfR0Fhn3sTCmI85YMpt1J/pzeBs9yhipb0j5nYJrQlz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\nSzPmXCHEPUKIxHAuJIQ4Rwj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XDL6k+WtEiGXLlrFs2bKwxgopm6t/HzRIiFz0GKiT/x3B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\nSQl+sdbu4h2nPlCXR2n911jESnbK8WSnTCEndSqDkicecb50w5r11D3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"text/plain": [ "<matplotlib.figure.Figure at 0x1144a39d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figsize(5,5)\n", "temp = gravdata.reshape(Y_PF.shape)\n", "plt.contourf(X_PF, Y_PF, temp, levels=[-10,-1.0], colors=\"olive\", linewidths = (3,), linestyles=\"-\", alpha=0.3)\n", "plt.contour(X_PF, Y_PF, temp, levels=[-10,-1.0], colors=\"olive\", linewidths = (3,), linestyles=\"-\")\n", "temp = magdata.reshape(Y_PF.shape)\n", "plt.contourf(X_PF, Y_PF, temp, levels=[100., 1000.], colors=\"blue\", linewidths = (3,), linestyles=\"-\", alpha=0.3)\n", "plt.contour(X_PF, Y_PF, temp, levels=[100.,1000.], colors=\"blue\", linewidths = (3,), linestyles=\"-\")\n", "\n", "temp = dcdata[src1,rx_x].reshape(Xx.shape,order=\"F\")\n", "plt.contourf(Xx, Yx, temp, levels=[0., 0.025], colors=\"crimson\", linewidths = (3,), linestyles=\"-\", alpha=0.3)\n", "plt.contour(Xx, Yx, temp, levels=[0., 0.025], colors=\"crimson\", linewidths = (3,), linestyles=\"-\")\n", "temp = dcdata[src3,rx_y].reshape(Xy.shape,order=\"F\")\n", "plt.contourf(Xy, Yy, temp, levels=[0., 0.025], colors=\"green\", linewidths = (3,), linestyles=\"-\", alpha=0.5)\n", "plt.contour(Xy, Yy, temp, levels=[0., 0.025], colors=\"green\", linewidths = (3,), linestyles=\"-\")\n", "plt.xlabel(\"Easting (m)\")\n", "plt.ylabel(\"Northing (m)\") \n", "plt.xlim(-280., 280.)\n", "plt.ylim(-280., 280.) \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": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
michaelaye/planet4
notebooks/download label files.ipynb
1
4072
{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", "<div class=\"toc\" style=\"margin-top: 1em;\"><ul class=\"toc-item\"></ul></div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from planet4 import region_data" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from hirise_tools.downloads import get_rdr_color_label, download_RED_product\n", "import hirise_tools as ht" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# regions = ['Giza', 'Ithaca', 'Manhattan2', 'Inca']\n", "# regions.remove('Inca')\n", "regions = ['Potsdam']\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "seasons = ['season2', 'season3']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "get_rdr_color_label(obsid)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "root = '/Volumes/Data/hirise/p4_input'" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from nbtools import execute_in_parallel" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for region in regions:\n", " print(region)\n", " for season in seasons:\n", " print(season)\n", " reg = getattr(region_data, region)\n", " seas = getattr(reg, season)\n", " execute_in_parallel(get_rdr_color_label, seas)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def get_p4_hirise_data(obsid):\n", " from hirise_tools.downloads import download_RED_product\n", " root = '/Volumes/Data/hirise/p4_input'\n", " for ccdno in [4,5]:\n", " for channel in [0, 1]:\n", " download_RED_product(obsid, ccdno, channel, saveroot=root)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from nbtools import display_multi_progress\n", "for region in regions:\n", " print(region)\n", " for season in seasons:\n", " print(season)\n", " reg = getattr(region_data, region)\n", " seas = getattr(reg, season)\n", " lbview.map_async(get_p4_hirise_data, seas)\n", " display_multi_progress?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for region in regions:\n", " print(region)\n", " for season in seasons:\n", " print(season)\n", " reg = getattr(region_data, region)\n", " seas = getattr(reg, season)\n", " print(sorted(seas))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:stable]", "language": "python", "name": "conda-env-stable-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": "3.6.3" }, "toc": { "nav_menu": { "height": "12px", "width": "252px" }, "number_sections": false, "sideBar": false, "skip_h1_title": false, "toc_cell": true, "toc_position": {}, "toc_section_display": "block", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 1 }
isc
netodeolino/TCC
TCC 01/Extração/plot_dbscan.ipynb
2
60893
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "\n", "# Demo of DBSCAN clustering algorithm\n", "\n", "\n", "Finds core samples of high density and expands clusters from them.\n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Automatically created module for IPython interactive environment\n" ] } ], "source": [ "print(__doc__)\n", "\n", "import numpy as np\n", "\n", "from sklearn.cluster import DBSCAN\n", "from sklearn import metrics\n", "from sklearn.datasets.samples_generator import make_blobs\n", "from sklearn.preprocessing import StandardScaler" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Generate sample data\n" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "centers = [[1, 1], [-1, -1], [1, -1]]\n", "X, labels_true = make_blobs(n_samples=550, centers=centers, cluster_std=0.4,\n", " random_state=0)\n", "\n", "X = StandardScaler().fit_transform(X)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Compute DBSCAN\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimated number of clusters: 3\n", "Homogeneity: 0.935\n", "Completeness: 0.845\n", "V-measure: 0.888\n", "Adjusted Rand Index: 0.928\n", "Adjusted Mutual Information: 0.845\n", "Silhouette Coefficient: 0.620\n" ] } ], "source": [ "db = DBSCAN(eps=0.3, min_samples=10).fit(X)\n", "core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n", "core_samples_mask[db.core_sample_indices_] = True\n", "labels = db.labels_\n", "\n", "# Number of clusters in labels, ignoring noise if present.\n", "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n", "\n", "print('Estimated number of clusters: %d' % n_clusters_)\n", "print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\n", "print(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\n", "print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\n", "print(\"Adjusted Rand Index: %0.3f\"\n", " % metrics.adjusted_rand_score(labels_true, labels))\n", "print(\"Adjusted Mutual Information: %0.3f\"\n", " % metrics.adjusted_mutual_info_score(labels_true, labels))\n", "print(\"Silhouette Coefficient: %0.3f\"\n", " % metrics.silhouette_score(X, labels))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Plot result\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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n44jhMw7wJzoyl/4sZjsK06TbS5yzbVoKxHzOAR42p3UeoaL4hH1e+fI7FoWB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\nL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"text/plain": [ "<matplotlib.figure.Figure at 0x7f005fd92b70>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# Black removed and is used for noise instead.\n", "unique_labels = set(labels)\n", "colors = [plt.cm.Spectral(each)\n", " for each in np.linspace(0, 1, len(unique_labels))]\n", "for k, col in zip(unique_labels, colors):\n", " if k == -1:\n", " # Black used for noise.\n", " col = [0, 0, 0, 1]\n", "\n", " class_member_mask = (labels == k)\n", "\n", " xy = X[class_member_mask & core_samples_mask]\n", " plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n", " markeredgecolor='k', markersize=14)\n", "\n", " xy = X[class_member_mask & ~core_samples_mask]\n", " plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n", " markeredgecolor='k', markersize=6)\n", "\n", "plt.title('Número estimado de clusters: %d' % n_clusters_)\n", "plt.show()" ] }, { "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 }
mit
amitkaps/hackermath
Module_1d_linear_regression_gradient.ipynb
1
398239
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear Regression (Gradient Descent)\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So far we have looked at direct matrix method for solving the $Ax = b$ problem. But most machine learning algorithms may not be directly computatable. So the alternative way to do is to define a cost function that needs to be minimised and use a gradient descent approach to solve it." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "plt.style.use('fivethirtyeight')\n", "plt.rcParams['figure.figsize'] = (9, 6)" ] }, { "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>brand</th>\n", " <th>model</th>\n", " <th>price</th>\n", " <th>kmpl</th>\n", " <th>bhp</th>\n", " <th>type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>Chevrolet</td>\n", " <td>Beat</td>\n", " <td>421</td>\n", " <td>18.6</td>\n", " <td>79</td>\n", " <td>Hatchback</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>Chevrolet</td>\n", " <td>Sail</td>\n", " <td>551</td>\n", " <td>18.2</td>\n", " <td>82</td>\n", " <td>Sedan</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>Chevrolet</td>\n", " <td>Sail Hatchback</td>\n", " <td>468</td>\n", " <td>18.2</td>\n", " <td>82</td>\n", " <td>Hatchback</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>Chevrolet</td>\n", " <td>Spark</td>\n", " <td>345</td>\n", " <td>16.2</td>\n", " <td>62</td>\n", " <td>Hatchback</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>Fiat</td>\n", " <td>Linea Classic</td>\n", " <td>612</td>\n", " <td>14.9</td>\n", " <td>89</td>\n", " <td>Sedan</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " brand model price kmpl bhp type\n", "0 Chevrolet Beat 421 18.6 79 Hatchback\n", "1 Chevrolet Sail 551 18.2 82 Sedan\n", "2 Chevrolet Sail Hatchback 468 18.2 82 Hatchback\n", "3 Chevrolet Spark 345 16.2 62 Hatchback\n", "4 Fiat Linea Classic 612 14.9 89 Sedan" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pop = pd.read_csv('data/cars_small.csv')\n", "pop.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Solving using Sklearn " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import linear_model\n", "from sklearn import metrics" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = pop['kmpl'].values.reshape(-1,1)\n", "y = pop['price']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = linear_model.LinearRegression(fit_intercept=True)\n", "model.fit(X,y)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-36.12619236])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.coef_" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "b0 = 1158.0 b1 = -36.0\n" ] } ], "source": [ "beta0 = round(model.intercept_)\n", "beta1 = round(model.coef_[0])\n", "print(\"b0 = \", beta0, \"b1 =\", beta1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting the Regression Line \n", "\n", "$$ price = 1158 - 36 * kmpl ~~~~ \\textit{(population = 42)}$$ \n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.PathCollection at 0x11acb1518>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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P0KDC5sGm400FF6YABReflTvxWbkTS78QcOsADeYP0mF0IgsuiIgodJixI/LD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\nSGixd0oQBCQmJqKysvKC993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gCLg8RY21E+NwZG4KVl8ei7FJgc/U/a7WhUfz63B9gQYL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\n1d674gKrwcZnFbsXXniB22+/ne7du1NSUsK6devYtm0b77/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IiFiEgp2IiIiIRSjYiYiI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\nIiIWoWAnIiIiYhH/DQqY77ARIZKaAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x118ab3128>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = np.arange(min(pop.kmpl),max(pop.kmpl),1)\n", "\n", "plt.xlabel('kmpl')\n", "plt.ylabel('price')\n", "\n", "y_p = beta0 + beta1 * x\n", "plt.plot(x, y_p, '-')\n", "plt.scatter(pop.kmpl, pop.price, s = 150, alpha = 0.5 )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calculating the Error term\n", "\n", "The error term as we saw is defined as:\n", "\n", "$$ E(\\beta)= \\frac {1}{n} {||y - X\\beta||}^2 $$" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Solving using Ax = b approach\n", "n = pop.shape[0]\n", "x0 = np.ones(n)\n", "x1 = pop.kmpl\n", "X = np.c_[x0, x1]\n", "X = np.asmatrix(X)\n", "y = np.asmatrix(pop.price.values.reshape(-1,1))\n", "b = np.asmatrix([[beta0],\n", " [beta1]])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Error calculation\n", "def error_term(X,y,b,n):\n", " M = (y - X*b)\n", " error = M.T*M / n\n", " return error[0,0]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13065.0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "round(error_term(X,y,b,n))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Shape of the error term\n", "\n", "Lets plot the error term and see that the error surface is **CONVEX**. And there is one absolute minimum point." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11ae80630>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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U0iyVShiGQTAYnDjQahqCo1qtomkawWBwbLHhrGVcTNOkXC4jy/IOsQHw3NYa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4lDmmQYmkx0Rx/knd+ziqmrFAyVZsNCVSV0VUaV57fwu9+9yfb1Dr3OlI2NOp2jCf3OjIKpUSxo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\nsuv8vNELjjOEpohV//a3v3HppZfSuXPnU277448/NsmLABQtyNq1a0lLS2P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"text/plain": [ "<matplotlib.figure.Figure at 0x11aef3860>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Calculate a range of values for b0 and b1\n", "b0_min, b0_max = -500, 2000\n", "b1_min, b1_max = -100, 100\n", "bb0, bb1 = np.meshgrid(np.arange(b0_min, b0_max, (b0_max - b0_min)/100), \n", " np.arange(b1_min, b1_max, (b1_max - b1_min)/100))\n", "\n", "# Calculate a mesh of values for b0 and b1 and error term for each\n", "bb = np.c_[bb0.ravel(), bb1.ravel()]\n", "errors = [error_term(X,y,np.asmatrix(i).T,n) for i in bb]\n", "\n", "from mpl_toolkits.mplot3d import Axes3D\n", "fig = plt.figure()\n", "ax = fig.gca(projection='3d')\n", "trisurf = ax.plot_trisurf(bb[:,0], bb[:,1], errors, cmap=plt.cm.viridis, linewidth=0)\n", "\n", "ax.view_init(azim=20)\n", "fig.colorbar(trisurf, shrink=0.3, aspect=5)\n", "\n", "ax.set_xlabel('Intercept')\n", "ax.set_ylabel('Slope')\n", "ax.set_zlabel(\"Error\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's plot the error term as a 2D plot - as a **contour plot** and make it more clear." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11b708fd0>" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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jUg+IZJkdk55urCFMCs8bJgmRwnrsscfQoEEDvPfee9xrWlwTmZ2djd69eyM3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"text/plain": [ "<matplotlib.figure.Figure at 0x11dd23240>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Get the errors\n", "Z = np.asmatrix(errors).reshape(bb0.shape)\n", "\n", "# Get the contour\n", "e_min, e_max = np.min(errors), np.max(errors)\n", "levels = np.logspace(0, np.log10(e_max), 40)\n", "\n", "# Plot the contour\n", "fig = plt.figure()\n", "cs = plt.contourf(bb0, bb1, Z, cmap=plt.cm.viridis, levels = levels, linewidth=0.3, alpha = 0.5)\n", "fig.colorbar(cs, shrink=0.5, aspect=5)\n", "\n", "plt.xlabel('Intercept')\n", "plt.ylabel('Slope')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Gradient Descent \n", "\n", "In our linear regression problem, there is only one minimum. Our error surface is convex. So we can start from one point on the error surface and gradually move down in the error surface in the direction of the minimum.\n", "\n", "\"At a theoretical level, gradient descent is an algorithm that minimizes the cost functions. Given a cost function defined by a set of parameters, gradient descent starts with an initial set of parameter values and iteratively moves toward a set of parameter values that minimize the cost function. This iterative minimization is achieved using calculus, taking steps in the negative direction of the function gradient.\"\n", "\n", "So the gradient in linear regression is:\n", "$$ \\nabla E(\\beta)= \\frac {2}{n} X^T(X\\beta−y) $$\n", "\n", "Lets start at an arbitary point for the features (0,0) and calculate the gradient\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "b_initial = np.asmatrix([[500],\n", " [-100]])" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "def gradient(X,y,b,n):\n", " g = (2/n)*X.T*(X*b - y)\n", " return g" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3371573.9285714286" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "error_term(X,y,b_initial,n)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "matrix([[ -3651.28571429],\n", " [-67557.49047619]])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gradient(X,y,b_initial,n)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "matrix([[ 536.51285714],\n", " [ 575.57490476]])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "b_next = b_initial - 0.01 * gradient(X,y,b_initial,n)\n", "b_next" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Learning Rate and Epochs\n", "\n", "Now we need to update our parameters in the direction of the gradient. And keep repeating the process. \n", "\n", "$$ \\beta_{i+1} = \\beta_{i} - \\eta * \\nabla E(\\beta)$$\n", "\n", "\n", "- Learning rate ($\\eta$) - how far we need to move towards the gradient in each step\n", "- Epoch - Number of times we want to execute this process" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "def gradient_descent (eta, epochs, X, y, n):\n", " \n", " # Set Initial Values \n", " b = np.asmatrix([[-250],[-50]])\n", " e = error_term(X,y,b,n)\n", " b0s, b1s, errors = [], [], []\n", " b0s.append(b.item(0)) \n", " b1s.append(b.item(1)) \n", " errors.append(e)\n", "\n", " # Run the calculation for those many epochs\n", " for i in range(epochs):\n", " g = gradient(X,y,b,n)\n", " b = b - eta * g\n", " e = error_term(X,y,b,n)\n", " \n", " b0s.append(b.item(0)) \n", " b1s.append(b.item(1)) \n", " errors.append(e)\n", " \n", " print('error =', round(errors[-1]), ' b0 =', round(b0s[-1]), 'b1 =', round(b1s[-1]))\n", " \n", " return errors, b0s, b1s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets see the error rate with different Learning Rate and Epochs" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "error = 48985.0 b0 = -240 b1 = 39\n" ] } ], "source": [ "errors, b0s, b1s = gradient_descent (0.001, 100, X, y, n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "\n", "Try with learning rate = 0.001, Epochs = 1000" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try with learning rate = 0.02, Epochs = 1000" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Try with learning rate = 0.001, Epochs = 50000" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting Epochs and Learning Rate" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def plot_gradient_descent(eta, epoch, gradient_func):\n", " \n", " es, b0s, b1s = gradient_func(eta, epoch, X, y, n)\n", " \n", " # Plot the intercept and coefficients\n", " plt.figure(figsize=(15,6))\n", " plt.subplot(1, 2, 1)\n", " #plt.tight_layout()\n", " \n", " # Plot the contour\n", " cs = plt.contourf(bb0, bb1, Z, cmap=plt.cm.viridis, levels = levels, \n", " linewidth=0.3, alpha = 0.5)\n", " \n", " # Plot the intercept and coefficients\n", " plt.plot(b0s, b1s, '-b', linewidth = 2)\n", " plt.xlim([-500,2000])\n", " plt.ylim([-100,100])\n", " plt.xlabel('Intercept')\n", " plt.ylabel('Slope')\n", " \n", " # Plot the error rates\n", " plt.subplot(1, 2, 2)\n", " plt.plot(np.log10(es))\n", " plt.ylim([3,10])\n", " plt.xlabel('Epochs')\n", " plt.ylabel('log(Error)') " ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "error = 49239.0 b0 = -245 b1 = 39\n" ] }, { "data": { "image/png": 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WPftAeGl85sg+CTUyLC/eEVyPTrIPBPbry+MaJPvyXCUBqdVT9qM1NIw6d2kw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M4jOpTyUMoC0MvDkq+AcJN8QL/1+iJIvgQl2RfhdaW2Xl+SfAmW/cDtA03hV1t4Tw/ZV3NOUbfc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\nceutt6JNmzb44Ycf8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"text/plain": [ "<matplotlib.figure.Figure at 0x11c6cb1d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_gradient_descent(0.0005, 15, gradient_descent)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exercise\n", "Plot with learning rate = 0.001, Epochs = 1000" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot with learning rate = 0.005, Epochs = 10000" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot with learning rate = 0.001, Epochs = 50000" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Challenges with Simple Gradient Descent" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "#### Convexity\n", "In our linear regression problem, there was only one minimum. Our error surface was convex. Regardless of where we started, we would eventually arrive at the absolute minimum. In general, this need not be the case. It’s possible to have a problem with local minima that a gradient search can get stuck in. There are several approaches to mitigate this (e.g., stochastic gradient search).\n", "\n", "#### Performance \n", "We used vanilla gradient descent with a learning rate of 0.001 in the above example, and ran it for 50000 iterations. There are approaches such a line search, that can reduce the number of iterations required and also avoid the overshooting problem with large learning rate.\n", "\n", "#### Convergence \n", "We didn’t talk about how to determine when the search finds a solution. This is typically done by looking for small changes in error iteration-to-iteration (e.g., where the gradient is near zero)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Line Search Gradient Descent\n", "\n", "To avoid the problem of setting the learning rate and overshooting, we can adaptively choose the step size\n", "\n", "$$ \\beta_{i+1} = \\beta_{i} - \\eta * \\nabla E(\\beta)$$\n", "\n", "\n", "First, fix a parameter $ 0 < \\gamma < 1$, then start with $\\eta = 1$, and while\n", "\n", "\n", "$$ E(\\beta_{i+1}) > E(\\beta) - \\frac{\\eta}{2} * {||\\nabla E(\\beta)||}^2 $$\n", "\n", "then\n", "\n", "$$ \\eta = \\gamma * \\eta $$\n", "\n", "Typically, we take $\\gamma = [0.1, 0.8] $" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def gradient_descent_line (eta, epochs, X, y, n):\n", " \n", " gamma = 0.5\n", " \n", " # Set Initial Values \n", " b = np.asmatrix([[-250],[-50]])\n", " es = error_term(X,y,b,n)\n", " b0s, b1s, errors = [], [], []\n", " b0s.append(b.item(0)) \n", " b1s.append(b.item(1)) \n", " errors.append(es)\n", "\n", " # Run the calculation for those many epochs\n", " for i in range(epochs):\n", " es_old = error_term(X,y,b,n)\n", " g = gradient(X,y,b,n)\n", " b = b - eta * g\n", " es = error_term(X,y,b,n)\n", " #print(e,e_old)\n", " if es > es_old - eta/2*(g.T*g):\n", " eta = eta * gamma\n", " \n", " b0s.append(b.item(0)) \n", " b1s.append(b.item(1)) \n", " errors.append(es)\n", " \n", " print('error =', round(errors[-1]), ' b0 =', round(b0s[-1]), 'b1 =', round(b1s[-1]))\n", "\n", " return errors, b0s, b1s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Avoid Overshooting\n", "Let see the performance difference between Simple Gradient Descent and Line Search Gradient Descent" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "error = 13928.0 b0 = 940 b1 = -24\n" ] } ], "source": [ "errors, b0s, b1s = gradient_descent_line (1, 50000, X, y, n)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "error = 18845.0 b0 = 597 b1 = -6\n" ] } ], "source": [ "errors, b0s, b1s = gradient_descent (0.0005, 50000, X, y, n)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting the Line Search Gradient Descent" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "error = 28314.0 b0 = 247 b1 = 13\n" ] }, { "data": { "image/png": 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j4uKQmpqq6ryla93BfIp+U/f1DP7tCrblS+nfBygHjhw4irL1lYhLTdZ0La3E4L+pqAJl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\nTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11dc57cf8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_gradient_descent(1, 10000, gradient_descent_line)" ] }, { "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": "3.6.2" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
swirlingsand/deep-learning-foundations
sentiment-network/Sentiment Classification - Mini Project 1.ipynb
2
127648
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sentiment Classification & How To \"Frame Problems\" for a Neural Network\n", "\n", "by Andrew Trask\n", "\n", "- **Twitter**: @iamtrask\n", "- **Blog**: http://iamtrask.github.io" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What You Should Already Know\n", "\n", "- neural networks, forward and back-propagation\n", "- stochastic gradient descent\n", "- mean squared error\n", "- and train/test splits\n", "\n", "### Where to Get Help if You Need it\n", "- Re-watch previous Udacity Lectures\n", "- Leverage the recommended Course Reading Material - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) (40% Off: **traskud17**)\n", "- Shoot me a tweet @iamtrask\n", "\n", "\n", "### Tutorial Outline:\n", "\n", "- Intro: The Importance of \"Framing a Problem\"\n", "\n", "\n", "- Curate a Dataset\n", "- Developing a \"Predictive Theory\"\n", "- **PROJECT 1**: Quick Theory Validation\n", "\n", "\n", "- Transforming Text to Numbers\n", "- **PROJECT 2**: Creating the Input/Output Data\n", "\n", "\n", "- Putting it all together in a Neural Network\n", "- **PROJECT 3**: Building our Neural Network\n", "\n", "\n", "- Understanding Neural Noise\n", "- **PROJECT 4**: Making Learning Faster by Reducing Noise\n", "\n", "\n", "- Analyzing Inefficiencies in our Network\n", "- **PROJECT 5**: Making our Network Train and Run Faster\n", "\n", "\n", "- Further Noise Reduction\n", "- **PROJECT 6**: Reducing Noise by Strategically Reducing the Vocabulary\n", "\n", "\n", "- Analysis: What's going on in the weights?" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "56bb3cba-260c-4ebe-9ed6-b995b4c72aa3" } }, "source": [ "# Lesson: Curate a Dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "nbpresent": { "id": "eba2b193-0419-431e-8db9-60f34dd3fe83" } }, "outputs": [], "source": [ "def pretty_print_review_and_label(i):\n", " print(labels[i] + \"\\t:\\t\" + reviews[i][:80] + \"...\")\n", "\n", "g = open('reviews.txt','r') # What we know!\n", "reviews = list(map(lambda x:x[:-1],g.readlines()))\n", "g.close()\n", "\n", "g = open('labels.txt','r') # What we WANT to know!\n", "labels = list(map(lambda x:x[:-1].upper(),g.readlines()))\n", "g.close()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "25000" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(reviews)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "nbpresent": { "id": "bb95574b-21a0-4213-ae50-34363cf4f87f" } }, "outputs": [ { "data": { "text/plain": [ "'story of a man who has unnatural feelings for a pig . starts out with a opening scene that is a terrific example of absurd comedy . a formal orchestra audience is turned into an insane violent mob by the crazy chantings of it s singers . unfortunately it stays absurd the whole time with no general narrative eventually making it just too off putting . even those from the era should be turned off . the cryptic dialogue would make shakespeare seem easy to a third grader . on a technical level it s better than you might think with some good cinematography by future great vilmos zsigmond . future stars sally kirkland and frederic forrest can be seen briefly . '" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews[1]" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "nbpresent": { "id": "e0408810-c424-4ed4-afb9-1735e9ddbd0a" } }, "outputs": [ { "data": { "text/plain": [ "'NEGATIVE'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Lesson: Develop a Predictive Theory" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false, "nbpresent": { "id": "e67a709f-234f-4493-bae6-4fb192141ee0" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "labels.txt \t : \t reviews.txt\n", "\n", "NEGATIVE\t:\tthis movie is terrible but it has some good effects . ...\n", "POSITIVE\t:\tadrian pasdar is excellent is this film . he makes a fascinating woman . ...\n", "NEGATIVE\t:\tcomment this movie is impossible . is terrible very improbable bad interpretat...\n", "POSITIVE\t:\texcellent episode movie ala pulp fiction . days suicides . it doesnt get more...\n", "NEGATIVE\t:\tif you haven t seen this it s terrible . it is pure trash . i saw this about ...\n", "POSITIVE\t:\tthis schiffer guy is a real genius the movie is of excellent quality and both e...\n" ] } ], "source": [ "print(\"labels.txt \\t : \\t reviews.txt\\n\")\n", "pretty_print_review_and_label(2137)\n", "pretty_print_review_and_label(12816)\n", "pretty_print_review_and_label(6267)\n", "pretty_print_review_and_label(21934)\n", "pretty_print_review_and_label(5297)\n", "pretty_print_review_and_label(4998)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Quick Theory Validation" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "NEGATIVE\t:\tthe storyline was okay . akshay kumar was good as always and that was the only g...\n", "POSITIVE\t:\tthe ship may have sunk but the movie didn t director james cameron from t...\n", "NEGATIVE\t:\tthis film is mediocre at best . angie harmon is as funny as a bag of hammers . h...\n", "NEGATIVE\t:\ti would have liked to write about the story but there wasn t any . i would hav...\n", "POSITIVE\t:\twhat s inexplicable firstly the hatred towards this movie . it may not be the...\n", "POSITIVE\t:\tto all the miserable people who have done everything from complain about the dia...\n", "POSITIVE\t:\tsure titanic was a good movie the first time you see it but you really should...\n", "POSITIVE\t:\tthe night listener held my attention with robin williams shining as a new york ...\n", "POSITIVE\t:\ttitanic has to be one of my all time favorite movies . it has its problems wha...\n", "NEGATIVE\t:\tthis film is about a male escort getting involved in a murder investigation that...\n", "POSITIVE\t:\tthe night listener is probably not one of william s best roles but he makes a ...\n", "NEGATIVE\t:\ti saw this movie at a drive in in . until howard the duck i considered th...\n", "NEGATIVE\t:\tsuch a long awaited movie . . but it has disappointed me and my friends who had ...\n", "POSITIVE\t:\tthere s so many things to fall for in aro tolbukhin . en la mente del asesino ...\n", "POSITIVE\t:\ti avoided watching this film for the longest time . long before it was even rele...\n", "NEGATIVE\t:\tshame on yash raj films and aditya chopra who seems to have lost their intellige...\n", "NEGATIVE\t:\tthis movie is horrible . the acting is a waste basket . no crying no action ho...\n", "POSITIVE\t:\ttitanic has to be one of my all time favorite movies . it has its problems wha...\n", "POSITIVE\t:\tdaniell steel s daddy what a refreshing story . this movie glorified the impor...\n", "NEGATIVE\t:\tok i am not japanese . i do know a little about japanese culture and a little ...\n", "NEGATIVE\t:\tfirst lesson that some film makers particularly those inspired by hollywood ne...\n", "POSITIVE\t:\tafter seeing several movies of villaronga i had a pretty clear opinion about hi...\n", "POSITIVE\t:\tsomewhat funny and well paced action thriller that has jamie foxx as a hapless ...\n", "POSITIVE\t:\tfamily problems abound in real life and that is what this movie is about . love ...\n", "NEGATIVE\t:\ta little girl s dead body is found stripped of all possible means of identifica...\n", "NEGATIVE\t:\tabsolutely awful movie . utter waste of time . br br background music is s...\n", "NEGATIVE\t:\ti just saw the movie in theater . the movie has very few good points to talk abo...\n", "POSITIVE\t:\ttitanic has to be one of my all time favorite movies . it has its problems wha...\n", "POSITIVE\t:\tfamily problems abound in real life and that is what this movie is about . love ...\n", "NEGATIVE\t:\tmasters of horror the screwfly solution starts as america is being infected by a...\n", "POSITIVE\t:\tfor me personally this film goes down in my top four of all time . no exceptions...\n", "NEGATIVE\t:\tthe worst movie i have seen since tera jadoo chal gaya . there is no story no h...\n", "NEGATIVE\t:\ti was fascinated as to how truly bad this movie was . was the viewer supposed to...\n", "NEGATIVE\t:\ti would have liked to write about the story but there wasn t any . i would hav...\n", "POSITIVE\t:\ttitanic is a long but well made tragic adventure love story that takes place dur...\n", "POSITIVE\t:\tevery once in a while the conversation will turn to favorite movies . i ll me...\n", "NEGATIVE\t:\ti m not alone in admiring the first superman movie a film that richard donner ...\n", "NEGATIVE\t:\tjewish newspaper reporter justin timberlake as joshua josh pollack is puzzle...\n", "NEGATIVE\t:\tthe author sets out on a journey of discovery of his roots in the southern t...\n", "POSITIVE\t:\tafter a brief prologue showing a masked man stalking and then slashing the throa...\n", "POSITIVE\t:\twow . what a wonderful film . the script is nearly perfect it appears this is th...\n", "NEGATIVE\t:\tmasters of horror the screwfly solution starts as america is being infected by a...\n", "POSITIVE\t:\tone of the most heart warming foreign films i ve ever seen . br br the y...\n", "NEGATIVE\t:\twarner bros . made many potboilers in the s and most of them are fast paced ...\n", "NEGATIVE\t:\ti ve now written reviews for several of the moh episodes and this is among the...\n", "POSITIVE\t:\tthis movie re wrote film history in every way . no one cares what anyone thinks...\n", "POSITIVE\t:\tanother aussie masterpiece this delves into the world of the unknown and the su...\n", "POSITIVE\t:\tback in do i remember that year clinton bans cloning research the unfortun...\n", "POSITIVE\t:\tfamily problems abound in real life and that is what this movie is about . love ...\n", "NEGATIVE\t:\t the curse of frankenstein sticks faithfully to mary shelley s story for one ...\n", "NEGATIVE\t:\ti would have liked to write about the story but there wasn t any . i would hav...\n", "NEGATIVE\t:\ti grew up on the superman ii theatrical version s t and as a kid i loved...\n", "POSITIVE\t:\tto all the miserable people who have done everything from complain about the dia...\n", "POSITIVE\t:\ttitanic is a classic . i was really surprised that this movie didn t have a sol...\n", "POSITIVE\t:\tevery once in a while the conversation will turn to favorite movies . i ll me...\n", "NEGATIVE\t:\tthis is the biggest flop of . i don know what director has is his mind of cr...\n", "POSITIVE\t:\tjust two comments . . . . seven years apart hardly evidence of the film s rele...\n", "POSITIVE\t:\tthis movie was excellent . a sad truth to how culture tends to clash with the se...\n", "NEGATIVE\t:\ti grew up on the superman ii theatrical version s t and as a kid i loved...\n", "NEGATIVE\t:\ttashan the title itself explains the nature of the movie . br br this typ...\n", "POSITIVE\t:\ti must admit when i read the description of the genre on netflix as steamy rom...\n", "NEGATIVE\t:\tthis movie is horrible . the acting is a waste basket . no crying no action ho...\n", "NEGATIVE\t:\tso . . . we get so see added footage of brando . . . interesting but not exactly...\n", "NEGATIVE\t:\tjewish newspaper reporter justin timberlake as joshua josh pollack is puzzle...\n", "NEGATIVE\t:\tall the world said that the film tashan would be a good movie with great pleasur...\n", "POSITIVE\t:\ti must admit when i read the description of the genre on netflix as steamy rom...\n", "NEGATIVE\t:\tthe author sets out on a journey of discovery of his roots in the southern t...\n", "NEGATIVE\t:\twarner bros . made many potboilers in the s and most of them are fast paced ...\n", "NEGATIVE\t:\ti m not alone in admiring the first superman movie a film that richard donner ...\n", "NEGATIVE\t:\tthe author sets out on a journey of discovery of his roots in the southern t...\n", "POSITIVE\t:\tthis movie is worth seeing for the visual beauty and moving acting alone but th...\n", "NEGATIVE\t:\tthe sight of kareena kapoor in a two piece bikini is about the only thing that ...\n", "NEGATIVE\t:\tall the world said that the film tashan would be a good movie with great pleasur...\n", "NEGATIVE\t:\ti wasn t terribly impressed with dante s st season offering in homecoming ...\n", "NEGATIVE\t:\tthis movie is horrible . the acting is a waste basket . no crying no action ho...\n", "POSITIVE\t:\tback in do i remember that year clinton bans cloning research the unfortun...\n", "POSITIVE\t:\twang bianlian is an old street performer who is known as a king of masks for h...\n", "NEGATIVE\t:\tthis is the biggest flop of . i don know what director has is his mind of cr...\n", "NEGATIVE\t:\ti found it very very difficulty to watch this after the initial minutes of the ...\n", "NEGATIVE\t:\twell at least my theater group did lol . so of course i remember watching grea...\n", "POSITIVE\t:\twang bianlian is an old street performer who is known as a king of masks for h...\n", "NEGATIVE\t:\ti grew up on the superman ii theatrical version s t and as a kid i loved...\n", "NEGATIVE\t:\tthis is an awful film . yea the girls are pretty but its not very good . the plo...\n", "POSITIVE\t:\twow . what a wonderful film . the script is nearly perfect it appears this is th...\n", "POSITIVE\t:\tfamily problems abound in real life and that is what this movie is about . love ...\n", "POSITIVE\t:\tthe filming is pleasant and the environment is keenly realistic . i liked that i...\n", "NEGATIVE\t:\tthis is an awful film . yea the girls are pretty but its not very good . the plo...\n", "NEGATIVE\t:\ti grew up on the superman ii theatrical version s t and as a kid i loved...\n", "POSITIVE\t:\tthe filming is pleasant and the environment is keenly realistic . i liked that i...\n", "NEGATIVE\t:\ti laughed all the way through this rotten movie . it s so unbelievable . a woma...\n", "NEGATIVE\t:\ti gave this stars because it has a lot of interesting themes many here have alr...\n", "POSITIVE\t:\tan unexpected pleasure as i had heard nothing about this film . br br sham...\n", "NEGATIVE\t:\twarner bros . made many potboilers in the s and most of them are fast paced ...\n", "NEGATIVE\t:\ti gave this stars because it has a lot of interesting themes many here have alr...\n", "NEGATIVE\t:\tthe author sets out on a journey of discovery of his roots in the southern t...\n", "POSITIVE\t:\tas i was watching this film on video last night i kept getting these tingles th...\n", "NEGATIVE\t:\tthis movie was okay but it certainly defeats the claim that homosexuals are bo...\n", "POSITIVE\t:\twang bianlian is an old street performer who is known as a king of masks for h...\n", "NEGATIVE\t:\t the curse of frankenstein sticks faithfully to mary shelley s story for one ...\n", "NEGATIVE\t:\ti saw this film at its premier at sundance . br br since american beauty...\n" ] } ], "source": [ "import random\n", "for i in range(100):\n", " pretty_print_review_and_label(i+random.randint(10, 100))\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from collections import Counter\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "positive_counts = Counter()\n", "negative_counts = Counter()\n", "total_counts = Counter()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for i in range(len(reviews)):\n", " if(labels[i] == 'POSITIVE'):\n", " for word in reviews[i].split(\" \"):\n", " positive_counts[word] += 1\n", " \n", " else:\n", " for word in reviews[i].split(\" \"):\n", " negative_counts[word] += 1\n", " total_counts[word] += 1\n", " " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('', 550468),\n", " ('the', 173324),\n", " 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('your', 2453),\n", " ('go', 2440),\n", " ('acting', 2437),\n", " ('plot', 2432),\n", " ('world', 2429),\n", " ('scenes', 2427),\n", " ('say', 2414),\n", " ('through', 2409),\n", " ('makes', 2390),\n", " ('better', 2381),\n", " ('now', 2368),\n", " ('work', 2346),\n", " ('young', 2343),\n", " ('old', 2311),\n", " ('ve', 2307),\n", " ('find', 2272),\n", " ('both', 2248),\n", " ('before', 2177),\n", " ('us', 2162),\n", " ('again', 2158),\n", " ('series', 2153),\n", " ('quite', 2143),\n", " ('something', 2135),\n", " ('cast', 2133),\n", " ('should', 2121),\n", " ('part', 2098),\n", " ('always', 2088),\n", " ('lot', 2087),\n", " ('another', 2075),\n", " ('actors', 2047),\n", " ('director', 2040),\n", " ('family', 2032),\n", " ('between', 2016),\n", " ('own', 2016),\n", " ('m', 1998),\n", " ('may', 1997),\n", " ('same', 1972),\n", " ('role', 1967),\n", " ('watching', 1966),\n", " ('every', 1954),\n", " ('funny', 1953),\n", " ('doesn', 1935),\n", " ('performance', 1928),\n", " ('few', 1918),\n", " ('bad', 1907),\n", " ('look', 1900),\n", " ('re', 1884),\n", " ('why', 1855),\n", " ('things', 1849),\n", " ('times', 1832),\n", " ('big', 1815),\n", " ('however', 1795),\n", " ('actually', 1790),\n", " ('action', 1789),\n", " ('going', 1783),\n", " ('bit', 1757),\n", " ('comedy', 1742),\n", " ('down', 1740),\n", " ('music', 1738),\n", " ('must', 1728),\n", " ('take', 1709),\n", " ('saw', 1692),\n", " ('long', 1690),\n", " ('right', 1688),\n", " ('fun', 1686),\n", " ('fact', 1684),\n", " ('excellent', 1683),\n", " ('around', 1674),\n", " ('didn', 1672),\n", " ('without', 1671),\n", " ('thing', 1662),\n", " ('thought', 1639),\n", " ('got', 1635),\n", " ('each', 1630),\n", " ('day', 1614),\n", " ('feel', 1597),\n", " ('seems', 1596),\n", " ('come', 1594),\n", " ('done', 1586),\n", " ('beautiful', 1580),\n", " ('especially', 1572),\n", " ('played', 1571),\n", " ('almost', 1566),\n", " ('want', 1562),\n", " ('yet', 1556),\n", " ('give', 1553),\n", " ('pretty', 1549),\n", " ('last', 1543),\n", " ('since', 1519),\n", " ('different', 1504),\n", " ('although', 1501),\n", " ('gets', 1490),\n", " ('true', 1487),\n", " ('interesting', 1481),\n", " ('job', 1470),\n", " ('enough', 1455),\n", " ('our', 1454),\n", " ('shows', 1447),\n", " ('horror', 1441),\n", " ('woman', 1439),\n", " ('tv', 1400),\n", " ('probably', 1398),\n", " ('father', 1395),\n", " ('original', 1393),\n", " ('girl', 1390),\n", " ('point', 1379),\n", " ('plays', 1378),\n", " ('wonderful', 1372),\n", " ('far', 1358),\n", " ('course', 1358),\n", " ('john', 1350),\n", " ('rather', 1340),\n", " ('isn', 1328),\n", " ('ll', 1326),\n", " ('later', 1324),\n", " ('dvd', 1324),\n", " ('whole', 1310),\n", " ('war', 1310),\n", " ('d', 1307),\n", " ('found', 1306),\n", " ('away', 1306),\n", " ('screen', 1305),\n", " ('nothing', 1300),\n", " ('year', 1297),\n", " ('once', 1296),\n", " ('hard', 1294),\n", " ('together', 1280),\n", " ('set', 1277),\n", " ('am', 1277),\n", " ('having', 1266),\n", " ('making', 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" ('grade', 316),\n", " ('peter', 316),\n", " ('suddenly', 315),\n", " ('missing', 315),\n", " ('form', 313),\n", " ('stick', 313),\n", " ('previous', 313),\n", " ('break', 313),\n", " ('soundtrack', 312),\n", " ('surprised', 311),\n", " ('front', 311),\n", " ('expecting', 311),\n", " ('parents', 310),\n", " ('surprise', 310),\n", " ('relationship', 310),\n", " ('shoot', 309),\n", " ('today', 309),\n", " ('painful', 308),\n", " ('ways', 308),\n", " ('leaves', 308),\n", " ('ended', 308),\n", " ('creepy', 308),\n", " ('concept', 308),\n", " ('somewhere', 308),\n", " ('vampire', 308),\n", " ('spend', 307),\n", " ('th', 307),\n", " ('future', 306),\n", " ('difficult', 306),\n", " ('effect', 306),\n", " ('fighting', 306),\n", " ('street', 306),\n", " ('c', 305),\n", " ('america', 305),\n", " ('accent', 304),\n", " ('truth', 302),\n", " ('project', 302),\n", " ('joe', 301),\n", " ('f', 301),\n", " ('deal', 301),\n", " ('indeed', 301),\n", " ('biggest', 300),\n", " ('rate', 300),\n", " ('paul', 299),\n", " ('japanese', 299),\n", " ('utterly', 298),\n", " ('begins', 298),\n", " ('redeeming', 298),\n", " ('college', 298),\n", " ('york', 297),\n", " ('fairly', 297),\n", " ('disney', 297),\n", " ('crew', 296),\n", " ('create', 296),\n", " ('cartoon', 296),\n", " ('revenge', 296),\n", " ('co', 295),\n", " ('outside', 295),\n", " ('computer', 295),\n", " ('interested', 295),\n", " ('stage', 295),\n", " ('considering', 294),\n", " ('speak', 294),\n", " ('among', 294),\n", " ('towards', 293),\n", " ('channel', 293),\n", " ('sick', 293),\n", " ('talented', 292),\n", " ('cause', 292),\n", " ('particular', 292),\n", " ('van', 292),\n", " ('hair', 292),\n", " ('bottom', 291),\n", " ('reasons', 291),\n", " ('mediocre', 290),\n", " ('cat', 290),\n", " ('telling', 290),\n", " ('supporting', 289),\n", " ('store', 289),\n", " ('hoping', 288),\n", " ('waiting', 288),\n", " ...]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "negative_counts.most_common()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pos_neg_ratios = Counter()\n", "\n", "for term,cnt in list(total_counts.most_common()):\n", " if(cnt > 100):\n", " pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)\n", " pos_neg_ratios[term] = pos_neg_ratio\n", "\n", "for word,ratio in pos_neg_ratios.most_common():\n", " if(ratio > 1):\n", " pos_neg_ratios[word] = np.log(ratio)\n", " else:\n", " pos_neg_ratios[word] = -np.log((1 / (ratio+0.01)))" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('superb', 1.7091514458966952),\n", " ('wonderful', 1.5645425925262093),\n", " ('fantastic', 1.5048433868558566),\n", " ('excellent', 1.4647538505723599),\n", " ('amazing', 1.3919815802404802),\n", " ('powerful', 1.2999662776313934),\n", " ('favorite', 1.2668956297860055),\n", " ('perfect', 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" ('hits', 0.095310179804324935),\n", " ('results', 0.094615976374849128),\n", " ('place', 0.094597844501735473),\n", " ('wall', 0.093218128832100788),\n", " ('screen', 0.093090423066012035),\n", " ('officer', 0.092592786827824888),\n", " ('three', 0.092400179829382326),\n", " ('mix', 0.092206193866733843),\n", " ('fred', 0.092170459799657101),\n", " ('missed', 0.091937495325685639),\n", " ('help', 0.089675228287008593),\n", " ('since', 0.089468159841429057),\n", " ('convincing', 0.088947486016496116),\n", " ('changes', 0.087911872322879892),\n", " ('water', 0.087647307058755675),\n", " ('big', 0.087462267506023539),\n", " ('whether', 0.086401434915215417),\n", " ('depth', 0.085990447855522512),\n", " ('cute', 0.085815919584856126),\n", " ('problems', 0.085637885161817195),\n", " ('film', 0.085618555650856729),\n", " ('spanish', 0.08515780834030677),\n", " ('government', 0.084956722475965391),\n", " ('mission', 0.083699018876646838),\n", " ('wish', 0.083555885690973566),\n", " ('falling', 0.083381608939051),\n", " ('issue', 0.083381608939051),\n", " ('wants', 0.082280679513991054),\n", " ('situations', 0.082013151660835074),\n", " ('which', 0.080721093142199482),\n", " ('set', 0.080695491821007242),\n", " ('rate', 0.079787116617831902),\n", " ('captain', 0.079552631701953688),\n", " ('kate', 0.079512062927733607),\n", " ('extras', 0.079336742236521013),\n", " ('lost', 0.078598068066187896),\n", " ('revealed', 0.077558234345874444),\n", " ('agent', 0.077386663615420195),\n", " ('part', 0.076751339578802424),\n", " ('satire', 0.076372978784573956),\n", " ('chris', 0.0758657507747134),\n", " ('little', 0.07583626238355369),\n", " ('my', 0.075531730165573088),\n", " ('majority', 0.07534943724178679),\n", " ('keep', 0.074941421292118657),\n", " ('woody', 0.074723546195936574),\n", " ('public', 0.074370542721069938),\n", " ('dad', 0.073203404023294921),\n", " ('player', 0.073122264828962585),\n", " ('ex', 0.072526444068262585),\n", " ('suffering', 0.071743904858841315),\n", " ('consider', 0.070067562616716844),\n", " ('devil', 0.069869679960486),\n", " ('decides', 0.069497794496921506),\n", " ('s', 0.069425993723984988),\n", " ('close', 0.069418765871583257),\n", " ('faces', 0.069391993423999793),\n", " ('difference', 0.068992871486951421),\n", " ('actress', 0.068879678889626456),\n", " ('witch', 0.068137805167218041),\n", " ('took', 0.067236944784686545),\n", " ('soft', 0.06713930283762852),\n", " ('hitler', 0.066691374498672143),\n", " ('numerous', 0.066445099408152755),\n", " ('does', 0.066343124451621341),\n", " ('creepy', 0.065751377562780433),\n", " ('hat', 0.065751377562780433),\n", " ('room', 0.065632014958942553),\n", " ('provide', 0.065382759262851711),\n", " ('martin', 0.065240521868400944),\n", " ('odd', 0.065203193810762075),\n", " ('death', 0.065012406669236716),\n", " ('tim', 0.064538521137571164),\n", " ('broken', 0.064538521137571164),\n", " ('effect', 0.063112423310056259),\n", " ('anderson', 0.062800901239030441),\n", " ('runs', 0.062276929521514507),\n", " ('station', 0.062131781107006179),\n", " ('create', 0.062010074784212534),\n", " ('ultimately', 0.061321890874318948),\n", " ('heavy', 0.060870715087025913),\n", " ('cage', 0.06062462181643484),\n", " ('filmed', 0.060148846690498754),\n", " ('food', 0.059898141581069014),\n", " ('the', 0.05902269426102881),\n", " ('woman', 0.058672047052498608),\n", " ('sci', 0.057679111586677989),\n", " ('now', 0.056463107132025653),\n", " ('till', 0.056089466651043578),\n", " ('second', 0.05605134890532238),\n", " ('flying', 0.055880458394456628),\n", " ('knew', 0.05563169598614863),\n", " ('robot', 0.055059777183027389),\n", " ('taking', 0.054406722207164263),\n", " ('a', 0.053580080386005403),\n", " ('type', 0.053298581724361922),\n", " ('ups', 0.052446475372542524),\n", " ('themselves', 0.052332615458067437),\n", " ('hearing', 0.051735674399188893),\n", " ('aged', 0.051293294387550481),\n", " ...]" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pos_neg_ratios.most_common()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[('boll', -4.0778152602708904),\n", " ('uwe', -3.9218753018711578),\n", " ('seagal', -3.3202501058581921),\n", " ('unwatchable', -3.0269848170580955),\n", " ('mst', -2.7753833211707968),\n", " ('incoherent', -2.7641396677532537),\n", " ('unfunny', -2.5545257844967644),\n", " ('waste', -2.4907515123361046),\n", " ('blah', -2.4475792789485005),\n", " ('horrid', -2.3715779644809971),\n", " ('pointless', -2.3451073877136341),\n", " ('atrocious', -2.3187369339642556),\n", " ('redeeming', -2.2667790015910296),\n", " ('prom', -2.2601040980178784),\n", " ('drivel', -2.2476029585766928),\n", " ('lousy', -2.2118080125207054),\n", " ('worst', -2.1930856334332267),\n", " ('laughable', -2.172468615469592),\n", " ('awful', -2.1385076866397488),\n", " ('poorly', -2.1326133844207011),\n", " ('wasting', -2.1178155545614512),\n", " ('remotely', -2.111046881095167),\n", " ('existent', -2.0024805005437076),\n", " ('boredom', -1.9241486572738005),\n", " ('miserably', -1.9216610938019989),\n", " ('sucks', -1.9166645809588516),\n", " ('uninspired', -1.9131499212248517),\n", " ('lame', -1.9117232884159072),\n", " ('insult', -1.9085323769376259),\n", " ('uninteresting', -1.8782515005814986)]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(reversed(pos_neg_ratios.most_common()))[0:30]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
slon1024/statistical_inference_coursera
week3.ipynb
1
4835
{ "metadata": { "name": "", "signature": "sha256:2b9a3e221d222dfa49edaf6f9561dccecf350aca63a2ea15f2d68aa90f645749" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from scipy.stats import t\n", "import numpy as np\n", "from __future__ import division\n", "\n", "def t_val(alpha, df):\n", " return t.ppf(1 - alpha/2, df)\n", "\n", "##https://en.wikipedia.org/wiki/Student%27s_t-test#Equal_or_unequal_sample_sizes.2C_equal_variance\n", "def calc_sigma_xy(nx, ny, var_x, var_y):\n", " var_xy = ( (nx - 1)*var_x + (ny - 1)*var_y ) / (nx + ny - 2)\n", " return np.sqrt(var_xy)\n", "\n", "def confidence_intervals(alpha, nx, ny, sigma_xy):\n", " return np.array([-1, 1]) * t_val(alpha, nx + ny - 2) * sigma_xy * np.sqrt(1/nx + 1/ny)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "#Question 1\n", "n = 9\n", "x_bar = 1100\n", "sigma = 30\n", "alpha = 0.05\n", "\n", "intervals = np.array([-1, 1]) * t_val(alpha, n - 1) * sigma * np.sqrt(1/n)\n", "x_bar + np.round(intervals, 0)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 2, "text": [ "array([ 1077., 1123.])" ] } ], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "#Question 2\n", "n = 9\n", "x_bar = -2\n", "right_bound = 0 - x_bar\n", "alpha = 0.05\n", "\n", "sigma_x = np.sqrt(n) * right_bound / t_val(alpha, n-1)\n", "round(sigma_x, 2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 3, "text": [ "2.6" ] } ], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "#Question 3\n", "# A paired interval" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "#Question 4\n", "nx = 10 #new\n", "ny = 10 #old\n", "x_bar, var_x = 3, 0.6\n", "y_bar, var_y = 5, 0.68\n", "alpha = 0.05\n", "\n", "sigma_xy = calc_sigma_xy(nx, ny, var_x, var_y)\n", "intervals = confidence_intervals(alpha, nx, ny, sigma_xy)\n", "(x_bar - y_bar) + np.round(intervals, 2)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "array([-2.75, -1.25])" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "#Question 5\n", "#The interval will be narrower." ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "#Question 6\n", "nx = 100 #new\n", "ny = 100 #old\n", "x_bar, sigma_x = 6, 2.0\n", "y_bar, sigma_y = 4, 0.5\n", "alpha = 0.05\n", "\n", "sigma_xy = calc_sigma_xy(nx, ny, sigma_x**2, sigma_y**2)\n", "intervals = confidence_intervals(alpha, nx, ny, sigma_xy)\n", "(x_bar - y_bar) + np.round(intervals, 2) #The new system appears to be effective." ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 9, "text": [ "array([ 1.59, 2.41])" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "#Question 7\n", "nx = 9\n", "ny = 9\n", "x_bar = -3\n", "y_bar = 1\n", "sigma_x = 1.5\n", "sigma_y = 1.8\n", "alpha = 0.1\n", "\n", "sigma_xy = calc_sigma_xy(nx, ny, sigma_x**2, sigma_y**2)\n", "intervals = confidence_intervals(alpha, nx, ny, sigma_xy)\n", "(x_bar - y_bar) + np.round(intervals, 3)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "array([-5.364, -2.636])" ] } ], "prompt_number": 8 } ], "metadata": {} } ] }
mit
kohleman/twitter_api
source/twitter_jupyter.ipynb
1
244477
{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# inspired by https://gist.github.com/yanofsky/5436496\n", "\n", "\n", "'''\n", "@author Manuel Kohler\n", "'''\n", "\n", "%matplotlib inline \n", "\n", "import twitter\n", "\n", "import tweepy\n", "import csv\n", "from tweepy import Stream\n", "from tweepy import OAuthHandler\n", "from tweepy.streaming import StreamListener\n", "import datetime\n", "from datetime import timezone\n", "from pytz import timezone\n", "from datetime import timedelta\n", "import numpy as np\n", "import pandas as pd\n", "from collections import Counter\n", "import matplotlib\n", "matplotlib.style.use('ggplot')\n", "import json\n", "import os\n", "\n", "screen_name = 'realDonaldTrump'\n", "#screen_name = 'rogerfederer'\n", "\n", "_count = 200\n", "\n", "base_dir = '/Users/kohleman/PycharmProjects/twitter/twitter_api/twitter_api/data/'\n", "raw_data_file = os.path.join(base_dir, 'raw_tweets_{0}.json'.format(screen_name))\n", "\n", "# Login to API\n", "\n", "\n", "auth = OAuthHandler(ckey, csecrect)\n", "auth.set_access_token(atoken, asecret)\n", "api = tweepy.API(auth)\n", "\n", "\n", "class listener(StreamListener):\n", "\n", " def on_data(self, raw_data):\n", " print(raw_data)\n", " return True\n", "\n", " def on_error(self, status_code):\n", " print(status_code)\n", "\n", " \n", "def extract_json(raw_tweets):\n", " return [[tweet._json] for tweet in raw_tweets]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Access current stream\n", "twitterStream = Stream (auth, listener())\n", "#twitterStream.filter(track=[\"#trump\"])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_all_tweets(screen_name):\n", " # taken some code from https://github.com/adilmoujahid/Twitter_Analytics/blob/master/analyze_tweets.py\n", " #Twitter only allows access to a users most recent 3240 tweets with this method\n", "\n", " #initialize a list to hold all the tweepy Tweets\n", " alltweets = []\n", "\n", " #make initial request for most recent tweets (200 is the maximum allowed count)\n", " new_tweets = api.user_timeline(screen_name = screen_name,count = _count)\n", "\n", " #save most recent tweets\n", " alltweets.extend(new_tweets)\n", " \n", " #save the id of the oldest tweet less one\n", " oldest = alltweets[-1].id - 1\n", "\n", " #keep grabbing tweets until there are no tweets left to grab\n", " while len(new_tweets) > 0:\n", " print(\"getting tweets before {0}\".format(oldest))\n", "\n", " #all subsiquent requests use the max_id param to prevent duplicates\n", " new_tweets = api.user_timeline(screen_name = screen_name, count = _count, max_id = oldest)\n", "\n", " #save most recent tweets\n", " alltweets.extend(new_tweets)\n", "\n", " #update the id of the oldest tweet less one\n", " oldest = alltweets[-1].id - 1\n", "\n", " print(\"...{0} tweets downloaded so far\".format(len(alltweets)))\n", "\n", " #transform the tweepy tweets into a 2D array that will populate the csv\t\n", " outtweets = [[tweet.id_str, tweet.created_at, tweet.text.encode(\"utf-8\")] for tweet in alltweets]\n", " \n", " #write the csv\n", " with open(os.path.join(base_dir, '{0}_all_tweets.csv'.format(screen_name)), 'w') as f:\n", " writer = csv.writer(f)\n", " writer.writerow([\"id\",\"created_at\",\"text\"])\n", " writer.writerows(outtweets)\n", "\n", " return alltweets\n", " \n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "getting tweets before 821719763214880768\n", "...400 tweets downloaded so far\n", "getting tweets before 808642018612310015\n", "...600 tweets downloaded so far\n", "getting tweets before 795057936565313535\n", "...800 tweets downloaded so far\n", "getting tweets before 789224624320028671\n", "...1000 tweets downloaded so far\n", "getting tweets before 785913754194104319\n", "...1200 tweets downloaded so far\n", "getting tweets before 781785509639118847\n", "...1400 tweets downloaded so far\n", "getting tweets before 774484342030602239\n", "...1600 tweets downloaded so far\n", "getting tweets before 766627569110249471\n", "...1800 tweets downloaded so far\n", "getting tweets before 759191265988653055\n", "...2000 tweets downloaded so far\n", "getting tweets before 754291925616852991\n", "...2200 tweets downloaded so far\n", "getting tweets before 746272130992644095\n", "...2400 tweets downloaded so far\n", "getting tweets before 738598954468659199\n", "...2600 tweets downloaded so far\n", "getting tweets before 732726105837277183\n", "...2800 tweets downloaded so far\n", "getting tweets before 725722027173249023\n", "...3000 tweets downloaded so far\n", "getting tweets before 718409541273194496\n", "...3200 tweets downloaded so far\n", "getting tweets before 711209246419845119\n", "...3243 tweets downloaded so far\n", "getting tweets before 710453513155960833\n", "...3243 tweets downloaded so far\n" ] } ], "source": [ "alltweets = get_all_tweets(screen_name)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# Historic tweets\n", "#http://trumptwitterarchive.com/data/realdonaldtrump/2009.json\n", "#http://trumptwitterarchive.com/data/realdonaldtrump/2010.json\n", "#http://trumptwitterarchive.com/data/realdonaldtrump/2011.json\n", "#http://trumptwitterarchive.com/data/realdonaldtrump/2012.json\n", "#http://trumptwitterarchive.com/data/realdonaldtrump/2013.json\n", "#http://trumptwitterarchive.com/data/realdonaldtrump/2014.json\n", "#http://trumptwitterarchive.com/data/realdonaldtrump/2015.json\n", "#http://trumptwitterarchive.com/data/realdonaldtrump/2016.json\n", "#http://trumptwitterarchive.com/data/realdonaldtrump/2017.json" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "\n", "\n", "# Accessing https://dev.twitter.com/rest/reference/get/statuses/user_timeline\n", "\n", "\n", "# include_rts = include retweets\n", "#new_tweets = api.user_timeline(screen_name = screen_name, count=count, include_rts=True)\n", "# max_id_tweets = api.user_timeline(screen_name = screen_name,count=count, include_rts=True, max_id=818643528905621504)\n", "\n", "\n", "outtweets = [[tweet.id_str, tweet.created_at, tweet.text.encode(\"utf-8\"), tweet._json] for tweet in alltweets]\n", "\n", "json_tweets = extract_json(alltweets)\n", "# older_tweets = extract_json(max_id_tweets)\n", "\n", "with open (raw_data_file, 'w') as jsonfile:\n", " json.dump(json_tweets, jsonfile, ensure_ascii=False, indent=4, separators=(',', ': '))\n", "# json.dump(older_tweets, jsonfile, ensure_ascii=False, indent=4, separators=(',', ': '))\n", " \n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got 3243 tweets\n" ] }, { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x111c0c4a8>" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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6jqPh7ozGz49GHBmambVWmp6Q9g5EHUrNmI4OyYlzvdaESAoBALYvX6fJY6tc\nl4sMAEDN2byvWLkoXXlDtlyWFCaGbJ5KIezC3KyUcGW8TNSR1JaXlnz6CjUbkkIAgO0r1rlSqCMR\njjpduUAHAKAWTNJTcH1SMmbtZkQQBDJJegphF6bHpf7WrRJa42UknwlkzYakEABgW6y1K9vH6lcp\nZIwJG1sXqRYCANTOQP8eXZhdUpDtkgo5BUGgC3NLGhw5FnVoaFJ2cUGqVKRsd9Sh1B6VQk2JpBAA\nYHuKRSneIRNz6nve5MoEMgAAasBWKvLmZjT02/9bl1LdujC3qEtOSkMPPMT0Mezc9Li0dzC8wdXq\nUp5UyMlWKlFHgm1gJD0AYHsKuXAaWL0xgQwAUEvXJ6Skp/TefRru3SOdc6UT98rEuI+OnbF5X8r5\n0pGRqEOpCxNzZBPJ8HtOt3j/pBbCv3AAgO0p5CNKCqXYPgYAqAlbKklTE9L+IUmScZzwd12OBtPY\nhelJac++9kosptP0FWoybbQ6AQBVEVlSyGX7GACgNiauSr17ZNb/fvMy0vJidDGhqdlSUZqflfr2\nRh1KfaUy9BVqMiSFAADbU8hLyTpOHlu1Uilkra3/uQEALcvmfGlhTurff/MT6SwVD9i561NSd59M\nvCPqSOqLSqGmQ1IIALA9hVw4CazOjONIMUcqFet+bgBACxu7LO3bLxN/S7tVLyMt8+EW22eDQJqZ\nkva2wRj6t3JTUrksWy5HHQm2iKQQAGDLbBBIQSAlEtEE4CZl87lozg0AaDl2/oZULkl9/W97zriu\nZCRbKEQQGZra7LSUyd68HbFNGGNWRtOTUG0WJIUAAFtXyEuJZHRjVZNJWfoKAQCqwFYqYZXQ/kO3\n/r3mZfhwi22x1krXJ6W9g1GHEh0vTV+hJsJIegDA1hXyYcPnqLipcPtaKh1dDACA1jAzJblJmWzX\nrV+TzobNpnv66hcXmpLv+xo/P6rK9QmZfE77D4/IizqoqHiZ8O8XmgKVQgCArSvkw8RMVFyX7WMA\ngF2z5ZI0OSbtP3T7F9JXCFvg+76uPP+cDgc5DReWdLgzqyvPPyff96MOLRpUCjUVkkIAgK2Lahz9\nKjdFUggAsHuTY1J3r8xm0zRTXjj5MgjqExea0vj5UQ13ZxQrFqRKICeb1XB3RuPnR6MOLRKmIyHF\nDP24mgRJIQDA1hVy0SaFEq5UKspWuDgHAOyMzfvSjRlp4MCmrzWxWJgYoq8QbsPmfTmOI83NSl29\nMsbIcZwolq91AAAgAElEQVRwrbWrFP24mgVJIQDA1hUKkSaFjDHh+bnzBADYqbErKyPoO7b2+nSW\nLWS4LZP0VF5aCifZZbKSpCAIZJJt21VISrOFrFmQFAIAbIktFSVjZOLRzigwyVS4jQ0AgG2yi/Ph\n75ANRtDfkpcJm00DtzA4ckwXLl1U0NUrE4spCAJdmFvS4MixqEOLTioj5UimNgOmjwEAtqaQl5IR\nbh1bYdykxFh6AMA2WWvfHEEf28a9cS8j5V6XtfbWo+vR1lKlgoZO3KtLcqR8TibpaeiBk/K8Nq4U\n8tJSzpetVLb39w11R1IIALA1UU8eW2GSKWl+LuowAADNZmZKinfIdPVs622mo0PWiYd99dp5OxA2\nZK2VJq7Ku2NYRzu3t7ZamXEc2YQr5XNhgggNi5QdAGBr8nnJdaOOQkqm6CkEANgWWy5vbQT9raQZ\nTY9bmJuVYo4MCaG3S6WlHH2FGh1JIQDA1hRyjVEp5CbDWAAA2KqpMamzWya1w0ofmk1jA7ZSkSau\nSoMHow6lMZFMbQokhQAAW1PIRzuOfoXpSEiSbLkUcSQAgGZgC3lp9ro0sIsP7mmaTWMDN65LCVcm\n0xl1JI2JSqGmQFIIALApW6lIpaKUaIDtY1JYscQEMgDAVoxdkfoHZDq2OIJ+I25KCsqyJW5IIGQr\ngTR5jSqh20mmpGJBNgiijgS3QVIIALC5YkHqSDTO9AjXZQIZAGBTdnFByvvSnoFdHccYE1Y9+GyF\nwYrrU1IqI+Nloo6kYZlYTEp5kk+1UCNrkKt7AEBDK+TDuz2Nwk3RVwgAcFtrI+gHD1bnpkY6S1II\nkhRWvkxPSIMHog6l8aUy/L1pcCSFAACbK+SkRPT9hNa4SSaQAQBuy85MSU5MpruvOgekrxBWTU9I\n2U6Z5A4bl7eTdJpKoQZHUggAsLlCQUo2WlKISiEAwMZsECgYu7LzEfQb8TJSzg/77KFt2XJJuj4p\n7aOX0JakMlKOSqFGRlIIALC5Qq4hJo+tcd2wcaG1UUcCAGhEU+My2a6q9nsxjhNWzeb8qh0TTWhq\nXOrulXEbZPhGgzOuK1WsbKkYdSi4BZJCAIDN5RtjHP0qE3OkeEfYABsAgHVssSDNTMo5cLj6B0/T\nH6Wd2VJRmp2W9u2POpTmks5Iy/y9aVQkhQAAt2XLZclamY5E1KHcjC1kAICNjF+R9uyTSdTg95ZH\nX6G2NnlN6utvvGuiRpdKSzn6CjUqkkIAgNsr5MPtWo0mmaLZNADgJnZ5MUza9A/W5gRMIGtbtpCX\n5m5IeweiDqX5UCnU0EgKAQBur5ALR8A3moRLpRAAYI21Vrp2WRoYCrcZ14BxXclKlpsS7WfiWliB\nFu+IOpLmk0qHTdrpBdmQ4lEHAABocIVCQ/UTWpNMSfM3oo4CANAo5mbD/++p0gj6W1ntK9SIVbSo\nCZvzpaUF6eCRqENpSrliUWNvvC47t6BYZ48GR47J87yow8IKKoUAALdXyDXWOPpVblIq5qOOAgDQ\nAGwlkMYvSwcOyRhT25PRV6j9TFyT+gfDCXTYFt/3deX553Q4Gdewyjoc5HTl+efk+0zxaxQkhQAA\nt1fIhyN4G01HQgoC2SCIOhIAQNSmJqR0Viadrf256CvUVuzykpRbkvr6ow6lKY2fH9Vwd0ZO0pOK\neTmOo+HujMbPj0YdGlaQFAIA3JK1NkwKNWClkDEmTFbRVwgA2potFaXrE9LAUH1OmPKkQp6bEu1i\n4qq076BMjI/OO2HzvhzHWanwDntxOY4jm6dSqFGwsgEAt1YsSvF4zRp27loyyQQyAGh341fDMeF1\n6vFjYrEwMeQzYrvV2cWFMJFR6z5VLcwkPQVBIMU7pFJJkhQEgUySnkKNgqQQAODWivnGnDy2ikoh\nAGhr1l+SFuel/v31PbGXYQtZO5i4Kg1QJbQbgyPHdGFuSRVjJGtVLhZ1YW5JgyPHog4NK1jdAIBb\ny+cac/LYKjcZbm8DALSnscvhh/Z6NwBOZ2k23eLs/A2pUpG6e6MOpal5nqehBx7SJSelC4F0ycY0\n9MBDTB9rIIykB4Am4vu+xs+PyuZ9maRX+5GehXxjJ4WSSWmapBAAtJPV34WV65My/rIG/+dhpesd\nhJeRrrwua23tp52h7qy1YZXQ4EF+vlXgeZ6GT75TtjsjdfbIkBBqKFQKAUCTWBvpGeQ0nIzXZ6Rn\noyeFEoylB4B2svq78FBpWcO5OR3u7dbVX/ys7uOtTUeH5MTZwtyq5malmCPT2RN1JK0lQYV3IyIp\nBABNYnWkZ6xYkK1U6jPSs9DYPYVMPC7FnHDyDACg5a2Nt15elBKu4plMdOOt0xlpmWbTrcZWKmtV\nQqgy1+VmXgMiKQQATcLmfcUqgTRxTZq8KhuUazrS01YCqVySEomaHL9qXFfKc4EBAO3A5n3F8r60\nMCf17pEU4Xhr+gq1phvXpYQrk+mMOpLW4zI1thGRFAKAJmGSnoLZGam7R3I9afyqyvlC7UZ6FgqS\nm2z8vfRuivJ9AGgXQaBgelLad0CmI7HyUETjrb0MSaEWYyuBNHmNKqFaSbhsH2tAJIUAoEkMHD6i\nC+MTCtKdMr17FGQ6dWH0Vxo4WKMLl0KDTx5bxV0nAGgLdmFOg25cF9ysKvFwXk4QBNGNt06mpHJZ\ntlyq/7lRG9enpFRGxstEHUlLMh0JyVZky+WoQ8E6TB8DgCbhLc1p6H+c0uVFP5w+1j2gobvulTd+\nWbYjLtPZXd0T5hu8yfQqNyktLUQdBQCghuzivHT5dXnH79Ghd8R0ad0kzqEHTkYy3toYI+ulJX9J\noiFx07NBIE1PSMN3RR1Ka0skpWJBipOKaBT8JACgCdhSUZqbkXfXvRru6Lj5uWyndPG87MBBmb69\n1TtpIS9lm2A/vcskCwBoZXZpQbp0QbpjRCadkSdp+OQ7ow4rlM5IyySFWsL0hJTtjGYrYjtZbTbt\npaOOBCvYPgYAzWBqQurZG47AfQuTzkhH3yFNjclOXK3eORt88tiahCuViuG0EABAS7HLi9LF16TD\nR2XS2ajDebt0NkwKoanZckm6Pinto5dQzbHtv+GQFAKABmdLJenGtNQ/cMvXGDcpHb1bWpiXvfx6\ndRIkhebYPmZiMakjwYhTAGgx1l+S3jgvHR6WadTK1VRayi1zY6LZTY1L3b0yrht1JK0vQYV3oyEp\nBACN7vqE1N23NmXlVkxHh3T0uBSUpTdGw73xO2RLJckYmWbZ751MMZYeAFqI9ZfDhNChO2SyXVGH\nc0smHg8/5Ob9qEPBDtlSUZqdlvbtjzqU9rC6fQwNg6QQADQwWy5JM1NS/+CWXm9ijnRkJKzwee1X\n4YXOThRyUrLxq4TW0FcIAFqGzfnSG6PSgcMyzdCrJ51mC1kzm7wm9e7d9OYbqoTtYw1n01vATz31\nlF566SV1dXXpG9/4hiRpaWlJ3/72tzU9Pa3+/n499thjax3/v//97+uVV16R67r69Kc/rSNHjtT0\nGwCAlnZ9UurqlUlsvZzZGCMdPCI7NSadPyd757HtN00sFMI7n83CTUnLi1FHAQDYJZvPSW+8Ku0/\nJNPdG3U4W+NlpcW5qKPADthCXpq7IR2/J+pQ2obpSMhWAtkgkHGcqMOBtlApdOrUKT3++OM3PfaD\nH/xA99xzj86cOaMTJ07omWeekSS9/PLLmpyc1He+8x196lOf0ne/+93aRA0AbcCWy9L1rVcJvZXp\n3y8NDkkXXg0nt2xH01UKuVQKAUCTs4W89Pp/SwNDMj19UYezdekMNyaa1eQ1ac8+mfjbB3mghugr\n1FA2TQodP35c6fTN4+JeeOEFve9975Mk/dZv/ZZeeOEFSdIvf/nLtcdHRkbk+77m5siaA8COzExJ\nnV1hE+kdMj190qFh6eJrsjdmtv7GZpk8tspNhYksAEBTsoWCdOG/pX0HZXr3RB3Othg3KVnJFtkS\n00xs3pcWF6S9tx7kgRpJ0Feokeyop9D8/Ly6u7slSd3d3Zqfn5ckzc7Oqq/vzax+b2+vZmdnqxAm\nALQXGwTh1rH+3Tc9NNlOafi4NH5Fdmp8a2/K58LqmyZhOjrCC/JyKepQAADbZIuFsEKof1Cmb2/U\n4eyMl6GvULMZvxauObYw1V+SvkKNpOaNpo0xtT4FALSemSkpk5VJVqdax6Q8aeRu6caM7NWLstbe\n8rW2UpFKxebqKSTRbBoAmpAtFcMKoT37ZPbsizqcnUtnJJ+kULOw/pKUW5L6+qMOpT2xfayh7GjW\ncHd3t+bm5tb+v6srHBPZ29urmZk3tyfMzMyop2fjiQFnz57V2bNn174+ffq0stnsTsJBC0okEqwH\nrGm39WArgcrLC4offYeMl978Dds5due7Fbz+qjQ9JueOkXBa2Vtfk8+p3Nmljq7GHAF8q/VQ7u1V\nLB5XrI3WCtrv3wfcHuuhudhSUeXLryl26A45Aweqfvx6roeKsapceUNx1l/DWr8eyhNXFBu+S7EG\nvdZpdRVbUaWwHOnfl3b8ffH000+v/fnEiRM6ceKEpC0mhay1N91Vvu+++/TTn/5UDz/8sH7605/q\n/vvvlyTdf//9+tGPfqQHH3xQo6OjSqfTa9vM3mp9EKsWF2nQhlA2m2U9YE27rQc7PSEpJhNUpBp8\n33bfQenqG9IrL0h3jLytuaKdvyFVrPIN+t/8VuvBlivS7HWZZuqFhF1rt38fcHush+Zhy6WwQqir\nVybdWZPfd/VcD7ZipRuz0vzchjdcED3HcTT60ouq3Lgus3BDg//rA0rz70UkbLkszd2QifC/f7v9\nvshmszp9+vSGz22aFDpz5ozOnTunxcVFPfroozp9+rQefvhhfetb39Kzzz6rPXv26Atf+IIk6V3v\nepdefvllfeYzn1EymdSjjz5a3e8EAFqcrVSk6XHpyEjNzmFiMenQsOz41ZWR9Xfd3My62ZpMr0om\nwwtyAEBDs+WydOFVqbNHpgYVQlEwsZhs0pP8ZSnTGXU4eAvf9zX9Hy/qsBtXLDenoKdbr//iZxp6\n4CF5nhd1eO0n3iEFjKVvFJsmhT73uc9t+Pif/MmfbPj4Jz7xid1FBADt7MaM5KZkvEzNT2UGD8om\nEtJrv5I9MiKTXjlnIS9VedtaXbgpJlkAQIOz5bL0+qtSplNm8GDU4VRXeqXZNEmhhjN+flTHu7Mq\nzM9JtiIn26nhSkWXzo9q+OQ7ow6v7RhjZBOuVCxIKZJyUdtRTyEAQPXZSkWaGpMO3Vm3c5q+ftmO\nDumNUS3vGdDE5JQqr/+3zMCQ9p/MNNXdM79S0fjZ/5K9sahYytPgyLGmih8AWp0NAumNUSmdkTlw\nKOpwqs/LSDeuRx0FNmDzvpxUNmwGnu6UMUaO44Rj6RGN1WbTJIUiV/PpYwCALZqblToSMun6Nr0z\nnT3yB4d05Sf/V4fmJjQclw47FV15/jn5fnNcLPm+r6u/+LkOO1bD8YoOB7mmih8AWp2tBNIbr0rJ\nlLS/BRNC0kql0OJtJ3wiGibpKQiCcHtfKqyGDoJAJklCIjKuS4V3gyApBAANwForTV6T9kXTW2Hi\n6jUNHzsuZ3FeqlQUd10Nd2c0fn40kni2a/z8qIa7M3LcpFQsynGcpoofAFpZmBA6H1YGHDwiY0zU\nIdWE6UhITpxR2w1ocOSYzk/PhIkg11UQBLowt6TBkWNRh9a+3KRUKEQdBcT2MQBoDPOzUrxDJhtN\nHwKb9xVPJmX3D0n5nCQ1VVm1zftyknHZzi5pZkrWceQkU00TPwC0KlupSBdfCxvLDt3RsgmhNd5K\nX6FkEw5saGGe5+nI3b+m114pSPmyTNLT0AMn2WYepUQy7KWJyJEUAoCIhVVCY9LgUGQxhGXVOTlO\nXFrZvtZMZdVr8aezsrGYNDmmcs8emc69UYcGAG3H932Nnx9VxV+WmZ/R4JE75N11rPUTQlK4hcxf\nkvr4/dNoUsW8hn/jPTKdPVGHAontYw2E7WMAELWFOckYmc7uyEIYHDmmC3NL4X57qenKqtfHb1Jp\nBXsHdeHiGxro6Yo6NABoK77v68rzz+lQ2dfw4rQOq6Qr18aUy+WiDq0+0llpeTHqKPAWtlyW9ZeZ\nDNdIOhJSuRxuL0WkSAoBQNQi7CW0yvM8DT3wkC45KV3Il3XJSWnogYeapqz6rfFf9ro19H9Oy/MX\nZa9dpuknANTJWo+3hTmpUpGz74CO9nS2T4+3ZEoqlWTLpagjwXqL8zLZTpmYE3UkWGGMoa9Qg2D7\nGABEyC7MSdZKEVYJrfI8T8Mn3xl1GDu2Ufw2nZEunpcuX5AdulMmxr0QAKglm/cVcyrS4px04IhM\nLCZn5fF2YIyR9dLhFjK2KTWOhTnF9g1EHQXeKpGUigXG0keMq2MAiNLkmNS/vz36LETAxOPSnXeF\nX7z+37LlcrQBAUCLM0lPwfUpqas3/DdYzdWjrirSGWl5OeoosMJaG1YKdZGkazgJVyq0ydbSBkZS\nCAAiYhcXpHJJ6u6NOpSWZmIx6dBwOBHmtXOylCkDQM0M7B/UhekZBeuGFjRTj7qqoK9QY/GXpI6E\nTMKNOhK8leuyfawBkBQCgKhMjUn7qBKqB2OMzP5DUl+/dOFc2GwSAFBV1lp5N6Y19L/+P13uSDdl\nj7qqSKWlnC9bqUQdCaRwoEcDbNPHBtyV7WOIFD2FACACdnkx/CXY3Rd1KG3F7B2QTbjS66/KHroz\n0olvANByblyXTEzp/Qc1vP9g1NFExsTjsomElPfDKlVEa2FOOnhH1FFgI4kk28caAJVCABCFyTGp\nf5DGxxEwXT3SHSPSlTdkZ6ajDgcAWoINAmniqrT/UNShNIZ0RlpeijqKtmcLBalUkrx01KFgIwnG\n0jcCPo0AQJ1Zfym8e9izJ+pQ2pZJZ6Wj75CmxmQnrkYdDgA0v6lxKd0pk6YyRlJYIeSTFIrc4pzU\n2cVW/QZljAmbTbOFLFJsHwOAepscl/ZSJRQ14yZlj94tvTEqWyxKB4/wM2lDvu9r/PyobN6XSXoa\nHDnWXr1PgCqwxYI0Mykd+7WoQ2kc6aw0cS3qKLAwJ/VyE66hucmw2XQ7TShsMFz9AkAd2Zwf3jns\n2xt1KJBkOjqko8eloBwmhwLKl9uJ7/u68vxzOhzkNJyM63CQ05Xnn5Pv+1GHBjSX8avSnn1Md1rH\nuEmpUgkTZoiErQThFr5MV9Sh4HYSSamQjzqKtkZSCADqaWpM2jsgE3OijgQrTMyRjoyEY1Ff+5Vs\nqRh1SKiT8fOjGu7OKGaM7PKiHMfRcHdG4+dHow4NaBp2eUlaXpD6B6MOpfGks2whi9LiguSlZeJs\njmlorisVSQpFiaQQANSJzefCC5S+/qhDwVsYY2QO3iF190rnz8nmqRRpBzbvy3EcKZ+TpidkKxU5\njsPPHw3B931d+I9X9NovfqYL//FK41awjV2WBg5ys2Mj6bS0vBx1FO2LUfTNYXX7GCJDUggA6mW1\nSsjhwrlRmX37pYGD0oVXZZcWog4HNWaSnoIgCBtcWisVcgqCQIa+BohYs2xttDdmJFthcMKtpLPS\n8mLUUbSvRZJCTYHtY5EjKQQAdWALeWlhniqhJmB690iHhqWLr8nOzUQdDmpocOSYLswtKcj5UsJV\nsLSkC3NLGhw5FnVoaHOrWxudlZsIjbi10VYCafyKdOAwk51uJeVJhRzjtiNg/WXJxMLeTmhsiYRU\nLslWKlFH0rZICgFAPUyNS3v62dfeJEy2Uxo+Lo1dkZ0ajzoc1IjneRp64CFdKga6kMjqkp/X0AMP\nMX0MkbN5P+x1dfn1cECB1HhbG6cnw34t6WzUkTQsE3PCiUo+W8jqbmFO6uyJOgpsgTFG6kgwlj5C\nfDoBgBqzxYI0PysdvzfqULANJuXJjtwtvf6qbKkov3uPJl47z+jyFpNKJDR8x53Sr71LOveKxPZO\nNACT9BQsXJdjTNjvauCgKo7TMFsbbakoTY9LI4yg35SXWZmA1Rl1JO1lYU4aHIo6CmyVu7KFLJmK\nOpK2RKUQANTa1LjU1y8T74g6EmyT6UhIw++QP3dDV/7vP+hQabmh+3tgB3K+lEqFdyqznWEPCiBi\ngyPHdGFiQkGmU+ruUzB+Ra/NzjfO1sbxq+HvNZcR9JtKZ5hAVme2VAqnWaUzUYeCrXJTVApFiKQQ\nANSQLRWluRlpz0DUoWCHTDyu8VJFw91ZOdPjaxOqGq2/B3Yotyyl0uGfs93S4ny08QCSUqmUhu64\nU5cye/R6Iq1LmT4NDfQrlYy+P4r1l8O/J/37ow6lOaQzNJuut8U5KdMpE+OjbtNIJGg2HSG2jwFA\nLU1NSD17ZTqoEmpqhbziAwdkxy6H48u9dOP198DO5PywQkgK///qRdlKhQ8TiJa/JC/bqaN3hduz\nrLXSpdekq2+EjfCjNHZZGjjAJM0tMh0J2Zgjm8/JsDWmPhhF33zcZPhzQyS44gGAGrHlknRjWuqn\nSqjZrY0u9zJhEkFidHmrWFcpZOIdYT8D7uojavM3pK43P9QaY6RDd0r5vOzkWGRh2blZKQik3r2R\nxdCU0lm2kNWJrVSkpYWw8hPNw02yfSxCJIUAoFamJ6Xu3rAvDZra2ujyRFLylxQEAaPLW4ANAqlU\nDC9GV2W72EKG6M3feNvkJBNzpDtGpJkp2RszdQ/JVirhCPr9Q4yg3y4vHTabRu0tL0puigrtZpNw\npVKRsfQRISkEADVgy2VpZlLaS8+FVrA2utzr0gW/qIuVOKPLW0HeDz88rN8q1klSCNGyeV+yVsZL\nv+0505GQ7jgmXbskW+8kw/VJKZmSyXbV97ytgEqh+lmYZ+tYE2IsfbToKQQAVeT7vsbPj6oyfkXG\niWn/ne8QaYPW4Hmejp78ddmeLslLy5AQan7+svTWn2MqLZVKssWCTILJSojA/O37oZiUJ3voDunS\nednhu+syAcyWSuEkzZG7a36ulpTypGJRtlyWifPxq6YWbkhHRqKOAjvhJsOpcfTeqjsqhQCgSnzf\n15XnnwvHlheXdLgzy9jyVsT2otaR89+cPLYiHE3PzxgRWpiTunpu+xLT2SPtHZQujoaVqbU2eVXq\n2SPjRj/9rBkZY8ItZFQL1ZTN5yRbkUlx06YpuUmpQKVQFEgKAUCVjJ8f1Z3ZlJz5WSnlKZ5MMra8\nFWU7paVF9r23gpwf3sF/q2xXuAUBqDNbKkqFXLjdaBNm74CU6ZQuvVbTf49szpfmbkj72A69K16G\nvkK1xtSx5pZwGUsfEZJCAFAFtpBX5dpFOeOXJVtZm8zC2PLWw4Sq1mArlfDD90YT5Dq7pOUFEn+o\nv4U5Kdt9c5+r29l/SDJGGrtUu5jGLkv79rPtabfSGSqFao2kUHNzkySFIkJSCAB2wS4uyL4xKp0/\nJ5NMKxgYktk7uHbxzNjyFkUlSfPbqMn0ChPvkFYmzQF19ZZR9JsxxkiHj0rLS7LTE1UPxy7cCCf0\n9fVX/dhtx8tI/rKstVFH0pJsuRxWf2Y6ow4FO8VY+siQFAKAbbKViuzstOyr/yVduxjelbr7pPY/\n8JBeX8orCAJJYmx5K+vskpZICjW1W20dW8UUMtSZDYJwe1F2e5UOxnHCiWTT47LzN6oXT6Uija2M\noN9q5RJuycTjUiIR/tuD6lual9IZmZgTdSTYqY4EY+kjQh0oAGyRLZWkmanwf8mUNHhQynaFd2q1\nbmz5+VHZvC+T9DT0wEnGlrciJlQ1v9zy7ZNC2S7p6iVpcKh+MaG9Lc6Fkw2d7X+oNQlX9siI9Pqo\nbEdiw3H22zYzJXUkwqbWqA4vE249rsbPBzdj61jTM7GY7EpiSDS1ryuSQgCwCZv3pelJaX5W6uqV\nhu+65ZYwz/M0fPKddY4Q9WaMkV2dUMW2iubk+1LPnls/72WkUkG2VJTpSNQvLrSv+c2njt2O8TKy\nB4+EE8lGTuxq3dpyWZock44e3/ExsIF0RlpciDqKlmOtDbd0DxyMOhTs1mqzaZJCdUUtKABswFor\nuzAne+G/pddfDX9JHb9XZugOegQhRF+hpnXbJtMr1kbT8zNGHdhKJawU2kVSSJJMd6+0Z5/0xmi4\nHW2nJsek7h5+31XbaqUQqstfCqvaqNxtfjSbjgSVQgCwjq0E0o0ZaXpCisWkPQNSdy/9FPB2nV3S\n2CXZSoX10WwK+fADxGbbdLJd4ZaEvr31iQvta3lJSiSrUpVm+vfL5vPS5ddljxxd2+K8VTafk25c\nl47fs+tYcDOTTIV9CalArK6FebaOtQqaTUeCpND/z969xcaRn3ef//27+sTqIw+iREkUJbWkGY/i\nzGThjQXPxfi1sxvAayDZC8+FjSDJXWLAMWwgMHKRyzgwEMAeJDCSqyQXvvEC8ezGyCZYrGO/m0kU\ne5CxX4/sGVGURFESJVFNNslm9bH6vxdFcjQzOvDQ3VXd/f0Axlij7q7HZrG7+qnnAGCkeJ6n5Udm\n/sycvyDXdWVbTenhfam8ImVy0snTMmywwFOYeEJ2Z0MV58pgqW0Fc6GeJVeU7t6StXbfX6yBfdlY\nO3SV0PucPB1UuS4vBWvr92N5SZqeCbbwofsy2SAJWJwIO5LhsVGRTs6FHQW6IZViyUMIuLUJYGR4\nnqely29ozq+plI5rzq/p1n//f7X17hXp3Z9LnY50/gWZM+dJCGFv2FA1mJ61eWybSSSC1lFW06PX\n9rmK/llMLCadPi+tr8mWH+z5eXZzXarXghY09MZOUghdYZuNYDCxmw07FHRDkvaxMJAUAjAyluev\nqlTMKhaLyW5tKvbgjkqNTS0vL0vPvyhzYk6GwXbYj1yRmTODaK+VQhI/Y/ScrXmSMV2f32Picens\nc9K9O0Gy51lxWBusoJ9hBX1PuVkSzd20UZHyBao5h0UyxVr6EPCOD2Bk2Lonx3GCO3SrD6X8uJy5\nkjTmBhfPwH65md0NVRgM1lqpVttTpZCk7WqwSm+DwmhbX5N6tPbdpNLSXElaXAg2aT7N6orkOMGw\nav6tPCoAACAASURBVPSOm5HqXjDDEIe3yTyhYWJiMWlnLT36hqQQgJFh0q583w8G2GXzMpmcOp0O\n21VwYGyoGkDNhhSP7z0RPJaRmg3ZVqu3cWF0bXS3deyDTDYfzBW6Mf/E89i229K9O/ufP4R9q9Ub\nWlhc0rX/74da+NlP5XnPSNbhiWzHl6qbUrYQdijopp219OgbkkIARsbM+QtaqFTlNxtSPCHf97VQ\nqWrm/IWwQ8Mgy1FJMlC8rb1XCWn7rmU2z88YPWGbDanZDBYc9JCZmJKKk9LN+ce3ZawsS7mCjLvH\ntkocyO5sw3RcJbU059e0dPkNEkMHVd2g2nsYsZa+70gKARgZrutq9tLLWmxLC77RojOm2Usvy3Wp\nFMIh5IpSdYP+90Gxn3lCO3JFaXOjN/FgtK2vSflif+ahHDshJZPS0o33/WvbaEjlB9LMyd7HMOJ2\nZhs6rivVa3IcR6ViVsvzV8MObTBtVGgdG0aspe870qoARorruiqdPi1duCiTSIYdDoaASSRkk6kg\n2dDju/3ogponHTm2v+fkC9Iyq+nRAxuVvm36MsbIzp6RFt7V1o153dvYCuYMVcqaee4FZfhM7Dlb\n9+Sk47JjGam8Ilvz5Iy5z573hMfbqEhnnw87CnQba+n7jkohACPFdnyp45MQQnexoWpwHKBSyCSS\nUiIVtJ4BXWLb7eCcyuX7dkwTc+QdPamlN36oU+sPdFYtzdU3tbQwTwtTH+zMNjSxmDR5RCo/ULvV\nYrbhAQRb+2Iy6bGwQ0G3sZa+70gKARgtzUaw1QDoJjZUDQTbbEixmEwisf8n8zNGt22uS9mcTMzp\n62Hv3biu0rkLciplaeWenKmjOjdeoIWpD3ZnG/q+TCYn33G0sHSb2YYHsVHp2dY+hGxnLb21YUcy\nMkgKARgtzUbwYQN0k5tlQ9UgqHn7nye0I8uWOXTZRu9W0T+NrXuKj41J08ekVFomm5PjOLQw9cHu\nbENnTAv1thanTml2dlZjTn8Tg0NhoxIk6zF0TCwmxRPBEH70BTOFAIyWRiMYYAd0kTFGdmdD1cSR\nsMPBk+xz89j7ZLJSoy7bbsnED1BpBDzCdjpBpdDxub4fO2hhqskZy+wmSX3fp4WpT1zXVenFl3b/\nbB/cle7clM4+F15QA8a2W1K9xhy/YZZKS816MF8IPUelEIDR0mxSKYTeyBUZjBh1B9k8tu291fRs\nIUMXVDek9NjBWhkP6dEWJilICC1UqrQwhWXqWNAqUymHHcng2FiXcvngfRnDKZVirlAf8ZsEYLQ0\n6ySF0Bv5grS5Tg98lNW8g1cKScHPeIO5QuiCkFrHpMe0MDljmr30slyXSqEwmFhMOnlaunsrGD6O\nZ9tkFf3QS6aD6n70Be1jAEYLM4XQIyaRlN3ZUJXJhh0OPsC2mpLtyBzm9z9XkJZvs5oeh2KtDZKL\npY+EFsMHW5gQLpPJyeaK0v070on+txQOkjBbL9FHqbS0tRl2FCODSiEAo4WkEHqJDVXRdZgh09tM\nMiXF40EbGnBQtS0p5sgw3w6PmjkpVVZlvWrYkUTbVlVKpkNpvUQf0T7WVySFAIwM22pJJibDlg/0\nSo4NVZFV8yT3cEkhScHsKH7GOIz1ilRglTbez8QT0sysdPsmbchPs0Hr2EhIpoKtrvwu9AVJIQCj\ngyoh9JqblZrBhipEzGE2jz1qe3YUcGAhzhNCtJmJKSnmSA/vhx1KdG2skRQaASbmsJa+j0gKARgd\nzQarLdFTJhaTMnkqSaLoEJvH3ieTk+o1BsLiQGyjLrXb3alaw3A6eVq6fzeYg4b3sY16MBuO35/R\nsLOWHj1HUgjA6KBSCP1AJUnk2HZb8v2u/P4Hq+lz/IxxMOtBlQODyvEkJj0mTU5LdxbDDiV6NipB\nCy9GQ5K5Qv1CUgjA6CAphH7IsZo+cmqeNDbWvS/iORJ/OKCNNanAl1o8w9EZqebJbrC44H2YJzRa\nUqyl7xdW0gMYHc2GVJwIOwoMOZNMySYSQbuSy2r6SKhtSWNd/FnkikF7B6vpI83zPC3PX5WtezJp\nVzPnL8h1uzBX6oBsuyXValK2EFoMGAwm5siePB0Mnc7mgvkqI876fjAbLpsPOxT0C2vp+4ZKIQCj\no1GnUgj9kWULWaTUvO4Mmd5mUikpFgteF5HkeZ6WLr+hOb+mUjquOb+mpctvyPNC/JltVKRcPmhB\nBJ7B5ArB7Kn7y2GHEg2b61ImywbZUUL7WN8cqlLo+9//vv71X/9VxhidOnVKX/ziF7W6uqrXXntN\n1WpVZ86c0Ze+9CU5/PICCJntdKR2S0okww4FoyBfkO7dkY6dCDsSSJJXlaZnuvua+WLwJYWBp5G0\nPH9VpWJWMWNkOx05jqNSMavF+asqvfhSOEGxih77dfyU9O7bsuMTMunwqtwigdax0ZN6by09Vbm9\ndeBbFaurq/rnf/5nfeMb39Bf/MVfyPd9/du//Zu+853v6LOf/axee+01ZTIZ/eAHP+hmvABwMK2m\nFE9whxb9sbuhitX0YbO+HySE02PdfeFcQdpk3kdU2boX3JSslKV7t2WtleM4svVwKoVsx5eqG3yp\nxb6YRDK4uXD75kjPqbPWMmR6BAVr6ePBNTx66lDfjjqdjur1unzfV7PZ1MTEhK5cuaKPf/zjkqRX\nXnlFP/7xj7sSKAAcSrMR9CYDffDehqqNsENB3ZNSXRwyvSObk2qspo8qk3bVbrel6qZkrbS+Jt/3\nw6u22NyQxlyZOOM8sU+T01LHSmsPw44kPN6WlEgErbsYLUmGTffDgZNCExMT+uxnP6svfvGL+oM/\n+AO5rqszZ84ok8kotn0nfnJyUmtra10LFgAOjM1j6DcqSaLB25J6MFzYxBwpkw2qPxA5M+cvaOH+\ninxjpOnj8tfKuvagrJnzF8IJaIPWMRyMMUY6eVpavj261ae0jo2uVFpqMleo1w6cFNra2tKbb76p\nb3/72/qbv/kbNRoNvfXWWx96HP1/ACKBpBD6LVeUNjdGuuQ/EmpedzePPYrV9JHluq5mz53XYm5K\n132jxckTmj1+TGPp/leM2u1KJeVJCuFgjJuRipPS3aWwQwkHSaHRlWLYdD8cuIb15z//uaanp5XN\nBhdav/7rv66rV69qa2tLnU5HsVhM5XJZ4+OP/wC8cuWKrly5svvnV199Vblc7qDhYMgkk0nOB+zq\nxvnQXnEUK04oxnk18Abm/SGXU+teVvF4TIbV9D3zrPOhbaxiR6YVy3T/Z2ATcbWvXlFiEM7HEbFz\nPtiOr7RjdPR//YxMMlgw0L72S5mtdTnHT/U1pk51Q51CQfGpqb4eFwP0ebEH9vzzal95S446iuUK\nYYfTN7bZVDvuKH505tDFBsN0PoyKTntKndUVxXvwcxvF8+G73/3u7n+/ePGiLl68KOkQSaGpqSnN\nz8+r2WwqkUjo5z//uUqlki5evKjLly/rE5/4hH70ox/pYx/72GOf/2gQOzY3Nw8aDoZMLpfjfMCu\nbpwPdm1NcvMynFcDb5DeH2w8Jd29I3P0eNihDK2nnQ+205Eqa9IJv2e/+7ZeV+3BfZkurrzHwe2c\nD7ZSlmRkGo3deRR2Ylq6ekVyEn1N1Nq7S1IixedPCAbp82Iv7PgR6Z23pQu/MjKLM2z5gRRPylSr\nh36tYTsfRoFttaW11Z68f47a+ZDL5fTqq68+9u8O/G5y7tw5Xbp0SV/72tf0x3/8x7LW6jd+4zf0\nhS98Qd///vf15S9/WdVqVZ/61KcOHDgAdA3tYwhDLs9coTDVPSmZ7u2XJ2ZHRdNaWRp/f2WOSSSD\nFd+3rgfbwPqF1jF0iSlOBDNWVu6FHUr/0Do22h5ZS4/eOdQKhM997nP63Oc+975/Nz09ra9//euH\nCgoAusn6vmQ7MolE2KFg1GRz0mKwoYqtQyGoeVKvK3jyBenBPWmaarCosO2WtLUpnSp96O/M+KTs\nxpp0706QIOp1LHUv+PxxMz0/FkbE8Tlp/opscXLot3HZjh9sEJw9G3YoCImJObKOE6yl5+Zuz3CF\nCmD4USWEkJiYI7uzoao4EVocnudpef6qbN2TSbuaOX9Bbg82ckVObav3SaFsXlq8Luv7Mo7T22Nh\nbyqrUq745J/HiTnp6tuy+aJMNt/bWNYrVAmhq0wqJTt9TLpzUzr7XNjh9FZ1Uxpzuaky6pKpoA04\nxGv5Yb+OGo1mVACjjaQQwhTyhirP87R0+Q3N+TWV0nHN+TUtXX5DnueFFlPfeJ7U4woNE3OCY7Ca\nPjrWytL45BP/2sQT0onT0tKNoJK0l1hFj16YOia1mtuzs4YYrWOQpNRYcC0fklG4jiIpBGD4NRtS\nsv9riAFJwQVtiDNnluevqlTMKtasy25uyHEclYpZLc9fDS2mfrCdjtSoSek+3MkL+WeM99h6LXjP\nf0YFkCmMS5mctHyrd7G0msE5mBmt7TboPROLSSdPS3dv9T6xGaaNStCii9GWTAXvpSHZvY7aXJdt\nt4fyOoqkEIDh12xI2yuJgX4zqbRkYrK1cO4o2bqnmLXSyn1p7aGstXIcJ5h1MswadSmR7E9LV64g\nbYRXDYb3dFYfSsWJvQ0XP35K2liX7VUl30YlaGMbkS1R6C+TyUm5onTvdtih9ISteZIxMv1I7CPa\nUqndLZJhsHVPMdmgCtULtuAN23UUn1IAhl+jHnygAGEJcUOVSbvyV+4FlRNOXKp58n1/+C+0a1vS\nWH+G+5r0mCQN1QXioOqsrkjFJ7eOPcrE49LsGWnpumy73f1g1tekAq0v6KGZk1JlVdY7/Lr2yKF1\nDDtCbh8zaVf+5oYUM8ECC2norqNICgEYfswUQtjy4VWSHJua0MLDivxcQcrl5W9UtFCpaub8hVDi\n6Zt+bB57VIg/YwR2vhibTHbPzzG5QjAI+u5id2PxfWmrGlRyAD1i4okgMXT75vCt7N4kKYRtyZTU\nrId2js+cv6CFu8vyCxNS3VO73R666yiSQgCGH2ssEbZsXqp5fZ/9YJsNuWsPNfuZ39KtREYLzpgW\nvaZO/s+XhmprxmP1sVJIUvDlP8SB4pC0VlZs4sj+n3d8Vtqqyq6vdS+WzXXJzbCRDj1nJo5IMUd6\neD/sULrGtltSjXlcCBjHCc7xdiuU448l4po9f0GLhWktNK0WfWn20stDdR3Ffj8AQ822mlLMCTYE\nASExMUd2Z0NVPzcRLd2QjhxTZmJSpYmgpcYuXpManpQb3otta23whaKflULZvHSL1fRhsdZKlVXF\nTpek1v5awUzMkT11Vrp5TdbNyiQShw9oY42tY+ifk6ela7+ULU7IJAZ3huLO2u/Oyj2ZdkvHSx8Z\nqi/eOIRUandWYN9VVuVOH9O5UyXZI5NSIiEzZOcllUIAhhutY4iKPm+osg/vS74vTc+8/y/Gp6TV\nh32LIxSNuhSPBzNj+sQ426vpt1hNH4rNdSmZ2p3vtF8mk5MmpqQ7Nw8diu10WEWPvjLpMWlyWrrb\nu216vfa+td+2obmx5NCt/cYhJNPhDZteX31vVl02L20O3+c8SSEAw61BUggR0ccNVbZRl+7dkU6d\nlTHmw3G0m8M9FLnf84R25PLMFQrLWlkqThzuNY6ekBp12bXy4V5nqyol0wNdsYEBdHRG8rZkN8JZ\nanBYu2u/Jam2JSebG7q13ziEVFpq1vt+WNtsSPV6kAySgpZGbytI/g8R2scADDcqhRARJj0mq2BD\nVS83VlhrpaXr0tHjj62aMMbIjh+Ryg+lE6d6Fkeo+j1PaEeuKN2c7/9xR5z1/aAK75Dns4nFZGfP\nStfflc3mDp7UoXUMITAxR/bEnLyFd7Vs4lKjIZN2NXP+wkC0YNm6p1jKkR7clbIFmXhcjtjqiG2p\nlFRZ7f9xK6tSYVwmFtTSmHhcNpUOVtPvJIqGAJVCAIYbSSFEST82VK3ck2SkqaNPfszElFQpD92d\nrl0hVQqZMVeynaBSC/2zUZHcbLCJ6ZCMmwl+d25dP/iLrK+xNQmhqMWTWpq/qrnyskrpuOb82sC0\nYJm0K3+tHLQ9jwetOsO29huHEFb7WGX1w1WoueFrISMpBGC4kRRClPR4Q5Wte9KDZWn2MW1jjzCp\ndHDXbVi3ZYVVKSQFP+MBbd8YWGsPg1lZ3TI9I/lt2fKDfT/V1jzJmCBBCPTZ8vxVlU6flrO1Kdts\nyHGcgWnBOnbiuBaWbsufPCoTi8n3/aFb+41DCKF9zDbqwfeID1YEZQvB4pAhQlIIwHBr1kkKITqy\n+aAXvQer6W2nE1Q3zMzKpPZwzo8fGcqB07bRkGKx7myQOohcgaRQH9lWKyjjL3SvMsfEYtKps9Ly\n7eB82o/1NSlP6xjCYeue4qlUkCRdWZb1fTmOE/kWLNtuyX1wR7O/+VndSuW0UG9r0RkburXfOLid\ntfS21ezfQSurUnH8wzfZMhmpXuvJtVxYmCkEYGjZTkdqt6Ukwz4RDcbZXk2/tdH9L44PlqV4Qmby\nyN4eX5yQlm/JtlrhJVB6IcwqISlI/C3dkO34MjFW0/dcpSzli13//9qkXdnpGWlpQbb0kadW3r3P\nxpp0fEhndSHyTNqV79fk5AuyrYb0YFntqaMy6RDfE5/BWistLkjjU8ocPa7S0eNhh4SoSqWCyp1+\nDfGvrD52Vp2J9fBaLiRUCgEYXq2mlEju/WIe6IcebCGz3pZUfiDNntnzc4zjBHNPKofctBQ1YW0e\n22bicWlsTKpuhhbDSFkrd7d17FFHjgX/fHh/Tw+3zYbUbAbbaYAQzJy/oIVKVb7vSxNH5FurhZs3\not2C9WBZsh3p2MmwI0HUJVPBJrA+sPWa5Lee/H6ezUubw/M5T1IIwPBinhCiKFfo6iwf2+kE28aO\nn9r/tqRhbCELu1JI6vnsKARsvRYk/3u0AcYYI82ele7fDY71LNsDprkRgbC4rqvZSy9r0RnT9Yav\nxWOnNXv+OY1tRXP+id3cCJKuc+f4vcGzJdPBtX0/VFalwsSTz8tsXqoOz+c87WMAhleDeUKIHjPm\nytqObL322JXx+3bvtpRKy2xva9mXbE7q+LLeVrB1aRiEXCkkKUj8LV6TTsyFG8ewq5Sl4mRPv0ya\nVFp25qR067rs+ReefqyNytO3/gF94LquSi++tPtn22xI134hm0zJFKLT6mJbLWlpQTp1dv83NDCa\nUumgRbcf1lelk6ef/PduRmo1h6YFn0ohAMOr2Qj6j4Go6VIlid3aDL4YP+3C5SmMMUHrzerKoWOJ\nAttqStbKhJwMNm5G6rCavufWyrurq3vJTE5LjhO0uTyBbbclbytYVQxEiEmmpNPng1ln3lbY4Uja\nniN0a0GaOCKTK4QdDgZFqj/tY7buSb4vudknPsYYI2XyQ7OFjKQQgOFF+xiiqgsbqqzvB9vGTpyW\niR/iLtX4lFRZDdrQBl1tK/wqoR1dbhPE+9mtarD6vV8VbqfOSiv3nvylenNdyuYYLo5IMm42uHlw\n82p/tzc9yf07wT+Pngg3DgyWfrWPra1Kxae0ju3I5kgKAUDkkRRCVO2spu8cYp3p8pKUyR66HcCk\nUkEipV8l2b3keUFJdxTkSQr11NrD3g2YfgyTSAZbxZauPz6BusEqekSbKU4E7Y03roa6StturgfV\nqadKzBHCvph4XDImaD3spUo52ND6LDkqhQAg+kgKIaKCDVXugTdU2c31oNLoeJdm1oxPDcfA6SjM\nE9qRLUjVzZ5XYHmep4Wf/VTX/vPftfCzn8rzvJ4eLwpspxMMAS32vnXsUWZiKrhTvVPl8Gg8m+vB\nNj8gwsz0cSntBjOyrO378W2rGVS4nioNxRwWhCCVlpq9ayHbqQY1T2kd22HS7nareJ+GX/cQSSEA\nQ8m225LV4dpqgF46YHuRbbelpRvS7JkgudQNxXHJq0ajreAworB5bNvuavqt3q2s9TxPS5ff0Jxf\nUykd15xf09LlN4Y/MVRdl9LpoMqt306ellYfBu1ru/FsSOkxvuRiMJw8LfntoNq0j2ynEwzgnzoq\n06ONgRgBqVSwSKZX1vd5wyE7HNVCJIUADCeqhBB1+eLB5grdvRWsve7icE4Tc6TCRNCSM6BsuxUM\nhozS7322IG30roXs7i/e1tlYW7GVe7LlFcU6HZWKWS3PX+3ZMSNhrdzX1rFHmUQi2Cp3a+G99s+N\nNaqEMDBMLBYMnl5fky0/6N+B79+RYo40PdO/Y2L4JNNSLytzKqt7ax3bMSSr6UkKARhOJIUQcWbM\n3feGKru+FlSezMx2P6CJKak8wFvItlvHIjWjIl+QNg83UPxRttOR3VyXvXNL9pc/k73xrpx2O5hr\nYCTdXVTs4X11KuWuHTNqrO8HibYQV2ub4oTkZuVdv6ZrP3tL1/7j37Rwa2n4K7QwNEw8Lp19Trp3\nJ2hH7jG7sRYkc0+djdZ7NAZPD9vHdhcY7KcNPZeXNjdCacfsJpJCAIYTSSEMgn1sIbPtlnTnZnBR\n7XR/w5HJ5ILj9LDdqaci1Dq2aywjtduyh9iWYlst2fKK7M156RdvSffuBOvR584pduGj6kxMyWRy\nMhNHpNmz8pMpmfVV2atXZNfKw7FV7lHrq1I2H3prsDcxraX/+JHmVpdVclM6HeuMRusehoZJpaW5\nkrS4EKzg7hHbbEhLN6W5Uui/txgCvWwf22+VkCSTTAWfyfVab2LqE5JCAIZTsyElk2FHATzdfjZU\n3b4pFSd3kzc9MXFkcAdO12rRGTK9zRiz7y1k1lpZryp777bs1SvSu/8jKE3PF6XnflXm/Asyx07I\nuBnNnL+ghUpV/vYmoY61ut6Ja+bTn5GOHpfKD6R3fib74G4wi2oYrJWl8f1dtPfCvesLKp0pyVlf\nldysHMcZjdY9DBWTzQdb9W7M92Sjk+10pJvXpCPHevvZhdHRo/Yxa620vsetYx80BFvIujShEgAi\nplFnxgOiL1uQlm7KdjrBnIcnsGvl4Jw+VeptPBNT0rs/lz1xKpgzNEi8qnQ0grMqcsXg7uPk9BMf\nYn0/SBxtVIJ/Ok7w/nV8VnKzTzw3XNfV7KWXtTh/VbbuyaRdzV56Ua7rSspIhfFgk8rDe0FyqDgZ\nfDlLpXv0P7a3bKsZVITlz4cdimzdUzyXk+20JTeoUHMcp6cVF0AvmImpoI355rxs6fmnfhbt2/Jt\nKZGQYY4QusTE47ImqJ7uauWZV5WcRLBRbL+y+eCGxZFj3Yunz0gKARhOtI9hAJh4XHZnQ9UTBkfb\nVjMYLn3mQncv1h8XTyIp62alylqQIBoQ1veldktKjYUdyod48YSWf/pfsvceKLZd3eO6bvAlbKMS\n/MfbChIL+aJ09MS+tmq5rqvSiy898e+Nm5FOlYLz6OEDaf4XspmsNHVMJjdgG4DWylJhIhIJS5N2\n5fs1OY/MNvJ9/2BfKICwHTsh3aoHmy3nunPzwVZWgyHs5y925fWAXcl0cKOsm0mhtQNWCUlBUmgP\nN/iibDCjBoCnsNZKrSbtYxgMueLTN1Qt3ZAmjwRf7vthYmrwtpDVPSkdsSHTClbG3/7Jf2ouYVRS\nU6eqZd36v/9Pbb31n9LCL4MZBFNHpRdekik9L3PkWM/WrJtEUmbmpPTCi1J+XLq7KPvu27KrK4Mz\nd2itvL9VwT30wdY93/e1UKlq5vyFkCMD9s8YI82ekZoN2Xu3D/16tlEPWp7nSsFQa6CbUt1tIQta\nx9YOnBQy8URwI7q21bWY+o2kEIDh025J8Xgk7iYDz5TLP3FDlS2vBOfz9PH+xZMfl2qebC9Xvnab\ntxW5eUKStDx/VaViVk42J92/K2djTaWJgpZbHZkXfk1m9oxMYbwng8OfxMQcmckjMs99NGhPWytL\nv/xZMMOoBzNFusXWPclvSdlozCXZbd1zxrRQb2vRGdPspZe3W/eAwWNiTrCqfq0se4jZcrbTkRav\nSUePy7jZLkYIbEuluruBrLoZtDkeprU6G2whG1SkbgEMn0ad1jEMjp0NVY3G+6pEbKMhLS9J57o8\n4+EZTCwmW5wIqoWOnejbcQ+l5kkRHGJq656cdFy2MC7lCzJOPLjwqkdj6LPJFaRcIUi4PLwvvfs/\nZPPjwdyh7SSb53lafmRm0U77W9+trQaD1iNUDfas1j1g0JhEQvbMeenaO7Kp1MGGQ9+9JSVTMgM8\nXwURl0zva4HDMx10wPSjcnnpwbKkAblu+gAqhQAMH+YJYYDsbqiqvneBY62Vlq5L0zPhzCjZbiGz\n1vb/2AdR8yJZKRTMnfFlYjEZJ7gPF8W5Mybtypw8Iz3/q0FZ/o13ZRfe0db9u7p1+d8059dUSsc1\n59dCWbturQ2SlOODM+cKGFQm7QZzhW5eC9rA9sGulYMv6yfP9Cg6QNvtY92pFLKdTjBHsXDI1uRM\nTqptyXb8rsTVbySFAAwfkkIYNB+cK/TwviQb2iYL42alWCwYgB1xtuMHZeTp6A2ZHrS5MyaekDl6\nXHr+RWl8Sss/uaxStazY1qasteGtXd/alJz4bvUSgN4yuYJ09Lh046pse2+VjbZek+4sSnPnmCOE\n3upm+1h1Q0qlDz3PzzhOcHNqq9qduPqMpBCA4UNSCIMml5eqG7KdTnBhff+uNHs23FaZiSPS6kp4\nx98jW6tJyXQkN34M6twZE4vJTExJR47JmZ4J5iSUH0gKae36WlkaP2RpP4B9MVNHgxsWi9eeOYze\ndnxpcUGaOdm/pQgYWTur6G27C3PwKquHbx3bkQ2u5QYRaVwAw4ekEAZMrdnS3cVbsuVVma2qZn71\n15Q5zMDDbihOSvfvyPp+Xwch75tXDda5R9Qgz50xaVcd3yg2c1Javi1bXlGnONHX9jfb6Ujrq9KF\nX+nbMQFsOz4r3ZwPKoBmn9ISdueWlB6TmZzuX2wYbcntDWSHWEtvOx1pY02aOdmdmLL5YKbWzGx3\nXq+PondbDQAOi6QQBojneVq6/Ibm3IRKm2XNGV9L81f7Prflg0wiIWXywV20CLPeViRbx4bBPPXl\nlwAAIABJREFUTvtbx1rp2An5XlXXbi72t/1tc11KuzK8pwN9Z4yRTpUkb0v2wd3HPsaurgQtnidP\n9zc4jLYuzBWyG5UgmZlIdicmNys1GntuuYwSkkIAhort+FK7LXXrDR7osfetLbcdOUdndK6Y6//c\nlseZmJLWot1CZr2tSFcKDbJH29+ut6wWj5c0WzqnsY0+JgoZMA2EyjiOdPaC9PC+7AduEti6J91d\nkk6fi3ZFKYZPF+YKddYeBlXRXWJiseB6ZABbyGgfAzBcmk0pkYzU2mLgaXbWlssZk509KxOLydn+\n96HLFaTbN2UbdZmw29keI5jBFM3NY8Pig+1vttWSFn4pa2LBUOoesu12MM+ITUZAqEwiKXv6gnT9\nXW35vu7dvqPOVlWmfE8z/9OvKxOxjYoYAYdcS287vux6RTp1rotBKbhuqm50b05Rn1ApBGC40DqG\nAbOztlzS7rDkqKwtN7FYcBdt9WHYoTxeoy4lUzIx7lD3i0kkpNLz0uqK7IPl3h5sfU3K5dlkBESA\ncTPypo5p6Z++p1ONTZW2yppLxXX73XdDb3fGCDps+9jGuoybCT7TumlAh02TFAIwXBr1oKQUGBCR\nX1u+3UJmrQ07kg+rbbHpJgQmkZTOfUQqP5Bdude7A9E6BkTKvfsPVDp5Us79O1KrIefIMZWK2Wi0\nO2O0HLZ9bH1VsYkefL6MuVK7Jdtqdv+1e4ikEIDh0mwGJaXAgIj62nIz5gbbPaJ456vmyYyRFAqD\nSSSls89LK/dkt9fVd5NtNqR6LSjFBxAJtu4pPjElFcal6eNBu7PjRKPdGSPFxBOS1YGGOlvfDyqF\netDiZYwJqoU2I3jN9BTU4wIYLs2GlMmGHQWwL5FfWz5xRFpdid4X9NqWzPHBW/06LEwqJVt6fnvG\nkJGZONK9F6+UpeL4bkslgPAF7c41OYXx3X8XlXZnjKCdaqH4Pq/7NypSJhsklnS4YdWPlStI1fWg\n0npA8EkLYLgwUwjovuKktLEeqTWr1lqpVqN9LGQmlQ4qhu7dll0rd++F18q0jgERE/l2Z4yWVFpq\nNPb/vMpqbwdBD+BcIZJCAIYLSSGg60w8LuULQfVGVDTqUjzOEOIIMOkx6exz0t1bsl04R2zNk3xf\ncqn6BKIk6u3OGDHJ/Q+btr4fJGzy489+8AEF21qNbL3Ws2N0G1dSAIaGbbckI74kAr0wPiXduyNN\nHQ07kkCNVfRRYtKu7NnnpOvvBuvqC4e44N4eMG2M6V6AALoi8u3OGB2p1P4rctbXpGyu998VdqqF\n0mO9PU6XUCkEYHhQJQT0Tq4gtZvRGSha25IYMh0pZsyVzpyXlm7IblQO9BrW2qAibbyHpf0AgMF3\nkPaxSjloie+1ARs2TVIIwPAgKQT0jDEmqBZafRh2KAEqhSLJuFnpzAXp1nXZzfX9v0B1U4onGFwL\nAHi6fbaP2XZb2qpK+WIPg9qWy0tbG8GNjgFAUgjA8CApBPTWxBFprSzb6YQdCZVCEWYy2aBiaHFB\ndr+l/dutYwAAPI1JJCRr974EY31NyuVlHKe3gUkyiaQUTwTXKgOApBCA4UFSCOgpk0oHPfwHqQDp\nIttoSLFYcEGISDKZnDR3Trp5TXZrc0/PsR1f2ljr7VYYAMDw2FlLvxeVcn8/X3KFgdlCRlIIwPBo\nkBQCem78SPgtZFQJDQSTy0tzJenGvKxXffYTNirSWCa4wwoAwLPsca6QbbeCa4d+tI7tGKC5QiSF\nAAyPZiP4cADQO8WJoE++3QovBuYJDQyTK0inzmwnhp5RRr+2Ko33YQAoAGA47HWu0PqalCvKxHrf\nOrYrk5O8rWi03D8DSSEAQ8FaK7WaEneYgZ4yjhPcaVsrhxcElUIDxeTHpZOnpRtXZWuP315n262g\nzL5A6xgAYI/22j621ufWMSlYe59KS3uplA0ZSSEAw6HVlOJxmRhva0DPhd1CRqXQwDGFcenEKenG\nu7L12ocfUFmV8oW+DAAFAAyJPbSP2VZTqnvBjJ9+yw1GCxnfngAMB+YJAf2TzUkd/9ntQD1gW03J\nWhl+3weOKU5Kx2al6+/IfrDcv1KmdQwAsD/J1LPbxyqrUr4Yzo3jbH4ghk2TFAIwHJqNoK8YQM8Z\nY4Iv8GshVAvVtqgSGmBmYko6elJaeCfYIqftbXL1upQN4S4uAGBgmURSsp2nr6VfX5WKId10yGSl\nuifr++Ecf4/iYQcAAF3BOnqgv8aPSPNXZGdm+3v3zfMkl3lCg8xMHpG1HXm/+KmWOzHZlXsy8biO\nn63LdUn4AQD2IZkOvgfEP5zasM2dmw75EAKTTMyRdbPS1oaUHw8lhr2gUgjAcCApBPSVSaWk9Fiw\nRryfmCc0FGpuTku3bmnuzjWV2p7m3LSWLr8hz3v8IGoAAB7racOmK6tSYTzcmaPZvLS5Gd7x94Ck\nEIDh0GwEHwoA+mfiiLS60t9jsnlsKCzPX9W52ZNyCuNSzCieyahUzGp5/mrYoQEABsnThk1XVvu+\ndexDBmCuEEkhAMOBSiGg/4rjklcNhj/3gW23JN/nd30I2Lonx3FkihPS8TlJkuM4snUqhQAA+5BM\nP3bYtG3Ug+3EIbWO7XIzUqsh22qFG8dTkBQCMPBsx5f8thRPhB0KMFJMzJEKE/0bOL3dOmaM6c/x\n0DMm7crfHry58/P0fV8mTWsgAGAfntQ+ttM6FvI1gzFGyuQiXS10qEHTnufpr//6r7W0tCRjjP7w\nD/9QMzMz+ta3vqWVlRVNT0/rK1/5CkMDAfTWdpVQ2G/6wEiamJKWbkjTx3t/LFrHhsbM+QtauPyG\nSsWsHMeR7/taqFQ1e+nFsEMDAAySJ7WPVValE6f6H8/j7LSQjYe0Be0ZDpUU+tu//Vv92q/9mr76\n1a/K9301Gg39wz/8gz760Y/qt37rt/T666/re9/7nr7whS90K14A+DBax4DQmExO1lrZrapMJtvb\ng9U8KVfs7THQF67ravbSy1qcvypb92TSrmYvvciNRADAvphEUrbjy/q+jONIkmy9JvmtoEInCnJ5\n6eH9sKN4ogO3j9VqNb3zzjv6b//tv0kK+sBd19Wbb76pV155RZL0yU9+Uj/5yU+6EykAPEmDpBAQ\nJm8sq4X/+O+69p//roWf/bR3G6S8LYmkwdBwXVelF1/SuY9/QqUXXyIhBAA4mA/OFaqsSoWJyHQR\nmLQrdTqyTxqIHbIDVwrdv39fuVxO3/72t7W4uKizZ8/q937v97S+vq5iMbiLVywWtbER3d45AEOi\n2QxKRwH0ned5Wpq/qtLWqpzJCXX8mhYuv6HZSy939Uu+9X2p3ZJSY117TQAAMASS23OF3O0W8/VV\n6eTpUEP6kJ0WstSRsCP5kANXCnU6Hd24cUO/+Zu/qW984xtKpVJ6/fXXuxkbAOxNs06lEBCS5fmr\nOjdZlONmpds3FVu5p7Odmu7+5D9kN9dlGw1Zaw9/oNqWlGbINAAA+ID0e3OFbN0LNpW6PW5p368I\nr6Y/cKXQxMSEJicnVSqVJEmXLl3S66+/rmKxqEqlsvvPQqHw2OdfuXJFV65c2f3zq6++qlwuIj1/\nCF0ymeR8wK5nnQ8tx1F8YlLGZQDtKOD9IVrSRspms7KZjNRqybYaUqul1MaW3Oq6bON+sBI2NSaT\nTsuk0jLpMSmVlkmNySSTzzyG53la/MXbshvripu4Tj73kd0qJM4HPIrzAY/ifMCjOB+GV6cxqU51\nU/FcTv7GmnRiVk7+6avo+30+2GRC7XfKSoR4Dn73u9/d/e8XL17UxYsXJR0iKVQsFjU5Oam7d+/q\n+PHj+vnPf66TJ0/q5MmT+uEPf6jf/u3f1g9/+EN97GMfe+zzHw1ix+bm5kHDwZDJ5XKcD9j1rPPB\nrq9JzZaMzzkzCnh/iJa6larVqpzt4Y6KxeXHjZpHCqrNBFs/bMcP7uA16lK9Lq2vBxV+9bpkbbBO\nNpUOZgKk3vuPiceD9rTLb+hsuypnLKvORlm/+H/+ebc9jfMBj+J8wKM4H/AozofhZZttaa0sM3lU\n9s4taa4k84yfdRjng200VHtwX2as/zP0crmcXn311cf+3aG2j/3+7/++/vIv/1LtdltHjx7VF7/4\nRXU6HX3zm9/Uv/7rv2pqakpf/epXD3MIAHgq22pJJra7bQBAf+1ltbiJOdKYG/znA2y7HSSIGg2p\nUZM214MNHY26rJHuLt7S2YTkNBpScVKO46hUzGpx/qpKL77Uz/+pAAAgitJpqdmQ9bYkSSZqrWM7\ndlrIQkgKPc2hkkKnT5/Wn//5n3/o3//pn/7pYV4WAPaOdfRAqA67WtzE41I8+9jef9tqyZYrcmKd\nYMD09u+64zjBzAAAAIB4IpgjtLoiFSfDjubJcnlprSwdORZ2JO9zqKQQAISu2QhaTwCEZme1eLeZ\nREKx4qQ6fu299jRJvu8H610BAMDIq9VquntzUfbK2zKl53W8MNHVDahdk81LSzdlOx2Z2IF3fnVd\ndCIBgIOgUggYajPnL2ihUpXv+5K02542c/5CyJEBAICw7cwenEsYlXKuTjtWS5ffkOdFr6LYxBPB\n95baVtihvA9JIQCDjaQQMNR229OcMS3U21p0xnaHTAMAgNG2PH81mGuYHpOy+d3Zg8vzV8MO7fGy\neWkzWqvpaR8DMNiaDak4EXYUAHqoV+1pAABgsNm6Jycdl8bfmyUU6dmDubz0YFnSibAj2UWlEIDB\nRqUQAAAAMJJM2t1tMd8R6dmDmZxU25Lt+M9+bJ+QFAIwsGynI7WaUiIZdigAAAAA+mzQZg8axwlW\n0m9Vww5lF0khAIOr1ZLiiUhN7wcAAADQHwM5ezCbl6rRmSvETCEAg6tZp3UMAAAAGGEDN3swm5fu\n3pJmZsOORBKVQgAGWbMppdJhRwEAAAAAe+NmpUZDtt0OOxJJJIUADDIqhQAAAAAMEBOLSW4mMi1k\nJIUADC42jwEAAAAYNLkCSSEAOLQGSSEAAAAAAyZCw6ZJCgEYXFQKAQAAABg0Y67Ubsm2mmFHQlII\nwGCyvi/ZjkwiEXYoAAAAALBnxpigWmgz/GohkkIABhNVQgAAAAAGVTYvVdfDjoKkEIABRVIIAAAA\nwKCKyLBpkkIABhNJIQAAAAADyqTSkiRbr4UaB0khAIOJpBAAAACAQZYNv1qIpBCAwdSokxQCAAAA\nMLgisJqepBCAwdRsSCmSQgAAAAAGVC5ICllrQwshHtqRAeAwmg0pQVIIAAAAwGCqtdq6e+OGbHld\nseKEZs5fkOu6fY2BSiEAA8e2mpITl3GcsEMBAAAAgH3zPE9Ll9/QXDqhkpqa82tauvyGPM/raxwk\nhQAMHoZMAwAAABhgy/NXVSpm5WRyUs2T4zgqFbNanr/a1zhICgEYPA2SQgAAAAAGl60HiSClx6RG\nXbbTkeM4snUqhQDg6agUAgAAADDATNqV7/vBSIxUUqrXgj+nmSkEAE9HUggAAADAAJs5f0ELlap8\n35fGsvKrG1qoVDVz/kJf4yApBGDwkBQCAAAAMMBc19XspZe16IxpIZbSotfQ7KWX+759jJX0AAYP\nSSEAAAAAA851XZVefEmSZH/5M8n0PwYqhQAMFNvpSO2WlEyGHQoAAAAAdEe+KG1W+n5YkkIABkur\nKSWSMiaENDoAAAAA9EK+KG2QFAKAp6N1DAAAAMCwyeSkWk223errYUkKARgsJIUAAAAADBkTi0nZ\nvLSx3tfjkhQCMFgadZJCAAAAAIZPCHOFSAoBGCzNhpQiKQQAAABgyOQL0uZ6sFynT0gKARgstI8B\nAAAAGEImkQy+63jVvh2TpBCAwUJSCAAAAMCw6vMWMpJCAAaGbbclK5l4IuxQAAAAAKD7SAoBwBNQ\nJQQAAABgmI1lJN+XbdT7cjiSQgAGB0khAAAAAEPMGNPXaiGSQgAGB0khAAAAAMMuVyApBAAf0mxI\nyWTYUQAAAABA7+QKkrcl6/s9PxRJIQCDo1GXUlQKAQAAABhexnEkNyNtrvf8WCSFAAwO2scAAAAA\njIJ8kaQQAOyw1kqtJkkhAAAAAMNve9i0tbanhyEpBGAwtFtSPC4Tc8KOBAAAAAB6yqTSkuNIta2e\nHoekEIDB0KhTJQQAAABgdPRhNT1JIQCDgXlCAAAAAEYJSSEA2EZSCAAAAMAocbNSsyHbavbsECSF\nAAwGkkIAAAAARoiJxaRcUdro3RYykkIABgNJIQAAAACjJlfoaQsZSSEAg4GkEAAAAIBRky9I1Q3Z\nTqcnL09SCEDk2Y4vtdtSIhl2KAAAAADQNyaekMbGpK3Nnrw+SSEA0ddsSomkjDFhRwIAAAAA/dXD\nLWQkhQBEnm3UaR0DAAAAMJpICgEYZbbRkFIkhQAAAACMHpN2JWtl617XX5ukEIDoa9SoFAIAAAAw\nunpULURSCEDk2WZDSqbDDgMAAAAAwkFSCMDIYqYQAAAAgFGWzUu1mmy73dWXJSkEIPJso0FSCAAA\nAMDIMrGYlM1Jm+tdfV2SQgAizbZbkjEy8XjYoQAAAABAeHrQQkZSCEC0NRsybB4DAAAAMOryRWmz\nImtt117y0LfeO52O/uRP/kQTExP62te+pgcPHui1115TtVrVmTNn9KUvfUmO43QjVgCjiCHTAAAA\nACCTSMomU5JXlTK5rrzmoSuF/umf/kknTpzY/fN3vvMdffazn9Vrr72mTCajH/zgB4c9BIBRRqUQ\nAAAAAAS63EJ2qKRQuVzWW2+9pU9/+tO7/+7tt9/Wxz/+cUnSK6+8oh//+MeHixDAaKNSCAAAAAAC\nuQglhf7+7/9ev/M7vyNjjCRpc3NT2WxWsVjwspOTk1pbWzt8lABGV4NKIQAAAACQJLkZqd0ONjR3\nwYFnCv3Xf/2XCoWCTp8+rStXrkiSrLUfGni0kzD6oCtXruw+T5JeffVV5XLd6YnD4Esmk5wPkCS1\n4o6S2ZxiDtvHEOD9AY/ifMCjOB/wKM4HPIrzAY8a9POhfey4TKclJze15+d897vf3f3vFy9e1MWL\nFyUdIin0zjvv6M0339Rbb72lZrOpWq2mv/u7v5Pneep0OorFYiqXyxofH3/s8x8NYsfm5uZBw8GQ\nyeVynA8IkszrFcVNTFXOB2zj/QGP4nzAozgf8CjOBzyK8wGPGvTzwToJ6e5tmbHsnh6fy+X06quv\nPvbvDpwU+vznP6/Pf/7zkqRf/OIX+sd//Ef90R/9kb75zW/q8uXL+sQnPqEf/ehH+tjHPnbQQwAY\nda2mFI/LxA49Ex8AAAAAhkOuIC3dkPV9mUNue+/6N60vfOEL+v73v68vf/nLqlar+tSnPtXtQwAY\nFY2GlGSeEAAAAADsMI4TzBaqbhz6tboypOOFF17QCy+8IEmanp7W17/+9W68LIBR1yQpBAAAAAAf\nsrOavvD4kT17RU8GgOhiHT0AAAAAfFi+KG1WPrTsa79ICgGILiqFAAAAAOBDTCotxRyp5h3qdUgK\nAYiuZkNKkRQCAAAAgA/JbbeQHQJJIQDRRaUQAAAAADxenqQQgCFlO77kt6V4IuxQAAAAACB6Mlmp\nWZdttQ78EiSFAETTdpWQMSbsSAAAAAAgckwsJuUKh6oWIikEIJpoHQMAAACApzvkXCGSQgCiqUFS\nCAAAAACeKl+Qqhuync6Bnk5SCEA0NZskhQAAAADgKUw8IaXHpK3NAz2fpBCAaGo2pFQ67CgAAAAA\nINoOsYWMpBCASPE8Tws/+6mu/dePtXD1XXmeF3ZIAAAAABBdJIUADAPP87R0+Q3N+TWV4tKc2lq6\n/AaJIQAAAAB4AjPmStbK1mv7fi5JIQCRsTx/VaViVrFmXTIxxZNJlYpZ3X73l2GHBgAAAADRlT/Y\nanqSQgAio1PzFNtYk1buS9PHJEmO46hDpRAAAAAAPNkBW8hICgGIBNtqypQfyPc86fgpmbGMJMn3\nfcVcN+ToAAAAACDCsnmp5sm22/t6GkkhAKGzG2vS1SuaufhRLaTz6hgjKUgILVSqOvncR0KOEAAA\nAACiy8QcKZuTNtf39bx4j+IBgGeynY60fFtaX5VOn1Mmk9Op6Rktzl+VrXsyaVezl16U67ra3NwM\nO1wAAAAAiK6dFrLxyT0/haQQgFDYRl1avCYlUtKFX5GJB29Hruuq9OJLIUcHAAAAAAMmV5CWb8ta\nK7PdffEsJIUA9J1dfSjdvSUdOyEzdTTscAAAAABg4JlkSjaRlLyqlMnt6TkkhQD0jfV96c5i8CZV\nel5mjAHSAAAAANA1Oy1ke0wKMWgaQF/YmifNXwn+cP4iCSEAAAAA6LZ9rqanUghAz9mH96V7d4JV\n8xNTYYcDAAAAAMPJzUitlmyzIZNMPfPhJIUA9Ixtt6WlG1KrIZ1/QSaVDjskAAAAABhaxhjZfCGo\nFtrD/FbaxwD0hN3alK6+LSVT0jkSQgAAAADQF/toIaNSCEBXWWulB8vSw/vS7GmZ/HjYIQEAAADA\n6MgWpKWbsh1fJuY89aEkhQB0jW01pVvXJWulCxdlEsmwQwIAAACAkWLicVk3I21uSIWn36QnKQSg\nK+xGJZgfNDktHT0uY0zYIQEAAADAaMoXpc0KSSEA3ed5npbnr8rWPSmV1kwuK7exJc2VZLL5sMMD\nAAAAgNGWL0oLy898GEkhAPvieZ6WLr+hUjGrmGPl376mha2aZj/zvytDQggAAAAAQmdSaVkTk/W2\nnvo4to8B2Jfl+atBQqjuSXdvyckXdO78Bd27fj3s0AAAAAAAO/Ljz9xCRlIIwL7YuqeYMcF2sWPH\nZQrjchwnaCUDAAAAAETDHlbTkxQCsC8m7crfWJfSrkxqTJLk+75M2g05MgAAAADADi/maOHK2099\nDEkhAPsyc/6CFu7eke9mJQUJoYVKVTPnL4QcGQAAAABACmbB3v7Pf9fcWOKpj2PQNIB9GYsZzV74\niBadlFSvy6RdzV56Ua5LpRAAAAAARMHuLNhnTPkgKQRgf9Yeyp05oXPHT4UdCQAAAADgMWzdk5OO\ny2ZyT30c7WMA9sxaK609lCamwg4FAAAAAPAEJu0Gs19jT0/7kBQCsHeb61I8yVBpAAAAAIiwmfMX\ntFCpyvf9pz6OpBCAvaNKCAAAAAAiz3VdzV56WYvO2FMfR1IIwJ7YdjuoFCpOhh0KAAAAAOAZXNdV\n6cWXnvoYkkIA9qZSlnIFmTjz6QEAAABgGJAUArA3qyvSxJGwowAAAAAAdEmkkkILP/upPM8LOwwA\nH2BrntRuSdl82KEAAAAAALokUkmhOb+mpctvkBgComb1oTR+RMaYsCMBAAAAAHRJpJJCjuOoVMxq\nef5q2KEA2GY7nWCeEFvHAAAAAGCoRCopZFdXFDNGtk6lEBAZm+tSKiWTSocdCQAAAACgiyKVFFKr\nKf/OLclJhB0JgB3brWMAAAAAgOESqaRQZ+qYFnyjGbVkK+WwwwFGnm21pK0NqTgRdigAAAAAgC6L\nhx3AoxadMZ36X/43jclKi9dkNzekE6dkYk7YoQGjqVKW8kUZh99BAAAAABg2kaoUKr34klzXlXEz\n0oVfkTodaf4XwTpsAP23uiJN0DoGAAAAAMMoUkmhRxnHkZkrSUeOSQvvyJYfhB0SMFKstxUkZjO5\nsEMBAAAAAPRApNrHHsdMHJF1s9LiguzmunTyjEw88mEDg291RRqfkjEm7EgAAAAAAD0Q2UqhR5n0\nmHT+BSmRlOavyG5Vww4JGGq205Eqq9L4VNihAAAAAAB6ZCCSQpJkYjGZE3PS8VPSzXnZB3dlrQ07\nLGA4baxJY65MKhV2JAAAAACAHhm4PixTGJcdc6VbC9LmhuypkkwiEXZYwHBZfUiVEAAAAAAMuYGp\nFHqUSaak0kckNyvNvx3MGgLQFbbVlLyqVBwPOxQAAAAAQA8NXKXQDmOMNHNSNpuXlhZkx6ekoydk\nYgOZ5wKiY+2hVJiQiTlhRwIAAAAA6KGBz6CYXF46/ytSzQtW1zcaYYcEDLbyijRxJOwoAAAAAAA9\nNvBJIUnBTKEzF6TCeLCdrLIadkjAQLJbm5IxMpls2KEAAAAAAHpsYNvHPsgYI03PyGZz0uKCbHVd\nOn6KFhhgP1YfShMMmAYAAACAUTA0SaEdxs3Knr8o3bkpzf9SW9PHde/WLdm6J5N2NXP+glzXDTtM\nIHJsx5fWV6XnPhp2KAAAAACAPjhwUqhcLuuv/uqvVKlUFIvF9OlPf1qf+cxnVK1W9a1vfUsrKyua\nnp7WV77ylb4nYUw8Ls2d09btW1r6v/4PlU6fVrw4Lt+vaeHyG5q99DKJIeCDKmuSm5VJJMOOBAAA\nAADQBweeKeQ4jn73d39X3/zmN/Vnf/Zn+pd/+RfduXNHr7/+uj760Y/qtdde08WLF/W9732vm/Hu\ny73yqkrPPS9na112fU2O46hUzGp5/mpoMQGRtUbrGAAAAACMkgMnhYrFok6fPi1JSqfTOnHihMrl\nst5880298sorkqRPfvKT+slPftKVQA/C/v/t3Xt01PWd//Hnd2aSTCb3KyQQQElYKohaLovoQVHc\n9dLdQrdFkWPLbgUPkdKtu1iR1tVuK7QFkRqhoBawtAruemNP/aG10FosCgrKTW7hJgRC7pdJQmby\n+f0xZpyQSYxJJhMyr8c5OTCT+Xy/78z3Pd/vzHs+l3o3jthYSOsHVRUYY7Db7Zh6d9hiEumNTEOD\nbwW/xJRwhyIiIiIiIiI9pFtWHysuLubEiRMMGzaMyspKkpOTAV/hqKqqqjt20SmW04XX68VyxoLN\nBvV1n93W0DGRFspLIDkVy9YnFiQUERERERGRDujyJ8D6+nqeeOIJZs6cidPp7I6Yuk1W3jCOVtTg\n9XohIQlvZTlHK2rIyhsW7tBEeg1jzGdDxzLCHYqIiIiIiIj0oC6tPub1elm6dCkTJ05k7NixgK93\nUEVFhf/fpKSkoG337dvHvn37/LenTZtGQkJCV8JpJSEhgYRbbuXTgwfw2qIw1W6+cssBCiF1AAAg\nAElEQVRNxCUld+t+pPtFR0d3ez5IcE1VlTTFx+Po1z/cobRJ+SCBlA8SSPkggZQPEkj5IIGUDxIo\nEvNh48aN/v+PGDGCESNGAGAZY0xnN1pQUEBCQgLf+c53/PetX7+e+Ph4pkyZwquvvkptbS0zZszo\n0PbOnDnT2VA6xJwsBKcTKzM7pPuRrktISKC6ujrcYUQEc/IoxMZhZfTeopDyQQIpHySQ8kECKR8k\nkPJBAikfJFCk5UN2dts1kE73FPrkk0945513GDRoEA8++CCWZTF9+nSmTJnCsmXL2LJlC+np6Tzw\nwAOd3UX3S8uEk0cxGVlYlhXuaETCzni9UFUB2YPCHYqIiIiIiIj0sE4XhYYPH86GDRuC/u7HP/5x\npwMKJSsuHmOzQ001JCSGOxyR8Ksog7hELEdUuCMRERERERGRHhZ5Sw2lZUBZcbijEOkdys9Danq4\noxAREREREZEwiLyiUHIaVFdiGhvDHYlIWJmGemhogITgk8GLiIiIiIhI3xZxRSHL4YDEFF8PCZFI\nVlYCyWlYtog7DYiIiIiIiAgRWBQCfBNOl56nCwuviVzSjDG+wmiaho6JiIiIiIhEqogsCllx8WCz\nQ01VuEMRCY+aKnBEYzld4Y5EREREREREwiQii0LAZxNOawiZRKgyTTAtIiIiIiIS6SK3KKQJpyVC\nGY8Hqit9rwERERERERGJWBFbFLIcDkhK1YTTEnkqSiEhyfcaEBERERERkYgVsUUhAFIzNOG0RJ6y\nEkjR0DEREREREZFIF9FFIU04LZHG1LvBcwESksIdioiIiIiIiIRZRBeFAN+E06XF4Y5CpGeUlUBK\nBpZlhTsSERERERERCTMVhZLToKZKE05Ln2eamqC8VKuOiYiIiIiICKCikCaclshRXQkxMVgxznBH\nIiIiIiIiIr1AxBeFgM+GkGnCaenjPhs6JiIiIiIiIgIqCgFguTThtPRtprERaqsgOTXcoYiIiIiI\niEgvoaJQM004LX1ZRSkkJmPZ7eGORERERERERHoJFYWapaRrwmnpuzR0TERERERERC6iotBnLLvd\nN+F0mSaclr7FuGuhyQvxCeEORURERERERHoRFYUCpWVAmSaclj6mvARS0rAsK9yRiIiIiIiISC+i\nolAATTgtfY1paoLyUg0dExERERERkVZUFLpYeqYmnJa+o6ocYl1YMTHhjkRERERERER6GUe4A+h1\nktOg6BSmsRErKirc0Yh0itvtpujwIZqOHcJKzyQ7axAulyvcYYmIiIiIiEgvop5CF9GE03Kpc7vd\nnNq+jUEN1Qy1eRjsjOLU9m243e5whyYiIiIiIiK9iIpCwWjCabmEFR0+xOVJcdhrqsCVgCMqiqHJ\n8RQdPhTu0ERERERERKQXUVEoCMsVD3ZNOC2XpqaKUuznTsOFOkhOBcBut2Pq1VNIREREREREPqc5\nhdqS9tmE0wlJ4Y5EpENM4wUoOoVVUYI3NRVH4ue56/V6sZyaU0hEREREREQ+p55CbUlOg5oq3wdt\nkV7MNDVhiovg4F6Iiibr5jsobPQVgsD379GKGrLyhoU5UhEREREREelN1FOoDZbdjklKhbIS6Jcd\n7nBEgjJVFXD6BDhjIe8KrBgncUDO+Os4cfgQpt6N5XSRM/4qrT4mIiIiIiIiLago1J60DDhxFJOZ\nhWVZ4Y5GxM801MOZk1BfBwMGYSWmtPi9y+Vi6FVXhyk6ERERERERuRSoKNQOyxWPaZ5wWnMLSS9g\nvF4oLvLNd5XZHwbnYtk0ClRERERERES+PBWFvogmnJZewpSXQtFJiEuEvxuJFRUd7pBERERERETk\nEqai0BdJToOiU5jGC/oQLmFh6txw+jh4m3w9g+ISwh2SiIiIiIiI9AEqCn0BTTgt4WI8jXD2NFSW\nQ/8BkJqhua1ERERERESk22gyko5Iy4Sy8xhjwh2JRABjDKbkHHyyx3fH312JlZapgpCIiIiIiIh0\nK/UU6gDLFacJp6VHmNpq+PQE2O0wdDhWrJaRFxERERERkdBQUaij0jKhRBNOS/dwu90UHT6EqXdj\nOV30v+wyXOUlUFsF2YOwktPCHaKIiIiIiIj0cRo+1lHJaVBbhWm8EO5I5BLndrs5tX0bg711XB5t\nY1DZGU698iJubxMMH6WCkIiIiIiIiPQIFYU6yLLbIfmzCadFuqDo8CGGJsdj81yA0yewN15g6PCv\ncLa6FstmD3d4IiIiIiIiEiFUFPoyUjOhtFgTTkuXmHo3drsd7A5Iy8Tql43D6cTUu8MdmoiIiIiI\niEQQFYW+BMsVBw4HVFeGOxS5hFlOF16vF8sR5csp8N12alJpERERERER6TkqCn1Zaf2g9Hy4o5BL\nWFbeMI5W1OD1egFfQehoRQ1ZecPCHJmIiIiIiIhEEhWFvqzkVE04LV3icrnIGX8dJ+yxHK33cMIe\nS87463C51FNIREREREREeo6WpP+SLLsd0zzhdL/skO7r4mXLs/KGqXDQR7hcLoZedXW4wxARERER\nEZEIpp5CndEDE04HLls+1OlgsLeOU9u34XZrMmIRERERERER6ToVhTrBN+F0VEgnnC46fIjL453Y\n3DWYsvPYTBNDk+MpOnwoZPsUERERERERkcih4WOdlZbpm3A6MbnbNmmMgTo3VFXQdPQAdocBp8tX\ngDp9EltiMk0x3m7bn4iIiIiIiIhELhWFOis5FYpOYhovYEVFd3ozxuv19TiqroSqCrDbISEZK2sQ\n3ihwREX5HpeUjPf8OayqGkxlOVZSSnf9JSIiIiIiIiISgVQU6qSuTDhtGup9BaCqCnDXgivO1+Mo\nMwsrxglAdko6hdu3MTQ5HrvdTpNlozA6gYFfHQVnP8WUnIMBg7GcsaH480RERERERESkj1NRqCtS\nM+H4YUxmFpZltfkw09QEtdVQVQlV5WCaICEZ0vtBfCKW3d6qjX/Z8oDVx3LGX4XL5cKkZ0BpMRw5\ngElJh37ZWA4dShERERERERHpOFUSusByxVHr8VL0t21gt7VYNt40Nvp6AlVXQE0VxMRCQhIMzvVN\nVN0BbS1bbtlskNEfk5wGZz+Fg3swWQMhJb3d4lRf43a7KQoomjU/9yIiIiIiIiLyxVQU6gK3282p\n48cZGm1h7z8Ar7uco//vdXJy83DZ8BWBElNgwBCsz+YG6k5WVBTkXIZx18Dpk1BSjBk4GMsV3+37\n6m3cbjenmofXOR14vXUc3b6NnPHXqTAkIiIiIiIi0gFakr4Lig4fYmh2f+wX6uHUMexl5xmaFEeR\nux6uuAZrcC5WanpICkKBLFc85H7FNxzt+GHMyUJfT6U+rOjwIS6PjcZWdh7j8WC32xmaHE/R4UPh\nDk1ERERERETkkqCeQl1g6t04nFGY/gPA5sCKivI9ofUe3xCvHmRZFqSmY5JS4Nxp35CyftmQltnj\nsYSSMQaqKmg6dhC73fgm6P5syJzdbsfUu8McoYiIiIiIiMilQUWhLrCcLrzeOuwxn68A5vV6sZzh\nG75k2e2QPQiTmgGnT0BpMWbAYKyEpLDF1B2M1wtl56HkHNgdWBn98cY4cAT0wgr3cy8iIiIiIiJy\nKek7XUjCICtvGEcravB6vYCvKHG0ooasvGFhjgwsZyzW0OGQlQOfHsccO4xpaAh3WF+audCAOXMS\nDnzkW8Ft0OVYw0aQfc1YCqvcvfK5FxEREREREbkUqKdQF7S3bHxvYSWlYBKS4PxZOLwPk54JmVlY\nNnu4Q2tXU0015sQRqK6ElAzIG4EVE+P//aXw3IuIiIiIiIj0ZioKdVFby8b3JpbNBv2yMSlpUHQK\nPtmDyc6hLjq2Vy3pboyByjI4fw5vlAPiEmHgZb4hcUFcCs+9iIiIiIiISG+lolAEsaJjYHAupqYK\n99FDnDqwl6FDLsMRGxvWJd2Nx/P5fEHR0ZCZhWNADlZNTY/GISIiIiIiIhJJVBSKQFZ8IkXYGdq/\nH/biM5hYF7aoaC6PcnBi70cMHT2uzd453ck01PsKQeUlvlXEhuRhueJ8MX62opiIiIiIiIiIhIaK\nQpGqvg5HShomMQnctdDYiL2uFlN8HmKjMDY7xDg/+4mBmFjfv9HODi9x73a7gw5PM9VVUHIWamsg\nLRP+7kqsqOgQ/8EiIiIiIiIiEkhFoQhlOV14vXXY7Q74bLl6r9eLrV8sjLwKGi9AQwNcqIf6eqg9\nD/V10HgB44gKKBgF/ETH+Hv4uN1uTm3fxtDkeOxOB57GWo6+/QY5gwbjiomGjP4weGivn/BaRERE\nREREpK8KWVFo9+7drF27FmMMkyZNYsqUKaHalXRCVt4wjjYXbex2/5LuOeOv8hV2omN8PyS2aGeM\ngQsN0FD/+U9Vhe9fTyMmOgZinJw5eozLowy2C/WY+nrs1ZUMtTs4UVvP0FGjNTxMREREREREJMxC\nUhRqamriueee45FHHiElJYUFCxYwduxYBgwYEIrdSSd0dkl3y7I+7xl0EdPU5OtZ1NCAOXQQO0Bd\nLTiiof8AHNExUO9RQUhERERERESkFwhJUejIkSNkZWWRkZEBwHXXXceOHTtUFOpluntJd8tmA6cL\nnC5s/XNo8tZhD5iw2uv1YjnDt+S9iIiIiIiIiHyuYzMGf0llZWWkpaX5b6emplJWVhaKXUkvlZU3\njKMVNXi9XgD/8LSsvGFhjkxEREREREREoAcnmtaQocjS2eFpIiIiIiIiItIzQlIUSk1NpaSkxH+7\nrKyMlJSUFo/Zt28f+/bt89+eNm0a2dnZoQhHwig3N7fTbRMSEroxErnUKR8kkPJBAikfJJDyQQIp\nHySQ8kECRVo+bNy40f//ESNGMGLECCBEw8dyc3M5e/Ys58+fx+PxsG3bNsaMGdPiMSNGjGDatGn+\nn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"text/plain": [ "<matplotlib.figure.Figure at 0x111c34278>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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q6Xhlw4dFiHdVMKwzWBVjB07zKedgLvnCs0IQgEoUFdN9S7x8ay88wJjCCGMB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"text/plain": [ "<matplotlib.figure.Figure at 0x10dfd76d8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "eastern = timezone('US/Eastern')\n", "zurich = timezone('Europe/Zurich')\n", "\n", "# Quick day light saving hack\n", "dls = timedelta(seconds=3600)\n", "\n", "fmt = '%Y-%m-%d %H:%M:%S %Z%z'\n", "simple_time_format = '%H:%M'\n", "# Used to put all timestamps in one day\n", "one_day_time_format = '2017-01-01 %H:%M:%S'\n", "\n", "\n", "print(\"Got {0} tweets\".format(len(outtweets)))\n", "\n", "dt_series = []\n", "\n", "for tweets in outtweets:\n", " timestamp = tweets[1]\n", " # print(timestamp)\n", " loc_dt = zurich.localize(timestamp) + dls\n", "# print(\"Localized Zurich Timezone: {0}\".format(loc_dt.strftime(fmt))) \n", " eastern_dt = loc_dt.astimezone(eastern)\n", "# print(\"Localized Eastern Timezone: {0}\".format(eastern_dt.strftime(one_day_time_format)))\n", " \n", " one_day_dt =datetime.datetime.strptime(eastern_dt.strftime(one_day_time_format), '%Y-%m-%d %H:%M:%S')\n", " \n", " dt_series.append(one_day_dt)\n", " \n", "\n", "series = pd.Series(1, index=dt_series)\n", "binned = series.resample('30T').sum()\n", "\n", "counted = Counter(dt_series)\n", "values = []\n", "\n", "for key, value in dict(counted).items():\n", " values.append(value)\n", "\n", "df2 = pd.DataFrame({ 'Date_Time' : list(counted),\n", " 'Count' : values})\n", "\n", "binned_df = pd.DataFrame({'Date_Time':binned.index, 'Count':binned.values})\n", "\n", "# print(binned.index)\n", "# print(binned.values)\n", "\n", "# print(binned_df.Date_Time.dt.time)\n", "binned_df.plot.line(x=binned_df.Date_Time.dt.time, y='Count', marker='o', alpha=0.3, figsize=(20,10))\n", "\n", "# print(df2.Date_Time.dt.hour)\n", "df2.plot.line(x=df2.Date_Time.dt.time, y='Count', marker='o', alpha=0.3, figsize=(20,10))\n", "\n", "# print(df2.Date_Time.dt.time)\n", "# df2.plot.line(x=df2.Date_Time.dt.time)\n", "# ppd = pd.Series(dt_series)\n", "# print(ppd)\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "list(counted)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "# series = pd.Series(1, index=dt_series)\n", "binned = series.resample('30T').sum()\n", "\n", "binned_df = pd.DataFrame({'Date_Time':binned.index, 'Count':binned.values})\n", "print(binned_df)\n" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_tweet_by_id(id):\n", " return [[tweet.id_str, tweet.created_at, tweet.text.encode(\"utf-8\"), tweet._json] for tweet in alltweets if tweet.id_str == id]" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " id likes\n", "833050081641234435 833050081641234435 69240\n", "832950628750127106 832950628750127106 79521\n", "832945737625387008 832945737625387008 147066\n", "832742165436579840 832742165436579840 86872\n", "832730328108134402 832730328108134402 105603\n", "Most liked tweets got 634311 likes\n", "Average likes 34321.68270120259\n" ] }, { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x11ba26ef0>" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ML2EUMIqKKQAAAKaXMKoVKHYBAAAAZghhFLQiBVAAAAA0iDAKWtr4VEoZHQAA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DAAAg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\nJ510EhYsWAAAmD9/vnu8Xbt2KC4uxq5duwAAFRUVKC8vR3l5ORKJhLtD1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"text/plain": [ "<matplotlib.figure.Figure at 0x11bb1c550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# favorite_count: Number of likes per tweet\n", "favourite_tweets_list = [tweet.favorite_count for tweet in alltweets]\n", "id_tweets_list = [tweet.id_str for tweet in alltweets]\n", "\n", "# print(len(favourite_tweets_list))\n", "# print(id_tweets_list)\n", "\n", "df_fav = pd.DataFrame({'likes':favourite_tweets_list, 'id':id_tweets_list}, index=id_tweets_list)\n", "print(df_fav.head())\n", "max_df = df_fav['likes'].max()\n", "print(\"Most liked tweets got {0} likes\".format(max_df))\n", "\n", "# df.loc[df['likes'].idxmax()]\n", "max_likes_index = df_fav['likes'].idxmax()\n", "# print(df_fav.loc[max_likes_index])\n", "\n", "# most_liked_tweet = api.user_timeline(screen_name = screen_name, count = _count, max_id = oldest)\n", "\n", "most_liked_tweet = [[tweet.id_str, tweet.created_at, tweet.text.encode(\"utf-8\"), tweet._json] for tweet in alltweets if tweet.id_str == max_likes_index]\n", "\n", "print(\"Average likes {0}\".format(df_fav['likes'].mean()))\n", "df_fav.plot(figsize=(20,10))\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2016-11-09 11:36:58\n", "b'Such a beautiful and important evening! The forgotten man and woman will never be forgotten again. We will all come together as never before'\n" ] } ], "source": [ "print(most_liked_tweet[0][1])\n", "print(most_liked_tweet[0][2])" ] }, { "cell_type": "code", "execution_count": 103, "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>id</th>\n", " <th>likes</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>796315640307060738</th>\n", " <td>796315640307060738</td>\n", " <td>634311</td>\n", " </tr>\n", " <tr>\n", " <th>795954831718498305</th>\n", " <td>795954831718498305</td>\n", " <td>573937</td>\n", " </tr>\n", " <tr>\n", " <th>823174199036542980</th>\n", " <td>823174199036542980</td>\n", " <td>396734</td>\n", " </tr>\n", " <tr>\n", " <th>815185071317676033</th>\n", " <td>815185071317676033</td>\n", " <td>351584</td>\n", " </tr>\n", " <tr>\n", " <th>822669114237943808</th>\n", " <td>822669114237943808</td>\n", " <td>294355</td>\n", " </tr>\n", " <tr>\n", " <th>741007091947556864</th>\n", " <td>741007091947556864</td>\n", " <td>293634</td>\n", " </tr>\n", " <tr>\n", " <th>822421390125043713</th>\n", " <td>822421390125043713</td>\n", " <td>272571</td>\n", " </tr>\n", " <tr>\n", " <th>828447350200926212</th>\n", " <td>828447350200926212</td>\n", " <td>271899</td>\n", " </tr>\n", " <tr>\n", " <th>827885966509604865</th>\n", " <td>827885966509604865</td>\n", " <td>260054</td>\n", " </tr>\n", " <tr>\n", " <th>826774668245946368</th>\n", " <td>826774668245946368</td>\n", " <td>256665</td>\n", " </tr>\n", " <tr>\n", " <th>826637556787838976</th>\n", " <td>826637556787838976</td>\n", " <td>255100</td>\n", " </tr>\n", " <tr>\n", " <th>755788382618390529</th>\n", " <td>755788382618390529</td>\n", " <td>246242</td>\n", " </tr>\n", " <tr>\n", " <th>829836231802515457</th>\n", " <td>829836231802515457</td>\n", " <td>239413</td>\n", " </tr>\n", " <tr>\n", " <th>796900183955095552</th>\n", " <td>796900183955095552</td>\n", " <td>232033</td>\n", " </tr>\n", " <tr>\n", " <th>825721153142521858</th>\n", " <td>825721153142521858</td>\n", " <td>227870</td>\n", " </tr>\n", " <tr>\n", " <th>810996052241293312</th>\n", " <td>810996052241293312</td>\n", " <td>223977</td>\n", " </tr>\n", " <tr>\n", " <th>825692045532618753</th>\n", " <td>825692045532618753</td>\n", " <td>222839</td>\n", " </tr>\n", " <tr>\n", " <th>797034721075228672</th>\n", " <td>797034721075228672</td>\n", " <td>222160</td>\n", " </tr>\n", " <tr>\n", " <th>822502270503972872</th>\n", " <td>822502270503972872</td>\n", " <td>221831</td>\n", " </tr>\n", " <tr>\n", " <th>823151124815507460</th>\n", " <td>823151124815507460</td>\n", " <td>221293</td>\n", " </tr>\n", " <tr>\n", " <th>823150055418920960</th>\n", " <td>823150055418920960</td>\n", " <td>217161</td>\n", " </tr>\n", " <tr>\n", " <th>827112633224544256</th>\n", " <td>827112633224544256</td>\n", " <td>215658</td>\n", " </tr>\n", " <tr>\n", " <th>803567993036754944</th>\n", " <td>803567993036754944</td>\n", " <td>214494</td>\n", " </tr>\n", " <tr>\n", " <th>824080766288228352</th>\n", " <td>824080766288228352</td>\n", " <td>213355</td>\n", " </tr>\n", " <tr>\n", " <th>813079058896535552</th>\n", " <td>813079058896535552</td>\n", " <td>212948</td>\n", " </tr>\n", " <tr>\n", " <th>802499192237080576</th>\n", " <td>802499192237080576</td>\n", " <td>210375</td>\n", " </tr>\n", " <tr>\n", " <th>830552079240409089</th>\n", " <td>830552079240409089</td>\n", " <td>208847</td>\n", " </tr>\n", " <tr>\n", " <th>827655062835052544</th>\n", " <td>827655062835052544</td>\n", " <td>208181</td>\n", " </tr>\n", " <tr>\n", " <th>797455295928791040</th>\n", " <td>797455295928791040</td>\n", " <td>202151</td>\n", " </tr>\n", " <tr>\n", " <th>824083821889015809</th>\n", " <td>824083821889015809</td>\n", " <td>197791</td>\n", " </tr>\n", " <tr>\n", " <th>...</th>\n", " <td>...</td>\n", " <td>...</td>\n", " </tr>\n", " <tr>\n", " <th>795120273070686208</th>\n", " <td>795120273070686208</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>760550754692263936</th>\n", " <td>760550754692263936</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>785293820258902016</th>\n", " <td>785293820258902016</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>779348578040963073</th>\n", " <td>779348578040963073</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>780585254583169078</th>\n", " <td>780585254583169078</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>743827444960899073</th>\n", " <td>743827444960899073</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>783488429442994176</th>\n", " <td>783488429442994176</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>783485558202916865</th>\n", " <td>783485558202916865</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>780444199737036800</th>\n", " <td>780444199737036800</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>783481871032123392</th>\n", " <td>783481871032123392</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>780550185365737472</th>\n", " <td>780550185365737472</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>780576225848885249</th>\n", " <td>780576225848885249</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>783481080078749696</th>\n", " <td>783481080078749696</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>783480864395055104</th>\n", " <td>783480864395055104</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>794022452233781248</th>\n", " <td>794022452233781248</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>780583741051768832</th>\n", " <td>780583741051768832</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>785287884718895104</th>\n", " <td>785287884718895104</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>780592630585499648</th>\n", " <td>780592630585499648</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>780590406501150723</th>\n", " <td>780590406501150723</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>788479634694246400</th>\n", " <td>788479634694246400</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>788449842854895616</th>\n", " <td>788449842854895616</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>760289166324215808</th>\n", " <td>760289166324215808</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>785289956541554689</th>\n", " <td>785289956541554689</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>785292936095424512</th>\n", " <td>785292936095424512</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>788077766063390724</th>\n", " <td>788077766063390724</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>788048800766099456</th>\n", " <td>788048800766099456</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>715215099082878977</th>\n", " <td>715215099082878977</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>788036646096822272</th>\n", " <td>788036646096822272</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>794320826237644805</th>\n", " <td>794320826237644805</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>778655495037091845</th>\n", " <td>778655495037091845</td>\n", " <td>0</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>3243 rows × 2 columns</p>\n", "</div>" ], "text/plain": [ " id likes\n", "796315640307060738 796315640307060738 634311\n", "795954831718498305 795954831718498305 573937\n", "823174199036542980 823174199036542980 396734\n", "815185071317676033 815185071317676033 351584\n", "822669114237943808 822669114237943808 294355\n", "741007091947556864 741007091947556864 293634\n", "822421390125043713 822421390125043713 272571\n", "828447350200926212 828447350200926212 271899\n", "827885966509604865 827885966509604865 260054\n", "826774668245946368 826774668245946368 256665\n", "826637556787838976 826637556787838976 255100\n", "755788382618390529 755788382618390529 246242\n", "829836231802515457 829836231802515457 239413\n", "796900183955095552 796900183955095552 232033\n", "825721153142521858 825721153142521858 227870\n", "810996052241293312 810996052241293312 223977\n", "825692045532618753 825692045532618753 222839\n", "797034721075228672 797034721075228672 222160\n", "822502270503972872 822502270503972872 221831\n", "823151124815507460 823151124815507460 221293\n", "823150055418920960 823150055418920960 217161\n", "827112633224544256 827112633224544256 215658\n", "803567993036754944 803567993036754944 214494\n", "824080766288228352 824080766288228352 213355\n", "813079058896535552 813079058896535552 212948\n", "802499192237080576 802499192237080576 210375\n", "830552079240409089 830552079240409089 208847\n", "827655062835052544 827655062835052544 208181\n", "797455295928791040 797455295928791040 202151\n", "824083821889015809 824083821889015809 197791\n", "... ... ...\n", "795120273070686208 795120273070686208 0\n", "760550754692263936 760550754692263936 0\n", "785293820258902016 785293820258902016 0\n", "779348578040963073 779348578040963073 0\n", "780585254583169078 780585254583169078 0\n", "743827444960899073 743827444960899073 0\n", "783488429442994176 783488429442994176 0\n", "783485558202916865 783485558202916865 0\n", "780444199737036800 780444199737036800 0\n", "783481871032123392 783481871032123392 0\n", "780550185365737472 780550185365737472 0\n", "780576225848885249 780576225848885249 0\n", "783481080078749696 783481080078749696 0\n", "783480864395055104 783480864395055104 0\n", "794022452233781248 794022452233781248 0\n", "780583741051768832 780583741051768832 0\n", "785287884718895104 785287884718895104 0\n", "780592630585499648 780592630585499648 0\n", "780590406501150723 780590406501150723 0\n", "788479634694246400 788479634694246400 0\n", "788449842854895616 788449842854895616 0\n", "760289166324215808 760289166324215808 0\n", "785289956541554689 785289956541554689 0\n", "785292936095424512 785292936095424512 0\n", "788077766063390724 788077766063390724 0\n", "788048800766099456 788048800766099456 0\n", "715215099082878977 715215099082878977 0\n", "788036646096822272 788036646096822272 0\n", "794320826237644805 794320826237644805 0\n", "778655495037091845 778655495037091845 0\n", "\n", "[3243 rows x 2 columns]" ] }, "execution_count": 103, "metadata": {}, "output_type": "execute_result" } ], "source": [ " df_fav.sort_values(by='likes', ascending=False)" ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2016-11-08 11:43:14\n", "b'TODAY WE MAKE AMERICA GREAT AGAIN!'\n" ] } ], "source": [ "second_highest = get_tweet_by_id('795954831718498305')\n", "print(second_highest[0][1])\n", "print(second_highest[0][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": "3.5.0" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
deehzee/cs231n
assignment1/knn.ipynb
1
412899
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# k-Nearest Neighbor (kNN) exercise\n", "\n", "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n", "\n", "The kNN classifier consists of two stages:\n", "\n", "- During training, the classifier takes the training data and simply remembers it\n", "- During testing, kNN classifies every test image by comparing to all training images and transfering the labels of the k most similar training examples\n", "- The value of k is cross-validated\n", "\n", "In this exercise you will implement these steps and understand the basic Image Classification pipeline, cross-validation, and gain proficiency in writing efficient, vectorized code." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Run some setup code for this notebook.\n", "from __future__ import absolute_import, division, print_function\n", "import random\n", "import numpy as np\n", "from cs231n.data_utils import load_CIFAR10\n", "import matplotlib.pyplot as plt\n", "import seaborn\n", "seaborn.set()\n", "\n", "# This is a bit of magic to make matplotlib figures appear inline\n", "# in the notebook rather than in a new window.\n", "%matplotlib inline\n", "# set default size of plots\n", "plt.rcParams['figure.figsize'] = (10.0, 8.0)\n", "plt.rcParams['image.interpolation'] = 'nearest'\n", "plt.rcParams['image.cmap'] = 'gray'\n", "\n", "# Some more magic so that the notebook will reload external python\n", "# modules;\n", "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n", "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training data shape: (50000, 32, 32, 3)\n", "Training labels shape: (50000,)\n", "Test data shape: (10000, 32, 32, 3)\n", "Test labels shape: (10000,)\n" ] } ], "source": [ "# Load the raw CIFAR-10 data.\n", "#cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n", "cifar10_dir = '../data/cifar10/'\n", "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n", "\n", "# As a sanity check, we print out the size of the training and\n", "# test data.\n", "print('Training data shape: ', X_train.shape)\n", "print('Training labels shape: ', y_train.shape)\n", "print('Test data shape: ', X_test.shape)\n", "print('Test labels shape: ', y_test.shape)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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NOjLzS3QtG5bK0Bmrl1otPN3/eiOEcOzNpz71KWeMXa/XXcywxcVFF4tOSulO\n5hcvXnTzzPM8d19K6VimRqPB9PS0u5+vr/fccw9TNvbbwYMHN6ypuaqu1Wpt8PLL1XOtVqunMukD\nu3aX6VqWWspFhuxsHB+rEPh2PGhF2jTvT14/RXjpPABDqkHLzj/Zydhmmb1yt4XOPShDZfTrgI/P\naGjqVa5UmK+YcVJfW2LVsv6HDh9komGYjsXVeVYWzP2leko2aZxsRkZrrK/W+6rfZvORra43P+ee\nuea/8nUTty6kBccbdE/R0PMl42tUdXrDDVsW3T97D+Y7PU1P71prQZbmBuFl5zUtpUdmzS+E19sP\nNmxxBbUwGCYT4OjRoxw+bBwC3vXOdzEzMwPAmdde4/HHHwfg8ce/wE7LON17773s338AgImJbc7c\nIdWaE68Y4/NH7jy6Zb1umqquiKuFJih60uUo3i/qV4sqPyHElmo4z/M2UKFbeTAUrzfbNN2IjRNa\nkDsxVkeGGRo1utmRyjitlllk5xdXqI0aL47Qr/DMV18E4OJsg+lp44nx/JOfpzRkBCTh+WAH1Ght\nlGC7oddLgU/S7bjya6ezlRtpWtHbijNymxJNZm0CUAlS9+/mLYSHyjchgZuvni+cN1kQhC6onCYk\ny8x1KMHPbYFSRWJVHaY/C/Y/zrZHONsFTeb0z5qCvRAScmFG+k4ll6gElWr3W30DklMwvJ0ksO7N\naQdtF9Y004g81oBWTI2ZjVB5Gct55INSSNcGQ5QyoGNdplYbTYaqph18WaJtp13qSTwbtLPiaZRV\n82VKFgIjer1gf1IibPskKum5z+o8VMH1UV+rI6z6MfAD196NeouuDUoZBKOUy0bIWbjSchtQ4IO0\ngTb37j3EpdNmDJZHQ1aXjP2S9gVq2LxzfKLM+HYzD7Sss816JS4t+MxdNptLuSrAtkFXetRqVuWB\noGuDH3aSJisXrYrnwevX8YtXMipVa1+kQ7BCTlk38ayw99B9RxmWRqh7+aXTvBobr9alS1dYu2IW\nXOH7dFdM/+8f6XD4oJmj1bBBoE2w0pGKRrWMsPHUszVe9824qAfbyDLrrq5L5EcpL9MEVv0QFjxH\n255AX8euooggCHj99dcBePLJJ5179sLCwoYglbnKYXFx0Xm0jY6OurXw0qVLjNo1aX5+3q2vnU5n\nQ1DNXLg6fvy4E64+/OEPc88995i2LZc3rLsN67m2tLTkBLlms1no3+sjSddppaYuftA7QHTxqViP\nRX+tRXb+ap1gAAAgAElEQVTOCEut05eo2efTMCOQecBVRSnI7WQTunbt8X2jFgfwRUaou/a6zeio\nDU6cwMpyHvqg6oLvbp+YYG3G2mvVNZkd8962YaqV/tXKRRT3tqur7uzBTHZB2e1bh/SEJ4Vv6yG0\ncnuSL4QLBJvplC72wCQEQvWC6gpnUqA2Ck7Fg/INWLForfH8nLzo2YAK4ZEk+XrTs3cSCCeceYWQ\nK2a/6al/e88XvEeFcHtDtVrlttuMivXwkSOsLJv5eunSZc6fM3ZwTzzxBE88YTzb9+/fz/iEmd8H\nojvodAequgEGGGCAAQYYYIA3BDeFcboaioaDm1mjrVR3Re+54jNXozaLKVY2P7cVTVo0Mt/qvdeC\nVoLQnqj3HzhEWDU0fdbRPPXMkwB88nd/j9FRw2bs+O4p/uunPwPAYqPJz/z0TwCwffsOksyc7uYX\nFpnYZlim6eldTl0h/V7QRc+TZMp6Q2WZYypM2+S6Q7Uh6F4vDseNqe2kFyD9PACTIidQPSFdkMag\nFCCsIa+mBNKcwIXUyNyA2NfOg0wjyTNBePS87YzHX55aJcWTOasmwKq6lPaQuSrQD0md56DX8/LL\nFJnoXwXihWWXkkZKDyHzE56Htuq/UuATHd5vn+lw/JRhKNpelYb1AlpbXabRMaecTHTwx02/D49t\no2tVO2GWMIY5jSd+QmZTnjSyhLY98WRZ6jwHg3IFz8YnIhVkNpCmFwaoPquotEKnucGlplw1/VOq\naPKgjq1uF7T5zuJym9EdhiW4/55DnHvFnO4np3dx/qxlZjLFgT1mvCdek1XrtbQwoyiXTb0rY10y\nZeo6OlqjPWdjbrUlwyN5TKxaIe1S4k6YWksTmLJPPLdeRmTWA5Iq2HFXWp7lLcOmLiOBx8WTzwLw\nZ8++yMtnDKPVajYoh3nMoi7tFZMuZKY9xx2Thra/6+A4um0YttGgzgVhTs5ngh1cUubUWqJERVtm\nUXpknmVYRYZLoJR10Dn7FAr27Z7ou46AY3LCMOTcORPj6Pz58y6e0vT0NLut526tVnNqsqNHjzrV\nntaaJcsW1mo1p/aYm5tzz3uet+F+3kdf+MIXXGydRx55xD2vtXYM1crKyoZUWnkwzH7QTVMCG2g2\n8ANUzjgIjbJxlryFVUoL+bea1K1jwsjYNkYrpsxJUqfRNm2lsg4ly16HniTIg/V6EAZ5OimFZ4Od\nim7GUM5e+oLLly+Z+0LRWDNjuLZ9hNIOa0i/bYhtY3lAzuvjavvNtWIQgvGs7i2WnnNWQSjn/KNT\n1TO6RhY0EIW9QfccjXRxP9jE0rvyFNR//SDNUjLraBIEJbSdE5nqoK3ThpChY4qMI5O5TroJpVxF\nr3DpqbRgy1hSUvQc4wQ45yIpBCPW6WFkdJQ77zSBVJuNJnPzxkv41Vdf5cxZE+j0+PETJHmD/tXv\n2bJeN0Vw2pDXrBAuoCjYFAeQlNJN/qKNU9H9dXMAuBxFu6Sid8dmQSuf2L7vuwlfjKJbzP/UF4Rw\nucaSl1/hyB13ATBSqTK90yymI7UhlhbMIrW4vMop6wFQ73S4cPEiACWZ8MSXnzB1R3B7ZOozPjHF\n1NSk/ZZPyS4KnhbUbaRmT0hEbhuje9Fhi54QmS6GMhCFPE/XRxiGVmAyC0dOqfpSEwZmKAVhuRdy\nwZdusGdA7vvuBSV8kYcd6BLYZ0y2vF4ATJVHO1epEwiNMZBV+WnRC08Rhvh57iO8niNVqkl0Lxjf\n9SClJrBeUkKGZLmQJj3y6KKB71OzG8nOsVG6lgZ+8cwimVWHpPV5Gg2bL80DmW+QrTZeaFQjQ0Ix\nWTP91QwC6m3TDstra3SSgndhfuV5SBuMT3rCbbpIief3px7QOkXazShNuqxYD7uk26VkbZ9W6+t0\nm+aZxeU6+/Ybm4D3P3o353eb6+VOl9Fho87bNTbOkf1m02+IJZpV005nzq+xOGvaY//oNsLQtMfI\nsM+Vy6auzXrK8HBu9yJJkjzidIswzMdRSM1Gbe8HHSRdmwcyFWU8G9zyyqJidNEIQgu7r9BdN5tg\nu95mfsn0ociEy8WltaBsbZPWW12OnzZrxoHDh9lmwyyk6got64Un0oBKZjbNatLAC42Q0ArKyLTg\ngZrnbNS9kB77pkY4uLN/NZZSynnAPfjgg25+f+pTn2JuzmwG09PTXLhg1BLHjh1zeb8eeOAB1qyH\nbqvVYscOo069cOGCU+ft2rXLBROcnZ1lcdHY8+zcudOt281mk/PnjSA9Pz/Pzp3GlvOhhx5ya2er\n1XKH3XK5vME25XrQnqYUGgHeE73cbFKnVNZMX4yvtzhz2ajSlttNJkaHbN33Uxk2fVFfnWX1jFGr\nqTR1ehbhewQ2AKz2AJuTcWxinIVF88768rorT7VWybWFzM8vsdS0OUXDJSa7Zm3uJAFZn9tGUeVU\n3OeuluVCCOHUZxpZcJn3nEc3wkRKB5D46Dz0jJDoXMemJX5+LbQzcdBkvfdsyPPwtV52/SJJU1pt\n66Hohwi71qdpgm/b3gTlzVVvksTmB12v1ymFvcNET3iTzsMOenZXapPtVl6BVGUUNj3SPIh2KWT/\ngQOAUdU1GmZMXb58idNnzl6zXgNV3QADDDDAAAMMMECfuCmMk+d5G3LAFVVvOTbnsytm+HaF9f0t\ng2deLaBlkdEqsi5Syg0pAarVXuj3zeq9fiGF5NIlc4L9V//m3/LIu94FwEc+9AFujw4B8PM/9zEW\n5g3j9MrpM1Rq5oTz8Lse4W0PvgWAL33hzzj7ujntlMsVRkbNSS94+RUuXDKnx51TOxwdOz4y6gx8\ns7S7MbpHIW5Hfo5QBSN2LaQLJtkPhoaGHO2qdeoCM/rSsDBg0pSUrTrJC0LHDgktHcerBYS2nZPU\ncwETfTKXusXEcTJHp0z1TulKZa7MgZD4OePke3iyZzSurIotFUkhbcb1ocEFCxUoZB7AM0vI7Dkj\nkMKlthkaqnDbfuONc/71M7SahrkYqkLXsxS/UrTtD9bXGuydtHFnSj1vweHaEHMrJkZMu5MW1FTK\ntCMQlirOOFyJjNDPVRdAqdpX/TxP0m4ZNWCj0aDZyNk14/lmihuwvmpT62QKra1KKNMcu8swqZ9/\n8RzlqjHE3Xdgkskpa1i57QBtGwBVdU8zs2aY1FCWKFkvs6TUJMvMO1eXFROT5ruJl+JbNqxSFi5/\nXBCUXFqTfiDaKZ2SNSwvCapN6xix1OVKauL8XG6scdaqHV95bZZmw/Z5liGsCjQIfWRgx5o/zKvn\nDWP20tkW73/fOwCYnXmJi+uGvSl3BVOJeX9NrzMa2nYoDbHeNmzSYlPRyFP6aM2h/caDc/c+n9Eb\nUClrrZmdNd86ceKEU9tdvnx5Q+qT+Xkzpk6dOkVsDeCzLHPsULPZdO+B3po5NTXl1o+lpSWnqtu/\nfz/LlmG9cuUK2216kdnZWXd/3759jIwYNnJoaGiD93Pu5dcPFJqmZStKvk/FpgSq1LsMrVgj8PlV\nZufMmqo8Sc0GxizVxmmlhkGoNxsu/U2qm46VUJ6GsjUgD3y0neu18Wl2TJv2vHRhhU7OQPsBOY3R\nybqsdC0jvrjG8Loppzc1vGUa0a2wORZhMZ3V5ufctTMOB8/Lved6ZhOgXcDJcljZoJ3L102BROqc\noe6xMcaBqGCMXYj1tKnkfdYQymEJz8Ytk0I7kwuVJS7opWkHV1kCq73Ytm3bBi3RxhyMvW/keyEb\n3iOcqk5QjB1Z8EjXmo6TAzRh2bTJ/sN7GZvads163TQbp2Jy3qIwVFStFUMB5Ndra2ucOWN0j0mS\nsGuXyUu0e/duJ/B4nrcxb9oWYQc2B73MKeMgCFyjdrvdLZNW9gMpAw4dOgLA4cNH+dPPfR6AZ554\nnG9577cA8P73f5Bd08bmYHzHBL/wix8z9fUEU5NGR377HXfwe580tk/Ndpclm6tndG2EsGIjDieK\n3TbEwYE9+5zoszB7ieaaEbQy6OmutXLqh0zjbHg00qmf+kGlUiFJc3dSryc4CeFsBaQfuMErpcCz\nnmJS9yK9y0yTpnl/lZB5slUtnOeaoBfgD62czl1kmatYMTK85/voPE+c8MisrYOvPTzVvzCsZIib\nFtqEIQDQSZs8g2+mBY01s0n4UyMMV4xK4M7dE4xiFmvtlVhdtwt3J3GRp8PUJ2mbjbaLT2gTlHa6\nGcura/lne5Pb9/Er1g6pVDWKfKCTdJ23CiLEq/RnV1Gv11GZWbCEkFRtvighNEobgapUHqJt61op\na4ZsAt/yWkrbqkiWlxNqvinlhZkLeHaxm/BGWLX18JOE2w5YT9Cahy/M5iu0YGqnEaJW19bZNmY2\n3yAMCir6Fp2GWdQaWeuGPFwPLHRorZvNo1XrUrKqtHY4xEjFlPnVM2eYnTflXJhbJiibTXl6cozb\nH4gAOBmfYsb2YUKCZ4X6U6dfZ71hNtYr8+eZHDPCz7G921l79RQAO9MOuxasbYeYZ8lGQZ8vV1m2\n3+pWAvYetOO3tYSa7z+q9sLCAp/9rEl6+vzzz29Y8/INqdVqOQ+4O++8ky9+8YsAfOxjH+P+++8H\n4LHHHnPeeb7vOyHq5Zdf5uDBg6ZsnucEnmq16oSxdrvtBDYpJVdsNOcnn3ySRx81oR62bdvm7Jra\n7fYNmT9IPyCxQmyqM7RNFlteaTBkVdnxufOsrFrvzuGKs8FcbdVJW6Y9V5YXyTXfifAIXdR3jbIq\nq1JQdp7B81fmqFvBYq4r8dasbd6VS3TqNghxGNCy61xnpUnbjtVRLyjYEl0fV/Pw3uqZLMvw8zmf\nKVRmbRx94UwopExcHsiMjMAeYpUCKXrrWp5PWste2AGT8SIXToRbhL7Wy6//fTFNU9fnpZLvbK7K\npZCqtR3rtLsuBAGILYUlW6i8QBsMrTbYOjuuQG0I8ukO7drk03TP5wGdM0WWJ01Pu06ddzUMVHUD\nDDDAAAMMMMAAfeKmxXHKGZ5iWpOi+qwYQG1mZoZXXnkFgDNnznDRGk53Oh1HAR86dIiHHnoIgCiK\nHJUspdzyW0WjxGJ6gKJE2+l0Nhif3wjjNFwbQWpzwvzu7/ooSyu/AcD50yf548+Yk+H62gqHD5tT\n3N3H3sp73v1WAJJWk0sz5rR27z138EM/8jcB4+WS5+rZuXOSsTFjVFwbrlEJc++qzHlCTIxPsLxo\nTowLCwsuhku320WneWwP7dKdUPAU6wdBECDzOI5K9uJtCOliplBgBtIs6anVPM/FrJFSEVrPJd8X\nkKvGtKRnHK5c3BZJLw8gWrgDnYBe4E3PI7NsjKKQ60lv7YFxNSTacznphBcao3DADwISqzrqqJQV\ne+quT9QYsob6E0NlytttLrTaOGdm7Cmw6dHKM5ioBGWNuj2/jLLtf2VhwdHGQnrOg0hWqgRDo648\nldyYVaVom45CeFX8PlV1Y7VJSqGhztudNVo2dlM7SUhtiphutkKqTZ0IoGKDDfpdxaz1MltfnXPU\n9gqauGMcHV759Be5dNGMwQffehvTh8y3dt02SdUGCeyUGgxZ79JOGwLrMSdETzVfKoW0WqZtGq11\nSmH/gRPvO3GJqmVD16oedctcLpV99t1pYrugFHe+w6jbSsGznDlrmKK7Dx/m7qNGtX7hwjnCen6q\nD1wMs7krC8xb9VCWZlSqpp8b8jwz80YdRqXG0f3G8zKducD0FbOGbStXadg1rM0wbasuVMurZOv9\nG04/99xzfOlLJmjn0NAQ995r0jc99dRTLCyYPgrD0JkkdDodfviHfxgwrFHOMp06dcqtqefOnXMM\n1drammP3p6eneemllwCTdT73wpuamnIG6sePH3equjAMHVu8urrKftsOu3fvviHm0Pd8x4B4gFw2\nzJW3sERjxVzPLS2y3jTjNvVLZDaYqk7baJ3n12vRtMb53VQ79jrIUkIbp60ifXw7zs+eP8uzV4xa\n9vmZOre3zTNlMdMLyLltjKxp5s7y7Ao77LXMgBtYU3NcK3ZTMT5Wq2nYr3IgjaE7kCRdwjCPv9SE\n1OZgDFKw674vPeelrBXO61hJicqDTErpzBHEphym/TBjW0GrjKZlJZOkSatlGNxut+uY0Yltky5e\nk1nte0zRhpQ0bk3Xzgtvs1fihrLlLJNSCPeejTn7ND35o5e3NXQey1fDTROcinTjVmq1NE35kz/5\nEwC+/OUvu6i4aZr21DGe57xBzp07x1e/+lUAbrvtNkcN33XXXW4hWFpa2qAWzD1DtNYuEJvneVf1\nyruRAeJ5gWMPO53EuUA/8tC7OHbM2IXMXH6Np595HIBnjz/F/fcdA2BypMa2bUb1tm/fAX74R/4a\nAFL6TsUmhHR0ptaarvU88IV00Q+DcpWJXWaRqm6bYs1G7F1bWyOz9C1akVrvtnOXl1zet37g+z5e\nTm0qQZJH3RWei+SrhOwFPJMeQcm0/1BYDJIZOLrfF8Ll1dPCc1Susf3JPUV07xrpopcLoTZQs3n0\nWaWzDarhG1EPZFr0otgWPFc86aETOzaERFr7omZ7xentwzLoSq4+Sxkumet2omg0baRpH2pWVSOk\nZK1u7q/U111gOSHoqfaGxqkMG3171mrRbNuF2w/IlFl4/PIowu/PdkSgnQCbJh3Wm2Y+rbc7eEEe\nGDBh1W7inU5A3SY8ra8IOh3TNju217jdemnppM7SitlMd+2eYMwGVByujCHapv/nz9TxbdLb8nCV\n0KpS02SZlrXJ8HXPqy5NU6eKL5XDG4o47WdtRm0OKh/A9tX6agZ2o3/g7W/nzrcYu8KHH3obM+df\nBcATKZdmzSEm0xlVqyZta2jZgH1rq02CwNSlFFbIMhtEtNPkihW0ZaXEOz/4raat0oT5P/4jALqv\nzzBik8MOza7TskFeldelG/Rfx7m5Obcubtu2jWHrQfbwww/z/PPPA0a1ngs2p06d4vbbbwdM4MoX\nXzTBd0+fPu3WQimlm5fr6+su8nKSJG4Dy7LMCWPLy8vs3WvUlLt27XIJVjudDp/73OcAIyydPn0a\ngA984ANEUdR3HYW2HrKA6KZkVphhrcFlW7ZWkiKsymeu3qLZNGtSp16n2baedEq7Q9VqV2zYOL2g\nl8NurW764ukzF1mfMAfW9W07+dKrJtTDWHmImlX1yu3jZAtGUFztZC6nocoy0hsKKvy1ZiWb4XYh\nrXnB9u3eyTGqZXO4OX/+Ivc/cCcATz39OCIxgsr48LBTQU9N76Y2YoTiNEndcirDKth5qVSIyvcD\nkyaiV4YNe2H/62ml5DFzwbTTM199Ct9mBkiSxI3ZRx5+O0ND5loKzx2+pfQ22DQXvfG3Cn5dlDOu\nFlA7y3oBlLNMOa9vpRTSjjWVFW3AtsZAVTfAAAMMMMAAAwzQJ24K41Q0ECt6DwjRi8PTaDTcKWh5\neXlD7KZiYMzQ5tNKksQZJj7//POcOmWo9gMHDrhMyHv37nV5ldrt9obv5uXJsmxLA/XN+e/6QZ6X\nLU1Tjhw2huKHpncRRSZw3p137+fsOZMD55nnXuB3/vO/A+C7Pvh+AntSPf7CsxywAbpqI6OUQhuv\nSZbpsacCPw/8qLTzqlNak1mp2S/7jNpTsQir1JeMx1/abeEM4tSmsPrXged5PUNCLQoeiMIxM9Lz\nnCea8CWZjaFz5tXTVG0MoiNHjnLbIRMj5tSpUyidq/mGHONkTgI9l5AsP8kLRWbjMiVphhZp7xk7\nZrpZ4tiwJE0KufCuDymEy/WksozMsk+B7yGsd56mN24rIoV1oxrJOk1SnccnqpPmhq3dDmnHUNRB\nNXT9lSQpK3Vzv5X0Yo5lSrswVEqHNJs2MOV6C5UZhmp8dBhsahuvPEzWp5H/yuoc5ZI1nG43XHvX\nRkbpuJSBmizPB9jSrM0u2zJWsLbSDFVGmcw9T5LQqflGxnyGSoZxCoRPuWqDoXoZetXMuXJYpepi\nx2hyV1CtNCrPF5UkziDZ1x6B138aixGvxLh9vOZljOVUXrvFzPMnTPnLw5Qt03LkrsMctjmrzpw9\ni7DzJgt8GjaFTqJg3aYRabXbTg0+treGZ5mo7swVhm0ajJGhcVqpKURr+w54x3tNEfSX8c5aT8M0\npZIzKlmJht9/kM99+/YxOWkM72dnZ51h9sMPP8wDDzwAwLd+67c6dug//af/5FR7+/fvd6YLhw4d\n4oknTNy4S5cuuXWxXC7z6U+b7PJSSsfov/rqqwV1asmZVGzfvt156p0+fdqxCe973/ucUfrJkydv\niHFCaYLcsaOpqJhmNmu/ZSZ1qUw2bvpxdb7FKzMmhlXodWnVzbycmBilagOfxpeWmbML6VhZM2nV\nxPNzLY7bIKJL0udd7zRq3Mb4Rb74sumvNV1myDNjoLJ9jNQz+0+9k9LN5y4Zqep/vdkqV6tBb+3L\nWZE0y0isal21GzRtzC2RNNAdU5aKp1BWPTc+EhjDcaCxOkvXMnAmlpIZd5Xh7YiSZWzwULkBuSow\n/mxOm9Y/49RYX2Xcsp7RbbeT2ByYQeg79btWWc8jWkpSp0XY6AlfLIMLcl2QJ0xOU1wdVcFL0e3x\nUjqNny+EUxFKBJmdE0KHyOvkjbxpqrqtkkEWXf/L5bKbVHNzc27CF1VpRSpucyLgPKDliRMnnBfe\nwYMHXRC31dVV97377ruPI0eMYJOmqcvttKGB2TxYro1UKJTdvLJuypBVLYRDZc6cM7Ty9K5Jbr/j\nnQDs2vMAB/Y9DUBbr/FHjxsvvCefinn43WbSftsHP8DttxsKtjY8SujsPLxeMDPZC2ymM0W+9CpP\nuMiywWiNId8ELrw8e4lu1y46SuHdoHCosp7AmavMMg2ZddPwPd/ZHbXbba7MWZfpZ79CyerhV1cW\nePjhRwA4emQfZ183Ql2z1UTmKhlPIqywpDGhAWx1e9O2MLHSLHXu60nWpZvm9g3JhtAT14MnN5oo\ntK3w01Eb9e1DVWvXVNa0Fox6I5Pg20CN5SFJUDELaDO5zKINTFoJfYZHjGCxXm+6g4DwPFTaC9mR\nRztvN5o0dT4XBNt3mH4UOnVhcn0/pN+4gtVqicDalykdEuRB97wQbb8jPc3YuFFRzc816Nik03Ks\n6gKs+gISa+9R9T1yhULSbeFVzSYlZIm69Taa3DZOzUYBFlmbqsgF0rILZeFLgW/7vBKWnMo3S28s\nsaj0Ja3cGE+Ab8fs9FCFpGHK/MoTX+HMGaNC2nd0n+k8oDK5g6l7jR1UG8HCslFB+n7oIhpXK2UC\n6y164MAuFpZM/8+sz+LlpgGB4stfMnN6Zm6eyR3mALfL8/AxG95URVCyVSungQtW2A9uu+02jh0z\nqv4XX3zR2R1dunTJCS2vvfaay2F35MgRd9Bst9sudMpdd93l1KDDw8POVnR6etqp8L7yla/wzDPP\nAGZtPnTI2IBVq1W31hZtSH3fZ2LCBC78xCc+4UIWrK+vu3L2Az/FHSiDrmJYmHI2Fazb8TZyaD9T\n95t9Y2olYfaJPwegVKuhrPfq5dWEJRvo9eWFNeo2IXUt0IwtWVWpv0xoy/m2v/RuJnaZebav3gY7\nDrtBmf17bC5CkZHaHb6tNdJG/W+prsuxeT1kSjn5qBhuMlOFhPRZF2kPJfXleaI95rBSSds0G0bl\nO+J1efUF0z/N5UW0NR24dGnRrTVplqG1aY9tE+OI3Jyi1WCoZNYsKaRLdKtJN9RDb7B56n/PWFqZ\nZ2zEqD137JhyIWakpyhbNWKlXCkcyBV5flZgg6zQ87LWeHkS92IIIYmz91VJ6gT8LMvcXiJ9r1fH\nQi48WbCh0rqNL9vXrNdAVTfAAAMMMMAAAwzQJ24K49RoNDYYbRWNw3OVXBiGPPKIYSGSJHEnnHq9\nvmVwyyAIHF1eDJhZ9Jg7c+aMM0yUUm44WeXeI/nfAfbs2eNOUzdqVKxQLNi0DSdeOsnqqjFkPHr7\nEReQ7OLlRVbWTJn37tnHI+808Z1eO/scswvm9JCkmk/+l98F4OSJk3zoQx8C4B3veDsHbFwVPywj\nRO5dpclc6hOJsBK6BzStaqGxvk7X0rftdhfPGqFKITenJLomzKnInoR0j+bsZp38wE7Fz1OnmJxu\nh/ebMpd9jbQKqKFywFNPGvXAW99yPw8+YIxWXztzwQWzS7MQ7dlAmn4J3zId6KwQJ0U6OlaR0lSm\nvp20Q8syTp20TZJeO9P1BsiNRoUVG2Oq2W47xs+TOG8438/wQ+vVODHlDO+TtES4Zvq9i3asxEhl\niLFJw4JKOc9q26gTtFKFVBCC0JahJFNK1nByx/Rexq3h9eVL51yMG09A0Cd9Pj62gzSzXkhJL6in\n9EoENr9imrVJbPqJ9vIiJb93vipZr76VTpd5G8x1+2iZqmXgOt2ExM4b5QvWbE6x7uw8260nXbbS\nJD5tWNhuVRLkKkdSkqyXailn1MpBiTS7NnVeRBmNygNXZkNo3zJjUnFUmjE13m6xNGecReqrawS+\nYRJqx4bwbN1LQQWRO2EEIRXLxgRhyE5rAjBSG2PmolGTddsa346Xy1fmuTxr+rzZbHLxnPGeO+V7\nTFqm7u2jw4xaVXMmOo5x7AcTExN85CMfMXUZH3epVbrdrlMjnzhxwjnEHDx40AXABPi+7/s+wHjD\n5evl29/+dsc4zc/P8+53v9u0W7XKn/7pnwLG4zmfH7VazTFIR48edXGfnnjiCbd2Li8vO6P0tbW1\nDcE2rwe/kyDsWNULyy6l0uJqnY6diwff8QByypS5msH+bYbRH59fYzgwZfhvf3YKbZnae/7ye2iu\nWlX1St053GzfuY37HzSeiROT43Rs8MzpyXGGRs1e8ezrl7l/h81BemUer2MZd08T2lhnSgl80Z9a\nOcsyxzNtyFOH7/bLUPoszBpV4asvH2dy3Mz/pXaHmRlzv91u02yaOq2uriLtnjo9PU1tNU9rIkks\n837p8jrKsp5hdYSwasbv0PhO9hwwmhiluxRVck6lCDe0Z6A7LsCxJyRDQ9Ypy1d4ubOQ51EO8uC7\nmkkKE04AACAASURBVGbD1CWsVPG93KwhQVqNRdpNSWwuz3Kl4tquvrbu1JqlsMRyfdmVPVcLLq+u\nUB7K40f11vRmsxAYNW1RX7tyzWrdFMFpfn7+a6J45/8teszl1w899JCjmy9cuMDly5cBQxPnuZSK\n1F0QBG6ipmnq6L2N1vQZDStIXL582XmMPPXUU06V88gjjziBqt1u31CSX+hFIB8fH+PV14yXzskT\nJ5mZMbT4Rz/63e6d586dY3TcLOLTO/dw5LuNSu7wwbfw4kvGDmN5ZYnf+s1/D8BnP/t53v/+9wPw\n6F96lKkpsxBUyjVn/6GSlBXrEhy/8goXbR2bjYbLB+kFAXfefe8N1SuHhp79kueRZ5bV0HPTFUZl\nCBD6AaGlhB991zuYmzXluTI7w5S1h5i5fNmNh/HxMUIbsG11vcWqVRFlnS6ZlcY0GZkVojKpXIgA\nlSZ0ra1JJ+04VV03bTs7q36QaIl0RKznbATM/Mr18LBgF99T2Rpq3QYpTUP8IUOlHz87T2g9SO44\neJC6zR/GyBTnl0zZQhU4j0iRpXh2Yyh5EmkXku1TU4ztMIKWliVa7VydFvZoaa1daIjrQWUBQpj2\nKJVCujakQckLnDDY1YpV60HYWG4wPG28VJutFspuCjrVzM/ZKOnBBErnkeBh3kZAX2lcommNovZM\nThLaKM3dtMnZ18yc7gx3uesBmyC1Inv0ulJusfO8ACn7FyqCRDnVrp9JM1YxXpgVL+/PEtusrZnn\nhbRsbrv2coNq17Trd33L+6lYm6X40gV27DJj9v777yewKtZnnn6a5VXTDoH0XSDYZqPBkF0PRkaG\n3SEvCAMO7jWqwGqrSeuS2fx8lLPz6Adaa+655x7A2Cn9zu/8DgAvvPCCU41Vq1XnhXzy5EkXLuC5\n555z5glCCOcN12q1XNLeEydOcPz4ccCEe8mfaTQabq3du3ev82BOksQJbDt37uTECbOGBUHAqvXu\nLZfLG7ycr4eMFl2bReHiyRnqNjfizOoqI/cau1E1PkTLCreJhtp2I9gszC6jbMLtVpKxd4dpk3se\nugttN93G0hpdays1MjrG6JjZ1NNuC2XXj1rZY7sVls6+eoZzCzaqtRAEbTM+y4HHUNlcV5WH6PO8\nnRXUmxsFp4xLM2Z+vHL8JRZmzfXS7CyJ/U0mSxuyCwzbtknUKC0btmFm9oLLrJAmiRMShUiQ0szL\nVGsIzJr7gQ99J7t2m/UlExm6YOezIXfeDQhOKxdP0Jgz/b9r137W5kx/+qFgyEZ5Hx4ZY/a0sS8r\nhSWu2PAShGW2bZuwbdX76PzsLMsL5kBw4NBh8ljgc4tLznN7qFph3UZzz7LMRSNfb9RdWJEsy0jy\nda7R6BEN9SVq1WuvNwNV3QADDDDAAAMMMECfuCmM0549exwL1Gq1XEbtPXv2OPVZkiTuVDY+Ps47\n32ko15WVFWfUODs7y8svG6+01157zZ1kkoIHjlLKMUu+7zvj206n497/9NNPu3eurKy4sj3//POO\n8p6amnJle+yxx/qqZ/6tY8fe4srwe5/+lKvv8vIKH/7wdwImvknJsihp0mLOnt6nd04zMma8ZT7+\n8Y8ze9lI1gvzq5w8aTwHf/+P/oAf/dEfN9964K10u1Y1kiSctXT8s88+6yj4Wq3mgmfmTB5wIzZ+\ngIkllef/kdJHWMldCukCVGZpSk5AKwEtG1dl6coMDZsOplL28e0JQAPPvmBOttPTuzhy8DAAo8ND\nlPaZ01Icn2LBeqVpvxB3y9OO9UrSrqOiu90OnSxnnDqoGzgiKRH2ArApTU66S89zXo2e77GQB+DL\nUqrC5lxqpKiuOcl/5cXT1IbNr+848BZ2WLr/bCfj+VdeB+DornG6duyZeFY9x4ecTRIyIMlsmpVO\nB209BJXGZWFXaUrWJzs67O1kdd2wHEnSdmpeUdZkefoJv8aIb9o10D5K5c4ZGr9i43Jpn6U1M8Zn\nF0ICG7MqrJTIrIpksT5D1jX9ttISBGt5eoMynjUUn9451MvyLiG16rmgXCawjJMvPbrd/tWtSilk\n1huP2no5dXQXz7pP+FJSyQ1zM0172OYP3DOFtqlVDh3Zzw/e8SMAnF+cZ3neqvbqdVptW6/6MiUb\nCPTYffc59fWJEyfIrHpcZQmrNp/dxOR23v4+42G3c3WVV/7wv5k2b3cpWTViP9Bau/Q0tVqNd9hg\nnjMzM+5+MfZNs9l0LFO32+W3f/u3Abj77rtdcMtWq+XUbXfeeeeG/KK5B1/RIy9JErfOnTp1iqef\nNs4ujz32mCvD5z73ORcbamRkxMXn6wciEP8/e28WY1eSnol9EXG2u+eeSTLJJFmsvbp6KXWrSlUt\ntVpbW2pby8iGZgTD8HtjoCfDMGDITzYML7BgjO0Hj+AZS6MHG7JnetQeSTPqkdSt3qvYtS8sFrdk\nJnO7mXmXs0aEH/7/xDm3il28BIx68fkeyMvDc8+N7fzxx/dvkIck4/e395B26XN3YxHty1S6aqRT\neEzcSKlQ8FrNoaHYuflXfv4LGJwhxswmx8h5veUih/E5uAQ5JuxsbUwObWjugsDH5YvEVt2+cQt2\niZj+ga+gTw94/FsIy8AHCGRzRvHaevkoWUWEQQpXTuX1115HzMx7y29BF9T2VErHOIVhC5lhq4OQ\nKJhRVh6QsPm30AY5150RVkKVW78ELnJE6WOPPQ5dujVIuDxOFh+K+nuIgKKWr1z03/BoH5oZHmsk\nNFsjiixF5nK5kYsHANze3cHo+JSfJBw7lMcxCpYH77z9FuJS7hcGBb9zuihcrsPR6NSt/SSdYsK5\n66yFM3EmSYqMc7/l2RSPP7r6sf36RBSnq1evOorW932ntIzHY6fM1E1v1lpHB/u+71IKbG1tuVDb\n/f19l4Lg5ZdfxvvvU+biOmVc91FaWlpyylUcx872X4/4u379+oxiUSpm88CaKplnr9fFl7/8C9Qv\nz3dt+9GPfoQ/+B/+RwDAL/7iL+Dnv0yFgFfWFiAFjcPhwQiHLCyOjk7x2c9+3rXNMHV6dHSEe/fI\nV+B73/s+8pz62W21oHmBtFotp8i1Wi2c2yRB8/Qzz2D/kJQ08RCmAQBQyqvq32m4qD0ALqmYNYVb\n4MLkUKy0dMIBvAGHBF+7hhaHByvfx+YWJVJcX1nF6QkXNYZBjyNVnrm8gRv8An2wveOoWWsMJL/E\nymjn76SLwtHgWmsIb/5+eipwfZFSuUTohTFVuKpSACszXtSCz1R3f2kJnS6tn8cu7COOSRBn0xRB\ni/p7sH0H/YgTFw46uHVEY5gW1iXALIxFyD5sUgUwKCNCBBfiBPLEkmQEAKXKMnoPxFb7Iu4c06Yf\nIcRxQRvZdJq4JJpt2Yaf0BpZ6bUx5fXV6XXQYdOMbyLErORsn5wi6lJber7GAWc3hm9x9ixR7UYP\n8e5tmtvxaR8LK2TG+tznNjHo8dqRGjGbV0yuMY1pznvtEHn+8VEudXjtCJJ3U5n5Ljw8FxLjMgLV\n95Dy8OUW2HqJkmE+/fe+ioT9lLZv38HlZykq7cmFz+Kf/qN/DAD48z/7l/jN3/xNAMDPvvA8znH9\nyS9/6ct4713ymTx/bgM3Obz9rTffwlKP/HBeevEFPPWpZ2j8d3axzQpJdDSEEPMrTvUEf1prJ0eP\nj49dpNvKyspM4diyZlyWZU4WjkYjd0+WZZVvTRA4+VGPWh4Oh87k99JLL2FtjSKmXnvtNffdP/3T\nP8Vjj5E58sKFCy5z+GQymT24PQBpAkx5rT5+6TzSjNrfXl2E7HOaFmER8pxKeAj4MOr1exATGs/V\nM2sI2afnYDSGv8j+aX0fKSvYWaFxxGZNY3L47CNXWODyWTLVvdXzMQ44IWqSYMJ167rnlhF1yoi/\nFFM7n+J0Ojxx+6IxxsljISyWF+g9e/rpp3HjGh28FQJ4LJC0TVz0shDCyawwbMEs0xgXukBaKgN5\n5j4LY12tOs+XuHKZ5ioeT13pUiMqpdsCs7XhHsL39813biEi0YccEsm0LCpu0S4LMu/tu1OgF7ac\nHHz11R/jiP2G42mKjPOlpNMJuOs4Ph1hsEgyJghb2OV90ZgqzVCR506Rz4u8igY3xl0XQrpM5v1e\nBycchfmT0JjqGjRo0KBBgwYN5sQnwjj94Ac/cCyQ7/vuJFOasADSmsvrYRg69qkoCvfdwWDgtELP\n81wtJSnlDGtUUskLCwvu5PPMM8+4U9bu7q57zu3bt53WWT991RNmzouycrWEcjlQvvKVX3P1eT7z\nmefwR3/0vwMA/tk/+xO88uNXAAC/+qu/giefJOfwfn+A/UNiAVZWlvGVr/wKAOCHP/whtu+Sc/Vv\n/86v49JlMmkdD0dQqjollie6yWTinNUvXryI9Q06GR4dHTl68mGd34XnuUgnAQNR1pKT0pklhDWu\n5IDJrUsklqUDLLG58PzmGcR5yd54zsy3v3sbsjQFFhne2aH+Rr6PJ56jiMsnL17CiE+Go9EpTk/p\nFDqdTFDUnMDLU4XV5qGqlUtY+GWOE8BFcxnLjBvIZNViR+fV5Q5UWZk8DNGJ6Ld+42efxu4dovLT\n0Rj3mG4/u7GK5z9Pzt67d25iwqcoqwKk7GCvIRGww7/yIwjF5WyMdubCwhhXW8lTAtrMt1Zb0wn8\nA1qPycEQZy7TO5QGApOytuFpDC/m6M+VFvYOabwH/gADxbX4ohDnLpP58fDGB9gekQOrHSeIuI7i\n2csrWFqkcWq3V7GZ0Dtx490EC5yhcnkQwmqOvMsKGB6PyfEJJvzepIMugmD+BJjRoAsreT2mPrwy\nQawCCjZp9C+cQ2+L5mHsAY9/jhinXr+HvXeIyVbDMVoTapsfpXjuaSqddLyzi6fZpHzhSxcQcj6a\nxeUlLH6BnvNTP/VZXGem+erLL+Mcy6qLjz+BZY4sSvYO4bE5MvADCDF/AswkSRyj/q1vfQt/8Ad/\nAAB4/fXXndyqy7zpdOrYDSGEC7LZ3d11sjbPc1eu5cKFCzhzhsxS5FxL4/baa6+5Z7733nv40pe+\nBIBcG0oLwMnJiYtOfvHFF11E3vPPP48LXKZnHug0gGmTzGhlYyg2X5kshmAzUmAEDNMnSllIzq8F\nT0KFxNqcCgufHaZtaxHBMucAUhYnbP04nSauPluWx+j1SHYOgi66imTV80+eRe8ssVV3fvAKCmbT\nW4sdlJTvJD515V0ehHu7u04GW1TBTEJoF3F26eIFbJ7h4BAtEfEeGcmpM19bW6tnJyU052nLjK5E\nnwXS0txtFJQNua8ZFEfNDg+PYFmWWVmlRlZK1sy/1uV+mwet3io2Nsn8e+78Bo4OKAozSaZYZ7ay\n3Yrw/e99HwDw7o2buPQY5eX6u2//LRS/u+PxFMfHZIm5sHkOV7jma1bkeO8dct+RnnffJNczsAKC\nGXyda5ebzVcKinmkIi8QTz+eVftEFCff993LWSpHAG3upe3xw/VnSpQUNEAKVfkC12vUWGudP9Lq\n6qpTnM6cOeMUicPDQ2ePT5LE1XAqisIpUe1229ngDw4OZiIBHwQppdtYFUQZLAVrLTyP2vzcc885\n4fX1r/9zvPoaCak7t+7ihZ8hxeArX/llxAm1c3l5AUvL1M7Lj1zEteskmC5sXcCZMxThc37zoitO\nGXo+Il7UFy5ccL+VJAl2OTLx3v4+llbW5u5XHUIqF/llYV3EBhBSQkZw9Aa/oLnJXQqCd959G1/4\nPBU13lhfxZTn1QDIE6ZFlYDnsWIsuq4gcr/Xx2hCL0RbBuhyuPj5tTOV0IfBG7cpIujNd95yL0QU\nhs5nZR6ooHolcqNd0dBAKRe6D63RZ+G0ttRDyPX4EC2gt0C0sT05QIejCA9OTnHKG3Bv0IFke/50\nmsJnE16nsMiGnKjTCudLpmFQymFK2FZmzAVyLhZsc7gQ5AchOd7Fzrs0TrdvHeLeDtdN21zGNOV6\nejDolAkGI41DVgyGMdBtcxJIT6LPddwe3TqP/ZTr8tljLC6SGWUwaMGCU40rA83Ct7/kYeMMfbcV\nGqQcxSa1QDek37K6cObHotAPlRqk50mn1IcichlNE18ht/SudLsdrK6S78qJLXDvhyS497/519g/\novX4xJdeRJsHX2cxHr9Mm373+RcQsZ6qDofIBjRv91BgcZHe11YnxCOPk4nq8ccuIQBHi8YjeDGN\nc3HnNjbYP9HvdJAU8yuHYRi6VAD/5J/8E+f7aYxxkXRvv/22M6VFUeQOc6urq07uHhwcOJeEuoIU\nRZFLI3D16lUnR8MwdJ89z8P3v0/jVhSFk+2f/vSnXeLhJEmcXB8Oh+7580D6AWKu+Xl8OMK5Lo3h\n9HgCeUJ9DDp958NoLZCxHEryHGFemu2WcZd9Rc3tffg+KWO7uzu4xtFrY20QcZby6WSMhQVaw5/7\nqafRY1+ZjTNtqHM0htPXu2h3OSJvcxkBK5NdZTBhf8wHYTIauzHzPA+yTOpotPPhMQZu/XpRCG1o\n3gqTIwgqP1FnltI5+04BrSBwUXiFLlw6B43C1YOLfK+271pkZdSuL1xSSpNXPk5KKff8efDq69fw\no9c5cjQALnN6mk8/+yxu3qFo88Ggj/OXKQ3Ca6++im+w39/qyjKeeJzW4MHBEV59lXzxzp8/izQj\nebqw0KOapQCyeOL2JGGrZJhKKSc/POUjTfjQrrWr2+kJBcEmfVNYJPHH7xmNqa5BgwYNGjRo0GBO\nfCKM03g8dhprnWKuO4d3Op0Zx8HyelEUjsJM09Q5lksp3emlKAr3/DLfCEBafHkqOzk5cYyTEMIx\nS1JKd319fd05Qdbr680DKSWMc5C2Vc0cSMc4KeXh089+htu5iL/91t8AAL7zne/jX/8l0dk3btzA\n1iN0sk2z1JXHWF1dxmRK7MBwOMSAo7Q6bekYpzzPMeb+xnHsnEFPT0+RF2WUSOAi2h4WQipXx01Y\nA1X2S1h4zCylJq/yLBWJK4NynBa4+hqdGD7z6U+h26Y2jycjdMvIlizBwoBOyOvnLkGFdLp7+9oN\nl2TQaONOy17NWb2/MEDB0RWeUog4QklL/XBRdUajyEuWU8Hzy6SXpmJNbYFOUEa6ZS46UgoLFdIa\n7m70MD0lx8btYYwRO8yvLq/ikNdbAR+rnDcs8CWmY5ovneUuqiZNY3gdpp9RsaxK+ZykjtaYUmU5\nno/H6WiI3X0aSysCTIe03qeTKdocWdZb7MMry7y0Aiz0ab3sn8bYYQZgK+wg4iidjhFY3jwPADgx\nPjLB+VMwcUlfkrjAkEvT9NprWF1jk59vEYI+S6UxZof6KAwASe9fUuiHCeRBaAGPzS5dq1B6m4bC\nwvDpNLl9G9ucjDHVBhFHE0ktsMpRRutXtpDy+sXePaRcGih79Q2knGtmvNBBZ5NMWr21dSQdzmEl\nAcHOrxvntyDY9GJHp9h9naJI47ffwaBkEz0Fyc758+Db3/42rl2jXHHD4dCx9HWG/vj42Mm2IAiq\n+oqtlmPop9Opk52bm5suL9O5c+fcc46OjmYSFdfLstTzNT33HJkpv/jFLzrZub297RiwIAgeyj2g\n0w+h2el6GPiwbFJKhHA1/kwRYfceMWZpEmOX86W1p8dY5wjXO+PrKI5oXXVxiFd+TPO4dzTCeMRm\nYmXR4oCMvDDQgtbG3e0j/PIFYia75xeQcvmTaDFCZ0zztbDWQpLTe5RlGp4337s46PfvW7fVM5QI\nGQCggEkZEZZN3TxoL0DGjtZBEDhrhzYCSFn+ZrljtAI/QhkiXJgCCZvH80Iji4lpiYLI5SHLdIZC\nV+VOyjWSxzHa0fzr9PbtHbx/m+TNaHKEbpvk4y//4i/im9+kPa+/sOAYytPRKQ72aH6uPHIZd9ld\nIwwj9DkgQCoy71HbPHTaNN5H8cRVa1Geh5XlJTe2JfrdPt5/7wO+XjjGCcY4K1GmBdIHmFs/sVp1\n5QTGcexebqWUMyelaeoSUdZpXyHEjDAoBYGUVbI8z/Oc3f3DiTZL22y9YGCdklZKOXt/kiTufs/z\n3PV5IFFFlhlY6DJ5WC1qT0qJiEOXty5uYXnttwEATzz1KfzFX/w5AODll3+EN96jlALPPPsplAHx\nVvounPR4fwizUdZBO8GEaet0GkPXoihK/5xkGmM4JH+bhcUFLC7RZq2sdrXH5oExluorMVz9Hyh4\n7IMiEcEU9KKncY6MKVVPKuxx0rKXX3kFl7Y4gWfoo82KUyYyLPLLURiDN3mBv3ntlqNgpbAQ/NLE\nWruxPdrdQcwmkMD3IfgNKoR0itw8sEUBm1L781wj7NJGojot10eMh+hEdP1geIrhCc3FYDFFwEVD\nw+4ywmXyg/HWC5wLSfhubG3h5ttvcjs9LDj/ksTVbBNpjoJ9wOLpBFGf6XZb1Qq0QkGyWVMqH/mc\nOv70ZAzfK2sYhi4DfSvy0e9z+Isx8DgRqRYRVlmG5Frj9h4dOEzQwgq7lFnk2GSFNznZdcVwW502\nxlOa2/29GG3Dvk8XlhGw7N05uAdPkDAVQiLlUG6/pZw/nS60S9Q5D6xSLuBTZgrSlgXDNXx+L4PM\nQGal2VmQAxSApN3CymcpsaR/dgWaldnk7etIf0yK/8K9IXxuTjI6AnizTsMOjnisjjttPP5zlFJl\nOhrj3fdfBgBM3ryG8BaZh9qTKQo283rGQJr5fUd+//d/3ykkSZI4c1s9ca+11h0yygMhMOtP6vt+\nLbKoUszfe++9mcLspZKWJInbvK9everuf+mll1zagW9961uu7uibb77pFK3Pfe5zTmGbB8JkWOrS\n88MFH4ecLsB/5Anco4947zuv4733aaNNC+D4hNbeIx0LtUJj+5c/PsEZTpfxC09vYP9tinbcS0ME\nRbnPTBH0yXwc6xyWD4XpXoroAo3nj24ewt6hcX7Wt4jW6H0xnsUkpgN9oDpzF04virRKCSOEixTL\njUXO5sEoitDiMUvTtBZtHjgfRz8IZqpoRLUC8F4tHY/7buAh5zQxWZa7pMaQgGZTZxiE8G25R0pX\nDBywTjbNB+F8f6MoQszK3tf/xZ9Vdfq8ANdvsL+zFNg8T0qU50ns7tDcnj9/Ad1Om9uvwJZ4bJ3f\nxIirdExOJy4FQbfXRYtdCcajEcYTmp/p+BSdVuk21IXnl3uzcWtTSolO++P9DRtTXYMGDRo0aNCg\nwZz4RBin0mQEzJregCrXUpZlM8xS6chYT2JZz10SBMGMc3j53fo9URQ5KvTk5MQ9sygKx24JIdyJ\n6+DgwLFMSil3fR4IW6/nU7FbEtbV2wGsy9Vh4KHLtPinP/tprK5TPpdz5zfxd9/7LrdniJ1dOs0e\nHx9jNKJT4/vvXsMVdrKjMihVNIDh08BoNMKdO1zj6No1jEaklT/z7DNYWFzi+zPnQDcPtM5cdWtr\ntYuGszDOFKGCACHXLUvS1Dn+ZnnhTh77e0McczLBduTjM09SBM7q4hncuEUU+Vs3fwwbUTvD9kJl\nBrXGsWRKFi5JW9SqKmyneeZyXklpUdj52QoJi5CDBXrdAGGX2uAFIbqco2kSD3HIp+uzTz2ChQGf\nhEyB7JCuT1IB0SZmb2NjC4HgqLO9UwTsECylO3R9pKRBVY0Pjk62Bi45J6RyDuFCKoh5AxmmGgvM\nABTKhy5PraqA4WSVYbuFFkd+jRKNxQ6fyjwPHxzQ+/Hqrds4y2bzrc01aF7jhRaQmk6qxwdT3Nvn\nvC3o4cx5jtIqYrz6OuU7KkSOhS5dV1Ig6nD0rfKR8DudZgWSeP530SgPRWm6EhRpBABGGnhMzYVG\nQLDsySEwZTZRndnA8uNUzkNLif1rNwAAp99/BR1es50c8MtyT0Y4FtDEOfyQnrPY76JbRvpevYof\n/CUxypuJwKMBjX8AgaRkxoSEtPObsYbDoZOLdTnl+74LjqlHBdfNLUBVdb4oCucikSSJY4fq8m9r\na8vJ1J2dHSdrrbX48pep3ubdu3fx3e9+1/1uGXDz4osvumTGg8HgoQJubF64iC+tJI5AbRueAK+/\nSoE1o70xfF4zhc5RsMuAsQo+J6WMzRRjZvNWVxbwxCaxRnt3huiucuTx5hmc2yAZfDiKoXmsnvZC\nbK7Tu/BXb25jvE8M3q/8xovoW1rbo5aBKu1gVs7tPK2kcEyg53nORUAp5eZqOp266OiwG85ERva7\nLGeTBIYd4QMvmNnbIg6M8UKvymWkC+feYW0tH5jRbl14gYewtu9W+cDm6ppDnmXuS77yETALZLVx\neah8FUKUqogQNeuRhcdlqzxfuTp0ge/h8ccpWr7b7mCBmfKWH2A8qgLATjjfmJTS7XOBJ7B5lnOn\ntXyEnFNPKgventAKQ4T+xzOjn5iPUz2bbak4GWNmstDWo+rK6/X0BUEQuAlM03TGBFZ/fqmMnZ6e\nuu/WCw2HYeg+lz5QAJkRy+fXP88DK+CyMENQdmmA6NIqVFy4yZdKuvDXMAxdkrjf+q3fwqc+Q0n3\n/s1f/RX+5E/+xI1Jaab8F1//M+ztk+ntySeexIDD/EcnJ7j5PkdM3b7jkoFZY/HY42Q2Wltbh1Rl\nckUPDyGrOQlhaRMyleIkKEs4QMUkJdthwtYC2FKALD9FwtEMq4tLiFggZnGMTp83VKUx5oizpeWz\nSCy18zSJYdgERmuHE8UZQJTps7WBZcpZ+YHzxbLSQs1Zxw0Aet0uuqz4he0+Jgln4a0ViQ67bfg5\nKQ03t3cRxxRJd7bbwgYLM2U1ci4yOcgzl8lcJ2NkCQluL/Ax5ERrk8nUFXkVQjhhIwWqGmbakoEf\ngNA5LPut5UUO5c8Xyp4aA1uaPZWAlGWUk4Lkelu9pUVYJqM9KRGyX8LE5ji3Rmst376HA16DXqRw\n+AMyGx2NdqE1+3vksdu8zpzxISI6QO0eH2BUmvPaHVeAut1uQfHGMBklyNNyDHyXkHAeiCCE4czn\nWdB2GciNjV3iPy004lJpWVvEwqefBADYtVWMy4SQ17cRv08+FmJcAAGNgwkl8jICyhNuM4ukHSCt\nNwAAIABJREFUgvTKhKYF9r9PmbTldIIvhlyPzxfw2JwgAGdS1tbMJJR9EKIocjIsiiK32dQPn3WF\nyhjj/D+Xlpawx+ZFpZTbmPM8d4rT4uKi8wMdjUZuMwbgzH8vvfSSk5/f/va33fWlpSWXqPiFF15w\n43N4eOiePw864QBFm33wOhlef/8GAODmnXfxzJPkB7r105/CwhL1/ThJ8PIPyM0hvHeIxx4hk88j\n2S2sLlIUcrQ6wOefJ1nYmcRYXKJNd2tzEW1OAZJkGtkxvbsLx1OM+V2IghB2QOv5sac/hxuHFOVc\nZKcuwlUjnzvL/eHhoUvE3O/33Z5XzgdASmg9g7vz76yF3tf3qbqvcN08Vy9yn+e5U2wBzOzB5fqp\n7831PbvenrlgNUqapDBVmhilvIpE0AbSKw+HFtZWPtAbG+tuTMp0M9N46hI6S0jscC2/dBIjTSsT\nZNnmhcVFtMv+2gJScOSgtPDZTcTzhastGnnCVaT4SWhMdQ0aNGjQoEGDBnPiE2GcpJTuRJRl2Uxy\ny1Ij1lrPOCOWp6m61txut50WXD4XICbKmcZqlHQcx+536yxWkiRO+xZCuNNUXROv/848MABszSmz\ndOSXUkA7JkG651rpwfPLxIYVS9bpdPDss88CAAYLC/jjP/5jAORkWbZzd3eEP/vG/wMA+OY3/9r1\nschzZHHi+lWePJ577jm89NLzAICti5crdkJIxyzMAxrXkqu1ZXoRMo+iTBop4fG8tNoSeVrmF0nR\n4hIqjz35FLrMbuzv7ODeAZ/uBhHOnKPorLC3AMnlDaZpjh++8ioAIIljiLLOnbWQ3P7cCBg24Vlp\nIVVZL8/iIdI4YWlpGV5Iv6uFj/iYWBVlNcanxOA9verh+Se/QO0/TbC/Q6f3QgXoLtJc6CKHZvOo\nSlK0JM17thLh7AKxi2+8+b472IRBxYKiFshAFcrLGnbameQENEJmYfJco5gzd4xREpYZR6EE8qJk\n4yy6QZmfx8eUnTilClHYkkXRLqFpN/TR5hI6/ZU29k45Uk9atDgXU0cFaLXpt4IwQ8ZmXj/yMPCI\ngSmKqrxSHMco2Dm80AUyZpyyrHio99H3A9iwZENbEOxgLGLlzGrwJWJ2hPWXF2DPkfN+6vk4eJNY\ni/j4GH2ux9ftDmoJCqWrKwgBqNI1wBgEpTybZsiYTVy2QJsdbWOdoeCTLYxAi2tcJLqoaNs5UBTF\nTOSxy+NTVGNVN83V5cpXv/pVxzitrq66BJUvv/yyi6qTUrqSMQcHB465X15ediWwDg8PnWvD1772\nNSePX3/9dZd4+OjoaKakS8mYzINu1MddwY6/0RJ2Uyolo3WBz3+G8v6snG0BzPasqi6Wma05vPoO\nugOSMb/56z+N8YTz2z32KPqaGN9VFPA5ytIYjTJ/rq88BDm/C/AxkjQmTz0zQMFsyCQf4SRm022/\n5eqFjtPclYF6EPr9/gzzU3dJKfenXq93370wjmPHKLbb7RlGqNwnptOpm5M0Td08RFHknlNfI/XI\nS2vtjNvK/fbsuVDkkOX9xmLCUbPWCCfL+osDVzsvSVNYW5osCyws0Dy0Oy2kKUcCmgWcO0/zf+v6\ndUwmdL8vFFoRPbPXaaEVlhGFChJsXkYO5bF7RxTA44SpnpJuDJNpxnL3J+MTUZzqC8HzqoRbvu/P\nRLqJGcWjqsNU3jMej2cWV2m6Go/HbmI/nDG0TiuWAxPHsWvPYDBwC6Runmu1WrOFDR8AbasM1RZV\n+6WQkGy2s9YCukxyhhlFsVyweZ67vjzyyCP42te+BoCE2gcfkBnu3s423n77Ldf3lH3IPKkQhlUy\nz6c40/Ev/NIvY4uzJEMIWNZ4CmNhH8JWR4nEeBO3FgKVqbQs1Cpt5bgjbIpWi4Ra1Alw8QK1obAG\nQ8743R0sQHMdsqNJivyY5rTXj9Hrk1lo6+JFnP+1XwIA/Mtv/CtMWDmkukYfpZC1Nc6fCspwYb05\n+9juwXCm7tH+NvavU3b3YjzFygqZOoK1CxiwX9PmlScwGXKk5+4dgJVkqQUyjgobjkfQrOzlIsS9\nERc77rYxTXnugsrMYzPAlDXpitQVHDXWwBdlRnEfXlmrKjRI9Xw+QFJKqrUHwAqJjJNehr4PX5bv\nR4KMFapo0AcH4CDNNeKEBFDY8vHoE6TkdpZ9RJzWIMmlq0/oecCUBWUynQAcVTedJuh2SCD6fuje\nxSzLsL9Pph9daAj2GykSCd+v/CIfhK4XAGV6DCEh0rIKbICUXReEEGgrmkOZGMSvkNnFKgU5oc29\nByAqa2yGEj7PiWcMVOkzAQVXClFqN28CAp0yRYQuYFnoB8oi9EoZplyUaktmmL9SHSk/pfyrFzD3\nPM+ZerIsc36dTzzxhFOE7t27h4sXLwKgzbvcaFdWVvD2228DAC5fvozf/d3fpTYHARY5bcbly5fx\n2muUTiFJEnz1q18FQLKqbMO1a9dwcEAHjtFo5ORrFEUzisKDYJMYJ1Pq4+k0xsYqteHKpXNYH3DN\nRK3RDsn8lysP/QV6Jy4//Qg+uEv97Q/6GLTKeQxhplzLNE6QctUFX0iXNDcuUpSBYwv9FUTs77LW\n8nF+ldwKpmaCxCkEFNYPAJH0ILz5knxGUTQT3V1+rsuy8Xjs9pLJZOJMnb7vz7i8lOZQa62bT9/3\n3XgLIWaSTddr5NVdZFzagTx3v9tut2euPwwWB33EfHhOT1KXLDhNcxhWQpUU5HcB8jGNp9T+C+c3\nMR5Tm32vgwl/Do5H+N53yJ9u+9atyqdMSnQ4SWoYepD8fE9osGiF7/vwQ3ZD8FQVUQi4GquwEg/a\nFRtTXYMGDRo0aNCgwZz4RBinNE1nqm7XczfVo+Tqzm6l9i2lvK8TXF1D931/hmUqNW4ppftsrXUn\nMc/z3PWTk5OZnCYfzgM1L1qdNt54k/LzTMZjx8aomu5ab78QCvfTW40xjrGvR8ydP38eKyvkhHyw\nfxH9BTp9HRwc1CL4BAI2wywuLeLRKxQdlBUaH9y85fpUMk7jSew+zw878xf3BpITa3jwncO5yQ1a\nLTohRb0ejjnXjALQ41xMrU4H4zHnLEqrvCZKSkg2ve3euYkv/fwvAABW/8O/jz/8p+QwP80LojUA\nGHc3kNvK7GtRuNwk80BEPTcr+eEd9BMyaawtLaLV4YCCO9fwVoue/5kX11FwXbfp4SE8XmNCKMeM\ndfsDtHt0Qv763/xbrF6mU+sLz38eVzk30J1tC98vI2aq90VnOcrjb9vvuNwkAoAtAyKS6dwnoDTP\nKsdvP4Lncd4kP4Auc19lFlGX2D7rRThhJ+E4y6HZ1LW6sYylVZrbqd5HEHC+NGEdExaEHoSk03oc\nW+iiTBCrEATVSXgyYQfg6RSnp2TeOjo8gcdJTEUa4CxHwsyDDiQ8Zg0zGbiTZyY1RJmsVCkXbaek\nhcelJpSnnClYa+2CDJSwrqyQLwW8cp0a7Upl5MLl2oSQwskAIX0X5elDAOyEbKCcWUda4cxA8+D3\nfu/3ZmRbiXqSSWvtjBwtGao7d+7gfa6jd3R0hF//9V+nvtQi6c6cOYMnn3zSfbcuF5eXSQ51u13n\nJjAcDl2EWBiGTqYOh0PHjIRh6NowD9JkguVVzqPW9vGpp8jEffnCORjDVoJ0jG5ArJpodZBZWj/9\nwTLkErGaIwiXY2/n3p6TGaEJXeknKIGQgwh8axCxw3miCxTsTBx2FIYnJA+MyQB2T4jHGTTn2Gu3\nQmTFfM7hWuuZXIHlXAVB4Mavvs9FUeTydbVaLccmApVrSRzHM8mmV1dX3XNK1N1lrLXu//I8nwkC\nKPer/f39mfY8TGTk+pkVCG7/8fjYmfoltPtdJYwrBxPnKUajjH93ikW2OmTTAuMjCkDReYa/+eZf\nAgBCpbCySPOsrIXH9K8QxrHUUaBc+aZ6pJ5Saka3KNd+ELbQaX98EMMnlgDT/aDnzWQCr4dXuobX\nlKuiKNxEeZ7nPkspZ8Jiy2fW76+b+XzfdwuwTm0GQeAUKlNLVul53kP5VfQHA+xwPbjd3d0qKqpm\nCjM1HyEBBYGPKmlKKWhXD66W4qDm96KUwiOPkI330qXL7hkCcHQzRJXa8vb2XRfCb6yBZSXHCs9F\n2D0sKO0D98toVw+OFEJWGpUH3yv9uDL4XFupG4Zoc9h2niU1ajlEzhEpR8Mhjodk0gp9D69epQSC\nL3zx5/CrX/llAMCf/J//nDLigjZCCfZbKyzVYwKF2Gr7MKHsbaiYfK4W7RSXeMPuRR1oFh5Howlu\n3qJoK7SuosXms2A8Rj4pw4sDBOzH1e50oNls9+jWJWw9TVE9eRK7EFig2uRo3ulqXuQuzULUChBy\nAtW8sCizwAWhByTzmbKyvIDiOfH8lktEKYUAPDZd+REEJ/jcOx7jkJWZaZwhYOp/cWURHhc01uPU\nrbtee1ALk8/QYb+25YU2yoPCNI6RcpLReDpBlpd+eRJLizTeC/11xGNORzDOqojVOdASEmGZjkBb\nV19RiwIhm1FK4QmAIm34XQ+F7+h7rQsIUwpi6YpXS0v+FAAgpHVtE8JzWeppPMpDhnHRkEZImPKd\nExKFU/AVHqKL8H3fybAPpxqoQscrmVGvyXn27Fnny9Tv9zEYDLj9Vdj5dDrFkMO56+aZIAhccfV6\nFQjP85zsnE6nLtpuf3/fRfMdHh7ORP09CO2FLrrLnNIDBn1uf56mSJMyzYWHkzG9c/kkdYpuIgU8\njnDtA0jY3F1IAan5sC4jd3BMshyW941u0EXBGU4taKMux7BMbCxqyWil9RCxf2CujSvK/SDcvn3b\nmamLonDzAFTZruuuJ77vuxqrnuc501vdtaWeuLndbrvr9fmvp++pu6TEcezuN8a4eyaTyX1dbeZB\nmkzQYpm1vNTF0ZDWRSACmHKv0hlMUR4UY5yyb+Da8gaKjN65g50dLPdpjIOWRMDyPZTCmdMDT8Hl\n8vSEK9buKQlrSj9juMh2Y7Q7CAohnZtLt9d7oEm5MdU1aNCgQYMGDRrMiU+EcVpaWnKUrrW2SrJV\nO6XUUT9NAbMRbmWkmLV2JjKu1BCTJJm5v376Kk9OWusZ+rgehVJSyb1ebyafxoMQtVp4kRO9JUlS\nluj6iU5mwkrHOJECz4yNlC5T0odT91cUfC1TPz6aPLFE3elPyipKy3KZjcJKbJzZmLuPxDKVTvhA\nmdOJEjRWJzTBp3HP98jTGYDvC5T+vbpIUAjOEZJlCNhzT3gKSpS14QQ0R1hJz0OWV1FDVx4hxub5\nL3weP7hK0XZ+GKCQtMZya6C5bcYC+oGufhWslQDnX1ryLNqK6PBARZgyJSCjDrbZifmNG3+ORzcp\np8wzZ1Yhmc2RQrgTTKfVxphPxd1OB8ND+u6bb7yOm1ydfRrrGvsKRzlpFEgLWpPTwykU96vb6kNy\n9J9st6D8+fLjUPatMhzSc6fjwgpoxeyd38FJQr+/s3+EU04qN40T9PnEu7y6jCQlE16SpCgyXpta\nIY9pbqeTxL2X6ThFxI6bRsPVhfI8H1GrdA4voHOm0b0AYIfeMPBdRM08CJRAVOZLswI+99cIibBk\nr+smdKNheX0FBlCqZIEsrClfKOnoflgy7wGzc2WNru4x2uWpEaJKXJpqA8sOycJTVdyCEfeVhT8J\n9Tw+UsoZpqC8XndhqDsce57nIuO01q7+Z/lvgFimUl5KWUUDF0XhGI16vdB6KZV6Tr7V1VUnUz/8\nWw+C7YUYl5GDWYaMWQkrBE7Z4dhXCkcTWofWKrS6HGFcJC6XXl0KGynhlayRhKvbGYgqP5XoRSg4\nOGIynaLgcfADDyHvCUlSsay+9F1dt8waBN582+qZM2dmmKL6nleytsaY++5b9ei2D+dGLD/7vu/m\nUylVRY3VSospqaB1Nbf15Knld9vttpvz09PTh2INp9NTFMzwrSx0MegxG2cC51AfpwnGbOZVKDA6\nIfm4c/cmEp7PyBN4/AoxnVLC1bH0pKxcAzzpNkA/DNysG2Mcm/hhdtbzKmuWK+vlKYwesE4/EcXp\nd37nd9xE1f2a4jh2dvE0TWcWSGm+qS+cejhmnuduobVarZlFUfdxqodvlrWd6qY9KaswxNFoNGMi\nfJhadYEX4rErT9Su1KTsffEQmSf/P4V1v11PLjAP6rWsrLVuM7DQ0IZfRFHAKwsJwbpoCVMkLvS9\nHYbIOZpLWYsoYmGU5zXFT8IPq6R+xyMu7uz5YJmCL770MzjmOb1++w4UF0lVRjtBpo2AeYhOKmsQ\n5fTMlqdctKAvFCVGA1B4Ida5FtfbH+xg94D8HjY6AbqszAwK4zaVdtTCwR5lFD9JRjg6or4fHJ1i\n6xKFbY/HEyTJ+zzO0plBlS6AMfk1+NagxQpnKx/DcLoGY1cgOUv5gxBnGjkLMisLp0SneQGZlRFh\nBsdjGoOT0QQTrhGW6wxLZ0hJlIHGnXuk9E2SY7S4IPN0fOL83XwZwmPufHh6ioSVH2NzZJwlud2K\nIEqfn0xDcwI7nWmkXHfR6xpkev4wdhl4gCyzy0t4pkxZIV36BxFWkUXCKGhVpQlxJgRBKRsAShbr\n1aJjSwu8BUUCAYCnq0ik2lkIAsIp77Z2HMqthWWhL42ALOZfqB8+dNb9J+vm/fqGWkIIMVO0t55U\nuJ7WpTzs1jfs+j31KKy67KyneImiaKamXvlOzIO9ZIwkL82jAnsj2lB9FbgNT2sNwSZxmQuoUvYE\nIYoyD0ngodxGjdFAUDtEmvIWiaysPKAMwLUR7dg4c63ODXIWUKmF80kMAh+G13OR5NDJfIpFFEUz\nNebqfkT1ihol6j5oH66oUYdX+iylGYqsSuCb8yE2Taq6mHmWVYcSCyc3/TB0st73fbcW6lHoc8Fq\nqDJpryeqbOQIHHGQ6RBLCyQ/4iRBygpV6Ft02jTe/baHULEfovLhsYuGklWCYCWFi1KV1iLng7ex\n1jkfFtBQZTRyjQjwvMqVR1uDB7n+Nqa6Bg0aNGjQoEGDOSEextGrQYMGDRo0aNDg/89oGKcGDRo0\naNCgQYM50ShODRo0aNCgQYMGc6JRnBo0aNCgQYMGDeZEozg1aNCgQYMGDRrMiUZxatCgQYMGDRo0\nmBON4tSgQYMGDRo0aDAnGsWpQYMGDRo0aNBgTjSKU4MGDRo0aNCgwZxoFKcGDRo0aNCgQYM50ShO\nDRo0aNCgQYMGc6JRnBo0aNCgQYMGDeZEozg1aNCgQYMGDRrMiUZxatCgQYMGDRo0mBON4tSgQYMG\nDRo0aDAnGsWpQYMGDRo0aNBgTnifxI984xvfsNba+/yPBGAAAEoYSKMBANYa5FD0GQrgz7ASQtBz\nhNT3/zErUeqDVhho5AAAjQLGlHqiuu9XhbWAcA+CVHTfb/7qV8V9v1DD3//3/x2Leh9N9VnwQ42w\nMJKue9qDtXRdiwxH924AAIrJCQTK7ypY41NrrIA2xrVN80drJSSPoZEGlr/riQJC8VgJVQ4zjLUo\nH2+EhNb0j++/tffAPv7af/z37KC/AADodftoRx0AQDvswFMhtcdYhD61eXHQQb9H9wTKh+X5NcYg\nLwoAwMlkjLt79wAAO4d7GOVTar/vIRA0/qHnI/IDN5ZlH421KKzhPmpIQV2QSsFX1AZf+fA9+vxf\n/if/xQP7+NhjV+x4nHAbfEhVzoWEKejrYejB6Iz65Um0WiG3B5jGMQCg1WqhXPNaa+zcoT4KK90y\noeby2jAa5cR4SkLy2hAA3BesdX2EqNaDEMJ9d3d88rF9XOwHVvAaXFoJMBnR9XhaQLpjlECSUP96\nvQidHomJvCiqvhoLa2k+FxYDFLyOpiOL8v0TwkKC5nl52UMUtqmN2wnygsap023h+DgFABQQAI+3\ngYDOA74H6C/QHL7y/Z0HzuFXf/oLVilqg+/7yFkGrK0t4pd+9iUAQD/0IflJcZbA5/UlpcDCwiIA\nIIhagGBZYi0sj/fB3V1cv/Y+AGB4fIxWq0V9PHsOf/3dH9FzPB/nVwf0XW1x49Y+XTcpzq8tURu6\nbUxSGh+lLB559DwA4Hf+0//ugX38R//4v7GdsA8ASLIY/UVqv7YBgpzac294A9rS+7S1tYUsoffp\n8HAfnk8/0e0uYHRC89jtBdg+/iEA4Nb+m3jisc8CAEbHBziZnAIAOi0FT9Ci2R4OsbK8Rv0SIU6n\nNI9HJ+9hOs74+euIglUAQDax8EL67v/8n337gX381E8/Y2+8c52eLy0GazSen3rpp1AKwJW1VSiW\n08l4hLu37tDnSQHLfVy5sIJBn8bqePuU5DyAheUlvP/uDQDA3ZvbyGN677XWSPmzAPD0s08BAAph\nYAX97sbGOg72DwAAJ6dHuPLIo9T3wynGLJNe+fO//dg+/sP/410reH1JIfg9BqzwAEHvXP3dBqp7\nqv/DzDUAkHjg0ALC1P5Bz5/Zomv7xOw91sm1//Y3Nh/4Q1/+7Z+z0xGN5fDuKQzLiXYnRFbQe5ln\nBr5P49Dp+Vjt0lruBT6CIAIA/IP/6B/iO9/6NwCAq1f/Dh2ez7MXnsLy0hkAwKVLF7G/T+srS2Kc\nX1unZy4u4+LlRwAARgCnE3on0jRx+308OkZW0Jj4rS4WB/T8L37h0/ft4yeiONUh6gvEAq7lQjr+\nSwgFT5RNk6wM8b3udokPzSzBSla2wIu8/K4HxYqTsBXRVlfohKyaY2HhyfsrWPfD//ZH/ze0rpQ5\nUxtut8BhAN5IjI0gPHr+9fd+hD/87/9zAMD44A581nI0gDijz7kGpjEttCzJwfIWWoWlTgQfGpGg\nNvTDFqJIcluUe06S5ihYqStMpWTOA2s0tOb2F4UTQLCWN37A5BoZC44kVwhy5e6Rbt5N9dLoHIY3\nYGs0FG9OHiw8j9qmfAn41ViWgkEZC8WdL7SunqMtDAvW3GaQD9HH9fWzsDjithloyxubAKSkz1IA\nEKWSI+BzOzUkZMZtVtVista6Tbou1Ky7g/9dru2agJz5fxoANw6K/1dY4drzIAjho9djJRcTtNoR\nt1FBF+UmrlAqHsYAecZ9DZR7X4oiL1sE5SkoHoNkmjkJLKSGZGFUJDlyS5upLxQMz0kQCCwu00Y/\niQsMRyTUwjCiDQRA4HsIw3Cu/gGAH0ZIU1bGTAbrsVKXFnj/1l16ppRYYuG4sb6CPgviVtRCu9ul\nvnsRPI/aIGU1JtHKGvzVFQDAK9/7IcB97LZbCEBrMPACrC706P4ownA0oTacJOj1uS+2wOnJCQCg\nP+ggYOVtHozybWTZEADQ9ts4OCZF9CQ5QsdSX7QQ8AOa09u7byKeBNy2FrqLpMROsg9QsCJ9khc4\nnt6kcfA92PQYABD6GYSg52RpitySEjWND7F7QP1Kph66bVL8+t4WeAiRiSkyswMASLWHk4N87j72\nuz1YllVpnsFnedxdHCBPaS3devsDWD5EL68voL9IfR8e3MXOB/S7y2eX4fP6sb5A4NHno+NjrKwt\nAwDOb27i+3/7dwCA48MxBAvwIPARhDRuyWSEk1MaEyUkbt6ksfJ9ieHhkMcH0Pfbl+4DC6+6U0hI\nId3n2vkIpRT4sNL0kxQn4f7+SXqNnZUrlV5Wv2X236UsA9zBdR4oX8CP+EDrebCa5t8Ki7BF4xqF\n1jVioeNjuU9rMxvnyHhfEVKgPSBZFXYDtDukRC+2OrBTUsymh0M8+fRPAQA8ncKMSY57C330eF14\nUiLiw1+W59i5RwfawmjkOa3x3E6xurL0sf1qTHUNGjRo0KBBgwZz4hNhnKy1MDNmBXzks7aAtjVd\n2ancFuBTHKyoVL06r2grLVhaDYnqVKP4NCWsRY23qlD7hxGmMgUC9fPAg/vohbCyxl6J+2j7wkIy\nRaqFQjYlWvHP/vSP8f7brwAAujJHxM+J8wKjlO7PcomsoB7E2iLmRybaQvFvrYQSAc+oNQKTKT1n\nkmvEBT8zM9CiNIN6EPJhloCFZcZJ64JZByCXGTxVmqUsJI+hNgUKvr+oMSSe5zmWpshS5CmdGGye\nw+d1onQOGVA7he/DcDOFtM6MpTTc/BlTmSmt1SiYPbHa0OKaExcvXcL6xhYA4N7hEfYO9qiPyQTW\nMOMkLZhUg5ACkukkIRUU27uklJD8udAVOyOFgGWOsH4ilELAyuoEWf5fnagHAFuaI2sGXSmEMyk9\nEFZCKRpMz/ORFvRuTSapO92HoefYFSll9W4JH0VGvz8di+o5KsB0SityPMqRJiU7kSMMea35IXxm\nAfeHGaI2fbcDA5/nuee3kPCpT3kBJmNin6JMwA/mZ2P+3X/vBUynU9d+qeikmuUGp8f0zskgwvmt\nywCAs+trjlmy1roxtlah0LzWmC8GAL/dwyNPfQoAsLi0jp3SPDQawphy7Rs3/0mSQvM493o9RBEx\nbKYoqjVSFDOM9YNwMj1Gv0XmvzROcCzpHRrlh+grYrqufXAbj1x8AgAQeCE8n8zsYT/F9f2r1AY7\nRTskU4eZAkVG7/TlzYs4HpIpKjeJY5QtDOIpsWSDxQKTYhsAEGsPQULPv3LpeWdm3T56BUfxqwCA\naVEgTuZnDl/+wcvQKf1u1AqxvEIsn/Ikbr5PYy5HBj4zF/3+AhaXyMx6871trC5Se9p+hGTCLhuZ\nRloQG3lvdwcXNi8AAFptH5cfpff+OjQO95l1jnNs72xziwzGJ7R+Wn6EcxsbAIDh8RB7uzQXraiH\nVme+d9FIVbHwUsKIcq3BydD6NiJEfV+xH2Klavd96O+PwkLWpMp9GaSPMNjW/WlhPnL7T0IyySoX\nh8hDZkpWGwhYNighkOc0z23fh2EZkGS5a5s2Gik3KUsNNrfIRLzse+gu0broDjpYXybXkHObj+P6\n9Q8AAPsHQ1x/n0y+vX4XlpnL8XiKGx8wayi1c+XxWpXF4ifhE1Gc6hN7f18nIg+tKE1jAqpUc2y1\nQCBM3drrPleqDt1TLgrqeulvIWq2EFttWrW2SWnd0xQA/yH4OF25EbEZpd65qs8F/49zFtMtAAAg\nAElEQVSExQ/+6v8CALzxra/Dt7H73dI/ShjA4/tTTRQ7ABSiQJoRRd4XBgssi1Y7EULefKdJCnbV\nQWqqV0NICYFyQzeOOp0HUsL5Kekih/NPU9VskDmPzTlZhnTKflwQELq8x7iNbTQaQWb0onQ935kC\nc13AFkTHayOhy9EVnjN3SQG3nQlRV0SMa5suCrcZzIPbN95H1Cbhu7l5FmtnyD9jf28Hh7u79HST\nOyUKUpIUAOCpymQlFfkqAYDKq/GRou7+ZmsHhGr9SFkzLlq49WNRCU4LOEXLCAGh5lus4zRGj03V\nF9bOYfsmKYbtyGIa03hnqeZ3AfA8gTb78IyOp9BswkhzjULTBrR8JNDp0PV2J0BMUwslPGxt0eZy\nPDoEQArMaTqGZj+4aOoD7IcjPB9pUpoFjfOz0jZEqz2/qFpq+1jp0qYZRS0E7FuVF0DGZseV1Q0M\nBmSm0blGmlX+aLIcS5U7hQoWAF+3iUUpiVbXz2F14xwAYO/OLay+8S4A4PqNW7h5l8Y2i1McHY0B\nAFtn1pCz8ulLH4MBtTPLY3e4nAdR1IWWtB6PT+6gu7oJAGh3z2PMvje9DkgbAtBrrSAKWzwOx9Ax\nK2nCYGmZNh5jPJxbpQ0Jeoox9z3w1hBa+hwqCdMjO9zUvwuPXwNbtOHF9N6kaYIpHwpPj04xSViJ\nDZawtHhu7j7CwMlsIRUEm8xafgDNwi2QfVy5RD5Iewd3EGd0fXVtDUc7hwCA5ETgwiIpResXLuGd\n63RI9SFwi33V4jzFxUs0hi/87Ofxzlt0/fR4hJVVWifHwyFS9uNSkFhaoLkLfB83b5By5csQRt1/\nj/swpJQz5ra6S4c7wH/EPEefZ9xcPuT7JN3fs6pTtXdaCGs+cn3miCY+ZM5zf9ufuIffD7qwSCZF\n9VjF8ksDBfubZNY6uak1MClNZsZCeVUr4jFdj6cZHnv0aQDApcefRc5KzjRJ8MZbbwAArr7+Y5Qi\n+uT4FJvnz/HzNQwfMqfTGAcH9K6sL/dxcYtMzVGnP+OjfD80proGDRo0aNCgQYM58QmZ6sSHIokI\nxhSoIuBUzWVNwHN0uXV0nYBxSra2NQ1dVg8trIeYlenJJHEnkEIDxplIqoYopRDw6dcLKtOeD4lu\n4M/dRwELIarnl2YpanoZBQZHzd9+7yr+9Z/+r/RbyQHaQUlbShR8uku0Rgw6datuF60enXw8hOhJ\nPn0pjSAj6lymY5xy9EuaWyT8HC2li8DSxiJjVicrLKSafwl4sjIzGVPAsDmsyFNINvkZraGZJcum\nFoKdOFFoGGZ+pKX7ACAwFiFHTrQAGGZDJvkUcUkXC+3WhrW2Yg6tgLQlk2NheWytkZCyNC1o5EzN\nz4N0MsTuLjkMHp0cYu0smTGuPHoF62vEnhwe7uNg7y7/Vg4jSsZJwmOHf8+rnIlnzHnWuug1a2oO\n86JiY33fcyceXehqfRfG3WOlgGW7rAwDLC0vz9W/dqAQWGpjRymskI8l+gt9vP0unb6K3CDweT6N\nwfGQ1lSrFTgHZpkXSDMa1047QrdVzpXBGjtx9rwAvSViOX58lMNTtF6WeiG8gPqUp0AY0ntWaOD0\nhNZI2ALipDQjGgwPp3P1DwB2tk/Q7hArsrbRcWyANgWefJJMbL1uB+MxsSLWympcrYXUTNn7AZQs\nA02sM/lKK909OrcudmVlYxO/9R/8AwDAv/rzv8Bbr1KEXZ5kEBzdUFhgwkEenrAIIxorFVhAzjIE\nH4fcZrhx4xo9c3yKuCD2Y7Cygd0dmi9feTg+ItYFRqGI6fmRJ9Dv0sRnxsPRPRqfpcVzGJ/S/a+/\n8zI6XVrvTz1yBZOcnfyLU9iCzCHHuz6MdxYAkB4rF5m4N7yO7TtkSsvTE0T8W354Ht3+ytx9fPpz\nT+GdV4nBU57AyTHJude++zLiI+rjKD+FZMd1GWmMDqmdJ3sjXH+fnMMXV89hc5WY4+1btwB25cht\ngcmEmHtfKHzwFpl21s6tY+MsRWSdO3sWRwc0JkoobGzQ9cOjQ5xOyCFcWIXhIbWt2+3jyctPzNW/\n+o5HNpeSSZ9lnNyuSLY6+gzU7qmizYGKaRKYZU3qJrw6Y+I8ZO5vnfvIJTv/MkUQKRedHp9qKI6+\nTqc5psxE+YGH5WWO1tUGRUlQWQvJ0T/aWvQ6ZILeOPsoru/ReL+z+zc4OiKz6r2DQxwc0+fFwSK+\n9NLPAgCOjg/w9BViHN99513c2mYzr1SImR7fXF/C449RZGQYtXD9ZmmevT8+IVNdZQKrLxUhFErP\no1xbjLgTea4R8u4y6HUQlL4uMCTAAEAq5Ow3EKeJMyFNtY+7x6QsxUkKw5u4scr5INkZ+3ABa8uN\nNYdXKk5WoMvU9q/O0UcJA8zQn7VXwkUZCezcfBMA8Ef/y3+F7etEKwbCVhFqXohTDhs20QrWzl8C\nACxvnEfOitAkD2DYjyEKA/RCVjYOr+Pg7W/R5+FdaMFReJmBdm5i1SsjlZ1ROh8ECi9nJVDnyDI2\nLwoBTwV8He6eIs6RZaQ42SzH3Ru3AACDTherpXlAa7d5RH6A1LCiFXguZFYD0BwxJy37LQEUQelo\nY1PR1TWTq7Ua2lRU8YMQqAK+pHGLJ0Ncv8ZRNJ6HNiuuZ863sbJGgvj4+BCTMSuuJp/xDarG1sKW\na6NmehZSVHullJQqAkC310Oe0JqcFlMnUD3Pcya5sNvG4jq1YfXsGVy4eHmu/q0udFGwmWZycoif\n+RkyzfzV3227qBJPBCj43Wp1FJYWaaMMwwCnHGY+TQqEbDqJ2hEyTi+g0xTLA1qnz396CW+8S4Js\nqRdAMu0+Ok1hMrrHD3y34eZJ4aSy1nChqQI+snT+dfrB9j5Wz9AzVy8tY2dICsag20LUI3PSNM2Q\nFqUJV834YHqyTH3hoUDp+1RFSSoroFmxDSApqhFAVhRYY+X6q7/2VaQJyaE33ngdbInAKJ0iLOVZ\nUaDls6LtCwrdnBOj4wwmo3amUw9v7LwDANjcSrGxSCaH+HiMxS7110JjnJC5sDdYgjEk2yDbznSR\n6RZORjQmRnYgfFJ47h3sYmrou6PJPUyHpEgUqcLGGVIkut4E/Q6tge29Q+zuk5ny/Lke+n2SYYnu\n4Mev/njuPrYHHYQcAeX7CsM9+t29W3cQRXR9dWMdgg+X51fPYpd9jdp+G6uswKt8iDfe+DYAYDg+\nxcIaR1NubeD919kkd/cIgn1x7t09gLFkBg1VhF020a+srLgD0Hg6hkj40JPBpT8RUmJ39+5c/VPS\nVspNzQwnK/1o1q+ppkShdr0eeVd+H4Bblx+GgP2QqcnW/vx4VJJ1PoSRhzBkNUNrFOyWkSba+TVF\nvocWr31d5KinAJGCxnUyniJhf7dbt3dwY5vTe7TaUCyH2q0u1nm/3Dp/AT1OnfPWG68h40Pe9u4O\n3nib9uDI87HKfnOnpyNkfLBvd7qO4PhJaEx1DRo0aNCgQYMGc+KTyeNk8ooKF54z5aSFwGjCSdNG\nMcYZaZSFqUxdi6nA2iKdjpRNXAK+JANu7hIbcJrklS+XUNClPmgk53uiCIZag9zpnixP5Zc9ZEzH\nZ9a6HBLzQBoxw2GamhNveUrZ2bmFP/yf/msAwJs//LcuX1OmPXQjTngWdHHpMjk7hstXcDQljfuD\n4Qi7h3TiyuMJQjaloNVDh6nwhYUNdB77OQBAeu07mGxf4yEJodisk6cpLNNPxuaubXPBameqs6aA\nzumkV0ivSrCpLSyf3Dwl0GIH06IoYDi3jgoiCGai8mkMkbLpottyTsACQeUcDgm2FEDrAj5TvwoU\nbUH3VGyfMajyTRn9UH30lHGso1QCKbNbRZZi+/Zt7mOGc0zlnz9/AXlO/ZqMjrB3j09CQjiTnBdo\ntLrEEPrSg+IxaXkS4xNaw7kBPEkn5MtXrmA8oRP+eDRyUZO+5zvH5eX1FSww67W0sY61M5tz9c8G\n1gU95NCI2VHZwIIt1rBaOgY3akkMFqroQMtmEQONbMrM3HTiGJVponAoacxuHcb44A6xyOPUokcH\nQGhpkBdliAzQKk3r49QFFiA3KGNFlGfwECnVEE9OcecmrZ29vX3E7Kh84YUvuGSJk8kERVGyTLPR\nsCVr6PmeO3nqmuO2EMJFwGmtZxx8y2jOpaUlfOlLPw8A2NnZwcE+mY1GIwOPvytMgRFHqF24uOry\ny8yDpW4foSC2cC8uEChaLyEEnr74HABgc/UCVJva/eYHb6DXI9OSTjVUQDI1bPWQc9Ty3ugIks2p\na5trODwik7U+BkJ2CEfRQjegU32sY2zfpWglG2iEATnhB22J9iItpkxo7HN0nkQb2Xj+6Mije0fV\nP6R0Ds1S+rC2lCsS6+vEtvrSx9pimXxXIOZ3SxiD27fJPLN6/gw8jtCUQv2/7L1JsCTZdSV2ns/u\nMQ9//pn5c64ha65CoUCgWQRHkEaRRkqUzGSazFobydq0kZl22mgrrdpaTamtuVGrRbHFFieQBAkC\nRQBEoYBCVaHmynn684+IH5PP/p4W9/qLSLBQGWkyq9V/C9ivgGeEP/c33HfPPefA8+ieexjACej5\nVxsBrn5yDQAQDvfhcMnG0eERUl6rpJI6o9xutlHk5bpYwHYXK/K3IB+E3vhz8UA26TP0muY//xnf\n82lNKPEAw/xTdZz+UZtn1S3epuMMBa/jaZRRVhlAluYwOMtkGQJuSbwwTA2P+34NDYZ2hZoxulMh\ndcbxygsvweJ3aMDU76HdbMLmBS2TCtKh6w3TQMzlIyordGY9jiLIomQ6KIiHsJQ/l8Aph4OkFEVM\nJcYh4cqDSYKQH2QBGwWn5ZQQUIoGaG88xTjh+gaVoVahCR9HOQYMOWTC0S/TFgo+BwlJEqNc79T8\nYFQS80VXswE4o14rKB38LNKEygCd7gegHP5dA+GYFqA//oP/BR99/68A0GZwxIrJXtDFpadeAQBc\neOoVOBUWsCsyvPsepeDffOdj9Pq0EDRsqemiY8vAGBQ4TfwVdE+RcFd37TEYggaLZQgtCDjsHSBj\nYTDDEnDdxZOOUhYoqQoSQMq1Q0oCAglfo2BwFJtZQM6LVMOv4tLlC3RNnDErDwg8B/U6BRWu7yFl\nWnWWpBAx10RJCYdrh2AbmtGUZCkKDmyUKfSmKwuJjJW9pcphPgIEIkxDB1oGDF1r5xgmPN5UDo8O\nce2Y3ml3eQVrpwkaaS6vwPYpOjANA2AIOA5zdJeoFqTZaeoUupr00d+jBb13HEIJGtuO76JZpXe3\nfGp1JnQpgJyD1WqtgoTZSlmaoRJUFupflMYwDJpntW4bwqlxxx0owcrJUmroajzKUAnoHZoWILUw\noAXbYbHVlgnB4304TmBaPPbNOgpryt8zRch1SuNQolx+K01HixNKTGciuAYegDezdPFDzHKrjoCj\nNK9SQ5IRXHXu3Dk9D6IoIkV9lMRIhs+E0Iup7UILb2ZZhiybsTPLg2Acx3qBthxH4yR5nmNriwKM\nL3zhJXz7W39LfUwSVCr0rtr1KhJWym+26iU5c6FmmGO4FGfj7Pmz6KzwvMxS+C59/9bFdXzwCckO\nOJ6NViksGAuMRqV8RITelA4Ewk6QTTjYsASGEwq0pnGBJYNqRM53n8Fagw527955G7cGVAtSa3fR\nalGNSDH8BK2CxnsWeRj0aK5sbTbx3JUvLtxHs7BQ43FdCKDgAxksoOpTX1aaHax0CB6Ne3uol+uT\n6eLiZapns30ft24TJHd4dxub50mCYL93iFufUA1VOJrCr9C4fezSaaQsxHp1fBseP2jTMpCXh/s0\n1xIZk3CqN/JarYIrzz6xWAeNOVgNM6RWQOg1YiZMwn/rpUzpPWw+WKIPyiTFXBCl8CDM96DICf/v\n3AFC/eMaqfKaR5EjmA4jBDzeg8BD7tD65XoCSUT7dDhNMBnTs2zXfQgWcXYtS++plYoPl9+DKoBW\nk/Y536vA4s+zLIPk8o5aUNH/1jCE3m9cx9QlNUUBDbmnWTqT3HjAQeTT2wlUd9JO2kk7aSftpJ20\nk7Zg+1wyTvsjhSmnx0ZRiJALtTIYKHPwZKTBLCQ8SDCJM4ZOLBe9EcNDaQrFwnYQlI4FAMcETE49\np2mmo1GKzkt221zCUc0VbwvxALtGPKRAbL4pzNh/SkldsJjGU/zx//kvAQCvf+uP0fDpO3eGCdw6\nncr+yS/9JtqnKRszVgEm+3TSe+xMF7/7ta8CABrVBr7xd+TV0zvaR8pFpUtBDStdStnXmmuQnOUo\nEgtLK6RdMRkNEHKxseN5MAp+JkYK236EbMxcolZJiZxP4IUqdF28KjDLVogMBwxdrHeWsMG+VtKM\nkU7Z061agVej06NUEuG0zKIkAGecZJQAnHGyAw8pFxlnMkdW/rBhah+kPC+YsUnF4Q9Luz7QR2PG\n0iQIp8wiSl3Ua5kSOcNzR4e7SLmgvd5qwBD0bJv1us6SpEmMCntQfPHLP6fTybufvIPTa5QN2dk/\nRq9PkFKehShtg4Qw9InXMAUi1loyTegTkmVY8Dld/bDmGiYO+5RZeucn97G8QtmSC+fXsLdNnx9n\nqRaBHE0kih2CgTY2K1CccQqHgGEy5CSgC6E9K8PpdXrP23cH2N8nKCrJMSsCjxQcl4v9rURnmqM4\nQ+mjB8NAyWigV7/4ODVVBptPrVXXRMAZXM/zMB7TMyaIja5XcxC7YTzIsJuHRyL2ISyKQvvTzUMp\nUklt52QYhtaAeuWVV7C/R5mZ999+C06ZhW00ENQIimg0bUi1OInBsHM0qkRWaPhLMHepGHtweIjj\nCUFjr3/wp9hhhmhcmHB8Pu3bq8j4eY7HfRSg4uciGyMNKXNiWjW4oFP9WnMNq1zgXcuWYI6pj4FY\nQiu4DACwDAVLlGzaBDaTRbxKE02P7vP0+lmkRbxwH32nirVLNJbefvsdTEIah5YlcIZh6ioK3Pzw\nfQDAFy9soM7aYjuH26g36P7dWgObT3+B+ptP4XI294XzV/DMqS0AwGQ61mQXQxi4z5mLSrWCIKC5\nVQkCXZJg15uakRzlESqczTMNA4VaLCMj5rSSTDEPnz0objnPhtP/dt4q7Kf+P2XIT/18/nvm68Zn\nUnKzD02lHsT65rTkFs83AUUuMRlR1tlyhBa+rVaqsB1a30eJBNeJI04LBG6ZCbYgVZnBLeD45Tpo\nwOL5Zzq2Tv/YgQcjZTHdRg0DLurPigI//OEbfA8ebN5L0ijR/pN5nut9yzCMOfzy09vnEjjdP5og\nKYUNoVAYJYw1qwtKkwxgMUbDMGEw3GYiR92jjq4vNVDlQXxvr4e9AS2C8751tgEgYyVqJWfstrkH\n8aCA14P1DfNNPUQEa74VEvolS6EgM0p5f/3/+QN84//91wAACwkGQ5ZHUE18+Rd+CwBQWz8Hs041\nM05hocP+YRubq9hqEQz31VckLIdGyO2rd2DVaeBs7g6Qs4ni4UYBgw1tVQ5EUxYGG+dwmf6tMoGc\nGWquobCgbiIAEro0y4hWQdfB5HmulVaFNLTcQW7lcD161/cHhzB4wG60l2Dx56ZtI2RY9rjfn5nn\nmgY89ipThUTEfZyOQwSrtBD7no8ooUkp5lLXgNJ1K4YptO/UQk0ILcKthNKmnhKFZrQJg0xZAUCY\nEpI3g2iqkET8u0WKOKIx0DsY4vRZMpkMKhXkzF5zXAeeT89kzXT09ysB5OV4tgUqrIzsuCY8l/ri\n+xVInr6uV4HjLgbVCcvQC1+vl+PHP6Tam4sXGnjqSdpojkcWbt8jOLc3mCDNSsNRD1lCm5cpC9Sr\nnDofZ7h0luHlLRt9ZrV+eHOEAT0CeL4Bt+LyM0g0zRgiRcgbommZMLMZu81mmQIhBOJ4cUmJVr2i\nU/aWAayfJmhGSqmhOiklLK4TNE1zFvxIqVlAlmXpmqU4jhBz4JTnuYbwbNvWS4gsCl0n4TiOro9q\nttp44gmCb+5dv4ZDNrWeHPdw/hLBvI1WdwZNLtAqfgdpxnDbcASPxXGrK2s4lPTuzKMhfJ8C9njS\nxxGr4A+OdrC6QuNxeamFHXZ6zooeLpz/JQDA81u/hnhEz2qjtoJsQu/o4HCIvR0aM2urZ9BtU2DT\nz97F3hHVBU3DBBX2Q2zVHViSoVhrB7k8WLiPAgIrfNjy3ACTCW/Ahok2l2x0Axu3uPYwWXZw4QLB\ndnURIUrpOaTTFKFFAWrFDnCwTfevPA9ffJ6MjG3fQqfLRXh5htde+wEA4Ec/+QQZj8n9oz5qDTro\ntDpLaLJiteMLmOxBOugNMB0vJrg7c1UlVrYWrBTipwQwZ3jeLKCa37cejHHmxZ21osv83/hZdUpz\nkgZCPBCcqX90xWItzwoUfLAMDBcG9ziaplBcL1b3bW1IP5zGiCK6phARqg1a16I413ub5fkwebyn\n00i7NThBgG6b1rB2vY7+LrEbDcPWa6VtOVjiMZXEsYbo582L5w9DP6udQHUn7aSdtJN20k7aSTtp\nC7bPJeMUpSFyjoILYSLlCH4yjTQ0E0XxLGthWnD4RHFuvYNLp+kUoUZ7kMd0hK05Lvqcjs9h6vSo\na5lIYjqJCeFotp1QSheLwTDwabLxUsoHIs1HkZaXagadRPEU3/v7vwAA/O2f/hu42YifQ8qFscCz\nP/8rWD1Hp53YFFoYLpnE8DkKbnW66HboxDieHOPUaYL2VrrrOGJ/LPHnr2OZdXaMp5Zh1+nvaSRh\nSPqe5W4T+weUBUimEjbTqlzbeEDS/mHNMk0t8KgKgUJDGlK/O2KAMWRmAQ6nZqVQ2GdxvXqjhlrA\nJ4a80P92ksUwSmZDbsJiiDYwbM10C8fHAH/uNQOYnK0qZDZ3SqOsJUBZjH9ku/QZTQhjNmaEYk0o\noFA5BIu3iRnCDCEkLKPMbshSegimKGBgBjXVqgxHSqUF4WzHQsECrX7VxVZA79c0Tc34skxbJ9Is\nz0QY06l7Oo0Rl+PcsCAWpJ1lqoDJGRUjN7B/yFpcdoLyZBu4TTzzOLH0bt66j919upfjgdKCo1un\nArz0EkHBjWqBkDXYbt7aw16P++06cCtlFkVCMnxdb3oox4jrzbzqVqw6JlPWmIoSeMz8EoAuGF2k\nub4B6dE82Dx/AZ0Wa7WMx8j5lCsMS1uxWK4HVWYBDQtKiz0WOgsUT0fIkzLjUSslxiCzQls+CEuh\nKP8PQ8HkjBlMC5vMejyz1sUxM+y6q0todSijLJXxAFTysBZ4TQhBzzMZ9lHxCbrKjCpsJsBVrQb6\nPVonbEehUnBxeJDAL6HS3IAvOUvjr2LZpZKBJbECp0XrxO692/jBD75F92m7uHWLIJBqtQtDUR/d\nM30MLMrwjKIIyi7nYoGcIfcEfYTp7sJ99DwLPmf2KtUaIiYJGSJHwOvQhc1ljA4ps/DxjRvotojs\nsNFZRzCmz30nx/YhiVti6uEMZ/Tv3HwX7w2pL51uF8HjBEdCxbiyQhnU5cdP6/XgID6NfWahVpc3\noDy6Js1CXP/JmwDIt9FYcKiakJr4YXCxCsDkKJ1ZmmWc5i1XxE8hJQ9as5TXzF2vfkr0Ul+MGQw3\nD/8p9UB6qZwHhMIsnhkNJzFW1ijDs7GxAZOzRt1mAzZXw9t5oZ+xlDMv0kqlgoC1mJaWV2FzOcIr\nL7+C5SVae2qVui6pmUzGaHIJiwOJi2e3AAD1eh0VZjUrJbF5lkgMw+EIN66xwKpp6ntQeHhx+OcS\nOF2//jZyxQwvt4aIhazSUCLjVLgUtLkCgGlI/PxjjwMAfvmfPI9zrN68fcPHP3z3NQBAbjrwBT3I\n46SAwYugbRqIGKoTpouS0yxUoeum1HxwhNkAUVAa8wTEA+nQh7WiyJBGlPL+k3//R/j3f0iq4M3s\nEBX+4XEu0F4n6uxzX/pFRIz9GyKHyVReYaSweED5lQCKB0JUhKg2WbTOMTH4FqWnm09u4tyzNOGL\neAKzRtdnWY7pmILMw/0djCczWLPcMFCMNNy2SLMNS6dF5011TUPpVKsQEvac6a1ivzHf91GwwFg/\nGqPepEVHRIrwdAB1z0fINSjKUHpsCKOAYL57vdlExH5R2f5QexyZgYtcpfw8BcyShVfMMSgXacJC\nqa9pwdCebYUiJWa6H0PXspDUKWPjArN8OJSmtUuZazq7MWdw7AUuLI8hOb+CZoM20WQaYXJM0Eia\nFBjyuzOsQF+j5BieV8KFAjGz7R7WPMdGo86sRxtQzDYZhQIF+7UN8wOstinYf+ZCA5agmhnTHaHL\n4pbLzTZ279JGeaPow/N5ETSraAYUjFmtGF3efPuDAp0O14TEmYY/fE/CcikgcT0HfoWuqacWYla6\nDiop1jYfBW41sH56CwBQqbcxZYhnOhoi5WKKSr0Fhzdl07Yx4mt6x4eo1xh2tBztJDCdjLSMh2NZ\nWvneEAIJ150pM4PNsiJFbs7euTDRbtFc31hbgc0W3esbywgqcxIEjyDJLM0civ0tO61VJJLWvIbT\nhOezaODoAKMpvbt6o4LVFdowfCdCzaHoqmVv4plTX+B+OfAlrQ3Twx0MUhqDH129iTc/JBju9r09\nrC/TRnhufRm3r5EcwZGRwznLqtDFEeJjqunqDSSMnDctQ2I4Wtw3ssgKZDw+TRPwmbnW7SzDY5i1\n39uF4tKDULo44hqXM90VFAXPockeVEpzqOZInGd5m412gEFMYzi/v4v9bTIjjoWtRVCr0RS+R8/z\n1MoWJmyUPEWOAdfiDJ0amryeuZbA2tJi6uiGyrVzgKGEhnaVmg9NZp9DzauLz/5Wah6SE5/+N366\nLmqu5KL8/GcETuTWMHfNI0DKp7bO4cwGQeXra+uwmWH38y+/jK3TFPwc9YdwuB7QEAZMMScizOu4\nXwl0icPFy49pGQnAQI+Vw29dv4bd7dsAAFvl2Nik7++2G7B9GoNSSnh+KSmhZl6UmAWH/4il+Cnt\nBKo7aSftpJ20k3bSTtpJW7B9LhmnW9d/AjegE5dXWYKUXBgsPVgMMeQmUHD07Qdk6EgAACAASURB\nVJo5cMAux9/ew+0OQRhetYsnnnoeALDUcHAYU9z3J3/3OpKQTl9tu4aEYYPYdBBOKDtRC1zShQCJ\nIz4oqc4wYl6QT1j50SPoOB0Pj/DNr/8xAODrf/yHyBiWgqMw5hPpJBZ4+sVX6f7PnMf9Ad1n3fNh\nMBQh8giNOnu3ORaOBhRNT9MMa+zSnb1zG1sv03NA1MPd1/6ernEk/A77Re3fw1GPU+fjMYRZas0Y\nGrFMkiGcRxGHNMyZD5KALpwWFukoARS1l+bggbAgSy/CXEJw9mwYTzBkUcKuHYDlgODYHkz+DykU\nImbtZSZQMOxl2C4ke31NjocQzDgLTte1YKKUuWZOSJHPhM0WaHluIC1ZnLbQFjWFBDzWJ4IwYWhb\nADlLac+l2KnInK8BNDznuK4WhLMcG0Ep8ug6qNfoNDaYRlCcnTvY3sM4pGxCx+yi0aKTrWWZcP2S\nZCGRZIuxla5cLgCGFqMoAdi3zjQMuDad4jw7RcKQYDwK8cQGndbW13JsbpRsFg/Co9P3/riOnBmN\npumgfNxSFgijUijSRMA6OWEc60xCu2uhWmOBTamQpkxuKGroDXhOWBG2Tj+COGRrHe02wU9ZJhFy\nYfN4MkJWWqUEPpystCyJsHefMrgfvP8+1tcJVnv6xS/CZVg7jpNZIe+cq32v18P2NmVXVtbaaHeo\neNi1XIgydWkUMDjL2F3fgMMF/tWqB6Bkfy7cPQDAO+99Hy5PtGcufwkB2xbluUI4pnmf5RJLHTrt\nnztzAVnIg3Pcx+kma485TUxYT+7GvatwBGemVY433yC2mt9sodmgd908HqNVp2xVlo7RXafxMGwJ\njJnRVq81YDGDMk8SjA6pj+NegmjiLdzH9lIH7RV6ns1uDSO2Nup0V1Ew7Htt5z6qW0SsqVeWcI3Z\ni92KjRqL7FpRrokABTIMQ2Itm4ZCkxdD0ygQp/T30tplmJxlOrj6AQyGODe6XZh1Qj9GYYz3D2mN\nT4I2Xv7Si3Q/736A06vnF+qfKFJoJooQDzAyxVyGp1xHSBuOPjbEzPf0p2G60jrEMIzZNQoQc2Qn\nbTFkGDPYbz77xP9d/lGy1pVSD2SuHtaeef5l+CWDcziEEZZwt4E2F9fvjQtUeS0zyk7oDtFvTaJY\nZ3AVDEzYYzBNEhzuE9nizvVrsFmX8PTWJpa7DF9vHzxQ3K6ZjKapi8PTJNUZLUil1/ef1T6XwCkc\nHkDypDLNFHYJFdkeClXSrh3Ygh5ekcW4d48G970bH6LC/lIw62i1aAJvrNVx6jwJnJ1ZqmG7R7BU\nHgLHPTaAtBIcM/ww8ioaEiJvNV6g5cybKoxCdJcJCnH8Obhngfb7//Kf44ev/Rn9x/QYdWdm8Bpx\n4UtjZQOXXngVAGAEPswRLVKuYaHGi6nhG9p7LpmOEE9oQ1zqrKPCgV80fR/227cBAG+8/QPcDGng\n3Kxa6G7S5psVqfZ5qlTriBgamU4SDI/ody2Vo+kuviEZQmioDkppZtl8Klfx/wcAthDaeDe3TYjS\nj0gpDBmSW15uwGOhziSWaHdpbBz0DjFmtpXtzGp4ZJJpo8ia62mZizyM4DSYvWMqfZ/CsWcq1Qu0\nLFcaRrKlQKlKWBQKomT5KQPQ0hlzKW1hzHkpGpAoaeqAxUGXVAouQw6e7+kASUHqYGI6HmM0oE3C\nMi24NrPRnACuxc8qSbU6eiGzhZW1v/Y7v4GirAvL8xlkZlVhCArkdw9+jCKhDfF730jgRHTvaVjM\nsWKmWO3SgebU+fMoWFSz2d2AcDm48+qQjMwIlWPMQrCD422kGfVvEt1Hp0ObuOdehGIKvOtXtLBk\n72gPlcriczHwq8gYuszyFFMO0pMkQsaRbRhNdZ3S5HiCm9cIivrkk49x+y55KlZaXZzmdH+e57NA\nWCktTfDNb34TN24SXPUbv/5LaDQoyDQVtKeiULneH0+dv4Q7DKtNB7swypt4sKTkoe3WzZvodCgI\n2R3egj2lNTJPR8hB319vnoHDX3p0/xgm1yN5SmB8nyC82DzCzTvEdLt77yoqDs253sEIWUTXV2o+\nOryWnP+5L2MypHd3cHyE2GbBTKeJVpeCiqa3ibu3qKZosNPDIZsO52mC0fHifTz72Bbg0hg/9+Qm\nDI8ZrkaO/SHtDxefOo/u5U2+Bwf779F7eef9d/DMBgXPTdPA8jqNsek0xCCjTde3FIy8rEM0YTTp\nGVaX1nF7l8bDneMxnAnXG44iBIp+N+rvIeAyhLfu3sXZFyhYkirVsPXDmswzXYupMHObsCwLBgcJ\nWZ5r4VXf92GVQq1QSHm9yLO5RIAAPJ8ZtkrqYEkVUkNghZQzcUiYs6BCPljWoD9XD3ptPkrt76jX\nw8GE3n8az3w3o+lEByqGKnS95vz3T+9sw+ZaWHu5DcnvSkqJnPseR5E2ap6EIZouBfX1eh1NjhX2\ne0OE6YytOy94XcaSaZpoP7s8zzX8+7PaCVR30k7aSTtpJ+2knbSTtmD7XDJOk9FA+3k5lsTRHkWI\nhuPB4hN0LWigWmnxTUn4NTpNdZaaSFjDJU5DHOzT6fEn796DAkFUK2tbuPA4CbG5ro3bbP8xnY6R\n8wk5lPGcApjSYpuymCuykxIZu3o3qxaq3uLZmNde+1s4EZ1GamYGTiAhyRRijt2vPP4EmmtUyJ2q\nHAFf1GkE2vqks9pGl6E6UWRYalI606s2oMb0/ZknkdQ5+n5mA90OMWFOVTsY2OwfNthFGFLfB0WE\nI87IDYdT5Pw8K66B/BEKUsUcRGGa5iylqubE+ucKGC1jlnFSpoCY08wIWTBxEsew2aNNOA5qfOqL\nZYH7O8QIOjg6QsAnD8u04JTCp7B0FjEdj+Eyg893bEg+MSghtDDfIi3OMfPFU1IXb8oi01otUAqS\nGYtSFTqzBGVAcIG6UFJnZ/I0R46Zp14J2xlOAMnzwjQsjCeU8QmPJxrCNms2qlWGdkwLNkNias4a\nxICBIKgt1L+VtctIEz5tmqZmgpqoYDIhxpNxvIOSM7B3cBtFn7ITFy9UYbFo3fBogp03yWXcsIQW\nGQ2aK6g0qXg4qLchVVksHeNwnzIz8XQPMqc1IFNDrG5QBmPzTIDVU2xZY6eQnJlptxqwmWG3SJuG\nI9g5Zb2yXCLjDEOaRQBnK7M8QcTMxclggIQhBNe1sLTc5b9dfbK1bVvb/liWpUknhmHA4cxYnqYo\nkhlBocw4AUor+gbNNmqsNbN98z2UuOa8+O4i7emnX4Dp0ngZTHbgGCXjLMSYrXgMt40m6zjduvsx\naoKeSVU6GB+zGKbv6JP/pYuPYXBI7+jGaBftFkMdWYJTzApcOb2G7/6AsjFoWKguU9bxIBxgfIfG\nz5ER4eqHlLXbvzuFWxb4Vm1UO4t71U2yMfo9+s5Cxrj0FP2WbVfQ32eSwuPrMDwae3Ylw7kXKOt1\nfTBGUoqgNlvwmbUs3RzRIXtO5jEkZ6MzqeCxNt5kfIyAU3XKdnHI9ive3WtoMpzuhPtoVWmt2r+9\nh5FHWZUgdzAYDxfqnyoySFVChYYW8E3TRGeWDCk1yxppqv1TBZQmKMRxjL9jceRqtYqv/QekDygM\nEzqlUhS63KHIC+15Se5j84Xf88hB+bF8QBT2UfTGBge7CMf0bAzDhMWsuiTLtBWWa81sUJQsdNIr\nV1L704ks0+QMpWZ9B2ZlEEIIpAzPRkmi10cF9UAfpZzNy5IclRQp4rS0nCoeWhz+uQROWZ7A5zRx\nxQTqbZrAhuOgVqGJ7dsOPIs3pqKAywuxLQTKR1TkEVzG8h3XQzil6+/dvo5rHxMj4qUvvYy1Nfp+\nP4owTUoBuxDMJEUsU+23YxgKZc7eKADFDxupo81qF2lFOESFYRpLSoDxasco4PGmv7x1BZU6QYG2\nUcDlupSqKdHhCVl1O7Ds0p/HhmvRhigMT8+BlV9/FerVZwAA0f37MEo/pzDFG2/9GAAwvplh+z5h\n8PsHR5rK2+k0kYb0DJNJH3gEmrfCHCNRzJGnf0phWTNFhILU3mNCB66mMJHzgjUcTxAwk6pVq8Py\n6FmtrG3giE2Nj4dDjdtbjoEo5ImoDFT5eR5Ppsg4cKp1O8j4nWaygGkvnlgtCgnJD1rKWf2SLFJA\nzSZu2RkJQPGmaELCEGV9wYzFKVUOh6FbAzlci56/51eQsFqxa5laEE4OxghcTlH7LhyGwbxCweZA\ntFqv4nBQ1gvYEFgsOHTtBKIoIacCwioXDgWTKfznL3wRB7v07KV5G60uBRudJQtJQs9ewcIxw4ki\nk/AZ+r7/4S5cNkWVUmDC9WJJnqHGG9xS04LHY9xzHWQ7BOHthN/A6OgtAEBr5QzqbdoEvaCNabx4\n4BSFE9jsuWY5DnIWVVWFgstMKJmmiDhoTeJQQ2xf3HwFG6fPAACq1aZe3E3Ph1uaw1oW/IDWrd/5\n3d/FrdsES+VpqA8EbYU501ABo1xpDQsdFuCrVBsY9RYXhJxvjWYTHiuH5+kUjlPWkeRoOPQeLRuI\nc4Le+oP72Fh+FgDQNTuIFQeTeY4mz7/hcISCIY3NU5vIeLMJmnX0Yzq0ffLWx3A3qe+dlXVYzCYz\n9/bwk7d/zNcrmNzhWt3XI7PbbSBoLj4Xn7r8OKSkQ2GWJlhdp+DHr9eQl6UfltSemWk2gPTo+aeP\nA6OrdPAyvRpiDiYHgwkGXNNVd4AGB3VGoZClbCIb72BphUoGzl28gD/71o8AAGpnH/4W1Yz5pguD\nGXn2dICb1+m5XTpzCtPJzkL969+9iu7yFgCC8KsBvcPp+Bj7fZpny2ubqNYpQIuiSAcShgX47I5w\nfO82hjcpmE3abaRc42t7vq6zc0xTv4dMFbpOKc1yyPJgKQyUeiqmVBquKgyFrKzFEwrmIwhDCyF0\nvZA5V1dc5IVmtCk1U1CntZflCNbXIHl/kkX+MwWp9eHPmO1JUZJosds0Tbm8Arye81XFzDMzTTMN\n+WVp8tCawxOo7qSdtJN20k7aSTtpJ23B9rlknExDQHKxZiNw0WYbEct3teaPK4A8oig7TiNs95gR\nsX4KS206WY2nY6RxeXrMdQTdqAW4unMbAPD33/g6zl2iU0pQraLCnjZtz4PNXjfK8hCnFJWPwmPt\ngYTEgMnF6mk2QSQXt3momgk4qQCZizmtKBMWnwBbnQ00GIJEGmnxyfWlFgKG6kSRa/d0yzXnILAM\nsLmQcamCwR5lBHrhFDXOFOz29vD6j34IALi/N8bxmE4Jlu3gygU6RXc6Xdy6Sanq3fQIrrd42lUq\nBaFmfj7zxbKzRNRMdE0qOdMjUbPMnlRCZ06iOMFYUNalVW3BZ0sd07Jw+XHS8gqnYxzuU8resRyM\n2fvIVAIiZZhPZki42N5oNOCUmQUhIc3FM4dQxSyzJHOIgv5tnufI+RgilUJpXCLnUr9KZdAsKRQa\nnsuQwebPgyJB1absRmQaiPnUmIQx2ibbSPgVDA7phC/GIQRnnAYqh7tGME+z2UTlkPo7PB5gyMWy\nD2txlCFL2JIozzVkrWxPa744to96jb3AmqaGjnf2BZIR9cn3gDKXsLMfoVFlodPEhORRWwlMne1x\nfWCpTdd02zY4CYvBNEGWsQZUGGKwfR0A0D84xsp51vdayrDUXYypBADD/kizLZvLXSh+h6awYXOB\nfziZanZpjgL1Fp3qVzZO6ay2yBNITpkmudTsWNg+wKflVquFJxtPAwAO9rbJYxFAkue6wBdCoMIW\nETaAzKL5Wm+fRhLxuyjyR2LxKhWjyNkPzq8A7PfXbCzB4oxff3SA3d07AICa56BgiP7uwU3Ua5Q1\nGgwHGI5oLTwajGGz/2BeFMh4jt7Zv4lxwmSFoA4zpH9rTCeQnHWReYQrV2idfuNHN1GENI9Pn65A\nmWy54VQQhouvqc89+xQszlZkaYYxFxlP03AmEClNOEzusewaHIcyYMETLfzgPer70fEUTZdJQv19\nfHib1pJ6PcCppTo/Hx8tn/YlUcS4f0j9cuprAGdTb98/gM3ff7auIGwWoJUZjna5WH39NOwF8xH9\n+zdgpjSOBr0DBA6NhS++9CzufkL3nkUTbF2gdXB4PMQSa0TZooDJ+m5Hb/0YF3163rllA5xdg2Vq\nwdoiizWkfDzsI+TsdqXdhmLiipKFzjgpZWiB48LINTIh8hT2gppxAMFkJQRpGDOILcszDe+TP+Qc\nfMbzQBkKGSMNMi9m2X8ojWrMZ/9NcyZsnaaZ1hlMkgSCS4IMCOR8/++/+64Wy86SBH1GOOLwGEkS\nfWa/Pp/ACUJ7omV5gphhI6gYNVYIL9IYkr2X4BiIFA2i3qiPi+epLuj+zi1MWDk8HieweQFqVNp4\n4Vli2L3xxht46zvfAwDUKx5q7HXTWmqjwT5DlWoVNovEdV0D60v0uWc2sb9H33/3cAeRubghpciO\nIZh5kiLX0EVh1CEt+v4sLjA6oDTuWreJTVYCr1c8RBOaqP3eDip12iQmY6XF+1r1GmyLTUazY0zZ\nX+rgYB9/8vVv0rM62keToatmy8IkpcXu8cefQMyqx2GYIOHg03By+FV74T7ObIwfzHjSl+kCMv25\ngh7rhOWrmShlSb0VICooABwdHmpl2UazidMsYjgZDnHM0gpRGGHEY6Bi2RiWRsM1W5tbqjSDzfTW\nQihd67BQm2OWqGIGNeaF0vVgSimURUBKKl0PVGRAwUFAkZkaYzdNF02+HzedYPsG9eWg19cBczQZ\nYhrRxN1YW9bebOPhGMOY3qOsV7S5pUxiDR31R0OMhovRlXZ2PtZBn2N4cC3aCGSs4PFcNKXEaoc2\nwReefxKvf4+ginvXJRpM617qKGS8yI5DCw577qWQqPLfti+1z2QmBWAyC1AUKHhBaLRdDPvUV99w\ntGRFfxJj2Kf7XN44BddaTFQQAIbDMdKSOeM4KIrZ4aA0pg6nIUwtaCr0YcXz/Tkz4gh379IGdvXm\nHSRc/Hblicdx5cqTAMiLuDT8XVpa0uwgUn9nz8ai0J6NjmloJtWpM+ewtkYB6gN1GAu0TsfDYESB\nxN3tfSwv0ZrhwoTHg/b2zVtIQxo7P/fUc1hzSXx36A6xz/ILu3u7yEt24XSKi2fpgLW6uoY7vJEM\nxncgGKpLRwlyhv8OB3dR63L9nSdg+zQGN85KpANmElcauM21T6kzRaO2WC0eANy6d1UzqSzD1P6A\nhcyRsf8ZlMCYYdkkmaDCh8hubQV2ldbCD2/exbNnSbKgWa/C4cB4GEkkB7QuVqsCZ2v0IKqmi9vM\nNDTcBBVe1weFQsz1lblVIOT1wGu2kN2gOsDh0RiHtcVqYwWAa58QNI0iQ6fO/ocG8MUXqRRjnArs\nHZK6+d7+ERoc8GaTMUJ2j3CUgRbXsrntJcRsEPnue+9rCZ5GvYaUD0z9wRCDkObBmXPnsLpO7MMi\niWFx0DcxbZQLuaNy1DmwaRkmosHhQv0DuHZPlHWZs8+lUjNGYVFolp+SEsosyxoEqlyGomSh11Op\nFOJkxoBL9N8FCl6T4ngG1WnmKqjkqywrUWJWAibnWIfR4BDhUbmefuFT+3UC1Z20k3bSTtpJO2kn\n7aQt2D6XjFOeStic7h9FIVJBJzHTczAtC/viCFNOxVZrVUxZ4Kzjmugus13BUQfxlEXHjBRpQuHi\njWuf4MmnrwAATm+dwVs/oiJFz7EQctoyGaQ4HFGk7DgeBKct7cClKkoAlUoDFWb2ZUIiTxdPSRpK\nwOB+uQ5gcLGm09jA9i6dav7iT/4Ezz7zGABgtbWOgD3DHEvAYiVEq+simnL622joAr3d7VvaviJN\nhzge0HPwPA+2TSfe51/8Eo74dPL2Bz/G8ZBOGHkuEbFo5K1bt3H/Pme91l0Y1uJFt6Zh6lNxURQP\nCLCVBYDEDpp5ApZNFTMPIiEBl5kQp1fX0GE5/JsfXcX+Lp1OL12+jMoFOkWfObOFwz3q19UPPoDN\n76tWrSFlPZ1wEMPm3w1HUy3SZ5kW0kdQyCmkhNI4gDWDsmBBlkZ0hQTyUiytQDylcRKPU0T892Ro\noMmQ9OMXn8SZFcoseCrBt//mGwCAOzsH+Oo/+Tn6mixCxOnzla2zaPDY8AYjhNzHYGkJ4IzP4fYR\nJgxNJrFCXixmZVFtdkr7PTSrS3CM0m/L0AXMtmOgEtC9/9rXvoqDHs3R1/76R1itEyQwyiJ96hPS\nQ2lJV28Dy6v0Ra6lEJW3ZXpwuSDc9wUs1gBz3QBDZnWFQwXh0d+Vio9f+MovAwCuvPQrmmyxSEvT\nHDEXrpt+gJUlKsYehyMUDBUIIbQ+jmk7qFbpZFsJAm3FMkwSXL9O0OG7738Ej0/1jaqHs2dI38m1\nTc2MdFxXZ0UAaFhCQiFiCM/wXG0j4dVqUKKm7+dhjuzzLZxk2L1PRfVv/uRtrHRpbTuztqHhv08+\nvIZf+/JXAQAvXXoZYPjMzg7w/X/4Pt2bKhDxe8ykxOoaZcG3zpxDEbAtUk+i1qA1+O4n93H5PJVC\n3Etv4iCi+dqo1REzLNs9ZUD5JZxXw3kWTR1OUzTqi683aRHBKO2bHAO2ww730oTPgzVHBtcus3lN\nTZJQponTTxLE9YOb1/HBbVrz2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CfkNIphxJQpeOF8G+fWOBPY62k9EhMGTE6xWYYAOFN0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bTSTikAb5ux/cwN37tDa8/OIr+OIXXwUAvPbDn+DHr70OAAjjDFsX6X1VWx4WFyjioBwX4zH1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LNVjtqIxtyr13HRp75pCQ1d4wSLVgOJT3tlzRWocNmZ2Gui2aJz6EE4Rpc1l7ZFjiP2ClhS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\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PVWiNovRRKApIlgfi8Qjj4fSFjN8VRIdJNkwhjqQCwCT5oCcUgtIQUhpljKcUAkKWyfis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\nrTeILfj3Tp3Do5+lSuGmAv71X3wLANDt9JGXtlhhiIyrW0ZOmfY6i5C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9hPV9ExD2/RAG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\nhBVn7iH3PIGSFjarfUqCc1di1CuBfpWUy96hFapOKQWlln0yoBefhIFey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"text/plain": [ "<matplotlib.figure.Figure at 0x112a0d810>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Visualize some examples from the dataset.\n", "# We show a few examples of training images from each class.\n", "classes = ['plane', 'car', 'bird', 'cat', 'deer',\n", " 'dog', 'frog', 'horse', 'ship', 'truck']\n", "num_classes = len(classes)\n", "samples_per_class = 7\n", "for y, cls in enumerate(classes):\n", " idxs = np.flatnonzero(y_train == y)\n", " idxs = np.random.choice(idxs, samples_per_class, replace=False)\n", " for i, idx in enumerate(idxs):\n", " plt_idx = i * num_classes + y + 1\n", " plt.subplot(samples_per_class, num_classes, plt_idx)\n", " plt.imshow(X_train[idx].astype('uint8'))\n", " plt.axis('off')\n", " if i == 0:\n", " plt.title(cls)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Subsample the data for more efficient code execution in this\n", "# exercise\n", "num_training = 5000\n", "mask = range(num_training)\n", "X_train = X_train[mask]\n", "y_train = y_train[mask]\n", "\n", "num_test = 500\n", "mask = range(num_test)\n", "X_test = X_test[mask]\n", "y_test = y_test[mask]\n", "\n", "# num_training = X_train.shape[0]\n", "# num_test = X_test.shape[0]" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5000, 3072) (500, 3072)\n" ] } ], "source": [ "# Reshape the image data into rows\n", "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n", "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n", "print(X_train.shape, X_test.shape)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from cs231n.classifiers import KNearestNeighbor\n", "\n", "# Create a kNN classifier instance. \n", "# Remember that training a kNN classifier is a noop: \n", "# the Classifier simply remembers the data and does no further\n", "# processing \n", "classifier = KNearestNeighbor()\n", "classifier.train(X_train, y_train)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps: \n", "\n", "1. First we must compute the distances between all test examples and all train examples. \n", "2. Given these distances, for each test example we find the k nearest examples and have them vote for the label\n", "\n", "Lets begin with computing the distance matrix between all training and test examples. For example, if there are **Ntr** training examples and **Nte** test examples, this stage should result in a **Nte x Ntr** matrix where each element (i,j) is the distance between the i-th test and j-th train example.\n", "\n", "First, open `cs231n/classifiers/k_nearest_neighbor.py` and implement the function `compute_distances_two_loops` that uses a (very inefficient) double loop over all pairs of (test, train) examples and computes the distance matrix one element at a time." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(500, 5000)\n" ] } ], "source": [ "# Open cs231n/classifiers/k_nearest_neighbor.py and implement\n", "# compute_distances_two_loops.\n", "\n", "# Test your implementation:\n", "dists = classifier.compute_distances_two_loops(X_test)\n", "print(dists.shape)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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VKs/nNG1PRHQ8Hov9UgzPgEANk9vtlsSNFBOr9nw+D5vNhlgsBqvVKs/JYrGg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\nIiRioIcIATD3RW0aC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"text/plain": [ "<matplotlib.figure.Figure at 0x112a0d210>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# We can visualize the distance matrix: each row is a single test\n", "# example and its distances to training examples.\n", "plt.imshow(dists, interpolation='none')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Inline Question #1:** Notice the structured patterns in the distance matrix, where some rows or columns are visible brighter. (Note that with the default color scheme black indicates low distances while white indicates high distances.)\n", "\n", "- What in the data is the cause behind the distinctly bright rows?\n", "- What causes the columns?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Your Answer**: *fill this in.*\n", "\n" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got 137/500 correct => accuracy: 0.274\n" ] } ], "source": [ "# Now implement the function predict_labels and run the code below:\n", "# We use k = 1 (which is Nearest Neighbor).\n", "y_test_pred = classifier.predict_labels(dists, k=1)\n", "\n", "# Compute and print the fraction of correctly predicted examples\n", "num_correct = np.sum(y_test_pred == y_test)\n", "accuracy = float(num_correct) / num_test\n", "print('Got {}/{} correct => accuracy: {}'.format(\n", " num_correct, num_test, accuracy))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should expect to see approximately `27%` accuracy. Now lets try out a larger `k`, say `k = 5`:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Got 139/500 correct => accuracy: 0.278\n" ] } ], "source": [ "y_test_pred = classifier.predict_labels(dists, k=5)\n", "num_correct = np.sum(y_test_pred == y_test)\n", "accuracy = float(num_correct) / num_test\n", "print('Got {}/{} correct => accuracy: {}'.format(\n", " num_correct, num_test, accuracy))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should expect to see a slightly better performance than with `k = 1`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference was: 0.0\n", "Good! The distance matrices are the same\n" ] } ], "source": [ "# Now lets speed up distance matrix computation by using partial\n", "# vectorization with one loop. Implement the function\n", "# compute_distances_one_loop and run the code below:\n", "dists_one = classifier.compute_distances_one_loop(X_test)\n", "\n", "# To ensure that our vectorized implementation is correct, we\n", "# make sure that it agrees with the naive implementation. There\n", "# are many ways to decide whether two matrices are similar; one\n", "# of the simplest is the Frobenius norm. In case you haven't\n", "# seen it before, the Frobenius norm of two matrices is the\n", "# square root of the squared sum of differences of all elements;\n", "# in other words, reshape the matrices into vectors and compute\n", "# the Euclidean distance between them.\n", "difference = np.linalg.norm(dists - dists_one, ord='fro')\n", "print('Difference was: {}'.format(difference))\n", "if difference < 0.001:\n", " print('Good! The distance matrices are the same')\n", "else:\n", " print('Uh-oh! The distance matrices are different')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Difference was: 0.0\n", "Good! The distance matrices are the same\n" ] } ], "source": [ "# Now implement the fully vectorized version inside\n", "# compute_distances_no_loops and run the code\n", "dists_two = classifier.compute_distances_no_loops(X_test)\n", "\n", "# check that the distance matrix agrees with the one we computed\n", "# before:\n", "difference = np.linalg.norm(dists - dists_two, ord='fro')\n", "print('Difference was: {}'.format(difference))\n", "if difference < 0.001:\n", " print('Good! The distance matrices are the same')\n", "else:\n", " print('Uh-oh! The distance matrices are different')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Two loop version took 28.3567130566 seconds\n", "One loop version took 58.8340101242 seconds\n", "No loop version took 0.314362049103 seconds\n" ] } ], "source": [ "# Let's compare how fast the implementations are\n", "def time_function(f, *args):\n", " \"\"\"\n", " Call a function f with args and return the time (in seconds)\n", " that it took to execute.\n", " \"\"\"\n", " import time\n", " tic = time.time()\n", " f(*args)\n", " toc = time.time()\n", " return toc - tic\n", "\n", "two_loop_time = time_function(\n", " classifier.compute_distances_two_loops, X_test)\n", "print('Two loop version took {} seconds'.format(two_loop_time))\n", "\n", "one_loop_time = time_function(\n", " classifier.compute_distances_one_loop, X_test)\n", "print('One loop version took {} seconds'.format(one_loop_time))\n", "\n", "no_loop_time = time_function(\n", " classifier.compute_distances_no_loops, X_test)\n", "print('No loop version took {} seconds'.format(no_loop_time))\n", "\n", "# You should see significantly faster performance with the fully\n", "# vectorized implementation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Cross-validation\n", "\n", "We have implemented the k-Nearest Neighbor classifier but we set the value k = 5 arbitrarily. We will now determine the best value of this hyperparameter with cross-validation." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Evaluating: k = 1\n", "Evaluating: k = 3\n", "Evaluating: k = 5\n", "Evaluating: k = 8\n", "Evaluating: k = 10\n", "Evaluating: k = 12\n", "Evaluating: k = 15\n", "Evaluating: k = 20\n", "Evaluating: k = 50\n", "Evaluating: k = 100\n", "k = 1, accuracy = 0.263\n", "k = 1, accuracy = 0.257\n", "k = 1, accuracy = 0.264\n", "k = 1, accuracy = 0.278\n", "k = 1, accuracy = 0.266\n", "k = 3, accuracy = 0.239\n", "k = 3, accuracy = 0.249\n", "k = 3, accuracy = 0.24\n", "k = 3, accuracy = 0.266\n", "k = 3, accuracy = 0.254\n", "k = 5, accuracy = 0.248\n", "k = 5, accuracy = 0.266\n", "k = 5, accuracy = 0.28\n", "k = 5, accuracy = 0.292\n", "k = 5, accuracy = 0.28\n", "k = 8, accuracy = 0.262\n", "k = 8, accuracy = 0.282\n", "k = 8, accuracy = 0.273\n", "k = 8, accuracy = 0.29\n", "k = 8, accuracy = 0.273\n", "k = 10, accuracy = 0.265\n", "k = 10, accuracy = 0.296\n", "k = 10, accuracy = 0.276\n", "k = 10, accuracy = 0.284\n", "k = 10, accuracy = 0.28\n", "k = 12, accuracy = 0.26\n", "k = 12, accuracy = 0.295\n", "k = 12, accuracy = 0.279\n", "k = 12, accuracy = 0.283\n", "k = 12, accuracy = 0.28\n", "k = 15, accuracy = 0.252\n", "k = 15, accuracy = 0.289\n", "k = 15, accuracy = 0.278\n", "k = 15, accuracy = 0.282\n", "k = 15, accuracy = 0.274\n", "k = 20, accuracy = 0.27\n", "k = 20, accuracy = 0.279\n", "k = 20, accuracy = 0.279\n", "k = 20, accuracy = 0.282\n", "k = 20, accuracy = 0.285\n", "k = 50, accuracy = 0.271\n", "k = 50, accuracy = 0.288\n", "k = 50, accuracy = 0.278\n", "k = 50, accuracy = 0.269\n", "k = 50, accuracy = 0.266\n", "k = 100, accuracy = 0.256\n", "k = 100, accuracy = 0.27\n", "k = 100, accuracy = 0.263\n", "k = 100, accuracy = 0.256\n", "k = 100, accuracy = 0.263\n" ] } ], "source": [ "num_folds = 5\n", "k_choices = [1, 3, 5, 8, 10, 12, 15, 20, 50, 100]\n", "\n", "X_train_folds = []\n", "y_train_folds = []\n", "################################################################################\n", "# TODO: #\n", "# Split up the training data into folds. After splitting, X_train_folds and #\n", "# y_train_folds should each be lists of length num_folds, where #\n", "# y_train_folds[i] is the label vector for the points in X_train_folds[i]. #\n", "# Hint: Look up the numpy array_split function. #\n", "################################################################################\n", "X_train_folds = np.array_split(X_train, num_folds)\n", "y_train_folds = np.array_split(y_train, num_folds)\n", "################################################################################\n", "# END OF YOUR CODE #\n", "################################################################################\n", "\n", "# A dictionary holding the accuracies for different values of k that we find\n", "# when running cross-validation. After running cross-validation,\n", "# k_to_accuracies[k] should be a list of length num_folds giving the different\n", "# accuracy values that we found when using that value of k.\n", "k_to_accuracies = {}\n", "\n", "################################################################################\n", "# TODO: #\n", "# Perform k-fold cross validation to find the best value of k. For each #\n", "# possible value of k, run the k-nearest-neighbor algorithm num_folds times, #\n", "# where in each case you use all but one of the folds as training data and the #\n", "# last fold as a validation set. Store the accuracies for all fold and all #\n", "# values of k in the k_to_accuracies dictionary. #\n", "################################################################################\n", "for k in k_choices:\n", " print('Evaluating: k = {}'.format(k))\n", " k_to_accuracies[k] = []\n", " for i in range(num_folds):\n", " X_cv = X_train_folds[i]\n", " y_cv = y_train_folds[i]\n", " X_tr = np.concatenate(X_train_folds[:i] + \n", " X_train_folds[i+1:])\n", " y_tr = np.concatenate(y_train_folds[:i] +\n", " y_train_folds[i+1:])\n", " knn = KNearestNeighbor()\n", " knn.train(X_tr, y_tr)\n", " y_pr = knn.predict(X_cv, k=k)\n", " accuracy = np.mean(y_pr == y_cv)\n", " k_to_accuracies[k].append(accuracy)\n", "################################################################################\n", "# END OF YOUR CODE #\n", "################################################################################\n", "\n", "# Print out the computed accuracies\n", "for k in sorted(k_to_accuracies):\n", " for accuracy in k_to_accuracies[k]:\n", " print('k = {}, accuracy = {}'.format(k, accuracy))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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IiEiBKSETERERKTAlZCIiIiIFpoRMREREpMCUkImIiIgUmBIyERERkQJTQiYiIiJSYErI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\nfTW7ujL85B2v5LGPPgwV+Gd3vLKz65CFEN4OXAH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"text/plain": [ "<matplotlib.figure.Figure at 0x118e56750>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# plot the raw observations\n", "for k in k_choices:\n", " accuracies = k_to_accuracies[k]\n", " plt.scatter([k] * len(accuracies), accuracies)\n", "\n", "# plot the trend line with error bars that correspond to standard\n", "# deviation\n", "accuracies_mean = np.array(\n", " [np.mean(v) for k,v in sorted(k_to_accuracies.items())])\n", "accuracies_std = np.array(\n", " [np.std(v) for k,v in sorted(k_to_accuracies.items())])\n", "plt.errorbar(k_choices, accuracies_mean, yerr=accuracies_std)\n", "plt.title('Cross-validation on k')\n", "plt.xlabel('k')\n", "plt.ylabel('Cross-validation accuracy')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best value for k = 10\n", "Got 141/500 correct => accuracy: 0.282\n" ] } ], "source": [ "# Based on the cross-validation results above, choose the best\n", "# value for k, retrain the classifier using all the training data,\n", "# and test it on the test data. You should be able to get above 28%\n", "# accuracy on the test data.\n", "best_k = k_choices[np.argmax(accuracies_mean)] # best_k = 10\n", "print('Best value for k = {}'.format(best_k))\n", "\n", "classifier = KNearestNeighbor()\n", "classifier.train(X_train, y_train)\n", "y_test_pred = classifier.predict(X_test, k=best_k)\n", "\n", "# Compute and display the accuracy\n", "num_correct = np.sum(y_test_pred == y_test)\n", "accuracy = float(num_correct) / num_test\n", "print('Got {}/{} correct => accuracy: {}'.format(\n", " num_correct, num_test, accuracy))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python [conda env:py27]", "language": "python", "name": "conda-env-py27-py" }, "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": 0 }
mit
goedman/RobGoedmansNotebooks.jl
notebooks/SheehanOlver/03.ipynb
2
33307
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Types\n", "\n", "Julia has two different kinds of types: bittypes (like `Int64`, `Int32`, `UInt32` and `Char`) and composite types. \n", "\n", "Here is an example of an inbuilt composite type representing complex numbers, for example, \n", "$$x= 1+i$$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1 + 2im" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x=1+2im" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Complex{Int64}" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "typeof(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A complex number consists of two fields: a real part (denoted `re`) and an imaginary part (denoted `im`). Fields of a type can be accessed using the `.` notation:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.re" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x.im" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also make our own types. Let's make a type to represent complex numbers in the format\n", "$$z=rexp(i\\theta)$$\n", "That is, we want to create a type with two fields: `r` and `θ`. This is done using the `type` syntax, followed by a list of names for the fields, and finally the keyword `end`" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "type MyComplex\n", " r\n", " θ\n", "end" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "MyComplex(1,0.1)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z=MyComplex(1,0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can access fields for our new type using `.r` and `.θ`:" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.1" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z.θ" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1,0.1)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "z.r, z.θ" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Tuple{Int64,Float64}" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "typeof(ans)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Functions\n", "\n", "Functions are created using the keyword `function`, followed by a name for the function, and in parentheses a list of arguments. Let's make a function that takes in a single number $x$ and returns $x^2$. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "sq (generic function with 1 method)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function sq(x)\n", " x^2 \n", "end" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(4,9)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sq(2),sq(3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Multiple arguments to the function can be included with `,`. Here's a function that takes in 3 arguments and returns the average. (We write it on 3 lines only to show that functions can take multiple lines.)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "av (generic function with 1 method)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function av(x,y,z)\n", " ret=x+y\n", " ret=ret+z\n", " ret/3\n", "end" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "av(1,2,3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Variables live in different scopes. In the previous example, `x`, `y`, `z` and `ret` are _local variables_: they only exist inside of `av`. So this means `x` and `z` are _not_ the same as our complex number `x` and `z` defined above.\n", "\n", "Note: This is only true for x, y and z because they are bit types. If they are arrays, a function will change 'surrounding' x, y and z values.\n", "\n", "Warning: if you reference variables not defined inside the function, they will use the outer scope definition. The following example shows that if we mistype the first argument as `xx`, then it takes on the outer scope definition `x`, which is a complex number" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "av2 (generic function with 1 method)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function av2(xx,y,z)\n", " (x+y+z)/3\n", "end" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4.666666666666667 + 0.6666666666666666im" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "av2(5,6,7)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You should almost never use this feature!! We should ideally be able to predict the output of a function from knowing just the inputs." ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5-element Array{Int64,1}:\n", " 1\n", " 2\n", " 3\n", " 4\n", " 15" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v=[1; 2; 3; 4; 5] # Note the use of ';' to create a vector\n", "\n", "function suminlastentry(v::Vector{Int64})\n", " v[end]=sum(v)\n", "end\n", "\n", "suminlastentry(v)\n", "v" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1x5 Array{Int64,2}:\n", " 1 2 3 4 5" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v=[1 2 3 4 5]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5-element Array{Int64,1}:\n", " 1\n", " 2\n", " 3\n", " 4\n", " 5" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v=[1, 2, 3, 4, 5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In Julia 0.5 this also works, but:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1x5 Array{Int64,2}:\n", " 1 2 3 4 5" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m=[1 2 3 4 5;]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2x3 Array{Int64,2}:\n", " 1 2 3\n", " 4 5 6" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m=[1 2 3;4 5 6] # Replacing the ';' by a ',' will give an error" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Functions of Vectors \n", "\n", "\n", "We can define functions for other types, for example vectors. Let's create a function that calculates the average of the entries of a vector. We want the function to work for general length vectors, so let's create vectors `v` and `w` of different lengths:" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(5,10)" ] }, "execution_count": 116, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v=rand(Int,5)\n", "w=rand(Int,10)\n", "\n", "length(v),length(w)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To implement the function, we need to use a for loop, using the `for` keyword. The following syntax evaluates the body of the for loop (between the lines after the `for` and before the `end`) one by one for `k` equal to every number in the range `1:10`." ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "4\n", "9\n", "16\n", "25\n" ] } ], "source": [ "for k=1:5\n", " k2=k^2\n", " println(k2) \n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is exactly the same as the following block of text, but without having to write it out explicitely" ] }, { "cell_type": "code", "execution_count": 119, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "4\n", "9\n", "16\n", "25\n" ] } ], "source": [ "k=1\n", "k2=k^2\n", "println(k2) \n", "\n", "k=2\n", "k2=k^2\n", "println(k2) \n", "\n", "k=3\n", "k2=k^2\n", "println(k2) \n", "\n", "k=4\n", "k2=k^2\n", "println(k2) \n", "\n", "k=5\n", "k2=k^2\n", "println(k2) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use a for loop to step `k` through every index of a vector. The following calculates the sum of the entries of the vector, printing out the current value for each value of k" ] }, { "cell_type": "code", "execution_count": 120, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "At step 1, the current sum is 1\n" ] }, { "data": { "text/plain": [ "15" ] }, "execution_count": 120, "metadata": {}, "output_type": "execute_result" }, { "name": "stdout", "output_type": "stream", "text": [ "At step 2, the current sum is 6\n", "At step 3, the current sum is 12\n", "At step 4, the current sum is 15\n" ] } ], "source": [ "v=[1,5,6,3]\n", "ret=0\n", "for k=1:length(v)\n", " ret=ret+v[k]\n", " println(\"At step $k, the current sum is $ret\")\n", "end\n", "ret" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now ready to write a function that calculates the average of the entries of a vector:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "vecav (generic function with 1 method)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function vecav(v)\n", " ret=0\n", " for k=1:length(v)\n", " ret=ret+v[k]\n", " end\n", " ret/length(v)\n", "end" ] }, { "cell_type": "code", "execution_count": 122, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3.5" ] }, "execution_count": 122, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vecav([1,5,2,3,8,2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "julia has an inbuilt `sum` command that we can use to check our code:" ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3.5" ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum([1,5,2,3,8,2])/6" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Functions with type signatures\n", "\n", "functions can be defined only for specific types using `::` after the variable name. The same function name can be used with different type signatures. \n", "\n", "The following defines a function `mydot` that calculates the dot product, with a definition changing depending on whether it is an `Integer` or a `Vector`. Note that `Integer` means any kind of integer: `mydot` is defined for pairs of Int64's, Int32's, etc. \n", "\n" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "mydot (generic function with 2 methods)" ] }, "execution_count": 124, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function mydot(a::Integer,b::Integer)\n", " a*b\n", "end\n", "\n", "function mydot(a::Vector,b::Vector)\n", " # we assume length(a) == length(b)\n", " ret=0\n", " for k=1:length(a)\n", " ret=ret+a[k]*b[k]\n", " end\n", " ret\n", "end" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "30" ] }, "execution_count": 125, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mydot(5,6) # calls the first definition" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "30" ] }, "execution_count": 126, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mydot(Int8(5),Int8(6)) # also calls the first definition" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "32" ] }, "execution_count": 127, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mydot([1,2,3],[4,5,6]) # calls the second definition" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false }, "outputs": [ { "ename": "LoadError", "evalue": "LoadError: BoundsError: attempt to access 3-element Array{Int64,1}:\n 4\n 5\n 6\n at index [4]\nwhile loading In[128], in expression starting on line 1", "output_type": "error", "traceback": [ "LoadError: BoundsError: attempt to access 3-element Array{Int64,1}:\n 4\n 5\n 6\n at index [4]\nwhile loading In[128], in expression starting on line 1", "", " in mydot at In[124]:9" ] } ], "source": [ "mydot([1,2,3,4],[4,5,6]) # an error is thrown because length(a) > length(b)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We should actually check that the lengths of `a` and `b` match. Let's rewrite `mydot` using an `if`, `else` statement. The following code only does the for loop if the length of a is equal to the length of b, otherwise, it throws an error.\n", "\n", "Note that `==` checks if two quantities are equal. This is _not the same_ as `=`, which assigns the value of one quantity to the other\n", "\n", "If we name something with the exact same signature (name, and argument types), previous definitions get overriden." ] }, { "cell_type": "code", "execution_count": 129, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "mydot (generic function with 2 methods)" ] }, "execution_count": 129, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function mydot(a::Vector,b::Vector)\n", " ret=0 \n", " if length(a) == length(b)\n", " for k=1:length(a)\n", " ret=ret+a[k]*b[k]\n", " end\n", " else\n", " error(\"arguments have different lengths\") \n", " end\n", " ret \n", "end" ] }, { "cell_type": "code", "execution_count": 130, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "70" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mydot([1,2,3,4],[5,6,7,8])" ] }, { "cell_type": "code", "execution_count": 131, "metadata": { "collapsed": false }, "outputs": [ { "ename": "LoadError", "evalue": "LoadError: arguments have different lengths\nwhile loading In[131], in expression starting on line 1", "output_type": "error", "traceback": [ "LoadError: arguments have different lengths\nwhile loading In[131], in expression starting on line 1", "", " in mydot at In[129]:8" ] } ], "source": [ "mydot([1,2,3,4],[5,6,7])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The fields in types point to locations in memory\n", "\n", "Let's return to the example we started last lecture:" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "r=Ref(1)\n", "r.x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Ref` is just a composite type with a single field called `x`. We can make our own version of `Ref` called `MyRef`:" ] }, { "cell_type": "code", "execution_count": 132, "metadata": { "collapsed": true }, "outputs": [], "source": [ "type MyRef\n", " x\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The function call `MyRef(52)` creates a new `MyRef`, with `x` initialized as 52:" ] }, { "cell_type": "code", "execution_count": 133, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "MyRef(52)" ] }, "execution_count": 133, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myref=MyRef(52)" ] }, { "cell_type": "code", "execution_count": 134, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "52" ] }, "execution_count": 134, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myref.x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "we can create another variable `n` that is equal to `myref`. " ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "MyRef(52)" ] }, "execution_count": 135, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n=myref" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unlike bittypes, the fields of composite types point to locations in memory that store the values. In this example, `myref.x` lives somewhere in memory, let's say at address 1543. But setting `n=myref` has the property that all the fields of `n` also point to the same location in memory. This means `n.x` also points to address 1543. \n", "\n", "So if we change the value of `myref.x` to 6, this changes the value living in address 1543 to 6, and so `n.x` is also automatically 6:" ] }, { "cell_type": "code", "execution_count": 136, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myref.x=6" ] }, { "cell_type": "code", "execution_count": 137, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 137, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n.x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is very different from bittypes. Here, `myrefx` and `nx` are in two different locations in memory, let's say 1765 and 1987, and the `=` copies the value 52 from `myrefx`'s address 1765 to `nx`'s address 1987. Then calling `myrefx=6` actually creates a new address in memory, let's say 2076, with the value of 6. But `nx` still corresponds to 1987, and is still 52." ] }, { "cell_type": "code", "execution_count": 138, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "52" ] }, "execution_count": 138, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myrefx=52\n", "nx=myrefx\n", "myrefx=6\n", "nx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is another example. Let's return to the composite type set-up:" ] }, { "cell_type": "code", "execution_count": 139, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "MyRef(52)" ] }, "execution_count": 139, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myref=MyRef(52)\n", "myref.x\n", "n=myref" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If instead of calling `myref.x=6` we call `myref=MyRef(6)`, this creates a brand new `MyRef`, \n", "with the new `myref.x` pointing to a new address in memory (let's say 6543) initialized with\n", "the value 6. Whereas `n.x` still points to the same address in memory as the old `myref.x`,\n", "so is still 52:" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "MyRef(6)" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "myref=MyRef(6)" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "52" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "n.x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Vectors work like composite types, not bittypes\n", "\n", "Vectors behave like composite types, where they point to an address in memory, and `=`\n", "copies the address in memory:" ] }, { "cell_type": "code", "execution_count": 141, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4-element Array{Int64,1}:\n", " 1\n", " 2\n", " 3\n", " 4" ] }, "execution_count": 141, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v=[1,2,3,4]\n", "\n", "w=v" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can change values of a vector using brackets and =:" ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "52" ] }, "execution_count": 142, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v[2]=52" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This has changed v:" ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4-element Array{Int64,1}:\n", " 1\n", " 52\n", " 3\n", " 4" ] }, "execution_count": 72, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "But it's also changed w, since w points to the same location in memory as v:" ] }, { "cell_type": "code", "execution_count": 143, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4-element Array{Int64,1}:\n", " 1\n", " 52\n", " 3\n", " 4" ] }, "execution_count": 143, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we assign v to a new vector, w still points to the old location in memory, so is unchanged.\n", "\n", "Makes sure it is clear the difference between `v=`, which reassigns the variable v to a new value, and `v[1]=`, which leaves v the same, but modifies a value in memory." ] }, { "cell_type": "code", "execution_count": 74, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2-element Array{Int64,1}:\n", " 6\n", " 75" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v=[6,75]" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4-element Array{Int64,1}:\n", " 1\n", " 52\n", " 3\n", " 4" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you actually want to copy the entries of a vector, without pointing to a new vector, use `copy`:" ] }, { "cell_type": "code", "execution_count": 147, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2-element Array{Int64,1}:\n", " 6\n", " 75" ] }, "execution_count": 147, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v=[6,75]\n", "w=v\n", "w2=copy(v)" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v[1]=2" ] }, { "cell_type": "code", "execution_count": 150, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2-element Array{Int64,1}:\n", " 2\n", " 75" ] }, "execution_count": 150, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w" ] }, { "cell_type": "code", "execution_count": 149, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2-element Array{Int64,1}:\n", " 6\n", " 75" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# On to floating point numbers\n", "\n", "Floating point numbers represent real numbers. They are also a bitstype, by default `Float64` which uses 64 bits, but not we interpret the bits in a different way than integers." ] }, { "cell_type": "code", "execution_count": 153, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Float64" ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x=1/2\n", "typeof(x)" ] }, { "cell_type": "code", "execution_count": 154, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"0011111111010101010101010101010101010101010101010101010101010101\"" ] }, "execution_count": 154, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y=1/3\n", "typeof(y)\n", "\n", "bits(y)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can create floats by adding .0 to the end. The following creates a Float64 to represent the integer 1:" ] }, { "cell_type": "code", "execution_count": 155, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "\"0011111111110000000000000000000000000000000000000000000000000000\"" ] }, "execution_count": 155, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x=1.0\n", "\n", "bits(x)" ] } ], "metadata": { "kernelspec": { "display_name": "Julia 0.4.5", "language": "julia", "name": "julia-0.4" }, "language_info": { "file_extension": ".jl", "mimetype": "application/julia", "name": "julia", "version": "0.4.5" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
lemonyhermit/CodingYoga
python-for-developers/Chapter9/Chapter9_Scope_of_names.ipynb
1
5432
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Python for Developers](http://ricardoduarte.github.io/python-for-desvelopers/#content)\n", "===================================\n", "First edition\n", "-----------------------------------\n", "\n", "Chapter 9: Scope of names\n", "=============================\n", "_____________________________\n", "\n", "The scope of names in Python are maintained by *Namespaces*, which are dictionaries that list the names of the objects (references) and the objects themselves.\n", "\n", "Normally, the names are defined in two dictionaries, which can be accessed through the functions `locals()` and `globals()`. These dictionaries are updated dynamically at <span class=\"note\" title=\"Although the dictionaries returned by locals() and globals() can be changed directly, this should be avoided because it can have undesirable effects.\">runtime</span>.\n", "\n", "![Namespaces](files/bpyfd_diags7.png)\n", "\n", "Global variables can be overshadowed by local variables (because the local scope is consulted before the global scope). To avoid this, you must declare the variable as global in the local scope.\n", "\n", "example:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def addlist(list):\n", " \"\"\"\n", " Add lists of lists, recursively\n", " the result is global\n", " \"\"\"\n", " global add\n", " \n", " for item in list:\n", " if type(item) is list: # If item type is list\n", " addlist(item)\n", " else:\n", " add += item\n", "\n", "add = 0\n", "addlist([[1, 2], [3, 4, 5], 6])\n", "\n", "print add # 21" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "21\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using global variables is not considered a good development practice, as they make the system harder to understand, so it is better to avoid their use. The same applies to overshadowing variables." ] }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<style>\n", " @font-face {\n", " font-family: \"Computer Modern\";\n", " src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n", " }\n", " div.cell{\n", " width:800px;\n", " margin-left:16% !important;\n", " margin-right:auto;\n", " }\n", " h1 {\n", " font-family: Helvetica, serif;\n", " }\n", " h4{\n", " margin-top:12px;\n", " margin-bottom: 3px;\n", " }\n", " div.text_cell_render{\n", " font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n", " line-height: 145%;\n", " font-size: 130%;\n", " width:800px;\n", " margin-left:auto;\n", " margin-right:auto;\n", " }\n", " .CodeMirror{\n", " font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n", " }\n", " .note{\n", " border-bottom: 1px black dotted;\n", " }\n", " .prompt{\n", " display: None;\n", " }\n", " .text_cell_render h5 {\n", " font-weight: 300;\n", " font-size: 16pt;\n", " color: #4057A1;\n", " font-style: italic;\n", " margin-bottom: .5em;\n", " margin-top: 0.5em;\n", " display: block;\n", " }\n", " \n", " .warning{\n", " color: rgb( 240, 20, 20 )\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", " \"HTML-CSS\": {\n", " styles: {'.MathJax_Display': {\"margin\": 4}}\n", " }\n", " });\n", "</script>" ], "output_type": "pyout", "prompt_number": 1, "text": [ "<IPython.core.display.HTML at 0x50f8f98>" ] } ], "prompt_number": 1 } ], "metadata": {} } ] }
gpl-2.0
davidgutierrez/HeartRatePatterns
Jupyter/MimicIII/02 Check existing waves in the data base-Copy1.ipynb
1
25180
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 02 Check existing waves in the data base" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1) Import de las librerias que utilizaremos" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import psycopg2\n", "import numpy as np\n", "import collections\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2) Conexion a la base de datos" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "conn = psycopg2.connect(\"dbname=mimic\")\n", "cur = conn.cursor()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3) Con cuantos archivos de ondas contamos" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Tenemos 23586 ondas de 10134 pacientes\n" ] } ], "source": [ "select_stament = 'select count(1) from waveformFields'\n", "cur.execute(select_stament)\n", "ondas = cur.fetchone()\n", "select_stament = 'select count(distinct subject_id) from waveformFields'\n", "cur.execute(select_stament)\n", "pacientes = cur.fetchone()\n", "print('Tenemos',ondas[0],'ondas de',pacientes[0],'pacientes')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4) De la ultima onda tomada del paciente, tomamos las señales" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "select_stament = 'SELECT lef.signame FROM waveformFields lef LEFT JOIN (SELECT MAX(recorddate) AS recorddate,subject_id FROM waveformFields GROUP BY subject_id) rig ON lef.subject_id = rig.subject_id AND lef.recorddate = rig.recorddate WHERE rig.subject_id IS NOT NULL ORDER BY lef.subject_id'\n", "cur.execute(select_stament)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "patient = []\n", "for row in cur:\n", " patient += row[0]\n", "# np.concatenate((patient, np.array(row)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Posibles Ondas que existen" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('PLETH L', 'RESP', 'MCL1', 'FAP', 'MCL1+', 'PLETH R', 'IC2', 'aVR', '???', 'I', '[0]', '[5125]', 'ABP', 'IC1', 'AOBP', 'CO2', '[0]+', 'ECG', 'P1', 'AVL', 'II+', 'PLETHl', 'MCL', 'I+', 'LAP', 'AVF', 'UAP', 'aVF', '!', 'Ao', 'V', 'III', 'BAP', 'P4', 'II', '[0]+++', 'V+', 'III+', 'PLETH', 'PLETHr', '[0]++++', 'CVP', '[0]++', 'ICP', 'aVL', 'V1', 'RAP', 'PAP', 'AVR', 'Resp', 'ART', 'UVP')\n" ] } ], "source": [ "labels, values = zip(*collections.Counter(patient).items())\n", "print(labels)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Muestrame el top 15 de las Ondas mas comunes" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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IKquk0o/qR6vfC9w7sugcDJsstf3hvwd+D+wIm+xYu+/Ml+HMEbXb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\nliRJkjIZqiVJkqRM/x+wsx1k+dRpIAAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x7fd4b32902e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels, values = zip(*collections.Counter(patient).most_common(14))\n", "indexes = np.arange(len(labels))\n", "width = 0.5\n", "fig_size = [12,9]\n", "plt.rcParams[\"figure.figsize\"] = fig_size\n", "\n", "fig, ax = plt.subplots() \n", "for i, v in enumerate(values):\n", " ax.text(i-0.3,v+12, str(v), color='blue', fontweight='bold')\n", " \n", "plt.bar(indexes, values, width)\n", "plt.xticks(indexes + width * 0.01, labels)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Se hara el analisis sobre las ondas de tipo II porque son de las que mas tenemos 9950 muestras\n" ] } ], "source": [ "select_stament = \"SELECT count(distinct lef.subject_id) FROM waveformfields lef LEFT JOIN (SELECT MAX(recorddate) AS recorddate,subject_id FROM waveformFields GROUP BY subject_id) rig ON lef.subject_id = rig.subject_id AND lef.recorddate = rig.recorddate WHERE rig.subject_id IS NOT NULL AND signame @> ARRAY['\"+labels[0]+\"']::varchar[]\"\n", "cur.execute(select_stament)\n", "result = cur.fetchone()\n", "print('Se hara el analisis sobre las ondas de tipo',labels[0],'porque son de las que mas tenemos',result[0],' muestras')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "conn.close()" ] } ], "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": 2 }
gpl-3.0
robertoalotufo/ia898
master/tutorial_numpy_1_10.ipynb
1
3614
{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#Clip\" data-toc-modified-id=\"Clip-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Clip</a></div><div class=\"lev2 toc-item\"><a href=\"#Exemplos\" data-toc-modified-id=\"Exemplos-11\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Exemplos</a></div><div class=\"lev2 toc-item\"><a href=\"#Exemplo-com-ponto-flutuante\" data-toc-modified-id=\"Exemplo-com-ponto-flutuante-12\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Exemplo com ponto flutuante</a></div><div class=\"lev1 toc-item\"><a href=\"#Documentação-Oficial-Numpy\" data-toc-modified-id=\"Documentação-Oficial-Numpy-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Documentação Oficial Numpy</a></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Clip\n", "\n", "A função clip substitui os valores de um array que estejam abaixo de um limiar mínimo ou que estejam acima de um limiar máximo, por esses limiares mínimo e máximo, respectivamente. Esta função é especialmente útil em processamento de imagens para evitar que os índices ultrapassem os limites das imagens.\n", "\n", "## Exemplos" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a = [11 1 2 3 4 5 12 -3 -4 7 4]\n", "np.clip(a,0,10) = [10 1 2 3 4 5 10 0 0 7 4]\n" ] } ], "source": [ "import numpy as np\n", "\n", "a = np.array([11,1,2,3,4,5,12,-3,-4,7,4])\n", "print('a = ',a)\n", "print('np.clip(a,0,10) = ', np.clip(a,0,10))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Exemplo com ponto flutuante\n", "\n", "Observe que se os parâmetros do clip estiverem em ponto flutuante, o resultado também será em ponto flutuante:" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a= [0 1 2 3 4 5 6 7 8 9]\n", "np.clip(a,2.5,7.5)= [ 2.5 2.5 2.5 3. 4. 5. 6. 7. 7.5 7.5]\n" ] } ], "source": [ "a = np.arange(10).astype(np.int)\n", "print('a=',a)\n", "print('np.clip(a,2.5,7.5)=',np.clip(a,2.5,7.5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Documentação Oficial Numpy\n", "\n", "- [clip](http://docs.scipy.org/doc/numpy/reference/generated/numpy.clip.html)" ] } ], "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" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "86px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": true, "toc_section_display": "block", "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }
mit
jdpigeon/neurodoro
classifier/.ipynb_checkpoints/ndro_train_tensorflow-checkpoint.ipynb
1
12617
{ "cells": [ { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import sklearn as sk\n", "import tensorflow as tf\n", "from sklearn import datasets\n", "from sklearn.model_selection import train_test_split\n", "from tensorflow.contrib import learn\n", "from sklearn.decomposition import PCA" ] }, { "cell_type": "code", "execution_count": 179, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Read from CSV where every column = [FFT freq bin]\n", "# every row = [epoch 1 (2s): electrode 1, 2, 3, 4] + [epoch 2: electrode 1, 2, 3, 4] + ...\n", "relax = pd.read_csv(\"../Muse Data/josh_relax_apr03_night.csv\", header=0, index_col=False)\n", "focus = pd.read_csv(\"../Muse Data/josh_corvo_task_apr03_night.csv\", header=0, index_col=False)\n", "\n", "# Chop off irrelevant frequencies\n", "relax = relax.iloc[:,0:54]\n", "focus = focus.iloc[:,0:54]\n", "\n", "# Add labels\n", "relax['label'] = 0\n", "focus['label'] = 1" ] }, { "cell_type": "code", "execution_count": 185, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ValueError", "evalue": "Found input variables with inconsistent numbers of samples: [1824, 912]", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-185-cf7b124cb782>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m# Split values and labels arrays into random train and test subsets (20% set aside for testing)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 12\u001b[0m \u001b[0mX_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/joshharris/anaconda3/lib/python3.6/site-packages/sklearn/model_selection/_split.py\u001b[0m in \u001b[0;36mtrain_test_split\u001b[0;34m(*arrays, **options)\u001b[0m\n\u001b[1;32m 1687\u001b[0m \u001b[0mtest_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.25\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1689\u001b[0;31m \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1690\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1691\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstratify\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/joshharris/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mindexable\u001b[0;34m(*iterables)\u001b[0m\n\u001b[1;32m 204\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 205\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 206\u001b[0;31m \u001b[0mcheck_consistent_length\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 207\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 208\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/joshharris/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_consistent_length\u001b[0;34m(*arrays)\u001b[0m\n\u001b[1;32m 179\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muniques\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m raise ValueError(\"Found input variables with inconsistent numbers of\"\n\u001b[0;32m--> 181\u001b[0;31m \" samples: %r\" % [int(l) for l in lengths])\n\u001b[0m\u001b[1;32m 182\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Found input variables with inconsistent numbers of samples: [1824, 912]" ] } ], "source": [ "# Combine focus and relax dataframes into a numeric values and complementary labels dataframe\n", "# rows = [relax data] + [focus data]\n", "values = pd.concat([relax.iloc[:,1:3], relax.iloc[:,4:54], focus.iloc[:,1:3], focus.iloc[:,2:54]]).reset_index(drop=True)\n", "labels = pd.concat([pd.DataFrame(relax['label']), pd.DataFrame(focus['label'])]).reset_index(drop=True)\n", "\n", "# Convert labels from a dataframe to a 1D matrix\n", "#c, r = labels.shape\n", "#labels = labels.as_matrix().reshape(c,)\n", "\n", "# Split values and labels arrays into random train and test subsets (20% set aside for testing)\n", "X_train, X_test, y_train, y_test = train_test_split(values,labels,test_size=0.2)\n", "X_train = X_train.as_matrix()\n", "\n", "# Convert labels from a dataframe to a 1D matrix\n", "c, r = y_train.shape\n", "y_train = y_train.as_matrix().reshape(c,)\n", "\n", "d, s = y_test.shape\n", "y_test = y_test.as_matrix().reshape(d,)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 178, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "ename": "TypeError", "evalue": "__init__() missing 1 required positional argument: 'example_id_column'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-178-36e7913b826b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;31m# Declare optimizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0mmy_opt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear_optimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSDCAOptimizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0mtrain_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmy_opt\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 39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 40\u001b[0m \u001b[0;31m# Initialize variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'example_id_column'" ] } ], "source": [ "# Create graph\n", "sess = tf.Session()\n", "\n", "# Declare batch size, get some sizes to use\n", "batch_size = len(X_train)\n", "x_length, x_width = X_train.shape\n", "y_length = len(y_train)\n", "\n", "# Initialize placeholders\n", "x_data = tf.placeholder(shape=[None, x_width], dtype=tf.float32)\n", "y_target = tf.placeholder(shape=[None, 1], dtype=tf.float32)\n", "\n", "# Create variables for linear regression\n", "A = tf.Variable(tf.random_normal(shape=[x_width, x_length]))\n", "b = tf.Variable(tf.random_normal(shape=[1, y_length]))\n", "\n", "# Declare model operations\n", "model_output = tf.subtract(tf.matmul(x_data, A), b)\n", "\n", "# Declare vector L2 'norm' function squared\n", "l2_norm = tf.reduce_sum(tf.square(A))\n", "\n", "# Declare loss function\n", "# Loss = max(0, 1-pred*actual) + alpha * L2_norm(A)^2\n", "# L2 regularization parameter, alpha\n", "alpha = tf.constant([0.01])\n", "# Margin term in loss\n", "classification_term = tf.reduce_mean(tf.maximum(0., tf.subtract(1., tf.multiply(model_output, y_target))))\n", "# Put terms together\n", "loss = tf.add(classification_term, tf.multiply(alpha, l2_norm))\n", "\n", "# Declare prediction function\n", "prediction = tf.sign(model_output)\n", "accuracy = tf.reduce_mean(tf.cast(tf.equal(prediction, y_target), tf.float32))\n", "\n", "# Declare optimizer\n", "my_opt = tf.contrib.linear_optimizer.SDCAOptimizer\n", "train_step = my_opt()\n", "\n", "# Initialize variables\n", "init = tf.global_variables_initializer()\n", "sess.run(init)\n", "\n", "# Training loop\n", "loss_vec = []\n", "train_accuracy = []\n", "test_accuracy = []\n", "for i in range(500):\n", " rand_index = np.random.choice(len(X_train), size=batch_size)\n", " rand_x = X_train[rand_index]\n", " rand_y = np.transpose([y_train[rand_index]])\n", " sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y})\n", " \n", " temp_loss = sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y})\n", " loss_vec.append(temp_loss)\n", " \n", " train_acc_temp = sess.run(accuracy, feed_dict={x_data: X_train, y_target: np.transpose([y_train])})\n", " train_accuracy.append(train_acc_temp)\n", " \n", " test_acc_temp = sess.run(accuracy, feed_dict={x_data: X_test, y_target: np.transpose([y_test])})\n", " test_accuracy.append(test_acc_temp)\n", " \n", " if (i+1)%100==0:\n", " print('Step #' + str(i+1))\n", " print('Loss = ' + str(temp_loss))\n", "\n", "\n", "# Plot train/test accuracies\n", "plt.plot(train_accuracy, 'k-', label='Training Accuracy')\n", "plt.plot(test_accuracy, 'r--', label='Test Accuracy')\n", "plt.title('Train and Test Set Accuracies')\n", "plt.xlabel('Generation')\n", "plt.ylabel('Accuracy')\n", "plt.legend(loc='lower right')\n", "plt.show()\n", "\n", "# Plot loss over time\n", "plt.plot(loss_vec, 'k-')\n", "plt.title('Loss per Generation')\n", "plt.xlabel('Generation')\n", "plt.ylabel('Loss')\n", "plt.show()" ] }, { "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
Divergent914/kddcup2015
explorer.ipynb
1
7537
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import pylab as pl\n", "import numpy as np\n", "import scipy as sp\n", "import pandas as pd\n", "from datetime import datetime, timedelta\n", "\n", "import util" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X = util.fetch(util.cache_path('train_X_before_2014-08-01_22-00-47'))\n", "y = util.fetch(util.cache_path('train_y_before_2014-08-01_22-00-47'))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X[X[:, :45] > 0] = 1." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X[:, 47] = X[:, 47] / 3." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X[:, 48] = X[:, 48] / 6." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X[:, 49] = X[:, 49] / 20104." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "X[:, 50] = X[:, 50] / 20104." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "X[:, 141:] = X[:, 141:] / np.amax(X, axis=0)[141:]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [], "source": [ "i = 55\n", "while i < 96:\n", " X[:, i] = X[:, i] / np.max(X[:, i])\n", " i += 5" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.svm import LinearSVC\n", "from sklearn.cross_validation import cross_val_score, StratifiedKFold" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "svc = LinearSVC(dual=False, class_weight='auto')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.89339357, 0.9134252 , 0.85240336, 0.86367327, 0.81409625])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val_score(svc, X, y, scoring='roc_auc', cv=StratifiedKFold(y, 5))" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "LinearSVC(C=1.0, class_weight='auto', dual=False, fit_intercept=True,\n", " intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n", " multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n", " verbose=0)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svc.fit(X, y)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.metrics import roc_auc_score" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.85831888308739623" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.calibration import CalibratedClassifierCV\n", "clf = CalibratedClassifierCV(svc, cv=StratifiedKFold(y, 5),\n", " method='isotonic')\n", "clf.fit(X, y)\n", "roc_auc_score(y, clf.predict_proba(X)[:, 1])" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.19560413, 0.17089759, 0.65764372, -0.16353356, -0.03430508,\n", " 0.03438299, 0.0305282 , -0.81062161, -0.18258275, -0.02177491,\n", " -0.61820751, -0.1728157 , 0.02736452, -0.05324924, -0.23152444,\n", " -0.13226027, 0.00292948, -0.13227422, -0.07393709, -0.01571165,\n", " -0.18954343, -0.1581372 , 0.04015358, 0.1046442 , -0.06462214,\n", " 0.1141877 , -0.0793758 , 0.09890401, -0.1229745 , -0.02287298,\n", " -0.00665293, 0.02671975, 0.6407645 , 0.72272215, 0.59951251,\n", " 0.42662929, -0.0743573 , 0.10653245, -0.2188587 , -0.08949457,\n", " -0.12845542, 0.27643448, -0.02939936, 0.27823046, -0.03132662])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "svc.coef_[0][0:45]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.85801240818802649" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LogisticRegressionCV\n", "lr = LogisticRegressionCV(cv=5, scoring='roc_auc')\n", "lr.fit(X, y)\n", "roc_auc_score(y, lr.predict_proba(X)[:, 1])" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 0.55524728, 0.58337573, 3.14866739, -0.42387956, -0.08976478,\n", " 0.13819725, 0.35633133, -3.41059384, -0.29468168, -0.03769797,\n", " -2.78221699, -0.59845394, -0.11601482, 0.0636001 , -0.54406701,\n", " -0.26319308, -0.25419769, 0.30933589, -0.50214663, -0.04211469,\n", " -0.49922947, -0.42282014, -0.13151163, 0.35713128, -0.17807037,\n", " 0.32982723, -0.21705695, 0.3376418 , -1.53369482, -0.03792951,\n", " -0.19220702, -0.04559449, 1.55751474, 1.77052335, 1.64272848,\n", " 1.67319217, -0.09988238, 0.69010081, -1.11456405, -0.2379652 ,\n", " -0.46870361, 1.34808414, -0.24040566, 2.74425955, -0.10113288])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "lr.coef_[0][0:45]" ] }, { "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 }
gpl-2.0
crscardellino/dnnwsd
graphics/pandas.ipynb
1
3234
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import os\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import matplotlib.pylab as pylab\n", "\n", "matplotlib.style.use('ggplot')\n", "pylab.rcParams['figure.figsize'] = 1, 10 # that's default image size for this interactive session" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "working_dir = \"../resources/results/results_supervised_sensem/\"\n", "\n", "experiments = {}\n", "\n", "for lemma_dir in (d for d in os.listdir(working_dir) if not (d.endswith(\".yaml\") or d == \".RData\" or d == \".Rhistory\")):\n", " experiments[lemma_dir] = {}\n", " \n", " for experiment_dir in os.listdir(os.path.join(working_dir, lemma_dir)):\n", " accuracy = np.loadtxt(os.path.join(working_dir, lemma_dir, experiment_dir, \"accuracy\"))\n", " mcp = np.loadtxt(os.path.join(working_dir, lemma_dir, experiment_dir, \"most_common_precision\"))\n", " lcr = np.loadtxt(os.path.join(working_dir, lemma_dir, experiment_dir, \"less_common_recall\"))\n", "\n", " experiments[lemma_dir][experiment_dir] = pd.DataFrame({\n", " 'accuracy': accuracy,\n", " 'mcp': mcp,\n", " 'lcr': lcr\n", " })" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "accuracy_sensem = pd.read_csv(\"scripts/test_accuracy.csv\", index_col=0)\n", "accuracy_semeval = pd.read_csv(\"scripts/test_accuracy_semeval.csv\", index_col=0)\n", "accuracy_semeval_verbs_only = pd.read_csv(\"scripts/test_accuracy_semeval_verbs_only.csv\", index_col=0)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "accuracy_sensem.boxplot(return_type='axes')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "accuracy_semeval.boxplot(return_type='axes')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "accuracy_semeval_verbs_only.boxplot(return_type='axes')" ] }, { "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 }
bsd-3-clause
karolaya/PDI
PS-05/.ipynb_checkpoints/problem_set_5-checkpoint.ipynb
1
529231
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# <center>Digital Image Processing - Problem Set 5</center>" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Student Names: \n", "* Karolay Ardila Salazar\n", "* Julián Sibaja García\n", "* Andrés Simancas Mateus" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Definitions" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Fall-Nature-Background-Pictures.jpg', 'Fig6.21(b).jpg', 'Woman.bmp', 'blown_ic.png', 'blurry_moon.png', 'bottles.png', 'building.jpg', 'cameraman.png', 'cameraman_new.png', 'check.png', 'chest.jpg', 'ckt_board_saltpep_prob_pt05.png', 'connected.jpg', 'contact_lens_original.png', 'crosses.png', 'darkPollen.jpg', 'dark_fountain.jpg', 'face.png', 'face.tif', 'fingerprint.jpg', 'flower.jpg', 'fruits.jpg', 'hiro.jpg', 'hubble-original.tif', 'hut.jpg', 'image_0001.jpg', 'image_0002.jpg', 'image_0003.jpg', 'image_0004.jpg', 'image_0005.jpg', 'image_0006.jpg', 'image_0007.jpg', 'image_0008.jpg', 'image_0009.jpg', 'image_0010.jpg', 'image_0011.jpg', 'image_0012.jpg', 'image_0013.jpg', 'image_0014.jpg', 'image_0015.jpg', 'image_0016.jpg', 'image_0017.jpg', 'image_0018.jpg', 'image_0019.jpg', 'image_0020.jpg', 'lena.jpg', 'lightPollen.jpg', 'lowContrastPollen.jpg', 'mms.jpg', 'moon.jpg', 'new_bottles.jpg', 'new_cameraman.png', 'new_chest.bmp', 'noisy_fingerprint.jpg', 'out.png', 'pollen.jpg', 'rectangle.png', 'rose.bmp', 'runway.jpg', 'shapes.PNG', 'skull.bmp', 'small_blobs.jpg', 'spheres.jpg', 'spine.jpg', 'squares.jpg', 'steve_blog.png', 'test_pattern_blurring_orig.png', 'three_bottles.jpg', 'translated_rectangle.png', 'weld_x-ray.jpg']\n" ] } ], "source": [ "'''This is a definition script, so we do not have to rewrite code'''\n", "\n", "import numpy as np\n", "import os\n", "import cv2\n", "import matplotlib.pyplot as mplt\n", "import random\n", "import json\n", "\n", "\n", "# set matplotlib to print inline (Jupyter)\n", "%matplotlib inline\n", "\n", "# path prefix\n", "pth = '../data/'\n", "\n", "# files to be used as samples\n", "# list *files* holds the names of the test images\n", "files = sorted(os.listdir(pth))\n", "print files\n", "\n", "# Usefull function\n", "def rg(img_path):\n", " return cv2.imread(pth+img_path, cv2.IMREAD_GRAYSCALE)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Problem 1" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Write a function that describes <i>each</i> object in a binary image using the Hu statistical moments. The Hu moments are invariant to rotation, scale and translation. These moments can be defined for <i>each</i> region in a binary image. The OpenCV function to compute these moments is <tt>cv2.HuMoments</tt>. Write down the equations that compute the seven Hu moments for a region." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Análisis\n", "La siguiente función tiene como objetivo encontrar los Hu moments que describen a cada objeto de la imagen <tt>shapes.png</tt>. Para esto primero guardamos la matriz de la imagen en una variable. Luego definimos nuestra función que recibe una imagen como parámetro. Aplicamos threshold a la imagen para luego con la resultante hallar los objetos con la función <tt>cv2.findContours</tt>. Ya que tenemos los valores de los contours, podemos mostrar cuantos de estos encontró con su longitud y confirmamos que sean la misma cantidad de objetos que se encuentran en la imagen. Ahora, para cada uno de estos objetos aplicamos <tt>cv2.huMoments</tt> y mostramos en pantalla los resultados.\n", "\n", "##### Seven Hu moments\n", "$ I_1 = n_{20} + n_{02} $ <br>\n", "$ I_2 = (n_{20} - n_{02})^2 + 4n_{11}^2 $ <br>\n", "$ I_3 = (n_{30} - n_{12})^2 + (n_{21} - n_{03})^2 $ <br>\n", "$ I_4 = (n_{30} + n_{12})^2 + (n_{21} + n_{03})^2 $ <br>\n", "$ I_5 = (n_{30} - 3n_{12})(n_{30} + n_{12})[(n_{30} + n_{12})^2 - 3(n_{21} + n_{03})^2] + (3n_{21} - n_{03})(n_{21} + n_{03})(3(n_{30} + n_{12})^2 - (n_{21} + n_{03})^2) $ <br>\n", "$ I_6 = (n_{20} - n_{02})[(n_{30} + n_{12})^2 - (n_{21} + n_{03})^2] + 4n_{11}(n_{30} + n_{12})(n_{21} + n_{03}) $ <br>\n", "$ I_7 = (3n_{21} - n_{03})(n_{30} + n_{12})[(n_{30} + n_{12})^2 - 3(n_{21} + n_{03})^2] - (n_{30} - 3n_{12})(n_{21} + n_{03})(3(n_{30} + n_{12})^2 - (n_{21} + n_{03})^2) $ <br>" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of contours detected = 17\n", "0 = [ 2.28516566e-01 1.51622505e-02 5.96665390e-03 5.40948254e-04\n", " 6.85106641e-07 3.28556348e-05 -6.89289716e-07]\n", "1 = [ 3.17013085e-01 6.36120231e-02 8.90898720e-03 1.53855917e-03\n", " 3.75848984e-06 1.21466674e-04 -4.28024355e-06]\n", "2 = [ 1.99008905e-01 1.19945861e-02 1.20201210e-27 1.93115646e-27\n", " 2.80774704e-54 1.75276435e-28 -8.79450435e-55]\n", "3 = [ 1.66666667e-01 3.31013624e-28 1.21865554e-24 9.78172683e-25\n", " 1.06054885e-48 1.42417596e-38 -1.25781086e-49]\n", "4 = [ 0.17468087 0.00273563 0. 0. 0. 0. 0. ]\n", "5 = [ 1.69595887e-01 9.84987155e-04 3.51718976e-25 8.84382491e-25\n", " 4.53997327e-49 2.60273073e-26 -1.92800200e-49]\n", "6 = [ 1.67320569e-01 2.18395085e-04 6.04141661e-26 9.70553149e-27\n", " -4.09612928e-53 -1.13085454e-28 -2.31419389e-52]\n", "7 = [ 0.16055151 0.00044483 0. 0. 0. 0. 0. ]\n", "8 = [ 1.59335088e-01 3.75621064e-05 0.00000000e+00 0.00000000e+00\n", " 0.00000000e+00 0.00000000e+00 0.00000000e+00]\n", "9 = [ 1.92605334e-01 5.75224452e-05 4.56454726e-03 1.73328362e-06\n", " 1.25684113e-10 1.03278959e-08 -8.92875585e-11]\n", "10 = [ 4.45430323e-01 1.61360408e-01 1.17355201e-02 1.17694241e-03\n", " -3.73389436e-06 -4.31886663e-04 -2.27823793e-06]\n", "11 = [ 1.59443034e-01 8.51478779e-05 3.63693328e-25 3.94573460e-25\n", " 1.49468737e-49 3.58169841e-27 9.67947241e-52]\n", "12 = [ 2.10668986e-01 7.29318139e-03 5.14334090e-03 1.48139184e-04\n", " -1.12337625e-07 -1.13815356e-05 -6.40389335e-08]\n", "13 = [ 1.78938677e-01 4.88891125e-03 3.06789442e-29 3.62255049e-30\n", " -2.93766069e-59 2.19659387e-31 2.44016147e-59]\n", "14 = [ 1.68247463e-01 5.29431001e-04 2.95389356e-30 2.78871078e-30\n", " 7.04940966e-60 5.37719928e-32 -3.79058412e-60]\n", "15 = [ 2.04941906e-01 1.66523218e-02 6.86356103e-28 6.28955975e-28\n", " 4.13224848e-55 7.93111328e-29 -3.83391967e-57]\n", "16 = [ 0.16825966 0.00053353 0. 0. 0. 0. 0. ]\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f07244fcb10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "image = rg(files[-11])\n", "\n", "def huMoments(image):\n", " ret, threshold = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n", "\n", " _, contours, _ = cv2.findContours(threshold, cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)\n", " print \"Number of contours detected = \" + str(len(contours))\n", "\n", " for i in range(len(contours)):\n", " Hu = cv2.HuMoments(cv2.moments(contours[i])).flatten()\n", " print str(i) + ' = ' + str(Hu)\n", "\n", " mplt.figure()\n", " mplt.imshow(image, cmap='gray')\n", " mplt.title(files[-11])\n", " \n", "huMoments(image)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Problem 2" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Write a function that detects corners on an image using the Harris corner detection method. You can use the OpenCV built-in functions. Your function should output the $N$ detected corner locations in a $2 \\times N$ matrix. Visualize your results by plotting the corners on top of the input image. Apply your function to the binary image <tt> shapes.png</tt> and to the grayscale image <tt>face.tif</tt>." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Análisis\n", "\n", "Primeramente cargamos las dos imágenes de interés. Luego creamos la función cornerDetection que recive como argumento una imagen. Guardamos los corners que hayamos por la función <tt>cv2.goodFeaturesToTrack</tt> en una variable y luego iteramos sobre estos para hayar las posiciones x,y donde los mostraremos en forma de círculo en la imagen a través de <tt>.ravel</tt>. Finalmente mostramos la imagen resultante." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "989 206\n", "880 211\n", "378 338\n", "836 351\n", "693 351\n", "944 321\n", "888 321\n", "944 265\n", "888 265\n", "390 265\n", "331 265\n", "637 256\n", "596 256\n", "836 246\n", "693 246\n", "637 222\n", "596 222\n", "390 211\n", "331 211\n", "157 98\n", "87 98\n", "555 96\n", "879 81\n", "817 81\n", "157 37\n", "87 37\n", "879 27\n", "817 27\n", "332 391\n", "601 90\n", "928 117\n", "449 404\n", "1036 331\n", "555 46\n", "207 158\n", "923 406\n", "1014 393\n", "581 190\n", "394 100\n", "307 56\n", "216 56\n", "307 45\n", "216 45\n", "75 171\n", "55 171\n", "75 130\n", "55 130\n", "933 405\n", "949 403\n", "437 403\n", "427 402\n", "965 401\n", "409 400\n", "980 399\n", "399 399\n", "996 397\n", "381 397\n", "371 396\n", "361 395\n", "344 393\n", 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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06e8c32c10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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o8SybnMiioiI88cQTapDz+Tw+8YlPoFAoIBQKYerUqZb+8DgKB2sd5GRL0lDG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N23v/3t8lwEnfe1+qBmEBXQ1tEEno0bBzIjJ2RycjI7duzIZZdd1tqunXSJZxRojT5YiE+GzLi2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\niy/Wvn37yrk+p/7iKfcgomcXxwftPfzx8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"text/plain": [ "<matplotlib.figure.Figure at 0x7f06e8b0bd50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "img = rg(files[-11])\n", "img_2 = rg(files[18])\n", "\n", "def cornerDetection(img):\n", " corners = cv2.goodFeaturesToTrack(img,100,0.01,10)\n", " corners = np.int0(corners)\n", "\n", " for k in corners:\n", " x,y = k.ravel()\n", " cv2.circle(img, (x,y), 3, 255, -1)\n", " print x,y\n", "\n", " mplt.figure()\n", " mplt.imshow(cv2.cvtColor(img, cv2.COLOR_GRAY2BGR))\n", " \n", "cornerDetection(img)\n", "cornerDetection(img_2)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Problem 3" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "A company that bottles a variety of industrial chemicals has heard\n", "of your success solving imaging problems and hires you to design an approach\n", "for detecting when bottles are not full. The bottles appear as shown below\n", "as they move along a conveyor line past an automatic\n", "filling and capping station. A bottle is considered imperfectly filled when the\n", "level of the liquid is below the midway point between the bottom of the neck and\n", "the shoulder of the bottle.The shoulder is defined as the region of the bottle\n", "where the sides and slanted portion of the bottle intersect. The bottles are\n", "moving, but the company has an imaging system equipped with an illumination\n", "flash front end that effectively stops motion, so you will be given images that\n", "look very close to the sample shown below.\n", "\n", "<img src=\"../data/files/bottles.png\" />\n", "\n", "Propose a solution for detecting\n", "bottles that are not filled properly. State clearly all assumptions that you\n", "make and that are likely to impact the solution you propose. Implement your\n", "solution and apply it to the images <tt>bottles.tif, new_bottles.jpg</tt> and <tt> three_bottles.jpg</tt>. Visualize the results\n", "of your algorithm by highlighting with false colors\n", "the regions that are detected as correctly\n", "filled bottles and the regions that are detected as not properly filled bottles." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Comentario\n", "La idea es encontrar las botellas que no están llenas apropiadamente. No se requirió de comandos demasiado complejos de OpenCV, de hecho ninguno además de `threshold`, `filter2D` y `erode`. La idea se puede resumir como sigue:\n", "\n", "* Encontrar el ancho promedio de las botellas, este ancho promedio es aproximadamente el mínimo valor posible de líquido en una botella correcta,\n", "* Encontrar la altura en la imagen en la que ocurre por primera vez el ancho promedio (este va a ser el nivel límite de líquido)\n", "* Encontrar la cantidad de botellas de la imagen,\n", "* Encontrar los centros de estas botellas,\n", "* Al nivel mínimo verificar qué botella tiene aire todavía e indexarla.\n", "\n", "Para empezar se suaviza la imagen con un `filtro2D` y se umbraliza la misma para separar las botellas del fondo; este valor se almacena. Luego la idea es encontrar el número de botellas que hay y su ancho; para esto creamos una regla (un vector de ceros) que sumamos con cada fila de la imagen. Lo resultante de la operación es un vector con intervalos de ceros y unos (unos son regiones de las botellas), se miden las regiones de unos y el número de las mismas; la moda del número de regiones es el número de botellas que hay, la media de máximo de cada medición es el ancho de las botellas. Debido a que este ancho hallado es ciertamente menor que el ancho real, sirve como el ancho de la botella en el que ocurre la altura de líquido mínima.\n", "Cabe notar que en la medición de las regiones hecha anteriormente se calcularon los centros de las botellas (centros de cada región) y se estimó la altura mínima del líquido a través del ancho promedio.\n", "\n", "Luego de esto se umbralizó la imagen inicial con el fin de obtener las regiones de aire únicamente (fue necesario hacer erosión para remover elementos pequeños). Esta imagen se evaluó en la fila de altura de agua mínima y se extrajeron las regiones de aire a esta altura; el centro de estas regiones se contrasto con el centro de las botellas para saber a qué botella pertenecían y de esta forma identificarlas." ] }, { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Final decision. Bottles not correct are marked with 1s: \n", "[0, 0, 1, 0, 0]\n", "Final decision. Bottles not correct are marked with 1s: \n", "[0, 1, 1, 1, 0]\n", "Final decision. Bottles not correct are marked with 1s: \n", "[0, 1, 0, 0, 0]\n" ] }, { "data": { "image/png": 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St3amaWJ1dRVPPvnk3GNTLsoKhUKhmAWpqGQBYO1NEATI5/Os2aHEwHVdnDlz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\n4zhs9ZAkkTmOtavVaol2F84iNoVCoVA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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0e015eac10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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uk/dxvcBLXX98LUyzB7AnGdlr29wWCRa3F/yZlim2W6Hilz337fu+xDDwfpbk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\nQSnb0chPVaTT6Uga+ipQuaN/jF1yo9EIxhgpt/lLlVThWJo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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0dfb5fb090>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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0Wi1ks1n4/X55L+1Gsg7jnjA4Opjmh+Jhrc3cEwYG22MvStafAPj+La/9CoC/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\n2Gg0kvfsed5Sx+BZmM1mEp8R9VGdnJyg2+1KxAQ/J5bjbNuWQFCWadP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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0e003828d0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Images to test\n", "MIN_DIST = 30\n", "LEVEL_OFFSET_INV = 10\n", "LEVEL_OFFSET = 90\n", "imgs = [files[i] for i in [5, -3, -20]]\n", "\n", "# Bad bottle detector\n", "def badBottleDetector(img):\n", " h, w = img.shape\n", " \n", " # Smooth\n", " kernel = np.ones((3,3),np.float32)/9\n", " simg = cv2.filter2D(img, -1, kernel)\n", " simg_c = simg.copy()\n", " \n", " _, simg_ct = cv2.threshold(simg_c, np.mean(simg_c)+LEVEL_OFFSET, 1, cv2.THRESH_BINARY)\n", " _, simg_cn = cv2.threshold(simg_c, LEVEL_OFFSET_INV, 1, cv2.THRESH_BINARY)\n", " kernel = np.ones((5,5), np.uint8)\n", " simg_ct = cv2.erode(simg_ct, kernel, iterations=1)\n", " \n", " bottles = list()\n", " sizes = list()\n", " indices = list()\n", " centers = list()\n", " for i in range(h):\n", " ruler = np.zeros([1, w]) + simg_cn[i: i+1]\n", " ruler = ruler[0]\n", " \n", " seed = 0\n", " break_points = list()\n", " for j in range(len(ruler)):\n", " if (ruler[j] != seed) or (j == len(ruler)-1 and ruler[j] == 1):\n", " break_points.append(j)\n", " seed = int(not seed)\n", " \n", " dist = list()\n", " center = list()\n", " for j in range(0, len(break_points)-1, 2):\n", " if break_points[j+1] - break_points[j] > MIN_DIST:\n", " dist.append(break_points[j+1] - break_points[j])\n", " center.append(int((break_points[j+1] + break_points[j])/2))\n", " \n", " if dist:\n", " bottles.append(len(dist))\n", " sizes.append(np.max(dist))\n", " indices.append(i)\n", " centers.append(center)\n", " \n", " counts = np.bincount(bottles)\n", " num_bottles = np.argmax(counts)\n", " base_size = int(np.mean(sizes))\n", " centers = [c for c in centers if len(c) == num_bottles]\n", " \n", " center = list()\n", " for i in range(num_bottles):\n", " bt = [c[i] for c in centers]\n", " center.append(int(np.mean(bt)))\n", " \n", " index = 0\n", " for i in range(len(sizes)):\n", " if sizes[i] > base_size:\n", " index = i\n", " break\n", " liquid_min_limit = indices[index]\n", " \n", " # Check which bottle has air at index liquid_min_limit\n", " ruler = np.zeros([1, w]) + simg_ct[liquid_min_limit: liquid_min_limit+1]\n", " ruler = ruler[0]\n", " \n", " seed = 0\n", " break_points = list()\n", " for j in range(len(ruler)):\n", " if (ruler[j] != seed) or (j == len(ruler)-1 and ruler[j] == 1):\n", " break_points.append(j)\n", " seed = int(not seed)\n", " \n", " dist = list()\n", " centers = list()\n", " for j in range(0, len(break_points)-1, 2):\n", " if break_points[j+1] - break_points[j] > MIN_DIST:\n", " dist.append(break_points[j+1] - break_points[j])\n", " centers.append(int((break_points[j+1] + break_points[j])/2))\n", " \n", " final = [0]*len(center)\n", " for c in centers:\n", " ct = [np.abs(cc - c) for cc in center]\n", " final[np.argmin(ct)] = 1\n", " \n", " print('Final decision. Bottles not correct are marked with 1s: ')\n", " print(final)\n", " \n", " printer([img, simg_cn, simg_ct], ['Original image', 'inv', 'liquid'])\n", " \n", "def printer(iss, des):\n", " # Printing\n", " f, ax = mplt.subplots(1, len(iss), figsize=(10,10))\n", " \n", " for i in range(len(iss)):\n", " ax[i].imshow(iss[i], cmap='gray')\n", " ax[i].set_title(des[i])\n", "\n", "\n", "for i in imgs:\n", " badBottleDetector(rg(i))" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Problem 4" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Suppose that you are observing objects in the night sky. Suppose that only ‘big’ objects are important to your observation. In this scenario, ‘small’ objects are considered noise. Write a python function that processes the image as follows:\n", "\n", "1. Use a 15x15 averaging filter to blur the image.\n", "\n", "2. Apply a threshold of 0.25 to binarize the resulting blurred image.\n", "\n", "3. Use the binary image to ‘mask’ the noise of the original image: simply perform an element-wise multiplication of the binary image and the original image.\n", "\n", "4. Use connected component analysis on the binary image to count the number of ‘big’ objects found.\n", "\n", "The function should take three inputs: an image matrix, the size of the averaging filter and threshold value. Make sure your function displays the intermediary results of each step outlined above.\n", "\n", "Apply your function to the input image ‘hubble-original.tif’. Try different values of smoothing kernel size and threshold value. Analyze the relationship between number of objects found and smoothing kernel size and threshold value. In particular, you might want to observe the result when using an averaging filter of size n=1 (i.e. no smoothing)." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Comentarios\n", "\n", "La idea del programa siguiente es encontrar cuerpos grandes en las imágenes; para ello se realizan los procedimientos anteriormente pedidos, esto es el filtro 15x15, la aplicación de la umbralización para crear la máscara, la eliminación de ruido de la imagen original y el análisis de cuerpo conexo. Estos pasos son sencillos de realizar y ya se han implementado anteriormente, a excepción del análisis de cuerpo conexo. Para comprobar conexión en un cuerpo, se elige un punto de semilla (aquel punto con intensidad de 1), a partir de este punto se analizan los vecinos de forma recursiva, esto es, se vuelve a llamar la función sobre los vecinos si estos tienen intensidad de 1. Esto arroja la cantidad de cuerpos conexos en la imagen, sin embargo, no hay garantía que estos cuerpos sean grandes. Para asegurar lo anterior se cuentan también el número de miembros de cada cuerpo, si este exede cierto umbral establecido, se cuenta como cuerpo grande." ] }, { "cell_type": "code", "execution_count": 174, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Objetos grandes encontrados: 9\n", "Se definió objeto grande aquel cuyo conjunto conexo tiene más de 80 miembros\n", "Este parámetro se puede variar\n", "Objetos grandes encontrados: 7\n", "Se definió objeto grande aquel cuyo conjunto conexo tiene más de 80 miembros\n", "Este parámetro se puede variar\n", "Objetos grandes encontrados: 33\n", "Se definió objeto grande aquel cuyo conjunto conexo tiene más de 80 miembros\n", "Este parámetro se puede variar\n", "Objetos grandes encontrados: 6\n", "Se definió objeto grande aquel cuyo conjunto conexo tiene más de 80 miembros\n", "Este parámetro se puede variar\n" ] }, { "data": { "image/png": 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FxElY2baNYrGo3DENw6gqsGibW+TpteXckLWwHvREMNWKllcT4PVY1OgHArdQ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\nQfqrDiGEeDVodgXRJiCYdgXRplriOM45Q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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0e001354d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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qArweixr9QOAWanq5Br09Xcz/zd/8DWKxmOO+yJ1yqs8kwzAMwzAMw9TDpAJO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hSPqqpwuVzo7u6GrutsE00uGO94zUTAybJsc6fI1aMCFU7hU6yBOL1O/ePIyaH3z1TAkatHPzt7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\nx+joKO644w4Ui0Vs2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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0e002f1310>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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jUaMfCNxCTS/XoLeni/m//uu/RiwWc1wXuVNO9Z5kGIZhGIZhmHqYVMAJIf5J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\ngI3K6+8C+O5JPP8inwP+HUCH+7wDYz1GA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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0dfb3b1210>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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IvEJNL9egt6eL+b/5m79BIpFw3Re5U071mWQYhmEYhmGYRphUwAkh/kkIcUQI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p/6N13LAX5MvmUCXQulLdJ10TlpDs50ShKPJHLp/KUiarQ2fJolpaeWS8+keVKELZfLIZlMYsuW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\ni/Hkk0966umKxaIcfWHbNtrb2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"text/plain": [ "<matplotlib.figure.Figure at 0x7f0dfae23850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "img_origin = rg('hubble-original.tif')\n", "\n", "BIG_OBJECT_COUNT = 80\n", "\n", "def printer(iss, des):\n", " # Printing\n", " f, ax = mplt.subplots(1, len(iss), figsize=(15,15))\n", " \n", " for i in range(len(iss)):\n", " ax[i].imshow(iss[i], cmap='gray')\n", " ax[i].set_title(des[i])\n", " \n", "def connectedLabeling(mat, i, j, h, w):\n", " mat[i][j] = 0\n", " if i+1 < h and mat[i+1][j] == 1:\n", " return 1 + connectedLabeling(mat, i+1, j, h, w)\n", " elif i-1 > -1 and mat[i-1][j] == 1:\n", " return 1 + connectedLabeling(mat, i-1, j, h, w)\n", " elif j+1 < w and mat[i][j+1] == 1:\n", " return 1 + connectedLabeling(mat, i, j+1, h, w)\n", " elif j-1 > -1 and mat[i][j-1] == 1:\n", " return 1 + connectedLabeling(mat, i, j-1, h, w)\n", " elif i-1 > -1 and j-1 > -1 and mat[i-1][j-1] == 1:\n", " return 1 + connectedLabeling(mat, i-1, j-1, h, w)\n", " elif i-1 > -1 and j+1 < w and mat[i-1][j+1] == 1:\n", " return 1 + connectedLabeling(mat, i-1, j+1, h, w)\n", " elif i+1 < h and j-1 > -1 and mat[i+1][j-1] == 1:\n", " return 1 + connectedLabeling(mat, i+1, j-1, h, w)\n", " elif i+1 < h and j+1 < w and mat[i+1][j+1] == 1:\n", " return 1 + connectedLabeling(mat, i+1, j+1, h, w)\n", " else:\n", " return 1\n", "\n", "def countObjects(img, avg_size, th):\n", " kernel = np.ones((avg_size,avg_size),np.float32)/(avg_size*avg_size)\n", " fimg = cv2.filter2D(img, -1, kernel)\n", " _, timg = cv2.threshold(fimg, 255*th, 1, cv2.THRESH_BINARY)\n", "\n", " masked = img * timg\n", "\n", " # Count the objects in the masked image\n", " timgc = timg.copy()\n", "\n", " h, w = maskc.shape\n", " for i in range(h):\n", " for j in range(w):\n", " if timgc[i][j] == 1:\n", " neighbors = connectedLabeling(timgc, i, j, h, w)\n", "\n", " if neighbors > BIG_OBJECT_COUNT:\n", " # print(neighbors)\n", " timgc[i][j] = 1\n", "\n", " count = np.sum(timgc)\n", " print('Objetos grandes encontrados: ' + str(count))\n", " print('Se definió objeto grande aquel cuyo conjunto conexo tiene más de ' + str(BIG_OBJECT_COUNT) + ' miembros')\n", " print('Este parámetro se puede variar')\n", "\n", " printer([img, timg, timgc, maskc], ['Original', 'Threshold', 'Counting', 'Masked'])\n", " \n", "sizes = [1, 5, 15, 25]\n", "ths = [0.5, 0.5, 0.25, 0.3]\n", "for i in range(len(sizes)):\n", " countObjects(img_origin, sizes[i], ths[i])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Problem 5" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Write a function that extracts local interest points and computes\n", "their descriptors using the SIFT transform. You can find implementations of\n", "the SIFT transform in OpenCV.\n", "\n", "\n", "Your function should return two matrices: A first matrix of size $3 \\times N$, where $N$ is the number of detected points in the image, and the 3 elements correspond to the $x$, $y$ locations and $s$ size of the detected points. A second matrix of size $128 \\times N$ that contains the SIFT descriptor of each interest point.\n", "\n", "Apply your function to all car images <tt>image_00XX.jpg</tt>.\n", "Store the results of each image in a separate data file." ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Comentarios\n", "La Siguiente función hace uso de la implementación de la transformada de SIFT que tiene OpenCV. La función utilizada fue detectAndCompute. La función que se creó, recibe la array de una imagen en escala de grises y regresa los puntos (x,y) y la escala de la imagen en la que fueron encontrados como una matriz de numpy 3xN y también los respectivos descriptores de SIFT para cada punto en otra matriz. También se puede pasar como segundo parametro a la función un booleano que determina si se imprime o no la imagen con los puntos sobre ella, en el caso por defecto, que es False, no imprime nada. Los datos se guardan en archivos de texto en formato json.\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Points and SIFT descriptors for image 1 extracted\n", "Points and SIFT descriptors for image 2 extracted\n", "Points and SIFT descriptors for image 3 extracted\n", "Points and SIFT descriptors for image 4 extracted\n", "Points and SIFT descriptors for image 5 extracted\n", "Points and SIFT descriptors for image 6 extracted\n", "Points and SIFT descriptors for image 7 extracted\n", "Points and SIFT descriptors for image 8 extracted\n", "Points and SIFT descriptors for image 9 extracted\n", "Points and SIFT descriptors for image 10 extracted\n", "Points and SIFT descriptors for image 11 extracted\n", "Points and SIFT descriptors for image 12 extracted\n", "Points and SIFT descriptors for image 13 extracted\n", "Points and SIFT descriptors for image 14 extracted\n", "Points and SIFT descriptors for image 15 extracted\n", "Points and SIFT descriptors for image 16 extracted\n", "Points and SIFT descriptors for image 17 extracted\n", "Points and SIFT descriptors for image 18 extracted\n", "Points and SIFT descriptors for image 19 extracted\n", "Points and SIFT descriptors for image 20 extracted\n", "Done!\n" ] } ], "source": [ "def getPointsAndDescriptors(img,show_img = False):\n", " \n", " # Getting Keypoint structure object and Descriptor Array\n", " sift = cv2.SIFT()\n", " kp, D = sift.detectAndCompute(img,None)\n", " \n", " # Getting the array of points (x,y,s)\n", " points = np.zeros((3,len(kp)))\n", " for i in range(len(kp)):\n", " points[0][i] = kp[i].pt[0]\n", " points[1][i] = kp[i].pt[1]\n", " points[2][i] = kp[i].size\n", " \n", " if show_img == True:\n", " img_s = cv2.drawKeypoints(img, kp, flags=cv2.DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS)\n", " mplt.imshow(img_s), mplt.xticks([]), mplt.yticks([]), mplt.figure()\n", " \n", " return points, D\n", " \n", "# 25 -45 ind of cars images\n", "for i in range(25,45):\n", " img_name = files[i]\n", " img = rg(img_name)\n", " points, D = getPointsAndDescriptors(img)\n", " f = open(\"data_image_\"+str(i-24)+\".json\",\"w\")\n", " data = {\"points\":points.tolist(),\"Descriptors\": D.tolist()}\n", " json.dump(data,f, sort_keys=True, indent=4)\n", " f.close()\n", " print \"Points and SIFT descriptors for image \"+str(i-24)+\" extracted\"\n", " \n", "print \"Done!\"\n" ] }, { "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" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
cernbox/entf
ROOT_testing/converted_notebooks/tree/copytree.C.nbconvert.ipynb
1
4788
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Copytree\n", "<hr style=\"border-top-width: 4px; border-top-color: #34609b;\">\n", "Copy a subset of a Tree to a new Tree\n", "\n", "The input file has been generated by the program in `$ROOTSYS/test/Event`\n", "with `Event 1000 1 1 1`.\n", "\n", "\n", "\n", "\n", "**Author:** Rene Brun \n", "<i><small>This notebook tutorial was automatically generated with <a href= \"https://github.com/root-mirror/root/blob/master/documentation/doxygen/converttonotebook.py\">ROOTBOOK-izer (Beta)</a> from the macro found in the ROOT repository on Tuesday, January 17, 2017 at 02:42 PM.</small></i>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[?1034h" ] }, { "name": "stderr", "output_type": "stream", "text": [ "sh: /cvmfs/sft.cern.ch/lcg/releases/ROOT/6.08.02-99084/x86_64-slc6-gcc49-opt/test/eventexe: No such file or directory\n" ] } ], "source": [ ".! $ROOTSYS/test/eventexe 1000 1 1 1 " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Error in <TUnixSystem::FindDynamicLibrary>: $ROOTSYS/test/libEvent[.so | .dll | .dylib | .sl | .dl | .a] does not exist in /cvmfs/sft.cern.ch/lcg/views/LCG_87/x86_64-slc6-gcc49-opt/lib64:/cvmfs/sft.cern.ch/lcg/views/LCG_87/x86_64-slc6-gcc49-opt/lib:/cvmfs/sft.cern.ch/lcg/contrib/gcc/4.9/x86_64-slc6/lib64:.:/cvmfs/sft.cern.ch/lcg/releases/ROOT/6.08.02-99084/x86_64-slc6-gcc49-opt/lib:/lib64/tls/x86_64:/lib64/tls:/lib64/x86_64:/lib64:/usr/lib64/tls/x86_64:/usr/lib64/tls:/usr/lib64/x86_64:/usr/lib64\n" ] } ], "source": [ "gSystem->Load(\"$ROOTSYS/test/libEvent\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get old file, old tree and set top branch address" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "input_line_29:9:22: error: unknown type name 'Event'\n", "Event *event = new Event();\n", " ^\n", "input_line_29:10:36: error: use of undeclared identifier 'event'\n", "oldtree->SetBranchAddress(\"event\",&event);\n", " ^\n" ] } ], "source": [ "TFile *oldfile;\n", "TString dir = \"$ROOTSYS/test/Event.root\";\n", "gSystem->ExpandPathName(dir);\n", "if (!gSystem->AccessPathName(dir))\n", " {oldfile = new TFile(\"$ROOTSYS/test/Event.root\");}\n", "else {oldfile = new TFile(\"./Event.root\");}\n", "TTree *oldtree = (TTree*)oldfile->Get(\"T\");\n", "Event *event = new Event();\n", "oldtree->SetBranchAddress(\"event\",&event);\n", "oldtree->SetBranchStatus(\"*\",0);\n", "oldtree->SetBranchStatus(\"event\",1);\n", "oldtree->SetBranchStatus(\"fNtrack\",1);\n", "oldtree->SetBranchStatus(\"fNseg\",1);\n", "oldtree->SetBranchStatus(\"fH\",1);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a new file + a clone of old tree in new file" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "input_line_31:2:3: error: use of undeclared identifier 'oldtree'\n", " (oldtree->CloneTree())\n", " ^\n", " __boot()\n", " import os\n", "Error in <HandleInterpreterException>: Trying to dereference null pointer or trying to call routine taking non-null arguments.\n", "Execution of your code was aborted.\n", "input_line_30:5:1: warning: null passed to a callee that requires a non-null argument [-Wnonnull]\n", "newtree->Print();\n", "^~~~~~~\n" ] } ], "source": [ "TFile *newfile = new TFile(\"small.root\",\"recreate\");\n", "TTree *newtree = oldtree->CloneTree();\n", "\n", "newtree->Print();\n", "newfile->Write();\n", "delete oldfile;\n", "delete newfile;" ] } ], "metadata": { "kernelspec": { "display_name": "ROOT C++", "language": "c++", "name": "root" }, "language_info": { "codemirror_mode": "text/x-c++src", "file_extension": ".C", "mimetype": " text/x-c++src", "name": "c++" } }, "nbformat": 4, "nbformat_minor": 0 }
agpl-3.0
simulkade/geothermal
.ipynb_checkpoints/geothermal_notebook-checkpoint.ipynb
1
163861
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Geothermal energy extraction\n", "Here I solve the equations for nonisothermal flow in porous media. The equations will be solved with different assumptions:\n", " + Viscosity does not change with temperature\n", " + Viscosity is a function of temperature\n", " \n", "The domains are:\n", " + 1D linear\n", " + 1D radial\n", " + 2D\n", " + 3D" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Start the toolbox" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AGMG 3.x linear solver is NOT available.\r\n", "warning: PVTtoolbox could not be found; Please run PVTinitialize.m manually\r\n", "FiniteVolumeToolbox has started successfully.\r\n" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "% start the FVTool\n", "cd('/home/ali/MyPackages/FVTool/');\n", "FVToolStartUp()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define the functions" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": {}, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function p_val=continuity(m, q)\n", "mu_val=1e-3; % (Pa.s)\n", "poros=0.2;\n", "perm_val=1.0e-12; % (m^2)\n", "% physical system\n", "p_out=250e5; % (Pa)\n", "% assign values to the domain\n", "k=createCellVariable(m, perm_val);\n", "labda_face=harmonicMean(k/mu_val);\n", "phi=createCellVariable(m,poros);\n", "% Define the boundaries\n", "BCp = createBC(m); % Neumann BC for pressure\n", "BCc = createBC(m); % Neumann BC for concentration\n", "% change the right boandary to constant pressure (Dirichlet)\n", "BCp.right.a(:)=0.0;\n", "BCp.right.b(:)=1.0;\n", "BCp.right.c(:)=p_out;\n", "% left boundary\n", "BCp.left.a(:)=0.0;\n", "BCp.left.b(:)=1.0;\n", "BCp.left.c(:)=p_out;\n", "Mdiffp=diffusionTerm(-labda_face);\n", "[Mbcp, RHSbcp] = boundaryCondition(BCp);\n", "RHSsp=constantSourceTerm(q);\n", "Mp= Mdiffp+Mbcp;\n", "RHSp=RHSsp+RHSbcp;\n", "p_val=solvePDE(m, Mp, RHSp);\n", "end\n", "\n", "function T=geotherm_iso(m, p, qin, qout)\n", "labda_s=2.6; % W/(m.K)\n", "labda_w=0.6; % W/(m.K)\n", "rho_w=1000; % kg/m^3\n", "rho_s=2650; % kg/m^3\n", "cp_w=4184; % J/(kg.K)\n", "cp_s=900; % J/(kg.K)\n", "poros=0.2;\n", "perm=1e-12; % m^2\n", "mu_w=1e-3; % Pa.s\n", "a=poros*rho_w*cp_w+(1-poros)*rho_s*cp_s;\n", "labda=createCellVariable(m,labda_w^poros*labda_s^(1-poros));\n", "T_init=80+273.15; % K\n", "T_inj=40+273.15; % K\n", "T_0=25+273.15;\n", "BC=createBC(m);\n", "BC.left.a(:)=0.0;\n", "BC.left.b(:)=1.0;\n", "BC.left.c(:)=T_init;\n", "BC.right.a(:)=0.0;\n", "BC.right.b(:)=1.0;\n", "BC.right.c(:)=T_init;\n", "[Mbc,RHSbc]=boundaryCondition(BC);\n", "T0=createCellVariable(m, T_init, BC)\n", "u=-perm/mu_w*gradientTerm(p); % m/s\n", "Mconv=convectionUpwindTerm(rho_w*cp_w*u);\n", "RHSconv=divergenceTerm(rho_w*cp_w*T_0*u);\n", "Mcond=diffusionTerm(harmonicMean(labda));\n", "RHSs1=constantSourceTerm(rho_w*cp_w*(T_inj-T_0)*qin);\n", "Ms=linearSourceTerm(-rho_w*cp_w*qout);\n", "RHSs2=constantSourceTerm(-rho_w*cp_w*T_0*qout);\n", "dt=100*24*3600; % s\n", "t_final=40*dt;\n", "for t=dt:dt:t_final\n", " [Mt, RHSt]=transientTerm(T0, dt, a);\n", " M=Mt+Mconv-Mcond+Mbc+Ms;\n", " RHS=RHSbc+RHSt+RHSs1+RHSs2+RHSconv;\n", " T=solvePDE(m, M, RHS);\n", " T0=T;\n", "end\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 2D domain" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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IJcGBAcH76qPEoxxBwONjHqn+EaRxuFT/iIcf5BS8w5fgcQ5KKPGC91DiY8kl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TxDAM\nwyQMmyKGYRgmYdgUMQzDMAnDpohhGIZJGDZFDMMwTML8f2gUUX6dyu1HAAAAAElFTkSuQmCC\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": {}, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Nx=100;\n", "Ny=50;\n", "Nz=1;\n", "Lx=2000;\n", "Ly=500;\n", "Lz=100;\n", "v_cell=(Lx*Ly*Lz)/(Nx*Ny*Nz)\n", "q_in=100/3600; %[m^3/s]\n", "m=createMesh2D(Nx,Ny, 2000, 400);\n", "q=createCellVariable(m, 0.0);\n", "qin=q;\n", "qout=q;\n", "q.value(25,25)=q_in/v_cell;\n", "qin.value(25,25)=q_in/v_cell;\n", "q.value(75,25)=-q_in/v_cell;\n", "qout.value(75,25)=-q_in/v_cell;\n", "p=continuity(m, q);\n", "visualizeCells(p)\n", "shading interp" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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2wuN41yIx7swWsBVlOfGC35Lc7nqev6LbEz0ciXEPV7k973kbFaXpZlTdhHFx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\nTk1dab5zE8DELU30l1PbWiM0LW1cA9CU3w0pAKCf0mV9C5PfB6C1boe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D76iFhYVkMhmN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"text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": {}, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "T=geotherm_iso(m, p, qin, qout)\n", "visualizeCells(T)\n", "%T.value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1D domain" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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L5+fDz/q+wvDS+CAALOkZd4xr5qy2wDlP0/R4PKZpeueYFec8/PVLoFKqdzrq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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": {}, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Nx=100;\n", "Ny=1;\n", "Nz=1;\n", "Lx=2000;\n", "Ly=400;\n", "Lz=100;\n", "v_cell=(Lx*Ly*Lz)/(Nx*Ny*Nz)\n", "q_in=100/3600; %[m^3/s]\n", "m=createMesh1D(Nx, Lx);\n", "q=createCellVariable(m, 0.0);\n", "qin=q;\n", "qout=q;\n", "q.value(25)=q_in/v_cell;\n", "qin.value(25)=q_in/v_cell;\n", "q.value(75)=-q_in/v_cell;\n", "qout.value(75)=-q_in/v_cell;\n", "p=continuity(m, q);\n", "visualizeCells(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3D domain" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "image/png": 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11Yr27NkTTdAdO3Zs586dX/ziF2+88cbXUSsaGxvbtWvXpk2b3v3udxuGEW28\n66677rnnnmhgdMsttwwPD994440vC5do5Xe0hC/6Mko1AJZlvf4JusXFxcXFxfNF0d69e++6665o\nHi/aomnaRz7ykbMXbKTTaSHE0NDQpk2bom5Hjx6tVCqvNYo+9alPnW/Xp//HZ9SkXigVrXk7EMxc\n3716aBq6kb58S+1H0+YvvFH29jnP/ljZsS3IFOyvfJP9xxtYJiOPToQXXI6Ww48cFcV+5Ars+HGZ\nH8BKlTWqMtWNEGz5FMwi/ADtVRRL8HysTCDdBwDNJSgcUJBMo7MKEYIzGCb6toKFEB5OPIVECqkM\nvA4Yg6pDBGhZSL9U9FZUtGqQEmYCegIARAhVRbUMQ4fC4XngCqRAvc7SSeTzTFEgBBKGdB2Uyyyb\nYxyy4zKE0naYpnFd49kU01SEITgPyitcV5V0UtEAASYEFO7XWlpS5xoXgVASqrA9b6WhZZOJYlI4\nngiFklCFG/hNx8gYXFMQhuCsU24pOk9kdK4ySAkBxtEst3Wdq4YSuGGyaHh20LGcZEY185oIZegJ\nPcFdO2ytuOmsCs48J4SEaweGwTUDqRRTNCZCKFyslEMzyRImVBUiBFcAIZt1mU5D0xEG0DRpt6Xd\nQiKBfAZCwvWQSMhOB4EvEwYLBQwNQsi5BeQzyKfgehACpoEgQLWOfBqGBseDqqDeRuAjoSNvQko4\nPgwFq20EARIawhASCAUsG1kDaQ2GAiFhKggEphvoNpBT4AYIJJIcQYhKgCJgcDgShkRNwBNIcBQ4\nQsCV0BhaEpzD4BAMJkcALDjI6cgl4EoIwNQQSNQcpE0kODohDBW1DsDBOQopSImOD0NFzT6z8CII\noTB4IZo2UiZyWSgKGIOuIQixUEEuDYWj1QHnUfpDM5DNIKHD86FqqNUhGUskkEoz30cYQlHQbktV\nY1xhCoOqww/Y0oLI5JRCj9JuSiGkYfIwQN0KkxnFSHDvzBoOIcE0Q1ESCgNEKJmquO2Aq5yrTAZS\nMxURyNWFTiJrDF5SMDKqqitCoL3irE63JKAlVCkkY9ESkEA3VRGKVDHBGISQADw74Co3kmp0RbNX\n3d5tOQBMYbUZ+zP/15fOeYn49xyN7drG7v/tn9nRuv/mNURRNEF3ySWXPPDAA+973/sajcb3v//9\nLVu23HXXXa/1dS3LKhaLd955Z39//6FDh7Zt25ZOp59++ulGoxFd3qPRzzm/d2Rk5GXhF41tHnvs\nsdc/QfeWt7xlcXHxfN0cx1lZWdE0bXBw8I//+I+/9a1vPfbYY1/4whfOjiLXdRlj6XT61ltvjWpF\nhw8ffh2Ly1dWVs6/kwWdYPM7d7QOHg1WnfU3vc1rOI7VMUsDXsC92WVRbQbTC6y3VwZgG9bJ5bJ8\n7HEEwMw8Oi4SaRYEmDrKHAGryqQipWCcwe1AhuCcMSDsMBmGWpIZWZkdhJFlqi4TORS38vKzorAO\nPVuV5WfDwnrk+vjMUyLThV/ey37wNbnlYpiclRclBNYNsdoyrFXmO0hmkc1ovFtxbKYw2e4Y3WnR\nbOuKcMG59NSUgZAh9HUNmeH+hMmEF1inV7yWFzTDrk1pbSjZXKzZq64IhWawrotTPRtNa77TrC63\nK64E0nmt6xJDhu7iqVURwrXF0Batf73mNAPfEQvTXhjCW5IbS2pqgC3N2fa8ZAJpQw52Kf2bZW1V\nrFTlalmGAgkDF25Fbw/aTTRaWKnKbA7rB9EqwulgahZSSnsKW9Zh/RVotsA5phegcPSk0DWEVh4z\nS6jXwEMkDVy6Dl1JtDqYLKPZgcpRTOMNRTgBphYggVYHF/RjMIOmjmYD8w0IgbaHHUWsU3G6gsUl\n+CGSKnoz2Gyg5WFyWbZ8BAI5DZeYaNcxtwSNoSOwLYXNSfQCzRrmbUjAFdiWxWAatQCujUoHOkd/\nGkNZ2CGm6/ADeCG607i0BEVF28d0FW6IRoh1RewcQLWJcgNChS9QHMJgD3pbqNaxXANjSOjYOoRQ\nYnoRix46LgZ6MTgERYPtYmoaroeKh65udvEAqitYqEJKhKHcsZ139/FGAytVLJZFICEClC7Ri31q\nqynaDWGthMkiT/eraUVxbLkw7fuOcDuyOKBv32BKRQ19WZnphKHMdxvZvsRAM7AWnLblhVwaaS03\nkMyvT3ONuw3f80OmcG6qZs0DIEK5WO5oOpcAY3CDINtnhkAIeIy1qp2+4aSUqLcDc6OuJ1VF47V5\ne/BCExJu229ONDZc1p1nTAIzz1Y2XtoDQNWVZqWzZVs++i898fD8u/5iZ9T+xn95/G2/f3HUfurO\nE9f8wSXdpWx3KXPnf3r4t750bbR99Dfu/c073j4zXgHwgwNHrt57UbT9mX+ceNNNWwFUJxvP/b+T\nucEzCzVXp5rnuziWSqV/tyiad3HL8+feNXIFRs61pOGWr5z3aL7vv/qXvuWWW26++ebbbrvtwx/+\ncDST9vWvfz2RSLz6I6yJFjt89atftSyrUCh84xvfKJVK73//+5988smoQ1SRuvHGG0dGRqI+lmWV\nSqUDBw68bKHE6OjowYMHb7311re//e2vOYpe5a/t+PHjAC6++OKxsbHx8fGxsbFkMmlZ1ne+851f\n+7Vfi/pIKTnnL6sVffnLX36tp7S2Svin9Vza11xo1qes9ozltb3GsXnF1DWmtH402To8q+0oyWaL\n5zOiXHUfelImM6xQZMVC6Aret062bLlSQ7sFrkinhYGtIgALPZnpRiHDmSKyg5Jn+PQTYX49ui5n\nh78p+y7D4jirzchEDlyHYkIKrEzJ5RlWnUFxA2TA5k/g2JPgki9MoLsbkIwxGQZMM1CtIJeBCJXN\nG+Thw8zkhf/8gdbBe9hqZfBjN7cO3oNW6+I//Y3lsR83fnT6V77/B0/t/XL9ZPlt/+3NT3zuuVRO\nec9fvuXJ0R+tzNrbdva8MFvN58UFb0g+8X3LnnVygwkhnCveiFo1qJTR39uZnZFZE1u2w7ZRbyKn\nOBkLBrBQh2IgqcFNorXsW3MoGkgCIRB2MP9iMCfhh0hqUCV6TIQeFo9gVQVjgIAioXRwYgaOD11B\nQYHC4aloLeL5WeQScAIIgZSBSh3LEm6AXAK6CqkgFJidRVkFA3Qgr8PU4XqYXIKhopgAV5BLYKWN\nlTbSBpwAZhIZE46P1QBlF7kivAChhB/ipIdjHQjATCOpIJWC52MuQMpEwUcgYDAs+Jh0AYCpUIrI\n5RljWAlRDpDMMceRyEIvKBM1KYGAIb2RmRxhyH0PR32Z0JnPERaRTnGm8IWmmGvDzPBEFuA88ORs\nKKfmkEgrvgFZhJ7RXEe82BAA9B5VZSwZoK2xGc0IJVw/TGxTTY2DQUpYBm+2bX09wkCqwHyorDQ1\nCRgblXTe9zthIqPZOm+1JdeYtk7t266GXmhLMM5artN/pQHAafn2qsu6swzwGr7Zp24Y7gFgW67f\nbG9/R/+FI0Mz45XKRGP4vVsAjB+cNHN8rX3VB3ZE/0dHx+ZvuHUnANvy7vxPD13zh5ec+dc7OPmO\nj12+1u4pZdqWB6B8vJ4f6vHanqrzCoOmMiEQJRkHJAA/FIFslW2NwxfgCn989MjaXeijB44AYAAU\n9vBtL/qeAOC2/M9ef0/Uobls33HzA71bsgMXFzRTKU/UAbhNv3KqMXvozEAhVTSic65ONpeOWq9w\nlfh3k+3ByHvOvau0Edhwju0jrfMe7cDYa/jYtoGBgdtuu23Pnj1RDu3du/dP//RP//7v//6cnaOL\n9it/HMMHPvCB3/zN34zao6Ojn/zkJ//wD//wzDmPjOzfv//WW2/dvXv32lqGaCHD2eu/o5LSs88+\nC+DBBx/8t/q0haNHjwohDh48eMsttxw+fBhANJV3zz33nB1Fmqbt27cvqhU99dRT5XL5dbzWK6Tj\nHd/5EtfUmYdO6bmE0ZMt3zPuNVwhoPQUtP4i48w5OslzGbWrS794e+fxcbbzShkKVl4VySQGN/Hj\nJ0TXAFIZdvyYzPfggmv5N/8ibLehmGxlGoFAsp8rmgg9PH07/DaO3MU4Q7qX+TbqM7JTYYaJdLdM\n5VnQkfOHZaGXQcr1F+D0IWGkkCuy5QWZTKO0HT9+Wv7CWxUNqJTxK7+qJRT52BPuoSP+xDRvNe1D\nx2UQKtlka2Jx5amTvtW65/rPhvW2mVWf+eILTs3xGu6jo0ebCx3DVJqrXm3ZyxYUrvHufhVSJlN8\noQ1dZxdcwDQNO7bhLVeKr3yV/Yc3yoyO49N8+5BgPo6e5G8siUIaJ6ZYEMqLhvDMceb4cmgA1VVm\nOSjkMddAUsOGPBYaSGhgGuZrsDrYNgDO4XhQdIQO1BCCY9vGM+vOXjiKZh0rCvJ5ZDTUWqhH9e1u\nbLiAdVysrMJtolVHPZTFIsvlmedjuSydANk8M3uUdE6xbRnYaCyFnittxge266mc4jlSBVs45XEV\niZKpBmhZEDlCiQAAIABJREFUYdAKbEvAgJnR+i7OOq2wZflty/PcsM217u1pDgiB1lw7cEX/jjxT\nGFe5DEXgina1wzj3AGZAM9VWgitZSAEuZNsNzZzOpNQA7kvVVKL/H68dpHvNFACGxlLHSKvJgiEl\nVqdbXZvSekozUtqR+2ZL164HkCroR8fmr9t35h743v3Pv/0jVwCYGa8cvnfulz96edS2Lffm0WuT\nBR3AZ6+/Z9dHLou2O01/+7WDZ7dnxiuVySZXWLrbBGBbLuMsvy4NYHWm2a75knFI6TR93w0b5Q5T\nORNChLIy2agcaACwV937/p8XzJzudwLPDh49cBQMvhM8OnoUkL4j6ovtz15/Zu5BChld+gGEgVi7\n9JcnGtuuHnjHvq0Avv47Dw/vLtmWC6BT9974vm1RH6fhXfn+M+1n/vHkm27aZlvu9HhVHKkVS2cK\n+scfWUxkNACMoXyq2V3KcAYh0V7hmW5DSAAI3TCV1xpLtl21y6fbQSd0W56e1rN95uSTyyKQrarj\nO8HfjnyPcYCxRNYYvvDcV4l/z+e3EkX9svfmz7f3nNe+y9573qMpf/hqCxmWZT3++OMf/vCHl5eX\nd+3aFdWKdu7c+Ud/9Ec/3Xl8fHzXrl1R4xWeHPrqV7/6rne96+677x4fH+/p6ZmYmNi588y4dmRk\n5ODBg1/84he/8pWvnL2Y+6mnntq3b9/+/fujbqOjo2c/6vRvFUW+7xuGsWvXLs/z2u12GIZCCM75\n2U8RMcY6nc7Z1aaFhYXX8VpdXV3n25UezDJNSQ9m5p5eFJIVd25xVhdCyc2+YrDa0basV6+41Hn2\nx8qWjUGmwF6cwNat/LJLwzvuxMIsylU2O41kFkHANF0unMLzj8nQ454NeDLXzxFg9hnh1BUoMt0t\nbVXpLsnWsmwuM64gkZVmVqo6kkXGhchswBXvYrPjIp3DwFY2dwTrt+Md17ODB2RtFSeOsPkZqTD0\nFJDJiMef8B54VFG5fc/DLGmq3ZnW00dktSoa7fKDhxnnZm82OZAVJrOOLOV/YbBVsY2k0l3K1mfr\njh38wo3rPattW+47biy6VqdpBZcMGzz0L7oMk0fC2XlwyFwGG9bB82F1MDGFmTnYjtKfloHgx+Zl\nswkJpelKy5Zpg4dCBkDdQVcSjAGMJVTBGZNSvrWE8VmWS8itG/Dsab6uKLiBVZsf/K9i7xdQafPf\nfbc4eC+ckH3hr+XB+7BY5RdcyJ4bl/2q2jfADx/GrMX716kJQzHW60VgdUUqKnc0FkBz6vXsxpSW\n4E3AagnNVNptt+cNqcaynek124GQSaM8V+/dluu9Ek7TP/7jla1vG8j0IAN4h6qXXL8x+jNYeXp5\n529f1F3KAnjsC0ff+nsXRtvPbn/jPz/+3s/8BwAXjAx94cax3/vmSHWyWZ1s/ODA0av3nunzyO1H\nrvnQmbmgB/72x307ztwzLhxejS79AMonG91b84EXgrEwkJJx1xZuxzMyeqPihr4on2o4Lf+RA0cB\n5jR9rrJHDhwFIDyhGvyRA8cAKXwBiTtufiA6Zn3BPvX4UqbPtC1XCLl0rOZ3gsUjNb8TmDk9vy69\nYbinPFG/+vcvAnBsbH72UHXXxy8/NjY/M15JpM6MeM4e/cyMVxpLnZ5SFoBtuW7Lyw2kwLAy1VQN\nJZHVJLAy3Ry6pNipuekeQ9FYMq8DcJv+6kzrxIPzABiH0wr8lscZghC6yZ/6+smnvn4SQH2h7f73\nF7a+bQCAZqpr0aUmtPJEPXDD0BPVU80oxq7+/Yuay/YNt+60LW9mvPLCt0+/6V/GVdR+6usnUz3J\nqG3NtAobslG7ueqlugwzpwmBlelW1/qU2/b7d2SteTs/mJQSTtOvTja6dp77KrH2tPW/gyYyh/CG\nn93xnn2V/cbHxycmJj72sY+lUqnV1dX+/v7nnnvuyiuvPGdVfm0k9MpPHTWbzYGBAVVVGWOu6+7e\nvfvswtXw8PDo6OiGDRve+c53Apibm7v77rsNw7jqqqvOPqu1WMK/XRQNDAycPn16cXFxZGTkscce\n0zSt2Wzqun72PYiUUkrpum5UK/rbv/3bRx99FC+tiDtnOfGcT6G/Qq3Ia3pgrHDtllZDdFad9Te9\nzT9wf8fqmKWB+otPC6YJsyqabe550rJYsSDvvS/8zncRgPUPoVGTXf1s5hRTFbla4z0b0b9JtiyW\nG5DzE+g0kepBdgASwiiCh8yuh5LB7GFMFS/ViqSZR1eJnT4tqy0EHmtUZCoDu4HudXj+B5h6Xnou\nYwy2jQ2bebUs66vgTHE6yvpBVYOSTriHJ1hDSWwd8hcCAKlSn2e1/YZt9gxZM5VkT+qKmy5yao5X\n71w4MmRNVBtLzulDVmXa8TrB3IRjGJwXlbFvtyvz3tIsk4HIpqHrrFJhC0vIp5nXAVfkQA9XpZyd\nl4bKGAOApBb2pME85gZC4VA5294trxiA4ErRCEtFrLosb8gjS5ixWC4h0zmYGoTAXBmrLVz/SVTa\nGOjDgYPcdaSq4MDX+WpTSo5jJ1g6Ix0fYDwaLXf1Kks/FiwhNl6cqdVs1eAXvLVndtKffrby1t+9\ncP0buvFSSeDo2DyA+pLdf8GZ/5PjDy1sf/sQgE7drU41FU2RQjhNP5HRW1VHCsFVLgWOPjiPB+YZ\nZyszrUe/eBQSjGFluhWNBgBkB5KPjh71nfD+//4je8WJ5oIu/7WNevLM/I8IpDXbWhsE1ObbibQW\ntd2mf+HIUNSefrYy/B83Re1H/u7Ipe9cNz1eBTB/yM/2RMuNzNpsq3dLNvBCu+bPPFvecGUKjHcs\npzZvb7gyAykWXlzdcGVP4AoAYMxrB3MvWjhcl37otLzKtAMRCi8wMsbkk2WvE3h2oBnKC9+ZBhA4\nodPwjj20kCroXZuy4qVBTOVkY+Wl8188UjMzenTOM+MVCLk2KXd2e3h3aWa80rY8a6bduy0PIN2D\n6nSrq5QD4NSdyulWprfAAEWiXXO7NqQBSInQC0NPHH9gngHWgh26YTT3Vluwm2U7mdMZkOk3p54u\nh7449tBCfaEdveGlN/cWN6TXoqvyUlwBWJlsBM6ZS2d7xV17wyun6m98z5ao/cw/Tlz8zvXRG27N\n25m+FIBsPzo173xXibNvjv+tTU0Ef7Tr+Dl3Xbr7gp17Lv/p7Xfs+sb5juY4zqt83ZGRkXe84x1X\nXXXVbbfd9olPfGLPnj179+6tVqvRQz8vUyqVTp06NTk5+QrrD6PJt8nJyU9/+tPXXHPNl7/85eee\ne+6DH/xgtHdtMffzzz+/Ni86MjKyefPmkydPrh1k69atV1111dVXXw1gfHxcfVk173+5/HH//v3R\ntxw7dgxANJQDED3KutZtYWEheiZu7bEezrnv+729vS97Rc/zRkdHC4XCvffeq+u64zjROXzuc5/7\n53/+5ze/+c1nd37ZhO/8/Pzs7KzjOJqmmaZ55iIKqKpqmiaAnl/say42h96y4dhdx7y2Zx06zU1d\nY4pvNUXHlfVmWLakH4iF5bDtSoXDMHgqGa40WCotuS5XauBcgoFxSCnUBOOqAMfmK1mjIrMD4Bnu\ntWSqCJ5h7qrsuwxagj39dzKRQ+gBHFLAaSAE7BWEPpjglWnYDSkC5rss34VGDYDMZuG2GIRs2UgY\nLJcVL5yW3Vll8zptQ0eTbvf7r2sdvMc5NtU3cqlntZ3Z8s5bb3jhln+avPvIwqHl+mwzWdDLE3Ut\noSYy6uGHyp4nuILxh5vt1UBVpOvIVIrlCyxwWKcj02mmcbZckRdvZYxJzsAkdFXkkmxLv8iYODHF\nOEOpF14HBRPXX4x/fIo3O+GRCmbqzEuiN4WkyoTErAUp5Uobk2UmAdtlAcC4TKcVF8L1ZE8vr4XS\naiCbZTOLUjeRzWBpAUqC/dI7NaEyayW85vqU1fYqFVy9u89FbXmyve0tXeP3nfDdsDzRiH6n1kxr\n9lB16NLizpu3/v3vPHLDrTuPjc0DeOHbp9euSq2q86b3bQFQnWw8/uXj6e4zVVmmNBJpLSpWcM6M\nVDT/w7jKElktkdEZZ83lTiKrg3k6V23LTWS1Ts078ciibwfHH16AgBBS0dixh84M3A1Ttevemb83\nna9dPZNFY+5HKzKUAKyZ9spU8+rfv6hrU3ryqcrb9lxYnWwAmH62snbO1kzzze/fimjCreG9+f0l\nAOMHJzlDtvfM+c+/4HdncuBKZ7XNOUukFUguhaKZKpgEZ6qhagYPQwnGjJSEhJHWK5NN35O25Xaa\nvpRwG157xYnOP3DCVEGPzrnT8L1OEF3xyxMNRWVr7dlD1fy69Dv2bb3rlqdvuHXnzHjVttzl49ba\nyQvBhneXANiW++jo0fRLo5bFI1YUSwCcppfI5qI6kNv0cv2mkdEUhnrVTXcZ8ALP5U5KS6ZVIbH4\n4qrvhCcemJcAY0wIufaGC4m1N1zRfvKGa6ay9oZXTjVWppqX/PL6bdcM3HHT2HX7roiWNvyc1IqS\nWwd/8f7/cr69p8+18RfvP++TKv+j+xOv/qXPrhXt3bs3Wlnw1reee2n55OTk+Pj48PDw+QZG0cRa\ntBZ8fHz8vvvu++EPf7j2CXvRzNuNN95YKBTODhTXdY8cObL2ZalU+vznP+95HgDLsv5FFL1sxBT1\niBY/rG2Znp5eXV3NZDLR+o2JiYlo+8s+Gm9+fn5qakrTNCnlwMBAo9Go1Wq6rq+tssBLyxYajca3\nv/1txli73Y6Wz91www1SynQ6HQSBqv5k3DY8PHz2OOnee+/NZrO33377XXfdNTs7++Mf/3jz5s2X\nX355Pp+v1WpRn7EnHuCa+sO/GEsUTaMn23ju1OqhaRhG4Ai1K6eWhgJXuMdPi3aHq5o8doLf8G4J\nQKpC0bB969m1IlEYgpphwpebLsOJcVY5JTtNGD1nakXWBKwZ2HchcJHu5b4Npy5bC8yuYP0bZeDw\n4oDM9MraLJorSBWwcAobLxS5InMcyWRUKxJbd0S1Innje1UmWHWpuO/3ln/3E/7ckn3ouDtXiWpF\nAOyF2j3Xf9ar1FP9mYXnl/WU1qm5D3/ucDLNGbD+0hwPfQXCzCozRwNIecHlxtxEUCyChVhdZV1d\nMrdePPUk27pJZHQcn+KDBXH5Jhy8j52Yl6aO6TLb0iOtNjQV47N4fg6BL4VgAxkUddFw2HeOIqmJ\npMHXd8ulBgyV64qcr0mVIeTIJVHqCS0b6RS76Z3hwXuRMtlNvxoeNFCu8d/8LfbgGDtySpmZEtMT\nwm7JyWMeA1IZft9X5g8/2VJ15Xt/fdxIqVzh5ZP1tuUyhkROn3h8KfDEU18/Gd1ER1XrtZtozw5W\np38yaunZkvtJ1fpY7U3v2xptf+TA0Z03nWn/4MDRq/ecmXz7pz96bPNb+qL0sled7k1Z23IDX6zO\ntPp25Dt1z8zpXGF6SgMQ+iL0w6gNoFNzjj5w5oppTTfXyu9dm9InH12KLqatcvsfP/xY6c29mT4z\nWTTKE3URSL8TNJY7ayOVtbZm8P6LCmsTbtPPVt78/jMTa2fXihplJ5nTAdiWyxWWyJ5p+52gsD5l\npFS35YWBNDJ6p+YWNmRUQ0l1J5jKnZprr7jHHlyAhBSyVXUCT4DBSClOw49O2Gm4UeOpr59sVTqf\nvX4lVdC3vm1gLQY8O4jiKvphBy8pri1tqE7U1ibZOg3vypcm2Zym/7afrHY7uRZd1dl2sZTz2p7b\nCpYeWezZkgXAGVKuke4yGIOQqJ4OzLQavbetsjjx4LyUUFWUJ1tBJ1RUFgQy229OPrF85L45778F\nvhN87lfu6duWKW7KKbry81AraiLzfBwf/bNWK5qamsrlckNDQ6VS6fnnn3/mmWd+uvP/sla0a9eu\nPXv2jI+P796927KsO+64Y3x8/LLLLluboFvLkbWxwZq5ubm19sc//vGJiYkXX3wxmUw+9thj6mv9\nZNb9+/d/97vf7XQ67Xa70+kUi8Voe6FQODvVLMtyXVdVVc55uVyOJiU9zzt48CD+5a9f0zQAxWIx\nCq0XXnjhPe95T19f38rKynPPPbcWbydPnmy1WgMDA/39/dGXnPNrr7327rvvfuyxxxzHMU3z9OnT\n/f39a7c/Qoj0xizTlMxQduLu42o+3XPVNq5rQkpjoNj50bSeNLVNfWGzHT1XxF6cQE83v/YafP2b\nYTqJE0fY7AyaTSRMBAECD46FxiqevIuBI9fPuYr6rKgvKFKRriuNnNJdQuCI+UPoWGzdMFM1FNbL\nRB5BS+hp9G9nflP2bYxqRbJ3Ha7bzQ4ekJnimVqR3YLwYCZkFPCatnjzR0W9mdg01HrmCFcU+/Ri\n+cHD7kor0Z1ODuQSJqufKAcN22v5ioJNO3uXflTmCttyZfHov6wVbb/ESCflRZeJbCIcHQWHTBqo\nNTC3iP48NI15AW6/C4GPjT283pH9ebw4zwdt1OpSV/nGgqzU5Gobh8tIadLUcUEPym2kVDGYQ8dj\neVNs3QCc5vmk4AZWmuzW38Yt/4SHX+CHjoYT07AaOPQiggBpExMT8umnZMfzx58Mkykt362Mfdf2\nAi64mrTRtSHJVW6ktBfuW3JbvqJzI60B6NS8ZNEEJCRkKMysXlvq1Jac2oJ99IEFp+4mcoaZ1yef\nrgAI3XB1prm24EpP6Ws30b4drF09V8+acHPqfnQ3DeDA7rG1G/9HDhyNRi0AHrn9yJvfv7U62ahM\nNjs1t3fLmXLFwuEgajstv3y8lsjoACBlZaLeszUbrQVzW4Ge0uZetPCiVZtrHbprOpHVABTWJaNz\nFl4YtUNPdOru0vF6NOGWKuhRdAGwLbe+9JPo8jvBDf/3W7o2pY+NzZ9dK3rx+zOX/PJ6/FR9iEMO\nv3fL0bF5hcG3g6hWtDrTlFIaaW2tVpQfSALQTcXM6dFzPLqpGGnVt8MTD85bM60TD853Gl4yrxsp\nZfqZssLRsaOlDWfe8LNHLSKUP3nDz7pXqJ5q7vjFdTtv3grg7z/4k2UOk08u7zzX0oZO3et6aWlD\np+ZGbeGH1RnbyGgKhyqwMtXs2pA2cxpjsOZtM6s1Km6nsbIy3fq5qBU18dzYuT+qh5U2o7T5p7fL\nsQfPd7TUq37+MqoVffSjH2232+vXry8Wi3/3d3/3iU984vXVir75zW9+5CMfOXjw4Ic//OF3vetd\nc3NzYRhec801ax2Gh4dvvvnmD37wg9lsNplMJhKJxcXFzZs3//CHP3zZMYeHh++77z7TNK+55prX\nXCvat29fFA+f+tSn9u/ff85hr2VZt99+O+f8kksuGRsbi1Lq13/91zudzv3337+WQ1EIfepTn0ql\nUgsLC4ODgx//+McB3HzzzQDuuOOO1dXVl83m3XfffVEjGo2Nj48fO3ZMCFEsFh3Hqdfrp0+fGeb2\n9/eXy2Wv5YOxN3/s6nZTdFad9Tdf7bVcx+pkh7c5duivPVe0bujMc0WnJsPouSJN542W7Bpkho5m\nDY0W9wLpeNKu84EdcmVZNipI9YArMHMiUYQSMncqlAxdFzC3LRM5GT1XxBXUF3h9Ri6dBMCtmVDb\neqZWtDyD0b+UnovZ0zxtordfcTsylQJj/KGHwmoFXGj9XcxM+HNL5o4N3vQ815SoVhQ0XbMnI9Uw\nqKd/8ZadT3zuOb/Z2XnT1h/W254dGCklqhVNHrajWtHMhPfiM87sBJuaCJ0OVsssnebbSzII2NMv\nQkrRm+ddebi2bNjgDJW6zCRYb0Zw/0ytqJBgvSm5tYgnZ3l3Smwt4pk5dlEvEgnMWLzhhNJAQoMQ\n4MBshV3/SVlpQ3L5vR8oaVNcuAVPPa+s1AXX5YMPMMYxsI4nUurs4UA3eOlCfWpSKgllcEvy6Lit\nJvjI75WEZjz/P6cu/KWhqFb0rX1POQ0PgJk3OnV//Rt6ol/06ScWc31mIq0DKB+v5YfSnbqX6koE\nbpjtSwJwmv7KZP0oJACn4asGiy79ADK95lq7uCn9z/uf/+f9zwNorzgvfHtq4OICAN1UyhN1zw5k\nKKNJQgA33LrzwO4HokEAgAO7x34yf3WsFo1gEEXXzWeuqt/a91TvljMfBFCbbeWHkpCIznnDm9KQ\nsmOJ2rzds01LdRnJgq6ZSuAKr+37nqjNtY89sACgerrRf0Fh8ukyAwt9AYl/+INHAfx0rah3ew5A\np+GfvcyhvmTPHqpe8svrQ1/8y6UN2jlrRcnCmWfprdkztSIA1elWfl3aqLuMoXK6NXRxoVP30l2G\norFoiOY0/dqCHU2yAXCawfTT5aidyOtTT5ejD3pIFo2zlzmMvbTMIRrmRm945aylDd//82fX3vDR\n37j3hlt3RutKVmZaZ0XXmeeKADxy++HuLWfOuVP/uagViWpT/NPD5953lYrOjnNsP1//l9Ykvxoj\nIyPXXnvtD37wg7/6q79aW8xdrVZ7e3t/unOpVLrzzjsfeeSR841SCoWCrutf+9rXTp48uWXLlptu\nuukzn/nMwYMH14YNa4u5r7vuumioNDw8fPDgwXMu5j59+rRlWX/2Z3/2mqNorVY0MzPTaDTWakVn\nP0M7Pj4ehmEQBI1G48YbbwRw6NAh13WllKOjo2s/YTT59pd/+ZfRZ8T+wz/8QxAEnPO16cXx8fHz\nvR0HDx5c28sYWxsbrn2UatToSfYDbPnQgj1nuU2vNbEYPVdU/vYT9vyqftHW6LmiYHJGKJpcXGK5\nPM9nAw+8f71s2ZibZ6ouOYeESHfBCBnjQk9h61vY4jGZHQLPsNM/RKqIddfioT9Duwq/jdoMEjno\naamYsCuYfkZme1gyC68jMr1s/jgCF7Uyy+ZRLIKB+Z4c6MP/1969h0dV3vsC/625ZpKZSSYXQiAk\nMQQqFJRkIljt3nhJnirdWrftRHY5WtxiUvTstrvak9hujo8bd0307GJPT9GgtbixFZJjrT4KSIKI\ntSqYMSiCgSRDLiRMbjPJZDL3Wev88YPlcnIxUHTZnu/nr2F4M2vNmpn1Xe9lva93QPJ4Bb+WjEax\n1K71jGiSdMZli6OuXpKimd/9hr9RnDh2KrtsORENH/zImGE+815ncNDf3zoQmYgmp5sGO8ZiEcnb\nH3q38XRyqs6aoT3y1oS3PyKJUjSqMyULWZmCUavxeCWdRujoEPsFmptB87IoGKKMVDEeoahOmGeT\nLCZqO6XRacTCORQJCDlWqW+MujxCkl6KxGk4oElPFn1hMumF3Sckq5lIEsMxYdRPvR4p2aDR6CUi\nSafX5s4RJyLS/Ll0vJMGvHRtrnhmiNKsVLRQen+UIiH6b3cb/m+jNDIYX+Mw/7ExMjRE/1y76P7r\nj4y5w6davYMdvlgoJvcVxcLx/NKs0dP+wfYxU5pRPiudeL1vxS0F/DjkC69aJze+HV+1rujj5j5r\ntingDXGtxTcYHGgbzb7UxGUG+ieyv5JGRN6ecZ1Ba517Nh5iofioOzDqDgpE3l6/byBgshqJKOMS\ny6nDQ+GJaNuB/tBYWK4EyI8vvzlfrsGIsU+ii4gkSZL3+fGyV+WalrfXv+qfPml8u2bjUvlxep55\nyDVORKO9E1kLrWF/RIyLYixusuiJyNPtz1pk5aENkiiRSGF/hIgMSVqSpLb9Zycvc7eNBn1nu1jE\nuOh6d6DtQL88tGHOQmuS1SAPbfCenhDPjc8e7PAtuyH37B+KJO/89jteX7bm7C0wgtbFfUV0bpjD\nx8195jnk6Z3IWJhKREFvyH1iNCs/RZJIIho4MZqRmyIRhX0RMS6lZpu0GhIlikfiUlQ8ub+PiMb6\nA+8+e9KWmyIQWeeaug8PRsJi24H+hPuK5GEO8n1FYkxS3lfkHw5VvXB2n5/41msJXQ8yXs90yv+6\n+Ezz6drpW6GmvPt1+vL6Pz49y81yA92mTZuUg7mffvrp7OzsyfOTOp3O22+/nYjGxsambKCz2+06\nna6wsPDnP/95SUnJ7t27/X7/hg0b5BoPD+beuXOnPFsrR8Z0g7ltNltubu6Fj6CzWCxms3nK/yor\nK1u9evX+/fslSTIajVxxMZvNCWMQjEYjp9Hw8LDP54vH48nJycqLF4/HM93WHQ5HY2Mjz8cnSZLL\n5XI4HHq9XtkXVVFRseeNvdFQ9NBjB0WRSBA+erBRjMRj/pA+3SLGpXD/CJEgBsNCiom0Oml8gnT6\nuCTRREj0eElrEMJRUaujFIswHqRYiHKW0FAPjQ/R6DBpjRSLUMxLY2coHKL2tygWpdCYEAtSyhwh\nGqCRTgoOCvEQaXWUvoACI6TVk6+PDCYSRYoEpaT5lJ4pDPRLWXOpsEg4OioVl2j0JA0Oadb9k75R\nFDo7kq5cEdj3liErNdRxWiJBjMRO/OKV8NC4FI32H2iT4pI119pz2B0NRUd7Qx/vj0+MhFPnGFNs\nBl8wPHAqMOdrKad9cb1ByJyj9Z4hWwbZS4SW9yjJQN4RSSsIiy6RLAZqcwmXLaYkot++rDHp47E4\n6XXSiI/2uEmM0tF+siaRUUcxUZgIk0knHh3Q+ELUOyplmYV5GdTrpZGAkCWQRiNFRErWSKkpwtLc\nuDdKnqBm3U1i42v0p1Zh3a1S4z4a9mkfrtX8W028ab90/MOYQJoUq6a7I3Li/QmtQbdpzftBX1xn\n0Ha85zWmaDU64Uybd8ITFjSk0QmeXv/ESGjCG9Yn6+SzUmA0LLcFGUy6/mOeeESkc21B3Ob25D++\nJvdgv1l/XI4B5eNdP/zzmn8rIaIe59BYv3/OwrMXywFvKG1eCjd6D5wYtc5NtmabiCjJok8yn/35\nhCx6Y7Iu5I+27e/znp44sb/Pc3rClpuScYml6/CgIFA0HI+F4/I+xyJx5TAHufEtOd2obHwzpRo4\nAH61Zg8PeZjyviLiYQ5aIXV+Cr/O+EDQaNJKEoUDseQ0Y5JZLwgUGo+O9QXSF5hNVhI0wvhgMDnN\n4HMHxtyBieFQcCwS8kV0Sdp4WOw6PCgRxUIxechAyPdJ7np7xguvmiv/yrjKReeGOfC4kh3//IY8\nrmRW6TmgAAAgAElEQVR8OGT/J0W/0aSB2sMuX//x0fzSs1foHe8MFKzMIokkiUZ6/IJAGXlmSaIJ\n/Sf3FYnheFqOKeSLDrZ5PnVf0VxT93uD0ZAokCTFxV9949VwMBaPSLFIfLpF3r7QdaHGidRYjs3p\ndEaj0YTB3Hl5eVPO1/CZDXSZmZnvvPPO5s2b8/Ly5s6d+9JLL0WjUV6LRFZfXy8IQmdnpyRJ0Wg0\nIyNDo9FYrdaLOZhbbqCb2be+9a2TJ0+OjIzs37/fZrNptVpJkrRarfKDz8vLGxkZkcddaLXab3zj\nG08/Pduob2hoKC8vl+dCffTRR+XmO+b1el/8/R+4jlVaWtrS0uJyua6//vprbrnGarW+9dZbfCCq\nq6vXrl1rNpt37dr1zW9+s7i4+NChQ5s3b/7a177W4z7T7RuViEijE1zHpOhuIkmQeLxqTJBEEnQU\njwjRMCWnUzwq6EwUj1A8TIJABgvpdFI8TJGAMNIjGZPIPyzEIpLFRpZ0IRKizPmUV0TtH1EoQEKE\nTneTKYlSTWQxi7/YEjl5QmfQeP/zGSHFFB/yDPyfxqQssxQXtSlJhrikkeKmudZQT3i8z5dqnzv0\ncchk1s1ZaO3sHQv4ojf/j7w36tuEXOMN6zIjvrB/LHajw0yRyNLLhGPOWG8PJZsoFKYlRRSJ0oen\nqNdN/QMUDtJcmzQ4Jrz+IWWbSRCEtBQKBqRUkzDHQsOj0ukxSjfRWFjKSKJ0k+QLCVpBWjyHfCEh\nP10syiNyCWkpYkYGdbqpdj19+39R34jU+jGddtOCudRxikZHye2O37xGHB6iBfnU8k70+DHRmqYN\nBcQUq1Zn0lvnGASToNEJSSnage6JWEQ0JuuSLLrgWMSabUqdayJJGj2ttWabUrNNEhHXWtr29wlE\nEpH3tD8WjhGRRGRKM3T+2d12oH9v3RGfe4qb8+lc4xsRhcejQW+EY0DQCEnWsxnQ4xwe6hyTG9kO\nbj22euPZOWkOPnFMvuXlwK8+yrzEaskmIvL2TmQuTOX+rcFO35yFVk+vP32BOSUjnJptIqJoMBbw\nhtuaz+7zmY+97uNeIhLjYpJFT6JERGJUNFgMbQf6OQzG+gPP//e3Lr85n+8r4ltz5Lgioox8c3K6\nkfuK9tUdkRvZEvqKktOM8uNIIJY630xnhznE0/PMAW+4/7hXZ9QaLfrgWMQ618QvTuf6jYiIJJoY\nCclVLv9wKOAJceObMUXrencgEozL40p4mIPBpP9ktFuSXj7gAyd8zsazEzMnmfXcOTfmDmi0gjxS\nv+8jz5yi1EgwTkQjXeOG5LPnK8/piXAgRkTRUNzb6/cNBOPRuCRKsXBcZ9RKoqTRaqLhuN6oJaLR\n/omsgrTpxgZ/octGD3bQb8un/q9sB82bqnLWOk15olD4wgdzE9GCBQv+4R/+YXLhzxzMfcUVV2Rm\nZu7fv//yyy8nor/7u7978803H330Ue5YYTyY++WXX+bJRdvb2w8cONDb26usvfAoPp4s/LwXifB6\nvdw/1NzcbLfbeYVar9er3Gme3s7pdA4PDweDwRtuuOEHP/iB2+1+8sknT5w44XA4eN4hh8NRUFDw\n8ccf9/f3BwKByy+//MUXX7z++uu5nlRVVdXS0qJsAORjJM/qSueWyG1qampubq6rq9u0aVM8Hq+o\nqKisrEw4iHfffXdXV9fSpUvHx8ctFkt6ejq/jtfrdTgc6enpxcXFZWVlf/jDH8xm87Zt215++eV4\nPJ6VlXXPPffcf//9mZmZ6enpTSeOEFF5eXl1dXVZWZn8gBSLIdrtdrfbnZKSunXr1tbW1qeeemrx\n4qVtbW1jYyNFBUX9/a4JrWnc7df6BqMSxfdsp70CaTRCUjJ1fERiVPjgiKgRSKeVYnHJlioKJHSd\nliYCgiBoTMbxM0Nag05v0ITcozz/dGgkEBmP9r/v9vX5x0Qxd7ktxaYfPDXx+m9OfdA0QiT95w9C\nyUmUV6R78ucenyc62C+YDFJqGklx8k+cu68oRDYr5WQIwQnq7ZdsKaTTULubRgNSIEy5acKibGlx\nFlHkbF9RJC6cHJEGJ0gQyD6fjrup10vvdpGuncJRuvFyOnSCAmFa8yAFREEj0K+eE0bGKByh4x1C\n1hxauFhIMZPJRO5BqWAhpVg0SSlCilnz3p8CPq9fpxdSs02WLONXrspcsMLk6Rq/9Lr58n1FK7+7\nqLd1eHwwlGQxrFx3tjc74A1nnavBDJwYNZr1PGqnxzm8aHWOiUjgsctWPdcAvL3+aCBOAglEnl5/\nJHC25zZ7SVr34UEShKA/Kt9XRESWTKN8Jo2F43L7j7Lxzdc/UfHLqzMKzET0qzV7PhnysPWjledi\n7JWHWlauW8S36Qx3+bOKUokoMhEZODmaz5PxjIaHOn2mVD3xbafjkdRsE++nFBOTUnRt+/vEqKgz\n6k5/MGJKNcQjosGsP3V4UIyKE6MRua/IkpWk1Wnkgdpy41vAGx7p8fe2Do/2TZw5PjrhDV9916VE\n1Ns6PDESSs1JTs1JHh8KkUTJacZoMO5uG5176bn+Idf43EvToqG4zx3wnp7QaIQxd5CIgqNhvUnL\nZSKBGI+Sl4ji4fhQh0+SpOP7TotR6djeHi4Tj4nHXuuRRNLoBEEjDLaPmtKM0UAsFhWfXf8GEUkS\nmayGDxp7iUir0euiSeNHdMFgMBKJJCVZo21nT2TSqE8vpKWkpJCG5hXpLRaLy+Wy2WyTc0Uecyv3\nOifcsOj1epubm7+gZXNjRUTTzIQ9QDQw5X9MO3N2UtJ5LE9+XhP/zDyY++GHH66qqnruuef47PfY\nY485nU6/36/8c44ieR0GXiQiJSVFWWzZsmW1tbW8CafTeR5R5PV6eRgfr1Hh9XobGxv5GfnUL09v\nV1hYWF5evmLFil27dg0NDRUUFBw9enTHjh080tzpdDY2NlqtVrPZnJqaumrVqiNHjlx55ZWiKHJQ\nOxyOoqKijo4OZQ1MeVz4VqTq6mp5i729vevWreO2yObm5traWvkLV1JS8uabb/b19f3yl7/ctGmT\nXq9/8skn77nnnu3btysHiAeDwc2bN2/YsIHfbG1tbV1d3ejo6OxXpB8cHLztttuOHj3Kaxru27dv\n9erVvLog72plZWVdXV1TU5PX6121apXVatXpdNyomJ2dnbPsqzqdzmazfe9733v22Wdra2tramo4\n5L7+9a8vWLDg7bffXmjKNS+/dM+ePeMHeogoeU5KeCIQC8YMyfojL57S6gSSpNbd/QJRJCQO9EY0\nRD2uiBiXtBoaHRFCASkSIb2eJiao4xTptZIkUjAo9J6RUk3k8Qn+oJRsJGuWUDiHLltAr7bQ4S6K\nxUmI0wIr+cJk1JJZT/3jRETO02QyCSlGmpcqGIz01gnJNSgYTaTXUU668E479Q/T35fSwAj5AnSl\nXdrzBvn8dOt3NHo9xUj49jqT1Cj1dUfffT2w/GsWSadbtNL21h9HzrT7A6PRhatzSRC8fRNagyYe\nEb3d/t7W4cEOX3g8IsfA339/6YnX++Qb9YfaR+VTf3A8KtdmDvzvo7aCs6PdQr4IxwARBX0Rc6Yx\nHhFJoO6WoYz8eQKR0ayTYmLaXJNEFBqPDpwciwbjROQ9PWFbYD51+Oz8LJa5ycf2dEsSSRKRTsPD\nB0gxMwIRGc3GwY6x8HiUiEb7A1wJsDsKTxzoW1I2f7B9LOyPavWDOUttRDR4cqz3iIejMeCNxCIi\nVwKioXj/MW96npmIgmOR0HhUjItEghgXBUGQREmj15AkCVqBRIoEYwFvODnN+MFLXUREGkGMiPxY\nkkgQqPfIsMBDPzzhJ/7xNd5njUYz2hWQBBLjohDTDX98JuITdTpdV9+YwWDw+XyxWGzMFdFqtWaz\nOVmypkVy0tJJHj1LU02+lXDGd7vdZ86ckUfAsimXGP7USCgLEZHL5Zo3b57y588/SeXr8+OEjSYM\nu2XKtQmm3ui5Z6Z8U5MTa8oYm/o8Ho2Tx5f45AWbdpKZRNxXVF1dzVUQvq+orKzsfCf+4apId3f3\nkSNHlAcnGo0qr/63bdumbPrjektNTc3NN9+8Z88eeYWkuXPniqK4ceNGnU7X399/HlFUUVFRW1sr\nb9Jms3HAyssfydPb8YTk/M43btx43XXXnT59WqvVVlRUhEKhwsJCTrKmpqZrr7321KlTPp9v4cKF\nR48ezc3N/elPf0rn7rTV6/XTVRKbm5srKyuVE+p5vV6TyWS327nhjr9tTqezrKxs7dq199xzz9VX\nX/3jH/84EonU1tY2Nzfn5uYuX76cv0NcTK/Xr169uq2trays7P7773e5XDfeeOOuXbv8fj8P9iPF\nt3/y17SqqspisaxZs+bo0aNTllH+6mw2W35+vsPh+MlPfsKT12ZkZLS3twcCAX53TU1Nyj83mUzP\nP/+8XBVLTU197rnneDrzXbt2Vd2/MRaLOZ3OpqYmLmO32/Py8l588UWeHLegoGBsbIx3KT09nQO7\ntrb2vvvuu/zyyxsbG08P+axWa44tGo34o9EIicK+o1LTUUkrkEkvvN9LZgMdOSMYtRSKkUYQglES\nBIrEBcMEhWMUF8lkJCLq9UhxIqNeSNJL3W7KyxGyMyWRaMArLF1MIYle3UsffUReL2kMdKojLpDg\nHYpr9cLg6bDWED/+Z685w/CVOZk9H44OdYxFQ7HOP7vH+iaIKCnNcOrw4PhgMOiLphdY5Bv1A6Nh\nRQ/2J3PMyHE12OEb7PRd+4Ozbdmn3nGbM84ODIuFxayFqWNnAmNnAuGJKEcOEXl6Jwwpep87QETj\ngyFBEMbOBGIR0d02qjfpiCQxLgmCIGgFbmST4lJv63CSRa8zaCPB2IFfnV1SUoxLx17r4TJiTPro\n1W4xLn3wUhdJtP2OA1qDJsVmFON0+LmOeFTSaIWkZIO3d8KgN4SHhfCIEO452wKTn1W0pODs/U/J\nycnHjx/v6upyD7pLSkpKSkpuu+22HTt2LFu2jIiKiz+ZV2bKpYRdLhd/D2PzYj6fj4h4aflC27JP\nCmUTEfHvNBwOu91uInK73V1dXU899VTCC3JdZPKGpjzjJzSh8/7MXJuhGWOGVyiYbqOHDh1atWrV\n5I3OJma4QELMtLa2EpFyo16vl9enVr4Un7Inb5Qoh2i6Doh5RDlTPT/tnbmzX0yH93Dbtm1bt259\n//3358+f73K5fvKTn/DtpQlm7isqKyt7+OGHly5dWl9ff+rUqRdeeMHtdvv9fuUqDU6n88MPP6Sp\n7itSns+5qvTII4/o9fo1a9bMNor4Gzw5GHj6B34sj4iQ3zlX04qKigYHB+12u/Jbwi/1hz/8gRNY\nr9fH4/Gf/vSns5z5u7CwkJdqV1ab5Ciurq5ubGysrKysqanhOpzdbjeZTPIihkTE0zHwW+Bil156\naWNj4+rVq//rv/5r4cKFvLehUGh8fFy5dAXnlvJBS0tLaWlpZWUl3zWldPLkSfr070p5/VVZWfn4\n44+Xl5c7HI7u7u7vfOc7zz77bHl5+a233lpXVzddK3Zzc7Ner9+9e/dLL71UXV1ttVr37t3b09Pz\n1a9+VS5TV1e3YMECflxTU1NUVDT5dex2u16vv+mmm375y18mtDfyUa2urg4Gg3feeefIyEhtbW15\nefm9995rNpsffvhhjUaTnZ191113cSWP/+pMc/OTzzUNDAzs2rXrjTfeSM9MnZMxcvioSBSPhMVf\n1AuSIBmNdPCAaDKRVifV/U+/REI0SoFxUdMb0xs1JksoOCEKgmAwabudQ2F/bOTU+GjfBJEQGo8k\nWfSxiBj2R13vDBiSdVqdJjQejQZj4YmYGBW7Dw/GY+Kxvb1ERAKRRPse+4D4tyDRb29/nT9DQaCB\nX5z7nQjC28+0E5HJYsy7LNt3jExacyQSscQzqSvlqwuWpaSk5HwtJycnh86d5ac7GSUcW6/XO/ns\n7HQ6r5jdhbbymtrv9/N0KcXFxe3t7Zs3b167du1tt93mdrvD4fC77777yiuvJCUl5eTkuFyuWZ52\nOWb4cXd3dzQa5ctkuQBv1Gw2y3PDcIPzbOoTk++Ll99Uwp5MN7kXv0LC85Ozzev1zvx+jx49yg3+\nFzdmLuAg80br6n5N9CuaWjpR+lTPd0xTniYmJuT+C2Uj0GRlZWWdnZ3crPXYY4/xhAsOh2PKoYMz\n9BVxm5bL5Tpw4IDH4zly5EhKSoogCJmZmVdffbXynXI9IWEwd3Nz83SDuZ9++unziKIpKyjKL1PC\niAjlRzh5xST5z1taWrievnPnztmPquT1MCwWS8LVh5zDZWVlvGPcLGa32w8dOtTT0/P73/+eC4yP\nj/MXUS62YcOGtra25cuX/+lPfyosLGxsbCwsLLzqqqvGxsbktgg5t+QH27dv1+v1PKG6Muq4cGVl\nJf8tf1nffPNNuQz/hvnO55qamuLi4qVLl/L+LFq0qKGhobS0NOEHKR9qvV5/8ODBlpYWm83W2NjY\n1NSUk5PD49e5THNz8/z58/kBL3U1ywPb2Nj49ttvb9u2rbCwsKOjg5tY5Y/ebDbzTQOZmZn5+fn8\nWPnnXHLdunWZmZmnTyvvrf5UmYQvA19VuM4pKyurrq4uLCx0Op0Jv2fuF7yApSdnY/J5lk9GPKpT\nueLkBZ+MaPqYkYtxS33CS3HMvP3228uXLz969CjXvHmjFovF4/HIfzLlRmeImdbW1pMnTybcos6U\nb9Pj8fB9ftPFDE2i3OiUDXSzbCuboeY0OduU16Y8xiphTjI2+5hRroHNG53N12/KC5SOjg7lqOBF\nixZNeY346T8pPHDgwN133z35v+bNm6ccKfCZ+8OdKcqJf1wu15Q9ZDP3FVVWVhYWFg4PD+/Zs4eI\nmpub77zzTuWZik8LCUeJr9SVW1QO5i4uLj6PBrrJV3kJlCMizovy+yTft+T1eqe7b4mICgsLa2tr\nb7/9do6c5ubmRYsWDQ8PJxTjxjqn06nRaI4dO3b33Xfzttxud19fn7yrXMxut3d1dZ06derhhx+u\nrKz0er2lpaU+n4/P6cpd5XgjokceeeQ//uM/fvSjHzkcjvLy8vr6eq/XGwwGlYX7+vq412fZsmW/\n+93v/vjHPxIRD9wgonfeeYeILrvsspGRkb1798rVyrKyMj7v80vJX2teR8Ttdt9xxx1yjx8RVVdX\n33fffVzmiSeeUPbecXsjH9Xx8XFBEORJCRPaG3m9+pUrVzY0NFRVVc2fP//gwYPy+eK8VhizWCz3\n3HPPv//7vyuflMd3JEg4g3AfZEtLyxe2phmb8mLrAr7PbMqT0Qwxk/Dk5Gzr6+vLz8+Xu4Lp3CdS\nXV39m9/85q677qJzZ3yz2ez3+1999dWTJ0/6/f5LLrmku7s7Pz9f+WryFj0eT3d3N2fMDDHDM37x\nIZplP9DkmNm9e3dXV9f4+PjIyIjVak1PT4/FYsnJyXl5n6zV4/F4+vr6wuFwKBS66aabJo9C4u7h\nVatWzZ8/f9euXQcPHly8eHFJScldd90lf1dfe+210dFRt9t95513BoNBi8VSVFSUMO5J/mTlkVbc\n8DN5i/JJiSsfjY2N3N4z+XTHl+NcWB7VJV9hX8AC583NzdyYlvB8VVXVvn37tm/fLjdm8JMc29xb\nLxeWe0zKysq4/YZ3iccV19TUcJDwJaDcV7Rly5bly5fzkzPv5KWXXsq5zjVafpyamurz+fhynPdQ\np9M98sgjTz31FO9nX1/fhQzmLisrq6qq4l6NhP+Sg47D8IJ/ukw5WHy6M5e8Szt27Fi/fr3H4zGZ\nTO3t7S6XKyG6lLWu995773e/+x0n+dDQkE6n49tvuXBLS8udd965ZcuWhx56yOl01tTUvPzyy0aj\nMRKJtLe3K+92kuNt0aJFmzZtWrx4cSwW4ysODsXDhw/LhTdt2vS9731v27ZtHR0dx44dS0tLs9vt\n3JRcX19fVVWVl5fX1NTE34adO3c+/vjjXN+qrKzs7Ox85plnSJFbclVsyZIl77//vvK/PB5Penr6\ntm3bHA7Hdddd99hjj/Gvq6ysbM+ePXJ7Y09Pz/z58/lvmbK9kXvgqqurle2NyusdLmy327u7u+Vn\n+IG8cjy78sorX3311YQomlJNTU3Cjcw8zFLesb9SCXNB/uW++93vFhcXK+tPdO5Ty8/PV573f/vb\n3w4ODhYVFXGb7fj4+GuvvSZJUmlpacJr8kfp8Xjq6+unvECWRxZw5Ynf0cz9QPzTm/ze+cL8gQce\ncDgc8rgnXl1U/gJwzPz4xz/Ozs4mIpvNJo9Ckl+Hxz0dOnSosLDwmWee4dNfTU2NshhvPeGrNeU1\nvjzuicNVzpKELXLHO7cp2Ww2jrSamhq73S4fN5fLxR3q3AzFF3Yul+vzuIWWm4USIodHRSVUi+lc\nj8nWrVs3bNgwf/78xsZG+afNXf5VVVVcfVQeJb1e73A4Zrgc5PMkt0Xn5+dzEHCNpaGhgVvkMjIy\nbr/9dh5aHI/Hq6qq6NxdoX6/v6GhgU9fb7zxxtmVGmajvr6+rKzM4/Eon+SBDPI/OW8n/2Ftba3y\nGc6bhGJEiTvDrZbKZ5qamhKemeUWJUnidiE2+d1xgfr6ekmSWlpampqaHnzwwauvvjo5OXn58uV8\nWmFcuKWl5dFHHy0oKFi1alVJSck111zD/1tSUqJ8I01NTWVlZS0tLS+++OKKFStyc3NvuOEGu91e\nX1+/fv36rKyslpYWSZI6Ozvtdvuvf/1rq9W6fv36goKC1NTUG264QZIkj8djt9u5mLyTlZWVJSUl\nW7Zs4f/yeDy5ubn33nsvv8fc3Nz6+vqWlhb+vOQ/b2ho+OY3v8ll+I1wGflBfX39Nddck5WVVV9f\n39DQUFlZyd91/rDkwtXV1evWrXM4HFyG959Hdig/4lWrViV8NPwbTvho5MMu6+zsnPzRK4/nlB/f\n3zz+4D6zGH+XZvPnnZ2dTU1Njz76aElJScLverK//MhPuf/19fXyqWD2e87nTeUzHo/HZrNN/sOZ\nd2n2W+Qn+Qs83avV1tYmnNY8Hs/ks9N5meGwKw+dvLkp3478X5N/aFO+1I4dO+64446CgoLZ7xWf\n5SZvtKmpadOmTWvXrpUkqbCwUP6a8d2cfILiEDmPBjq5TscfCY/HLysrU/ZE1dfX19TUcBk610Zh\nt9uVlxhVVVV8SeVyufhvuf5IRNzGJec8d5VzbMqbS7iC/swtulwujmLOcM58nqYh4cKNBwLJL+Vy\nuc6cOfPSSy9NeW3L7dRyxVmulQuCYLPZ5MoZ1/C48C233PLGG29UVVVdddVVjzzySFdX15IlS+rq\n6hoaGgoLC9esWfPAAw8Q0QsvvGC1WpOSkvLy8vhaj+tb/IJcJ1u8ePHw8PDPfvaz0tLSxsbG3bt3\nh8PhzZs3V1RUVFVV7dix4/7777fb7R0dHcXFxTwYxOv1btiwYd68eUNDQ3x9xzvZ2tp6++2379ix\ng4hGRkb+/Oc/5+bmfvzxxzt37uQG+oceeujBBx90Op18JcgVr5qamtzc3A0bNtx6660VFRUnTpy4\n6aabeBAKf4IOh8PpdNpsNvl4Op1OnU43eTKnyYedt/WF3gb/NyRhLI+Mx/Iov8xyv/2+ffs+7zs9\nz2vcU4LJe06Tal18RjrfvTqvLRJRbW3tDFUcbqXn1ku5JVPZJHhxVVZWcney/NnV1dVNuXvKIc3K\n50tLS7nvqrKycuHChTzCS574Z7r6UEVFxbXXXktE3PfDw3ET2mx5mNiCBQuSkpJCoVBXV1dxcfHK\nlSuVX7P8/PykpKTLLrvs61//usFgOI9akTISZ6hbyGE4c5lZmuVLfR5bnBzyF2DKixqudU15Hcqb\n5quVGYq1tLRs3bq1oKBg/fr1a9euXbFixeS9lati3Oa2ZMmSJUuWcFek8kqNL5cqKys3btyo1WqX\nLFny/e9/f/Hixf/6r//Km1ZebMqFCwoK8vPzf/aznzU1NR04cEBZb5NxdYeP54svvrh48eIZDuks\nD/v/z7Uih8Mxm6/3DK2CUx66WR7Sv/DINzU1TW4ISTD7PZ9ls8pn7vB5HavZ1Er5Gp97RwoLC7mZ\nfeY/mdnatWsLCgqqFZT7INdmmpqaHA6H1Wqtrq6efMbgSai52eayyy5btGgRP6ZPN2NwcwVflRLR\nihUrpjz5bN26lSfQsVqtJpNp7ty5P/rRj+SSfGHNNw+lp6cvXLgwPT3dbDavXbtWeSj4vVRWVq5Y\nsSIlJWX9+vUXMgfdZ7aAX8RW8lm+1Be/xdnj+hwphk7NPOxSfjxDMa5mbdy4Ue4gna7MLbfcws39\nfMkzuZjcnfb6668vWLDAarUePnx43rx5R48e5apqwu1QXPjGG2986KGHhoaGuNtAWW+TyZ1qPDrr\nvvvum+EdfeZh53fxZVgATS3cE/mZX84LHj30efv8xj1dsIu+xekG4FxwpXPnzp0lJSUJdVn5MVeM\nHn/88eeee+6GG2646qqrwuEwb1H5InxRyINUv/KVr/A9nQkb4h5im822bdu2e++91+12X3HFFZNf\niohWrly5Zs2al19+ef369QcPHrzlllteeeUVfoPcPcbvVxCEjo4Op9PZ2tra39+/ZcuWye+Ol7L7\n6KOPOjo6LqRWBLPX2dkpX878hddHX0Iz1NvOq8xs8AVgdXX1DI31f9s8Ho+ytV1J2f12vtWXL6ZW\nJH26q0BJ/l3MfhMXq1Z0Xm/qM2tFU1b7ZtnDNx2api4r27JlS1paGg9ulHdj8m+kpaWF2xtmeMvc\ne80tH/yhTPlSHo8nJyfnmmuukV9KLqYsL38cDQ0Nk4+M/FuW/0tzYVkNs8SDztnfXucHt4nPfMU3\nmzKzwWMoZm6s/9vGHQ/l5eUJ1QseGiv/k5uGuH9Uadu2bdMtlPDFqK6urqioSOjRqaurk3f1ou/5\n5PkmeGy0/M+Lu0Wn08mDvBN24HP94fPadNyDy89UVlYm7AZNdQ/WZIsWLUpJSeG+dt7nKV+K55fY\nwVYAAAcZSURBVAFQrq8tF+N7AZWF6+rqfvjDH/LkZwn7wwdHvo3kwheJAIAvGLe0VFRUyCMOLmws\nDxHJI2t4MK78z4Sb8GdZbDb+lsY9EdFsxj1dxAE4PJPblPfS3HzzzXV1dZ2dnfwMj26/gE34fD6r\n1apc4C7hpfjT7+jo0Gg03Obf3d3NR5iL8bhwvkeTiFJTU5OTk+fNm/fEE088//zze/fuXbHi7Hrq\nDoejubl5+/bt3d3djY2NXq9XkBRT2gDAXwX5dp8Zuo6UXWtfqju05HuSpturi7jns3ypz2OLU05r\ndL4EQcjIyCgoKODw5rt0lYFUUVGRm5trMBgSrjOmuyNzhvlKplzWYPJLycWUL6UsxtWdXbt23Xvv\nvXL2bNu2LWHiHyK68sorv/3tb1933XV2ux21IoC/PrM5x130ATgXC8Y9zd6GDRsOHTrEMWOfallu\nr9f7wQcfKJcMvWCzrEt9ZjGusicchMkT/xCRxWIpLi4uLCwsLS1FXxEAwJfUAw88sGbNGh6nynMI\nJcy029fXV1JS8nnfFnZRcP9QwjN+v5+nqkGtCADgS4rHPSmf4RM6z4xFRMFg8OTJk+Xl5Q0NDX9h\nIM2yOfEizgwZDof/5V/+ZdOmTZWVlagVAQD8lamtreVbwm+77baKioqmpiZlDl3w7XdTLiF4wcUS\nNDY2KmPM6/W+88476enpPJ4FUQQA8Fcj4YTOQ9ESClzYTbuzfKnZFCsvL09YvI1XSErIy6Ghob6+\nvqVLl5aXlyOKAAC+jGZzQpeXreF/8qz/F3bv3Sxfqrm5ubW1denSpTU1NU6n89prr92wYcMHH3yg\nLNPQ0NDc3FxaWlpTU1NTU7Nw4UJeukJZpqysrLOzs729PTk5ubCwEIO5AQC+jHjxC3m1FK58JJzQ\n6dzqG3zXEc/CNTk/lOvAyTeW0qR14GbzUudVjBvupluFT+Z0OhFFAABfXrM8ofOtZp950p+NWb7U\nRdwiESGKAABAZegrAgAAlSGKAABAZYgiAABQGaIIAABUhigCAACVIYoAAEBliCIAAFAZoggAAFSG\nKAIAAJUhigAAQGWIIgAAUBmiCAAAVIYoAgAAlSGKAABAZYgiAABQGaIIAABUhigCAACVIYoAAEBl\niCIAAFAZoggAAFSGKAIAAJUhigAAQGWIIgAAUBmiCAAAVIYoAgAAlSGKAABAZYgiAABQGaIIAABU\nhigCAACVIYoAAEBliCIAAFAZoggAAFSGKAIAAJUhigAAQGWIIgAAUBmiCAAAVIYoAgAAlSGKAABA\nZYgiAABQGaIIAABUhigCAACVIYoAAEBliCIAAFAZoggAAFSGKAIAAJUhigAAQGWIIgAAUBmiCAAA\nVIYoAgAAlSGKAABAZYgiAABQGaIIAABUhigCAACVIYoAAEBliCIAAFAZoggAAFSGKAIAAJUhigAA\nQGWIIgAAUBmiCAAAVIYoAgAAlSGKAABAZYgiAABQGaIIAABUhigCAACVIYoAAEBliCIAAFAZoggA\nAFSGKAIAAJUhigAAQGWIIgAAUBmiCAAAVIYoAgAAlSGKAABAZYgiAABQGaIIAABUhigCAACVIYoA\nAEBliCIAAFAZoggAAFSGKAIAAJUhigAAQGWIIgAAUBmiCAAAVIYoAgAAlSGKAABAZYgiAABQGaII\nAABUhigCAACVIYoAAEBliCIAAFAZoggAAFSGKAIAAJUhigAAQGWIIgAAUBmiCAAAVIYoAgAAlSGK\nAABAZYgiAABQGaIIAABUhigCAACVIYoAAEBliCIAAFAZoggAAFSGKAIAAJUhigAAQGWIIgAAUBmi\nCAAAVIYoAgAAlSGKAABAZYgiAABQGaIIAABUhigCAACVIYoAAEBliCIAAFAZoggAAFSGKAIAAJUh\nigAAQGWIIgAAUBmiCAAAVIYoAgAAlSGKAABAZYgiAABQGaIIAABUhigCAACVIYoAAEBliCIAAFAZ\noggAAFSGKAIAAJUhigAAQGWIIgAAUBmiCAAAVIYoAgAAlSGKAABAZYgiAABQGaIIAABUhigCAACV\nIYoAAEBliCIAAFAZoggAAFSGKAIAAJUhigAAQGWIIgAAUBmiCAAAVIYoAgAAlSGKAABAZYgiAABQ\nGaIIAABUhigCAACVIYoAAEBliCIAAFAZoggAAFSGKAIAAJUhigAAQGWIIgAAUBmiCAAAVIYoAgAA\nlSGKAABAZYgiAABQGaIIAABUhigCAACVIYoAAEBliCIAAFAZoggAAFSGKAIAAJUhigAAQGWIIgAA\nUBmiCAAAVIYoAgAAlSGKAABAZYgiAABQGaIIAABUhigCAACVIYoAAEBliCIAAFAZoggAAFSGKAIA\nAJUhigAAQGWIIgAAUBmiCAAAVIYoAgAAlSGKAABAZYgiAABQGaIIAABUhigCAACVIYoAAEBl/w/m\nhuaG+23MQwAAAABJRU5ErkJggg==\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": {}, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Nx=100;\n", "Ny=50;\n", "Nz=10;\n", "Lx=2000;\n", "Ly=400;\n", "Lz=200;\n", "v_cell=(Lx*Ly*Lz)/(Nx*Ny*Nz)\n", "q_in=100/3600; %[m^3/s]\n", "m=createMesh3D(Nx,Ny,Nz, Lx, Ly, Lz);\n", "q=createCellVariable(m, 0.0);\n", "qin=q;\n", "qout=q;\n", "q.value(25,25,:)=q_in/v_cell;\n", "qin.value(25,25,:)=q_in/v_cell;\n", "q.value(75,25,:)=-q_in/v_cell;\n", "qout.value(75,25,:)=-q_in/v_cell;\n", "p=continuity(m, q);\n", "visualizeCells(p)\n", "%shading interp" ] } ], "metadata": { "kernelspec": { "display_name": "Octave", "language": "octave", "name": "octave" }, "language_info": { "file_extension": ".m", "help_links": [ { "text": "GNU Octave", "url": "https://www.gnu.org/software/octave/support.html" }, { "text": "Octave Kernel", "url": "https://github.com/Calysto/octave_kernel" }, { "text": "MetaKernel Magics", "url": "https://github.com/calysto/metakernel/blob/master/metakernel/magics/README.md" } ], "mimetype": "text/x-octave", "name": "octave", "version": "4.0.0" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
rsignell-usgs/notebook
xarray/.ipynb_checkpoints/epic_to_xray-Copy0-checkpoint.ipynb
1
82237
{ "metadata": { "name": "", "signature": "sha256:d95330a156c1b0b7abeb9ba203695aab76e7c4739888a48c55a0cbfe3bde9016" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import xray\n", "import pandas as pd\n", "import numpy as np\n", "%matplotlib inline" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { "cell_type": "code", "collapsed": false, "input": [ "url='http://geoport.whoi.edu/thredds/dodsC/usgs/data2/emontgomery/stellwagen/Data/FI14/10001whp-cal.nc'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "code", "collapsed": false, "input": [ "ds = xray.open_dataset(url)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 3 }, { "cell_type": "code", "collapsed": false, "input": [ "def convert_epic_time(ds):\n", " \"\"\" convert EPIC time and time2 variables to datenum64 \"\"\"\n", " t1 = np.array(ds.coords['time'].values - 2440000,dtype='int64')*3600*24*1000\n", " t2 = np.array(ds.data_vars['time2'].values, dtype='int64')\n", " dt64 = [np.datetime64('1968-05-23T00:00:00Z') + np.timedelta64(a,'ms') for a in t1+t2]\n", " ds.coords['time'] = dt64" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# if we find a time2 variable, convert EPIC time and time2 variables to datetime64 object\n", "if 'time2' in ds.data_vars.keys():\n", " convert_epic_time(ds)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "ds.coords['time'][0]" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "<xray.DataArray 'time' ()>\n", "numpy.datetime64('2014-02-07T09:51:29.999000000-0500')\n", "Coordinates:\n", " time datetime64[ns] 2014-02-07T14:51:29.999000" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "ds.data_vars" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 8, "text": [ "Data variables:\n", " time2 (time) int32 53489999 57090000 60690000 64289999 67890000 ...\n", " burst (time) int32 ...\n", " dspecfirstdir (time) int16 ...\n", " wh_4061 (time, lat, lon) float64 ...\n", " wp_4060 (time, lat, lon) float64 ...\n", " mwh_4064 (time, lat, lon) float64 ...\n", " hght_18 (time, lat, lon) float64 ...\n", " wp_peak (time, lat, lon) float64 ...\n", " wvdir (time, lat, lon) float64 ...\n", " dspec (time, frequency, direction, lat, lon) int32 ...\n", " pspec (time, frequency, lat, lon) int32 ...\n", " sspec (time, frequency, lat, lon) int32 ...\n", " vspec (time, frequency, lat, lon) int32 ..." ] } ], "prompt_number": 8 }, { "cell_type": "code", "collapsed": false, "input": [ "df = ds.data_vars['hght_18'].to_dataframe()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 13 }, { "cell_type": "code", "collapsed": false, "input": [ "df.plot(figsize=(12,4))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 15, "text": [ "<matplotlib.axes._subplots.AxesSubplot at 0x7f532f6b8f90>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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YGNB1S12kKlJZTjZAqYXhFK1j8+qIjTuvrpcrLPmuu8x+53cWf/2WLe3ndbx4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CAAAg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\nncEk6zPg8fH+KExBRFuDvTFAs7HzUbDA3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"text": [ "<matplotlib.figure.Figure at 0x7f5331759110>" ] } ], "prompt_number": 15 }, { "cell_type": "code", "collapsed": false, "input": [ "t1.dtype()" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'numpy.dtype' object is not callable", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-55-2cedeb1df8cf>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: 'numpy.dtype' object is not callable" ] } ], "prompt_number": 55 }, { "cell_type": "code", "collapsed": false, "input": [ "t1.type" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'numpy.ndarray' object has no attribute 'type'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-56-d5515c393857>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mt1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'type'" ] } ], "prompt_number": 56 }, { "cell_type": "code", "collapsed": false, "input": [ "t1.dtype" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 57, "text": [ "dtype('int32')" ] } ], "prompt_number": 57 }, { "cell_type": "code", "collapsed": false, "input": [ "t2.dtype" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 58, "text": [ "dtype('int64')" ] } ], "prompt_number": 58 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
scikit-rf/scikit-rf
doc/source/examples/networksets/Sorting Network Sets.ipynb
3
7601
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sorting Network Sets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Frequently a set of `Networks` is recorded while changing some other variable; like voltage, or current or time. So... now you have this set of data and you want to look at how some feature evolves, or calculate some representative statics. This example demonstrates how to do this using [NetworkSets](../../tutorials/NetworkSet.ipynb)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generate some Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For the purpose of this example we use a predefined `skrf.Media` object to generate some networks, and save them as a series of touchstone files. Each file is named with a timestamp, generated with the convenience function `rf.now_string()`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from time import sleep \n", "import skrf as rf \n", "%matplotlib inline\n", "from pylab import * \n", "rf.stylely()\n", "\n", "\n", "!rm -rf tmp\n", "!mkdir tmp\n", "\n", "wg = rf.wr10 # just a dummy media object to generate data\n", "wg.frequency.npoints = 101\n", "\n", "\n", "for k in range(10):\n", " # timestamp generated with `rf.now_string()`\n", " ntwk = wg.random(name=rf.now_string()+'.s1p')\n", " ntwk.s = k*ntwk.s\n", " ntwk.write_touchstone(dir='tmp')\n", " sleep(.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets take a look at what we made" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ls tmp" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Not sorted (default)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When created using `NetworkSet.from_dir()`, the `NetworkSet`'s stores each `Network` randomly" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ns = rf.NS.from_dir('tmp')\n", "ns.ntwk_set " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sort it" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ns.sort()\n", "ns.ntwk_set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sorting using `key` argument " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also pass a function through the `key` argument, which allows you to sort on arbitrary properties. For example, we could sort based on the sub-second field of the name, " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ns = rf.NetworkSet.from_dir('tmp')\n", "ns.sort(key = lambda x: x.name.split('.')[0])\n", "ns.ntwk_set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extracting Datetimes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also convert the ntwk names to datetime objects, in case you want to plot something with pandas or do some other processing. There is a companion function to `rf.now_string()` which is `rf.now_string_2_dt()`. How creative.." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ns.sort()\n", "dt_idx = [rf.now_string_2_dt(k.name ) for k in ns]\n", "dt_idx" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Put into a Pandas DataFrame and Plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The next step is to slice the network set along the time axis. For example we may want to look at S11 phase, at a few different frequencies. This can be done with the following script. Note that NetworkSets can be sliced by frequency with human readable strings, just like Networks." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd \n", "dates = pd.DatetimeIndex(dt_idx)\n", "\n", "# create a function to pull out S11 in degrees at a specific frequency\n", "\n", "s_deg_at = lambda s:{s: [k[s].s_deg[0,0,0] for k in ns]}\n", "\n", "for f in ['80ghz', '90ghz','100ghz']:\n", " df =pd.DataFrame(s_deg_at(f), index=dates)\n", " df.plot(ax=gca())\n", "title('Phase Evolution in Time')\n", "ylabel('S11 (deg)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualizing Behavior with `signature`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It may be of use to visualize the evolution of a scalar component of the network set over all frequencies. This can be done with a little bit of array manipulation and `imshow`. For example if we take the magnitude in dB for each network, and create 2D matrix from this," ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mat = array([k.s_db.flatten() for k in ns])\n", "mat.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This array has shape ( 'Number of Networks' , 'Number frequency points'). This can be visualized with imshow. Most of the code below just adds labels, and axis-scales. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "freq = ns[0].frequency\n", "\n", "# creates x and y scales\n", "extent = [freq.f_scaled[0], freq.f_scaled[-1], len(ns) ,0]\n", "\n", "#make the image\n", "imshow(mat, aspect='auto',extent=extent,interpolation='nearest')\n", "\n", "# label things\n", "grid(0)\n", "freq.labelXAxis()\n", "ylabel('Network #')\n", "cbar = colorbar()\n", "cbar.set_label('Magnitude (dB)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This process is automated with the method `NetworkSet.signature()`. It even has a `vs_time` parameter which will automatically create the DateTime index from the Network's names, if they were written by `rf.now_string()` " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ns.signature(component='s_db', vs_time=True,cbar_label='Magnitude (dB)')" ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
willwhitney/hydrogen
spec/helpers/test-notebook.ipynb
2
13048
{ "cells": [ { "cell_type": "code", "source": [ "import pandas as pd" ], "outputs": [], "execution_count": 1, "metadata": { "collapsed": false, "outputHidden": false, "inputHidden": false } }, { "cell_type": "code", "source": [ "pd.util.testing.makeDataFrame()" ], "outputs": [ { "output_type": "execute_result", "execution_count": 2, "data": { "text/plain": [ " A B C D\n", "gLcuqKZ0Ol 1.545515 2.497467 -0.133160 0.270383\n", "sdIj1UgyOQ -0.073220 2.253143 -0.451220 -1.317645\n", "jCLJVl0gXB 1.368376 -1.165832 0.861381 -0.466035\n", "0NXcX2qYaK -0.372654 -1.050680 -2.143551 0.438508\n", "NiDIC9rF9C 0.341255 0.025531 -0.565850 0.787239\n", "wNZw1kII22 -0.397931 -0.858899 0.565173 1.900312\n", "0C3fNmKUw3 0.376922 -1.535493 0.895807 -0.502226\n", "mmuBdhEWXo -1.935622 -1.300806 1.023292 0.691432\n", "IY9pUaq5nS -0.479837 -0.658911 -0.225613 -1.590860\n", "jc1StEbMiR 1.409729 -0.122310 -0.006437 2.162515\n", "nFsRYNuhku 0.638121 -0.236174 0.045062 -0.404694\n", "4jfoTHJiUG -0.682048 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mit
rileyrustad/pdxapartmentfinder
analysis/Munge-Copy3.ipynb
1
87293
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "from pandas import DataFrame, Series\n", "import json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the data from our JSON file. \n", "The data is stored as a dictionary of dictionaries in the json file. We store it that way beacause it's easy to add data to the existing master data file. Also, I haven't figured out how to get it in a database yet." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with open('../pipeline/data/Day90ApartmentData.json') as g:\n", " my_dict2 = json.load(g)" ] }, { "cell_type": "code", "execution_count": 8, "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>content</th>\n", " <th>laundry</th>\n", " <th>price</th>\n", " <th>dog</th>\n", " <th>bed</th>\n", " <th>bath</th>\n", " <th>feet</th>\n", " <th>long</th>\n", " <th>parking</th>\n", " <th>lat</th>\n", " <th>smoking</th>\n", " <th>getphotos</th>\n", " <th>cat</th>\n", " <th>hasmap</th>\n", " <th>wheelchair</th>\n", " <th>housingtype</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>4663</td>\n", " <td>4663</td>\n", " <td>4663</td>\n", " <td>4663</td>\n", " <td>4663</td>\n", " <td>4663</td>\n", " <td>4663</td>\n", " <td>4663.000000</td>\n", " <td>4663</td>\n", " <td>4663.000000</td>\n", " <td>4663</td>\n", " <td>4663</td>\n", " <td>4663</td>\n", " <td>4663</td>\n", " <td>4663</td>\n", " <td>4663</td>\n", " </tr>\n", " <tr>\n", " <th>unique</th>\n", " <td>1965</td>\n", " <td>5</td>\n", " <td>1173</td>\n", " <td>2</td>\n", " <td>5</td>\n", " <td>7</td>\n", " <td>481</td>\n", " <td>542.000000</td>\n", " <td>7</td>\n", " <td>559.000000</td>\n", " <td>1</td>\n", " <td>24</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>7</td>\n", " </tr>\n", " <tr>\n", " <th>top</th>\n", " <td>1085</td>\n", " <td>w/d in unit</td>\n", " <td>1730</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>700</td>\n", " <td>-122.692177</td>\n", " <td>attached garage</td>\n", " <td>45.532763</td>\n", " <td>no smoking</td>\n", " <td>9</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>wheelchair accessible</td>\n", " <td>apartment</td>\n", " </tr>\n", " <tr>\n", " <th>freq</th>\n", " <td>67</td>\n", " <td>4144</td>\n", " <td>89</td>\n", " <td>4525</td>\n", " <td>2447</td>\n", " <td>3557</td>\n", " <td>124</td>\n", " <td>173.000000</td>\n", " <td>2408</td>\n", " <td>173.000000</td>\n", " <td>4663</td>\n", " <td>545</td>\n", " <td>4528</td>\n", " <td>4663</td>\n", " <td>4663</td>\n", " <td>4417</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " content laundry price dog bed bath feet long \\\n", "count 4663 4663 4663 4663 4663 4663 4663 4663.000000 \n", "unique 1965 5 1173 2 5 7 481 542.000000 \n", "top 1085 w/d in unit 1730 1 1 1 700 -122.692177 \n", "freq 67 4144 89 4525 2447 3557 124 173.000000 \n", "\n", " parking lat smoking getphotos cat hasmap \\\n", "count 4663 4663.000000 4663 4663 4663 4663 \n", "unique 7 559.000000 1 24 2 1 \n", "top attached garage 45.532763 no smoking 9 1 1 \n", "freq 2408 173.000000 4663 545 4528 4663 \n", "\n", " wheelchair housingtype \n", "count 4663 4663 \n", "unique 1 7 \n", "top wheelchair accessible apartment \n", "freq 4663 4417 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dframe = DataFrame(my_dict2)\n", "dframe = dframe.T\n", "dframe = dframe[['content', 'laundry', 'price', 'dog', 'bed', \n", "'bath', 'feet', 'long', 'parking', 'lat', 'smoking', 'getphotos', \n", "'cat', 'hasmap', 'wheelchair', 'housingtype']]\n", "dframe = dframe.dropna()\n", "dframe.describe()" ] }, { "cell_type": "code", "execution_count": 32, "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>content</th>\n", " <th>laundry</th>\n", " <th>price</th>\n", " <th>dog</th>\n", " <th>bed</th>\n", " <th>bath</th>\n", " <th>feet</th>\n", " <th>long</th>\n", " <th>parking</th>\n", " <th>lat</th>\n", " <th>smoking</th>\n", " <th>getphotos</th>\n", " <th>cat</th>\n", " <th>hasmap</th>\n", " <th>wheelchair</th>\n", " <th>housingtype</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223</td>\n", " <td>32223.00000</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " </tr>\n", " <tr>\n", " <th>unique</th>\n", " <td>3873</td>\n", " <td>5</td>\n", " <td>2004</td>\n", " <td>2</td>\n", " <td>9</td>\n", " <td>16</td>\n", " <td>1461.000000</td>\n", " <td>6131.000000</td>\n", " <td>7</td>\n", " <td>6000.00000</td>\n", " <td>2</td>\n", " <td>24</td>\n", " <td>2</td>\n", " <td>2</td>\n", " <td>1</td>\n", " <td>11</td>\n", " </tr>\n", " <tr>\n", " <th>top</th>\n", " <td>967</td>\n", " <td>w/d in unit</td>\n", " <td>995</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>903.863061</td>\n", " <td>-122.631076</td>\n", " <td>off-street parking</td>\n", " <td>45.51843</td>\n", " <td>no smoking</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>no wheelchair access</td>\n", " <td>apartment</td>\n", " </tr>\n", " <tr>\n", " <th>freq</th>\n", " <td>174</td>\n", " <td>21669</td>\n", " <td>678</td>\n", " <td>20511</td>\n", " <td>12459</td>\n", " <td>22550</td>\n", " <td>3764.000000</td>\n", " <td>1829.000000</td>\n", " <td>16284</td>\n", " <td>1829.00000</td>\n", " <td>19621</td>\n", " <td>3224</td>\n", " <td>21850</td>\n", " <td>30434</td>\n", " <td>32223</td>\n", " <td>25285</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " content laundry price dog bed bath feet \\\n", "count 32223 32223 32223 32223 32223 32223 32223.000000 \n", "unique 3873 5 2004 2 9 16 1461.000000 \n", "top 967 w/d in unit 995 1 1 1 903.863061 \n", "freq 174 21669 678 20511 12459 22550 3764.000000 \n", "\n", " long parking lat smoking getphotos \\\n", "count 32223.000000 32223 32223.00000 32223 32223 \n", "unique 6131.000000 7 6000.00000 2 24 \n", "top -122.631076 off-street parking 45.51843 no smoking 8 \n", "freq 1829.000000 16284 1829.00000 19621 3224 \n", "\n", " cat hasmap wheelchair housingtype \n", "count 32223 32223 32223 32223 \n", "unique 2 2 1 11 \n", "top 1 1 no wheelchair access apartment \n", "freq 21850 30434 32223 25285 " ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dframe2 = DataFrame(my_dict2)\n", "dframe2 = dframe2.T\n", "dframe2 = dframe2[['content', 'laundry', 'price', 'dog', 'bed', \n", "'bath', 'feet', 'long', 'parking', 'lat', 'smoking', 'getphotos', \n", "'cat', 'hasmap', 'wheelchair', 'housingtype']]\n", "dframe2.describe()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dframe = pd.get_dummies(dframe2, columns = ['laundry', 'parking', 'smoking', 'wheelchair', 'housingtype'])" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pd.set_option('display.max_columns', 500)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dframe = df" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.cross_validation import train_test_split, ShuffleSplit\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.metrics import accuracy_score as acc" ] }, { "cell_type": "code", "execution_count": 71, "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>content</th>\n", " <th>price</th>\n", " <th>dog</th>\n", " <th>bed</th>\n", " <th>bath</th>\n", " <th>feet</th>\n", " <th>long</th>\n", " <th>lat</th>\n", " <th>getphotos</th>\n", " <th>cat</th>\n", " <th>hasmap</th>\n", " <th>laundry_laundry in bldg</th>\n", " <th>laundry_laundry on site</th>\n", " <th>laundry_no laundry on site</th>\n", " <th>laundry_w/d hookups</th>\n", " <th>laundry_w/d in unit</th>\n", " <th>parking_attached garage</th>\n", " <th>parking_carport</th>\n", " <th>parking_detached garage</th>\n", " <th>parking_no parking</th>\n", " <th>parking_off-street parking</th>\n", " <th>parking_street parking</th>\n", " <th>parking_valet parking</th>\n", " <th>smoking_no smoking</th>\n", " <th>smoking_smoking</th>\n", " <th>wheelchair_no wheelchair access</th>\n", " <th>housingtype_apartment</th>\n", " <th>housingtype_condo</th>\n", " <th>housingtype_cottage/cabin</th>\n", " <th>housingtype_duplex</th>\n", " <th>housingtype_flat</th>\n", " <th>housingtype_house</th>\n", " <th>housingtype_in-law</th>\n", " <th>housingtype_land</th>\n", " <th>housingtype_loft</th>\n", " <th>housingtype_manufactured</th>\n", " <th>housingtype_townhouse</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.00000</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.00000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " <td>32223.000000</td>\n", " </tr>\n", " <tr>\n", " <th>unique</th>\n", " <td>3873</td>\n", " <td>2004</td>\n", " <td>2</td>\n", " <td>9</td>\n", " <td>16</td>\n", " <td>1461.000000</td>\n", " <td>6131.000000</td>\n", " <td>6000.00000</td>\n", " <td>24</td>\n", " <td>2</td>\n", " <td>2</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", " <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>top</th>\n", " <td>967</td>\n", " <td>995</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>1</td>\n", " <td>903.863061</td>\n", " <td>-122.631076</td>\n", " <td>45.51843</td>\n", " <td>8</td>\n", " <td>1</td>\n", " <td>1</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", " <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>freq</th>\n", " <td>174</td>\n", " <td>678</td>\n", " <td>20511</td>\n", " <td>12459</td>\n", " <td>22550</td>\n", " <td>3764.000000</td>\n", " <td>1829.000000</td>\n", " <td>1829.00000</td>\n", " <td>3224</td>\n", " <td>21850</td>\n", " <td>30434</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", " <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>mean</th>\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>0.108277</td>\n", " <td>0.151755</td>\n", " <td>0.006083</td>\n", " <td>0.061416</td>\n", " <td>0.67247</td>\n", " <td>0.198243</td>\n", " <td>0.079664</td>\n", " <td>0.062005</td>\n", " <td>0.013438</td>\n", " <td>0.505353</td>\n", " <td>0.141110</td>\n", " <td>0.000186</td>\n", " <td>0.608913</td>\n", " <td>0.391087</td>\n", " <td>1</td>\n", " <td>0.784688</td>\n", " <td>0.037427</td>\n", " <td>0.001428</td>\n", " <td>0.022717</td>\n", " <td>0.010086</td>\n", " <td>0.097880</td>\n", " <td>0.001583</td>\n", " <td>0.000279</td>\n", " <td>0.004127</td>\n", " <td>0.000497</td>\n", " <td>0.039289</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\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>0.310734</td>\n", " <td>0.358789</td>\n", " <td>0.077755</td>\n", " <td>0.240095</td>\n", " <td>0.46932</td>\n", " <td>0.398683</td>\n", " <td>0.270776</td>\n", " <td>0.241169</td>\n", " <td>0.115141</td>\n", " <td>0.499979</td>\n", " <td>0.348141</td>\n", " <td>0.013645</td>\n", " <td>0.488001</td>\n", " <td>0.488001</td>\n", " <td>0</td>\n", " <td>0.411045</td>\n", " <td>0.189808</td>\n", " <td>0.037757</td>\n", " <td>0.149001</td>\n", " <td>0.099923</td>\n", " <td>0.297157</td>\n", " <td>0.039753</td>\n", " <td>0.016710</td>\n", " <td>0.064114</td>\n", " <td>0.022278</td>\n", " <td>0.194284</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\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>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\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>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.00000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\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>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.00000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\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>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.00000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\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>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.00000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " content price dog bed bath feet long \\\n", "count 32223 32223 32223 32223 32223 32223.000000 32223.000000 \n", "unique 3873 2004 2 9 16 1461.000000 6131.000000 \n", "top 967 995 1 1 1 903.863061 -122.631076 \n", "freq 174 678 20511 12459 22550 3764.000000 1829.000000 \n", "mean NaN NaN NaN NaN NaN NaN NaN \n", "std NaN NaN NaN NaN NaN NaN NaN \n", "min NaN NaN NaN NaN NaN NaN NaN \n", "25% NaN NaN NaN NaN NaN NaN NaN \n", "50% NaN NaN NaN NaN NaN NaN NaN \n", "75% NaN NaN NaN NaN NaN NaN NaN \n", "max NaN NaN NaN NaN NaN NaN NaN \n", "\n", " lat getphotos cat hasmap laundry_laundry in bldg \\\n", "count 32223.00000 32223 32223 32223 32223.000000 \n", "unique 6000.00000 24 2 2 NaN \n", "top 45.51843 8 1 1 NaN \n", "freq 1829.00000 3224 21850 30434 NaN \n", "mean NaN NaN NaN NaN 0.108277 \n", "std NaN NaN NaN NaN 0.310734 \n", "min NaN NaN NaN NaN 0.000000 \n", "25% NaN NaN NaN NaN 0.000000 \n", "50% NaN NaN NaN NaN 0.000000 \n", "75% NaN NaN NaN NaN 0.000000 \n", "max NaN NaN NaN NaN 1.000000 \n", "\n", " laundry_laundry on site laundry_no laundry on site \\\n", "count 32223.000000 32223.000000 \n", "unique NaN NaN \n", "top NaN NaN \n", "freq NaN NaN \n", "mean 0.151755 0.006083 \n", "std 0.358789 0.077755 \n", "min 0.000000 0.000000 \n", "25% 0.000000 0.000000 \n", "50% 0.000000 0.000000 \n", "75% 0.000000 0.000000 \n", "max 1.000000 1.000000 \n", "\n", " laundry_w/d hookups laundry_w/d in unit parking_attached garage \\\n", "count 32223.000000 32223.00000 32223.000000 \n", "unique NaN NaN NaN \n", "top NaN NaN NaN \n", "freq NaN NaN NaN \n", "mean 0.061416 0.67247 0.198243 \n", "std 0.240095 0.46932 0.398683 \n", "min 0.000000 0.00000 0.000000 \n", "25% 0.000000 0.00000 0.000000 \n", "50% 0.000000 1.00000 0.000000 \n", "75% 0.000000 1.00000 0.000000 \n", "max 1.000000 1.00000 1.000000 \n", "\n", " parking_carport parking_detached garage parking_no parking \\\n", "count 32223.000000 32223.000000 32223.000000 \n", "unique NaN NaN NaN \n", "top NaN NaN NaN \n", "freq NaN NaN NaN \n", "mean 0.079664 0.062005 0.013438 \n", "std 0.270776 0.241169 0.115141 \n", "min 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 \n", "75% 0.000000 0.000000 0.000000 \n", "max 1.000000 1.000000 1.000000 \n", "\n", " parking_off-street parking parking_street parking \\\n", "count 32223.000000 32223.000000 \n", "unique NaN NaN \n", "top NaN NaN \n", "freq NaN NaN \n", "mean 0.505353 0.141110 \n", "std 0.499979 0.348141 \n", "min 0.000000 0.000000 \n", "25% 0.000000 0.000000 \n", "50% 1.000000 0.000000 \n", "75% 1.000000 0.000000 \n", "max 1.000000 1.000000 \n", "\n", " parking_valet parking smoking_no smoking smoking_smoking \\\n", "count 32223.000000 32223.000000 32223.000000 \n", "unique NaN NaN NaN \n", "top NaN NaN NaN \n", "freq NaN NaN NaN \n", "mean 0.000186 0.608913 0.391087 \n", "std 0.013645 0.488001 0.488001 \n", "min 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 \n", "50% 0.000000 1.000000 0.000000 \n", "75% 0.000000 1.000000 1.000000 \n", "max 1.000000 1.000000 1.000000 \n", "\n", " wheelchair_no wheelchair access housingtype_apartment \\\n", "count 32223 32223.000000 \n", "unique NaN NaN \n", "top NaN NaN \n", "freq NaN NaN \n", "mean 1 0.784688 \n", "std 0 0.411045 \n", "min 1 0.000000 \n", "25% 1 1.000000 \n", "50% 1 1.000000 \n", "75% 1 1.000000 \n", "max 1 1.000000 \n", "\n", " housingtype_condo housingtype_cottage/cabin housingtype_duplex \\\n", "count 32223.000000 32223.000000 32223.000000 \n", "unique NaN NaN NaN \n", "top NaN NaN NaN \n", "freq NaN NaN NaN \n", "mean 0.037427 0.001428 0.022717 \n", "std 0.189808 0.037757 0.149001 \n", "min 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 \n", "75% 0.000000 0.000000 0.000000 \n", "max 1.000000 1.000000 1.000000 \n", "\n", " housingtype_flat housingtype_house housingtype_in-law \\\n", "count 32223.000000 32223.000000 32223.000000 \n", "unique NaN NaN NaN \n", "top NaN NaN NaN \n", "freq NaN NaN NaN \n", "mean 0.010086 0.097880 0.001583 \n", "std 0.099923 0.297157 0.039753 \n", "min 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 \n", "75% 0.000000 0.000000 0.000000 \n", "max 1.000000 1.000000 1.000000 \n", "\n", " housingtype_land housingtype_loft housingtype_manufactured \\\n", "count 32223.000000 32223.000000 32223.000000 \n", "unique NaN NaN NaN \n", "top NaN NaN NaN \n", "freq NaN NaN NaN \n", "mean 0.000279 0.004127 0.000497 \n", "std 0.016710 0.064114 0.022278 \n", "min 0.000000 0.000000 0.000000 \n", "25% 0.000000 0.000000 0.000000 \n", "50% 0.000000 0.000000 0.000000 \n", "75% 0.000000 0.000000 0.000000 \n", "max 1.000000 1.000000 1.000000 \n", "\n", " housingtype_townhouse \n", "count 32223.000000 \n", "unique NaN \n", "top NaN \n", "freq NaN \n", "mean 0.039289 \n", "std 0.194284 \n", "min 0.000000 \n", "25% 0.000000 \n", "50% 0.000000 \n", "75% 0.000000 \n", "max 1.000000 " ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dframe.describe(include='all')" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.754005915225\n" ] } ], "source": [ "from sklearn.cross_validation import train_test_split\n", "# X_train, X_test, y_train, y_test = train_test_split(\n", "# dframe.drop('price', axis = 1), dframe.price, test_size=0.33)\n", "# print X_train.shape\n", "# print y_train.shape\n", "\n", "from sklearn.ensemble import RandomForestRegressor\n", "reg = RandomForestRegressor()\n", "reg.fit(X_train, y_train)\n", "print reg.score(X_test, y_test)" ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "ename": "ValueError", "evalue": "Can't handle mix of continuous and unknown", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-69-94020e93119e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mscore\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0macc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_test\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 15\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscore\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/mac28/anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.pyc\u001b[0m in \u001b[0;36maccuracy_score\u001b[0;34m(y_true, y_pred, normalize, sample_weight)\u001b[0m\n\u001b[1;32m 177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 178\u001b[0m \u001b[0;31m# Compute accuracy for each possible representation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 179\u001b[0;31m \u001b[0my_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_check_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 180\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0my_type\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'multilabel'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 181\u001b[0m \u001b[0mdiffering_labels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcount_nonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/Users/mac28/anaconda/lib/python2.7/site-packages/sklearn/metrics/classification.pyc\u001b[0m in \u001b[0;36m_check_targets\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m 82\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_type\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 83\u001b[0m raise ValueError(\"Can't handle mix of {0} and {1}\"\n\u001b[0;32m---> 84\u001b[0;31m \"\".format(type_true, type_pred))\n\u001b[0m\u001b[1;32m 85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 86\u001b[0m \u001b[0;31m# We can't have more than one value on y_type => The set is no more needed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Can't handle mix of continuous and unknown" ] } ], "source": [ "scores = []\n", "for thous in range(1000,len(dframe),1000):\n", " temp_dframe = dframe[:thous]\n", " X_train, X_test, y_train, y_test = train_test_split(\n", " temp_dframe.drop('price', axis = 1), temp_dframe.price, test_size=0.33)\n", "\n", " reg = RandomForestRegressor()\n", " reg.fit(X_train,y_train)\n", " pred = reg.predict(X_test)\n", "# pred = [float(x) for x in pred]\n", "# y_test = [float(x) for x in y_test]\n", "\n", "\n", " score = acc(pred, np.array(y_test))\n", " scores.append(score)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[0.73038945182127368,\n", " 0.76783412576815901,\n", " 0.72333012996097401,\n", " 0.63272085317437254,\n", " 0.71341161605529479,\n", " 0.67656391098594792,\n", " 0.80613678534851196,\n", " 0.82341574908025217,\n", " 0.82427209375950294,\n", " 0.81218132714823654,\n", " 0.82160609258065909,\n", " 0.7331384325788064,\n", " 0.80666799343482587,\n", " 0.66936839485506483,\n", " 0.70857552047088457,\n", " 0.62080223594763995,\n", " 0.78484023199495745,\n", " -6.3530665769874979,\n", " 0.65042422484048446,\n", " -15.781387798716221,\n", " -22484.253861126595,\n", " -0.00057525776928479821,\n", " -0.036980996491301932,\n", " -13793.067898631314,\n", " -0.029488864192833031,\n", " -0.10251644550537442,\n", " -0.075238720156028505,\n", " -0.065372801021961857,\n", " -0.71309465414734552,\n", " -0.060054368247673739,\n", " -0.071102196392859085,\n", " -0.78642652087412457]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[<matplotlib.lines.Line2D at 0x11bf4fb50>]" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ZwNPAx81sEbDU3RcDVwA3hseuBm5392XAI8AnzSwJXA+cB5wNfMLMOqutUUQm\nVnHIK51KEI/Hxjlamk0tQ16bgWtGtZ3t7jvDz5NAP7AE2Ajg7s8DCTObE7ZvCI+9myBE3gg85e7d\n7j4I3A8sraFGEZlAxRPxWhiyNY3bJzWzq4BVQIGgN1IgGJ6608yWlR7r7i+H33MZQQ/jL4DPADtL\nDusBpgNZYO9h2krbRWQSKA556ZLh1jTuq+7u64B15d6hmf1X4H3ABe4+YGbdBEFR1AHsBortufDj\nnrCto+TYYvthdXZmxzukoan++lL90Tl63wAAHVPTZdXVSLVXY7LXH7VI30aY2Z8Di4Dz3D0XNm8G\nrjWz64D5QMzdd5nZZuBi4DbgImAT8ARwvJnNAHoJhru+ON7jdnX1RPljTKjOzqzqryPVH61cbxAo\nyfj4/y8brfZKNUP9UYssUMxsLsGJ9l8AG8ysAHzb3f/ezO4HHiAYMlsZfstaYL2ZrSAYErvS3fNm\n9mmCcy4x4BZ3fymqGkXkyGrPhOdQMlPqXInUQ6xQKNS7hloVJvu7BNVfP6o/ehsefA47dgYLju44\n7HGNWHslmqD+yC/D05kzEYnUhYuPrXcJUieaKS8iIpFQoIiISCQUKCIiEgkFioiIREKBIiIikVCg\niIhIJBQoIiISCQWKiIhEQoEiIiKRUKCIiEgkFCgiIhIJBYqIiERCgSIiIpFQoIiISCQUKCIiEgkF\nioiIREKBIiIikVCgiIhIJGraAtjM3gu8390/OKr9z4CT3f2K8PZq4BJgEFjl7lvMbDZwB5ABtgHL\n3b3fzC4FPhce+zV3v6WWGkVEZGJU3UMxsxuAtUBsVPtFwMVAIby9CFjq7ouBK4Abw0NXA7e7+zLg\nEeCTZpYErgfOA84GPmFmndXWKCIiE6eWIa/NwDWlDWZ2HLCCICyKlgAbAdz9eSBhZnPC9g3hMXcT\nhMgbgafcvdvdB4H7gaU11CgiIhNk3CEvM7sKWEXQ44iFH5e7+51mtqzkuKkEvY8PA28quYsOYGfJ\n7R5gOpAF9h6mrbRdREQa3LiB4u7rgHVl3Nc7gXnAt4GZwNFm9qcEAZEtOa4D2A10h+258OOesK2j\n5Nhiu4iINLiaTsqXcve7gLsAwp7LJ939r83sNOBaM7sOmA/E3H2XmW0mONdyG3ARsAl4AjjezGYA\nvQTDXV8c56FjnZ3ZcQ5pbKq/vlR//Uzm2mHy1x+1I37ZsLs/RBAWDwB3AivDL60FrjCzTcAZwFfc\nPQ98muBoFQp0AAAEO0lEQVScy2bgFnd/6UjXKCIitYsVCoV61yAiIk1AExtFRCQSChQREYmEAkVE\nRCKhQBERkUhEdtnwRDOzGPBV4BSgH/i4uz9T36r2M7NfsH+S5u+ALwC3AsPAY+6+MjxuBfAJgrXL\n1rr7D80sA3wDmEswN+cj7v7KBNW9GPjf7v6OcOWDmmo2szOAG8Jj73X3NRNY/6nAD4Anwy/fFE7I\nbbj6w2WH1gGvB1IEV0E+ziR5/g9R//NMnuc/DtwMGMHzfTXBHLlbafDn/xC1p6jDcz+ZeyjvAdLu\n/jbgswRrgDUEM0sDuPs54b+PEdT3Z+HaZXEz+30zmwf8EXAmcCHwV2Y2hWBJm1+5+1Lg6wSLZU5E\n3Z8h+MVMh01R1HwTcLm7nwUsNrNTJrD+04HrSl6HOxu4/g8BO8PHvxD4CpPr+S+t/6Kw/tOYPM//\npUDB3ZeEj/0FJs/zP1btdfndn8yBMrIWmLs/CLy5vuUc4BRgqpndY2Y/Ct81n+bum8Kv3w2cD7wV\nuN/d8+7eDTwVfu9Y65xNhKeB95bcPr2Gms81syyQcvetYfs9HNmf5aD6gUvM7D4zu9nMpjVw/d9h\n/3/kBJCntt+ZetYfJ3hXezrwrsnw/Lv7PxG8cwd4HcFqHpPi+R9V++vD2uvy3E/mQOngwHW/8mHX\nrxH0Al909wsI0v92DlyVuYeg/tFrl+1j7HXOSpejOWLC1Q7yJU211Fxs6x51H0dsbbYx6n8Q+Ez4\nDvMZ4C85+PemIep39153fzX8j3wn8OdMoud/jPr/AvgP4E8mw/Mf/gzDZnYr8LcEW2tMpue/WPuX\nCP7ePEgdnvtG+QNcjeJaYEVxdx+uVzGjPEnwouLuTwGvEKxzVnS4tctK1zkrPbYeSp/PamoeHYYT\n/bN8z90fLn4OnErwH6ch6zez+cCPgfXu/i0m2fM/Rv2T6vkHcPePAm8AbgHaxnjshn3+R9W+sR7P\n/WQOlOJaYIQnjx6tbzkHuAq4DsDMXkPwwmwsWZ25uHbZFmCJmaXMbDpwIvAY8G+EP1v4cRP18ZCZ\nFbcPqLhmd+8Bcma2ILyI4gIm9me5x8yKQ6HnAr9o1PrD8e17gD919/Vh88OT5fk/RP2T6fn/kJn9\nj/BmPzAE/LyW/7MTVf8YtQ8D3zWzt4RtE/bcT9qrvAgWojzfgkUmAZbXs5hR/i/wNQvWKRsGPkrQ\nS7klPAn2G+Af3b1gZn9LsO9LjOAE4ICZ3QSsD78/B1xZjx8C+BPg5hprvppg+CBO8K5pywTWfw3w\nZTMbALYDn3D3fQ1a/2eBGcDnLNjhtAB8Kqx/Mjz/Y9W/Crhhkjz/3yX4P3sfwd/FPyZYrLbW/7MT\nUf/o2j9FcIXdVyb6uddaXiIiEonJPOQlIiINRIEiIiKRUKCIiEgkFCgiIhIJBYqIiERCgSIiIpFQ\noIiISCQUKCIiEon/DxeVGRO6cbwkAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x119350190>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline\n", "\n", "plt.plot(range(1000,len(dframe),1000),scores)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def listing_cleaner(entry):\n", " print entry\n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df = pd.DataFrame({'A': ['a', 'b', 'a'], 'B': ['b', 'a', 'c'],\n", " 'C': [1, 2, 3]})\n", "df" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pd.get_dummies(df, columns=['A','C'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "listing_cleaner(my_dict['5465197037'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "type(dframe['bath']['5399866740'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Clean up the data a bit\n", "Right now the 'shared' and 'split' are included in number of bathrooms. If I were to convert that to a number I would consider a shared/split bathroom to be half or 0.5 of a bathroom." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe.bath = dframe.bath.replace('shared',0.5)\n", "dframe.bath = dframe.bath.replace('split',0.5)\n", "dframe.smoking = dframe.smoking.replace(np.nan, 'smoking')\n", "dframe.furnished = dframe.furnished.replace(np.nan,'not furnished')\n", "dframe.wheelchair = dframe.wheelchair.replace(np.nan, 'not wheelchair accessible')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe.describe()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe.bed.unique()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.preprocessing import Imputer, LabelEncoder" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def meanimputer(column):\n", " imp = Imputer(missing_values='NaN', strategy='mean', axis=1)\n", " imp.fit(column)\n", " X = imp.transform(column)\n", " return X[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arr = np.array([np.nan, 'house', 'boat', 'houseboat', 'house', np.nan, 'house','houseboat'])\n", "prac_df = DataFrame()\n", "prac_df['arr'] = arr\n", "prac_df['arr']\n", "modeimputer(prac_df['arr'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "def modeimputer(column):\n", "\n", " le = LabelEncoder()\n", " column = le.fit_transform(column)\n", " print le.classes_\n", " print type(le.classes_[0])\n", " print column\n", " nan = le.transform([np.nan])[0]\n", " print nan\n", " print type(column)\n", " column = list(column)\n", " for _,i in enumerate(column):\n", " if i == nan:\n", " column[_] = np.nan\n", " \n", " imp = Imputer(missing_values='NaN', strategy='most_frequent', axis=1)\n", " imp.fit(column)\n", "\n", " X = imp.transform(column)\n", " \n", " for _,i in enumerate(X[0]):\n", " if np.isnan(i):\n", " X[_] = 0\n", " X = X.astype(int)\n", "\n", "\n", " Y = le.inverse_transform(X)\n", "\n", " return Y" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "from sklearn.base import TransformerMixin\n", "class ModeImputer(TransformerMixin):\n", "\n", " def __init__(self):\n", " \"\"\"Impute missing values.\n", "\n", " Columns of dtype object are imputed with the most frequent value \n", " in column.\n", "\n", " Columns of other types are imputed with mean of column.\n", " \n", " Credit:http://stackoverflow.com/questions/25239958/\n", " impute-categorical-missing-values-in-scikit-learn\n", "\n", " \"\"\"\n", " def fit(self, X, y=None):\n", "\n", " self.fill = pd.Series([X[c].value_counts().index[0]\n", " if X[c].dtype == np.dtype('O') else X[c].mean() for c in X],\n", " index=X.columns)\n", "\n", " return self\n", "\n", " def transform(self, X, y=None):\n", " return X.fillna(self.fill)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = [\n", " ['a', 1, 2],\n", " ['b', 1, 1],\n", " ['b', 2, 2],\n", " [np.nan, np.nan, np.nan]\n", "]\n", "\n", "X = pd.DataFrame(data)\n", "xt = ModeImputer().fit_transform(X)\n", "\n", "print('before...')\n", "print(X)\n", "print('after...')\n", "print(xt)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe = ModeImputer().fit_transform(dframe)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe.describe(include = 'all')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe.bed.mean()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe.parking.unique()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "u_dframe = DataFrame()\n", "dframe['bath'] = meanimputer(dframe['bath'])\n", "dframe['bed'] = meanimputer(dframe['bed'])\n", "dframe['feet'] = meanimputer(dframe['feet'])\n", "dframe['lat'] = meanimputer(dframe['lat'])\n", "dframe['long'] = meanimputer(dframe['long'])\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe.head()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe.describe(include='all')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = dframe[dframe.lat > 45.4][dframe.lat < 45.6][dframe.long < -122.0][dframe.long > -123.5]\n", "plt.figure(figsize=(15,10))\n", "plt.scatter(data = data, x = 'long',y='lat')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### It looks like Portland!!!\n", "Let's cluster the data. Start by creating a list of [['lat','long'], ...]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "XYdf = dframe[dframe.lat > 45.4][dframe.lat < 45.6][dframe.long < -122.0][dframe.long > -123.5]\n", "data = [[XYdf['lat'][i],XYdf['long'][i]] for i in XYdf.index]\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use K Means Clustering because that's the clustering method I recently learned in class! There may be others that work better, but this is the tool that I know" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.cluster import KMeans\n", "km = KMeans(n_clusters=40)\n", "km.fit(data)\n", "neighborhoods = km.cluster_centers_\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%pylab inline\n", "figure(1,figsize=(20,12))\n", "plot([row[1] for row in data],[row[0] for row in data],'b.')\n", "for i in km.cluster_centers_: \n", " plot(i[1],i[0], 'g*',ms=25)\n", "'''Note to Riley: come back and make it look pretty'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### We chose our neighborhoods!\n", "I've found that every once in a while the centers end up in different points, but are fairly consistant. Now let's process our data points and figure out where the closest neighborhood center is to it!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "neighborhoods = neighborhoods.tolist()\n", "for i in enumerate(neighborhoods):\n", " i[1].append(i[0])\n", "print neighborhoods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a function that will label each point with a number coresponding to it's neighborhood" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def clusterer(X, Y,neighborhoods):\n", " neighbors = []\n", " for i in neighborhoods:\n", " distance = ((i[0]-X)**2 + (i[1]-Y)**2)\n", " neighbors.append(distance)\n", " closest = min(neighbors)\n", " return neighbors.index(closest)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "neighborhoodlist = []\n", "for i in dframe.index:\n", " neighborhoodlist.append(clusterer(dframe['lat'][i],dframe['long'][i],neighborhoods))\n", "dframe['neighborhood'] = neighborhoodlist\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Here's the new Part. We're breaking out the neighborhood values into their own columns. Now the algorithms can read them as categorical data rather than continuous data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn import preprocessing\n", "def CategoricalToBinary(dframe,column_name):\n", " le = preprocessing.LabelEncoder()\n", " listy = le.fit_transform(dframe[column_name])\n", " dframe[column_name] = listy\n", " unique = dframe[column_name].unique()\n", " serieslist = [list() for _ in xrange(len(unique))]\n", " \n", " \n", " for column, _ in enumerate(serieslist):\n", " for i, item in enumerate(dframe[column_name]):\n", " if item == column:\n", " serieslist[column].append(1)\n", " else:\n", " serieslist[column].append(0)\n", " dframe[column_name+str(column)] = serieslist[column]\n", "\n", " \n", " return dframe\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pd.set_option('max_columns', 100)\n", "dframe = CategoricalToBinary(dframe,'housingtype')\n", "dframe = CategoricalToBinary(dframe,'parking')\n", "dframe = CategoricalToBinary(dframe,'laundry')\n", "dframe = CategoricalToBinary(dframe,'smoking')\n", "dframe = CategoricalToBinary(dframe,'wheelchair')\n", "dframe = CategoricalToBinary(dframe,'neighborhood')\n", "dframe\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe = dframe.drop('date',1)\n", "dframe = dframe.drop('housingtype',1)\n", "dframe = dframe.drop('parking',1)\n", "dframe = dframe.drop('laundry',1)\n", "dframe = dframe.drop('smoking',1)\n", "dframe = dframe.drop('wheelchair',1)\n", "dframe = dframe.drop('neighborhood',1)\n", "dframe = dframe.drop('time',1)\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "columns=list(dframe.columns)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division\n", "print len(dframe)\n", "df2 = dframe[dframe.price < 10000][columns].dropna()\n", "print len(df2)\n", "print len(df2)/len(dframe)\n", "\n", "price = df2[['price']].values\n", "columns.pop(columns.index('price'))\n", "features = df2[columns].values\n", "\n", "from sklearn.cross_validation import train_test_split\n", "features_train, features_test, price_train, price_test = train_test_split(features, price, test_size=0.1, random_state=42)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, lets put it through Decision Tree!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What about Random Forest?" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.metrics import r2_score\n", "reg = RandomForestRegressor()\n", "reg = reg.fit(features_train, price_train)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "forest_pred = reg.predict(features_test)\n", "forest_pred = np.array([[item] for item in forest_pred])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print r2_score(forest_pred, price_test)\n", "plt.scatter(forest_pred,price_test)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df2['predictions'] = reg.predict(df2[columns])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df2['predictions_diff'] = df2['predictions']-df2['price']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sd = np.std(df2['predictions_diff'])\n", "sns.kdeplot(df2['predictions_diff'][df2['predictions_diff']>-150][df2['predictions_diff']<150])\n", "sns.plt.xlim(-150,150)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = df2[dframe.lat > 45.45][df2.lat < 45.6][df2.long < -122.4][df2.long > -122.8][df2['predictions_diff']>-150][df2['predictions_diff']<150]\n", "plt.figure(figsize=(15,10))\n", "plt.scatter(data = data, x = 'long',y='lat', c = 'predictions_diff',s=10,cmap='coolwarm')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "dframe" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print np.mean([1,2,34,np.nan])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def averager(dframe):\n", " dframe = dframe.T\n", " dframe.dropna()\n", " averages = {}\n", " for listing in dframe:\n", " try:\n", " key = str(dframe[listing]['bed'])+','+str(dframe[listing]['bath'])+','+str(dframe[listing]['neighborhood'])+','+str(dframe[listing]['feet']-dframe[listing]['feet']%50)\n", " if key not in averages:\n", " averages[key] = {'average_list':[dframe[listing]['price']], 'average':0}\n", " elif key in averages:\n", " averages[key]['average_list'].append(dframe[listing]['price'])\n", " except TypeError:\n", " continue\n", " for entry in averages:\n", " averages[entry]['average'] = np.mean(averages[entry]['average_list'])\n", " return averages\n", " \n", " \n", " \n", " \n", " " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "averages = averager(dframe)\n", "print averages" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe['averages']= averages[str(dframe['bed'])+','+str(dframe['bath'])+','+str(dframe['neighborhood'])+','+str(dframe['feet']-dframe['feet']%50)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dframe.T\n" ] }, { "cell_type": "raw", "metadata": {}, "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Wow! up to .87! That's our best yet! What if we add more trees???" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "reg = RandomForestRegressor(n_estimators = 100)\n", "reg = reg.fit(features_train, price_train)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "forest_pred = reg.predict(features_test)\n", "forest_pred = np.array([[item] for item in forest_pred])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print r2_score(forest_pred, price_test)\n", "print plt.scatter(pred,price_test)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.tree import DecisionTreeRegressor\n", "reg = DecisionTreeRegressor(max_depth = 5)\n", "reg.fit(features_train, price_train)\n", "print len(features_train[0])\n", "columns = [str(x) for x in columns]\n", "print columns\n", "from sklearn.tree import export_graphviz\n", "export_graphviz(reg,feature_names=columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Up to .88!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So what is our goal now? I'd like to see if adjusting the number of neighborhoods increases the accuracy. same for the affect with the number of trees" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def neighborhood_optimizer(dframe,neighborhood_number_range, counter_num):\n", " XYdf = dframe[dframe.lat > 45.4][dframe.lat < 45.6][dframe.long < -122.0][dframe.long > -123.5]\n", " data = [[XYdf['lat'][i],XYdf['long'][i]] for i in XYdf.index]\n", " r2_dict = []\n", " for i in neighborhood_number_range:\n", " counter = counter_num\n", " average_accuracy_list = []\n", " while counter > 0:\n", " km = KMeans(n_clusters=i)\n", " km.fit(data)\n", " neighborhoods = km.cluster_centers_\n", " neighborhoods = neighborhoods.tolist()\n", " for x in enumerate(neighborhoods):\n", " x[1].append(x[0])\n", " neighborhoodlist = []\n", " for z in dframe.index:\n", " neighborhoodlist.append(clusterer(dframe['lat'][z],dframe['long'][z],neighborhoods))\n", " dframecopy = dframe.copy()\n", " dframecopy['neighborhood'] = Series((neighborhoodlist), index=dframe.index)\n", " df2 = dframecopy[dframe.price < 10000][['bath','bed','feet','dog','cat','content','getphotos', 'hasmap', 'price','neighborhood']].dropna()\n", " features = df2[['bath','bed','feet','dog','cat','content','getphotos', 'hasmap', 'neighborhood']].values\n", " price = df2[['price']].values\n", " features_train, features_test, price_train, price_test = train_test_split(features, price, test_size=0.1)\n", " reg = RandomForestRegressor()\n", " reg = reg.fit(features_train, price_train)\n", " forest_pred = reg.predict(features_test)\n", " forest_pred = np.array([[item] for item in forest_pred])\n", " counter -= 1\n", " average_accuracy_list.append(r2_score(forest_pred, price_test))\n", " total = 0\n", " for entry in average_accuracy_list:\n", " total += entry\n", " r2_accuracy = total/len(average_accuracy_list)\n", " r2_dict.append((i,r2_accuracy))\n", " print r2_dict\n", " return r2_dict" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "neighborhood_number_range = [i for _,i in enumerate(range(2,31,2))]\n", "neighborhood_number_range" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "r2_dict = neighborhood_optimizer(dframe,neighborhood_number_range,10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "r2_dict[:][0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.scatter([x[0] for x in r2_dict],[x[1] for x in r2_dict])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like the optimum is right around 10 or 11, and then starts to drop off. Let's get a little more granular and look at a smaller range" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "neighborhood_number_range = [i for _,i in enumerate(range(7,15))]\n", "neighborhood_number_range" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "r2_dict = neighborhood_optimizer(dframe,neighborhood_number_range,10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print r2_dict\n", "plt.scatter([x[0] for x in r2_dict],[x[1] for x in r2_dict])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Trying a few times, it looks like 10, 11 and 12 get the best results at ~.85. Of course, we'll need to redo some of these optomizations after we properly process our data. Hopefully we'll see some more consistency then too." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "r2_dict = neighborhood_optimizer(dframe,[10,11,12],25)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note #1 to Riley: (From Last time) Perhaps look into another regressor? see if there's one that's inherantly better at this kind of thing.\n", "\n", "Note #2 to Riley: Figure out how to process data so that you don't have to drop null values\n", "\n", "Note #3 to Riley: convert categorical data into binary\n", "\n", "Note #4 to Riley: I wonder if increasing the number of neighborhoods would become more accurate as we collect more data? like you could create a bunch of little accurate models instead of a bunch of bigger ones.\n", "\n", "Learned: If you plan on using Decision Tree/Random Forest from SKLearn, make sure you collect your discrete variables in separate columns and make them binary yes or no(0 or 1)." ] } ], "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
clarkchen/leetcode_python
algorithms/Container With Most Water/scrips.ipynb
1
12709
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 暴力搜索版本，超时" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Solution(object):\n", " def maxArea(self, height):\n", " \"\"\"\n", " :type height: List[int]\n", " :rtype: int\n", " \"\"\"\n", " max_area = 0\n", " for m, a_m in enumerate(height):\n", " for n, a_n in enumerate(height):\n", " if n==m: continue\n", " temp = abs(m-n)*min(a_m, a_n)\n", " if temp>max_area:\n", " max_area = temp\n", " return max_area" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s = Solution()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.maxArea([1,1])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f= open(\"demo.json\", \"r\")\n", "demo_input = json.loads(f.read())" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "([1, 2, 3, 4, 5], 15000)" ] }, "execution_count": 117, "metadata": {}, "output_type": "execute_result" } ], "source": [ "demo_input[:5],len(demo_input)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(1, 4)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.maxArea([1,2]),s.maxArea([2,2,2]), " ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 9 µs, sys: 1 µs, total: 10 µs\n", "Wall time: 13.1 µs\n" ] }, { "data": { "text/plain": [ "1" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time s.maxArea([1,2])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6.66 s, sys: 7.59 ms, total: 6.66 s\n", "Wall time: 6.68 s\n" ] }, { "data": { "text/plain": [ "4913370" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time s.maxArea(demo_input)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 剪枝1" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Solution(object):\n", " def maxArea(self, height):\n", " \"\"\"\n", " :type height: List[int]\n", " :rtype: int\n", " \"\"\"\n", " max_area = 0\n", " \n", " for m, a_m in enumerate(height):\n", " for n in range(m+1, len(height)):\n", " a_n = height[n]\n", " temp = abs(m-n)*min(a_m, a_n)\n", " if temp>max_area:\n", " max_area = temp\n", " return max_area" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s = Solution()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3.22 s, sys: 3.48 ms, total: 3.22 s\n", "Wall time: 3.22 s\n" ] }, { "data": { "text/plain": [ "4913370" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time s.maxArea(demo_input)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 剪枝2\n", "[新思路](https://discuss.leetcode.com/topic/3462/yet-another-way-to-see-what-happens-in-the-o-n-algorithm)\n", "\n", "应该怎么办\n", "\n", "花了半个小时思考下能否使用动态规划的方法来解决这道题目，发现无法证明\n", "\n", "所有转向了剪枝\n", "\n", "问题变为了 max(abs(m-n)*min(a[m],a[n]))\n", "\n", "一个数组二维搜索问题，最直观的方式当时是通过矩阵搜索剪枝来完成这个任务\n", "\n", "矩阵的每一个元素表示以行和列做下标的两条线所能承受的最大水量\n" ] }, { "cell_type": "code", "execution_count": 162, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Solution(object):\n", " def maxArea(self, height):\n", " \"\"\"\n", " :type height: List[int]\n", " :rtype: int\n", " \"\"\"\n", " max_area = 0\n", " # 初始化矩阵\n", " x = 0\n", " y = len(height)-1\n", " ret = []\n", " cal =0\n", " while x!=y:\n", " if height[x]>height[y]:\n", " ret.append(abs(y-x)*height[y])\n", " y-=1\n", " elif height[x]<=height[y]:\n", " ret.append(abs(y-x)*height[x])\n", " x+=1\n", " cal+=1\n", " print cal\n", " max_area = max(ret)\n", " return max_area" ] }, { "cell_type": "code", "execution_count": 163, "metadata": { "collapsed": true }, "outputs": [], "source": [ "s = Solution()\n" ] }, { "cell_type": "code", "execution_count": 164, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14999\n", "CPU times: user 6.25 ms, sys: 933 µs, total: 7.18 ms\n", "Wall time: 6.37 ms\n" ] }, { "data": { "text/plain": [ "56250000" ] }, "execution_count": 164, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time s.maxArea(demo_input)" ] }, { "cell_type": "code", "execution_count": 148, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] }, { "data": { "text/plain": [ "(1,)" ] }, "execution_count": 148, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.maxArea([1,2])," ] }, { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 142, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.maxArea([2,1])" ] }, { "cell_type": "code", "execution_count": 135, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 135, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.maxArea([2,2,2]) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 疑问，类似的思路，为什么会超时" ] }, { "cell_type": "code", "execution_count": 200, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def test4(m=10000):\n", " for i in iter(range(m)):\n", " for j in iter(range(m)): break " ] }, { "cell_type": "code", "execution_count": 201, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def test1(m=10000):\n", " for i in range(m):\n", " for j in range(m): break \n", "def test3(m=10000):\n", " i=j=0\n", " while i<m:\n", " i+=1\n", " j=0\n", " while j<m: \n", " j+=1\n", " break \n", "def test2(m=10000):\n", " for i in range(m):\n", " break\n", " " ] }, { "cell_type": "code", "execution_count": 202, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 267 µs, sys: 3 µs, total: 270 µs\n", "Wall time: 271 µs\n", "CPU times: user 1.44 ms, sys: 345 µs, total: 1.78 ms\n", "Wall time: 1.51 ms\n", "CPU times: user 1.18 s, sys: 5.82 ms, total: 1.19 s\n", "Wall time: 1.21 s\n", "CPU times: user 1.2 s, sys: 8.05 ms, total: 1.2 s\n", "Wall time: 1.21 s\n" ] } ], "source": [ "%time test2()\n", "%time test3()\n", "%time test1()\n", "%time test4()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "通过以上例子说明一个问题，python range的方式来进行两重迭代是要动态生成 list的\n", "\n", "所以下面的代码虽然和正确代码表达的是一个疑似，但是执行效率完全不是一个数量级" ] }, { "cell_type": "code", "execution_count": 160, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Solution(object):\n", " def maxArea(self, height):\n", " \"\"\"\n", " :type height: List[int]\n", " :rtype: int\n", " \"\"\"\n", " max_area = 0\n", " # 初始化矩阵\n", " len_h = len(height)\n", " len_v = len(height)\n", "\n", " ret = []\n", " cal = 0\n", " for m in range(0, len_h):\n", " for n in range(len_v-1, m,-1):\n", " cal+=1\n", " if height[m]>=height[n]:\n", " len_v-=1\n", " ret.append((n-m)*height[n])\n", " elif height[m]<height[n]:\n", " ret.append((n-m)*height[m])\n", " break\n", " max_area = max(ret)\n", " print len(ret), cal\n", " return max_area" ] }, { "cell_type": "code", "execution_count": 183, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14999 14999\n", "CPU times: user 7.48 ms, sys: 980 µs, total: 8.46 ms\n", "Wall time: 7.62 ms\n" ] }, { "data": { "text/plain": [ "56250000" ] }, "execution_count": 183, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = Solution()\n", "%time s.maxArea(demo_input)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [default]", "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 }
apache-2.0
edwarddh101/IMDB_ABC
code/IMDbpy Exploration.ipynb
2
32907
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import imdb" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "imdb_access = imdb.IMDb()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<Company id:0000001[http] name:_Panorama Video_>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "imdb_access.get_company(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "i = imdb.IMDb()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# movie_list is a list of Movie objects, with only attributes like 'title'\n", "# and 'year' defined.\n", "movie_list = i.search_movie('the passion')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# the first movie in the list.\n", "first_match = movie_list[0]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Movie\n", "=====\n", "Title: Passion of the Christ, The (2004)\n", "\n" ] } ], "source": [ "# only basic information like the title will be printed.\n", "print first_match.summary()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# update the information for this movie.\n", "i.update(first_match)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Movie\n", "=====\n", "Title: Passion of the Christ, The (2004)\n", "Genres: Drama.\n", "Director: Mel Gibson.\n", "Writer: Benedict Fitzgerald, Mel Gibson.\n", "Cast: Jim Caviezel (Jesus), Maia Morgenstern (Mary), Christo Jivkov (John), Francesco De Vito (Peter), Monica Bellucci (Magdalen).\n", "Runtime: 127, 120::(cut).\n", "Country: USA.\n", "Language: Aramaic, Latin, Hebrew.\n", "Rating: 7.1 (176909 votes).\n", "Plot: A depiction of the last twelve hours in the life of Jesus of Nazareth, on the day of his crucifixion in Jerusalem. The story opens in the Garden of Olives where Jesus has gone to pray after the Last Supper. Betrayed by Judas Iscariot, the controversial Jesus--who has performed 'miracles' and has publicly announced that he is 'the Son of God'--is arrested and taken back within the city walls of Jerusalem. There, the leaders of the Pharisees confront him with accusations of blasphemy; subsequently, his trial results with the leaders condemning him to his death. Jesus is brought before Pontius Pilate, the Roman Governor of Palestine, for his sentencing. Pilate listens to the accusations leveled at Jesus by the Pharisees. Realizing that his own decision will cause him to become embroiled in a political conflict, Pilate defers to King Herod in deciding the matter of how to persecute Jesus. However, Herod returns Jesus to Pilate who, in turn, gives the crowd a choice between which prisoner they would rather to see set free--Jesus, or Barrabas. The crowd chooses to have Barrabas set free. Thus, Jesus is handed over to the Roman soldiers and is brutally flagellated. Bloody and unrecognizable, he is brought back before Pilate who, once again, presents him to the thirsty crowd--assuming they will see that Jesus has been punished enough. The crowd, however, is not satisfied. Thus, Pilate washes his hands of the entire dilemma, ordering his men to do as the crowd wishes. Whipped and weakened, Jesus is presented with the cross and is ordered to carry it through the streets of Jerusalem, all the way up to Golgotha. There, more corporal cruelty takes place as Jesus is nailed to the cross--suffering, he hangs there, left to die. Initially, in his dazed suffering, Jesus is alarmed that he has been abandoned by God his father. He then beseeches God. At the moment of his death, nature itself over-turns.\n" ] } ], "source": [ "# a lot of information will be printed!\n", "print first_match.summary()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[u'In an interview with Newsweek magazine, Jim Caviezel spoke about a few of the difficulties he experienced while filming. This included being accidentally whipped twice, which has left a 14-inch scar on his back. Caviezel also admitted he was struck by lightning while filming the Sermon on the Mount and during the crucifixion, experienced hypothermia during the dead of winter in Italy.', u'Jim Caviezel experienced a shoulder separation when the 150lb cross dropped on his shoulder. The scene is still in the movie.', u'This film had more pre-ticket sales than any other film in history until Star Wars: The Force Awakens (2015) presales beat it.', u'This is the highest grossing foreign language film and/or subtitled film in US box office history. It is also the highest grossing religious film in worldwide box office of all time.', u'Jim Caviezel required heart surgery after the completion of The Passion, following the extreme stress in the making of this film and the several accidental injuries received.', u\"During the scourging scene, Jim Caviezel accidentally got whipped twice. The first time knocked the wind out of him, and the second time hurt so much it caused him wrench his hand quickly from his shackles, scraping his wrist badly. The remainder of the scourging scenes were finished by using visual effects: the actors playing Roman soldiers held sticks without the leather tails, and acted out the whipping motion, while Caviezel would react as if hit. The tails were later digitally composited into the shots. Make-up wounds on Caviezel's body were digitally covered until the actual hit by the whip, creating the illusion that they suddenly appeared.\", u'According to Mel Gibson, the long shot of Jesus lying in Mary\\'s arms after having been taken from the cross, was greatly inspired by Michelangelo\\'s famous statue \"La Piet\\xe0,\" a work of art that inspired many other depictions of this scene.', u'Rated #1 of the 25 most controversial movies of all time. Entertainment Weekly, 16 June 2006.', u'In one scene while hanging on the Cross, Jim Caviezel can be seen to have a blue colouration of skin. This was not a special effect, but a case of asphyxiation, the cause of death for crucified victims.', u'Because of their experiences during film production, many of the cast and filming crew converted to Catholicism after the completion of the film. Among those who converted was an atheist who played Judas Iscariot.', u\"The name of the Roman soldier who pierced Christ with a spear is Cassius, as we heard Abenader shout his name when giving him a spear. This is a reference to the Catholic tradition that the name of the soldier who pierced Christ's side was Cassius Longinus, who was later believed to convert to Christianity and is venerated in Roman Catholicism as a saint.\", u'This was the highest-grossing rated R film in US box office history earning $370 million until the release of Deadpool.', u'Originally, the second confrontation between Pilate and the Sanhedrin included a line where the Sanhedrin say, \"His blood be on our heads and on the heads of our children!\" Although this line comes from the Gospels, Mel Gibson removed the subtitle of the line to avoid further allegations of the movie having an anti-Semitic message. The actual line in Aramaic was left in the movie.', u'It would usually take over 10 hours to put Jim Caviezel into the scourged makeup. On some of those days, it would happen that the weather conditions turned out to be unsuitable for filming. To avoid spending more hours to have it removed and re-applied the next day, he kept it on and went to bed in full make-up.', u'When this Latin and Aramaic language film was announced, Mel Gibson stated that his intent was to release it without subtitles, letting the performances speak for themselves. However, subtitles were added later. Also, he stated that regardless of the cost of the project, that this would be \"good for the soul\".', u'According to Mel Gibson, Maia Morgenstern, who played Mary, was pregnant during the shoot. She didn\\'t tell anyone, until one day she approached Jim Caviezel (Jesus) and said in broken English and a thick Romanian accent, \"I have baby. In stomach.\"', u'As of 2010, it has not been commercially released in Israel for \"lack of interest\".', u\"According to Caleb Deschanel, the majority of the movie was shot with a speed above the normal 24 frames per second. This created a sense of relative 'slow motion' in most scenes, which gave the performances and events more weight and drama.\", u\"At actual Roman crucifixions, the nails were driven through the wrists and not through the palms as in the film. The structure of the hand is not strong enough to support the weight of the body and the nail would have torn through between the fingers. However, the Christian tradition shows the nails as driven through the palms. In the Bible, the nails were said to be driven into the hands of Jesus. Medically, the hands include the wrists. - Edit: Recent study of ancient crucifixions have revealed that it is likely that the nails were in fact driven through the hands as opposed to the wrists. The hands have a nerve running through the middle of them that would have caused unbelievable amounts of pain when the nail was driven through. To support the body's weight, ropes were tied at the wrists and the elbows to tie them to the crossbeam, then the legs were broken causing the weight of their body to pull on the upper body, eventually resulting in death by slow suffocation.\", u'The film begins without opening credits. The title of the film is stated only in the closing.', u\"The figure of Christ during the crucifixion is actually Jim Caviezel in many scenes. The movie's make-up effects creator/producer Keith VanderLaan also forged an articulated, rubber stand-in for Caviezel that even made breathing motions, who could be suspended on the cross for certain wide shots to allow the actor some physical relief, and for some dangerous shots (such as the turning of the cross).\", u'Jim Caviezel was given a prosthetic nose and a raised hairline. His blue eyes were digitally changed to brown on film.', u'Assistant director Jan Michelini was also hit twice by lightning during filming.', u'Mel Gibson had a Canadian priest, Fr. Stephen Somerville, celebrate the Traditional Roman Catholic Latin Mass of the Apostolic Rite for the film crew each day before production began.', u'Malaysia did not ban the film as is commonly believed. The Malaysian government allowed Christians to the film. Tickets were allowed to be sold only by Christian churches.', u'Banned in Kuwait and Bahrain, for religious reasons (forbidding visual depictions of a prophet, as Jesus is considered a prophet in Islam, not the Son of God).', u'During production the film was originally supposed to be titled simply \"The Passion\". However in October 2003, it was revealed the Miramax studios already had a movie in production with that title. Mel Gibson retitled the film \"The Passion of Christ\". He retitled it yet again a month later in November 2003 to \"The Passion of the Christ\".', u\"A new type of fake blood with added viscosity was developed for Jesus' scourging makeup, which contained red dyes suspended in glycerin, fatty gums and a stabilizing base. It also made Jim Caviezel's skin smell very sweet for numerous days, and had to be rubbed off with alcohol.\", u'In a rarity for Hollywood releases, re-entered the #1 spot at the box office for the weekend of Good Friday, 2004.', u'Maia Morgenstern, who plays Mary (mother of Jesus), is only six years older than Jim Caviezel.', u'Foreseeing damage to box office, its release in Mexico had to be moved one week earlier (from March 25 to March 19) because pirate copies were already available a few days after it premiered in the USA.', u\"Although many Jews consider this film as Anti-Semitic, still there are several Jewish Rabbis who don't agree with this claim. This is because Judaism began only with the completion of Babylonian Talmud in sixth century AD, Judaism is not the religion of the bible, and Jews are different from Hebrews.\", u'The Bible verse from Isaiah 53:5 which appeared in the beginning of the film (\"He was wounded for our transgressions, crushed for our iniquities; by His wounds we are healed\") is abbreviated. Here is the full verse for those who are not familiar with the Bible: \"But He was wounded for our transgressions; He was crushed for our iniquities; upon Him was the chastisement that brought us peace, and with His stripes we are healed.\" (ESV).', u'The first names of actors Hristo Shopov (Pontius Pilate) and Christo Jivkov (Apostle John) are variations of the name Christ.', u'On the first day of general release, Ash Wednesday, Peggy Scott, a 56-year-old advertising sales manager from Wichita, Kansas collapsed of apparent heart failure while watching the crucifixion scene. She later died at the hospital.', u'Was voted the most pro-Catholic film of all time by readers of Faith & Family magazine and the National Catholic Register newspaper. It received more votes from readers than the next three films on the list combined: The Sound of Music (1965), A Man for All Seasons (1966), and The Song of Bernadette (1943).', u'Mel Gibson originally opted to not use a musical score for the film.', u\"Mel Gibson's first writing credit.\", u\"Mel Gibson has stated that he will give $100 million of the film's gross to the Traditional Catholic Movement.\", u'While the characters of the film mostly speak Latin and Aramaic, there are instances where Hebrew was spoken: a) The gathering of the Sanhedrin (Jewish chief Rabbis); b) Simon of Cyrene speaking; and c) The woman who gave water to Jesus on his way to Golgotha.', u\"There are several specifically Catholic influences in the film, such as the prominent role of Jesus' mother Mary; the Stations of the Cross; the floating cross on which Jesus was crucified; and the depiction of Satan.\", u'The line mentioned by Jesus to High Priest Caiaphas and others in this film - \"I AM and you will see the Son of Man seated at the right hand of power and coming on the clouds of heaven.\" comes from Mark 14:62. This is also a reference to Jeremiah 4:13 where it says \"Behold, he (God) shall come up as clouds, and his chariots shall be as a whirlwind: his horses are swifter than eagles. Woe unto us! for we are spoiled.\" This is interesting to note, because Elijah was taken by a chariot of fire in 2 Kings 2:11-12 and Elijah being taken by a chariot is also mentioned by Annas the priest in this film. Right before the destruction of Jerusalem in 70 AD (which was prophesied by Jesus Christ in Matthew 24, Mark 13, and Luke 21), chariots and troops of soldiers in their armor were seen running about among the clouds, and surrounding of cities. This incident was recorded by Josephus in Jewish Wars (Book Six), Tacitus in Histories (Book 5), Eusebius in his Ecclesiastical History (Book 3), and Jewish History Document \"Sepher Yosippon\" (Chapter 87 - Burning of the temple) written in Hebrew. God and his chariots are also mentioned in Isaiah 66:15, 2 Kings 6:17, Zachariah 6:1-6, and other verses in the bible. It must be noted that God is called \"Lord of Hosts\" more than 275 times in the bible. In New Testament, God is called \"Lord of Hosts\" in Romans 9:29 and in James 5:4.', u'In this film, Jesus Christ (Yeshua Meshikha in Aramaic) calls Peter \"Kaypha\" which means stone in Aramaic. The name \"Peter\" comes from Greek name \"petros\" which is the translation of Aramaic word \"Kaypha.\" In New Testament English Bible, Kaypha is written as Cephas in John 1:42, Galatians 2:9, 1 Corinthians 1:12, 1 Corinthians 9:5, and other verses in the Bible.', u'Although some Hebrew words are used in this film, still Hebrew was never the spoken language of first century Israel. According to Dead Sea Scrolls archaeologist, Yigael Yadin, Aramaic was the spoken language of Hebrews until Simon Bar Kokhba tried to revive Hebrew and make it as the official language of Jews during Bar Kokhba revolt (132-135 AD). Yigael Yadin noticed the shift from Aramaic to Hebrew during the time of Bar Kokhba revolt (132-135 AD). In Book \"Bar Kokhba: The rediscovery of the legendary hero of the last Jewish Revolt Against Imperial Rome\" Yigael Yadin notes, \"It is interesting that the earlier documents are written in Aramaic while the later ones are in Hebrew. Possibly the change was made by a special decree of Bar-Kokhba who wanted to restore Hebrew as the official language of the state\"(page 181). In Book \"A Roadmap to the Heavens: An Anthropological Study of Hegemony among Priests, Sages, and Laymen (Judaism and Jewish Life)\" by Sigalit Ben-Zion (Page 155), Yadin said: \"it seems that this change came as a result of the order that was given by Bar Kokhba, who wanted to revive the Hebrew language and make it the official language of the state.\" According to Book \"Naming the Witch: Magic, Ideology, and Stereotype in the Ancient World\" written by Kimberly B. Stratton (p. 232), Yigael Yadin suggests that Bar Kokhba was trying to revive Hebrew by decree as part of his messianic ideology.', u'A year after the film\\'s initial release, a \"toned-down\" version retitled as \"The Passion Recut\" was released. Though some of the more graphic elements were removed, the MPAA still assigned the movie an \"R\" rating - so the version was distributed to theaters as \"unrated\".', u'Aramaic was the most used language spoken by the Hebrews in this film. Aramaic was the language of the Jews in Israel during first century AD. \"Bar\"tholomew, \"Bar\"abbas, \"Bar\"nabbas, \"Bar\" Jesus, Simon \"Bar\" Jonas, \"Bar\"sabbas, and \"Bar\"timaeus are examples of names in the New Testament which use the Aramaic word Bar meaning \\'Son\\' rather than Ben in Hebrew. When Jews say \"Hebrew\" in New Testament, they were referring to their Hebrew tongue which was Aramaic in first century AD. \"Golgotha\" in John 19:17 is a Greek transliteration of an Aramaic word. In Hebrew, Golgotha will become \\'Ha Gulgoleth\\'.', u'In first century AD, Hebrew Priest Josephus points out that Aramaic (the language used in this film) was a widespread language and \"understood accurately\" by Parthians, Babylonians, the remotest Arabians, and those of his nation beyond Euphrates with Adiabeni (Jewish Wars, Preface). Josephus differentiates Hebrew from both his language and the language of the first century Israel which is Aramaic. Josephus calls Hebrew as Hebrew tongue while he calls Aramaic as \"our tongue\" or \"our language\" in both of his works - Jewish Wars and Antiquities of Jews. This is also agreed by Yigael Yadin who points out that Aramaic was the lingua franca of this time period (Source - Yadin\\'s Book \"Bar Kokhba: The rediscovery of the legendary hero of the last Jewish Revolt Against Imperial Rome\" Page 234). Unlike Hebrew Priest Josephus and other Hebrew priests at Jerusalem, the people of first century Israel had no knowledge of Hebrew. That is why whenever the apostles say Hebrew in New testament (John 19:13, John 19:17, etc.), the word comes up is transliteration of an Aramaic word.', u'Rosalinda Celentano lived on a diet of nothing but beans and rice so she would have a malnourished and emaciated appearance', u'Included among the \"1001 Movies You Must See Before You Die\", edited by Steven Schneider.', u'Rumors circulated that James Horner was interested in composing the music for this film. Later on, female composers such as Lisa Gerrard and Rachel Portman were in talks, and Portman did indeed have the job until her pregnancy caused her to bow out gracefully. John Debney took the job in the end.', u'Contrary to this film, Western Christian Traditions believe that Jesus spoke Greek and New Testament was written in Greek. So Western Christianity uses Greek NT manuscripts. But Several Eastern Christian Traditions (in Middle East regions and in South India) believe that Jesus spoke Aramaic and New Testament is written in Aramaic. Not in Greek. So Several Eastern Christian Traditions support the use of Aramaic language in this film and consider Aramaic NT (known as Aramaic Peshitta) as the original text of New Testament.', u'Just like in this film, Aramaic was the spoken language of first century Israel. In first century AD, Hebrew wasn\\'t used as a spoken language at all among Hebrews although Hebrew word \"adonai\" (my lord) and some Hebrew words are mentioned in this film. In Acts 1:19, it says \"And it became known to all the inhabitants of Jerusalem, so that the field was called in their own language Akeldama, that is, Field of Blood.\" \"Akel dama\" is Greek transliteration of Aramaic words \"Khqel Dama.\" \"Field of Blood\" was called \"Khqel Dama\" by all the inhabitants of Jerusalem in their \"own\" language which was Aramaic in first century Israel. If Aramaic words \"Khqel Dama\" are translated into Hebrew, then \"Khqel Dama\" will become \"Shadeh Hadam.\" If Hebrew was used as a spoken language in first century Israel, then \"Shadeh Hadam\" would have been mentioned along with \"Khqel Dama\" (akel dama in Greek NT and English NT) in Acts 1:19. So through this verse (Acts 1:19), it is confirmed that all the inhabitants of Jerusalem spoke in their \"own\" language in first century Israel which was Aramaic.', u'Just like Aramaic spoken by Hebrews in this film, the native language of Abraham and his family was also Aramaic. Abraham lived in Haran (Aramaic speaking region) till the age of 75 when he and his family moved to Canaan (Genesis 12:4-5). The relatives of Abraham in Aramaic speaking regions also spoke Aramaic. For Example, Laban the Aramean called \"Witness heap\" as \"Jegar Sahadutha\" which is Aramaic (Genesis 31:47).', u'The music for the 1st trailer, released in 2003, is from the score Peter Gabriel composed for the movie Rabbit-Proof Fence (2002). The specific track is titled \"Running to the Rain.\"', u'Just like we see in the film, Hebrews (a.k.a Judeans) did not speak Greek in first century Israel. This is confirmed by Hebrew Historian Flavius Josephus who wrote: \"I have also taken a great deal of pains to obtain the learning of the Greeks, and understand the elements of the Greek language, although I have so long accustomed myself to speak our own tongue, that I cannot pronounce Greek with sufficient exactness; for our nation does not encourage those that learn the languages of many nations, and so adorn their discourses with the smoothness of their periods; because they look upon this sort of accomplishment as common, not only to all sorts of free-men, but to as many of the servants as please to learn them. But they give him the testimony of being a wise man who is fully acquainted with our laws, and is able to interpret their meaning; on which account, as there have been many who have done their endeavors with great patience to obtain this learning, there have yet hardly been so many as two or three that have succeeded therein, who were immediately well rewarded for their pains.\" - Antiquities of Jews XX, XI. In Jewish Wars (Book 1, Preface, Paragraph 1), Josephus states this - \"I have proposed to myself, for the sake of such as live under the government of the Romans, to translate those books into the Greek tongue, which I formerly composed in the language of our country, and sent to the Upper Barbarians. Joseph, the son of Matthias, by birth a Hebrew, a priest also, and one who at first fought against the Romans myself, and was forced to be present at what was done afterwards, [am the author of this work].\" In Antiquities of Jews Book 3, Josephus points out that Hebrews called Pentecost \"Asartha.\" Asartha is Aramaic, because Aramaic places Aramaic definite article \"tha\" at the end of a feminine noun in an emphatic state. If \"Asartha\" is translated into Hebrew, then it will become \"Ha Atzeret.\" Unlike Aramaic, Hebrew places the definite article (\"Ha\") at the beginning of a word.', u'The Catholic priest and scholar consulted by director, Mel Gibson, in the depiction of almost every scene, was Father John Bartunek, L.C. He wrote an authorized, behind-the-scenes book about the film called, \"Inside the Passion: An Insider\\'s Look at the Passion of the Christ.\" The Foreword to this book was written by Mel Gibson.', u\"South Park (1997) did an episode featuring the movie as its main focus. The episode title is a spin off of the movie's title, South Park: The Passion of the Jew (2004).\", u'The names of Jim Caviezel and Jesus Christ share the same initials.', u'This was the highest grossing R-rated film at the worldwide box office until it was beaten by Deadpool (2016).', u'Mel Gibson: Gibson\\'s hands nail Christ to the cross during the Crucifixion scene. Gibson said \"It was me that put him on the cross. It was my sins\" that put him there. According to special edition commentaries, Gibson also supplied the foot of Jesus (washed by Mary Magdalene) and the arms that tie Judas\\' suicide rope. His crying, screaming voice is heard during the latter scene.', u'In this film, Jesus Christ asks \"Who are you looking for?\" A Soldier replies \"We are looking for Jesus of Nazareth.\" Jesus Christ says \"I am he.\" The importance of this incident is mentioned in Gospel of John. In Gospel of John 18:4-6 (in Aramaic NT), Jesus Christ says \"I AM\" (ENA NA in Aramaic NT) instead of \"I am he.\" When Jesus Christ said that \"I AM\", they went backward and fell to the ground in John 18:6. This is interesting to note, because the name of God is \"I AM\" which was revealed to Moses in Exodus 3:14.', u'Although Sadducees are not mentioned in this film, still the High Priest and all of his associates (seen in this film) belonged to the sect of Sadducees. This can be read in Acts 5:17. James, a disciple of Jesus Christ (Galatians 1:19) was murdered by High Priest Ananus who belonged to the sect of Sadducees. This was mentioned by Josephus in his \"Antiquities 20.9.1.\"In Matthew 22 & Mark 12, Jesus Christ criticized Sadducees for their lack of the knowledge in the scriptures and the power of God. Sadducees\\' lack of knowledge in the scriptures can be seen in both bible and in this film.When the High Priest asked \"Are you the Christ, the son of Blessed?\" Jesus Christ told the High Priest and his associates with him this - \"Jesus said, \"I am, and you will see the Son of Man seated at the right hand of Power, and coming with the clouds of heaven\" (Mark 14:61-62). When Jesus Christ said this to the High Priest and his associates with him, the High Priest tore his garments (Matthew 26:65, Mark 14:63). When High Priest tore his garments, he violated Leviticus 21:10 which prohibited the High Priest from tearing his clothes. The sect of Sadducees completely disappeared with the destruction of Jerusalem and Jerusalem temple in 70 AD which was prophesied by Jesus Christ (Matthew 24, Mark 13, Luke 21).', u'YA\" is Aramaic form of Hebrew \"YH\" in EHYH (God\\'s name revealed to Moses in Exodus 3:14). This can be seen with the names in New testament. In this film, Judas the traitor (\"Yuda\" in Aramaic where H is silent), John (\"Yochanan\" in Aramaic), and Joseph of Arimathea (\"Yoseph\" in Aramaic). When False Messiah Bar Kokhba revived Hebrew during Bar Kokhba revolt (132-135 AD), the names started changing from Aramaic form (\"YA\") to Hebrew form (\"YH\"). So Yuda (in Aramaic) became Yehuda (in Hebrew), Yochanan (in Aramaic) became Yehochanan (in Hebrew), and Yoseph (in Aramaic) became \"Yehoseph\" (in Hebrew). The names with Aramaic word \"Bar\" (for example, the criminal \"Bar\"abba in this film) was replaced with Hebrew word \"Ben\" during Bar Kokhba revolt (132-135 AD).']\n" ] } ], "source": [ "# retrieve trivia information and print it.\n", "i.update(first_match, 'trivia')\n", "print first_match['trivia']" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "ename": "KeyError", "evalue": "'quotes'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-15-297db2a4a28a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[0mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfirst_match\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'quotes'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'goofs'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mprint\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'quotes'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[1;32mprint\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'goofs'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;32m/home/hao/anaconda/lib/python2.7/site-packages/imdb/utils.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1467\u001b[0m \u001b[1;31m# Handle key aliases.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1468\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys_alias\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1469\u001b[1;33m \u001b[0mrawData\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1470\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeys_tomodify\u001b[0m \u001b[1;32mand\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m\\\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1471\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodFunct\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmodNull\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", "\u001b[1;31mKeyError\u001b[0m: 'quotes'" ] } ], "source": [ "# retrieve both 'quotes' and 'goofs' information (with a list or tuple)\n", "m = first_match\n", "i.update(m, ['quotes', 'goofs'])\n", "print m['quotes']\n", "print m['goofs']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# retrieve every available information.\n", "i.update(m, 'all')" ] } ], "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 }
gpl-3.0
intellimath/pyaxon
examples/axon_with_python.ipynb
1
23672
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "> [**AXON**](http://axon.intellimath.org) is e**X**tended **O**bject **N**otation. It's a simple notation of objects,\n", "documents and data. It's also a text based serialization format in first place. \n", "It tries to combine the best of [JSON](http://www.json.org), [XML](http://www.w3.org/XML/) and [YAML](http://www.yaml.org)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[pyaxon](https://pypi.python.org/pypi/pyaxon) is reference implementation of the library for processing AXON with [python](http://www.python.org)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- TEASER_END -->" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import print_function\n", "from axon import loads, dumps\n", "from pprint import pprint" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two API functions `loads`/`dumps` for loading/dumping from/to unicode string.\n", "\n", "By default loading and dumping are safe. By the word \"safe\" we mean that there is no user code is executed while loading and dumping. Unicode strings are converted only into python objects of given types. There is \"unsafe\" mode too. It allows to transform unicode string into user defined objects and to dump objects into unicode string under user control. But this is the topic of another post." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Simple example" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "text = '''\\\n", "note {\n", " from: \"Pooh\"\n", " to: \"Bee\"\n", " posted: 2006-08-15T17:30\n", " heading: \"Honey\"\n", " \"Don't forget to get me honey!\" }\n", "'''\n", "vals = loads(text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here `vals` is always *list of objects*." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "axon._objects.Node" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ob = vals[0]\n", "type(ob)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that the message is converted to the instance of class `Element`. Attribute `vals.mapping` is dictionary containing objects's attributes:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'axon.odict.OrderedDict'>\n", "OrderedDict([('from', 'Pooh'), ('to', 'Bee'), ('posted', datetime.datetime(2006, 8, 15, 17, 30)), ('heading', 'Honey')])\n" ] } ], "source": [ "print(type(ob.__attrs__))\n", "print(ob.__attrs__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Attributes of the object are accessable by methods `get/set`:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('from', 'Pooh'),\n", " ('to', 'Bee'),\n", " ('posted', datetime.datetime(2006, 8, 15, 17, 30)),\n", " ('heading', 'Honey')]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[(attr, getattr(ob, attr)) for attr in ob.__attrs__]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Element` objects has content - list of values. They are accessible by python's sequence protocol. In our case the first value is the message body of the `note`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Don't forget to get me honey!\n" ] } ], "source": [ "print(ob[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For dumping objects there are *three* modes. First mode is *compact*:" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "note{from:\"Pooh\" to:\"Bee\" posted:2006-08-15T17:30 heading:\"Honey\" \"Don't forget to get me honey!\"}\n" ] } ], "source": [ "print(dumps([ob]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Second mode is pretty dumping mode with indentations and without braces:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "note\n", " from: \"Pooh\"\n", " to: \"Bee\"\n", " posted: 2006-08-15T17:30\n", " heading: \"Honey\"\n", " \"Don't forget to get me honey!\"\n" ] } ], "source": [ "print(dumps([ob], pretty=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Third mode is *pretty* dumping mode with *indentation* and *braces*:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "note {\n", " from: \"Pooh\"\n", " to: \"Bee\"\n", " posted: 2006-08-15T17:30\n", " heading: \"Honey\"\n", " \"Don't forget to get me honey!\"}\n" ] } ], "source": [ "print(dumps([ob], pretty=1, braces=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At the end let's consider `JSON`-like representation too:" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "text = \"\"\"\\\n", "{note: {\n", " from: \"Pooh\"\n", " to: \"Bee\"\n", " posted: 2006-08-15T17:30\n", " heading: \"Honey\"\n", " body: \"Don't forget to get me honey!\"\n", "}}\n", "\"\"\"\n", "vals = loads(text)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It has converted into python dicts:" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'note': {'body': \"Don't forget to get me honey!\",\n", " 'from': 'Pooh',\n", " 'heading': 'Honey',\n", " 'posted': datetime.datetime(2006, 8, 15, 17, 30),\n", " 'to': 'Bee'}}]\n" ] } ], "source": [ "pprint(vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Compact dump is pretty small in size." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{note:{body:\"Don't forget to get me honey!\" from:\"Pooh\" heading:\"Honey\" posted:2006-08-15T17:30 to:\"Bee\"}}\n" ] } ], "source": [ "print(dumps(vals))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dumping in pretty mode is also pretty formatted." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{note: {\n", " body: \"Don't forget to get me honey!\"\n", " from: \"Pooh\"\n", " heading: \"Honey\"\n", " posted: 2006-08-15T17:30\n", " to: \"Bee\"}}\n" ] } ], "source": [ "print(dumps(vals, pretty=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> JSON-like objects are pretty dumps only in indented form with braces." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### JSON-like example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's consider now `JSON`-like example with *crossreferences* and *datetimes*:" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[{'posts': [{'body': '...',\n", " 'date': datetime.datetime(2012, 1, 2, 12, 15, tzinfo=datetime.timezone(datetime.timedelta(0, 10800))),\n", " 'id': 1,\n", " 'topic': {'python': 'Python related'}},\n", " {'body': '...',\n", " 'date': datetime.datetime(2012, 1, 12, 9, 25, tzinfo=datetime.timezone(datetime.timedelta(0, 10800))),\n", " 'id': 2,\n", " 'topic': {'axon': 'AXON related'}},\n", " {'body': '...',\n", " 'date': datetime.datetime(2012, 2, 8, 10, 35, tzinfo=datetime.timezone(datetime.timedelta(0, 10800))),\n", " 'id': 3,\n", " 'topic': {'json': 'JSON related'}}],\n", " 'topic': [{'python': 'Python related'},\n", " {'axon': 'AXON related'},\n", " {'json': 'JSON related'}]}]\n" ] } ], "source": [ "text = '''\\\n", "{\n", " topic: [\n", " &1 {python: \"Python related\"}\n", " &2 {axon: \"AXON related\"}\n", " &3 {json: \"JSON related\"}\n", " ]\n", " posts: [\n", " { id: 1\n", " topic: *1\n", " date: 2012-01-02T12:15+03 \n", " body:\"...\" }\n", " { id: 2\n", " topic: *2\n", " date: 2012-01-12T09:25+03 \n", " body:\"...\" }\n", " { id: 3\n", " topic: *3\n", " date: 2012-02-08T10:35+03 \n", " body:\"...\" }\n", " ]\n", "}\n", "'''\n", "vals = loads(text)\n", "pprint(vals)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's easy to see that crossreference links just works:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "assert vals[0]['topic'][0] is vals[0]['posts'][0]['topic']\n", "assert vals[0]['topic'][1] is vals[0]['posts'][1]['topic']\n", "assert vals[0]['topic'][2] is vals[0]['posts'][2]['topic']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pretty dump looks like this one:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{ posts: [\n", " { topic: &1 {python: \"Python related\"}\n", " id: 1\n", " body: \"...\"\n", " date: 2012-01-02T12:15+03}\n", " { topic: &3 {axon: \"AXON related\"}\n", " id: 2\n", " body: \"...\"\n", " date: 2012-01-12T09:25+03}\n", " { topic: &2 {json: \"JSON related\"}\n", " id: 3\n", " body: \"...\"\n", " date: 2012-02-08T10:35+03}]\n", " topic: [*1 *3 *2]}\n" ] } ], "source": [ "print(dumps(vals, pretty=1, crossref=1, sorted=0))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> Note that `sorted` parameter defines whether to sort keys in dict." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### XML-like example" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "html{xmlns: 'http://www.w3.org/1999/xhtml' head{title{'Form Example'}, link{rel: 'stylesheet', href: 'formstyle.css', type: 'text/css'}}, body{h1{'Form Example'}, form{action: 'sample.py' div{class: 'formin' '(a)', input{type: 'text', name: 'text1', value: 'A textbox'}}, div{class: 'formin' '(b)', input{type: 'text', size: 6, maxlength: 10, name: 'text2'}}, div{class: 'formb' '(c)', input{type: 'submit', value: 'Go!'}}}}}\n" ] } ], "source": [ "text = '''\\\n", "html {\n", " xmlns:\"http://www.w3.org/1999/xhtml\"\n", " head {\n", " title {\"Form Example\"}\n", " link {\n", " rel:\"stylesheet\"\n", " href: \"formstyle.css\"\n", " type: \"text/css\" }}\n", " body {\n", " h1 {\"Form Example\"}\n", " form { \n", " action: \"sample.py\"\n", " div {\n", " class: \"formin\" \n", " \"(a)\"\n", " input {type:\"text\" name:\"text1\" value:\"A textbox\"}}\n", " div {\n", " class: \"formin\"\n", " \"(b)\"\n", " input {type:\"text\" size:6 maxlength:10 name:\"text2\"}}\n", " div {\n", " class: \"formb\"\n", " \"(c)\"\n", " input {type:\"submit\" value:\"Go!\"}}\n", " }\n", " }\n", "}\n", "'''\n", "vals = loads(text)\n", "val = vals[0]\n", "print(val)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's examine the value:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'axon._objects.Node'>\n", "OrderedDict([('xmlns', 'http://www.w3.org/1999/xhtml')])\n" ] } ], "source": [ "print(type(val))\n", "print(val.__attrs__)\n", "head, body = val[0], val[1]" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'axon._objects.Node'>\n", "title{'Form Example'}\n", "link{rel: 'stylesheet', href: 'formstyle.css', type: 'text/css'}\n" ] } ], "source": [ "print(type(head))\n", "title, link = head\n", "print(title)\n", "print(link)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "h1{'Form Example'}\n" ] } ], "source": [ "h1, form = body\n", "print(h1)" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[div{class: 'formin' '(a)', input{type: 'text', name: 'text1', value: 'A textbox'}}, div{class: 'formin' '(b)', input{type: 'text', size: 6, maxlength: 10, name: 'text2'}}, div{class: 'formb' '(c)', input{type: 'submit', value: 'Go!'}}]\n" ] } ], "source": [ "print(form.__vals__)\n", "div1, div2, div3 = form" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OrderedDict([('class', 'formin')])\n", "(a)\n", "input{type: 'text', name: 'text1', value: 'A textbox'}\n" ] } ], "source": [ "print(div1.__attrs__)\n", "label1, input1 = div1\n", "print(label1)\n", "print(input1)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "OrderedDict([('class', 'formin')])\n", "(b)\n", "input{type: 'text', size: 6, maxlength: 10, name: 'text2'}\n" ] } ], "source": [ "print(div2.__attrs__)\n", "label2, input2 = div2\n", "print(label2)\n", "print(input2)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'axon._objects.Node'>\n", "OrderedDict([('class', 'formb')])\n", "(c)\n", "input{type: 'submit', value: 'Go!'}\n" ] } ], "source": [ "print(type(div3))\n", "print(div3.__attrs__)\n", "label3, input3 = div3\n", "print(label3)\n", "print(input3)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "html\n", " xmlns: \"http://www.w3.org/1999/xhtml\"\n", " head\n", " title\n", " \"Form Example\"\n", " link\n", " rel: \"stylesheet\"\n", " href: \"formstyle.css\"\n", " type: \"text/css\"\n", " body\n", " h1\n", " \"Form Example\"\n", " form\n", " action: \"sample.py\"\n", " div\n", " class: \"formin\"\n", " \"(a)\"\n", " input\n", " type: \"text\"\n", " name: \"text1\"\n", " value: \"A textbox\"\n", " div\n", " class: \"formin\"\n", " \"(b)\"\n", " input\n", " type: \"text\"\n", " size: 6\n", " maxlength: 10\n", " name: \"text2\"\n", " div\n", " class: \"formb\"\n", " \"(c)\"\n", " input\n", " type: \"submit\"\n", " value: \"Go!\"\n" ] } ], "source": [ "print(dumps([val], pretty=1))" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "html {\n", " xmlns: \"http://www.w3.org/1999/xhtml\"\n", " head {\n", " title {\"Form Example\"}\n", " link {\n", " rel: \"stylesheet\"\n", " href: \"formstyle.css\"\n", " type: \"text/css\"}}\n", " body {\n", " h1 {\"Form Example\"}\n", " form {\n", " action: \"sample.py\"\n", " div {\n", " class: \"formin\"\n", " \"(a)\"\n", " input {\n", " type: \"text\"\n", " name: \"text1\"\n", " value: \"A textbox\"}}\n", " div {\n", " class: \"formin\"\n", " \"(b)\"\n", " input {\n", " type: \"text\"\n", " size: 6\n", " maxlength: 10\n", " name: \"text2\"}}\n", " div {\n", " class: \"formb\"\n", " \"(c)\"\n", " input {\n", " type: \"submit\"\n", " value: \"Go!\"}}}}}\n" ] } ], "source": [ "print(dumps([val], pretty=1, braces=1))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dataset example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's consider simple tabular dataset:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dataset\n", "OrderedDict([('fields', ('id', 'date', 'time', 'territory_id', 'A', 'B', 'C'))])\n", "[(1, datetime.date(2012, 1, 10), datetime.time(12, 35), 17, 3.14, 22, 33500),\n", " (2, datetime.date(2012, 1, 11), datetime.time(13, 5), 27, 1.25, 32, 11500),\n", " (3, datetime.date(2012, 1, 12), datetime.time(10, 45), -17, -2.26, -12, 44700)]\n", "\n", "Pretty form of dataset:\n", "dataset\n", " fields: (\"id\" \"date\" \"time\" \"territory_id\" \"A\" \"B\" \"C\")\n", " (1 2012-01-10 12:35 17 3.14 22 33500)\n", " (2 2012-01-11 13:05 27 1.25 32 11500)\n", " (3 2012-01-12 10:45 -17 -2.26 -12 44700)\n" ] } ], "source": [ "text = '''\\\n", "dataset {\n", " fields: (\"id\" \"date\" \"time\" \"territory_id\" \"A\" \"B\" \"C\")\n", " (1 2012-01-10 12:35 17 3.14 22 33500)\n", " (2 2012-01-11 13:05 27 1.25 32 11500)\n", " (3 2012-01-12 10:45 -17 -2.26 -12 44700)\n", "}\n", "'''\n", "ob = loads(text)[0]\n", "print(ob.__tag__)\n", "pprint(ob.__attrs__)\n", "pprint(ob.__vals__, width=132)\n", "print(\"\\nPretty form of dataset:\")\n", "print(dumps([ob], pretty=1, hsize=10))\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'tuple'> (1, datetime.date(2012, 1, 10), datetime.time(12, 35), 17, 3.14, 22, 33500)\n", "<class 'tuple'> (2, datetime.date(2012, 1, 11), datetime.time(13, 5), 27, 1.25, 32, 11500)\n", "<class 'tuple'> (3, datetime.date(2012, 1, 12), datetime.time(10, 45), -17, -2.26, -12, 44700)\n", "\n", "\n", "<class '__main__.Datarow'> Datarow(id=1, date=datetime.date(2012, 1, 10), time=datetime.time(12, 35), territory_id=17, A=3.14, B=22, C=33500)\n", "<class '__main__.Datarow'> Datarow(id=2, date=datetime.date(2012, 1, 11), time=datetime.time(13, 5), territory_id=27, A=1.25, B=32, C=11500)\n", "<class '__main__.Datarow'> Datarow(id=3, date=datetime.date(2012, 1, 12), time=datetime.time(10, 45), territory_id=-17, A=-2.26, B=-12, C=44700)\n" ] } ], "source": [ "from collections import namedtuple\n", "Datarow = namedtuple(\"Datarow\", ob.fields)\n", "rows = []\n", "for line in ob:\n", " print(type(line), line)\n", " rows.append(Datarow(*line))\n", "print(\"\\n\")\n", "for row in rows:\n", " print(type(row), row)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "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 }
mit
computational-class/cjc2016
code/Python performance optimization.ipynb
1
3197
{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Python performance optimization" ] }, { "cell_type": "markdown", "metadata": { "ExecuteTime": { "end_time": "2017-01-28T11:29:43.100718", "start_time": "2017-01-28T11:29:43.009165" }, "slideshow": { "slide_type": "slide" } }, "source": [ "## Membership testing is faster in dict than in list. \n", "\n", "Python dictionaries use hash tables, this means that a lookup operation (e.g., if x in y) is O(1). A lookup operation in a list means that the entire list needs to be iterated, resulting in O(n) for a list of length n. http://www.clips.ua.ac.be/tutorials/python-performance-optimization" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-02-25T15:32:34.036617", "start_time": "2017-02-25T15:32:34.028978" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [], "source": [ "import timeit\n", "def test_ifin(d):\n", " if 5000 in d:\n", " a = 1\n", " else:\n", " a = 2\n", "\n", "d1 = dict.fromkeys(range(10000), True)\n", "d2 = range(10000)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2017-02-25T15:32:44.772804", "start_time": "2017-02-25T15:32:44.700130" }, "slideshow": { "slide_type": "subslide" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.000880002975464\n", "0.0672550201416\n" ] } ], "source": [ "print (timeit.timeit(lambda: test_ifin(d1), number=1000))\n", "print (timeit.timeit(lambda: test_ifin(d2), number=1000))" ] } ], "metadata": { "celltoolbar": "Slideshow", "kernel_info": { "name": "python3" }, "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": "3.7.3" }, "latex_envs": { "LaTeX_envs_menu_present": true, "autoclose": false, "autocomplete": true, "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": false, "eqNumInitial": 0, "hotkeys": { "equation": "Ctrl-E", "itemize": "Ctrl-I" }, "labels_anchors": false, "latex_user_defs": false, "report_style_numbering": false, "user_envs_cfg": false }, "nteract": { "version": "0.14.3" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }
mit
savkov/BootstrapSplit
demo/bootstrapsplitdemo.ipynb
1
10811
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bootstrapping" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What is bootstrapping?\n", "> Bootstrapping is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution function of the observed data. In the case where a set of observations can be assumed to be from an independent and identically distributed population, this can be implemented by constructing a number of resamples with replacement, of the observed dataset (and of equal size to the observed datasert [population]). -- [Wikipedia](http://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29 \"Bootstrapping (Statistics)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What is it used for?\n", "> Bootstrapping allows assigning measures of accuracy (defined in terms of bias, variance, confidence intervals, prediction error or some other such measure) to sample estimates. -- [Effron & Tibshirani, (1993)](https://books.google.co.uk/books/about/An_Introduction_to_the_Bootstrap.html?id=gLlpIUxRntoC&hl=en \"An Introduction to the Bootstrap\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Usage\n", "\n", "The `BootstrapSplit` class is a modified version of the `Bootstrap` iterator from the [cross_validation](http://scikit-learn.org/stable/modules/cross_validation.html \"Cross-validation\") module in [scikit-learn](http://scikit-learn.org/stable/index.html \"sklearn\"). Provided with the number of observations in the population the iterator returns a tuple containing two lists of index references to the population, representing the two split subsets resampled from the population. The size of the samples and the number of iterations can be controlled with keyword parameters." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "BootstrapSplit(13, n_iter=3, train_size=7, test_size=6, random_state=0)\n" ] } ], "source": [ "import numpy as np\n", "from __future__ import print_function\n", "from bootstrapsplit import BootstrapSplit\n", "\n", "# population\n", "pop = np.array(list('ABDEFGHIJKLMN'))\n", "bs = BootstrapSplit(len(pop), random_state=0)\n", "print(bs)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "POPULATION: ['A' 'B' 'D' 'E' 'F' 'G' 'H' 'I' 'J' 'K' 'L' 'M' 'N']\n", "TRAIN: ['H' 'D' 'F' 'M' 'B' 'B' 'H'] TEST: ['K' 'N' 'K' 'N' 'I' 'K']\n", "TRAIN: ['D' 'I' 'I' 'L' 'N' 'J' 'J'] TEST: ['G' 'H' 'M' 'E' 'A' 'A']\n", "TRAIN: ['J' 'L' 'B' 'B' 'E' 'L' 'L'] TEST: ['K' 'K' 'N' 'N' 'A' 'N']\n" ] } ], "source": [ "print('POPULATION:', pop)\n", "for tr_idx, te_idx in bs:\n", " print(\"TRAIN:\", pop[tr_idx], \"TEST:\", pop[te_idx])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Contrary to other resampling strategies, bootstrapping will allow some observations to occur several times in each sample. However, an observation that occurs in the train sample will never occur in the test sample and vice-versa." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What is weight-limited bootstrapping?\n", "\n", "In classic bootstrapping, observations are sampled uniformly with replacement until the sample is equal in size to the population. But in some cases we want to use a different limiting criterion. For example, in the well-known knapsack problem items (observations) are selected until their combined weight reaches a threshold. Weight-limited bootstrapping is similar in that each observation is assigned a weight and the total weight of the sample must not exceed a threshold. For example, sentences are made up of different number of words. Suppose we wanted to fill a page with a random sample of sentences from a long document (population). However, in this case if we simply sampled sentences (observations), we may run out of space as sentences are of different length and a page can contain no more than `t` words. The solution is to _weigh_ each sentence (observation) by its word count, and to _limit_ the sample to a maximum weight of `t`.\n", "\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WeightLimitedBootstrapSplit(13(18), n_iter=3, train_size=9, test_size=4, random_state=None)\n", "POPULATION: ['A' 'B' 'D' 'E' 'F' 'G' 'H' 'I' 'J' 'K' 'L' 'M' 'N'] (weight=18)\n", "TRAIN: ['I' 'B' 'I' 'K' 'F' 'E'] (weight=8) TEST: ['D' 'M'] (weight=3)\n", "TRAIN: ['A' 'A' 'A' 'L'] (weight=8) TEST: ['G' 'N' 'G'] (weight=3)\n", "TRAIN: ['G' 'A' 'M' 'M' 'B' 'N' 'G' 'G'] (weight=9) TEST: ['D' 'F' 'E'] (weight=4)\n" ] } ], "source": [ "from bootstrapsplit import WeightLimitedBootstrapSplit\n", "\n", "w = np.random.randint(low=1, high=3, size=len(pop))\n", "wb = WeightLimitedBootstrapSplit(w, n_iter=3, train_size=9, test_size=4)\n", "print(wb)\n", "print(\"POPULATION:\", pop, \"(weight=%s)\" % w.sum())\n", "for tr, te in wb:\n", " print(\"TRAIN:\", pop[tr], \"(weight=%s)\" % w[tr].sum(), \"TEST:\", pop[te], \"(weight=%s)\" % w[te].sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `WeightLimitedBootstrapSplit` class re-implements the `BootstrapSplit` accounting for individual sample weight. In contrast to `WeightLimitedBootstrapSplit` though it only sets a maximum weight for each sample split, which means that the returned sample is of the closest lower weight given a random resampling with replacement. This introduces a small degree of inaccuracy that needs to be kept in mind when working with very small samples. If the weight of a sample split is smaller than that of the first token in the resampled sequence, an empty list is returned.\n", "\n", "In the following example we set the weight limit of the test sample to `1` while the lowest word weight is `2`. " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WeightLimitedBootstrapSplit(13(38), n_iter=3, train_size=31, test_size=1, random_state=None)\n", "TRAIN: [12 12 0 6 7 1 9 8 10] (28) TEST: [] (0)\n", "TRAIN: [ 2 7 2 12 7 10 12 7 7] (29) TEST: [] (0)\n", "TRAIN: [ 4 7 4 0 5 9 12 2 7 6 2] (29) TEST: [] (0)\n" ] } ], "source": [ "w = np.random.randint(low=2, high=5, size=len(pop))\n", "wb = WeightLimitedBootstrapSplit(w, n_iter=3, train_size=0.8, test_size=1)\n", "print(wb)\n", "for tr_idx, te_idx in wb:\n", " print(\"TRAIN:\", tr_idx, \"(%s)\" % w[tr_idx].sum(), \"TEST:\", te_idx, \"(%s)\" % w[te_idx].sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###Bootstrapping without splitting\n", "\n", "The module also contains classes for bootstrapping and weight-limited bootstrapping without splitting the sample. The first is just plain sampling with replacement," ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Bootstrap(13, n_iter=3, random_state=None)\n", "POPULATION: ['A' 'B' 'D' 'E' 'F' 'G' 'H' 'I' 'J' 'K' 'L' 'M' 'N']\n", "BOOTSTRAP: ['D' 'N' 'G' 'L' 'G' 'D' 'I' 'D' 'K' 'I' 'D' 'M' 'K']\n", "BOOTSTRAP: ['N' 'H' 'A' 'E' 'B' 'H' 'M' 'B' 'K' 'N' 'N' 'M' 'K']\n", "BOOTSTRAP: ['G' 'N' 'G' 'L' 'M' 'N' 'I' 'N' 'D' 'M' 'H' 'I' 'G']\n" ] } ], "source": [ "from bootstrapsplit import Bootstrap\n", "\n", "b = Bootstrap(len(pop), n_iter=3)\n", "print(b)\n", "print(\"POPULATION:\", pop)\n", "for s in b:\n", " print(\"BOOTSTRAP:\", pop[s])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "the latter offers the option to set the maximum sample weight the same way `WeightLimitedBootstrapSplit` does." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "WeightLimitedBootstrap(13, n_iter=3, limit=13 random_state=None)\n", "POPULATION: ['A' 'B' 'D' 'E' 'F' 'G' 'H' 'I' 'J' 'K' 'L' 'M' 'N'] (weight=32)\n", "BOOTSTRAP: ['K' 'H' 'I' 'K' 'B'] (weight=8)\n", "BOOTSTRAP: ['M' 'D' 'A' 'M' 'K'] (weight=8)\n", "BOOTSTRAP: ['B' 'G' 'D' 'N' 'E'] (weight=8)\n" ] } ], "source": [ "from bootstrapsplit import WeightLimitedBootstrap\n", "\n", "w = np.random.randint(low=1, high=5, size=len(pop))\n", "wb = WeightLimitedBootstrap(w, n_iter=3, max_weight=len(pop))\n", "print(wb)\n", "print(\"POPULATION:\", pop, \"(weight=%s)\" % w.sum())\n", "for s in wb:\n", " print(\"BOOTSTRAP:\", pop[s], \"(weight=%s)\" % w[tr].sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##See Also\n", "* [jackknife resampling](http://en.wikipedia.org/wiki/Jackknife_resampling \"Jackknife resampling\")\n", "* [randomisation tests](https://www.uvm.edu/~dhowell/StatPages/Resampling/RandomizationTests.html \"Randomisation tests\")\n", "* [exact tests](http://en.wikipedia.org/wiki/Exact_test \"Exact tests\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## References\n", "Efron, Bradley; Tibshirani, Robert J. (1993). An introduction to the bootstrap, New York: Chapman & Hall" ] } ], "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-3-clause
ICGC-TCGA-PanCancer/pcawg-infrastructure-paper
pcawg-infrastructure-paper.ipynb
1
107146
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# About\n", "\n", "This Jupyter notebook contains the beginnings of our analysis code for the [PCAWG](https://dcc.icgc.org/pcawg) infrastructure paper. This paper describes our efforts to run the core analysis for the project, namely the alignment workflow with 3 variant calling workflows. We built various infrastructure components that let us do this in a distributed way across many (14) cloud and HPC environments.\n", "\n", "The main text of the paper is currently in a private Google document. This notebook will be used (maybe) to generate our figures if I can figure out how to program in Python!\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10 Longest Running Normals for BWA (icgc_specimen_id|icgc_sample_id|submitter_specimen_id|submitter_sample_id)\n", "[('SP127702|SA557379|Tb188_Blood_DNA|EXTERN-MELA-20140924-028', 566.8733333333333), ('SP111840|SA529519|HX13L|HX13L', 465.2969444444444), ('SP112804|SA530455|CPCG0117-B1|CPCG0117-B1', 357.8644444444444), ('SP112810|SA530461|CPCG0124-B1|CPCG0124-B1', 316.81666666666666), ('SP102745|SA507170|C0005N|C0005N', 263.1227777777778), ('SP112808|SA530459|CPCG0123-B1|CPCG0123-B1', 258.01944444444445), ('SP112942|SA530585|CPCG0358-B1|CPCG0358-B1', 253.6363888888889), ('SP112814|SA530465|CPCG0128-B1|CPCG0128-B1', 251.16916666666665), ('SP103296|SA507138|C0085N|C0085N', 247.1302777777778), ('SP102632|SA506748|CPCG0102-B1|CPCG0102-B1', 241.47722222222222)]\n", "10 Longest Running Tumors for BWA (icgc_specimen_id|icgc_sample_id|submitter_specimen_id|submitter_sample_id)\n", "[('SP114898|SA538863|A10A-0015_CRUK_PC_0015_M1|A10A-0015_CRUK_PC_0015_M1_DNA', 578.661111111111), ('SP114920|SA538893|A22K-0016_CRUK_PC_0016_M9|A22K-0016_CRUK_PC_0016_M9_DNA', 441.41416666666663), ('SP114974|SA538898|A21D-0096_CRUK_PC_0096_M3|A21D-0096_CRUK_PC_0096_M3_DNA', 394.1886111111111), ('SP114914|SA538886|A22G-0016_CRUK_PC_0016_M5|A22G-0016_CRUK_PC_0016_M5_DNA', 386.1169444444444), ('SP116267|SA541756|EOPC-037_tumor_04|EOPC-037_tumor_04', 351.1425), ('SP114984|SA538907|A21I-0096_CRUK_PC_0096_M8|A21I-0096_CRUK_PC_0096_M8_DNA', 347.56388888888887), ('SP114912|SA538884|A22F-0016_CRUK_PC_0016_M4|A22F-0016_CRUK_PC_0016_M4_DNA', 337.15527777777777), ('SP114900|SA538867|A10C-0015_CRUK_PC_0015_M2|A10C-0015_CRUK_PC_0015_M2_DNA', 329.87833333333333), ('SP116266|SA541754|EOPC-037_tumor_03|EOPC-037_tumor_03', 300.5863888888889), ('SP114992|SA538918|A17D-0095_CRUK_PC_0095_M3|A17D-0095_CRUK_PC_0095_M3_DNA', 295.41777777777776)]\n", "BWA Timing Histogram\n" ] }, { "data": { "image/png": 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Qvij6ET6sznX3HU2j7r7IwvNwfkAYBrqaELzsR/om8nQS00OfS7hn+yzhy6Ct21PpZMqb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\nq+nTpzN9+vQWaYsWLWLKlCn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"text/plain": [ "<matplotlib.figure.Figure at 0x13c4587f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.mlab as mlab\n", "import numpy as np\n", "import gzip\n", "from io import StringIO\n", "from io import BytesIO\n", "import urllib\n", "import json\n", "import statistics\n", "import traceback\n", "import sys\n", "import operator\n", "\n", "# file content\n", "file_content = \"\"\n", "\n", "# https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_151215020209.small.jsonl.gz\n", "# http://pancancer.info/gnos_metadata/latest/donor_p_151215020209.jsonl.gz\n", "# https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.1.jsonl.gz\n", "# small\n", "# request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_151215020209.small.jsonl.gz')\n", "# large\n", "request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.1.jsonl.gz')\n", "request.add_header('Accept-encoding', 'gzip')\n", "response = urllib.request.urlopen(request)\n", "buf = BytesIO( response.read())\n", "f = gzip.GzipFile(fileobj=buf)\n", "file_content = f.read()\n", "request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.2.jsonl.gz')\n", "request.add_header('Accept-encoding', 'gzip')\n", "response = urllib.request.urlopen(request)\n", "buf = BytesIO( response.read())\n", "f = gzip.GzipFile(fileobj=buf)\n", "file_content += f.read()\n", "\n", "# BWA Analysis\n", "bwa_normal_timing_list = []\n", "bwa_tumor_timing_list = []\n", "\n", "# timing hash\n", "sorted_normal = {}\n", "sorted_tumor = {}\n", "\n", "for line in file_content.splitlines():\n", " json_struct = json.loads(line.decode(encoding='UTF-8'))\n", " #print(json_struct)\n", " #print(json_struct['normal_specimen'])\n", " \n", " try:\n", " bwa_normal_timing = 0\n", " submitter_specimen_id = json_struct['normal_specimen']['submitter_specimen_id']\n", " submitter_sample_id = json_struct['normal_specimen']['submitter_sample_id']\n", " icgc_specimen_id = json_struct['normal_specimen']['icgc_specimen_id']\n", " icgc_sample_id = json_struct['normal_specimen']['icgc_sample_id']\n", " name_str = icgc_specimen_id+\"|\"+icgc_sample_id+\"|\"+submitter_specimen_id+\"|\"+submitter_sample_id\n", " for timing in json_struct['normal_specimen']['alignment']['timing_metrics'] :\n", " #print(timing['metrics']['bwa_timing_seconds'])\n", " bwa_normal_timing += timing['metrics']['bwa_timing_seconds']\n", " #print(bwa_normal_timing)\n", " bwa_normal_timing_list.append(bwa_normal_timing/60/60)\n", " sorted_normal[name_str] = bwa_normal_timing/60/60\n", " except:\n", " pass\n", " try:\n", " bwa_tumor_timing = 0\n", " #print (json_struct['aligned_tumor_specimens'])\n", " for aligned_tumor in json_struct['aligned_tumor_specimens'] :\n", " submitter_specimen_id = aligned_tumor['submitter_specimen_id']\n", " submitter_sample_id = aligned_tumor['submitter_sample_id']\n", " icgc_specimen_id = aligned_tumor['icgc_specimen_id']\n", " icgc_sample_id = aligned_tumor['icgc_sample_id']\n", " name_str = icgc_specimen_id+\"|\"+icgc_sample_id+\"|\"+submitter_specimen_id+\"|\"+submitter_sample_id\n", " sorted_tumor[name_str] = bwa_tumor_timing/60/60\n", " for timing in aligned_tumor['alignment']['timing_metrics'] :\n", " bwa_tumor_timing += timing['metrics']['bwa_timing_seconds']\n", " #print(bwa_tumor_timing)\n", " bwa_tumor_timing_list.append(bwa_tumor_timing/60/60)\n", " except Exception as err:\n", " pass\n", " \n", "print(\"10 Longest Running Normals for BWA (icgc_specimen_id|icgc_sample_id|submitter_specimen_id|submitter_sample_id)\")\n", "sorted_n = sorted(sorted_normal.items(), key=operator.itemgetter(1), reverse=True)\n", "print (sorted_n[:10])\n", "print(\"10 Longest Running Tumors for BWA (icgc_specimen_id|icgc_sample_id|submitter_specimen_id|submitter_sample_id)\")\n", "sorted_t = sorted(sorted_tumor.items(), key=operator.itemgetter(1), reverse=True)\n", "print (sorted_t[:10])\n", "print(\"BWA Timing Histogram\")\n", "#print(bwa_normal_timing_list)\n", "#print(bwa_tumor_timing_list)\n", "\n", "# histogram\n", "mu = statistics.mean(bwa_normal_timing_list) # mean of distribution\n", "sigma = statistics.stdev(bwa_normal_timing_list) # standard deviation of distribution\n", "num_bins = 30\n", "n, bins, patches = plt.hist(bwa_normal_timing_list, num_bins, normed=1, facecolor='green', alpha=0.5)\n", "n, bins, patches = plt.hist(bwa_tumor_timing_list, num_bins, normed=1, facecolor='blue', alpha=0.5)\n", "# add a 'best fit' line\n", "y = mlab.normpdf(bins, mu, sigma)\n", "plt.plot(bins, y, 'r--')\n", "plt.xlabel('Timing in Hours')\n", "plt.ylabel('Frequency')\n", "plt.title(r\"Histogram BWA Runtime (Hours): $\\mu$=%3d, $\\sigma$=%3d\" % (mu, sigma))\n", "\n", "# Tweak spacing to prevent clipping of ylabel\n", "plt.subplots_adjust(left=0.15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Longest Running Normal for Sanger (donor_unique_id)\n", "PRAD-CA::CPCG0212,1575.0086111111111\n", "UCEC-US::f6d136c7-c250-4361-9fed-50f513959a40,1479.4283333333333\n", "RECA-EU::C0089,1407.3833333333334\n", "BRCA-US::786e8dbe-442e-4551-87b3-b4c333b04dd4,1281.8236111111112\n", "MELA-AU::MELA-0223,828.5161111111112\n", "BRCA-UK::CGP_donor_1212361,824.17\n", "PACA-CA::PCSI_0106,714.6741666666666\n", "PACA-AU::ICGC_0088,689.9052777777778\n", "PBCA-DE::ICGC_MB147,637.125\n", "LINC-JP::HX31,602.8836111111111\n", "RECA-EU::C0010,562.475\n", "RECA-EU::C0012,552.4602777777778\n", "LINC-JP::HX18,550.7916666666666\n", "MELA-AU::MELA-0226,538.2705555555556\n", "PBCA-DE::ICGC_MB12,537.9680555555556\n", "PACA-CA::PCSI_0077,534.7111111111111\n", "ESAD-UK::OCCAMS-AH-182,532.7783333333333\n", "MALY-DE::4188879,525.5252777777778\n", "CLLE-ES::684,519.1416666666667\n", "READ-US::e6827400-0d95-46b0-8874-6ce9e9d5011b,518.4338888888889\n", "ESAD-UK::OCCAMS-PS-001,502.3336111111111\n", "BRCA-US::3fc3755d-a3f8-4e2c-813f-ff124f2a75c1,495.06\n", "PACA-CA::PCSI_0175,489.89166666666665\n", "ESAD-UK::OCCAMS-PS-013,486.48333333333335\n", "RECA-EU::C0001,483.4194444444445\n", "LIRI-JP::RK040,481.97083333333336\n", "GACA-CN::CGP_donor_GC00004,479.52944444444444\n", "ESAD-UK::OCCAMS-ED-036,477.71777777777777\n", "RECA-EU::C0026,475.6122222222222\n", "READ-US::0cb21fb0-520f-4105-99ec-697a335115b5,469.6363888888889\n", "ESAD-UK::OCCAMS-PS-012,463.0708333333333\n", "ESAD-UK::OCCAMS-AH-071,460.1458333333333\n", "LINC-JP::HX28,456.61277777777775\n", "BRCA-US::1174f6e4-ffbe-4e59-a000-8d861c968369,452.4536111111111\n", "RECA-EU::C0032,450.2838888888889\n", "ESAD-UK::OCCAMS-PS-014,450.1458333333333\n", "ESAD-UK::OCCAMS-PS-003,448.5708333333333\n", "LINC-JP::HX16,446.1794444444444\n", "PACA-CA::PCSI_0081,445.9675\n", "RECA-EU::C0060,441.90833333333336\n", "PACA-AU::ICGC_0135,441.2\n", "BRCA-US::4bf50455-9ab7-4521-a791-089f66d3b877,438.2580555555556\n", "PACA-CA::PCSI_0073,436.3461111111111\n", "ESAD-UK::OCCAMS-RS-022,435.9\n", "LAML-KR::SNU_WGS_09,435.26666666666665\n", "ESAD-UK::OCCAMS-WG-019,434.91277777777776\n", "PRAD-CA::CPCG0117,434.84805555555556\n", "ESAD-UK::OCCAMS-RS-035,434.7608333333334\n", "LINC-JP::HX25,434.6875\n", "RECA-EU::C0059,432.56694444444446\n", "Sanger Somatic Variant Timing Histogram\n" ] }, { "data": { "image/png": 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dkS6fCEwFRgILgFXAaeXqpqu+DrhO0hxgDXBKehZhZmZVkudlJSJiKkkCKJw3seB9AGdl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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1460b6b00>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.mlab as mlab\n", "import numpy as np\n", "import gzip\n", "from io import StringIO\n", "from io import BytesIO\n", "import urllib\n", "import json\n", "import statistics\n", "import traceback\n", "import sys\n", "import operator\n", "\n", "# file content\n", "file_content = \"\"\n", "\n", "# https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_151215020209.small.jsonl.gz\n", "# http://pancancer.info/gnos_metadata/latest/donor_p_151215020209.jsonl.gz\n", "# https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.1.jsonl.gz\n", "# small\n", "#request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_151215020209.small.jsonl.gz')\n", "# large\n", "request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.1.jsonl.gz')\n", "request.add_header('Accept-encoding', 'gzip')\n", "response = urllib.request.urlopen(request)\n", "buf = BytesIO( response.read())\n", "f = gzip.GzipFile(fileobj=buf)\n", "file_content = f.read()\n", "request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.2.jsonl.gz')\n", "request.add_header('Accept-encoding', 'gzip')\n", "response = urllib.request.urlopen(request)\n", "buf = BytesIO( response.read())\n", "f = gzip.GzipFile(fileobj=buf)\n", "file_content += f.read()\n", "\n", "# multi-tumor\n", "multi_tumor = {}\n", "multi_tumor_list = 'https://raw.githubusercontent.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/develop/donor_multitumor.txt'\n", "response = urllib.request.urlopen(multi_tumor_list)\n", "data = response.read() # a `bytes` object\n", "text = data.decode('utf-8') # a `str`\n", "for line in text.splitlines():\n", " line.rstrip(\"\\n\\r\")\n", " multi_tumor[line] = True\n", "\n", "# Sanger Analysis\n", "timing_list = []\n", "\n", "# timing hash\n", "sorted_timing = {}\n", "\n", "for line in file_content.splitlines():\n", " json_struct = json.loads(line.decode(encoding='UTF-8'))\n", " #print(json_struct)\n", " #print (type(json_struct['variant_calling_results']['sanger_variant_calling']['workflow_details']['variant_timing_metrics']))\n", " #print (json_struct['variant_calling_results']['sanger_variant_calling']['workflow_details']['variant_timing_metrics'].keys())\n", " try:\n", " donor_unique_id = json_struct['donor_unique_id']\n", " if donor_unique_id in multi_tumor.keys():\n", " #print (\"MULTITUMOR FOUND!!!\")\n", " continue\n", " submitter_specimen_id = json_struct['normal_specimen']['submitter_specimen_id']\n", " submitter_sample_id = json_struct['normal_specimen']['submitter_sample_id']\n", " icgc_specimen_id = json_struct['normal_specimen']['icgc_specimen_id']\n", " icgc_sample_id = json_struct['normal_specimen']['icgc_sample_id']\n", " #name_str = icgc_specimen_id+\"|\"+icgc_sample_id+\"|\"+submitter_specimen_id+\"|\"+submitter_sample_id\n", " name_str = donor_unique_id\n", " sanger_timing = 0\n", " timing = json_struct['variant_calling_results']['sanger_variant_calling']['workflow_details']['variant_timing_metrics']['workflow']['Wall_s']\n", " #print (\"TIMING: \" + str(timing))\n", " sanger_timing += timing\n", " #print(bwa_normal_timing)\n", " timing_list.append(sanger_timing/60/60)\n", " sorted_timing[name_str] = sanger_timing/60/60\n", " except:\n", " pass\n", " \n", "#print(\"10 Longest Running Normal for Sanger (icgc_specimen_id|icgc_sample_id|submitter_specimen_id|submitter_sample_id)\")\n", "print(\"Longest Running Normal for Sanger (donor_unique_id)\")\n", "sorted_t = sorted(sorted_timing.items(), key=operator.itemgetter(1), reverse=True)\n", "topn = sorted_t[:50]\n", "for item in topn:\n", " print (item[0]+\",\"+str(item[1]))\n", " \n", "# now just dump this out to a file\n", "f = open('sanger_timing.tsv', 'w')\n", "for item in sorted_timing.keys():\n", " f.write(item+\"\\t\"+str(sorted_timing[item])+\"\\n\")\n", "f.close()\n", "\n", "print(\"Sanger Somatic Variant Timing Histogram\")\n", "#print(bwa_normal_timing_list)\n", "#print(bwa_tumor_timing_list)\n", "\n", "# histogram\n", "mu = statistics.mean(timing_list) # mean of distribution\n", "sigma = statistics.stdev(timing_list) # standard deviation of distribution\n", "num_bins = 30\n", "n, bins, patches = plt.hist(timing_list, num_bins, normed=1, facecolor='blue', alpha=0.5)\n", "# add a 'best fit' line\n", "y = mlab.normpdf(bins, mu, sigma)\n", "plt.plot(bins, y, 'r--')\n", "plt.xlabel('Timing in Hours')\n", "plt.ylabel('Frequency')\n", "plt.title(r\"Histogram Sanger Runtime (Hours): $\\mu$=%3d, $\\sigma$=%3d\" % (mu, sigma))\n", "\n", "# Tweak spacing to prevent clipping of ylabel\n", "plt.subplots_adjust(left=0.15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Longest Running Normal for DKFZ/EMBL (donor_unique_id)\n", "PRAD-CA::CPCG0354,392.1788888888889\n", "LINC-JP::HX12,388.8333333333333\n", "MELA-AU::MELA-0226,367.16416666666663\n", "PACA-CA::PCSI_0047,266.7988888888889\n", "LINC-JP::HX28,263.29333333333335\n", "RECA-EU::C0002,259.33194444444445\n", "PRAD-CA::CPCG0358,255.66611111111112\n", "PACA-CA::PCSI_0235,248.76083333333332\n", "RECA-EU::C0091,239.24166666666667\n", "RECA-EU::C0010,238.7447222222222\n", "PACA-CA::PCSI_0297,233.0538888888889\n", "BOCA-UK::CGP_donor_1691206,226.88833333333332\n", "RECA-EU::C0098,221.67055555555555\n", "MELA-AU::MELA-0238,208.7813888888889\n", "PACA-CA::PCSI_0509,204.43722222222223\n", "PACA-CA::PCSI_0250,203.3086111111111\n", "PACA-CA::PCSI_0290,202.50916666666666\n", "PACA-CA::PCSI_0286,201.57222222222222\n", "PACA-CA::PCSI_0326,201.42388888888888\n", "PACA-CA::PCSI_0228,200.3838888888889\n", "BRCA-UK::CGP_donor_1230722,200.1197222222222\n", "LIRI-JP::RK047,199.81194444444446\n", "RECA-EU::C0001,199.6913888888889\n", "PACA-CA::PCSI_0325,197.4388888888889\n", "PACA-CA::PCSI_0508,191.72777777777776\n", "PACA-CA::PCSI_0324,189.36083333333332\n", "PACA-CA::PCSI_0292,184.83083333333335\n", "PACA-CA::PCSI_0305,181.00222222222223\n", "PACA-CA::PCSI_0356,179.49166666666667\n", "BRCA-UK::CGP_donor_1167080,179.22\n", "PACA-CA::PCSI_0504,178.72666666666666\n", "PACA-CA::PCSI_0329,177.2811111111111\n", "PACA-AU::ICGC_0392,175.67833333333334\n", "PACA-CA::PCSI_0506,174.42805555555555\n", "PACA-CA::PCSI_0227,173.80194444444444\n", "PACA-CA::PCSI_0253,173.0586111111111\n", "MELA-AU::MELA-0234,171.80499999999998\n", "MELA-AU::MELA-0236,169.14722222222224\n", "ESAD-UK::OCCAMS-AH-182,166.5375\n", "BOCA-UK::CGP_donor_1397077,164.9072222222222\n", "PRAD-CA::CPCG0346,164.27305555555554\n", "RECA-EU::C0012,161.56333333333333\n", "PRAD-CA::CPCG0339,161.0975\n", "PACA-CA::PCSI_0294,161.01416666666668\n", "MELA-AU::MELA-0259,157.9072222222222\n", "ESAD-UK::OCCAMS-RS-035,155.8552777777778\n", "MELA-AU::MELA-0223,155.73777777777778\n", "PACA-CA::PCSI_0302,155.5938888888889\n", "PACA-CA::PCSI_0048,154.85194444444446\n", "PACA-CA::PCSI_0019,153.89166666666668\n", "DKFZ/EMBL Somatic Variant Timing Histogram\n" ] }, { "data": { "image/png": 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INzRJ7YELSJqU6io7jqSpij59+mQcmZlZ85LlkcNCoHfOcK90XH3L5NoZ6Ac8L+n1tPwz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\naSvgOGBqXpmpwElKDAFWRMT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"text/plain": [ "<matplotlib.figure.Figure at 0x142927320>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.mlab as mlab\n", "import numpy as np\n", "import gzip\n", "from io import StringIO\n", "from io import BytesIO\n", "import urllib\n", "import json\n", "import statistics\n", "import traceback\n", "import sys\n", "import operator\n", "\n", "# file content\n", "file_content = \"\"\n", "\n", "# https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_151215020209.small.jsonl.gz\n", "# http://pancancer.info/gnos_metadata/latest/donor_p_151215020209.jsonl.gz\n", "# https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.1.jsonl.gz\n", "# small\n", "#request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_151215020209.small.jsonl.gz')\n", "# large\n", "#request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.1.jsonl.gz')\n", "request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.1.jsonl.gz')\n", "request.add_header('Accept-encoding', 'gzip')\n", "response = urllib.request.urlopen(request)\n", "buf = BytesIO( response.read())\n", "f = gzip.GzipFile(fileobj=buf)\n", "file_content = f.read()\n", "request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.2.jsonl.gz')\n", "request.add_header('Accept-encoding', 'gzip')\n", "response = urllib.request.urlopen(request)\n", "buf = BytesIO( response.read())\n", "f = gzip.GzipFile(fileobj=buf)\n", "file_content += f.read()\n", "\n", "# multi-tumor\n", "multi_tumor = {}\n", "multi_tumor_list = 'https://raw.githubusercontent.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/develop/donor_multitumor.txt'\n", "response = urllib.request.urlopen(multi_tumor_list)\n", "data = response.read() # a `bytes` object\n", "text = data.decode('utf-8') # a `str`\n", "for line in text.splitlines():\n", " line.rstrip(\"\\n\\r\")\n", " multi_tumor[line] = True\n", "\n", "# Sanger Analysis\n", "timing_list = []\n", "\n", "# timing hash\n", "sorted_timing = {}\n", "\n", "for line in file_content.splitlines():\n", " json_struct = json.loads(line.decode(encoding='UTF-8'))\n", " #print(json_struct['variant_calling_results']['dkfz_embl_variant_calling']['workflow_details']['variant_timing_metrics']['dkfz']['timing_metrics'][0]['workflow']['embl_timing_seconds'])\n", " try:\n", " timing = 0\n", " donor_unique_id = json_struct['donor_unique_id']\n", " if donor_unique_id in multi_tumor.keys():\n", " #print (\"MULTITUMOR FOUND!!!\")\n", " continue\n", " submitter_specimen_id = json_struct['normal_specimen']['submitter_specimen_id']\n", " submitter_sample_id = json_struct['normal_specimen']['submitter_sample_id']\n", " icgc_specimen_id = json_struct['normal_specimen']['icgc_specimen_id']\n", " icgc_sample_id = json_struct['normal_specimen']['icgc_sample_id']\n", " #name_str = icgc_specimen_id+\"|\"+icgc_sample_id+\"|\"+submitter_specimen_id+\"|\"+submitter_sample_id\n", " name_str = donor_unique_id\n", " embl_timing = json_struct['variant_calling_results']['dkfz_embl_variant_calling']['workflow_details']['variant_timing_metrics']['dkfz']['timing_metrics'][0]['workflow']['embl_timing_seconds']\n", " dkfz_timing = json_struct['variant_calling_results']['dkfz_embl_variant_calling']['workflow_details']['variant_timing_metrics']['dkfz']['timing_metrics'][0]['workflow']['dkfz_timing_seconds']\n", " #print (\"TIMING: \" + str(timing))\n", " timing = embl_timing + dkfz_timing\n", " #print(bwa_normal_timing)\n", " timing_list.append(timing/60/60)\n", " sorted_timing[name_str] = timing/60/60\n", " except:\n", " pass\n", "\n", "#print(\"10 Longest Running Normal for DKFZ/EMBL (icgc_specimen_id|icgc_sample_id|submitter_specimen_id|submitter_sample_id)\")\n", "print(\"Longest Running Normal for DKFZ/EMBL (donor_unique_id)\")\n", "sorted_t = sorted(sorted_timing.items(), key=operator.itemgetter(1), reverse=True)\n", "topn = sorted_t[:50]\n", "for item in topn:\n", " print (item[0]+\",\"+str(item[1]))\n", "\n", "# now just dump this out to a file\n", "f = open('dkfz_embl_timing.tsv', 'w')\n", "for item in sorted_timing.keys():\n", " f.write(item+\"\\t\"+str(sorted_timing[item])+\"\\n\")\n", "f.close()\n", " \n", "print(\"DKFZ/EMBL Somatic Variant Timing Histogram\")\n", "\n", "# histogram\n", "mu = statistics.mean(timing_list) # mean of distribution\n", "sigma = statistics.stdev(timing_list) # standard deviation of distribution\n", "num_bins = 30\n", "n, bins, patches = plt.hist(timing_list, num_bins, normed=1, facecolor='blue', alpha=0.5)\n", "# add a 'best fit' line\n", "y = mlab.normpdf(bins, mu, sigma)\n", "plt.plot(bins, y, 'r--')\n", "plt.xlabel('Timing in Hours')\n", "plt.ylabel('Frequency')\n", "plt.title(r\"Histogram DKFZ/EMBL Runtime (Hours): $\\mu$=%3d, $\\sigma$=%3d\" % (mu, sigma))\n", "\n", "# Tweak spacing to prevent clipping of ylabel\n", "plt.subplots_adjust(left=0.15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Longest Running Normal for Broad (donor_unique_id)\n", "LIRI-JP::RK128,887.9975\n", "PACA-CA::PCSI_0101,808.3105555555555\n", "LIRI-JP::RK012,731.3836111111111\n", "BRCA-US::9938ce5c-e74e-446e-a932-f096f85cc3b1,673.1127777777779\n", "OV-AU::AOCS-108,515.2622222222222\n", "PBCA-DE::ICGC_PA166,440.8430555555555\n", "OV-AU::AOCS-115,427.60527777777776\n", "OV-AU::AOCS-079,405.40222222222224\n", "PACA-CA::PCSI_0106,396.8277777777778\n", "OV-AU::AOCS-109,389.0097222222222\n", "PACA-AU::ICGC_0021,382.81305555555554\n", "PACA-CA::PCSI_0109,376.62166666666667\n", "OV-AU::AOCS-111,371.3366666666667\n", "PACA-CA::PCSI_0044,365.83888888888885\n", "UCEC-US::5ffa2ec6-2d94-4b09-8fb9-3591cf38fa4f,363.04249999999996\n", "LIRI-JP::RK093,342.84833333333336\n", "UCEC-US::748e38b1-2ead-4a0a-8881-d640617b856b,332.9183333333333\n", "PACA-CA::PCSI_0046,327.11583333333334\n", "CESC-US::9aa36ac2-8418-4109-a3c1-63ca8742baab,324.48527777777775\n", "THCA-US::c7a2f394-3e3f-4c90-9f1e-f2be3e5b0d6b,319.5588888888889\n", "BRCA-US::e9f4f373-37a5-48ad-a1a0-b0d47820111a,308.87166666666667\n", "BRCA-US::d8d7a6c2-6427-4f47-968c-6c3affba4617,308.05527777777775\n", "BOCA-UK::CGP_donor_1691213,300.6105555555556\n", "PACA-CA::PCSI_0305,298.39166666666665\n", "OV-AU::AOCS-164,297.4441666666667\n", "PACA-AU::ICGC_0391,294.22527777777776\n", "PACA-CA::PCSI_0351,290.87722222222226\n", "LUSC-US::9a874b64-d0d6-416e-97bc-e9071ed0b16b,290.2005555555555\n", "UCEC-US::d2aa7d70-a25f-4a9e-9d52-37e372461824,287.0886111111111\n", "PBCA-DE::ICGC_MB143,283.2452777777778\n", "THCA-US::acb80b76-2394-4250-8687-60e40e16e83b,280.7347222222222\n", "LUAD-US::a8d6694c-a213-4544-ac0b-63bce16d8f4e,279.70916666666665\n", "THCA-US::cfdd2155-57a9-499c-92e1-fb82b577b050,277.5852777777778\n", "PACA-AU::ICGC_0099,272.86805555555554\n", "UCEC-US::79ecc142-8f6e-4260-a7ce-c8e2a6212c35,268.00972222222225\n", "OV-AU::AOCS-159,266.0225\n", "PACA-AU::ICGC_0354,264.36055555555555\n", "LUSC-US::aecf85cc-058c-46f3-9cdc-3573ac3b8438,263.3197222222222\n", "PACA-CA::PCSI_0355,263.24694444444447\n", "PACA-AU::ICGC_0395,261.77555555555557\n", "OV-AU::AOCS-096,258.44138888888887\n", "UCEC-US::c88e3901-f3ee-436a-ba80-143c8eab7d69,254.09722222222223\n", "OV-AU::AOCS-097,253.80777777777777\n", "OV-AU::AOCS-104,252.09305555555557\n", "OV-AU::AOCS-163,247.75222222222223\n", "OV-AU::AOCS-167,243.1161111111111\n", "OV-AU::AOCS-088,236.43638888888887\n", "OV-AU::AOCS-092,236.17111111111112\n", "UCEC-US::c10b2885-70ea-4d32-bc48-8b85530df702,236.10972222222225\n", "OV-AU::AOCS-165,234.22861111111112\n", "Broad Somatic Variant Timing Histogram\n" ] }, { "data": { "image/png": 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2Xp1tJT0q6ZX07zaV3Aczs1pUseQgqQ1wNTAC6A+MkdQ/r9gIoF/6GAtcm7PsZmB4A6u+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\nHMzMrMD/B6XcCYgGk6fgAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1461a0390>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "import matplotlib.mlab as mlab\n", "import numpy as np\n", "import gzip\n", "from io import StringIO\n", "from io import BytesIO\n", "import urllib\n", "import json\n", "import statistics\n", "import traceback\n", "import sys\n", "import operator\n", "\n", "\n", "# file content\n", "file_content = \"\"\n", "\n", "# https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_151215020209.small.jsonl.gz\n", "# http://pancancer.info/gnos_metadata/latest/donor_p_151215020209.jsonl.gz\n", "# https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.1.jsonl.gz\n", "# small\n", "#request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_151215020209.small.jsonl.gz')\n", "# large\n", "#request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.1.jsonl.gz')\n", "request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.1.jsonl.gz')\n", "request.add_header('Accept-encoding', 'gzip')\n", "response = urllib.request.urlopen(request)\n", "buf = BytesIO( response.read())\n", "f = gzip.GzipFile(fileobj=buf)\n", "file_content = f.read()\n", "request = urllib.request.Request('https://github.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/raw/develop/donor_p_160408020209.2.jsonl.gz')\n", "request.add_header('Accept-encoding', 'gzip')\n", "response = urllib.request.urlopen(request)\n", "buf = BytesIO( response.read())\n", "f = gzip.GzipFile(fileobj=buf)\n", "file_content += f.read()\n", "\n", "# multi-tumor\n", "multi_tumor = {}\n", "multi_tumor_list = 'https://raw.githubusercontent.com/ICGC-TCGA-PanCancer/pcawg-infrastructure-paper/develop/donor_multitumor.txt'\n", "response = urllib.request.urlopen(multi_tumor_list)\n", "data = response.read() # a `bytes` object\n", "text = data.decode('utf-8') # a `str`\n", "for line in text.splitlines():\n", " line.rstrip(\"\\n\\r\")\n", " multi_tumor[line] = True\n", "\n", "# Sanger Analysis\n", "timing_list = []\n", "\n", "# timing hash\n", "sorted_timing = {}\n", "\n", "for line in file_content.splitlines():\n", " json_struct = json.loads(line.decode(encoding='UTF-8'))\n", " #print(str(json_struct['variant_calling_results']['broad_variant_calling']['workflow_details']['variant_timing_metrics']['MuSE'][0]['runtime_seconds']))\n", " #['dkfz']['timing_metrics'][0]['workflow']['embl_timing_seconds'])\n", " #['MuSE', 'gatk_bqsr', 'cghub_genetorrent', 'gatk_indel', 'broad_variant_pipline']\n", " try:\n", " timing = 0\n", " donor_unique_id = json_struct['donor_unique_id']\n", " if donor_unique_id in multi_tumor.keys():\n", " #print (\"MULTITUMOR FOUND!!!\")\n", " continue\n", " #print (json_struct['variant_calling_results']['broad_variant_calling']['workflow_details']['variant_timing_metrics']['MuSE'][0]['runtime_seconds'])\n", " muse_timing = float(json_struct['variant_calling_results']['broad_variant_calling']['workflow_details']['variant_timing_metrics']['MuSE'][0]['runtime_seconds'])\n", " #print (\"here!!!!!!!!\")\n", " gatk_bqsr_timing = float(json_struct['variant_calling_results']['broad_variant_calling']['workflow_details']['variant_timing_metrics']['gatk_bqsr'][0]['runtime_seconds'])\n", " cghub_genetorrent_timing = float(json_struct['variant_calling_results']['broad_variant_calling']['workflow_details']['variant_timing_metrics']['cghub_genetorrent'][0]['runtime_seconds'])\n", " gatk_indel_timing = float(json_struct['variant_calling_results']['broad_variant_calling']['workflow_details']['variant_timing_metrics']['gatk_indel'][0]['runtime_seconds'])\n", " broad_variant_pipline_timing = float(json_struct['variant_calling_results']['broad_variant_calling']['workflow_details']['variant_timing_metrics']['broad_variant_pipline'][0]['runtime_seconds'])\n", " timing = muse_timing + gatk_bqsr_timing + gatk_indel_timing + broad_variant_pipline_timing\n", " submitter_specimen_id = json_struct['normal_specimen']['submitter_specimen_id']\n", " submitter_sample_id = json_struct['normal_specimen']['submitter_sample_id']\n", " icgc_specimen_id = json_struct['normal_specimen']['icgc_specimen_id']\n", " icgc_sample_id = json_struct['normal_specimen']['icgc_sample_id']\n", " name_str = donor_unique_id\n", " #name_str = icgc_specimen_id+\"|\"+icgc_sample_id+\"|\"+submitter_specimen_id+\"|\"+submitter_sample_id\n", " #print (\"TIMING: \" + str(timing))\n", " #print(bwa_normal_timing)\n", " timing_list.append(timing/60/60)\n", " sorted_timing[name_str] = timing/60/60\n", " except:\n", " #print (\"Unexpected error:\", sys.exc_info()[0])\n", " pass\n", "\n", "#print(\"10 Longest Running Normal for Broad (icgc_specimen_id|icgc_sample_id|submitter_specimen_id|submitter_sample_id)\")\n", "print(\"Longest Running Normal for Broad (donor_unique_id)\")\n", "sorted_t = sorted(sorted_timing.items(), key=operator.itemgetter(1), reverse=True)\n", "topn = sorted_t[:50]\n", "for item in topn:\n", " print (item[0]+\",\"+str(item[1]))\n", " \n", "# now just dump this out to a file\n", "f = open('broad_timing.tsv', 'w')\n", "for item in sorted_timing.keys():\n", " f.write(item+\"\\t\"+str(sorted_timing[item])+\"\\n\")\n", "f.close()\n", "\n", "print(\"Broad Somatic Variant Timing Histogram\")\n", "\n", "# histogram\n", "mu = statistics.mean(timing_list) # mean of distribution\n", "sigma = statistics.stdev(timing_list) # standard deviation of distribution\n", "num_bins = 30\n", "n, bins, patches = plt.hist(timing_list, num_bins, normed=1, facecolor='blue', alpha=0.5)\n", "# add a 'best fit' line\n", "y = mlab.normpdf(bins, mu, sigma)\n", "plt.plot(bins, y, 'r--')\n", "plt.xlabel('Timing in Hours')\n", "plt.ylabel('Frequency')\n", "plt.title(r\"Histogram Broad Runtime (Hours): $\\mu$=%3d, $\\sigma$=%3d\" % (mu, sigma))\n", "\n", "# Tweak spacing to prevent clipping of ylabel\n", "plt.subplots_adjust(left=0.15)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
biof-309-python/BIOF309-2016-Fall
Week_03/Week03 - 03 - Working with Files.ipynb
1
22101
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Working with Files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook builds upon the [Python for Biologists](http://pythonforbiologists.com/index.php/introduction-to-python-for-biologists/reading-and-writing-files/) and [Software Carpentry](https://swcarpentry.github.io/python-novice-inflammation/04-files/) materials." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Text versus Binary Files\n", "\n", "A text file is a file that contains charaster or string data. Openning a text file in a text editor will display the file. Some exmaples of bioinformatics text documents include:\n", "\n", "- FASTA files of DNA or protein sequences\n", "- files containing output from command-line programs (e.g. BLAST)\n", "- FASTQ files containing DNA sequencing reads\n", "- HTML files\n", "- word processing documents\n", "- and of course, Python code\n", "\n", "In contrast, many other files will be binary files – ones which are not made up of characters and lines, but of bytes. Examples include:\n", "\n", "- BAM files\n", "- image files (JPEGs and PNGs)\n", "- audio files\n", "- video files\n", "- compressed files (e.g. ZIP files)\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making a new file and writing to it" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sequence_description = \">gb:AF333238|A/Brevig Mission/1/1918(H1N1)|Segment:8|Subtype:H1N1|Host:Human\"\n", "sequence = \"ATGGATTCCAACACTGTGTCAAGCTTTCAGGTAGACTGCTTTCTTTGGCATGTCCGCAAACGGTTTGCAG\\n\\\n", "ACCAAGAACTGGGTGATGCCCCATTCCTTGATCGGCTTCGCCGAGATCAGAAGTCCCTAAGAGGAAGAGG\\n\\\n", "CAGCACTCTTGGTCTGGACATCGAGACAGCCACCCGTGCTGGAAAGCAGATAGTGGAGCGGATTCTGAAG\\n\\\n", "GAAGAATCCGATGAGGCACTTAAAATGACCATTGCCTCTGTACCTGCTTCGCGCTACCTAACTGACATGA\\n\\\n", "CTCTTGAGGAGATGTCAAGGGACTGGTTCATGCTCATGCCCAAGCAGAAAGTGGCAGGCTCTCTTTGTAT\\n\\\n", "CAGAATGGACCAGGCGATCATGGATAAGAACATCATACTGAAAGCGAACTTCAGTGTGATTTTCGACCGG\\n\\\n", "CTGGAGACTCTAATACTACTAAGGGCTTTCACCGAAGAGGGAGCAATTGTTGGCGAAATTTCACCATTGC\\n\\\n", "CTTCTCTTCCAGGACATACTGATGAGGATGTCAAAAATGCAGTTGGGGTCCTCATCGGAGGACTTGAATG\\n\\\n", "GAATGATAACACAGTTCGAGTCTCTGAAACTCTACAGAGATTCGCTTGGAGAAGCAGTAATGAGAATGGG\\n\\\n", "AGACCTCCACTCCCTCCAAAACAGAAACGGAAAATGGCGAGAACAATTAAGTCAGAAGTTTGAAGAAATA\\n\\\n", "AGATGGTTGATTGAAGAAGTGAGACATAGACTGAAGATAACAGAGAATAGTTTTGAGCAAATAACATTTA\\n\\\n", "TGCAAGCCTTACAACTATTGCTTGAAGTGGAGCAAGAGATAAGAACTTTCTCGTTTCAGCTTATTTAA\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets say that we want to save the information above into a FASTA file. How do we do it?\n", "\n", "1. We have to create a new file\n", "2. We have to give the file a name\n", "3. We have to tell python that we want to write to teh file, not just read form it.\n", "4. As a good programmer we will close teh file when we are done with it\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### How do we create a new file?\n", "\n", "We create a new file using the _open()_ function of python" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function open in module io:\n", "\n", "open(file, mode='r', buffering=-1, encoding=None, errors=None, newline=None, closefd=True, opener=None)\n", " Open file and return a stream. Raise IOError upon failure.\n", " \n", " file is either a text or byte string giving the name (and the path\n", " if the file isn't in the current working directory) of the file to\n", " be opened or an integer file descriptor of the file to be\n", " wrapped. (If a file descriptor is given, it is closed when the\n", " returned I/O object is closed, unless closefd is set to False.)\n", " \n", " mode is an optional string that specifies the mode in which the file\n", " is opened. It defaults to 'r' which means open for reading in text\n", " mode. Other common values are 'w' for writing (truncating the file if\n", " it already exists), 'x' for creating and writing to a new file, and\n", " 'a' for appending (which on some Unix systems, means that all writes\n", " append to the end of the file regardless of the current seek position).\n", " In text mode, if encoding is not specified the encoding used is platform\n", " dependent: locale.getpreferredencoding(False) is called to get the\n", " current locale encoding. (For reading and writing raw bytes use binary\n", " mode and leave encoding unspecified.) The available modes are:\n", " \n", " ========= ===============================================================\n", " Character Meaning\n", " --------- ---------------------------------------------------------------\n", " 'r' open for reading (default)\n", " 'w' open for writing, truncating the file first\n", " 'x' create a new file and open it for writing\n", " 'a' open for writing, appending to the end of the file if it exists\n", " 'b' binary mode\n", " 't' text mode (default)\n", " '+' open a disk file for updating (reading and writing)\n", " 'U' universal newline mode (deprecated)\n", " ========= ===============================================================\n", " \n", " The default mode is 'rt' (open for reading text). For binary random\n", " access, the mode 'w+b' opens and truncates the file to 0 bytes, while\n", " 'r+b' opens the file without truncation. The 'x' mode implies 'w' and\n", " raises an `FileExistsError` if the file already exists.\n", " \n", " Python distinguishes between files opened in binary and text modes,\n", " even when the underlying operating system doesn't. Files opened in\n", " binary mode (appending 'b' to the mode argument) return contents as\n", " bytes objects without any decoding. In text mode (the default, or when\n", " 't' is appended to the mode argument), the contents of the file are\n", " returned as strings, the bytes having been first decoded using a\n", " platform-dependent encoding or using the specified encoding if given.\n", " \n", " 'U' mode is deprecated and will raise an exception in future versions\n", " of Python. It has no effect in Python 3. Use newline to control\n", " universal newlines mode.\n", " \n", " buffering is an optional integer used to set the buffering policy.\n", " Pass 0 to switch buffering off (only allowed in binary mode), 1 to select\n", " line buffering (only usable in text mode), and an integer > 1 to indicate\n", " the size of a fixed-size chunk buffer. When no buffering argument is\n", " given, the default buffering policy works as follows:\n", " \n", " * Binary files are buffered in fixed-size chunks; the size of the buffer\n", " is chosen using a heuristic trying to determine the underlying device's\n", " \"block size\" and falling back on `io.DEFAULT_BUFFER_SIZE`.\n", " On many systems, the buffer will typically be 4096 or 8192 bytes long.\n", " \n", " * \"Interactive\" text files (files for which isatty() returns True)\n", " use line buffering. Other text files use the policy described above\n", " for binary files.\n", " \n", " encoding is the name of the encoding used to decode or encode the\n", " file. This should only be used in text mode. The default encoding is\n", " platform dependent, but any encoding supported by Python can be\n", " passed. See the codecs module for the list of supported encodings.\n", " \n", " errors is an optional string that specifies how encoding errors are to\n", " be handled---this argument should not be used in binary mode. Pass\n", " 'strict' to raise a ValueError exception if there is an encoding error\n", " (the default of None has the same effect), or pass 'ignore' to ignore\n", " errors. (Note that ignoring encoding errors can lead to data loss.)\n", " See the documentation for codecs.register or run 'help(codecs.Codec)'\n", " for a list of the permitted encoding error strings.\n", " \n", " newline controls how universal newlines works (it only applies to text\n", " mode). It can be None, '', '\\n', '\\r', and '\\r\\n'. It works as\n", " follows:\n", " \n", " * On input, if newline is None, universal newlines mode is\n", " enabled. Lines in the input can end in '\\n', '\\r', or '\\r\\n', and\n", " these are translated into '\\n' before being returned to the\n", " caller. If it is '', universal newline mode is enabled, but line\n", " endings are returned to the caller untranslated. If it has any of\n", " the other legal values, input lines are only terminated by the given\n", " string, and the line ending is returned to the caller untranslated.\n", " \n", " * On output, if newline is None, any '\\n' characters written are\n", " translated to the system default line separator, os.linesep. If\n", " newline is '' or '\\n', no translation takes place. If newline is any\n", " of the other legal values, any '\\n' characters written are translated\n", " to the given string.\n", " \n", " If closefd is False, the underlying file descriptor will be kept open\n", " when the file is closed. This does not work when a file name is given\n", " and must be True in that case.\n", " \n", " A custom opener can be used by passing a callable as *opener*. The\n", " underlying file descriptor for the file object is then obtained by\n", " calling *opener* with (*file*, *flags*). *opener* must return an open\n", " file descriptor (passing os.open as *opener* results in functionality\n", " similar to passing None).\n", " \n", " open() returns a file object whose type depends on the mode, and\n", " through which the standard file operations such as reading and writing\n", " are performed. When open() is used to open a file in a text mode ('w',\n", " 'r', 'wt', 'rt', etc.), it returns a TextIOWrapper. When used to open\n", " a file in a binary mode, the returned class varies: in read binary\n", " mode, it returns a BufferedReader; in write binary and append binary\n", " modes, it returns a BufferedWriter, and in read/write mode, it returns\n", " a BufferedRandom.\n", " \n", " It is also possible to use a string or bytearray as a file for both\n", " reading and writing. For strings StringIO can be used like a file\n", " opened in a text mode, and for bytes a BytesIO can be used like a file\n", " opened in a binary mode.\n", "\n" ] } ], "source": [ "help(open)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## How to we read the help information?\n", "\n", "- What arguments (or options) are reguired? (Hint: file is a required argument in the open function)\n", "\n", "- What arguments are optional? (Hint: all other arguments are optional)\n", "\n", "- How do I know? (Hint: do you see any equal signs?)\n", "\n", "- Positional versus keyword" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So, these python commands are the same:\n", " \n", " open(\"new_file.txt\")\n", " open(\"new_file.txt\", 'r')\n", " open(file=\"new_file.txt\", mode='r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lets write our data to a new file" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Now that we have the file name we will create a few file,\n", "# which RETURNS a _file ahdnle_\n", "\n", "my_file_connector = open(\"flu_seg_8.fasta\", 'w')" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "76" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result", "text": [] } ], "source": [ "# Now that we ahve a file handle we can write to the file handle\n", "\n", "my_file_connector.write" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "849" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result", "text": [] } ], "source": [ "my_file_connector.write(sequence)" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "849" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result", "text": [] } ], "source": [ "len(sequence)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is the number that is returned? It is the number of characters written by the _write()_ function." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# No lets close our file taht we have written to\n", "\n", "my_file_connector.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# %load flu_seg_8.fasta\n", ">gb:AF333238|A/Brevig Mission/1/1918(H1N1)|Segment:8|Subtype:H1N1|Host:HumanATGGATTCCAACACTGTGTCAAGCTTTCAGGTAGACTGCTTTCTTTGGCATGTCCGCAAACGGTTTGCAG\n", "ACCAAGAACTGGGTGATGCCCCATTCCTTGATCGGCTTCGCCGAGATCAGAAGTCCCTAAGAGGAAGAGG\n", "CAGCACTCTTGGTCTGGACATCGAGACAGCCACCCGTGCTGGAAAGCAGATAGTGGAGCGGATTCTGAAG\n", "GAAGAATCCGATGAGGCACTTAAAATGACCATTGCCTCTGTACCTGCTTCGCGCTACCTAACTGACATGA\n", "CTCTTGAGGAGATGTCAAGGGACTGGTTCATGCTCATGCCCAAGCAGAAAGTGGCAGGCTCTCTTTGTAT\n", "CAGAATGGACCAGGCGATCATGGATAAGAACATCATACTGAAAGCGAACTTCAGTGTGATTTTCGACCGG\n", "CTGGAGACTCTAATACTACTAAGGGCTTTCACCGAAGAGGGAGCAATTGTTGGCGAAATTTCACCATTGC\n", "CTTCTCTTCCAGGACATACTGATGAGGATGTCAAAAATGCAGTTGGGGTCCTCATCGGAGGACTTGAATG\n", "GAATGATAACACAGTTCGAGTCTCTGAAACTCTACAGAGATTCGCTTGGAGAAGCAGTAATGAGAATGGG\n", "AGACCTCCACTCCCTCCAAAACAGAAACGGAAAATGGCGAGAACAATTAAGTCAGAAGTTTGAAGAAATA\n", "AGATGGTTGATTGAAGAAGTGAGACATAGACTGAAGATAACAGAGAATAGTTTTGAGCAAATAACATTTA\n", "TGCAAGCCTTACAACTATTGCTTGAAGTGGAGCAAGAGATAAGAACTTTCTCGTTTCAGCTTATTTAA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Questions...\n", "\n", "1. Did we create a file?\n", "2. Is the file correctly formatted?\n", "3. If not, how do we fix it?\n", "4. How to we check it again?" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "849" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result", "text": [] } ], "source": [ "my_file_connector.write(sequence_description + '\\n')\n", "my_file_connector.write(sequence)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# We ahve closed the stream to our file\n", "\n", "my_file_connector = open('flu_seg_8.fasta', 'w')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%load flu_seg_8.fasta\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Opening a file\n", "\n", "In Python, as in the physical world, we have to open a file before we can read what’s inside it. The Python function that carries out the job of opening a file is very sensibly called open. It takes one argument – a string which contains the name of the file – and returns a file object:\n", "\n", " sequence_file = open('flu_seg_8.fasta\")" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Type the above command here and run it\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Files, contents and file names\n", "\n", "When learning to work with files it’s very easy to get confused between a file handle, a file name, and the contents of a file. Take a look at the following bit of code:\n", "\n", " my_file_name = \"flu_seg_8.fasta\"\n", " my_file_handle = open(my_file_name)\n", " my_file_contents = my_file_handle.read()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Type the above commands here and run it\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What _type_ of object is each variable above? Try:\n", "\n", " type(my_file_name)\n", " type(my_file_handle)\n", " type(my_file_contents)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Type the above command here and run it\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Common errors:\n", " \n", "A common error is to try to use the read method on the wrong thing. Recall that read is a method that only works on file objects. If we try to use the read method on the file name:\n", "\n", " my_file_name = \"flu_seg_8.fasta\"\n", " my_contents = my_file_name.read()\n", " \n", "we’ll get an AttributeError – Python will complain that strings don’t have a read method3 :\n", "\n", " AttributeError: 'str' object has no attribute 'read'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another common error is to use the file object when we meant to use the file contents. If we try to print the file object:\n", "\n", " my_file_name = \"flu_seg_8.fasta\"\n", " my_file = open('/Users/squiresrb/Doc/BIOF309/week3/my_file_name')\n", " print(my_file)\n", "\n", "we won’t get an error, but we’ll get an odd-looking line of output:\n", "\n", " <open file 'flu_seg_8.fasta', mode 'r' at 0x7fc5ff7784b0>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Missing files\n", "\n", "What happens if we try to read a file that doesn’t exist?\n", "\n", " my_file = open(\"nonexistent.txt\")\n", "\n", "We get a new type of error that we’ve not seen before:\n", "\n", " IOError: [Errno 2] No such file or directory: 'nonexistent.txt'\n", "\n", "Ideally, we’d like to be able to check if a file exists before we try to open it. To do this we use " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Advanced: Working with many files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have almost everything we need to process all our data files. The only thing that’s missing is a library with a rather unpleasant name:\n", "\n", " import glob\n", "\n", "The glob library contains a function, also called glob, that finds files and directories whose names match a pattern. We provide those patterns as strings: the character * matches zero or more characters, while ? matches any one character. We can use this to get the names of all the CSV files in the current directory:\n", "\n", " print(glob.glob('*'))" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['flu_seg_8.fasta']\n" ] } ], "source": [ "import glob\n", "print(glob.glob('*.fasta'))" ] }, { "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": 0 }
mit
ithemal/Ithemal
learning/pytorch/notebooks/Graph Explorer.ipynb
1
3221
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import common_libs.utilities as ut\n", "import pandas as pd\n", "import os\n", "import sys\n", "sys.path.append(os.pardir)\n", "import data.data_cost as dt" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "EMBED_FILE = os.path.join(os.environ['ITHEMAL_HOME'], 'learning', 'pytorch', 'inputs', 'embeddings', 'code_delim.emb')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import imp; imp.reload(dt); imp.reload(ut); imp.reload(dt)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "data = dt.load_dataset('../inputs/embeddings/code_delim.emb', data_savefile='../inputs/data/time_skylake_test.data', arch=1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "instr_is_idempotent = lambda instr: len(set(instr.srcs) & set(instr.dsts)) == 0" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(set(sd.values()))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print(', '.join(map(\"'{}'\".format, [sd[i] for i in range(49, 65)])))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def foo(block):\n", " for instr in block.instrs:\n", " if instr_is_idempotent(instr):\n", " if not(instr.has_mem()) and 'and' in ut._global_sym_dict.get(instr.opcode):\n", " print(instr.intel)\n", " print(instr)\n", " return True\n", "\n", "i = 0\n", "while i < len(data.data) and foo(data.data[i].block):\n", " i += 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "block.clear_edges()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "block.create_dependencies()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "block.random_forward_edges(0.05)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "block.transitive_reduction()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "block.transitive_closure()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "block.draw()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "language_info": { "name": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
quantopian/research_public
notebooks/data/quandl.fred_gdp/notebook.ipynb
3
43634
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Quandl: United Stated Gross Domestic Product\n", "\n", "In this notebook, we'll take a look at data set , available on [Quantopian](https://www.quantopian.com/data). This dataset spans from 1947 through the current day. It contains the value for the United Gross Domestic Product (GDP) as provided by the US Federal Reserve via the [FRED data initiative](https://research.stlouisfed.org/fred2/). We access this data via the API provided by [Quandl](https://www.quandl.com). [More details](https://www.quandl.com/data/FRED/GDP-Gross-Domestic-Product-1-Decimal) on this dataset can be found on Quandl's website.\n", "\n", "### Blaze\n", "Before we dig into the data, we want to tell you about how you generally access Quantopian partner data sets. These datasets are available using the [Blaze](http://blaze.pydata.org) library. Blaze provides the Quantopian user with a convenient interface to access very large datasets.\n", "\n", "Some of these sets (though not this one) are many millions of records. Bringing that data directly into Quantopian Research directly just is not viable. So Blaze allows us to provide a simple querying interface and shift the burden over to the server side.\n", "\n", "To learn more about using Blaze and generally accessing Quantopian Store data, clone [this tutorial notebook](https://www.quantopian.com/clone_notebook?id=561827d21777f45c97000054).\n", "\n", "With preamble in place, let's get started:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import the dataset\n", "from quantopian.interactive.data.quandl import fred_gdp\n", "# Since this data is public domain and provided by Quandl for free, there is no _free version of this\n", "# data set, as found in the premium sets. This import gets you the entirety of this data set.\n", "\n", "# import data operations\n", "from odo import odo\n", "# import other libraries we will use\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " <th>asof_date</th>\n", " <th>value</th>\n", " <th>timestamp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>1947-01-01</td>\n", " <td>243.1</td>\n", " <td>1947-01-01</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>1947-04-01</td>\n", " <td>246.3</td>\n", " <td>1947-04-01</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>1947-07-01</td>\n", " <td>250.1</td>\n", " <td>1947-07-01</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>1947-10-01</td>\n", " <td>260.3</td>\n", " <td>1947-10-01</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>1948-01-01</td>\n", " <td>266.2</td>\n", " <td>1948-01-01</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>1948-04-01</td>\n", " <td>272.9</td>\n", " <td>1948-04-01</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>1948-07-01</td>\n", " <td>279.5</td>\n", " <td>1948-07-01</td>\n", " </tr>\n", " <tr>\n", " <th>7</th>\n", " <td>1948-10-01</td>\n", " <td>280.7</td>\n", " <td>1948-10-01</td>\n", " </tr>\n", " <tr>\n", " <th>8</th>\n", " <td>1949-01-01</td>\n", " <td>275.4</td>\n", " <td>1949-01-01</td>\n", " </tr>\n", " <tr>\n", " <th>9</th>\n", " <td>1949-04-01</td>\n", " <td>271.7</td>\n", " <td>1949-04-01</td>\n", " </tr>\n", " <tr>\n", " <th>10</th>\n", " <td>1949-07-01</td>\n", " <td>273.3</td>\n", " <td>1949-07-01</td>\n", " </tr>\n", " </tbody>\n", "</table>" ], "text/plain": [ " asof_date value timestamp\n", "0 1947-01-01 243.1 1947-01-01\n", "1 1947-04-01 246.3 1947-04-01\n", "2 1947-07-01 250.1 1947-07-01\n", "3 1947-10-01 260.3 1947-10-01\n", "4 1948-01-01 266.2 1948-01-01\n", "5 1948-04-01 272.9 1948-04-01\n", "6 1948-07-01 279.5 1948-07-01\n", "7 1948-10-01 280.7 1948-10-01\n", "8 1949-01-01 275.4 1949-01-01\n", "9 1949-04-01 271.7 1949-04-01\n", "..." ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fred_gdp.sort('asof_date')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The data goes all the way back to 1947 and is updated quarterly.\n", "\n", "Blaze provides us with the first 10 rows of the data for display. Just to confirm, let's just count the number of rows in the Blaze expression:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "275" ], "text/plain": [ "275" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fred_gdp.count()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's go plot for fun. 275 rows are definitely small enough to just put right into a Pandas Dataframe" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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aPrqDqjrZWzsSShjlCQAAALhL6Zk5+tene5SXb9bkYe3l513N2pFgAZQnAAAA\n4C4t/f6oLl25qsd6NFX75j7WjgMLoTwBAAAAd+H42cv6YecZ1fFx0cBuTawdBxZEeQIAAADuUL7Z\n0IJVUTIM6elH2sjejl+vKzJGFwAAALhDP+w8o+hzV3RvkJ9a+3taOw4sjPIEAAAA3IEzcan6bMMR\nVXW00+gHWlo7DkoB5QkAAAC4TUlXsvTa4l3Kys7TMwPbqIaro7UjoRRQngAAAIDbkJWdp9c/3q1L\nKVka0ae5QgP9rB0JpYTyBAAAANyGd1dE6tT5K+rVsZ4Gdm1s7TgoRZQnAAAAoJgORF/Ur1EX1Ly+\nu8Y+EiCTyWTtSChFlCcAAACgGPLNhhavPSSTSRrTr7XsbPlVurJhxAEAAIBi2LL3rE5fSNV9QXXk\nX8fN2nFgBZQnAAAAoAiZV3O17PujquJgqxF9mls7DqyE8gQAAAAUYcWPx5WSlq0B9zWWR3Una8eB\nlVCeAAAAgFuI/CNRq7dFy9fDWf3vbWTtOLAiyhMAAABQiMupVzX3i/2ytTHppeHt5ehgZ+1IsCLK\nEwAAAHAT+WZD73wRoZT0bI18oKUa16lh7UiwMsoTAAAAcBMrtx5X1IlL6tDCVw/d09DacVAGUJ4A\nAACA/+PwqSR98cMxeVZ31PNDAnkYLiRRngAAAIDrpGbkaM7yfZLJpBeHtZdrVQdrR0IZQXkCAAAA\n/pdhGJr/1X5dunJVj/dqqpYNPawdCWUI5QkAAAD4X+t+OaW9RxLUtrGXBnZtYu04KGMoTwAAAICk\nE7GXtWT9Ybm5VNHEx9vJ1ob7nHA9yhMAAAAqvYysXL29bJ/yzYYmPt5ONVwdrR0JZRDlCQAAAJWa\nYRj6zze/Kz4pUwO7NlZgU29rR0IZRXkCAABApbZ2+yntiLqg5vXdNbRXM2vHQRlGeQIAAEClte9o\ngj797pDcXavo5RHtZWvLr8coHP91AAAAoFI6G5+q2cv3yc7WRtNGhcijupO1I6GMozwBAACg0knP\nytUbn+xR5tU8PT8kUE3q1rB2JJQDlCcAAABUKoZh6L0VkYpLytCgbo0VGuhn7UgoJ+xu9WZ2drY2\nbdqknTt3KjY2VpLk5+enTp06qVevXqpSpUqphAQAAABKSvivp7XzYJxaNfLQ0PubWzsOypFCy9Pi\nxYv11VdfKTg4WIGBgerVq5ckKSEhQTt37tR7772nIUOG6O9//3uphQUAAADuxslzKVq87rBcqzro\nxaFBPAh0xWshAAAgAElEQVQXt6XQ8uTk5KTw8HA5ODjc8N7gwYOVk5Ojb775xqLhAAAAgJKSm2fW\n7OX7lJdv1guPtWOCCNy2QsvT0KFDb7mhg4NDkesAAAAAZUX4b6d1/mKG+nSur/bNfawdB+VQkRNG\nfPLJJ0pNTZUkTZ48Wb169dIvv/xi8WAAAABASUnPzNGKH/9QVUc7Pc6DcHGHiixPq1evlqurq3bt\n2qXk5GT961//0rx580ojGwAAAFAivt5yQmmZuRrcvYmquzDpGe5MkeXJxubaKrt379YDDzygoKAg\nGYZh8WAAAABASYhPytB3v5ySdw0nPdClobXjoBwrsjw5Ojpq0aJFWr9+vbp06SKz2azc3NzSyAYA\nAADclfx8sxauPqi8fLNG9GkhB3tba0dCOVZkefr3v/+txMRETZ48WV5eXoqNjdUDDzxQGtkAAACA\nO5afb9bcL/dr39EEBfh76p62ta0dCeXcLR+SK0kNGzbU9OnTC17Xq1dPY8eOtWgoAAAA4G7kmw3N\n/ypS2yPPq3l9d00b1UE2PNMJd6nI8nTy5EktWLBAsbGxysvLkySZTCatXLnS4uEAAACA25Wbl6+5\nX+zXjqgLalavhmY+1VHOjvbWjoUKoMjyNHHiRPXu3VsDBgwomDzCZKK1AwAAoOxJz8zRrCV7dOhk\nklo0cNerT1KcUHKKLE+GYXCZHgAAAMq80xeuaM7nETobn6bOATU16fEgJohAiSqyPLVt21bHjh1T\ns2Y8TAwAAABlz+FTSVq59YT2HU2QJD14T0M9+VAr2XKPE0pYkeUpKipK3377rRo0aCAHBwdJ3PME\nAAAA68u8mquFqw9q675YSVKLBu4a1K2Jgpp5c5sJLKLI8jR16tQblvEfIwAAAKzpROxlzV4eobhL\nGfL3q66/P9xaLRt6WDsWKrgiy1NISIgkKTMzU5Lk7Oxs2UQAAABAIcxmQ2u2RWvZ90eVl2/okXv9\nNax3c9nbFfn4UuCuFVmezp49qxdffFFHjx6VJLVs2VKzZ89WnTp1LB4OAAAA+FNy6lXN+3K/fj9+\nUTWqVdELj7VTYFNva8dCJVJkRX/11Vc1ePBgRUVFKSoqSoMGDdKrr75aGtkAAAAASddm0nt+7s/6\n/fhFtW/uo/cm3UdxQqkrsjwlJydr4MCBsrGxkY2NjQYMGKCkpKTSyAYAAADo6OlkTfnwV6WkZWv0\ngy316pMhcqtWxdqxUAkVWZ5sbW118uTJgtenTp2SnV2RV/sBAAAAd8UwDO06FKdXFv2mrOw8TXq8\nnfrf68/kZbCaIlvQCy+8oGHDhhU85+nYsWN6++23LR4MAAAAlYdhGEpOvaq8fEOGYehg9CV9t+OU\nTl9Ilb2djaaN7KAOLX2tHROVXJHlKTQ0VOvXr1dUVJSkaw/NdXd3t3gwAAAAVA45ufmavXyfdh2K\nv265jY1JXdrU0sCujdXIz81K6YD/r1jX33l4eKhr166WzgIAAIBKJvNqrt74ZI8Onrwk/zpuqutT\nTSaT5OXmrJ4h9eRVw8naEYEChZanESNGaOnSpQoJCbnhulKTyaSdO3daPBwAAAAqpqvZeTp6JlnL\nvj+qE7Ep6tS6piYPC5K9na21owGFKrQ8zZ49W5K0atWqUgsDAACAiu30hStatOagjp5OVr7ZkCR1\nC66j5wa1la0tD7pF2VZoefLx8ZEk+fn5lVoYAAAAVFw7os5r/leRys7JV5O6bmrdyFNtGnupbRMv\nZtBDuVBoeRowYEChG5lMJq1cudIigQAAAFCx5Obl64uNf2jl1hNyqmKrqSOD1al1LWvHAm5boeXp\npZdeKs0cAAAAqGAMw9BvB+L06frDSkjOVE3Pqpo2qoPq+bpaOxpwRwotTyEhIaWZAwAAABVIvtnQ\n6x/v0v5jibKzNenh0EYa0rOpXJzsrR0NuGOFlqfx48cXupHJZNK7775rkUAAAAAo/37cHaP9xxLV\nupGnxg1uo1qeLtaOBNy1QsvTvffeK5PJJMMwbnivuDf0TZkyRdu2bZOHh4e+++47SVJKSopeeOEF\nXbhwQbVr19b8+fPl6nrt1O3ChQu1atUq2djYaPr06erSpYsk6dChQ5oyZYqys7MVGhqq6dOnS5Jy\ncnL00ksv6ciRI3Jzc9O8efNUu3bt2/sGAAAAUKIysnK1/IejcqpiqxeHBcnd1dHakYASUWh5euSR\nR+565wMGDNDw4cP18ssvFyxbtGiROnfurKeeekqLFi3SokWL9OKLLyo6Olrh4eHasGGDEhISNGrU\nKG3atEkmk0kzZ87UrFmzFBAQoKeeekrbt29XaGiovvnmG7m5uWnTpk0KDw/XnDlzNG/evLvODQAA\ngDv39ebjupKeoxF9mlOcUKEUWp6WLFmikSNH6q233rrhDJTJZCrWhBLt27fXuXPnrlu2detWLV++\nXJLUv39/DR8+XC+++KK2bNmivn37yt7eXn5+fqpbt66ioqJUq1YtZWRkKCAgQJLUr18/bd68WaGh\nodq6dWvB5YU9e/bU66+/fvvfAAAAAErMhUvpWvfLSXnXcNLDoY2sHQcoUYWWJ0fHa/9K4OzsfNPy\ndKeSkpLk6ekpSfL09FRSUpIkKTExUW3atClYz9fXVwkJCbKzs5Ovr2/Bch8fHyUmJhZs8+d7dnZ2\nqlatmlJSUuTm5nbH+QAAAHBn0jJz9P7Xvysv39CoB1vKwd7W2pGAElVoeRoyZIgk6bnnnrPYh5tM\nJh6IBgAAUAEcOZ2kOZ9H6OLlLHVo4au/BfAcJ1Q8hZanP6Wnp+uDDz7Q7t27JUkdO3bUM888IxeX\nO5sxxcPDQxcvXpSXl5cSExPl7u4u6doZpfj4+IL14uPj5evre9PlPj4+kiRvb2/FxcXJx8dHeXl5\nSktLK9ZZp4iIiGLnvZ11UXYxjhUD41gxMI4VA+NYMZTUOEZEp2v93hRJ0n2tXXVPS1vt37+/RPaN\novHzWHqKLE9Tp06Vi4uLpk+fLsMw9O2332rq1Kl677337ugDu3btqtWrV2vMmDFas2aNunfvXrB8\n0qRJGjlypBISEhQTE6OAgACZTCa5uLgoKipKAQEBWrt2rYYPH37dvtq2bauNGzeqU6dOxcoQFBRU\nrPUiIiKKvS7KLsaxYmAcKwbGsWJgHCuGkhrHmLhUfb9im1yrOugfI4LVqpFnCaRDcfHzaBmFFdIi\ny9OJEyf0/fffF7wOCgpS7969i/WhEydO1J49e5SSkqKwsDCNHz9eY8aM0YQJE7Rq1aqCqcolyd/f\nX71791bfvn1la2urGTNmFFzSN2PGDE2ZMkVXr15VWFiYQkNDJUmDBg3S5MmT1bNnT7m5uWnu3LnF\nygUAAIC7l59v1vwVkcrLN2v84ECKEyq8IsuTt7e3kpOTCy6vS05OLrhsriiFlZklS5bcdPnYsWM1\nduzYG5a3atWq4DlRf+Xg4MDDegEAAKzk25+jFR2bovuC/NShpW/RGwDlXKHl6a233pIk1ahRQw89\n9JC6du0qwzD0888/Kzg4uNQCAgAAoOyJiU/VFxv/kLtrFY3p19racYBSUWh5+nOKcn9/fzVq1Kjg\nErrBgwczQx4AAEAldjn1qt74ZLfy8s16dmBbuTg7WDsSUCoKLU+WnKIcAAAA5VPm1VzN/O8uxSdl\n6tEeTbhcD5WKTWFvhIeHF7lxcdYBAABAxZCTm69Zn+7RqQtX1KtjPQ3t1czakYBSVeiZp4MHD+o/\n//mPunfvrrZt2xZMEhEfH6+oqCht3rxZYWFh6tOnT6mFBQAAgHVcSsnSm5/t1R9nL6tT65p6ekAb\nbuVApVNoeXr55Zf197//XWvXrtWKFSt07tw5SVLt2rUVEhKiZcuWycPDo9SCAgAAwDoORl/S28v2\nKSU9W/cG+em5QW1la0NxQuVzy6nKPTw8NHr0aI0ePbq08gAAAKAM+X7nGX307QGZJI3p11oPdGnA\nGSdUWkU+5wkAAACVT77Z0JL1h7Vm20m5VnXQ1JEd1LIhVx2hcqM8AQAA4Dp5+Wa9tXSvdh2Kl5+3\ni159sqNqela1dizA6ihPAAAAuM7H6w5p16F4Bfh7asoTwTzHCfhfhU5VLkkpKSk6dOiQ0tPTSysP\nAAAArGjrvrNav+O06vpW0/TRIRQn4C9u+ZynsLAwjRkzRvfee6927txZmrkAAABQyqJjU/TBN1Gq\n6minaSM7yKkKFykBf1XoT8SCBQv01VdfqXnz5tq1a5c++OADderUqTSzAQAAoJRcSsnSrCV7lJtv\n1j+eCFYtLxdrRwLKnELPPNnY2Kh58+aSpI4dOyotLa3UQgEAAKD0XEnP1isLf9OllCyN6NNCwS18\nrR0JKJMKPfOUk5Oj6OhoSZJhGMrOzi54LUn+/v6WTwcAAACLyryaq5mLd+lcYrr6hTXSgPv4HQ8o\nTKHlKTs7W2PGjLlu2V9fb9261XKpAAAAYHGXUrL05tK9io5NUY8OdTX6wZY8ABe4hULLE+UIAACg\n4jpxIUvvrPlZaZk5uredn54d2IbiBBShyClUTp48qRMnTkiSGjdurEaNGlk8FAAAACwjNy9fy78/\npm9/TpKdrY2eHhCg3p3qU5yAYrjlZXsTJkzQzp07Va9ePRmGobNnz6pz586aP3++HByY8x8AAKA8\nOROXqnc+j9CZuFTVcLHVq091kb+fm7VjAeVGoeXpv//9ryRp+/btcnV1lSRduXJFU6dO1aJFizRu\n3LjSSQgAAIC7YjYbWrv9pJaGH1Vevlm9OtZTuzq5FCfgNhU6VfmPP/6oWbNmFRQnSapevbr++c9/\n6scffyyVcAAAALg7icmZmvbRr/rku8NycbbXK0+GaNygtqpiX+ivgQAKUeiZp9zcXLm7u9+w3N3d\nXTk5ORYNBQAAgLsX+Uei3ly6V5lX89Sxla/GDWqr6i5VrB0LKLcKLU9VqhT+g+Xo6GiRMAAAACgZ\n+48l6o1Pd0uSnn+0rboF12VSCOAuFVqeYmNj9fzzz8swjBveO3funEVDAQAA4M7tO5qgfy3ZI5Ok\naaND1K6pt7UjARVCoeVp6tSphW503333WSQMAAAA7s7vxxM169M9sjFJrzwZorZNKE5ASSm0PD3y\nyCOlmQMAAAB36Y+YZM36dI9MFCfAIgotTydPntTp06fVvXt3SdKsWbOUnp4uSRoxYoSaN29eOgkB\nAABQpJi4VM387y7l5Jk15YlgihNgAYXOUfnuu+/K3t6+4PX27dvVqlUrNWjQQIsWLSqVcAAAACja\nsZhkTV/4m9KzcjV+cFt1bFXT2pGACqnQM08xMTEKCwsreO3k5KShQ4dKkh5//HHLJwMAAECRNu2O\n0YJVB2Q2m/X0gAB1C65r7UhAhVVoecrPz7/u9Zw5cwr+nJqaarlEAAAAKFK+2dDitQe1fsdpVXO2\n10vDuccJsLRCy1NeXp7S09Pl4uIiSfL395ckpaenKzc3t3TSAQAA4AbZufl65/MI7TwYp3q+1TR9\ndIh8PapaOxZQ4RV6z1Pfvn01depUpaWlFSxLS0vTtGnT1KdPn1IJBwAAgOulZ+bo1YW/aefBOAX4\ne+qtcfdQnIBSUuiZp7Fjx2rKlCkKDQ1VvXr1JF27D6pr16565plnSi0gAAAArjl6OlnzvtyvuKQM\ndWlTSxMfbyd7O1trxwIqjULLk729vebMmaMzZ87oyJEjkqQWLVqofv36pZUNAAAAknLzzPpy0zGt\n2npChqRB3Rpr2P3NZWNjsnY0oFIptDz9qX79+hQmAAAAK8k3G3pr6V7tPhwvb3dnTXysnVo29LB2\nLKBSKrI8AQAAwHqWbjii3Yfj1aaxp6aO7CBnR/uiNwJgEYVOGAEAAADr2rwnRt/+HK3aXi76x4hg\nihNgZZx5AgAAKGMMw9Avv5/XByuj5OJkr1efDJGLs4O1YwGVHuUJAACgDElOvaoFq6K061C8HOxt\nNWVksGp5uVg7FgBRngAAAMoEwzC0ZW+sFq87pIysXLVq5KHnBrdVLU+KE1BWUJ4AAACsLDE5Ux+s\njNL+PxLlVMVWzwwIUK+O9ZmKHChjKE8AAABWYhiGfoqI1UffHlBWdr7aNfPWswPbyLuGs7WjAbgJ\nyhMAAIAVZGTl6sNVUdoeeV7OjnZ6/tFAdQuuI5OJs01AWUV5AgAAKEVX0rO1aXeMNvx6WklXrqpp\nvRp6cWiQfD2qWjsagCJQngAAAEpB3KUMrfrphH7aF6ucPLMcHWw1pEdTPdqjiexsefQmUB5QngAA\nACwo7lKGvth4TNsjz8lsSL4eznqgS0N1C64rFyceeguUJ5QnAAAACzkYfUmzluxRRlau6td01eBu\nTdS5TS3ZMoseUC5RngAAACzg54hYvbsiUpI0fnBbdQuuy9TjQDlHeQIAAChBGVm5+mLjMa375ZSq\nOtpp6qgOCvD3snYsACWA8gQAAFACzGZDW/ae1dLwo0pJz1ZNj6qaNrqD6vm6WjsagBJCeQIAALhL\n+flmzV8RqZ8jzqmKg62G926ufmGN5GBva+1oAEoQ5QkAAOAu5OaZNXv5Pu08GKem9WroHyOC5enm\nZO1YACyA8gQAAHCHMq/mavbyCO07mqDWjTz1ypMhcqrCr1dARcVPNwAAwB2Ijk3R28v3Ke5Shto1\n89bUkR1Uhcv0gAqN8gQAAHAbDMPQ2u0n9dmGI8rLNzTgPn8Nvb+57O1srB0NgIVRngAAAIopP9+s\nBd8e0MZdMapRrYpeeKydApt6WzsWgFJCeQIAACiG7Nx8zV62T7sPx6th7eqa+feOquHqaO1YAEoR\n5QkAAKAIx2KStWj1QZ2ITVGbxp6aOrKDnB3trR0LQCmjPAEAABQiJj5Vy8KPavfheEnSfUF+em5w\nW9nbMTEEUBlRngAAAP6P9Mwcfb7xmMJ/OyOz2VCLBu4a0aeFWjb0sHY0AFZEeQIAAPhf8UkZ2rb/\nnNZuP6W0zBzV8qyqJx9upeDmPjKZTNaOB8DKKE8AAKDSO3jykpZuOKJjMZclSU5VbDXqgRZ68J5G\nTEEOoADlCQAAVFqGYejbn6K1NPyIDEltGnvq3nZ+6tS6lqo6MSEEgOtRngAAQKWUnpWr91ZEaufB\nOLm7OurlEe3VogH3NAEoHOUJAABUOvuOJug/3/yupCtX1aqRh14a3l41qvHMJgC3RnkCAACVRlZ2\nnv675qB+3HNWdrYmDb2/mQZ1bSxbW+5rAlA0yhMAAKgULlxK178+3aOY+DQ1rFVdEx4LVINa1a0d\nC0A5QnkCAAAV3t4j8Xrn8whlXM3TA39roNEPtWIWPQC3jfIEAAAqrJzcfC0NP6q120/Kwc5GE4YE\nqltwXWvHAlBOUZ4AAECFdCYuVe98HqEzcamq7eWiycOC1MjPzdqxAJRjlCcAAFBhGIahyOMX9d0v\npxRxLEGGId3fqb6efLClHKvwaw+Au8PfIgAAoNwzDEN7jyZoWfhRnYlLlSQ1rVdDj3ZvouAWvlZO\nB6CioDwBAIBy7VhMsj5Zd1hHzyTLxiTd285PD4U2VOM6NawdDUAFQ3kCAADlUubVXC0LP6oNv52W\nYUgdW/lqWO/mqufrau1oACooyhMAACizDMNQ3KUM5eWb5ehgJ5mkU+ev6NiZZG3bf06XrlxVbS8X\njRvURq0aeVo7LoAKjvIEAADKnLTMHP0ccU4/7onR6QupN13HztZGQ3o01eDujWVvZ1vKCQFURpQn\nAABQZuTlm/XdL6f05aZjysrOl62NSSEtfVXD1VFXc/KUn2+onm81NavnrsZ13eTsaG/tyAAqEcoT\nAAAoEw6fStIHK6MUm5Cmas4OGvVAU93Xvo5qVHO0djQAkER5AgAAZcDWfbF6b0WkzIah3p3qa3if\n5qrm7GDtWABwHcoTAACwGsMwtOqnaH224YiqOtlr2qgOas3EDwDKKMoTAAAodXn5Zu0/lqhNu2O0\n+3C8PKs7auaYTkwzDqBMozwBAIBSk5Obr9XborVu+ymlZuRIkvzruGnayA7ydHOycjoAuDXKEwAA\nsDjDMLTncLwWrzuk+KRMVXN20IP3NNR9QX7y93OTyWSydkQAKBLlCQAAWFR6Vq4+XBmlX34/L1sb\nk/qFNdKQHk1V1YlpxgGUL5QnAABgMUdPJ2vO5/uUeDlLzeu767nBbVXHp5q1YwHAHaE8AQCAEmcY\nhtb9ckqffHdYMgwN6dFUQ3o0ka2tjbWjAcAdozwBAIASdTUnTx98E6Wf959TjWpVNHl4e6YfB1Ah\nUJ4AAECJSUzO1Kwle3Tq/BU1rVdDU54Ilkd1ZtEDUDFQngAAQImIOnFRby3dp7TMHPUMqaexj7SW\nvZ2ttWMBQImhPAEAgLtiNhtau/2klmw4IhuT9MzANurdqb61YwFAiaM8AQCAO3b+Yrre//p3HT6V\npBrVqugfTwSrRQMPa8cCAIugPAEAgNtmGIbWbj+lpeFHlJtnVqfWNfX0IwGq4epo7WgAYDFWK09d\nu3ZV1apVZWtrKzs7O61cuVIpKSl64YUXdOHCBdWuXVvz58+Xq6urJGnhwoVatWqVbGxsNH36dHXp\n0kWSdOjQIU2ZMkXZ2dkKDQ3V9OnTrXVIAABUCrn5huZ/Famt+2Ll5lJFYx8J0N/a1LJ2LACwOKs+\nbGHZsmVas2aNVq5cKUlatGiROnfurI0bN6pjx45atGiRJCk6Olrh4eHasGGDFi9erNdee02GYUiS\nZs6cqVmzZmnTpk2KiYnR9u3brXY8AABUdMmpV/XZlovaui9WTeq66d1J91KcAFQaVi1PfxagP23d\nulX9+/eXJPXv31+bN2+WJG3ZskV9+/aVvb29/Pz8VLduXUVFRSkxMVEZGRkKCAiQJPXr169gGwAA\nUHIuXs7SojUH9dS/NuvcpRzd285P/3qmi9y5TA9AJWK1y/ZMJpNGjRolGxsbDRkyRIMHD1ZSUpI8\nPa89RM/T01NJSUmSpMTERLVp06ZgW19fXyUkJMjOzk6+vr4Fy318fJSYmFi6BwIAQAVmGIa+3nJc\nX236Q3n5hjzdnNSxcRWNebSdTCaTteMBQKmyWnn68ssv5e3treTkZI0aNUoNGza87n2TyWSxv5Qj\nIiIssi7KLsaxYmAcKwbGsfzIyze0bvdlHTiTKVdnW90X4KrW9ZxlZ2vS/v37rR0PJYCfx4qBcSw9\nVitP3t7ekiR3d3f16NFDBw4ckIeHhy5evCgvLy8lJibK3d1d0rUzSvHx8QXbxsfHy9fX96bL/9zv\nrQQFBRUrY0RERLHXRdnFOFYMjGPFwDiWH5dTr+rt5ft06EymmtatoWmjO6hGtWuX6DGOFQPjWDEw\njpZRWCG1yj1PWVlZSk9PlyRlZmZqx44datKkibp27arVq1dLktasWaPu3btLujYz34YNG5STk6PY\n2FjFxMQoICBAXl5ecnFxUVRU1P9r787jo6rv/Y+/Zsu+hyQkEAIESAioiOxgoGxKEYiyuFxrb7FS\nvK4Xa1vurT+tVsS1WotFbRGtGxZQioKsrSgiEpE1rGExQPaE7Mls5/fHwBQEMXCFyUzez8cjj8yc\nOWfyOXweJ5M353y/xzNl6pIl3m1ERETk/DXanSxYtYdpT6xmR345gy9P4fH/GuwNTiIirZlPzjyV\nlZVx9913A+ByuRg3bhxDhgyhZ8+e3H///SxatMg7VTlAly5dGDNmDGPHjsVisfDwww97L+l7+OGH\nmTlzJo2NjQwdOpTs7Gxf7JKIiIhfMwyDz7cV8pcl2ymraiQ6Ioip43pwzYCOmM0a2yQiAj4KT6mp\nqSxZsuSM5TExMcyfP/+s20yfPp3p06efsbxnz54sXbr0hy5RRESk1ThWVsvLi7ezeU8JVouZScO7\nMnlEV8JCbL4uTUSkRfHZmCcRERHxrYLiGhb/cz//2lyA02XQq1sCd95wOSkJEb4uTUSkRVJ4EhER\naUUa7U427Szmn5sLyN1VjGFAu4QI/uPaTIZckaLpx0VEzkHhSUREpBU4XFTNkk/y+XTLURrtLgC6\ndYhh0vCu9O+RrHFNIiLNoPAkIiISoOobHWzdV8rKjd+Qu6sYgKS4MMb3bk/2le1Iaxvl4wpFRPyL\nwpOIiEgAabQ7+WTzEf751RF2H6rA5TYAyOoUxw3DutA3q63OMomIXCCFJxERkQBQXFHPR+sPsmrj\nYWobHJhN0DU1lt6ZifTLakuX1Bhflygi4vcUnkRERPzYtv2l/GPdATblFeE2IDoiiBtHdmPMoI7E\nR4f6ujwRkYCi8CQiIuKHKmsaeXnxdtZvOwZAl/bRjLu6M0OuaEeQzeLj6kREApPCk4iIiB9xuQ0+\n2VzAX5bsoKbeQfeOcUwd14OMtFhNMy4icpEpPImIiPgBh9PF2twCFv1zP4VldQQHWZiWcxljB3fS\nBBAiIpeIwpOIiEgLl7urmD8v2kpJZQNWi5nR/dOYMrIbSXFhvi5NRKRVUXgSERFpocqrGnh1yQ7W\nbz2GxWxiQnY61w9L10QQIiI+ovAkIiLSghiGwa5DFXz02UHWbzuGy22QmRbLXZN70TFZN7UVEfEl\nhScREZEWwDAMtu0r482Pd7H7cCUAaW0jmZCdzoi+HTSuSUSkBVB4EhER8bFDhdW88v52tueXAdC/\nR1smDE2nZ+d4zaAnItKCKDyJiIj4iGEYLFt/kL8u3YnD6aZP9yT+45pMuqTG+Lo0ERE5C4UnERER\nH6itt/P8u1+zcWcRkWFB/PonvejfM9nXZYmIyDkoPImIiFxiBcU1PDZvI4VldVzepQ0zbumtGfRE\nRPyAwpOIiMgllLurmKffzKW+0cnkEV35j2u7Y9FkECIifkHhSURE5BJwuQ3eWbmb91bvxWYx88v/\nuIqhvdv7uiwRETkPCk8iIiIXWXlVA8+89RU78stJjAvjN7f1oWtqrK/LEhGR86TwJCIichFt3l3C\nc+98RVWtnYGXJXPvjVcSEWrzdVkiInIBFJ5EREQuApfLzVsrdvP3NfuwWkxMy7mM64Z00n2bRET8\nmAKdqmEAACAASURBVMKTiIjID6zseANPv5lL3sEK2saH8auf6DI9EZFAoPAkIiLyA8rdVcxzb2+m\npt7O4MtTuGdKL8J1mZ6ISEBQeBIREfkBlFc1sGDVXpZvOITVYmb6DZfz40EddZmeiEgAUXgSERG5\nQIZhUFBcw4ovDrN8wyEcTjftEsL55a196NI+xtfliYjID0zhSURE5DxU1jSydW8pX+8tZcveUiqq\nGwFIjA3lxlEZDO+TitVi9nGVIiJyMSg8iYiIfI/aBgdrN33Dmk0FHDhW5V0eFR5Edq92XNU9iat7\ntcNmVWgSEQlkCk8iIiLA/oLjbN5TwpGSGo6W1uJyG0SGBmGzmdm6rwy7w4XVYqJX1wR6dfN8dUqJ\nxmzWmCYRkdZC4UlERFq1RruTvy3fxdJPD2AYnmVWixmLxUST3QVAUlwYYwZ2ZGS/DkRHBPuwWhER\n8SWFJxERaZXKqxrYtr+Md1fu4VhZHe0SwvnJmCw6tYsiKTYMi8WMw+mivtFJZFiQzjCJiIjCk4iI\ntC5f5hXx2tKdHCmpBcBkgpyh6dw6pjvBNstp69qsFqIjLGd7GxERaYUUnkREpNX4ZPMRnntnMxaz\niasyE7m8Sxv6dE+iQ9soX5cmIiJ+QOFJRERahRVfHGbOwi2EBVt5+OcD6d4pztcliYiIn1F4EhGR\ngPePdfm8umQHkWFBPPqLgbqBrYiIXBCFJxERCWjvrd7L35bvIi4qmMd+MUiX6ImIyAVTeBIRkYDk\ncrl58+PdLFy7j4TYUH4/fRApbSJ8XZaIiPgxhScREfF7hmFgd7pxOt3UNTr4ZPMRlq0/SFlVIylt\nwnls+iASY8N8XaaIiPg5hScREfFbNfV2Pt5wiI/WH6S8qvG010KDLYwd3ImbRmUQE6kb24qIyP+d\nwpOIiPido6W1LFmXz9rcAprsLkKDLVzZLYEgmwWrxUxW5zhG9u1AWIjN16WKiEgAUXgSERG/kXew\nnIVr97EprxiAhNhQxl3TmdH90wgPVVASEZGLS+FJRERavIPHqnhj2S5yd3lCU0ZaLDlD0xnYMxmL\nxezj6kREpLVQeBIRkRZrz+EKFv9rPxu2F2IY0KNzPD8Z050eneN9XZqIiLRCCk8iItKi1DU42LC9\nkNWbvmHngXIAuqTGcOu1mfTOSMRkMvm4QhERaa0UnkREpEX4pqiat1fs4cu8IhxONwC9MxOZ+KMu\nXJbeRqFJRER8TuFJRER8qrbBwTsrd/PhZwdxuw1SkyIZemU7rr6ynW5qKyIiLYrCk4iI+ER5VQMf\nrT/IxxsOUVPvoG18GHdMuIy+WUk6yyQiIi2SwpOIiFxSTpebvyzZwYovDuF0GUSGBXHbj7szITud\nIJvF1+WJiIh8J4UnERG5ZOxON7+ft5GvdpeQ0iacnGFd+NFV7QkJ0seRiIi0fPq0EhGRS6Kqtok3\n1pZxpMzOVZmJ/Oa2voQE62NIRET8hz61RETkoikoruHLnUVs3lNC3sFynC6DoVe25/6br8Sqm9uK\niIifUXgSEZEfVFF5Hau//IbPtx+joLjWu7xLagyd2xjcdUtvzGZNCCEiIv5H4UlERP7PDMMg72AF\nS9bls3FHIW4DgqxmBvRsy8DLUuidkUhMZDBfffWVgpOIiPgthScREblghmHw9Z5SFqzeQ97BCgC6\ntI9m3NXpDLwsmVCNaRIRkQCiTzURETlvdQ0O1m05ysovDrH/SBUAfbOSmPijrmR1itN9mkREJCAp\nPImISLPYHS427ynh0y1H+WJ7IXanG7MJBl2ezI0jM+jcLtrXJYqIiFxUCk8iInJWLpebg4XV5B0s\nJ+9ABZv3lNDQ5AQgpU04I/t1YHifVOKjQ31cqYiIyKWh8CQiIl6HC6v5fHsheQfL2XO4goYml/e1\nxLgwxgzsyJBeKXRpH6NL80REpNVReBIRaeUam5x8uuUoKzYeZs/hSu/y1KQIsjrFk9UpjqxO8STF\nhSkwiYhIq6bwJCLSSu0vOM6KjYf5ZPMRGpqcmEzQOzORkX07cHmXNkRHBPu6RBERkRZF4UlEpBWp\nb3TwyeYjfPzFYQ4c9cyS1yY6hAnZ6Yzq14HEuDAfVygiItJyKTyJiAQoh9NF2fFGSirr2X24gm37\nysg7WIHT5cZsNtG/R1uuGZBG78wkLLpxrYiIyPdSeBIRCRD1jQ7yDnpmxft6TwlHSmrPWKdzSjSD\nrkhmZN8OmiVPRETkPCk8iYj4qcrqRjbvKWHb/jL2FVRypKQWw/C8FhJkoWd6PImxYSTEhJKWHKVx\nTCIiIv9HCk8iIi2cYRgcKanl670lFFfUU3a8gWOldRwqrPauExpsoUfneDLT4rgyI4HuHeOwWS0+\nrFpERCTwKDyJiLQghmFQXFHPocJqisrrOFpax5a9JRSV15+2ns1qplfXBHpnJnJlRiKpSZEatyQi\nInKRKTyJiPiY3eHiq90l5O4qZsu+UkoqTg9KocFWBl+eQp/uSXRMjiI+JoTo8GDMCksiIiKXlMKT\niIgP1NTb2bavjI07C/liRxENTU4AIkJtDLwsmW4dYkluE05yfDipSZHYrGYfVywiIiIKTyIiF1mT\nw8Xew5UcKqzmm+Ia9h85Tv6R497JHRJjQxkzsCODr0ghvX2MLr8TERFpoRSeRER+YG63wcFjVWzZ\nW8qWvaXsPFiOw+n2vm4xm8jqFE+vbgn0zkika2oMJpMCk4iISEun8CQicoGqaps4WlrLsdJaSo83\nUlnTSEVVI7sOVVBdZ/eu1ykliiu6JpDePoa0tpG0S4ggyKaZ8ERERPyNwpOISDPU1tv5Mq+IbfvL\nOFLiCUw19Y6zrhsXFcLwPqlc2S2BK7olEBsZcomrFRERkYtB4UlEWj2X26C6tonyqkaKK+oprqij\ntLKBJocLp8tNZXUT2/PLcLk9g5QsZhNt48Pp3jGedokRtEuIIDE2lLioEGIig4kKD9JleCIiIgFI\n4UlEAlqj3UlFVSPlVY2UVTVQXtVI+fEGyqsbTyxvoKKmCfeJYPRd0ttHM/jyFPpltaVdYgRWi2a/\nExERaW0UnkTErxmGQU29g5KKeg4XVXOosJqC4hrKjnuCUm3D2S+tA7BaTMRFhdAtNYb46FDio0NI\niA2jbXwYibFhhARZsFjMhARZiI4IvoR7JSIiIi2RwpOI+IW6Rhdf7ynh4LEqjpXVUXq8gdLKekor\nG2i0u85YPzzESlx0KF1PCUbxMSe+R4UQHx1KVHiQbjQrIiIizabwJCItRqPd6bm8rrKB0uMNHCur\n5eCxag4craKiuhEoPG39iFAbKW0iSIgNJSEmlNS2kXRMjiKtbRThoTbf7ISIiIgELIUnEbko3G6D\nmno71XV2jtc2cbymiaoT34/Xeh43NrlocrhoaPKEppp6+1nfq010CN1SQrgyqwOd2kXTPjGChJhQ\nwkIUkEREROTSUXgSkQtiGAYV1Y0cLqqhoNjzVVhWR1VtE1W1dqrr7d87CQOA2QTBQVbio0NIbx9N\nQkwobU58JcWF0SklmqjwIL766iuuuqr7JdgzERERkbNTeBKRszIMg8qaJkor6yk77pmpruy456u0\nsoEjJTXUNTrP2C481EZ0eBDJbcKJjggiOiKY6IhgYiKCiYn89/foiGBCg61YLSZN6y0iIiJ+QeFJ\npBVptDupqrV7Lp879VK62iZq6x00OVw02V1UVDdSWFZLQ9OZEzGA5z5HKQnh9OoWRWpSJB2SIunQ\nNpKUhHBsVssl3isRERGRS0PhScTPuN0GjXYnDU1O6hs93xsandQ3eR7X1Nu9Y4tODUpVtU1nnZXu\nbIJsFlLahJPcJpy28eG0OTFTXcKJ2epiIkOwaJY6ERERaWUUnkR8xO02qG1wUFXbRHWdneq6E2OF\n6uxU1Z1YdmLsUEOjwxuWmhuATrJaTMREBNMuMcJ7+Zzn++mX1EWE2QgJshIcZCHYZtEU3iIiIiLf\novAkcgrDMGhoclJZ00RtvR2ny8DlduNyGbjcBk6Xm9p6B5U1jVTWNGF3uDAMz3YAbsPgxEOME48N\nAwwMHE63NyRV19mpqbPTjPkUCLJZCAuxEhpsJSYyhNBgq/f5ya+wYCuhJ5ZFhnlC0clxReEhVo0p\nEhEREfkBKDyJ33C7PcGmrsGB0+XG5TZwGwZu94kv72MwmyHkRLCorndRVF5Hk8NFbb2D4zVNVNY0\neqfMPu15TRN2p/ui7YPJBBGhQUSFB9EuIYKocE/QiQoPIio8mOgIz2vR4SeWRQQREqTDVERERKQl\n0F9lctG53AYNjQ7qT4zLqT/5+NvfT3mt4cRYnpPjeDzjehzNOlNzVh8UnvNlq8VMTGQwHZKjiIkI\nJjYymMiwIKxWMxazCYvFhMVsxmoxERpsIy4qmNioEEKCLJhMJkzg+X7iBM+3l5lMnp8REWrDYjFf\n4E6IiIiIiC8pPAUgl9ugye6kye6i0e65CWmj3UlT0ymPTyw/9bvb+O5k8u3Lvk595nS5qW90Utfo\nODFxgYO6BicNTY4LGqNzksVs8l6e1iY6hPC2kYSH2ggPtWGzmDGbTZjNJiwmk/ex+cTjk2epGpqc\nlJWX0zYxAZvVTESYjZjIYGIjQ7zTZsdGBhMeatOlbSIiIiJyTgpPLZDD6aam3u4dF+MdJ/OtZQ1N\nnmDy7aDkuIiXnTWHzWomLMRKWIiN2KgQwoJtJ557lp0MROGhNsKCPctCQ6yEn/JaaLAVm9X8gwQa\nz81Vr/wB9kxEREREWjOFp+9gGAYOp8HxmibvtNCNTZ6zNi634Z0MwGo1E2Q1E2SzeL6snjMiTpcb\np8s48d0z4YDT6cZx4nl9o5Paes9MaqWVDZRU1lNa2eANRc1hMkFIkIXgICshQRbiQ0+ZLS3I4nls\ns5xY59/reV6zEGz798xqQTYzFvO/Lycz+PdZqHOckALAbDZ5g09YiFX3+RERERGRgNQqw9OchVtx\nOF04nZ5w0+RwnXX8jdttAEcvSU0mE8RGBtM2Poyo8CAiw4K8kwhEhtuIOjmBwInlkeFB3vE2IiIi\nIiJy8QVEeFq3bh2zZs3C7XYzadIkpk2bds71P95w6IxlJhMnpnu2ERcdQmqIDXtjHUmJcZ7LyIKs\nhAR7ztxYLGY8t8Ax4XC5cDjc2J1u7A4XdocLl9vAZjVjtZixWEzYLJ7H1hPLPJMOWIkICyIi1EZC\nTCgJsaE6YyMiIiIi0oL5fXhyuVw89thjvPbaayQlJTFp0iRGjBhBenr6d27z0q+Ge8ONzer5Cgmy\nnnFTUM9Ymasu9i6IiIiIiIgf8PvwtG3bNjp06ED79u0BGDt2LGvWrDlneEpNirxU5YmIiIiISIDw\n+xvOFBcXk5yc7H2elJREcXGxDysSEREREZFA5PfhSRMmiIiIiIjIpeD3l+0lJSVRWFjofV5UVERS\nUtI5t/nqq6+a/f7ns660XOpjYFAfA4P6GBjUx8CgPgYG9fHS8fvw1LNnTw4fPsyRI0dITExk2bJl\nPPfcc9+5viaAEBERERGRC+H34clqtfLQQw9x++23e6cqP9dkESIiIiIiIhfCZBiG4esiRERERERE\nWjq/nzBCRERERETkUlB4EhERERERaQaFJxERERERkWZQeDpBQ78Cg/oYGNTHwKA+Bgb1MTCoj/6t\nurra1yXICa06POXl5fHee+9RUlKim+36MfUxMKiPgUF9DAzqY2BQH/3f1q1bufPOO/ntb3/L3//+\nd5qamnxdUqtneeSRRx7xdRGXmsPh4JFHHuHdd9+lvr6e3Nxc4uPjadu2ra9Lk/OgPgYG9TEwqI+B\nQX0MDOpjYNixYwcPP/wwEydOJCMjg88++4wuXboQHx/v69JatVZ55mnHjh0cP36c999/n2effRbD\nMIiNjfV1WXKe1MfAoD4Ghry8PPUxAOh4DAw7d+5UHwPA1q1bSU1NJScnh8GDB9PU1ERKSoqvy2r1\nWk14ys3N5dChQ4DnxrqrV6+mpqaGFStWsGXLFr744gt27tzp2yLle3322Wd89tlnANhsNvXRTy1f\nvpy33noLUB/92YoVK/j9738PgMlkUh/91M6dOzlw4ACg49GfFRQU0NjYCIDZbFYf/dDSpUt54YUX\nWL16NQCjR4/miy++4Pnnn2fs2LEUFxfz+OOP88orr/i40tatVVy2l5eXx4033khsbCzdu3cnNTUV\ns9nM8uXLmTdvHvfffz8FBQUsW7aMHj166H9nWqC9e/fy+9//nk8//ZThw4eTmJhIUlISVqtVffQj\ndXV1zJgxg88++4x+/frRuXNnkpOT1Uc/s2/fPmbNmsXKlStZu3YtkydPJj09HYvFwrJly9RHP1FQ\nUMCDDz7IypUrWbVqFSkpKfTp00efj36moKCABx54gFWrVrFu3Tq6du1KVlYWwcHBLF26lNdee019\nbOEMw+Cdd97hzTffZMiQIbz44osEBQXRr18/cnJyyM3NZcKECTz00ENERUWxbNky2rVrR3Jysq9L\nb5UCMjwZhnHawMidO3dSWVlJUlISTU1NdO7cmauuuoqNGzcyffp0Ro0aRUZGBnv27MHpdJKVleXD\n6uWkk32srKxk4sSJ9OjRgz/+8Y8kJSV5X+vduzdffPGF+tiCnXo8FhQUsH//fl566SUyMjIwDAOz\n2aw++oGTfdy0aROPP/44V199NU888QTV1dWYzWa6dOlCnz591Ec/8tRTT5GWlsazzz5LRUUF69at\n49prr9Xno5954YUXSEtLY/bs2VRUVLBo0SIyMzMZOXKkjkc/YTKZePPNN7nhhhvIyckhPT2d1atX\nExISQlZWFs888wzDhw8nLS2N6OhocnNz6dWrF4mJib4uvVUKyMv2vj0TSWRkJGlpaZjNZnbu3ElN\nTQ1ms5ng4GBWrFgBQFxcHEVFRXTp0sUXJctZnLz8IDY2lqlTp2K32wFYvHgx69ev59ChQ5jNZqxW\nKytXrgTUx5bo1ONx9+7dFBUVAfDWW2/x5z//mQ0bNmA2m4mIiNDx2IKdPB7T09OZN28et912G3a7\nncOHD2O1WgHPHwBWq5VVq1YB6mNLdLKPDoeDsLAwLBYLALW1tXTp0oX9+/djNpsJCQnR8diCneyj\n0+kE8Pbm1ltvZefOnSxatAiHw0FoaKg+H1uoDz74gC+//JLjx48Dnt+txcXFOJ1OBg0aREZGBhs3\nbqS0tJTJkyfzl7/8BbfbzfLly9m3bx8xMTE+3oPWK6DOPK1fv57/9//+H3l5edTW1tKtWzcA1q1b\nR1JSEtdddx25ubls3bqVyspKBgwYwKuvvkphYSFz5swhKiqK6667jrCwMB/vSet2so+7du2ipqaG\nbt260bNnT+bMmcOcOXNoaGigvLycV199lUGDBpGZmcnLL7+sPrYwpx6P1dXVZGRkYLPZ2Lt3L6tW\nreLo0aOkpqbywQcfUF9fz5AhQ5g3bx5Hjx7lpZdeUh9biFP7WFdXxxVXXIHNZqOxsZHg4GD27dvH\nl19+yZgxYwBISkrS8dgCfbuPmZmZ2Gw2vvrqK/70pz9x6NAhOnXqxKuvvkpKSgq9e/fW52ML9O0+\nZmRk8PXXX1NSUkJcXBxlZWXs3bsXh8NBt27dyMzMZO7cuepjC2EYBiUlJdx5553s3r2b4uJi1qxZ\nw6BBgygtLeXo0aMkJycTFxdHYmIiH374IZdddhnDhw9nw4YNLF68mN27d/O73/2Ojh07+np3Wq2A\nCU+HDx/m0UcfZerUqfTr14/Fixezf/9++vXr503ybdq0Ye7cuXz66acMHTqUwYMH07dvX9xuNwMG\nDOC//uu/9AvFx77dx/fff58DBw4wcOBAkpKS6N27N7/85S/Jzs5m+/btFBQUMHbsWPr166c+tiBn\n6+PRo0fJzs5m06ZNbNu2jblz53LVVVdhGAY7duxg4sSJ9O/fX31sQU7tY//+/b3HY58+fXC73Vgs\nFsxmM7t27aJPnz6EhYURHx/PgAEDcLlc6mML8e3jcdGiRRw+fJicnBw6derEtm3beOutt+jfvz+l\npaXs3buX66+/Xr9XW5hv93HhwoWUl5dzyy23sHPnTpYsWcKKFSt44IEH2LJlC06nk2HDhqmPLYTT\n6cRisVBSUkJeXh5z584lOzubjRs3smbNGn7xi1+wfPlybDYb7dq1IyEhgXXr1lFUVMTAgQMZNmwY\nV199NTfffDNxcXG+3p1WzerrAv4v3G434JlVZsuWLfTo0YORI0cCMHDgQJ544gluueUWysrKWLBg\nAXPmzGHYsGEMHjyYuro6nE4nmZmZZGZm+nI3Wr1z9XHAgAHMnj2byZMnM2LECO/6ZrOZYcOG8fnn\nn2MYBhkZGWRkZPhsH+T7+/jkk08yceJERo4cSV5eHsuXL2f8+PFkZmaycuVKDMOgW7du3jPG4hvf\n18cnnniCSZMm0aZNG8DzB0FtbS3R0dHecVFdu3ala9euPtsH+f7Px9mzZzNx4kSampqIjY0lPz+f\n9PR0BgwYwOuvv47b7dbv1RbgXH0cNGgQTz75JNdeey133XUX33zzDR06dACgd+/eBAcHA6iPPuZy\nuXj++edxu91kZ2dTV1fnvdTZarXy0EMPMWTIEPLz87nuuutYtWoVRUVFTJ8+HbPZTK9evQDPTJgK\nTS2D3455WrhwIdnZ2Tz//POA55fDRx99REFBAeD5QG/fvj1z5sxh9OjRDBo0iAULFvC///u/dOvW\njfDwcF+WLyd8Xx9dLhdpaWk89dRTAN4JBt5//33+9Kc/cfXVV+uu6S1Ac/rYrl07nn76afr27ctP\nf/pT5s+fzyuvvMKMGTO46qqrAE9/xXea83v11OMRYPDgwWzfvp3NmzfrWGwhmnM8pqam8sILL9Cp\nUydMJhNvvPEGb7zxBg8//DADBw5UL1uA5vSxQ4cOzJo1C4D27dsD8O6777Jo0SJNCtECfPnll9xw\nww1UV1eTlpbGCy+8gNVqZePGjWzbtg0Ai8XC3XffzTPPPMOgQYO48cYb2bx5M5MnT6a6upp+/fr5\neC/k2/zysr26ujpefvllxo8fz4oVK+jbty/dunWjtLSUNWvWMH/+fGpqavjNb37DsmXLGDVqFCNG\njCA0NBTwDMrLysrCbPbb7BgQmtvHX//616xdu5ZBgwZht9v54x//yCeffMJDDz3EwIEDfb0brd75\nHI+rVq1iwIABXHbZZXTv3p2qqiqmTJnC2LFjMZvN+oPNh873eBw4cCDh4eHY7Xaio6PJysoiOjra\n17vR6p3P8bhixQquvfZasrKyaGhoIC8vj3vvvZdRo0bpWPSx8zke//nPfzJo0CDCwsJ4/fXXef/9\n93nkkUe47LLLfL0brd6xY8dIT0/nzjvvpEePHmzbto2goCAGDRrEiy++yI033ojL5SIlJYUNGzbQ\ns2dP0tPTyc7OZvTo0UyZMoWgoCBf74Z8m+Gnjh49ahiGYTz99NPGfffdZxiGYTidTqOiosLYtGmT\nd51f//rXRmNjo+F2uw2Xy+WzeuXszqePDofDsNvtRkFBgc/qlbM7nz42NDT4rE45t/P9vSotU3P7\n+OCDDxpNTU0+q1PO7XyOx5N9rKur802xclYNDQ1GY2Oj4XQ6DcMwjCVLlhjPPPOMYRiGMX78eOP1\n1183DMMwtm3bZvz3f/+3z+qU8+O3p15SUlIA+OlPf0pBQQGffvopFouFqKgo+vTpA8CCBQsIDg7G\narViMpl0pqkFOp8+guea35OXJkjLcT59tNlsvixVzuF8f69Ky9TcPoaGhnqnKpeW53yOx5N/32gy\niJYlJCSE4OBg73H2+eefe29QPGvWLPLz85k2bRoPPPCALrP0I36fJhISEpg8eTJz584FPNeObtu2\njenTp5OXl8ddd92lDwc/0Jw+6o+1lk/HY2BQHwOD+hgY9Pno/5xOJy6Xi7KyMu/kVxEREcyYMYNp\n06bxxhtv8POf/9zHVUpzmQzDv0doGydmd7rnnntITEzEZrMxcOBAOnbsSFpamq/Lk2ZSHwOD+hgY\n1MfAoD4GBvUxMDQ1NfHb3/6WUaNGsXDhQmJjY3nooYeIiIjwdWlynvz+zJPJZPLeNPXDDz8kOTmZ\noUOH6heKn1EfA4P6GBjUx8CgPgYG9TEw5OXlsXTpUl577TVGjRrFk08+qeDkpwLiPO8777xDVlYW\n8+fP16wkfkx9DAzqY2BQHwOD+hgY1Ef/l5yczP3338/UqVPVQz/n95ftwb9vmir+TX0MDOpjYFAf\nA4P6GBjUR5GWIyDCk4iIiIiIyMWm/8YQERERERFpBoUnERERERGRZlB4EhERERERaQaFJxERERER\nkWZQeBIREREREWkGhScREREREZFmUHgSEZHTVFVVcfnll/P4449f0PaFhYXce++9jBw5ktGjR3P7\n7bezb98+7+uVlZXcdNNN5OTkMG/evDO2z8zMZPz48YwfP55rrrmGBx54gPz8/Gb97Pnz51NRUXFB\ndRuGwS233EJRUdEFbX8uDoeDO+64g/HjxzN79uxmb5eZmUlDQ8M51zl69Cjvvfdes97Pbrdzww03\nUF9f3+waRETk3xSeRETkNB9++CFDhgxh+fLlOByO89rW4XAwdepUevfuzerVq1m5ciVTpkzhZz/7\nGdXV1QBs2LCB6OhoPvjgA6ZOnXrW91mwYAH/+Mc/+Pjjj+nbty8333wzR44c+d6f/8Ybb1BeXn5e\nNZ+0du1a0tLSaNu27QVtfy55eXkUFhbyj3/8g9/85jc/6HsfOXKEBQsWNGvdoKAgxowZw5tvvvmD\n1iAi0looPImIyGkWLVrE7bffTo8ePVizZo13+erVqxk3bhw5OTmMGzeOL7/88oxtP/roI6Kjo/nP\n//xP77JrrrmGvn378uabb7Jx40aefvppNm/eTE5ODrm5ueesxWQycdNNNzFkyBDefvttAJYuXcqU\nKVO4/vrruf7669mwYQMAf/7znykpKeHee+8lJyeH/Px87HY7Tz75JJMnT2bChAn86le/+s6ze6+0\n1wAABrBJREFULn//+98ZO3as9/m8efOYNGkS119/PTfddBO7d+8GoKGhgXvvvZexY8cyYcIE7r//\nfu82r7zyCuPGjWPcuHHMnDmT+vp6Dhw4wIMPPsiRI0fIyclh2bJl37m/K1euZMyYMeTk5DBnzpzT\nXvvlL3/JxIkTGTduHHfffbc3jD766KPk5+eTk5PDfffdB8CBAwe44447mDRpEhMmTGDx4sXe9/nx\nj3/MokWLzvnvLiIi38EQERE5YdeuXcaoUaMMwzCMjz76yPj5z3/ufW38+PHGli1bDMMwDLfbbdTU\n1Jyx/ezZs41Zs2adsfy1114z7rnnHsMwDGPx4sXex2eTkZFh1NfXn7H9HXfcYRiGYVRWVnqX5+fn\nG9nZ2d7nP/rRj4x9+/Z5n8+ZM8d46aWXvM+feuop47nnnjvjZ7pcLqN3796n7VN5ebn38fr1640p\nU6YYhmEYK1euNKZOnep9rbq62jAMw/jXv/5lXHfddUZtba1hGIbxq1/9ynj66acNwzCMjRs3Gjfc\ncMN37rNhGEZpaanRr18/4+DBg4ZhGMarr7562r9FRUWFd93nnnvOeOaZZ8763g6Hw7j++uuN/Px8\nwzAMo6amxrjmmmu8zw3DMIYMGWIUFhaesx4RETmT1dfhTUREWo6FCxcyfvx4AEaMGMHvfvc7SkpK\nSExMZMCAAcyaNYvRo0eTnZ1N165dz/oehmGc82d83+vft80333zDjBkzKCkpwWq1UlZWRnl5OfHx\n8Wdst3btWurq6lixYgXgGfPTvXv3M9arrKzE7XYTERHhXbZjxw5efvllqqurMZlMHDp0CPCMQzpw\n4ACPPvoo/fr1Y9iwYYDncsSxY8cSHh4OwJQpU7zjxpqzz1u3bqVHjx507NgRgBtvvJFnnnnG+/oH\nH3zA0qVLcTgcNDQ00KlTp7O+96FDhzhw4AAzZszwLnM4HBw4cIDOnTsD0LZtWwoKCi7KJYoiIoFM\n4UlERABPsPjwww8JDg72XubldDpZvHgx06dPZ+bMmezbt48NGzZw33338bOf/YzJkyef9h4ZGRm8\n8847Z7z3li1byMzMvODatm/fTrdu3QCYMWMGM2fOZMSIERiGwRVXXEFTU9N3bvvII4/Qv3//8/p5\ndrud++67j7fffpvu3btTXFzM0KFDAUhNTeWjjz7i888/Z926dfzhD39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"text/plain": [ "<matplotlib.figure.Figure at 0x7fd90cb8de10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "gdp_df = odo(fred_gdp, pd.DataFrame)\n", "\n", "gdp_df.plot(x='asof_date', y='value')\n", "plt.xlabel(\"As Of Date (asof_date)\")\n", "plt.ylabel(\"GDP (billions)\")\n", "plt.title(\"United States GDP\")\n", "plt.legend().set_visible(False)" ] }, { "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": 0 }
apache-2.0
atlas-outreach-data-tools/notebooks
september_2018_v-2.0/.ipynb_checkpoints/python_analysis_example_Invariant_mass_Zll_with_TLorentz-checkpoint.ipynb
1
4174
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<CENTER><img src=\"../images/opendata-top-transblack.png\" style=\"width:40%\"></CENTER>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<CENTER><h1>Simple pyROOT notebook example using TLorentz Vectors</h1></CENTER>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "require(['codemirror/mode/clike/clike'], function(Clike) { console.log('ROOTaaS - C++ CodeMirror module loaded'); });" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "IPython.CodeCell.config_defaults.highlight_modes['magic_text/x-c++src'] = {'reg':[/^%%cpp/]};" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Welcome to ROOTaaS 6.06/08\n" ] } ], "source": [ "import ROOT" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "## f = ROOT.TFile.Open(\"mc_105986.ZZ.root\")\n", "## f = ROOT.TFile.Open(\"mc_147770.Zee.root\")\n", "f = ROOT.TFile.Open(\"http://opendata.atlas.cern/release/samples/MC/mc_147770.Zee.root\")" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "c = ROOT.TCanvas(\"testCanvas\",\"a first way to plot a variable\",800,600)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "t = f.Get(\"mini\")" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h = ROOT.TH1F(\"variable\",\"Example plot: Number of Leptons\",4,0,4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "h_Mll = ROOT.TH1F(\"h_Mll\",\"Invariant mass of the two Leptons\",50,0,200)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "w = 1\n", "for event in t:\n", " \"\"\"This is the cut #1: request at least 2 leptons\"\"\"\n", " if t.lep_n > 1:\n", " \"\"\"Let's define one TLorentz vector for each, e.i. two vectors!\"\"\"\n", " leadingLep = ROOT.TLorentzVector(t.lep_pt[0], t.lep_eta[0], t.lep_phi[0], t.lep_E[0])\n", " secondLep = ROOT.TLorentzVector(t.lep_pt[1], t.lep_eta[1], t.lep_phi[1], t.lep_E[1])\n", "\n", " \"\"\"Next line does the addition of the two TLorentz vectors above and so,\n", " we can ask the mass very easy\"\"\"\n", " TL_ll = leadingLep + secondLep\n", " \n", " \"\"\"We devide the value of the combined vector by 1000 to get the value in GeV\"\"\"\n", " mll = TL_ll.M()/1000.\n", " h_Mll.Fill(mll,w)\n", "\n", "print \"Done!\"" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "\"\"\"Now les't plot the mass of the lepton-lepton system\"\"\"\n", "h_Mll.Draw()\n", "c.Draw()" ] }, { "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": 1 }
gpl-3.0
ES-DOC/esdoc-jupyterhub
notebooks/noaa-gfdl/cmip6/models/gfdl-esm2m/aerosol.ipynb
1
85411
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Aerosol \n", "**MIP Era**: CMIP6 \n", "**Institute**: NOAA-GFDL \n", "**Source ID**: GFDL-ESM2M \n", "**Topic**: Aerosol \n", "**Sub-Topics**: Transport, Emissions, Concentrations, Optical Radiative Properties, Model. \n", "**Properties**: 70 (38 required) \n", "**Model descriptions**: [Model description details](https://specializations.es-doc.org/cmip6/aerosol?client=jupyter-notebook) \n", "**Initialized From**: -- \n", "\n", "**Notebook Help**: [Goto notebook help page](https://es-doc.org/cmip6-models-documenting-with-ipython) \n", "**Notebook Initialised**: 2018-02-20 15:02:34" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### Document Setup \n", "**IMPORTANT: to be executed each time you run the notebook** " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# DO NOT EDIT ! \n", "from pyesdoc.ipython.model_topic import NotebookOutput \n", "\n", "# DO NOT EDIT ! \n", "DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'gfdl-esm2m', 'aerosol')" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Authors \n", "*Set document authors*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_author(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Contributors \n", "*Specify document contributors* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set as follows: DOC.set_contributor(\"name\", \"email\") \n", "# TODO - please enter value(s)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Publication \n", "*Specify document publication status* " ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# Set publication status: \n", "# 0=do not publish, 1=publish. \n", "DOC.set_publication_status(0)" ], "outputs": [], "metadata": {} }, { "source": [ "### Document Table of Contents \n", "[1. Key Properties](#1.-Key-Properties) \n", "[2. Key Properties --&gt; Software Properties](#2.-Key-Properties---&gt;-Software-Properties) \n", "[3. Key Properties --&gt; Timestep Framework](#3.-Key-Properties---&gt;-Timestep-Framework) \n", "[4. Key Properties --&gt; Meteorological Forcings](#4.-Key-Properties---&gt;-Meteorological-Forcings) \n", "[5. Key Properties --&gt; Resolution](#5.-Key-Properties---&gt;-Resolution) \n", "[6. Key Properties --&gt; Tuning Applied](#6.-Key-Properties---&gt;-Tuning-Applied) \n", "[7. Transport](#7.-Transport) \n", "[8. Emissions](#8.-Emissions) \n", "[9. Concentrations](#9.-Concentrations) \n", "[10. Optical Radiative Properties](#10.-Optical-Radiative-Properties) \n", "[11. Optical Radiative Properties --&gt; Absorption](#11.-Optical-Radiative-Properties---&gt;-Absorption) \n", "[12. Optical Radiative Properties --&gt; Mixtures](#12.-Optical-Radiative-Properties---&gt;-Mixtures) \n", "[13. Optical Radiative Properties --&gt; Impact Of H2o](#13.-Optical-Radiative-Properties---&gt;-Impact-Of-H2o) \n", "[14. Optical Radiative Properties --&gt; Radiative Scheme](#14.-Optical-Radiative-Properties---&gt;-Radiative-Scheme) \n", "[15. Optical Radiative Properties --&gt; Cloud Interactions](#15.-Optical-Radiative-Properties---&gt;-Cloud-Interactions) \n", "[16. Model](#16.-Model) \n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "# 1. Key Properties \n", "*Key properties of the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 1.1. Model Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of aerosol model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.model_overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.2. Model Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Name of aerosol model code*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.model_name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.3. Scheme Scope\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Atmospheric domains covered by the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.scheme_scope') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"troposhere\" \n", "# \"stratosphere\" \n", "# \"mesosphere\" \n", "# \"mesosphere\" \n", "# \"whole atmosphere\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.4. Basic Approximations\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Basic approximations made in the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.basic_approximations') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.5. Prognostic Variables Form\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Prognostic variables in the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.prognostic_variables_form') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"3D mass/volume ratio for aerosols\" \n", "# \"3D number concenttration for aerosols\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.6. Number Of Tracers\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of tracers in the aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.number_of_tracers') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 1.7. Family Approach\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Are aerosol calculations generalized into families of species?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.family_approach') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 2. Key Properties --&gt; Software Properties \n", "*Software properties of aerosol code*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 2.1. Repository\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Location of code for this component.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.software_properties.repository') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.2. Code Version\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Code version identifier.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_version') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 2.3. Code Languages\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Code language(s).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.software_properties.code_languages') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 3. Key Properties --&gt; Timestep Framework \n", "*Physical properties of seawater in ocean*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 3.1. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Mathematical method deployed to solve the time evolution of the prognostic variables*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.method') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses atmospheric chemistry time stepping\" \n", "# \"Specific timestepping (operator splitting)\" \n", "# \"Specific timestepping (integrated)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.2. Split Operator Advection Timestep\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for aerosol advection (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_advection_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.3. Split Operator Physical Timestep\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Timestep for aerosol physics (in seconds).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.split_operator_physical_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.4. Integrated Timestep\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Timestep for the aerosol model (in seconds)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_timestep') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 3.5. Integrated Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Specify the type of timestep scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.timestep_framework.integrated_scheme_type') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Explicit\" \n", "# \"Implicit\" \n", "# \"Semi-implicit\" \n", "# \"Semi-analytic\" \n", "# \"Impact solver\" \n", "# \"Back Euler\" \n", "# \"Newton Raphson\" \n", "# \"Rosenbrock\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 4. Key Properties --&gt; Meteorological Forcings \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 4.1. Variables 3D\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Three dimensionsal forcing variables, e.g. U, V, W, T, Q, P, conventive mass flux*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_3D') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.2. Variables 2D\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Two dimensionsal forcing variables, e.g. land-sea mask definition*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.variables_2D') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 4.3. Frequency\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Frequency with which meteological forcings are applied (in seconds).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.meteorological_forcings.frequency') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 5. Key Properties --&gt; Resolution \n", "*Resolution in the aersosol model grid*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 5.1. Name\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *This is a string usually used by the modelling group to describe the resolution of this grid, e.g. ORCA025, N512L180, T512L70 etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.name') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.2. Canonical Horizontal Resolution\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Expression quoted for gross comparisons of resolution, eg. 50km or 0.1 degrees etc.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.canonical_horizontal_resolution') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.3. Number Of Horizontal Gridpoints\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Total number of horizontal (XY) points (or degrees of freedom) on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.number_of_horizontal_gridpoints') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.4. Number Of Vertical Levels\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Number of vertical levels resolved on computational grid.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.number_of_vertical_levels') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 5.5. Is Adaptive Grid\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Default is False. Set true if grid resolution changes during execution.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.resolution.is_adaptive_grid') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 6. Key Properties --&gt; Tuning Applied \n", "*Tuning methodology for aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 6.1. Description\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *General overview description of tuning: explain and motivate the main targets and metrics retained. &amp;Document the relative weight given to climate performance metrics versus process oriented metrics, &amp;and on the possible conflicts with parameterization level tuning. In particular describe any struggle &amp;with a parameter value that required pushing it to its limits to solve a particular model deficiency.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.description') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.2. Global Mean Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List set of metrics of the global mean state used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.global_mean_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.3. Regional Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List of regional metrics of mean state used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.regional_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 6.4. Trend Metrics Used\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *List observed trend metrics used in tuning model/component*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.key_properties.tuning_applied.trend_metrics_used') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 7. Transport \n", "*Aerosol transport*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 7.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of transport in atmosperic aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.2. Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Method for aerosol transport modeling*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.scheme') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Specific transport scheme (eulerian)\" \n", "# \"Specific transport scheme (semi-lagrangian)\" \n", "# \"Specific transport scheme (eulerian and semi-lagrangian)\" \n", "# \"Specific transport scheme (lagrangian)\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.3. Mass Conservation Scheme\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Method used to ensure mass conservation.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.mass_conservation_scheme') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Mass adjustment\" \n", "# \"Concentrations positivity\" \n", "# \"Gradients monotonicity\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 7.4. Convention\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Transport by convention*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.transport.convention') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Uses Atmospheric chemistry transport scheme\" \n", "# \"Convective fluxes connected to tracers\" \n", "# \"Vertical velocities connected to tracers\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 8. Emissions \n", "*Atmospheric aerosol emissions*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 8.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of emissions in atmosperic aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.2. Method\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Method used to define aerosol species (several methods allowed because the different species may not use the same method).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.method') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"None\" \n", "# \"Prescribed (climatology)\" \n", "# \"Prescribed CMIP6\" \n", "# \"Prescribed above surface\" \n", "# \"Interactive\" \n", "# \"Interactive above surface\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.3. Sources\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Sources of the aerosol species are taken into account in the emissions scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.sources') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Vegetation\" \n", "# \"Volcanos\" \n", "# \"Bare ground\" \n", "# \"Sea surface\" \n", "# \"Lightning\" \n", "# \"Fires\" \n", "# \"Aircraft\" \n", "# \"Anthropogenic\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.4. Prescribed Climatology\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Specify the climatology type for aerosol emissions*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Constant\" \n", "# \"Interannual\" \n", "# \"Annual\" \n", "# \"Monthly\" \n", "# \"Daily\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.5. Prescribed Climatology Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of aerosol species emitted and prescribed via a climatology*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_climatology_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.6. Prescribed Spatially Uniform Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of aerosol species emitted and prescribed as spatially uniform*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.prescribed_spatially_uniform_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.7. Interactive Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of aerosol species emitted and specified via an interactive method*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.interactive_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.8. Other Emitted Species\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of aerosol species emitted and specified via an &quot;other method&quot;*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.other_emitted_species') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 8.9. Other Method Characteristics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Characteristics of the &quot;other method&quot; used for aerosol emissions*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.emissions.other_method_characteristics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 9. Concentrations \n", "*Atmospheric aerosol concentrations*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 9.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of concentrations in atmosperic aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.2. Prescribed Lower Boundary\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of species prescribed at the lower boundary.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_lower_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.3. Prescribed Upper Boundary\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of species prescribed at the upper boundary.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_upper_boundary') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.4. Prescribed Fields Mmr\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of species prescribed as mass mixing ratios.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_mmr') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 9.5. Prescribed Fields Aod Plus Ccn\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *List of species prescribed as AOD plus CCNs.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.concentrations.prescribed_fields_aod_plus_ccn') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 10. Optical Radiative Properties \n", "*Aerosol optical and radiative properties*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 10.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of optical and radiative properties*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 11. Optical Radiative Properties --&gt; Absorption \n", "*Absortion properties in aerosol scheme*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 11.1. Black Carbon\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Absorption mass coefficient of black carbon at 550nm (if non-absorbing enter 0)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.black_carbon') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.2. Dust\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Absorption mass coefficient of dust at 550nm (if non-absorbing enter 0)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.dust') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 11.3. Organics\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** FLOAT&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *Absorption mass coefficient of organics at 550nm (if non-absorbing enter 0)*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.absorption.organics') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 12. Optical Radiative Properties --&gt; Mixtures \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 12.1. External\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there external mixing with respect to chemical composition?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.external') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.2. Internal\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is there internal mixing with respect to chemical composition?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.internal') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 12.3. Mixing Rule\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If there is internal mixing with respect to chemical composition then indicate the mixinrg rule*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.mixtures.mixing_rule') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 13. Optical Radiative Properties --&gt; Impact Of H2o \n", "**" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 13.1. Size\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does H2O impact size?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.size') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.2. Internal Mixture\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does H2O impact aerosol internal mixture?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.internal_mixture') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 13.3. External Mixture\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does H2O impact aerosol external mixture?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.impact_of_h2o.external_mixture') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 14. Optical Radiative Properties --&gt; Radiative Scheme \n", "*Radiative scheme for aerosol*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 14.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of radiative scheme*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.2. Shortwave Bands\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of shortwave bands*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.shortwave_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 14.3. Longwave Bands\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of longwave bands*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.radiative_scheme.longwave_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 15. Optical Radiative Properties --&gt; Cloud Interactions \n", "*Aerosol-cloud interactions*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 15.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of aerosol-cloud interactions*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.2. Twomey\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Is the Twomey effect included?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.3. Twomey Minimum Ccn\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.1\n", "### *If the Twomey effect is included, then what is the minimum CCN number?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.twomey_minimum_ccn') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.4. Drizzle\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the scheme affect drizzle?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.drizzle') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.5. Cloud Lifetime\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** BOOLEAN&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Does the scheme affect cloud lifetime?*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.cloud_lifetime') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# Valid Choices: \n", "# True \n", "# False \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 15.6. Longwave Bands\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** INTEGER&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Number of longwave bands*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.optical_radiative_properties.cloud_interactions.longwave_bands') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(value) \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "# 16. Model \n", "*Aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "source": [ "### 16.1. Overview\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** STRING&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.1\n", "### *Overview of atmosperic aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.overview') \n", "\n", "# PROPERTY VALUE: \n", "# Set as follows: DOC.set_value(\"value\") \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.2. Processes\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Processes included in the Aerosol model.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.processes') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Dry deposition\" \n", "# \"Sedimentation\" \n", "# \"Wet deposition (impaction scavenging)\" \n", "# \"Wet deposition (nucleation scavenging)\" \n", "# \"Coagulation\" \n", "# \"Oxidation (gas phase)\" \n", "# \"Oxidation (in cloud)\" \n", "# \"Condensation\" \n", "# \"Ageing\" \n", "# \"Advection (horizontal)\" \n", "# \"Advection (vertical)\" \n", "# \"Heterogeneous chemistry\" \n", "# \"Nucleation\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.3. Coupling\n", "**Is Required:** FALSE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 0.N\n", "### *Other model components coupled to the Aerosol model*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.coupling') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Radiation\" \n", "# \"Land surface\" \n", "# \"Heterogeneous chemistry\" \n", "# \"Clouds\" \n", "# \"Ocean\" \n", "# \"Cryosphere\" \n", "# \"Gas phase chemistry\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.4. Gas Phase Precursors\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of gas phase aerosol precursors.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.gas_phase_precursors') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"DMS\" \n", "# \"SO2\" \n", "# \"Ammonia\" \n", "# \"Iodine\" \n", "# \"Terpene\" \n", "# \"Isoprene\" \n", "# \"VOC\" \n", "# \"NOx\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.5. Scheme Type\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *Type(s) of aerosol scheme used by the aerosols model (potentially multiple: some species may be covered by one type of aerosol scheme and other species covered by another type).*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.scheme_type') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Bulk\" \n", "# \"Modal\" \n", "# \"Bin\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### 16.6. Bulk Scheme Species\n", "**Is Required:** TRUE&nbsp;&nbsp;&nbsp;&nbsp;**Type:** ENUM&nbsp;&nbsp;&nbsp;&nbsp;**Cardinality:** 1.N\n", "### *List of species covered by the bulk scheme.*" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "# PROPERTY ID - DO NOT EDIT ! \n", "DOC.set_id('cmip6.aerosol.model.bulk_scheme_species') \n", "\n", "# PROPERTY VALUE(S): \n", "# Set as follows: DOC.set_value(\"value\") \n", "# Valid Choices: \n", "# \"Sulphate\" \n", "# \"Nitrate\" \n", "# \"Sea salt\" \n", "# \"Dust\" \n", "# \"Ice\" \n", "# \"Organic\" \n", "# \"Black carbon / soot\" \n", "# \"SOA (secondary organic aerosols)\" \n", "# \"POM (particulate organic matter)\" \n", "# \"Polar stratospheric ice\" \n", "# \"NAT (Nitric acid trihydrate)\" \n", "# \"NAD (Nitric acid dihydrate)\" \n", "# \"STS (supercooled ternary solution aerosol particule)\" \n", "# \"Other: [Please specify]\" \n", "# TODO - please enter value(s)", "\n" ], "outputs": [], "metadata": { "collapsed": true } }, { "source": [ "### \u00a92017 [ES-DOC](https://es-doc.org) \n" ], "cell_type": "markdown", "metadata": {} } ], "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": "2.7.10", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
gpl-3.0
openearth/notebooks
delft3d io sea.ipynb
1
20908
{ "metadata": { "name": "webinar_delft3d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "cd C:\\Users\\boer_g\\Desktop\\Webinar\\OEM" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "C:\\Users\\boer_g\\Desktop\\Webinar\\OEM\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "import openearthtools.io.delft3d as d3d\n", "dir(d3d)" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 5, "text": [ "['Grid',\n", " '__builtins__',\n", " '__doc__',\n", " '__file__',\n", " '__name__',\n", " '__package__',\n", " '__path__',\n", " 'grid',\n", " 'mdf',\n", " 'read',\n", " 'tekal',\n", " 'tekalblock',\n", " 'write']" ] } ], "prompt_number": 5 }, { "cell_type": "code", "collapsed": false, "input": [ "help(d3d.Grid)\n", "# example from https://svn.oss.deltares.nl/repos/openearthmodels/trunk/deltares/brazil_patos_lagoon_52S_32E/\n", "G = d3d.Grid.fromfile('lake_and_sea_5_ll.grd')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Help on class Grid in module openearthtools.io.delft3d.grid:\n", "\n", "class Grid(__builtin__.object)\n", " | Create a Delft3D grid file\n", " | # Create an empty grid\n", " | grid = Grid()\n", " | # Load a grid from file\n", " | grid = Grid.read('filename.grd')\n", " | # Write grid to file\n", " | Grid.read(grid,'filename.grd')\n", " | \n", " | Methods defined here:\n", " | \n", " | __init__(self, *args, **kwargs)\n", " | \n", " | write(self, filename, **kwargs)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " | \n", " | fromfile(filename, **kwargs)\n", " | \n", " | ----------------------------------------------------------------------\n", " | Data descriptors defined here:\n", " | \n", " | __dict__\n", " | dictionary for instance variables (if defined)\n", " | \n", " | __weakref__\n", " | list of weak references to the object (if defined)\n", "\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# curvilinear grids typically have many invalid matrix positions\n", "# to represent islands, thick dams or cul-de-sacs (estuaries/rivers) \n", "# therefore the matrices are stored as Masked array type\n", "G.X" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "pyout", "prompt_number": 7, "text": [ "masked_array(data =\n", " [[-- -- -- ..., -51.21892397029205 -51.25650464051964 -51.28911971484214]\n", " [-- -- -- ..., -51.25256180611927 -51.274277884869164 -51.29888408312184]\n", " [-- -- -- ..., -51.26523724097448 -51.288310621047295 -51.31035946306346]\n", " ..., \n", " [-51.92346119182868 -51.94226259182987 -51.96086078342037 ..., -- -- --]\n", " [-51.92608902265055 -51.94527933494353 -51.96403316815339 ..., -- -- --]\n", " [-51.928765963284064 -51.948398147668485 -51.967149196828366 ..., -- -- --]],\n", " mask =\n", " [[ True True True ..., False False False]\n", " [ True True True ..., False False False]\n", " [ True True True ..., False False False]\n", " ..., \n", " [False False False ..., True True True]\n", " [False False False ..., True True True]\n", " [False False False ..., True True True]],\n", " fill_value = 1e+20)\n" ] } ], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "G.dep = G.z" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "matplotlib.pyplot.pcolor(G.X,G.Y,G.dep)\n", "gca().set_aspect(1./cos(mean(ylim())/180*pi),adjustable='box') # see Matlab axislat()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "display_data", "png": 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IOwBCT08PIyMjkfep9aBm1h/Yog9s/xL+RiBRyPTidvvrKORjxOJ5ruS4SRMu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\n" } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
gpl-3.0
catalyst-cooperative/pudl
notebooks/work-in-progress/ferc714-output.ipynb
1
19524
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# Standard libraries\n", "import logging\n", "import os\n", "import pathlib\n", "import sys\n", "\n", "# 3rd party libraries\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import sqlalchemy as sa\n", "\n", "# Local libraries\n", "import pudl\n", "import pudl.output.ferc714" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Configure Display Parameters" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "sns.set()\n", "%matplotlib inline\n", "mpl.rcParams['figure.figsize'] = (10,4)\n", "mpl.rcParams['figure.dpi'] = 150\n", "pd.options.display.max_columns = 100\n", "pd.options.display.max_rows = 100" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Use Python Logging facilities\n", "* Using a logger from the beginning will make the transition into the PUDL package easier.\n", "* Creating a logging handler here will also allow you to see the logging output coming from PUDL and other underlying packages." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "logger=logging.getLogger()\n", "logger.setLevel(logging.INFO)\n", "handler = logging.StreamHandler(stream=sys.stdout)\n", "formatter = logging.Formatter('%(message)s')\n", "handler.setFormatter(formatter)\n", "logger.handlers = [handler]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Define Functions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Define Notebook Parameters" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'pudl_in': '/home/zane/code/catalyst/pudl-work',\n", " 'data_dir': '/home/zane/code/catalyst/pudl-work/data',\n", " 'settings_dir': '/home/zane/code/catalyst/pudl-work/settings',\n", " 'pudl_out': '/home/zane/code/catalyst/pudl-work',\n", " 'sqlite_dir': '/home/zane/code/catalyst/pudl-work/sqlite',\n", " 'parquet_dir': '/home/zane/code/catalyst/pudl-work/parquet',\n", " 'datapkg_dir': '/home/zane/code/catalyst/pudl-work/datapkg',\n", " 'notebook_dir': '/home/zane/code/catalyst/pudl-work/notebook',\n", " 'ferc1_db': 'sqlite:////home/zane/code/catalyst/pudl-work/sqlite/ferc1.sqlite',\n", " 'pudl_db': 'sqlite:////home/zane/code/catalyst/pudl-work/sqlite/pudl.sqlite'}" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Engine(sqlite:////home/zane/code/catalyst/pudl-work/sqlite/ferc1.sqlite)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "Engine(sqlite:////home/zane/code/catalyst/pudl-work/sqlite/pudl.sqlite)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pudl_settings = pudl.workspace.setup.get_defaults()\n", "display(pudl_settings)\n", "\n", "ferc1_engine = sa.create_engine(pudl_settings['ferc1_db'])\n", "display(ferc1_engine)\n", "\n", "pudl_engine = sa.create_engine(pudl_settings['pudl_db'])\n", "display(pudl_engine)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Load Data" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "pudl_out = pudl.output.pudltabl.PudlTabl(pudl_engine=pudl_engine)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Running the interim EIA 861 ETL process! (~2 minutes)\n", "Extracting eia861 spreadsheet data.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/zane/code/catalyst/pudl/src/pudl/extract/eia861.py:39: UserWarning: Integration of EIA 861 into PUDL is still experimental and incomplete.\n", "The data has not yet been validated, and the structure may change.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n", "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n", "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n", "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n", "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n", "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n", "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n", "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n", "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n", "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n", "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n", "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n", "Transforming raw EIA 861 DataFrames for service_territory_eia861 concatenated across all years.\n", "Assigned state FIPS codes for 100.00% of records.\n", "Assigned county FIPS codes for 99.64% of records.\n", "Transforming raw EIA 861 DataFrames for balancing_authority_eia861 concatenated across all years.\n", "Started with 37622 missing BA Codes out of 38882 records (96.76%)\n", "Ended with 12674 missing BA Codes out of 38882 records (32.60%)\n", "Transforming raw EIA 861 DataFrames for sales_eia861 concatenated across all years.\n", "Tidying the EIA 861 Sales table.\n", "Dropped 0 duplicate records from EIA 861 Demand Response table, out of a total of 301045 records (0.0000% of all records). \n", "Performing value transformations on EIA 861 Sales table.\n", "Transforming raw EIA 861 DataFrames for advanced_metering_infrastructure_eia861 concatenated across all years.\n", "Tidying the EIA 861 Advanced Metering Infrastructure table.\n", "Transforming raw EIA 861 DataFrames for demand_response_eia861 concatenated across all years.\n", "Tidying the EIA 861 Demand Response table.\n", "Dropped 0 duplicate records from EIA 861 Demand Response table, out of a total of 10644 records (0.0000% of all records). \n", "Performing value transformations on EIA 861 Demand Response table.\n", "Transforming raw EIA 861 DataFrames for distribution_systems_eia861 concatenated across all years.\n", "Transforming raw EIA 861 DataFrames for dynamic_pricing_eia861 concatenated across all years.\n", "Tidying the EIA 861 Dynamic Pricing table.\n", "Performing value transformations on EIA 861 Dynamic Pricing table.\n", "Transforming raw EIA 861 DataFrames for green_pricing_eia861 concatenated across all years.\n", "Tidying the EIA 861 Green Pricing table.\n", "Performing value transformations on EIA 861 Green Pricing table.\n", "Transforming raw EIA 861 DataFrames for mergers_eia861 concatenated across all years.\n", "Transforming raw EIA 861 DataFrames for net_metering_eia861 concatenated across all years.\n", "Tidying the EIA 861 Net Metering table.\n", "Transforming raw EIA 861 DataFrames for non_net_metering_eia861 concatenated across all years.\n", "Tidying the EIA 861 Non Net Metering table.\n", "Building an EIA 861 BA-Util-State-Date association table.\n", "Building an EIA 861 Util-State-Date association table.\n", "Completing normalization of balancing_authority_eia861.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/zane/miniconda3/envs/pudl-dev/lib/python3.8/site-packages/pandas/core/missing.py:49: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " mask = arr == x\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Running the interim FERC 714 ETL process! (~11 minutes)\n", "Extracting respondent_id_ferc714 from CSV into pandas DataFrame.\n", "Extracting id_certification_ferc714 from CSV into pandas DataFrame.\n", "Extracting gen_plants_ba_ferc714 from CSV into pandas DataFrame.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/zane/code/catalyst/pudl/src/pudl/extract/ferc714.py:82: UserWarning: Integration of FERC 714 into PUDL is still experimental and incomplete.\n", "The data has not yet been validated, and the structure may change.\n", " warnings.warn(\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Extracting demand_monthly_ba_ferc714 from CSV into pandas DataFrame.\n", "Extracting net_energy_load_ba_ferc714 from CSV into pandas DataFrame.\n", "Extracting adjacency_ba_ferc714 from CSV into pandas DataFrame.\n", "Extracting interchange_ba_ferc714 from CSV into pandas DataFrame.\n", "Extracting lambda_hourly_ba_ferc714 from CSV into pandas DataFrame.\n", "Extracting lambda_description_ferc714 from CSV into pandas DataFrame.\n", "Extracting description_pa_ferc714 from CSV into pandas DataFrame.\n", "Extracting demand_forecast_pa_ferc714 from CSV into pandas DataFrame.\n", "Extracting demand_hourly_pa_ferc714 from CSV into pandas DataFrame.\n", "Transforming respondent_id_ferc714.\n", "Transforming id_certification_ferc714.\n", "Transforming gen_plants_ba_ferc714.\n", "Transforming demand_monthly_ba_ferc714.\n", "Transforming net_energy_load_ba_ferc714.\n", "Transforming adjacency_ba_ferc714.\n", "Transforming interchange_ba_ferc714.\n", "Transforming lambda_hourly_ba_ferc714.\n", "Transforming lambda_description_ferc714.\n", "Transforming description_pa_ferc714.\n", "Transforming demand_forecast_pa_ferc714.\n", "Transforming demand_hourly_pa_ferc714.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/zane/miniconda3/envs/pudl-dev/lib/python3.8/site-packages/pandas/core/missing.py:49: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " mask = arr == x\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "We've already got the 2010 Census GeoDB.\n", "Extracting the GeoDB into a GeoDataFrame\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/home/zane/miniconda3/envs/pudl-dev/lib/python3.8/site-packages/pandas/core/missing.py:49: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " mask = arr == x\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 10min 52s, sys: 60 s, total: 11min 52s\n", "Wall time: 12min 39s\n" ] } ], "source": [ "%%time\n", "ferc714_out = pudl.output.ferc714.Respondents(pudl_out)\n", "annualized = ferc714_out.annualize()\n", "categorized = ferc714_out.categorize()\n", "summarized = ferc714_out.summarize_demand()\n", "fipsified = ferc714_out.fipsify()\n", "counties_gdf = ferc714_out.georef_counties()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 2968 entries, 0 to 2785\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 eia_code 2954 non-null Int64 \n", " 1 respondent_type 2870 non-null category \n", " 2 respondent_id_ferc714 2968 non-null Int64 \n", " 3 respondent_name_ferc714 2968 non-null string \n", " 4 report_date 2968 non-null datetime64[ns]\n", " 5 balancing_authority_id_eia 1806 non-null Int64 \n", " 6 balancing_authority_code_eia 1176 non-null category \n", " 7 balancing_authority_name_eia 1806 non-null string \n", " 8 utility_id_eia 1064 non-null Int64 \n", " 9 utility_name_eia 994 non-null string \n", "dtypes: Int64(4), category(2), datetime64[ns](1), string(3)\n", "memory usage: 229.3 KB\n" ] } ], "source": [ "categorized.info()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 2968 entries, 0 to 2967\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 report_date 2968 non-null datetime64[ns]\n", " 1 respondent_id_ferc714 2968 non-null Int64 \n", " 2 demand_annual_mwh 2968 non-null float64 \n", " 3 eia_code 2954 non-null Int64 \n", " 4 respondent_type 2870 non-null category \n", " 5 respondent_name_ferc714 2968 non-null string \n", " 6 balancing_authority_id_eia 1806 non-null Int64 \n", " 7 balancing_authority_code_eia 1176 non-null category \n", " 8 balancing_authority_name_eia 1806 non-null string \n", " 9 utility_id_eia 1064 non-null Int64 \n", " 10 utility_name_eia 994 non-null string \n", "dtypes: Int64(4), category(2), datetime64[ns](1), float64(1), string(3)\n", "memory usage: 252.5 KB\n" ] } ], "source": [ "summarized.info()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "Int64Index: 99747 entries, 0 to 2785\n", "Data columns (total 14 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 eia_code 99733 non-null Int64 \n", " 1 respondent_type 99649 non-null category \n", " 2 respondent_id_ferc714 99747 non-null Int64 \n", " 3 respondent_name_ferc714 99747 non-null string \n", " 4 report_date 99747 non-null datetime64[ns]\n", " 5 balancing_authority_id_eia 91893 non-null Int64 \n", " 6 balancing_authority_code_eia 82262 non-null category \n", " 7 balancing_authority_name_eia 91893 non-null string \n", " 8 utility_id_eia 7756 non-null Int64 \n", " 9 utility_name_eia 7339 non-null string \n", " 10 state 98255 non-null string \n", " 11 county 98255 non-null string \n", " 12 state_id_fips 98255 non-null string \n", " 13 county_id_fips 98238 non-null string \n", "dtypes: Int64(4), category(2), datetime64[ns](1), string(7)\n", "memory usage: 10.5 MB\n" ] } ], "source": [ "fipsified.info()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'geopandas.geodataframe.GeoDataFrame'>\n", "Int64Index: 99747 entries, 0 to 99746\n", "Data columns (total 16 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 county_id_fips 98238 non-null string \n", " 1 county_name_census 98221 non-null object \n", " 2 geometry 98221 non-null geometry \n", " 3 eia_code 99733 non-null Int64 \n", " 4 respondent_type 99649 non-null category \n", " 5 respondent_id_ferc714 99747 non-null Int64 \n", " 6 respondent_name_ferc714 99747 non-null string \n", " 7 report_date 99747 non-null datetime64[ns]\n", " 8 balancing_authority_id_eia 91893 non-null Int64 \n", " 9 balancing_authority_code_eia 82262 non-null category \n", " 10 balancing_authority_name_eia 91893 non-null string \n", " 11 utility_id_eia 7756 non-null Int64 \n", " 12 utility_name_eia 7339 non-null string \n", " 13 state 98255 non-null string \n", " 14 county 98255 non-null string \n", " 15 state_id_fips 98255 non-null string \n", "dtypes: Int64(4), category(2), datetime64[ns](1), geometry(1), object(1), string(7)\n", "memory usage: 12.0+ MB\n" ] } ], "source": [ "counties_gdf.info()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# This takes 45 minutes so...\n", "#respondents_gdf = ferc714_out.georef_respondents()\n", "#display(respondents_gdf.info())\n", "#respondents_gdf.sample(10)" ] } ], "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": "3.8.5" } }, "nbformat": 4, "nbformat_minor": 4 }
mit
schaber/deep-learning
autoencoder/Simple_Autoencoder.ipynb
1
332105
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A Simple Autoencoder\n", "\n", "We'll start off by building a simple autoencoder to compress the MNIST dataset. With autoencoders, we pass input data through an encoder that makes a compressed representation of the input. Then, this representation is passed through a decoder to reconstruct the input data. Generally the encoder and decoder will be built with neural networks, then trained on example data.\n", "\n", "![Autoencoder](assets/autoencoder_1.png)\n", "\n", "In this notebook, we'll be build a simple network architecture for the encoder and decoder. Let's get started by importing our libraries and getting the dataset." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets('MNIST_data', validation_size=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below I'm plotting an example image from the MNIST dataset. These are 28x28 grayscale images of handwritten digits." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.image.AxesImage at 0x119175908>" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11907c7b8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "img = mnist.train.images[2]\n", "plt.imshow(img.reshape((28, 28)), cmap='Greys_r')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll train an autoencoder with these images by flattening them into 784 length vectors. The images from this dataset are already normalized such that the values are between 0 and 1. Let's start by building basically the simplest autoencoder with a **single ReLU hidden layer**. This layer will be used as the compressed representation. Then, the encoder is the input layer and the hidden layer. The decoder is the hidden layer and the output layer. Since the images are normalized between 0 and 1, we need to use a **sigmoid activation on the output layer** to get values matching the input.\n", "\n", "![Autoencoder architecture](assets/simple_autoencoder.png)\n", "\n", "\n", "> **Exercise:** Build the graph for the autoencoder in the cell below. The input images will be flattened into 784 length vectors. The targets are the same as the inputs. And there should be one hidden layer with a ReLU activation and an output layer with a sigmoid activation. The loss should be calculated with the cross-entropy loss, there is a convenient TensorFlow function for this `tf.nn.sigmoid_cross_entropy_with_logits` ([documentation](https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits)). You should note that `tf.nn.sigmoid_cross_entropy_with_logits` takes the logits, but to get the reconstructed images you'll need to pass the logits through the sigmoid function." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "784\n" ] } ], "source": [ "print(len(img))" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Size of the encoding layer (the hidden layer)\n", "encoding_dim = 32 # feel free to change this value\n", "\n", "inputs_ = tf.placeholder(tf.float32, [None,len(img)], name='inputs')\n", "targets_ = tf.placeholder(tf.float32, [None,len(img)], name='targets')\n", "\n", "# Output of hidden layer\n", "encoded = tf.layers.dense(inputs_, encoding_dim, activation=None)\n", "#encoded = tf.contrib.layers.fully_connected(inputs_, encoding_dim, activation_fn=None)\n", "\n", "# Output layer logits\n", "logits = tf.layers.dense(encoded, len(img), activation=None)\n", "#logits = tf.contrib.layers.fully_connected(encoded, len(img), activation_fn=None)\n", "# Sigmoid output from logits\n", "decoded = tf.sigmoid(logits)\n", "\n", "# Sigmoid cross-entropy loss\n", "loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits)\n", "# Mean of the loss\n", "cost = tf.reduce_mean(loss)\n", "\n", "# Adam optimizer\n", "opt = tf.train.AdamOptimizer(learning_rate=0.01).minimize(cost)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create the session\n", "sess = tf.Session()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here I'll write a bit of code to train the network. I'm not too interested in validation here, so I'll just monitor the training loss. \n", "\n", "Calling `mnist.train.next_batch(batch_size)` will return a tuple of `(images, labels)`. We're not concerned with the labels here, we just need the images. Otherwise this is pretty straightfoward training with TensorFlow. We initialize the variables with `sess.run(tf.global_variables_initializer())`. Then, run the optimizer and get the loss with `batch_cost, _ = sess.run([cost, opt], feed_dict=feed)`." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch: 1/20... Training loss: 0.6945\n", "Epoch: 1/20... Training loss: 0.6604\n", "Epoch: 1/20... Training loss: 0.5190\n", "Epoch: 1/20... Training loss: 0.4161\n", "Epoch: 1/20... Training loss: 0.3379\n", "Epoch: 1/20... Training loss: 0.3079\n", "Epoch: 1/20... Training loss: 0.2975\n", "Epoch: 1/20... Training loss: 0.3089\n", "Epoch: 1/20... Training loss: 0.3112\n", "Epoch: 1/20... Training loss: 0.3004\n", "Epoch: 1/20... Training loss: 0.2920\n", "Epoch: 1/20... Training loss: 0.2806\n", "Epoch: 1/20... Training loss: 0.2889\n", "Epoch: 1/20... Training loss: 0.2752\n", "Epoch: 1/20... Training loss: 0.2588\n", "Epoch: 1/20... Training loss: 0.2610\n", "Epoch: 1/20... Training loss: 0.2571\n", "Epoch: 1/20... Training loss: 0.2564\n", "Epoch: 1/20... Training loss: 0.2558\n", "Epoch: 1/20... Training loss: 0.2444\n", "Epoch: 1/20... Training loss: 0.2396\n", "Epoch: 1/20... Training loss: 0.2409\n", "Epoch: 1/20... Training loss: 0.2446\n", "Epoch: 1/20... Training loss: 0.2346\n", "Epoch: 1/20... Training loss: 0.2385\n", "Epoch: 1/20... Training loss: 0.2208\n", "Epoch: 1/20... Training loss: 0.2267\n", "Epoch: 1/20... Training loss: 0.2159\n", "Epoch: 1/20... Training loss: 0.2215\n", "Epoch: 1/20... Training loss: 0.2121\n", "Epoch: 1/20... 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Training loss: 0.0937\n" ] } ], "source": [ "epochs = 20\n", "batch_size = 200\n", "sess.run(tf.global_variables_initializer())\n", "for e in range(epochs):\n", " for ii in range(mnist.train.num_examples//batch_size):\n", " batch = mnist.train.next_batch(batch_size)\n", " feed = {inputs_: batch[0], targets_: batch[0]}\n", " batch_cost, _ = sess.run([cost, opt], feed_dict=feed)\n", "\n", " print(\"Epoch: {}/{}...\".format(e+1, epochs),\n", " \"Training loss: {:.4f}\".format(batch_cost))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Checking out the results\n", "\n", "Below I've plotted some of the test images along with their reconstructions. For the most part these look pretty good except for some blurriness in some parts." ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11dd792b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, axes = plt.subplots(nrows=2, ncols=10, sharex=True, sharey=True, figsize=(20,4))\n", "in_imgs = mnist.test.images[:10]\n", "reconstructed, compressed = sess.run([decoded, encoded], feed_dict={inputs_: in_imgs})\n", "\n", "for images, row in zip([in_imgs, reconstructed], axes):\n", " for img, ax in zip(images, row):\n", " ax.imshow(img.reshape((28, 28)), cmap='Greys_r')\n", " ax.get_xaxis().set_visible(False)\n", " ax.get_yaxis().set_visible(False)\n", "\n", "fig.tight_layout(pad=0.1)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "sess.close()" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Up Next\n", "\n", "We're dealing with images here, so we can (usually) get better performance using convolution layers. So, next we'll build a better autoencoder with convolutional layers.\n", "\n", "In practice, autoencoders aren't actually better at compression compared to typical methods like JPEGs and MP3s. But, they are being used for noise reduction, which you'll also build." ] } ], "metadata": { "anaconda-cloud": {}, "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
mit
vyan2000/ocml-public
roc_to_rocLog10/roc_to_rocLogE_8DOC.ipynb
1
792063
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# this is a file for calculating the Lagrangian rate of change of the log-scale $Chl_a$ using $\\frac{D \\ ( log_{e} Chl_a)}{Dt}:=\\frac{1}{Chl_a}\\frac{D \\ ( Chl_a)}{Dt}$\n", "* A few remarks:\n", " * Unit $mg/(m^3 \\cdot day)$\n", " * Natural logarithm added\n", " * All the rates on the same time frequency\n", " * validate the rate of change of the log-scale rate Chl-a by FD\n", " * Monthly trends \n", " * aaa\n", " * aaa" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/vyan2000/local/miniconda3/envs/condapython3/lib/python3.5/site-packages/IPython/html.py:14: ShimWarning: The `IPython.html` package has been deprecated. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n", " \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n" ] } ], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import xarray as xr\n", "from datetime import datetime\n", "import datetime" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x1153422b0>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "in_filename and path: ./data_collector_modisa_chla9km/df_chl_out_8DOC_modisa_3.csv\n", "out_filename: df_chl_out_8DOC_modisa_4.csv\n" ] }, { "data": { "text/html": [ "<div>\n", "<table border=\"1\" class=\"dataframe\">\n", " <thead>\n", " <tr style=\"text-align: right;\">\n", " <th></th>\n", " 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<td>0.293834</td>\n", " <td>-0.531898</td>\n", " <td>0.130402</td>\n", " <td>-0.884716</td>\n", " <td>940.2960</td>\n", " </tr>\n", " <tr>\n", " <th>5960</th>\n", " <td>11089</td>\n", " <td>2003-01-04</td>\n", " <td>28.521781</td>\n", " <td>-26.206813</td>\n", " <td>0.000134</td>\n", " <td>59.107094</td>\n", " <td>14.353250</td>\n", " <td>0.000070</td>\n", " <td>5.199344</td>\n", " <td>0.003628</td>\n", " <td>26.104250</td>\n", " <td>0.241114</td>\n", " <td>-0.617778</td>\n", " <td>0.013459</td>\n", " <td>-1.870987</td>\n", " <td>456.8570</td>\n", " </tr>\n", " <tr>\n", " <th>5986</th>\n", " <td>34721</td>\n", " <td>2003-01-04</td>\n", " <td>9.439625</td>\n", " <td>6.296906</td>\n", " <td>0.000130</td>\n", " <td>66.806469</td>\n", " <td>15.917500</td>\n", " <td>0.000069</td>\n", " <td>-0.686937</td>\n", " <td>0.001784</td>\n", " <td>27.099719</td>\n", " <td>0.252779</td>\n", " <td>-0.597259</td>\n", " <td>0.072795</td>\n", " <td>-1.137898</td>\n", " <td>668.1950</td>\n", " </tr>\n", " <tr>\n", " <th>6245</th>\n", " <td>34721</td>\n", " <td>2003-01-12</td>\n", " <td>5.829688</td>\n", " <td>0.587656</td>\n", " <td>0.000143</td>\n", " <td>66.872000</td>\n", " <td>16.100937</td>\n", " <td>0.000073</td>\n", " <td>3.039469</td>\n", " <td>0.001790</td>\n", " <td>26.924969</td>\n", " <td>0.784225</td>\n", " <td>-0.105559</td>\n", " <td>0.531446</td>\n", " <td>-0.274541</td>\n", " <td>647.3570</td>\n", " </tr>\n", " <tr>\n", " <th>6480</th>\n", " <td>15707</td>\n", " <td>2003-01-20</td>\n", " <td>29.502000</td>\n", " <td>-2.061375</td>\n", " <td>0.000165</td>\n", " <td>53.534156</td>\n", " <td>9.159719</td>\n", " <td>0.000084</td>\n", " <td>-23.901281</td>\n", " <td>1000.000000</td>\n", " <td>NaN</td>\n", " <td>0.567945</td>\n", " <td>-0.245694</td>\n", " <td>0.138772</td>\n", " <td>-0.857698</td>\n", " <td>273.2370</td>\n", " </tr>\n", " <tr>\n", " <th>6482</th>\n", " <td>27139</td>\n", " <td>2003-01-20</td>\n", " <td>39.732875</td>\n", " <td>-11.357875</td>\n", " <td>0.000154</td>\n", " <td>60.161406</td>\n", " <td>21.526563</td>\n", " <td>0.000077</td>\n", " <td>-30.396656</td>\n", " <td>0.003372</td>\n", " <td>25.101438</td>\n", " <td>0.889541</td>\n", " <td>-0.050834</td>\n", " <td>0.324802</td>\n", " <td>-0.488381</td>\n", " <td>70.0870</td>\n", " </tr>\n", " <tr>\n", " <th>6504</th>\n", " <td>34721</td>\n", " <td>2003-01-20</td>\n", " <td>11.056531</td>\n", " <td>-6.067625</td>\n", " <td>0.000134</td>\n", " <td>67.260125</td>\n", " <td>15.943687</td>\n", " <td>0.000070</td>\n", " <td>8.365000</td>\n", " <td>0.001864</td>\n", " <td>26.808875</td>\n", " <td>0.450702</td>\n", " <td>-0.346111</td>\n", " <td>-0.333523</td>\n", " <td>NaN</td>\n", " <td>639.3160</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", " </tr>\n", " <tr>\n", " <th>160511</th>\n", " <td>114945</td>\n", " <td>2016-01-24</td>\n", " <td>16.426625</td>\n", " <td>-2.015563</td>\n", " <td>0.000059</td>\n", " <td>63.093344</td>\n", " <td>11.397188</td>\n", " <td>0.000130</td>\n", " <td>-14.821188</td>\n", " <td>0.001803</td>\n", " <td>27.841750</td>\n", " <td>0.261716</td>\n", " <td>-0.582170</td>\n", " <td>0.038290</td>\n", " <td>-1.416910</td>\n", " <td>940.9580</td>\n", " </tr>\n", " <tr>\n", " <th>160544</th>\n", " <td>147127</td>\n", " <td>2016-01-24</td>\n", " <td>16.039875</td>\n", " <td>-13.398062</td>\n", " <td>0.000025</td>\n", " <td>63.918406</td>\n", " <td>17.120500</td>\n", " <td>0.000056</td>\n", " <td>-6.248188</td>\n", " <td>0.001685</td>\n", " <td>25.938562</td>\n", " <td>1.535089</td>\n", " <td>0.186134</td>\n", " <td>1.208838</td>\n", " <td>0.082368</td>\n", " <td>640.4780</td>\n", " </tr>\n", " <tr>\n", " <th>160554</th>\n", " <td>60150420</td>\n", " <td>2016-01-24</td>\n", " <td>29.255969</td>\n", " <td>24.009875</td>\n", " <td>0.000005</td>\n", " <td>60.651625</td>\n", " <td>6.411062</td>\n", " <td>0.000003</td>\n", " <td>-4.516375</td>\n", " <td>0.001684</td>\n", " <td>27.916875</td>\n", " <td>0.151033</td>\n", " <td>-0.820928</td>\n", " <td>-0.028541</td>\n", " <td>NaN</td>\n", " <td>953.3690</td>\n", " </tr>\n", " <tr>\n", " <th>160769</th>\n", " <td>114917</td>\n", " <td>2016-02-01</td>\n", " <td>19.631719</td>\n", " <td>0.287500</td>\n", " <td>0.000047</td>\n", " <td>71.655594</td>\n", " <td>13.694594</td>\n", " <td>0.000104</td>\n", " <td>-0.602406</td>\n", " <td>0.001819</td>\n", " <td>28.963156</td>\n", " <td>0.175277</td>\n", " <td>-0.756275</td>\n", " <td>0.038361</td>\n", " <td>-1.416112</td>\n", " <td>238.7190</td>\n", " </tr>\n", " <tr>\n", " <th>160803</th>\n", " <td>147127</td>\n", " <td>2016-02-01</td>\n", " <td>16.704312</td>\n", " <td>-6.202406</td>\n", " <td>0.000021</td>\n", " <td>63.489719</td>\n", " <td>16.346813</td>\n", " <td>0.000046</td>\n", " <td>-2.310031</td>\n", " <td>0.001752</td>\n", " <td>25.847969</td>\n", " <td>0.637804</td>\n", " <td>-0.195313</td>\n", " <td>-0.897285</td>\n", " <td>NaN</td>\n", " <td>662.0290</td>\n", " </tr>\n", " <tr>\n", " <th>160813</th>\n", " <td>60150420</td>\n", " <td>2016-02-01</td>\n", " <td>20.689094</td>\n", " <td>9.689937</td>\n", " <td>0.000005</td>\n", " <td>60.247719</td>\n", " <td>7.097969</td>\n", " <td>0.000003</td>\n", " <td>2.720687</td>\n", " <td>0.001684</td>\n", " <td>27.543437</td>\n", " <td>0.201691</td>\n", " <td>-0.695314</td>\n", " <td>0.050658</td>\n", " <td>-1.295353</td>\n", " <td>868.9360</td>\n", " </tr>\n", " <tr>\n", " <th>161028</th>\n", " <td>114917</td>\n", " <td>2016-02-09</td>\n", " <td>16.747969</td>\n", " <td>-1.324813</td>\n", " <td>0.000049</td>\n", " <td>71.394656</td>\n", " <td>13.744531</td>\n", " <td>0.000110</td>\n", " <td>-6.097062</td>\n", " <td>0.001770</td>\n", " <td>28.736531</td>\n", " <td>0.196238</td>\n", " <td>-0.707216</td>\n", " <td>0.020962</td>\n", " <td>-1.678577</td>\n", " <td>251.8690</td>\n", " </tr>\n", " <tr>\n", " <th>161062</th>\n", " <td>147127</td>\n", " <td>2016-02-09</td>\n", " <td>16.373969</td>\n", " <td>-2.064625</td>\n", " <td>0.000020</td>\n", " <td>63.043063</td>\n", " <td>16.160937</td>\n", " <td>0.000047</td>\n", " <td>-14.384594</td>\n", " <td>0.001683</td>\n", " <td>25.865125</td>\n", " <td>0.679547</td>\n", " <td>-0.167780</td>\n", " <td>0.041743</td>\n", " <td>-1.379415</td>\n", " <td>635.7060</td>\n", " </tr>\n", " <tr>\n", " <th>161072</th>\n", " <td>60150420</td>\n", " <td>2016-02-09</td>\n", " <td>28.188344</td>\n", " <td>23.421656</td>\n", " <td>0.000005</td>\n", " <td>61.167812</td>\n", " <td>8.367313</td>\n", " <td>0.000003</td>\n", " <td>14.079844</td>\n", " <td>0.001684</td>\n", " <td>27.543937</td>\n", " <td>0.165788</td>\n", " <td>-0.780447</td>\n", " <td>-0.035903</td>\n", " <td>NaN</td>\n", " <td>859.9870</td>\n", " </tr>\n", " <tr>\n", " <th>161287</th>\n", " <td>114917</td>\n", " <td>2016-02-17</td>\n", " <td>17.292531</td>\n", " <td>1.582844</td>\n", " <td>0.000039</td>\n", " <td>71.042531</td>\n", " <td>13.715094</td>\n", " <td>0.000090</td>\n", " <td>-7.299969</td>\n", " <td>0.001794</td>\n", " <td>28.704438</td>\n", " <td>0.185869</td>\n", " <td>-0.730792</td>\n", " <td>-0.010369</td>\n", " <td>NaN</td>\n", " <td>261.9900</td>\n", " </tr>\n", " <tr>\n", " <th>161288</th>\n", " <td>114945</td>\n", " <td>2016-02-17</td>\n", " <td>16.583125</td>\n", " <td>3.474531</td>\n", " <td>0.000074</td>\n", " <td>60.167750</td>\n", " <td>11.973937</td>\n", " <td>0.000160</td>\n", " <td>-15.263531</td>\n", " <td>0.001968</td>\n", " <td>27.163656</td>\n", " <td>0.385036</td>\n", " <td>-0.414498</td>\n", " <td>0.039529</td>\n", " <td>-1.403086</td>\n", " <td>616.6080</td>\n", " </tr>\n", " <tr>\n", " <th>161321</th>\n", " <td>147127</td>\n", " <td>2016-02-17</td>\n", " <td>13.602531</td>\n", " <td>0.513469</td>\n", " <td>0.000027</td>\n", " <td>62.402781</td>\n", " <td>16.112031</td>\n", " <td>0.000063</td>\n", " <td>-3.445594</td>\n", " <td>0.001675</td>\n", " <td>25.694813</td>\n", " <td>0.672385</td>\n", " <td>-0.172382</td>\n", " <td>-0.007162</td>\n", " <td>NaN</td>\n", " <td>582.4100</td>\n", " </tr>\n", " <tr>\n", " <th>161331</th>\n", " <td>60150420</td>\n", " <td>2016-02-17</td>\n", " <td>15.845250</td>\n", " <td>-2.976969</td>\n", " <td>0.000005</td>\n", " <td>61.526156</td>\n", " <td>8.957031</td>\n", " <td>0.000003</td>\n", " <td>2.012250</td>\n", " <td>0.001684</td>\n", " <td>27.387125</td>\n", " <td>0.164424</td>\n", " <td>-0.784036</td>\n", " <td>-0.001364</td>\n", " <td>NaN</td>\n", " <td>862.7080</td>\n", " </tr>\n", " <tr>\n", " <th>161542</th>\n", " <td>114873</td>\n", " <td>2016-02-25</td>\n", " <td>46.406563</td>\n", " <td>31.911688</td>\n", " <td>0.000046</td>\n", " <td>56.937562</td>\n", " <td>7.920000</td>\n", " <td>0.000103</td>\n", " <td>-26.101250</td>\n", " <td>0.001738</td>\n", " <td>27.758813</td>\n", " <td>0.161817</td>\n", " <td>-0.790975</td>\n", " <td>-0.002742</td>\n", " <td>NaN</td>\n", " <td>576.1700</td>\n", " </tr>\n", " <tr>\n", " <th>161546</th>\n", " <td>114917</td>\n", " <td>2016-02-25</td>\n", " <td>15.755031</td>\n", " <td>1.721063</td>\n", " <td>0.000041</td>\n", " <td>70.649250</td>\n", " <td>13.835156</td>\n", " <td>0.000093</td>\n", " <td>-4.553625</td>\n", " <td>0.001898</td>\n", " <td>28.897437</td>\n", " <td>0.227879</td>\n", " <td>-0.642297</td>\n", " <td>0.042009</td>\n", " <td>-1.376657</td>\n", " <td>295.7130</td>\n", " </tr>\n", " <tr>\n", " <th>161547</th>\n", " <td>114945</td>\n", " <td>2016-02-25</td>\n", " <td>17.389031</td>\n", " <td>0.625844</td>\n", " <td>0.000080</td>\n", " <td>59.187937</td>\n", " <td>12.082219</td>\n", " <td>0.000170</td>\n", " <td>-16.443625</td>\n", " <td>0.001964</td>\n", " <td>27.346812</td>\n", " <td>0.209156</td>\n", " <td>-0.679530</td>\n", " <td>-0.175880</td>\n", " <td>NaN</td>\n", " <td>506.9260</td>\n", " </tr>\n", " <tr>\n", " <th>161580</th>\n", " <td>147127</td>\n", " <td>2016-02-25</td>\n", " <td>4.892375</td>\n", " <td>-0.772781</td>\n", " <td>0.000018</td>\n", " <td>62.385563</td>\n", " <td>16.147094</td>\n", " <td>0.000040</td>\n", " <td>-0.198031</td>\n", " <td>0.001698</td>\n", " <td>26.104094</td>\n", " <td>0.774010</td>\n", " <td>-0.111254</td>\n", " <td>0.101624</td>\n", " <td>-0.993003</td>\n", " <td>576.3870</td>\n", " </tr>\n", " <tr>\n", " <th>161801</th>\n", " <td>114873</td>\n", " <td>2016-03-04</td>\n", " <td>22.003844</td>\n", " <td>-6.779937</td>\n", " <td>0.000037</td>\n", " <td>55.139125</td>\n", " <td>8.443687</td>\n", " <td>0.000084</td>\n", " <td>-20.524625</td>\n", " <td>0.001729</td>\n", " <td>28.017688</td>\n", " <td>0.151360</td>\n", " <td>-0.819989</td>\n", " <td>-0.010457</td>\n", " <td>NaN</td>\n", " <td>443.0950</td>\n", " </tr>\n", " <tr>\n", " <th>161805</th>\n", " <td>114917</td>\n", " <td>2016-03-04</td>\n", " <td>17.934188</td>\n", " <td>0.119406</td>\n", " <td>0.000045</td>\n", " <td>70.025500</td>\n", " <td>13.959344</td>\n", " <td>0.000098</td>\n", " <td>-13.646313</td>\n", " <td>0.001863</td>\n", " <td>29.127500</td>\n", " <td>0.162528</td>\n", " <td>-0.789072</td>\n", " <td>-0.065351</td>\n", " <td>NaN</td>\n", " <td>349.1780</td>\n", " </tr>\n", " <tr>\n", " <th>161806</th>\n", " <td>114945</td>\n", " <td>2016-03-04</td>\n", " <td>11.021281</td>\n", " <td>-1.083656</td>\n", " <td>0.000207</td>\n", " <td>58.335156</td>\n", " <td>12.127812</td>\n", " <td>0.000377</td>\n", " <td>-10.133969</td>\n", " <td>0.001882</td>\n", " <td>27.922812</td>\n", " <td>0.227034</td>\n", " <td>-0.643909</td>\n", " <td>0.017878</td>\n", " <td>-1.747684</td>\n", " <td>415.5460</td>\n", " </tr>\n", " <tr>\n", " <th>161839</th>\n", " <td>147127</td>\n", " <td>2016-03-04</td>\n", " <td>17.594531</td>\n", " <td>-3.890563</td>\n", " <td>0.000019</td>\n", " <td>62.116156</td>\n", " <td>15.841812</td>\n", " <td>0.000044</td>\n", " <td>-8.121344</td>\n", " <td>0.001655</td>\n", " <td>26.949656</td>\n", " <td>0.376218</td>\n", " <td>-0.424560</td>\n", " <td>-0.397791</td>\n", " <td>NaN</td>\n", " <td>570.3880</td>\n", " </tr>\n", " <tr>\n", " <th>162060</th>\n", " <td>114873</td>\n", " <td>2016-03-12</td>\n", " <td>9.301438</td>\n", " <td>-1.854719</td>\n", " <td>0.000030</td>\n", " <td>54.368750</td>\n", " <td>8.112656</td>\n", " <td>0.000066</td>\n", " <td>-4.882906</td>\n", " <td>0.001779</td>\n", " <td>28.597687</td>\n", " <td>0.122877</td>\n", " <td>-0.910531</td>\n", " <td>-0.028483</td>\n", " <td>NaN</td>\n", " <td>414.2320</td>\n", " </tr>\n", " <tr>\n", " <th>162064</th>\n", " <td>114917</td>\n", " <td>2016-03-12</td>\n", " <td>14.665219</td>\n", " <td>-8.076125</td>\n", " <td>0.000036</td>\n", " <td>69.226937</td>\n", " <td>13.619938</td>\n", " <td>0.000083</td>\n", " <td>-7.315469</td>\n", " <td>0.001815</td>\n", " <td>29.071594</td>\n", " <td>0.158982</td>\n", " <td>-0.798652</td>\n", " <td>-0.003546</td>\n", " <td>NaN</td>\n", " <td>391.6030</td>\n", " </tr>\n", " <tr>\n", " <th>162065</th>\n", " <td>114945</td>\n", " <td>2016-03-12</td>\n", " <td>9.326250</td>\n", " <td>-4.065969</td>\n", " <td>0.000120</td>\n", " <td>57.813969</td>\n", " <td>11.889969</td>\n", " <td>0.000253</td>\n", " <td>-5.984000</td>\n", " <td>0.002030</td>\n", " <td>28.350969</td>\n", " <td>0.169983</td>\n", " <td>-0.769593</td>\n", " <td>-0.057051</td>\n", " <td>NaN</td>\n", " <td>363.7230</td>\n", " </tr>\n", " <tr>\n", " <th>162098</th>\n", " <td>147127</td>\n", " <td>2016-03-12</td>\n", " <td>21.346375</td>\n", " <td>-8.517750</td>\n", " <td>0.000016</td>\n", " <td>61.957500</td>\n", " <td>15.564375</td>\n", " <td>0.000038</td>\n", " <td>-0.578969</td>\n", " <td>0.002710</td>\n", " <td>28.131781</td>\n", " <td>0.342775</td>\n", " <td>-0.464991</td>\n", " <td>-0.033443</td>\n", " <td>NaN</td>\n", " <td>577.0800</td>\n", " </tr>\n", " <tr>\n", " <th>162108</th>\n", " <td>60150420</td>\n", " <td>2016-03-12</td>\n", " <td>27.715719</td>\n", " <td>-5.131031</td>\n", " <td>0.000005</td>\n", " <td>61.016375</td>\n", " <td>10.009031</td>\n", " <td>0.000003</td>\n", " <td>-22.813438</td>\n", " <td>0.001795</td>\n", " <td>28.580750</td>\n", " <td>0.125524</td>\n", " <td>-0.901275</td>\n", " <td>0.002054</td>\n", " <td>-2.687491</td>\n", " <td>760.1980</td>\n", " </tr>\n", " <tr>\n", " <th>162323</th>\n", " <td>114917</td>\n", " <td>2016-03-20</td>\n", " <td>29.604250</td>\n", " <td>-17.772313</td>\n", " <td>0.000051</td>\n", " <td>68.712938</td>\n", " <td>13.048219</td>\n", " <td>0.000114</td>\n", " <td>-7.033125</td>\n", " <td>0.001830</td>\n", " <td>29.248094</td>\n", " <td>0.144908</td>\n", " <td>-0.838907</td>\n", " <td>-0.014074</td>\n", " <td>NaN</td>\n", " <td>411.6270</td>\n", " </tr>\n", " <tr>\n", " <th>162367</th>\n", " <td>60150420</td>\n", " <td>2016-03-20</td>\n", " <td>36.445312</td>\n", " <td>-7.017594</td>\n", " <td>0.000005</td>\n", " <td>59.155281</td>\n", " <td>9.990875</td>\n", " <td>0.000003</td>\n", " <td>-33.476469</td>\n", " <td>0.001734</td>\n", " <td>28.605438</td>\n", " <td>0.216656</td>\n", " <td>-0.664229</td>\n", " <td>0.091133</td>\n", " <td>-1.040326</td>\n", " <td>576.3650</td>\n", " </tr>\n", " <tr>\n", " <th>162582</th>\n", " <td>114917</td>\n", " <td>2016-03-28</td>\n", " <td>29.713500</td>\n", " <td>12.361594</td>\n", " <td>0.000046</td>\n", " <td>69.281031</td>\n", " <td>12.596750</td>\n", " <td>0.000100</td>\n", " <td>22.120531</td>\n", " <td>0.001927</td>\n", " <td>29.890781</td>\n", " <td>0.144557</td>\n", " <td>-0.839961</td>\n", " <td>-0.000351</td>\n", " <td>NaN</td>\n", " <td>331.8890</td>\n", " </tr>\n", " <tr>\n", " <th>162626</th>\n", " <td>60150420</td>\n", " <td>2016-03-28</td>\n", " <td>32.750687</td>\n", " <td>-9.834937</td>\n", " <td>0.000005</td>\n", " <td>58.262562</td>\n", " <td>8.938125</td>\n", " <td>0.000003</td>\n", " <td>10.682125</td>\n", " <td>0.001684</td>\n", " <td>28.979375</td>\n", " <td>0.195704</td>\n", " <td>-0.708399</td>\n", " <td>-0.020952</td>\n", " <td>NaN</td>\n", " <td>569.0470</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>683 rows × 16 columns</p>\n", "</div>" ], "text/plain": [ " id time spd vn var_lon lon \\\n", "index \n", "3886 10206 2002-11-01 11.188906 6.509875 0.000996 67.351188 \n", "3888 11089 2002-11-01 13.679406 4.337844 0.000106 65.099156 \n", "3908 34710 2002-11-01 12.432687 11.684344 0.000123 63.145031 \n", "4145 10206 2002-11-09 3.428062 1.562844 0.003551 67.108219 \n", "4147 11089 2002-11-09 19.677781 -6.951906 0.000126 64.193281 \n", "4149 15707 2002-11-09 23.783812 -15.661781 0.000150 67.022625 \n", "4167 34710 2002-11-09 26.598219 25.294281 0.000126 63.000156 \n", "4173 34721 2002-11-09 18.274406 12.886094 0.000124 68.182750 \n", "4426 34710 2002-11-17 51.033437 42.687531 0.000152 62.227781 \n", "4432 34721 2002-11-17 12.116437 11.203437 0.000104 68.325406 \n", "4681 34315 2002-11-25 34.884469 -22.903094 0.000104 57.697625 \n", "4684 34709 2002-11-25 26.364529 -18.946647 0.000130 74.747500 \n", "4685 34710 2002-11-25 65.392656 4.843219 0.000098 62.329031 \n", "4691 34721 2002-11-25 11.410500 9.268219 0.000108 68.210844 \n", "4944 34710 2002-12-03 46.047312 -8.093344 0.000150 63.903438 \n", "5203 34710 2002-12-11 14.257563 10.818781 0.000123 64.458906 \n", "5440 10206 2002-12-19 9.617437 4.556469 0.004192 64.896875 \n", "5458 34315 2002-12-19 14.123563 5.683906 0.000094 52.956562 \n", "5462 34710 2002-12-19 10.502969 -6.181844 0.000107 64.695437 \n", "5468 34721 2002-12-19 13.939531 -1.223813 0.000113 66.889000 \n", "5699 10206 2002-12-27 12.251438 -1.765500 0.001212 64.271031 \n", "5701 11089 2002-12-27 18.858156 13.433844 0.000123 58.528844 \n", "5727 34721 2002-12-27 11.281406 9.685406 0.000108 66.840469 \n", "5958 10206 2003-01-04 12.856875 -5.715375 0.002190 63.550156 \n", "5960 11089 2003-01-04 28.521781 -26.206813 0.000134 59.107094 \n", "5986 34721 2003-01-04 9.439625 6.296906 0.000130 66.806469 \n", "6245 34721 2003-01-12 5.829688 0.587656 0.000143 66.872000 \n", "6480 15707 2003-01-20 29.502000 -2.061375 0.000165 53.534156 \n", "6482 27139 2003-01-20 39.732875 -11.357875 0.000154 60.161406 \n", "6504 34721 2003-01-20 11.056531 -6.067625 0.000134 67.260125 \n", "... ... ... ... ... ... ... \n", "160511 114945 2016-01-24 16.426625 -2.015563 0.000059 63.093344 \n", "160544 147127 2016-01-24 16.039875 -13.398062 0.000025 63.918406 \n", "160554 60150420 2016-01-24 29.255969 24.009875 0.000005 60.651625 \n", "160769 114917 2016-02-01 19.631719 0.287500 0.000047 71.655594 \n", "160803 147127 2016-02-01 16.704312 -6.202406 0.000021 63.489719 \n", "160813 60150420 2016-02-01 20.689094 9.689937 0.000005 60.247719 \n", "161028 114917 2016-02-09 16.747969 -1.324813 0.000049 71.394656 \n", "161062 147127 2016-02-09 16.373969 -2.064625 0.000020 63.043063 \n", "161072 60150420 2016-02-09 28.188344 23.421656 0.000005 61.167812 \n", "161287 114917 2016-02-17 17.292531 1.582844 0.000039 71.042531 \n", "161288 114945 2016-02-17 16.583125 3.474531 0.000074 60.167750 \n", "161321 147127 2016-02-17 13.602531 0.513469 0.000027 62.402781 \n", "161331 60150420 2016-02-17 15.845250 -2.976969 0.000005 61.526156 \n", "161542 114873 2016-02-25 46.406563 31.911688 0.000046 56.937562 \n", "161546 114917 2016-02-25 15.755031 1.721063 0.000041 70.649250 \n", "161547 114945 2016-02-25 17.389031 0.625844 0.000080 59.187937 \n", "161580 147127 2016-02-25 4.892375 -0.772781 0.000018 62.385563 \n", "161801 114873 2016-03-04 22.003844 -6.779937 0.000037 55.139125 \n", "161805 114917 2016-03-04 17.934188 0.119406 0.000045 70.025500 \n", "161806 114945 2016-03-04 11.021281 -1.083656 0.000207 58.335156 \n", "161839 147127 2016-03-04 17.594531 -3.890563 0.000019 62.116156 \n", "162060 114873 2016-03-12 9.301438 -1.854719 0.000030 54.368750 \n", "162064 114917 2016-03-12 14.665219 -8.076125 0.000036 69.226937 \n", "162065 114945 2016-03-12 9.326250 -4.065969 0.000120 57.813969 \n", "162098 147127 2016-03-12 21.346375 -8.517750 0.000016 61.957500 \n", "162108 60150420 2016-03-12 27.715719 -5.131031 0.000005 61.016375 \n", "162323 114917 2016-03-20 29.604250 -17.772313 0.000051 68.712938 \n", "162367 60150420 2016-03-20 36.445312 -7.017594 0.000005 59.155281 \n", "162582 114917 2016-03-28 29.713500 12.361594 0.000046 69.281031 \n", "162626 60150420 2016-03-28 32.750687 -9.834937 0.000005 58.262562 \n", "\n", " lat var_lat ve var_tmp temp chlor_a \\\n", "index \n", "3886 10.873656 0.000352 -6.823625 1000.000000 NaN 0.132783 \n", "3888 14.269219 0.000057 -11.122000 0.003679 28.969813 0.150789 \n", "3908 17.038563 0.000064 0.757312 0.001698 28.970219 0.388257 \n", "4145 11.155719 0.000984 -0.786375 1000.000000 NaN 0.135089 \n", "4147 14.220969 0.000065 -17.539250 0.003868 28.742188 0.201879 \n", "4149 12.926656 0.000075 -12.393500 1000.000000 NaN 0.153961 \n", "4167 17.952812 0.000065 -2.723375 0.001878 28.255188 0.501054 \n", "4173 12.879281 0.000063 8.236687 0.001813 29.291313 0.150119 \n", "4426 20.239094 0.000075 -25.647250 0.001815 27.549469 0.480306 \n", "4432 13.776062 0.000056 -1.096344 0.001754 29.060656 0.133791 \n", "4681 6.886781 0.000056 -20.598188 0.004397 28.923875 0.142745 \n", "4684 11.582167 0.000066 -11.325471 1000.000000 NaN 0.236595 \n", "4685 22.561781 0.000052 35.795281 0.001683 27.312969 0.482353 \n", "4691 14.380031 0.000058 -2.194313 0.001749 28.983563 0.142531 \n", "4944 20.918094 0.000076 9.094031 0.001760 26.786125 1.431313 \n", "5203 21.914281 0.000064 5.068844 0.001715 26.455813 0.592263 \n", "5440 12.434812 0.001140 -8.368125 1000.000000 NaN 0.156649 \n", "5458 8.837594 0.000050 -7.807813 0.003568 27.552594 0.270939 \n", "5462 21.996188 0.000057 1.270844 0.001660 26.456375 0.484379 \n", "5468 15.015063 0.000060 -5.765406 0.001752 28.101500 0.157701 \n", "5699 12.549094 0.000417 -11.493313 1000.000000 NaN 0.163432 \n", "5701 14.770719 0.000065 4.662594 0.003657 26.580312 0.227655 \n", "5727 15.328750 0.000058 0.916906 0.001784 27.666062 0.179984 \n", "5958 12.280437 0.000691 -11.053437 1000.000000 NaN 0.293834 \n", "5960 14.353250 0.000070 5.199344 0.003628 26.104250 0.241114 \n", "5986 15.917500 0.000069 -0.686937 0.001784 27.099719 0.252779 \n", "6245 16.100937 0.000073 3.039469 0.001790 26.924969 0.784225 \n", "6480 9.159719 0.000084 -23.901281 1000.000000 NaN 0.567945 \n", "6482 21.526563 0.000077 -30.396656 0.003372 25.101438 0.889541 \n", "6504 15.943687 0.000070 8.365000 0.001864 26.808875 0.450702 \n", "... ... ... ... ... ... ... \n", "160511 11.397188 0.000130 -14.821188 0.001803 27.841750 0.261716 \n", "160544 17.120500 0.000056 -6.248188 0.001685 25.938562 1.535089 \n", "160554 6.411062 0.000003 -4.516375 0.001684 27.916875 0.151033 \n", "160769 13.694594 0.000104 -0.602406 0.001819 28.963156 0.175277 \n", "160803 16.346813 0.000046 -2.310031 0.001752 25.847969 0.637804 \n", "160813 7.097969 0.000003 2.720687 0.001684 27.543437 0.201691 \n", "161028 13.744531 0.000110 -6.097062 0.001770 28.736531 0.196238 \n", "161062 16.160937 0.000047 -14.384594 0.001683 25.865125 0.679547 \n", "161072 8.367313 0.000003 14.079844 0.001684 27.543937 0.165788 \n", "161287 13.715094 0.000090 -7.299969 0.001794 28.704438 0.185869 \n", "161288 11.973937 0.000160 -15.263531 0.001968 27.163656 0.385036 \n", "161321 16.112031 0.000063 -3.445594 0.001675 25.694813 0.672385 \n", "161331 8.957031 0.000003 2.012250 0.001684 27.387125 0.164424 \n", "161542 7.920000 0.000103 -26.101250 0.001738 27.758813 0.161817 \n", "161546 13.835156 0.000093 -4.553625 0.001898 28.897437 0.227879 \n", "161547 12.082219 0.000170 -16.443625 0.001964 27.346812 0.209156 \n", "161580 16.147094 0.000040 -0.198031 0.001698 26.104094 0.774010 \n", "161801 8.443687 0.000084 -20.524625 0.001729 28.017688 0.151360 \n", "161805 13.959344 0.000098 -13.646313 0.001863 29.127500 0.162528 \n", "161806 12.127812 0.000377 -10.133969 0.001882 27.922812 0.227034 \n", "161839 15.841812 0.000044 -8.121344 0.001655 26.949656 0.376218 \n", "162060 8.112656 0.000066 -4.882906 0.001779 28.597687 0.122877 \n", "162064 13.619938 0.000083 -7.315469 0.001815 29.071594 0.158982 \n", "162065 11.889969 0.000253 -5.984000 0.002030 28.350969 0.169983 \n", "162098 15.564375 0.000038 -0.578969 0.002710 28.131781 0.342775 \n", "162108 10.009031 0.000003 -22.813438 0.001795 28.580750 0.125524 \n", "162323 13.048219 0.000114 -7.033125 0.001830 29.248094 0.144908 \n", "162367 9.990875 0.000003 -33.476469 0.001734 28.605438 0.216656 \n", "162582 12.596750 0.000100 22.120531 0.001927 29.890781 0.144557 \n", "162626 8.938125 0.000003 10.682125 0.001684 28.979375 0.195704 \n", "\n", " chlor_a_log10 chl_rate chl_rate_log10 dist \n", "index \n", "3886 -0.876858 -0.017698 NaN 520.4050 \n", "3888 -0.821630 0.025481 -1.593784 822.7430 \n", "3908 -0.410881 0.064084 -1.193250 584.0640 \n", "4145 -0.869380 0.002306 -2.637141 545.1970 \n", "4147 -0.694909 0.051090 -1.291664 858.3870 \n", "4149 -0.812589 -0.004697 NaN 580.7750 \n", "4167 -0.300115 0.112797 -0.947702 513.0270 \n", "4173 -0.823564 0.002713 -2.566549 457.1560 \n", "4426 -0.318482 -0.020748 NaN 327.3360 \n", "4432 -0.873573 -0.016328 NaN 481.8100 \n", "4681 -0.845439 0.004513 -2.345534 713.3930 \n", "4684 -0.625994 -0.070532 NaN 68.7629 \n", "4685 -0.316635 0.002047 -2.688882 258.0500 \n", "4691 -0.846091 0.008740 -2.058489 528.2990 \n", "4944 0.155735 0.948960 -0.022752 448.3200 \n", "5203 -0.227485 -0.839050 NaN 360.6660 \n", "5440 -0.805072 0.019815 -1.703006 795.6110 \n", "5458 -0.567128 -0.074232 NaN 241.1240 \n", "5462 -0.314815 -0.107884 NaN 351.7960 \n", "5468 -0.802166 0.016133 -1.792285 685.7980 \n", "5699 -0.786663 0.006783 -2.168578 866.4100 \n", "5701 -0.642723 -0.080533 NaN 381.1380 \n", "5727 -0.744766 0.022283 -1.652026 703.3780 \n", "5958 -0.531898 0.130402 -0.884716 940.2960 \n", "5960 -0.617778 0.013459 -1.870987 456.8570 \n", "5986 -0.597259 0.072795 -1.137898 668.1950 \n", "6245 -0.105559 0.531446 -0.274541 647.3570 \n", "6480 -0.245694 0.138772 -0.857698 273.2370 \n", "6482 -0.050834 0.324802 -0.488381 70.0870 \n", "6504 -0.346111 -0.333523 NaN 639.3160 \n", "... ... ... ... ... \n", "160511 -0.582170 0.038290 -1.416910 940.9580 \n", "160544 0.186134 1.208838 0.082368 640.4780 \n", "160554 -0.820928 -0.028541 NaN 953.3690 \n", "160769 -0.756275 0.038361 -1.416112 238.7190 \n", "160803 -0.195313 -0.897285 NaN 662.0290 \n", "160813 -0.695314 0.050658 -1.295353 868.9360 \n", "161028 -0.707216 0.020962 -1.678577 251.8690 \n", "161062 -0.167780 0.041743 -1.379415 635.7060 \n", "161072 -0.780447 -0.035903 NaN 859.9870 \n", "161287 -0.730792 -0.010369 NaN 261.9900 \n", "161288 -0.414498 0.039529 -1.403086 616.6080 \n", "161321 -0.172382 -0.007162 NaN 582.4100 \n", "161331 -0.784036 -0.001364 NaN 862.7080 \n", "161542 -0.790975 -0.002742 NaN 576.1700 \n", "161546 -0.642297 0.042009 -1.376657 295.7130 \n", "161547 -0.679530 -0.175880 NaN 506.9260 \n", "161580 -0.111254 0.101624 -0.993003 576.3870 \n", "161801 -0.819989 -0.010457 NaN 443.0950 \n", "161805 -0.789072 -0.065351 NaN 349.1780 \n", "161806 -0.643909 0.017878 -1.747684 415.5460 \n", "161839 -0.424560 -0.397791 NaN 570.3880 \n", "162060 -0.910531 -0.028483 NaN 414.2320 \n", "162064 -0.798652 -0.003546 NaN 391.6030 \n", "162065 -0.769593 -0.057051 NaN 363.7230 \n", "162098 -0.464991 -0.033443 NaN 577.0800 \n", "162108 -0.901275 0.002054 -2.687491 760.1980 \n", "162323 -0.838907 -0.014074 NaN 411.6270 \n", "162367 -0.664229 0.091133 -1.040326 576.3650 \n", "162582 -0.839961 -0.000351 NaN 331.8890 \n", "162626 -0.708399 -0.020952 NaN 569.0470 \n", "\n", "[683 rows x 16 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# load the floats data \n", "# ********************\n", "# *** CSV files ***\n", "# ********************\n", "# load the floats data, take the lon and lat as list out and calculate the distance\n", "\n", "# load CSV output \n", "# some how the CSV Format has some compatibility issues here\n", "# see readme file for the file convetion in the experiments,\n", "# for instance \"3\" indicates distance is addd to the dataset \n", "\n", "\n", "plt.close('all')\n", "plt.cla() # axis\n", "plt.clf() # figure\n", "plt.show()\n", "\n", "\n", "\n", "# freqency\n", "freq = 8\n", "suf = 'DOC'\n", "in_filename = 'df_chl_out_'+str(freq)+ suf +'_modisa_3.csv'\n", "out_filename = 'df_chl_out_'+str(freq)+ suf +'_modisa_4.csv'\n", "folder = './data_collector_modisa_chla9km/'\n", "direc = folder + in_filename\n", "direc\n", "\n", "print('in_filename and path:', direc)\n", "print('out_filename:', out_filename)\n", "\n", "df_chl_out_3 = pd.read_csv(direc, index_col='index')\n", "df_chl_out_3" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# All the rates on the same time frequency\n", "check1 = df_chl_out_3.chl_rate/ df_chl_out_3.chlor_a \n", "check1 = check1/ freq\n", "check2 = df_chl_out_3.chl_rate.divide(freq *df_chl_out_3.chlor_a, axis = 'index')\n", "# an check \n", "np.sum(abs(check1 - check2))" ] }, { "cell_type": "code", "execution_count": 5, "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>id</th>\n", " <th>time</th>\n", " <th>spd</th>\n", " <th>vn</th>\n", " <th>var_lon</th>\n", " <th>lon</th>\n", " <th>lat</th>\n", " <th>var_lat</th>\n", " <th>ve</th>\n", " <th>var_tmp</th>\n", " <th>temp</th>\n", " <th>chlor_a</th>\n", " <th>chlor_a_log10</th>\n", " <th>chl_rate</th>\n", " <th>chl_rate_log10</th>\n", " <th>dist</th>\n", " <th>chlor_a_logE_rate</th>\n", " </tr>\n", " <tr>\n", " <th>index</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", " <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>3886</th>\n", " <td>10206</td>\n", " <td>2002-11-01</td>\n", " <td>11.188906</td>\n", " <td>6.509875</td>\n", " <td>0.000996</td>\n", " <td>67.351188</td>\n", " <td>10.873656</td>\n", " <td>0.000352</td>\n", " <td>-6.823625</td>\n", " <td>1000.000000</td>\n", " <td>NaN</td>\n", " <td>0.132783</td>\n", " <td>-0.876858</td>\n", " <td>-0.017698</td>\n", " <td>NaN</td>\n", " <td>520.405</td>\n", " <td>-0.016661</td>\n", " </tr>\n", " <tr>\n", " <th>3888</th>\n", " <td>11089</td>\n", " <td>2002-11-01</td>\n", " <td>13.679406</td>\n", " <td>4.337844</td>\n", " <td>0.000106</td>\n", " <td>65.099156</td>\n", " <td>14.269219</td>\n", " <td>0.000057</td>\n", " <td>-11.122000</td>\n", " <td>0.003679</td>\n", " <td>28.969813</td>\n", " <td>0.150789</td>\n", " <td>-0.821630</td>\n", " <td>0.025481</td>\n", " <td>-1.593784</td>\n", " <td>822.743</td>\n", " <td>0.021123</td>\n", " </tr>\n", " <tr>\n", " <th>3908</th>\n", " <td>34710</td>\n", " <td>2002-11-01</td>\n", " <td>12.432687</td>\n", " <td>11.684344</td>\n", " <td>0.000123</td>\n", " <td>63.145031</td>\n", " <td>17.038563</td>\n", " <td>0.000064</td>\n", " <td>0.757312</td>\n", " <td>0.001698</td>\n", " <td>28.970219</td>\n", " <td>0.388257</td>\n", " <td>-0.410881</td>\n", " <td>0.064084</td>\n", " <td>-1.193250</td>\n", " <td>584.064</td>\n", " <td>0.020632</td>\n", " </tr>\n", " <tr>\n", " <th>4145</th>\n", " <td>10206</td>\n", " <td>2002-11-09</td>\n", " <td>3.428062</td>\n", " <td>1.562844</td>\n", " <td>0.003551</td>\n", " <td>67.108219</td>\n", " <td>11.155719</td>\n", " <td>0.000984</td>\n", " <td>-0.786375</td>\n", " <td>1000.000000</td>\n", " <td>NaN</td>\n", " <td>0.135089</td>\n", " <td>-0.869380</td>\n", " <td>0.002306</td>\n", " <td>-2.637141</td>\n", " <td>545.197</td>\n", " <td>0.002134</td>\n", " </tr>\n", " <tr>\n", " <th>4147</th>\n", " <td>11089</td>\n", " <td>2002-11-09</td>\n", " <td>19.677781</td>\n", " <td>-6.951906</td>\n", " <td>0.000126</td>\n", " <td>64.193281</td>\n", " <td>14.220969</td>\n", " <td>0.000065</td>\n", " <td>-17.539250</td>\n", " <td>0.003868</td>\n", " <td>28.742188</td>\n", " <td>0.201879</td>\n", " <td>-0.694909</td>\n", " <td>0.051090</td>\n", " <td>-1.291664</td>\n", " <td>858.387</td>\n", " <td>0.031634</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id time spd vn var_lon lon \\\n", "index \n", "3886 10206 2002-11-01 11.188906 6.509875 0.000996 67.351188 \n", "3888 11089 2002-11-01 13.679406 4.337844 0.000106 65.099156 \n", "3908 34710 2002-11-01 12.432687 11.684344 0.000123 63.145031 \n", "4145 10206 2002-11-09 3.428062 1.562844 0.003551 67.108219 \n", "4147 11089 2002-11-09 19.677781 -6.951906 0.000126 64.193281 \n", "\n", " lat var_lat ve var_tmp temp chlor_a \\\n", "index \n", "3886 10.873656 0.000352 -6.823625 1000.000000 NaN 0.132783 \n", "3888 14.269219 0.000057 -11.122000 0.003679 28.969813 0.150789 \n", "3908 17.038563 0.000064 0.757312 0.001698 28.970219 0.388257 \n", "4145 11.155719 0.000984 -0.786375 1000.000000 NaN 0.135089 \n", "4147 14.220969 0.000065 -17.539250 0.003868 28.742188 0.201879 \n", "\n", " chlor_a_log10 chl_rate chl_rate_log10 dist chlor_a_logE_rate \n", "index \n", "3886 -0.876858 -0.017698 NaN 520.405 -0.016661 \n", "3888 -0.821630 0.025481 -1.593784 822.743 0.021123 \n", "3908 -0.410881 0.064084 -1.193250 584.064 0.020632 \n", "4145 -0.869380 0.002306 -2.637141 545.197 0.002134 \n", "4147 -0.694909 0.051090 -1.291664 858.387 0.031634 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# add the column to the dataframe and output the dataset\n", "df_chl_out_3['chlor_a_logE_rate'] = pd.Series(np.array(check2), index=df_chl_out_3.index)\n", "df_chl_out_3.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "count 683.000000\n", "mean -0.032549\n", "std 0.166130\n", "min -1.971716\n", "25% -0.033506\n", "50% -0.005183\n", "75% 0.021785\n", "max 0.120403\n", "Name: chlor_a_logE_rate, dtype: float64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_chl_out_3.chlor_a_logE_rate.describe() # more scattered on the left hand side" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x117748860>" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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m0EmcaxkZuXI63YFOIyg4HDbVqlWNPi9H9Hn5o8/LH31e/ujz8keflz/6vPwd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\n2bhRVFaW4vYIiN94fyPPvrmakxblz3C3RmIse28DI0qLmDN1zKC/hv4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MEzYTFJpR4p5D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\noVAoUjn//POT8ceyb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6tcXK4r00pLSkfS1DiEPwADyEi6zz/e1MhPX1yB07ba+e81tXyw\nppaFJ0wd4pH1jpF0zXcVdlxzhSJbnHfeeVRWVnLbbbfhOA5SSgzD4KyzzuKyyy7rU599EslLlizh\nN7/5DQcddBDvvPMOxx57LHvvvTf/93//x1tvvcU3vvGNPg2mM/fccw9Lly7l0UcfTbatXLmSiRN7\nl9nWcVxsW/3wDSbqmg8+fb7mwgOTD8O75l2ETAhlJ1RMtHxf6HF/AtvIB6OtLJyk231Hew0CmqDB\nctEFFHl0PEKknIPeRUIfVzIs7q/d+T5vjTusrA0T8uhMLQokRcz2SObkX/VROyvXane+5kNFNq55\nZUsMAexRGMBsc7UUQhBxJfUxh1xDQ0BaGIYBWBEHzRAIIXC7S/Q1jLBdyertrViuy7SiAD6j5+WY\nRsJ9/scPNiUF8g7eXF3LWbPLGVsw8gTQSLjmCoUiM5qm8dOf/pQrr7ySdevWAQnDan/KBvdJJMfj\n8aTP98SJE/n888/Ze++9mT9/Pueee26fB9OZo446igcffJBHH32UY489lnfeeYcXX3xxpzHRCoWi\n99ijp2AXjMNo3IJr+nFzSwYlQVbI1AllSNS1g7jw4ZOtiA5TZwnExcibgO1KfFjZyMMfbSbeZv6r\nyPfxw7kTyPUaFHThUp3vVfVadzfcWAy7oR6zeBSGEHg8WlIgdyTiSvJIuFW30i6UDWBHwIZrS9Il\n9PClpjXOw0u20NiW+dujC741q5SpRSOgjFQP2dqUOTXj1qboiBTJCkVP6W/td0V22LJlS8b2HTHI\nTU1NNDUlQvbKysp63X+fRHJ5eTmrV6+mtLSUiRMnsnLlSgBc1yUc7qF7Zhd0dKmaNWsW99xzD7/6\n1a/41a9+RXl5OXfddRd77713v46hUCgyYPqwiycN9ShSkEKnRcvD77ZgYOOgE9WCOMKkPmJR2Ryj\nOGBSEhq8rMwCiUkcqzmOB4mLnsi6vZsQtR1+t6QyKZABvmyI8pcVVXx7v3JG+U3GBj1sDrdHugcM\njen5atK8O1H77DNsf/Zp3NZWjFGjqfjWRVRO3y/jth2qO5FDIh+XgBH9V/W3z2uTAhkg7kieW1nF\nNXMn7DIl7GaU5PDWmu0pbaYumDp6aBMrKhSK3YOjjz46LQFkZ3aE6+zQqr2hTyL5lFNO4eqrr+bO\nO+/kyCOP5LzzzqOsrIz33nuPadN2XvqhOzqfxNFHH83RRx/drz4VCsXIxREeWvTCxNJt24/hG+vr\neG9TY9KuNE02cOIXr2Lk5eM79ESMsooBGo0kaMTQhUQ6YAowDJtm24scYVP6qOPSart4dY1gLzJP\nr9neSjSDS+Lyqpbk/+eW5FAZjlMdsQiaOhNzvHjaLIgtlkNdPOHWX+jZuSeBYmTS/P6/qfn975Lv\n7ZpqxK9/wdi77idq+fF1+s6DHUSjAHaFO2JNfXqMf1PMoaY1PqiLei5gae0LD4ZMvLLBuQeN47Ot\nzdS2LYgJ4PyDx5PnN7NzAIVCodgJO0oTDxR9EskLFizA6/UipWTvvffmu9/9Lvfffz+lpaXceeed\n2R6jQqFQJAXy5qYo725qTPnoc5FPecxkxpJ3iS1bRN6C6zDGZd8qbgoHXaTOMIUAr+YQdUeOSN7c\narE93p6oLdfUmBAwu12RBcj1ZX5s5HVoF0IwNuRlbCcxUBuz2dghWVttzGF80KQ4C1mvFcOHxjf/\nmdYmbYvRyxeTP+kUGmyXqExYikO6ILQLloPJ9Ro0dKoFrwkIeQZvCUACca09E68ELAG4MitCuSTX\nxwNn78N76+poitocMD6fccrNWrEb4Cp/62HBgQce2Kvt6+rq+PrXv867777bo+37NDMRQnD++ecn\n3y9YsIAFCxb0pSuFQjFCsR2HF99cyvvL1jC2pJBvnngIxQV9z17cU9bUZ85qu7F4CjM2LwXbIvLO\n38n55hVZP7YmMj8YNTFykr00W06KQAZoslzq4w6FPRCr4/P8zBgVZEVNamjNCXvsvA6hlJKtrelJ\nvba0WhR50mvm9gQJuCIhAoSUaDJhzcpENO4QidsUDKIVb3fknaWfc9+yJmqtCg7SWjhDq8Pb9ncj\ndB1dCIp2A++BIyoK+OvnNSlt+5fmEvIM3oKQS+ZSNY7InjXZZ+ocM21UdjpTKBSKAcR1XbZv3979\nhm30+Nf6hRde6HGn8+fP7/G2CoViZLLglkd55T/Lk+8f/es7/O1XP2TsmJ3XS+0vOV1YYgKx5uT/\nnboaHClZtKWJz2rD2K5kjwI/88bm4+uFa3FnbJl5X1uOnEl/SxfZW5ttl8Ie6sfLDx7P//u8ho+3\nNhPy6Bw3uYj9y/N2uo8twcowMbdl4mX2UiNLwNYEMumqK3ClxHBkilB2dHAqkwAAIABJREFUXMk9\n/285z/57HdG4w17jCrjhzP2ZUrbz8Sp6z+uLPuOCGx/CdSXgZ6XjZ4Xr5zZzM8LnI/eww5PbyjZL\nTF8WR0YCh4zNI2BoLNrShOW47D0mxNyx+UM9LGAkpT9TKBSKoaPHInnhwoU92k4IoUSyQrGLs2j5\n2hSBDFBd18QDz/2TW793+oAee+aoEG992UBzB2uoYceZtfGD5Htz4nQWbWliSYc42c/rIkRsl693\nY/HcGY7UiTk6Xr392LarEXeHXiQ7tduw1q9CKxyNOWnPLsWH2UXSoK7aM+EzdE7fq4TT9yrp8T6G\nAI8miHcqGePRBEYfdJIr6CCQE0ghcIVE73CIP7y5mife/CL5/rNN9Vz58Hu8+NMTMXZBN9+h5DfP\nvNEmkNv5QIbYVDqJQ79/GUZBAVJK6m2XZlsigf/P3pvHyVWV+f/vc5fauqr3dHf2kBCyAUkIS0DC\nvrmAyAjqKDCIX1wRHLdBHRn0N4gjOqKo48gy6jg4OqLivrEIimgCSchOyJ50kt6XWu+95/z+qOrq\nrq6qpJfq7urOeb9e/Ur61K1Tt6qrbp3PeZ7n84QMgY3iYMwh7koitsncsI/gKDazjoVhCoQg3Upq\njNXi8qYIy5vGPrumGAbkeDn0YWqVrNGMCv0ROjEYskjetm3bWJ6HRqOZRGzdXdh2f+uuwuN9CCcB\nSqJ8I2+D4rcM3rliOs/s7eRAT5Ka3hZWvvAYNdF0Co3ZOJPgRVezeWdP3n33dSfpSblERpHymJA+\npKEIhyx6Yy5prT6x0bDY735E7Pc/zvalsE5aRNWtH0P4AnnH1vhMjibcnKiuAdSPca2kEIJZIZtd\nvamc8ZlDrIUeTKE00uz4APXzi7X78o453Bln3astnHNK47AfV1OcQy0dBcfFre+n4rRTAeh0Jd1u\n/98nJhUpV9KZ2fRKJl26HI9VdSGsUrpAC/AFTIzMnEop3JTEc6fuclcAPgmOobKfC7OExl0ajUYz\nldFuKRqNZtgsWzCr8PjJhceFmyJ48EWs3qMAuMEa4rNWjVgsVwds3pitg5uFu6QBZ+cmjKpafEtW\nIkwLV3YXvK8rR79ClJiY/hAyFiVd+TdxuIcPEPvd47lju7cTf+43hC55Y97xphCcHPFzNOES8yQB\nQ9AQsLLu02NJtc9kaZWf9mTG3dpvEhjh4wqlKLQ5IbShypiydm8HP1x7gJaeJCvnVHPj6jlUh3wA\nnL/yFB779V9yjg/4bc5a1m+i11tAlPosA8sQ2c+mIxWtCZemUOlcki3byApkSG/aWD4Dz/MmfVhI\nKUVL0qMt5eKptBHf9ICNbQhMwJCgSJchlHtyu1SKQz1JAGZE/BhTNB1fo9GUP1okazSaYXPWspO4\n5sKVPPHMS9mxGdOqee/1hdu1BZo3ZgUygBXvIHjwRWInnV+S87EaZ2I1zswZO7kmyPb2XJOvuqBF\nTWBqtSdxdm4qMr4ZCohkSKc4zyqhABkOAdNgRmj0gtxQ6QV1TkQ5Y941kNefOYcHfp77GjVVB1m1\nQJsNDZeX9nXyyR9vom+faX9HnE0Hu/nGO1ZiCMFHb34da7fs5pV9RwCwTIN/ff+bqY70b4YV06OD\npZBTgs2sgRhmgQ0VITAMkU69HnASRfz5ypbWlMeRAT2ZuxxJSqY4OWNSNxnEMUBb3OGJV1rpzmQV\nVPpM3riwnlrdUkpTZpT48qQpU7RI1mg0I+Jrd93ENReu5PmNO5nVWMv1l59NTWVF/oFSYnU35w1b\nsXaEE0fZY9MyZM2samKOZH8mKlEXsLjqpLE1FZsIjOq6wuNV5fFcFaAGOFCLYzhQDwcBWJ5CCnVM\nd+t3XHQK7b3JHOOuu9+6Stcjj4DHXzyYtzjc1Rpl3d5OzppXQ2NdFb//5sd5Zt02Wjt7uWjVYhrr\ncg3SKkxBj5c7iePJPFFc4y9t+r+SKt2DafC4UigB0hL9tbtSYbhqUghLINt3fCBxTxH3JMFJ9D7/\n3Z72rEAG6E55/H5PBzcsaZjAs9JoNCcqWiRrNJoRYRgGrz1/Oa89f/mxDxSAMEDlLuTSkZuxW8AF\nLIM3LqynO+niKTWqCPLR9m7+80dPsWnnAZbMn8H7briUmpoCGwITgG/JSsymWXiHD/QPWjbBNVdN\n3EllUIA0+x2oFZm6yBIJEEHGhOgYKdamIfjQNafz3quW6RZQo6Q9lio43jlg3DJNLj17WdE5amwD\nT0liGVFsC0h5/SULApgb9hEucZso15EZ067+d57nSqQEaYtccytDoEwQg8T82i27+elTL2IYgusv\nP5tTi5SXjDfFerYO3tCQSpFQ6QIRvwC7jFKZY47HkWh+i7jmaIqEK0fVlUCj0WigL3to6NcSLZI1\nGs3YIgyc6ln4OvbmDLuRRpQ19oKlcgi9f49FV2+Mq+/4dw4caQfg2Zd28LNn1rPuB/dgMvELN2Fa\nVL37U8Sf+hnO7q0YNdMIXXQ11ox5E31q6Qjy4OidEChDMd6tpQM+k8AYm5NNRZRSmHgIQ3LWvBp2\nDHCMh/QmxMo5Q29tZAhBg9/EUwqpMq7qAYvZFZK4JwlbRsH6+JQnaU24WIagPmANrVbVAJFJs1ae\nIpXwsGwDBEhXpU27BHnuzwDKAAbs6/33L/7Mxx/43+zvj/70Wb72iZu4+oKVQ37uY0WVbeb1PrcF\nhEyRjpSTFsbtnso6KPQCEQMqSmmONgpsQ+TUpfdhZcY1mnJCae+LssR1Xdra2tJeE6T/TqlUipdf\nfplrrrmGuro6Nm/ePOT5tEjWaCYpUio27W0l4Ld4TZlENYuRaEo729qdB0BJdicCfOevR5g/bx2v\nX7MC28oVL72OR8KTVPmsYbUmGgt+8Nu/ZgVyH82tnTz6k2d517UXTtBZ5WJURKh4w99P9GnkcUwH\n6snuljRJEMpDoJBi+F/3BhKnp52AocCAW89tYPOhbtbv7wLANgUfuHgB9SOIzptCMLBMOGgZRds+\nHY6lWN8Wy0ZGKyyDsxvCx24TZQoMu/92YYJMSZzkoN2ZIbwNU47L5//r5/hsCyklrifxpORzD/+M\nN6xZMeG9nhsDFo5UdGd6oPsMweygRa+EeOb5CVSexWCPVAQFZWGOZZsGp9ZXsP5o7ibMadMqtEjW\naDTH5bnnnuPjH/847e3tebcFAgGuueaaYc+pRbJGU4Z4UrF+XycJV3LG3GqCg1IPXznYwZ3feoYD\nrekFxbJ59Txw24VMqxyb+t5RY5gkZiynq24pb7vra7ywaXf2pm//7Dm+f9/78fssPKXY2BajNZE2\noTEELK0OMr3CN1Fnzr7mtoLjuw4cLTiu6UeoPk/d/PFiKKX40R/W8qvnNhAOBXjH61/DWctOGsvT\nnJooSYXThk9GEYArfPTa9Uhj6J8ln0jlpLKH/SbfeOti1h5I0tKT5LSZlVln67HCk4qX2+M5qcNR\nV7KtM87K+uKbg6JA821hC1Qy970nIJ2XPEiI9aVa/2n9K/z6Txu45uIzuOu2a7BMg+//6i/c87Uf\ns7e5jWg8STiU32ptPDGFYG6FD0cqPKXwG4LoAIEM6VIHgcrbE3BUOvV6rBGpGP6W7ZixDqQ/TLJ+\nITJUA0BHLMWPNzSzsyVK0G9RXxciHLBYXBdi1QT2mdZoNJOHL33pSyxdupQbb7yRO+64g/vvv59D\nhw7xla98hc997nMjmlOLZI2mzGjuSvBPP9rEwY60M3PYb/Ev1yxhxYCUxru+/aesQAbYvKeVzz72\nF77y7ovH/XyHw4+fWpcjkAH+umkXP3l6HW+54hz29iSzAhnSQZ5dvUnaUh5By2B60CY0zrVpZ582\nn0d++se88fPPWDSu51GI9liKV1pjTK/0M6e6/DZIhCJfgKhjp1p/+uuP57zejz+5loc+fSuvv+A4\nte+aHIJuJ34Zzf5uqRQR5yhdvpkF04sHI1CYBWyeTUOxpCnM4nESL10pr6DT9cDrRB6CgtFdIUTB\nwLHhKpSZSbEmLZCFhH/6yg/47s//lD3uyRe28MTX/pF3XnchjuPx7Z88O+ECeSC2IbAzm1KJAhtR\nhWRyAdPv0uO5VOx+DsNNpB8z1YvV20J0/hp6RIiPPL6ZIxmDRYCawz185frTqBnjDRiNZqRod+vy\nY+fOndx7770sXryYJUuWEAqFuPHGGwmFQjz88MNcdtllw55z4gvqNBpNDl9/8tWsQAboTbp8/lfb\n8TJX5QOtvbxyqDPvfs9uOoTj5rucjhTTEvgCBrbfYKg+Bx7gULhz8JZdB/na//6+4P3Wb0vXK7fE\ncxe+EZ+F3zJJSEVHymNbV4KEN77FrK97zXKuOu+0nLHLzlnKDVecNa7nMZjHNx3m/T/ZzBef3c1H\nfrGNf392d/Y9Ui4IwPQUhicRMv3vsUy7jrZ3852fP5czJqXiS//96zE/16mG34vmjZnKxVSFzbcG\nU8wPTanxTZT3F1FxgWOpO1W4ZlAV+XwIwPAUppP+MSRsfvVAjkCGdFbJ17+fvoa99fWr+fRthVus\nTRaCAqxxSLW2uw9mBXIfQnn42vfw1CutOQIZoCPm8LutR/CluggkWzG93PtqNBrNYEzTJBJJb97O\nnTuXHTt2ALB69WpeffXVEc2pI8kaTZnxtz0deWMtvSn2tEZZ0BCmImBhGiJPEKXHS7PvZfsNrAER\nW9MUpJIS6RVeZCogTlog9xEA+ioVt+9p5o13fplYovACfdG86QD4TJGdxGcKzEEpkBI4mnCZM47p\n16Zp8PC/vIs/b3gl62590ZmLsW0LSB73/mPBno4Y39+Q21br+X2dnNbUxmUL68flHJKeZEtbjJa4\nQ5XfZGltBZECxlgCMpHj40urfYfbcAtsguzcd4R33v0Qza2dnHv6ybzvhstyeu9q8hm9kBU4ysIn\ncjeuktJkPLvuVtgmTUGbw/Fc5+P5lceO4CpHgm1kI8pKKZQ79A22l7buLTj+4uY9AERCAa4497SC\nx5QDAQGxQW8CA/CRNtQLCEFgnP6Mwi18nRRuksPd+bfVBwU3zOohEu/OjBwh5m8gHtC9zTUaTWEW\nLlzIk08+yY033sj8+fNZt24dN998M4cPHx7xnFokazRlRk2Fj5ZBO+uGIFv7VxMOcOUZc/nl2j05\nx7zlgkUYJTA4EYIcgZweE1i2QcorHKlOkSuQARKATXph9tCPnykqkE+Z28T1l58NwOywb0A9cuHn\nUij1cjw4b/lCzlu+ECicyjmevHSwu/D4oe5xEcmuVPxqTzudyfT74VAUdncluHp+HRWjaN2z5KQZ\nREIBemK5kaOk4/KLZzcA8OLWvTyzbju/evDDw2rlcKKRtCKE3NyME1fYeMbQTbYcbILBAKlYLO0S\nKk0cOf6v+fL6EJXdSQ7HHWwhmBvx0XS8VFwJKiX73dWLbPAVY+GcxsLjc5uA9MZPzJO4CoKmGFFE\nViCxcFAYuFiUcvOhwgApIZF52hZQaYI1hm33iuGGG+DotoLjp02P8JNBG363nOqjLpD79womj5L0\nVQ2rpl6j0Zw43HbbbXzwgx/Etm3e8IY38NWvfpXbbruN7du3s3r16hHNqVcYGk2ZccOZM/PGLlnc\nQF24f3Fw99+v5qZLltBQHWJWfZiPv+VsPvjGFSV5fFFEaB9rDVisOrBPOA92h+7j8nOW8dMv30lF\nML1wrw/YLK8LUeUzKVw9CJUl7p86GakKFu75XBUYn33PPd2JrEDuI+EptrXHRjVvRdDPv7znTTmb\nPbZl5qXObtp5gGfWbR/VY011EmYVcbMSlfkkpYwgvb7Cwu9YmL4ACeUn6vpwxjmKnD0HITi5KsD5\nTRHOaQwfXyD3oUiL42EKZIDVp5/MJWctyRmrCgd579suxXMl+3pSHEi4HE667Ik5dDnDK3WxSVJJ\nFxXECNNLhG5EwUKVkbWbEUJQaQrqTagzodYamZAvBTJYTXLaKTlXdKdyBk7NbM6aV8OaBbU5x6+e\nkf/3FYDlxvPGNZqJQJXpz4nMZZddxg9/+ENWrFjB9OnTeeihhzBNk0svvZTPfOYzI5pTqBOg2VdH\nRxR3GGlWmpFjWQY1NRX6NR8lv9l0hJ9vbCbpSi5YWM9bzp6FXaB3KBz/NT8aS/FKZxxHKuZGApxU\ndXyjmUDIzIuWum6B9ikZYuRHkgGCpNP7vvrY77jv0Z/n3f7bb3yMZSc1wf6t0H4IqptgzlIw02Lv\nQDTF4QEGPdW2yfyIb8Jblkz0+zzheHzo51tpi/W/6rYhuPeqRcytGXsDr5eO9rKhNb/mdV6ln4tm\nDb1nbjF2H2zhN39+mXAowPd//Rde2p6f+vpvd76Ft7/uvFE/1phjgLAMhJHpWeuOTLSNGJVZPo0g\ngjjR7/OJJuW4/PB3f+W5l3Ywq7GWm97wGmY31dGaculw8l+Pk4L2kNoVCSSVdOVtNyTxsS9ms7cn\nRaUpmF0bosdTeIAJVJqCwCRuhyRSMcx4J9JfgQxU5dy2ubmbnS1R5taGOK+mi5gZImpFMJRHVaqd\niNtFZ8V8PKv017cT/X0+EfS95pOVtp7RbQiPFXW6DOmYtLe3c8011/Dcc88d/2B0urVGU5ZceWoj\nV546/KjPYPb1JHhqf1d2h3FPd5LWuMNZBZxp23qSbNrfyczaIItmVWH7+uv5pFS4qeKLBx/5IlmQ\nTrcGuPXaC3jyb1v466Zd2dvf/5ZLWTavEf7wbTiyp/+O22fBZbeAZTOrwkd9wCLqSgKmQcU4O1uX\nKwHb5J7LF/LDlw+zvSXKjIifN53aOC4CGaAhVDiS3VgiN9qTZk7jPddfAqR7Ug8WyUIIzl95Skke\na0wRIAbUxQohELZAKq+wu92YnINgIqK/UwGfbfH2152XtxkTy2xyGAKq/RZ+yyDpSuJSEjGOn+li\n4hb8izy2/gjfXd+evV4vaorwngsX4LMMPKDDU0wbJ7OtsUD5Qri+wov4ZdMrWTa9EoAWGcYR/a/j\nUSuEkwygxkAgazSaEwcpJW1thdt6FkKLZI2mDLCEi0+4GELhKJOktCnFwnZ9SzQvBWdre4zT6isI\nDBCc//Pcbr766224mcXfRcsa+dzbVuL3m6DAKxb5yiSiWEIQIm1jJUlfWAIDnkEo6OfxL36QZ1/c\nzt7mNs45bQGnzG2CXRtyBTJA6wHYvQEWnglAwDQIFImin8g0hP28/9y5E/LYMyp8zK8KsKurv3a4\nIWizcAzaUL33+kt47qUdrN2Sbh1mGIK73nk1c6ePj0HZqDBE4VZEhijqtKwpf6yMv+DsSj++zLUp\n7DORUkGBCPNgVIFKt10dSb6zPrcsZfvhHv64o4XLlvZvmMalIjIufZtyUUpxMJriSMzBbwrmRgIF\njfpGi6fIEch9dPnrqCz5o2k0I0Nfvk8MtEjWaCYYS7iEjGS25tcUEkt4RL3RC46uZH618MywhcDF\nEBZSGext6eXLv9ya0+7l6c1H+P6f9/CONfMLT6wUQdmDX8UBhSP8xIxK7AKLmz6EEFywanHuYPvB\n7H978fFnax4dRpClB9s5beFwnqlmPBFCcMHMKhbVBGmJOVT5LWaFfWNiaBYOBfjJv9/BC5tepaM3\nxvKFs5lRX1Pyx9Fohkq1bWJZKiuQ+zAMkW48fJx0eg8LBwt7gJvDukOF6223He7OEcmDZxaZqnPJ\n2GYMbGiLsXeAoeSeniTnNVVSW2IfhGKvXPHGcRqNRjM2aJGs0UwwPuHkmWJZQmLi4THCnXqlMFWK\nkyMm27u9zJzwxgVhZoUt0ksRh6Rn8ucdLQX7oX7tp3/j6pVNVIXz0+OCspeA6q/J8akkhtdJj1U3\nvPOsagCgRVRwb+ASuoz0xsBvOuCi9Ye4acWM4c2nGVcaQ76SpVgfi7706lLXDaakotPxcBQEDEG1\nbWCWUuhLhVIqb/NAR5EnNyHTwC6W3DLEt0+UMH4S2DgoBJGKMJCfBlg96PMVzNYkK4JGClu4CAGe\nEsQ9/8i/M45Br+PlCGRI7wPs6IyzukDpzjERYNoGQqQ/B56b23i7zxpu8CekcIGHRqPRjB06h1Gj\nGWdSStHjKXo9hasUhii8YBZFxgdiuHECHa/i7V6Hv3s/QrqYMkVV6iBVqWZeX9/L22YkCZmK0+v9\nGYHcj9/0WNBQuEasu7uHbz3+dMHbfCrftMLCwVDFfK6LcNLpUN3Iz+0lWYHcx9O72znYnShyR41m\ndDhScTDh0uMpElLR6UoOJlxkKb0sVbpfb58oVkohHTl+9ciaYdPjeOzqSfJqT5LuVHHHaruYGh7y\n31aQJEgvlUSJsGp2HTMHmSrapuDiRenewAZQZQrszIaLXzj4DHdABpIiZCYYC4/b3iLO3d3DdPRG\ngOU3MEyBMASGZWD5B7cbhMHfSCbp8h2NplxQSpXlj6a06EiyRjOOxKSid8AiKuZB0DAJmbniUinw\n1LEjAoYbp6p9M0KlJ/TTiRnvgIpqzAFpfNMDkjfPVFAk4nf+wkoifkFPsv8Cq5QkenQfL7w8XJE6\nzIu05YMrbmXv77enC5oHsaczzszKiV8ePb12Kz99+kVsy+ItV57DOacVSUPXTBq6XJmnZxwFUU8R\nsUoZTc706y3djJoxoiXhsLOnv5/70YTL/LCPxkIt16RK/wx0m+4bGwG2afDZqxbxk82H2Xo0SkOF\njzcua2RBXSjrbj0wI8E28gWqIcASHq5KL+2UUijSkdmB9zXMdN97AM+TeM6xz7nKZxWM7tb4h7eE\nNMz8Gn0hBIYpkANS1G0BVSrdWlDQ99yH9VAajUYzarRI1mjGCakU0QJRhhbHZqYhsUT6RqUgLn3H\nrcEKxA5nBXIflhsDxwbbnzNeZyVxzMILIRuX9104g7v/5wV84VrcVIze5t04sS7mzVha8D4pESCg\ncmvoXCykGEFSnC/AzIY69u3vzLtpVhkI5G/84A/8fw89kf39sV8/zzc+eTO3XHfBBJ6VZrS4RXbd\nHb0bf0KilGJfNL+R3b5oimkBq3DbOUemQ7xCpC/co8wQqA7avGv13LyygkILteLv0vR5xjxJuyNx\nVbpMusYyCFsGpiWw/f0bsIZpYgiJl0xhk94gcPAjB/hLBC2DU6oDbO/s3zT1GYLFwzTqKyp0C4wL\noVOsNRrNxKLTrTWaccKj8MImhSDqBej1AsQ8Pz1eCEcVXh4Y0iHgdBBMtWHJVMFjlMyPMCgErlRZ\nN+os0sOTgjddcBonBXs5uvlZ2l9ZR6q3nXDIz21/d1HBx4gbEVIikH0+LjZRc+T9cd+waBoVdm7k\n/OxZVcwdA7fk4RBPpHjgf36bMyal4nMP5/d8Lje6emP889d+xLk3fYbX3/5Ffvzk2ok+pbKiWL/Z\n4CTuQ1uOKKXoSLq0J0ucyl5iXJWuUS807hwrOixJF+iOcwp9SuZLZ08JXGXgSMXRVFogkzm9VkeS\n8BRmgWJqwxKE6SaoYgRVjIjqwFK53y+La0KsmR7hlKoAp9aGuGRW1bDdrWWR10jX6GsmG7JMf05k\n1q5di+Pkb3QORAiBYQxd+upIskYzThQzJEl/CAWeMhksbw9HU2xoidLruCypFJwX6cHom8FngwpC\nalBE145gD5opaUZIEsKfaAfbB8IE6YKTImnWYhoG37/vfTz602d5fuNOZjfV8v/edBEnzynSq1kY\nRM3qTCRboY7haj0Upkf83HPJAp7Z00FHwmFZQ5izZlaNas5ScKi1k55Yfsr5nkOtpJxh1l8fByUl\nyR1bUU6KwOJTEfbo4ii3fPohXtj0KgD7Drfxgfu+i1Jw3aVnluJ0Jz2VlkE0U488cCyo242VjJgr\n2dAeI5FJpfUbgtNqg0Ts0ptLjRZLpM8vOUiw+QyBb5gbJ0mpSKn09T5oUFozuAyOsol7Cr/hIlC4\nyiQhfYAg6hVeLvd6kqqCUVsBwoBMZpIAgipKj8gt0akN2NQGRn5dUp5CuhIj035QKYV0FepEX91r\nNJpRc/vtt/PQQw+xbNmyosfU1dWxefPmIc+pRbJGM04YQlBh5NYkA4SLrMlbYil+tac9W+I2x0r0\nC+QMyhdCpPrNWhKhRmLBmYTcDnxeFBAkzTBxqxqEoMeoJuBEMVUCT1gkzJqswA2HAtz+tsu5/W2X\nD/k5KTEyQRF1Jb2uxBSCKtvANgS1IR9vWlpElE8Qsxtrqa8O09rZmzO+bMFMfLZFtFAh9QhwW49y\n9IufwTl0AACjsoppH7yLwMLFx7lnYV5+ZX9WIA/koR8/rUVyBkMIZvhN4lLhSEXANPDrKHJJ2daV\nyApkSIvHrZ0Jzp5WMYFnVRghBPPCPnZ0J7NXWQHMG2Zrsx5PER9wmY57UG2qrOFWKUkpHynPB9nK\n4+MjPYU5uOZeegiZu+ln4qUzj0p83p6j8Fwvm6Gui/U1Gk0pqK2tpaenp6RzapGs0YwjIUNgC0VC\nppc0AQOsIouQzW2xHA+YRn/+drsQgkTNSQQDFj2en5RIpyfH7Dpidn47JiksYubERmiPJlzanf7n\n0p7ymBOyCJRhBM9nW9z97jdx5xe+h5fJFQz4be5575tyjnM9jz+8sIWDRzt4zYqFLJo3fViP0/7d\nb2UFMoDs7qLtm//OjH/7BmIYqUF9tHX1FhmP5o1JpfKMfU4UhBCETMEYdM054fGkoquAO3TUlSQ8\nWZaf91q/xfJag9ZEWjDW+y2C1tDP01O5AhnSGjAqoXpM32O5n90KU9BZINGlwhS4KZl2ls5sCCml\n8CW68yS2HMtqvAKVPxrNZEK/f8uPCy64gHe/+91ceOGFzJ07F78/15vnAx/4wLDn1CJZoxlnbCEY\nSrZhbFAv2JaUwaxA7phC4ATqqKirRHZEoUT9Y8cKR6ocgQzpOprWlMesYPktmiGdnnz6wtn87I/r\n8dkm1158BnNn1Gdv7+iOcsPHvsaWXQezYx96x5V85KbXDfkx4i+/mDfmthzBOXQA36w5wz7ns5fN\np7IiSHc0NxX/snP605BcpYhmHGQB/EpRIU5MsawpPUKkDaO8QYtFays5AAAgAElEQVRJg+Ibg+VA\n0DSYXTGy3t9ukYVzsfGR4EhFa9Il5ikChmCa38Q3aMPBNgTTfAbtKYlH+jWvttOlBEpBKu6lnaZt\ngUIgDB+m158Vo4C4CE2opXSh/uIajUZTjN/85jfU1dWxadMmNm3alHObEEKLZI1mKjEr7Kc52m+e\n8my7zZubkgz0XUlYVShj8oTBEkUMWnpSHi8e6eX02VVYZRhhOnlOIx96x5UFb/vGD/+QI5ABvvy9\n3/KmS85kwayGIc1vhivxujpyB4WBGY6M6HxDQT8PfOwdfPDz383WVK9aOo+P3pwW7kopegZ5DSUB\nQ0FIr0s1JcAQglkVPvb25hpANYVsrCma1l6sc5h9jKdrWumewUoqvOOoaU8pdvamsgZjvUCn43FK\nxI9tDI4mG4QCAi/jbj1YcEqlMDLR4pQdwjMsbDeOQpCSPryRdCooAS/u7+SR5/eyqzXGSXUh/mH1\nHM6aWzMh56LRaCYPTz75ZMnn1CJZoylTltaFaI4mOZBZZB5MmLyUrOO0iIdAkjJDuGZoUn2Ii9V8\nrt/dwdee2EJd2Me9N5zOslkTb9o1VP7ycn7tr1KKv2zcOWSRHLnyajp/8J2csdDq8zGrR744vOLc\nU1n32Gd44eVXqa2qYMWiudnbXAo7YSaB0IgfcXS4UnGgN0nSUzSFbKqG2YNVU36cFPZhC0Fz3EEB\njUGLOSOM0k4GTCEICUVsgNYVQEWRfT9fwMQw+6+Jpq2QBXoWdyZdXm6L4iioq8hNIXQVtCVdmgr0\nchZCFBXuyNxorWf68EwfylMoZ2Iykg50xvmXX2xLd2IAdrfF+OyvtvPgDaczp3airkwaTT7akL08\nUUrx7LPPsmPHDizLYuHChaxevRrTHFkwSa9CNJoyxTIEV86rpSXm0OO4NIZ8VNgm8WPcx4i24T+4\nESPeiReqJTXzdGSofHbhfYagxjboGLAIiyddnnh+LwBtvSn+5fFN/O/t5xXuS1qGzG6sZd2WPfnj\nTfk14cWoev11GIEAPU//DpVKUnnu+TTe8FYM28RxFcnk8BatCpACAiE/F529tOx7/UUdj2cOdWdL\nDF5uh1NrQyyumdgWYJrRIYRgdtjH7PDUFcaDCZsCn1QkFRgCAqKwu7VpiRyBDGAYAmXkrr5jjsfv\n9nfiSEVDkQ2GwY7cQ0W5CjEgzK2UQpWwZEcJUJmNUeGp41qL/WF7S1Yg9+FKxe+2tXDreXOL3Euj\n0Wigs7OTW2+9lc2bNxOJRFBK0dvby7Jly3j00UeprKwc9pxaJGs0E0zSkzTHXaKuxG8KmgIW4QFF\ny9NCNtM4fuqbSPQQ2va7rEupkYpi9RwheuobUL7y2YVvDFiELEnUlXzvuT088/Jh2nv66+GaOxPs\nPhplQWN4As9y6LznzZfw6z+/TCLZ35/vzKUnsWblKcOaJ3Lp64hc+joCfgN7QE69zxYIIDFEoawA\nJ51fmR3rjjtUGCLb3sgiXac4eEb/GO1LtPcm2Xqoh1m1QebW5zsbb+mI59Xgb26PMTfiH5ZxkkZT\nDvgMwfG2BUSRrBphiHSEV6VNz17tTmT7NMecfCM0gPBIPyOeQkovreYzv5cKaQrUgE0AZYDhKsQx\nHiJVRKCnirS00mg0mj4+//nPk0gk+MlPfsLixenOINu2beOjH/0oX/ziF7nnnnuGPacWyRrNBOIp\nxY6eVHYRlJSKHifF4sqhiQNDuQiZPs48ujOvjYfwUtitu0jNOLX0Jz8KIpZBxDL448Zm2gfVLAog\nEpw8l6bTFs7miS9/iG89/jQHj3Zw/spTeNebLhyx6YxVID/SsgRD7TblGiLPcCccsNjVHmd+2EfA\nNBBCUImid4BxVwAYi7jt95/fxzef3ImTWYBfdmojn7p2KdYA1+62hJN3PwW0Jxxmhv15t2k0kx1V\nJPpreA5OeychJdMbXgOEa2/KozPhUD2gV3HYMqjxjcKXQlFScdw3pRr89SUE0gTzGHXXaxbU8eMN\nzQXHNZpyQml767Ljqaee4itf+UpWIAMsXryYT33qU/zjP/6jFskazWSjM+VlBXIfCmhJusyxisci\nhPIIu21YKgUOuC0dCKdwIrZwS9PLdyx489mz+eaTuTW9Fy6ZRkNlYFzPI+VJEq6kcoR1sMsWzOTL\nH317ic9qZKgC2twQAp9l0JL0mB3KbKoIQZUY2xZQu1t6efB3r+SM/X7TEVbMrebaVbOyY2HbpLdA\nHWR4KDbwGs0kxHMVpqVyUq6lJ/G7/X0+hYD5FYptnf33O9idoD2W4rT6ChqDNmHLKD8X6PQFpfD4\nMVjcFOG9a+bxnRf2E015BG2TG8+ezakzhp8mqdFoTixc16W+vj5vvL6+nt7ewm0xj4cWyRrNBDJY\nIB9vvI8Ktz0tkPtIxbHClXA0/1i3euZoTnFMecdr5uK3DZ5Yd5CEI7l4aQO3XjR/3B5fKsUvXmnl\n+f1dOFIxPezj+qWNzK4qrUg3DDAtAyUV7vEcbD2VF032hhHpEaQ3WgailCLpKQq1XB7L2u/nX2kr\nOP7nHW05InlxTZCjcSfHDGVmhU+bd2mmNKmEh2GlexYrqbDcOIOzsOeGDZZWW2wZ0Ph4dtjPyeO8\nkTgsFOlGsoOvLUO4jF192nQuX9xAc3eCpsoAQb1RptFohsCyZct47LHH+OQnP5kz/thjj7FkyZIR\nzalXIBrNBFJpmxyKu3njVcdYGAglsVR+dNiKVBJrOAX76CsIVLqVR9NSvMqmkp5zKRFCcMM5c7jh\nnOH3Ai4Ff9rXyR/39odpmntTPLL+EJ84fx52iVpR2baBP9D/95RSEY+5FMvWSiQlASGwMhEm11ND\nrkcGMKXCMXIjw50JF1cqwqNJyxwB1aHCtfTVFenxnoTD91/Yz/p9ncxpCHPmomkE/CZNIR8nVeo0\na83UR7oKmVGPxRZkqxsCLKj20Zl0qfZbRMb5czxcBCAkqIGnqRTGEDf7ArbJSXX53gUaTbmgq+TL\njzvvvJObbrqJ9evXc8YZZwCwbt06tm3bxkMPPTSiObVI1mgmkJBlMD1o0TxAKFf7TOr8xRdBxZYZ\nCkjOOZNU01KMeCcyWIPy64XGsXixuSdvrDfl8Up7jKXTRm8cJgT4/Lli2zAEPr9JMlHYhEcpiMe9\nbBBmuKVPhgLbU/RIiaMUPSmP7qRHhSmO+b4aCy5a2sBDT+/iaHf/po5lCK47cxaeVHz4+xt55Ug6\nDWrLoR7+8PJhHnz7ck4PdOOLR5HCIGlVkbAmT0swjWakpPDhJ5mTlawUONhEfGbZi+OBGJ5CSZV2\nt1YKIY+bba3RaDQjZuXKlXzve9/jkUce4bnnnkMpxaJFi/j0pz/N6aefPqI5tUjWaMYBBXiZfy1y\nFwvTgzZ1fouoKwkY4viGXcIgZYTwy1jOsGuGQBgofxjPPzmcoSeaYpnGokTLOcMQBesFTfP484/G\nF8QAqgxBwlMIQ1BfMTG1iyGfxVdvXsXDT+9i475OZtWGuGnNPBbPqOT5V9uyArkPx1P8+IWdrL4i\n3bbMVB4hpx0A164d13PXaMYbhUHCCBMSSZTn4CmDBAG8SbpUEyrd+kmj0WjGg9NPP50vf/nLJZtv\ncl55NZpJhASi9KfnCCBE7ofPZwh8w4gSxMxqFCIrlI2KKhIynFbimiGzakYl+7tbcsYq/SYL60rT\nMksWqS0vNl5qAqZBYIRp4+1Jl5gjqfabIzDQUphG+r0+sybAp9+0LO+Ilu7ChnJHe/Kdrv1uNy6j\nE8mbXj3Ag9//Pa/uP8qqpfO44++vYHp99ajm1AyRTKG8KxUtCQdbCOoCVvkZTpUBUtjYNdV0tPfi\nTjGB2fds9F9dM9nR5tblwYMPPjjkYz/wgQ8Me34tkjWaMSZBbv2KAmJAhFEsFoRB3KohTg2WZVBT\nXQEdA6W4ZiicN6uKnqTHn/Z3knAlc6oC/N2SBqwiPUyHi1LgODKn77FSilSqfP9OnlK82BKjLdlf\nAnBSxM+i6qEZBRlCEfLJrAGRVBBLGchBttsr5hQWqGfPyq9FFqNckezcf4Q3fegBYom02d2WXQd5\n+m9befI//4lQUNc+jxW238Aw09kUSUfy/L5OOpPpnbywbbC6ITKp+mB3Ox4tSQ9XKiK2QWPAwhwr\noS8KWfBNThSQFCBFOvXaBHxKi2WNRjM6Hn/88SEdJ4TQIlmjKUfy42L96ddW5v+KjNnJOJ7XeNMS\nd+hJuUwL+sqmtk4IwVUn13HZ/FocT46Jk2oy4eF5Ess0MqLZQ5avRmZ/bypHIAPs7knSFLKpGsLf\nLWDLHIdeQ0DQlkRTufedUxfinWvm8eize7JSYOmMCG8/o5rB4iBlja62/ttPPJcVyH3sP9LOE8+8\nxFuvWj2quTWFsXwG5gAB7LcNzplVyW9e7QCg15Fs6YixqgS1/+NBl+OxJ9p/NU8kPWKu5OTI5Nhk\n8ZSiPZU+Z79pUOczsUu0GXg8sgIZQAg8IIXCPzX2ADQazQTx5JNPjun8WiRrNGOMQeH4rgCkANcQ\n6aiBUhgq7U48lcSyJxVPH+ziULRfpJxeF2J5GS2OLUNgGWMn3F1H4TqTIxe+LZHvtt43fnyRrCgU\nGEynXqcd1wdy43lzuWRJA+v3ddJUFeCMudUomcRLHcVU6fNIGUFidu2ovqwOt3UWHG9u7RrFrJpj\nYVr5V7FKv0V1wKIz8x5rKfJeK0cKnWvUU8RcSWiMouEpqUhJRcAQo8pukUrxam+KeF/6tiNpT7os\njPjHXChLBgjkAfR5dEyl7zrNiYPU+dZlS3t7O8lkEjXobzRjxoxhz6VFskYzxvhIp1wPxCItnp0+\ngQwgBFKkU0vNKXT93dEZzxHIABvbYsyJBKgJTK1LkFSK1qRHj5uOptb6zGO28ypHmsI+TpkWImAZ\ntMQctrXGSHmKwBDMxqBIe1RVPHF0Zk2QmTXB7O+eGaArMBtTpVAYSKNwG6nhcP7KRfzyuY154xes\nWjTquQfSV3MrFUwLWPhK1EZsUlJEAQ1cXPqHKdCUUsQVJJXCAEKGwB6numanyBvYGYPFslKKg1GH\njlT/xlq9z6R+hO70XY7sF8gZHAVtSZem4Og/XxqNRjPRbNy4kTvvvJPm5uaccaUUQgi2bt067Dmn\n1gpVoylD/KTXiin63a0DpKPIheyVpSEwp5BhS/MggdzHkd5eagJTyzjpYNylx5U5v0sFNWWSXn5c\nDMHsmv7a41mVfir9Ji8e6qWxSM/jXAQpT+C3Bi3IvWEWEwiBJ0qXxvq2q1bz1N+28ru/bMpML3jv\n9Zewasm8kj1GT8pjXWsUJ2PKZpDOmJh2gooQz1VYvty/eXvcoTvZL/zmVw6tzr2PLqlIDHhrxT1F\nrQm+UQrlvvIXCZiZn8FELIO2VG42iADCI9wISSlF1EuX41hAhdG/adDSm8oRyACtKY8KSxAcweMl\nvcL1HYlxMBA0AEOpvGiyiY4iazSa0nHPPffQ2NjIJz7xCSorK0sypxbJGs044Mv8DKToAmHq6GOA\nosY8tWYSlAQxNaJtKalyBHIfbSkvK5IN5RIkioWLxCBBEEcMTyiMJaJIiuy5TeEhGxQlXYFU4Muk\nQzheWjiPFk8qDvckiCZcwqbAGIYw8tkW//WZ/8dL2/by6oGjnLFkLvNnNoz6nAayrTOeFciQFlxb\nO+LUBaxhnetUwXXSjXH70q49T9HcmSRiG1iGYF4kwKyKwVfFY8yncgVyH71SUTvELIdCKCBOZtMy\ng6XSG5kDaQpYxLz+iKwAZodszBGkK3tK0TlAA7tAl4RaobCA9ljhjcVeV45IJIcsA5L55R6hccp0\n8Kt0DXLfGfQZd2k0kxX99i0/XnnlFR5//HFOPvnkks05NVanGs0kRCgK9hEwplity+KaEIPXsFW2\nYkFYYalCtmaTE7dIVCY7rhRhurFxEYCJpIIoliq8IC4n7KGKEAMQAscziKZMoimTlGcw2phRr+Ox\nvi3GhkPd7OxOsqkzQazAhsTxWLl4Lm++7KySC2SlFJ2pfBGSlGpE5zlVcFOSZMwjGfNwk5IFlQEu\nmlHF+U2VwxLIAG6Ry2Kx8aHikCuQAVyR303PMgQLwz4WhH3MDdksrfSPOEMkXuQt0TduFRGv1gg3\nWyKWQeWgzcqAKagbYfr2cBGkhXIw8+PXztYaTVmQSqX4xCc+wVlnncWaNWt49NFHix67ZcsWbrjh\nBlasWMH111/P5s2bc27/+c9/zuWXX86KFSv4wAc+QEdHR87t999/P+eeey7nnHMOX/jCF0r+XBob\nG0kkBhc3jg4tkjWaCUIAtqcQUmWKNhWmJ6dUPTJAtd/kNfUGTQFFnU9xepXizTMVpgApJkka8hAI\nmCJvMwAgnFmc2qQwCuw/+/Mq1ieQAkJfKZXjPBdPpvjJU+v49s+e4+DR9vSgAHwG2Cb4TLBL+9Xy\n+1fa+P36Zrbs7UAqhatgX5E0/olACFGwZtsA/CdyXXIJsYuoKt8o1VYxO71C40IIwpZBtc8clZFW\nMbuyvsecXunPE5GmgMoRfq6EEMyrsJlXYdPgN5kdslkY9o1d+6pi54EWxxpNOfH5z3+eLVu28N3v\nfpe7776bBx98kN/+9rd5x8XjcW677TbOOussHn/8cVasWMG73/3urCjduHEjn/rUp7j99tv5wQ9+\nQFdXF3fddVf2/o888gi//OUv+frXv85Xv/pVfvaznx1TkI+E973vffzrv/4ru3fvzjPtGik63Vqj\nKTVKIbwUyrSzqcTCFAgj7e+r3H4XIwHY41AXNlEkXMmzzd10pQRgYaKYFfSosCApAkgxdS5BhhDM\nDNociDlZTek3BI0ZczIxCRK0lKtAKERG8CmlUE6/Qt59sIXrP/ogza1pt+h//prB/f/4Nm54w+rc\n+npDgCVGHeaTSnHPT7fyx+0t2bH5TRFue+0iYhh4Uo0o3XUsOCkSYGtnPGdsVtg3bm12pjqmEISN\ndHp1HwYQHuXra1BYEI/l1oZRpAVyX7VDhc9iXsTH0ZhDUiqCpkG9zxyVqBVCUGVPPiNBjaYcmQrL\ntng8zv/93//x8MMPs3jxYhYvXsy73vUu/vu//5srrrgi59hf/OIXBINBPvrRjwLwyU9+kj/+8Y/8\n+te/5tprr+V73/ser33ta7nmmmsA+MIXvsDFF1/MwYMHmTlzJt/97ne54447WLlyJQAf+chHeOCB\nB7jllltG9RwWL16MGHBdVErxute9ruCx2rhLo5lgrGgLgaNbMJ0Y0vSRrF2A2zA/KzoEAmUoZEqe\nEEUtm9tjdA1IQ/UQPNtuUR/2o4zR9b4tR8KWwcKIj5grMYQgZIrsBdzBhyKaF0lx8qrVJxblSJRL\npkdZ7m33PvxEViADeFLyz1//P1536UrCoUFVnMWUwDB4YVd7jkAG2HW4h7/taGHNskbKSX/OCvvw\nmYKD0RRSKRqDPmZWTC7Trl7Hoz3lIYA6v5WXojvRhA2BX0BSpUVsQDDqem+bjHP1wD0eVdi8q1TY\nQuAIxUB/RlOAf8BzqbAMZg/JLE+j0WiGz7Zt2/A8jxUrVmTHVq1axTe/+c28Yzdu3MiqVatyxs44\n4wxeeuklrr32WtavX8+73/3u7G1NTU1Mnz6dDRs2YNs2zc3NnHnmmTmPc+jQIVpbW6mvrx/xc7j3\n3ntzRHJXVxcVFRVYVlre9qV819TUjGh+LZI1mhIhnDihQy8iVFpZGF6KYMtW4uEIXqS/BlIIgWEZ\nSGfq1yoeiefXHLsKDiV9TK8oI4VTQkwhiBSI1ihhEFNhgkQxUCggRYAUpXNxLhmKrL61ZAJbJZGY\nbH31QN6hvbEkL2/fx7krTyn5aWw6ULiP8a7DvfzdqpkIIdjXFuPZHa34bYNLlzRQM8xa11LSELRp\nmKRu1i0Jl/2x/s9rS9LjZGBkS4uxwxaiaOr1SDCAEGmh3OdubTO2acF+IGUIDKWynbJsIfSCTKPR\njBstLS1UV1dnBSVAXV0dyWSSjo6OHGF59OhRTjkl9zu+rq6OnTt3ZudqaMj1+qivr+fw4cO0tLQg\nhMi5vb6+HqUUhw8fHpVIvu6667L/37x5M+985zu57rrr+PjHPw7AJZdcQiqV4pFHHhnR/OW1TazR\nTGLs3sNZgTwQqyNfWJwohVnFnK2LjU91HOGnmxp6qKKbGuKiomAbsHIh5HUS8doJyCgh2c3TX3w7\ncxurco4RQjCroTb/zqN1VAJmVAcLji+oDzE9aPOrjYe55eG/8dAfd/O1P7zKjf/5V7Yc6h71455o\nSKU4VGBDa39v+dR9jyUGaeEaJN2FoBSfSKUUnlI5faGzjycgAgSFICAEFUJQQVlfCsYEV0qe3tPB\nV/6yj68+v5edbdGJPiWNZkgoVZ4/wyEej+Pz5W4q9/2eSuVe+xOJRMFj+4471u3xeDxn7mM9zmi4\n7777uOSSS/jQhz6UHfvtb3/LmjVruO+++0Y054m5UtVoxpMCFy41FQpahsApBUROY9Cm2n/8mMn2\nPc088cxL7DnUOhanNnEIgScsVJm3vjKUg1/GcsamVYX42A3n5oz93aVnMru+BlyZLtSSChxZ1ARs\nV3uMfYNqd4txyZIG5tSGcsZqK3y89cyZJByPrz/5as7DRFMe33xq1xCfoaYPR+am/vaRlKqoa7um\nOK5SdAFdQCfQq1SekYwh0uniIZE2HzsRBLJUim5X0pbyiHqSb68/xH+9sI8nNx/hD1uO8vEntvKj\njc0TfZoazQmB3+/PE6l9vweDwSEdGwgEjnu73+/PmftYjzMaNm3axPve974cMW5ZFrfddhsbNmwY\n0Zw6u0ejKRFOuIlA6468aHKqcmbO70pmzLtOAGZW+HhNU4SdXQmSnmR6hY9FRaKDfUgpufP+/+FH\nv/8bkI5U3vZ3F/Hp264dj1PWZCjWnmvlyY34fTZnLJ7L69cs58Y3vCZ9g6coqLQyHOhK8MVnd9Pc\nkwRgQV2ID59/ErXHqLsM+ky+8o4VPL72INsP97B4djVXn95IXcjHjsM99CbzfYI360hyDhLwjPS/\nAjBlfr2tbYiCPmsBU4zKxflERClFD7l7oyn607pPVDylOJRwSWVemNbOJC/s7aRzQLaCJxXfW3uA\nixfUUTuBZRMazYlAY2MjnZ2dSCkxjPSmfWtrK4FAgMrKyrxjW1py/UFaW1uZNm0aAA0NDbS2tubd\n3tDQQGNjI0opWltbmTFjBkA2Bbvv/qWgoqKC/fv3M3v27Jzxo0eP5kW5h0p5hzI0mkmEsoPEZpyB\nZ6eXQtK0iU9bihusx0t6SEfipby0adcJxPQKH2tmVHLZ7GqW1YaOu+j+6dMvZgUypBed3/y/p/jz\nhlfG+lRPaITy8HlRbBkHpfCKOI9v3ttKMuXw5svO4pY3XoBlDs3i6ME/780KZIBX22I8snb/ce9X\nFbS5Zc087n/bcj569VIaK9M7101VgYL9mwdHnk9kFOAYIEU6VKmEwDXy/NjSzuyDNisEMCeshcpw\ncSlsV3diJK4Xp9uVWYEM0JN06Y3lb8RJBS/s68wb12jKCYkqy5/hsGTJEizLYv369dmxtWvXcuqp\np+Ydu3z5cl566aWcsRdffDHrVr1ixQrWrVuXva25uZnDhw+zYsUKGhoamDFjRs7ta9euZfr06aOq\nRx7MlVdeyT333MPzzz9PNBolGo3yl7/8hXvuuYfLL798RHPqSLJGU0Lcimn0zrsgrwUUCtQxomya\nfp5eu63g+DNrt3He8oXjfDYnBrYXo8Jrz9Ziepj02tNIiSA+1Z8a3d4T5ws/eB6Atq7eIc9/tDfJ\nngIp1i8e6saVakTRysqgzQ1nz+Z7z+/LjhkCblkzb9hzTVWkID+PVwg8oTAGXY7q/BZB06Aj425d\n6zcJD6EsYqgMfLhJE5tWCkslMKWLa/jwjJGb7E2a5zxGJAel7TdGAhhFPveBE9SzQqMZTwKBAG98\n4xu5++67uffeezly5AiPPvpotn63tbWVSCSC3+/nyiuv5Etf+hL33nsvb3nLW3jssceIx+NcddVV\nALztbW/jpptuYvny5Zx66qnce++9XHzxxdnI8Vvf+lbuv//+bFT5S1/6ErfeemtJn8+HP/xh9u3b\nxy233JLjeH355ZfzsY99bERzapGs0ZQaIVBWGToWTxIa6yoLjk+rjYzzmZwgKEmF15GziDfxCHpd\nRM1annppH0eamznU1svDv17PwdYeAC49e2nONEfjDq90Joi7kmlBi8U1QfxmerEbsEyEyDcW8VvG\nqNo4veuCkzilMcwz21sI2CavXz6dpTMKv3/GmkFdhMqCottyRbpzhSyDqqCF7TMQAqRMlz+M+jwM\n0j2OMm8C5SlEuSfUKEnYacFWmewHD5JGBTG77ph3s0in6A1+eif6N4Itct90IZ/JeSfX8duNh3OO\ni/gtVs8rN091jWZqctddd3HPPfdw8803E4lEuOOOO7jssssAOP/887nvvvu49tprCYfD/Md//Ad3\n3303P/jBD1i0aBHf+ta3sjXJK1as4DOf+QwPPPAAXV1dnH/++Xz2s5/NPs673vUuOjo6uP322zFN\nk+uvv56bb765pM8lFArxrW99i927d7Njxw4sy2LBggXMmzdvxHMKNdhNYgrS0RHFdcv9G3lqYFkG\nNTUVJ/Rr7niSbz29i19tPIwrFZcsaeD9ly4gVMKozECm2mu+t7mVK977b/TG+lNzp9VEePJbd1Fb\nWR69lafSa27JBBE33xxNYtDlm4HnSe68/3s8/oe1APhsk4//wxt4z/WXZI9tiTs819yTo7uqfCaX\nzKzM7uh++U97+MugNMqrFzfw9pUzhnaeZfqae0CSdNRWqLQ7crk0gZKk060HR5MtqTALfPNbliAQ\nzL1OCSGIx9wRv+ZKANYgZyqlwFWIMl59+L0eQm5H3niP3YBrBArcox9PKWKAQ3o/IpD5EUNw5yrX\n9/locZXiQMLNsS3wG4KXdrby+MuH6Yo7LGuKcOvqOcwb56utpEIAACAASURBVJKJqfqalzN9r/lk\nZduR8vS+WNw4MZvEUxUdSdackByMptjXm8KTisaQzfyIH7NEBjX/8eSr/Gjtwezvv9jQTE/C4TPX\n5dd5aPKZP2sav/r6h/jit3/D1t3NrFg0h9vfekXZCOSphhSFa4r7xk3T4Ksfv5E7/v4Kdh9sYcWi\nOUyryf0i3tmVyAtMdqU8jsZdGjO1ru85ZzbVAYs/7+3EMgQXL6jlumVNJX8+44kCEmSEYObfJGmx\nXA5frgZgKXBR2SiuochLte7D9uWnuSqlMEaT/WoUsG4WIn1yZVyCYslE0fHjiWRTCCKkX7uhCOMT\nAUsIZgUsuh2JoxQBQxCxDGad2sTVp07u64BGo5malMP3uEYzruzrTbKlo38B1NudpNfxWFk/ehHm\nScUvNuS3sHhuRysd0RQ12rHzuAQCBstPmc13/vVd2bGUI0km9Q7/sBBg+QyEIUCB50pkAVd1KWxS\nRhCf7K8ZVkDCzE1vP3l2IyfPbiz4UAmv8N9m4HjAMvmHVbP4h1WzRvBkyhOXfoE8EIfy+XI1M6JY\noRAcLyVcC7o+ZBHjumLjhdACORdLCGp9QzP602g0mommXL7HNZpxY09Pvs/okbhLzJWERmkYopQi\nVSBdSyoKjmtyMU2BUWBhaZmCZIHjNcWx/WZaIENGMJu4ykMWiN5FzVpc0YtPxpEYJM3wcaNlA2kM\n2nQkvZwxAUwLlkvi8RRAKex9L2Ef2AhuEq92DqmFa1CB8HHvenxxnMZ1JWYBt/JRlSVLlY4aD063\nLvP+y0kzgs+LYgyoLvaERcrQ7ukazYlOmV++NCVCWwhqTjiSRaJeqSLjw8EyDV6zMN/SftH0CAml\n6IgX7j2rSVPMImHqOyeUFmGIfoE8AKPYJpAQJM0IPXYDUbt+WAIZYGF1kPpA/56rAE6vC41606nc\nsaCgAVYpd5+TrkQqhXXwZXy7X0A4cYSSWG17CLz8i5J+OJyUxHFk9nOopMJnj26jQyjSadV956nS\n/bTLuR4Z0hHjHl8jSSOMI/zEzUp67Mb+jgUajUajmdLoSLLmhGNawObwILHqNwSVJUoD+9BVp9Ad\nd9iwvwuAWbVBqhrDPPi3A2nx0Bjmrac2Yo2q0G9qIiW4nsIa1P/WcXQUflgUCxuOUfanbQgumFFJ\nW8Ih7krqA/YJ0calz5QpqTJp1ypt2lWKL9b9XQm+u/4QO9tjhH0mrw+0cN2gS5QRbcfoPoKsKl1N\nZzLhkcqUERuGQSg0+uuikGnB3bejMFmSkKWwidm1E30aGo1Go5kAtEjWnHAsrg7Q63j0ZtKfLQGn\n1QULpvmOhNoKHw+8YyUHO+JEky7f3nKEVCbFVQEbjvQyPezn0vnHWXwphV9F8f3/7N15nGRVffD/\nz7lLrV29L9Oz7wwMMKyDI7ixiYgkYTGYGA1R8YmiGJdH1OT5vZREMfFxi5JEfLK4PdGIYgwgPwER\nI4PAwLAOAzPMvvR0z/Ra613O80d1V3d1VU3vVdVd3/fr1S/oM3XvPX37dtX93vM93+On8ZVBWkVw\nZ7BO53yRTHoEAwaWpdA6GyA7RebSitK0p4sWDfLnOOW/JVR76dUWYJIdIJ1sWvNEHM/ny4/upT/l\nAjCU8fhhpoOmSD9vCnblv9h3i+4j4fmkPI1tKOpMNaX5sXp44Hc2n+PNl8BYCCEmItlttUGCZFFz\nQpbBhYvqOJH28LSmOWhhzVJl67GWNIV55uhgLkAe67ljQxMGyVG/j8DIGp0abJ0mTiPOFFNh56N0\nxiddOHVcTIGb9nOFu7TW+J4uWrhrspSlYDjY0p5Gu/5JFuKtLbMVHI94rmsoFyCP9XCmIy9I9gMR\n/IbOgtcdTbsMjnkgEjQUS0IWZo0VkvK05sBQhuMph5BpsCIWpM6WwlFCCCEmJkGyqElKKVpCc3/5\nB0uknE6UimpodzRAHqaAkB+viSBZzJz2NU7KG1n5Z2ZMhRpzzSpTgTLQmcmNTGut+fZPf80P73+M\ntOPxe288mw+/43ICtnwEFeOX+IW5VhiNQqHxQ/WkT7sUjPygL+n5eQEyQNrX9Dt+zVUW3tYdp2fM\nw4aD8QxbOmITTq3RnPyhh681aV9jKYU9Bw9Ya1E8meauB59g1/4uzt6wgqtefza2VVvXqxCiusgd\nihBzaF1zhNaITU8ifw70lmUNJ93O0F7xdoq3C1HKbKSFKbMwEFCGys3DnchXvvcL/vd3fzHm+/s5\n3N3Hlz/2RzPv3Dyg0Fhm9lS5XrblZE7vqCNqm8Sd/L/3C9avILl0A7gpdKSpcP1hIFli7eGU75NN\nDK8NJ1JuXoAM2fphrw6kOKvIcn8ayJBdvkuTvTkKUljddMj16Uq7uZrXdaaiI2jN2nSdWtQ/lOD3\n/+JrvLzvaK7th/f/ju9//s8xzYVf20DMP76kUdUEefcRtUH7qIFuSCfKeljTUNx0zhI6gha+55NI\nOCR74gQmmBvqKhuNIuUrdiUtdiZsBl2Fo2SdZVHI05o+x6cr7XHC8XDLNWFqEnGB1pr/c/cjBe13\nPfAEJwbic9Cp6mKbmrqQJhzQRAKauqBGTVDaOWSZfHjLchbVZf/eA6bi8jUtvGl1MzoYQUebiwbI\nQMmRTbvGgri4W/yBYrzEe68DZNRwATYFroIk+c+APK05OiZABhjysn97Yvq+d8+jeQEywG+efpn/\nf+tzFeqREELISLKoAerYHqzt96JSQ2hl4C8/E+/My8q2lMe+riHueuCVvBG9D716gh9/5CIaoyWC\nXmVw2I3xdL+Lp7M3ty8lbE6pD9Ihy3SKMXytOZr2cIavr6SfDRA6QybWLAVG2tMFS0pprWESsYHr\n+fQPJYu3DyZori8c1TsZw1QEAyZKKXxP42Q8dNXGKJqQrfPiWcOAkK1JZk7+u1nXEuVvLl3H8YRD\nNGASnuRc2jpTETQU6TELeZpAY43NxW0KFr+9aSqRal1scT5fZddDHdki4RUfPxryfJpraJR+tj23\n62DJ9rdctKnMvRFCiCwZSRblNY179qTr8Yvdx/n7Jw7yracP8fjhgZLr6RZwUlhP3I1KDWUPr33M\nfdsx9jw19Y5M08+ePFiQ8jqUcnng+aPFNxi2M65zAXKWYvegU3K+4mw4/OCvefwT/x9P/dUXOPHc\ni3N2HDF74p7OBcgjPGBoNiuCDxfqGrt+7mTnI9uWyevPOaWgfe2yDlYtaZtSNzKui2GNVmo2TEUg\nVL3BiWkUH/CdbAapUorWaGDSAfLINktDFi22SdRUNFoGy8J2zc2drbNNVsfyVwOIWAZrGorXdCj1\n1zK23SjxAVbq12laCss2ZGnlCWxYWVh8DmDDqsVl7okQkzOyAkC1fYnZJSPJoiwMS+VubrXWeI5G\nl5g7N97PdvZwYDBbxCrlwa/396GA8xfXT3zcrldRXmGZZOPQS/irz5vSzzBdKad42l/6JCl6WmsG\ni/y7qzVJ1yc6B6NCL3ztn9jxjW/nvt/zHz9jyze+yOJL3zDrxxKzp1RqtTPJT8w9vUn+86VjHBxI\nsaQ+xO9taGNVU2G6gnY1uHpaM7G+8OHr+ZPP/BO7Dx4DYFFLA1//5DunvB+3SAqtUgrDzI4qVxt/\n+MZlfKA81zczhlLDRbqq9wFCOWxoCrMoYtOTcglbBovCNmapdHSyc5LHUjr/DEZMha0oeCjVMO79\nWCkIhMxc9oWFgZvxcSUtu6h3ve0i/uOXj7P3cE+u7fyNq3jLhWdWsFdCiFonQbKYc8oA0x5TGVcp\nTBtcX0OJm8gRPQknFyCP9XTX0KSCZG2WuMRLtc+Bi09fxK9ePJZ/eEPxhtPaS26jlCJsGiS9/Jsq\nQ0FwDgqZZPoH2Hnnd/PatOfxwlf/UYLkKhc0ilfPCk5i5PB4IsNXt+4jPXydvdQTZ8/WBH/1xjW0\nRmZv/vuKzlYe/vaneOKFPaQdly1nrq2JyrVaKxxPE7DGtkHaqa1R3WJcrUnobNaDBUQUc7JEVWPQ\norFE6vVYAbIPNVwAlQ2Qw+QnPymlWBKy6cl4JDwfSymabIPYuNUKTNsomJ5g2grPldGeYprro9zz\n9x/j3+9/LFfd+tpLz6+J9wghRPWSIFnMOaNYZVyl0IbiRNpjb3+K3qSDaSiWRQOsqQ/m0ikzXvEn\n76Xax9Ptq9Ghuly69Qh/RfnmOV12xiJePjLAD7fuJ+P61Idt/uLKU1jafPLJxStjAXb0pfLalkUD\nc7Kmc/zgEfx04cOIgd17Zv1YYnaFDUXEUCTGzEENGtm5qRN59EBfLkAekfY0j+7v4+oNpR/iTIdh\nGFxwxpoZ7cOyTLxxad4ja0BXq5Sj8PxsAS8NZFyF59d2kOxpTf+YX1mG7OhsI7piVaIV2aDYJxvI\nGhSfHWQbis4Jlg8s9ZmnDDXpDKpa0xiL8D+uu7jS3RBiUuZy2puoHhIki4rJAK+cSDCYyaZQur5m\nz2AaBbl5Y4vqAsQCZu41I9Y1hyd3ENPC2fKHWM89gOrZB6EY3rrX4C85dRZ/kol98PL1vPOiVXT1\nJ1nRGiU4iXTptpBNsNngaDI7D7ktZM/Z2s6x1SuwY3U4g/kPE5rP3DgnxxOzRylFe9Ak6fmkfQgY\n2cBZTSLYSJRI/xy/9FC1sE2TpJtBmdmf2/c1Tro6+zpK4XjgeLUdGI+VKnJ/qYE02UC1XJKuj6kg\nMCY7ZzbydLSvs2k/xdqFEELMCxIkiznnexpl6rybdq01/Wm3IPgFOBjP5IJkQymuXtfKz1/pYWD4\ntSsaQrxhedPkOxBrxX3tDSfP6y6DhohNQ8Se0jb1AZP6EtVYZ5MVDnHmrbew7S8/n8sHtKIRzrz1\nljk/tpgdYdMgPMVLZVNHHb/ac6KwfVFslno1u0YqWrtpmds5n5UKFX3NtIo7TlXc8dh6dJBjSQcF\nLKsLcsGi2KwVN3MdH8PMf1Dlub6kWgshxDwiQbKYc9oHL+Nn5yWr7PdJx6fUUsHj01gWx4K87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0QipDz3tmoq9o5oyZ7Ct7X4QYy5N865ogQbIQoiqphlawg+Dkp7Gq1iUFr40GzKL7qAtM/y1O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\nIiIiIqJCZOsgeebMmdA0De+//3607Z133sHs2bNz2CsiIiIiIiIqVLYOkt1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"text/plain": [ "<matplotlib.figure.Figure at 0x1177434e0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# visualize the ROC of log(chl_a) around the arabian sea region\n", "fig, ax = plt.subplots(figsize=(12,10))\n", "df_chl_out_3.plot(kind='scatter', x='lon', y='lat', c='chlor_a_logE_rate', cmap='RdBu_r', vmin=check2.median()-0.5*check2.std(), vmax=check2.max(), edgecolor='none', ax=ax, title = 'rate of change of the log-scale chl-a')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x117ab8438>" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ASyKoAgAAwJIIqgAAALAkgioAAAAsiaAKAAAASyKoAgAAwJIIqgAAALAkgioA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\n+kfCKtX1D3JqTz+yQ4EtIquoKsDaKuz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"text/plain": [ "<matplotlib.figure.Figure at 0x115079be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# histogram for non standarized data\n", "axdf_chl = df_chl_out_3.chlor_a_logE_rate.dropna().hist(bins=100,range=[-1.5,0.5]) # there are very a few small values on the left\n", "axdf_chl.set_title('histogram of the rate of change of the log-scale chl-a')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x118b32400>" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Wml8tuTceujueV07z5JjPnTtX1apV0+jRo/XCCy8oOTlZf//731WnTh2nHofv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\nH7zevBcV1R/PPrtwqWXLlqpRo4ZGjRq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"text/plain": [ "<matplotlib.figure.Figure at 0x11893c588>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# standarized series\n", "tmp = df_chl_out_3.chlor_a_logE_rate.dropna()\n", "tmp = (tmp - tmp.mean())/tmp.std()\n", "axdf_chl_stdan = tmp.hist(bins=100,range=[-1.5,0.5]) # there are very a few small values on the left\n", "axdf_chl_stdan.set_title('histogram of the standardized rate of change of the log-scale chl-a')" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "0.0021522004602332889" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "(np.log(0.135089)-np.log(0.132783)) / freq" ] }, { "cell_type": "code", "execution_count": 11, "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>id</th>\n", " <th>time</th>\n", " <th>spd</th>\n", " <th>vn</th>\n", " <th>var_lon</th>\n", " <th>lon</th>\n", " <th>lat</th>\n", " <th>var_lat</th>\n", " <th>ve</th>\n", " <th>var_tmp</th>\n", " <th>temp</th>\n", " <th>chlor_a</th>\n", " <th>chlor_a_log10</th>\n", " <th>chl_rate</th>\n", " <th>chl_rate_log10</th>\n", " <th>dist</th>\n", " <th>chlor_a_logE_rate</th>\n", " </tr>\n", " <tr>\n", " <th>index</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", " <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>3886</th>\n", " <td>10206</td>\n", " <td>2002-11-01</td>\n", " <td>11.188906</td>\n", " <td>6.509875</td>\n", " <td>0.000996</td>\n", " <td>67.351188</td>\n", " <td>10.873656</td>\n", " <td>0.000352</td>\n", " <td>-6.823625</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.132783</td>\n", " <td>-0.876858</td>\n", " <td>-0.017698</td>\n", " <td>NaN</td>\n", " <td>520.405</td>\n", " <td>-0.016661</td>\n", " </tr>\n", " <tr>\n", " <th>4145</th>\n", " <td>10206</td>\n", " <td>2002-11-09</td>\n", " <td>3.428062</td>\n", " <td>1.562844</td>\n", " <td>0.003551</td>\n", " <td>67.108219</td>\n", " <td>11.155719</td>\n", " <td>0.000984</td>\n", " <td>-0.786375</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.135089</td>\n", " <td>-0.869380</td>\n", " <td>0.002306</td>\n", " <td>-2.637141</td>\n", " <td>545.197</td>\n", " <td>0.002134</td>\n", " </tr>\n", " <tr>\n", " <th>5440</th>\n", " <td>10206</td>\n", " <td>2002-12-19</td>\n", " <td>9.617437</td>\n", " <td>4.556469</td>\n", " <td>0.004192</td>\n", " <td>64.896875</td>\n", " <td>12.434812</td>\n", " <td>0.001140</td>\n", " <td>-8.368125</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.156649</td>\n", " <td>-0.805072</td>\n", " <td>0.019815</td>\n", " <td>-1.703006</td>\n", " <td>795.611</td>\n", " <td>0.015812</td>\n", " </tr>\n", " <tr>\n", " <th>5699</th>\n", " <td>10206</td>\n", " <td>2002-12-27</td>\n", " <td>12.251438</td>\n", " <td>-1.765500</td>\n", " <td>0.001212</td>\n", " <td>64.271031</td>\n", " <td>12.549094</td>\n", " <td>0.000417</td>\n", " <td>-11.493313</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.163432</td>\n", " <td>-0.786663</td>\n", " <td>0.006783</td>\n", " <td>-2.168578</td>\n", " <td>866.410</td>\n", " <td>0.005188</td>\n", " </tr>\n", " <tr>\n", " <th>5958</th>\n", " <td>10206</td>\n", " <td>2003-01-04</td>\n", " <td>12.856875</td>\n", " <td>-5.715375</td>\n", " <td>0.002190</td>\n", " <td>63.550156</td>\n", " <td>12.280437</td>\n", " <td>0.000691</td>\n", " <td>-11.053437</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.293834</td>\n", " <td>-0.531898</td>\n", " <td>0.130402</td>\n", " <td>-0.884716</td>\n", " <td>940.296</td>\n", " <td>0.055474</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id time spd vn var_lon lon lat \\\n", "index \n", "3886 10206 2002-11-01 11.188906 6.509875 0.000996 67.351188 10.873656 \n", "4145 10206 2002-11-09 3.428062 1.562844 0.003551 67.108219 11.155719 \n", "5440 10206 2002-12-19 9.617437 4.556469 0.004192 64.896875 12.434812 \n", "5699 10206 2002-12-27 12.251438 -1.765500 0.001212 64.271031 12.549094 \n", "5958 10206 2003-01-04 12.856875 -5.715375 0.002190 63.550156 12.280437 \n", "\n", " var_lat ve var_tmp temp chlor_a chlor_a_log10 chl_rate \\\n", "index \n", "3886 0.000352 -6.823625 1000.0 NaN 0.132783 -0.876858 -0.017698 \n", "4145 0.000984 -0.786375 1000.0 NaN 0.135089 -0.869380 0.002306 \n", "5440 0.001140 -8.368125 1000.0 NaN 0.156649 -0.805072 0.019815 \n", "5699 0.000417 -11.493313 1000.0 NaN 0.163432 -0.786663 0.006783 \n", "5958 0.000691 -11.053437 1000.0 NaN 0.293834 -0.531898 0.130402 \n", "\n", " chl_rate_log10 dist chlor_a_logE_rate \n", "index \n", "3886 NaN 520.405 -0.016661 \n", "4145 -2.637141 545.197 0.002134 \n", "5440 -1.703006 795.611 0.015812 \n", "5699 -2.168578 866.410 0.005188 \n", "5958 -0.884716 940.296 0.055474 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "###########################\n", "# On 2D-subsampling Dataset\n", "###########################\n", "# Val 1:\n", "# id:10206, time:2002-11-09\"\n", "# (0.135089- 0.132783) / (freq*0.135089) == 0.0021337784719703077\n", "#########\n", "# Val 2:\n", "# id:10206, time:2002-11-09\"\n", "# (np.log(0.135089)-np.log(0.132783)) / freq == 0.0021522004602332889 # very close to the value above\n", "\n", "df_chl_out_3.sort_values(by=['id', 'time']).head()" ] }, { "cell_type": "code", "execution_count": 12, "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>id</th>\n", " <th>time</th>\n", " <th>spd</th>\n", " <th>vn</th>\n", " <th>var_lon</th>\n", " <th>lon</th>\n", " <th>lat</th>\n", " <th>var_lat</th>\n", " <th>ve</th>\n", " <th>var_tmp</th>\n", " <th>temp</th>\n", " <th>chlor_a</th>\n", " <th>chlor_a_log10</th>\n", " <th>chl_rate</th>\n", " <th>chl_rate_log10</th>\n", " <th>dist</th>\n", " <th>chlor_a_logE_rate</th>\n", " </tr>\n", " <tr>\n", " <th>index</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", " <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>3886</th>\n", " <td>10206</td>\n", " <td>2002-11-01</td>\n", " <td>11.188906</td>\n", " <td>6.509875</td>\n", " <td>0.000996</td>\n", " <td>67.351188</td>\n", " <td>10.873656</td>\n", " <td>0.000352</td>\n", " <td>-6.823625</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.132783</td>\n", " <td>-0.876858</td>\n", " <td>-0.017698</td>\n", " <td>NaN</td>\n", " <td>520.405</td>\n", " <td>-0.016661</td>\n", " </tr>\n", " <tr>\n", " <th>4145</th>\n", " <td>10206</td>\n", " <td>2002-11-09</td>\n", " <td>3.428062</td>\n", " <td>1.562844</td>\n", " <td>0.003551</td>\n", " <td>67.108219</td>\n", " <td>11.155719</td>\n", " <td>0.000984</td>\n", " <td>-0.786375</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.135089</td>\n", " <td>-0.869380</td>\n", " <td>0.002306</td>\n", " <td>-2.637141</td>\n", " <td>545.197</td>\n", " <td>0.002134</td>\n", " </tr>\n", " <tr>\n", " <th>5440</th>\n", " <td>10206</td>\n", " <td>2002-12-19</td>\n", " <td>9.617437</td>\n", " <td>4.556469</td>\n", " <td>0.004192</td>\n", " <td>64.896875</td>\n", " <td>12.434812</td>\n", " <td>0.001140</td>\n", " <td>-8.368125</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.156649</td>\n", " <td>-0.805072</td>\n", " <td>0.019815</td>\n", " <td>-1.703006</td>\n", " <td>795.611</td>\n", " <td>0.015812</td>\n", " </tr>\n", " <tr>\n", " <th>5699</th>\n", " <td>10206</td>\n", " <td>2002-12-27</td>\n", " <td>12.251438</td>\n", " <td>-1.765500</td>\n", " <td>0.001212</td>\n", " <td>64.271031</td>\n", " <td>12.549094</td>\n", " <td>0.000417</td>\n", " <td>-11.493313</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.163432</td>\n", " <td>-0.786663</td>\n", " <td>0.006783</td>\n", " <td>-2.168578</td>\n", " <td>866.410</td>\n", " <td>0.005188</td>\n", " </tr>\n", " <tr>\n", " <th>5958</th>\n", " <td>10206</td>\n", " <td>2003-01-04</td>\n", " <td>12.856875</td>\n", " <td>-5.715375</td>\n", " <td>0.002190</td>\n", " <td>63.550156</td>\n", " <td>12.280437</td>\n", " <td>0.000691</td>\n", " <td>-11.053437</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.293834</td>\n", " <td>-0.531898</td>\n", " <td>0.130402</td>\n", " <td>-0.884716</td>\n", " <td>940.296</td>\n", " <td>0.055474</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id time spd vn var_lon lon lat \\\n", "index \n", "3886 10206 2002-11-01 11.188906 6.509875 0.000996 67.351188 10.873656 \n", "4145 10206 2002-11-09 3.428062 1.562844 0.003551 67.108219 11.155719 \n", "5440 10206 2002-12-19 9.617437 4.556469 0.004192 64.896875 12.434812 \n", "5699 10206 2002-12-27 12.251438 -1.765500 0.001212 64.271031 12.549094 \n", "5958 10206 2003-01-04 12.856875 -5.715375 0.002190 63.550156 12.280437 \n", "\n", " var_lat ve var_tmp temp chlor_a chlor_a_log10 chl_rate \\\n", "index \n", "3886 0.000352 -6.823625 1000.0 NaN 0.132783 -0.876858 -0.017698 \n", "4145 0.000984 -0.786375 1000.0 NaN 0.135089 -0.869380 0.002306 \n", "5440 0.001140 -8.368125 1000.0 NaN 0.156649 -0.805072 0.019815 \n", "5699 0.000417 -11.493313 1000.0 NaN 0.163432 -0.786663 0.006783 \n", "5958 0.000691 -11.053437 1000.0 NaN 0.293834 -0.531898 0.130402 \n", "\n", " chl_rate_log10 dist chlor_a_logE_rate \n", "index \n", "3886 NaN 520.405 -0.016661 \n", "4145 -2.637141 545.197 0.002134 \n", "5440 -1.703006 795.611 0.015812 \n", "5699 -2.168578 866.410 0.005188 \n", "5958 -0.884716 940.296 0.055474 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# convert into datetime\n", "df_chl_out_3['time'] = pd.to_datetime(df_chl_out_3['time']) # ,format='%m/%d/%y %I:%M%p'\n", "df_chl_out_3.sort_values(by=['id', 'time']).head() # a check" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "out_filename: df_chl_out_8DOC_modisa_4.csv\n" ] }, { "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>time</th>\n", " <th>spd</th>\n", " <th>vn</th>\n", " <th>var_lon</th>\n", " <th>lon</th>\n", " <th>lat</th>\n", " <th>var_lat</th>\n", " <th>ve</th>\n", " <th>var_tmp</th>\n", " <th>temp</th>\n", " <th>chlor_a</th>\n", " <th>chlor_a_log10</th>\n", " <th>chl_rate</th>\n", " <th>chl_rate_log10</th>\n", " <th>dist</th>\n", " <th>chlor_a_logE_rate</th>\n", " </tr>\n", " <tr>\n", " <th>index</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", " <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>3886</th>\n", " <td>10206</td>\n", " <td>2002-11-01</td>\n", " <td>11.188906</td>\n", " <td>6.509875</td>\n", " <td>0.000996</td>\n", " <td>67.351188</td>\n", " <td>10.873656</td>\n", " <td>0.000352</td>\n", " <td>-6.823625</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.132783</td>\n", " <td>-0.876858</td>\n", " <td>-0.017698</td>\n", " <td>NaN</td>\n", " <td>520.405</td>\n", " <td>-0.016661</td>\n", " </tr>\n", " <tr>\n", " <th>4145</th>\n", " <td>10206</td>\n", " <td>2002-11-09</td>\n", " <td>3.428062</td>\n", " <td>1.562844</td>\n", " <td>0.003551</td>\n", " <td>67.108219</td>\n", " <td>11.155719</td>\n", " <td>0.000984</td>\n", " <td>-0.786375</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.135089</td>\n", " <td>-0.869380</td>\n", " <td>0.002306</td>\n", " <td>-2.637141</td>\n", " <td>545.197</td>\n", " <td>0.002134</td>\n", " </tr>\n", " <tr>\n", " <th>5440</th>\n", " <td>10206</td>\n", " <td>2002-12-19</td>\n", " <td>9.617437</td>\n", " <td>4.556469</td>\n", " <td>0.004192</td>\n", " <td>64.896875</td>\n", " <td>12.434812</td>\n", " <td>0.001140</td>\n", " <td>-8.368125</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.156649</td>\n", " <td>-0.805072</td>\n", " <td>0.019815</td>\n", " <td>-1.703006</td>\n", " <td>795.611</td>\n", " <td>0.015812</td>\n", " </tr>\n", " <tr>\n", " <th>5699</th>\n", " <td>10206</td>\n", " <td>2002-12-27</td>\n", " <td>12.251438</td>\n", " <td>-1.765500</td>\n", " <td>0.001212</td>\n", " <td>64.271031</td>\n", " <td>12.549094</td>\n", " <td>0.000417</td>\n", " <td>-11.493313</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.163432</td>\n", " <td>-0.786663</td>\n", " <td>0.006783</td>\n", " <td>-2.168578</td>\n", " <td>866.410</td>\n", " <td>0.005188</td>\n", " </tr>\n", " <tr>\n", " <th>5958</th>\n", " <td>10206</td>\n", " <td>2003-01-04</td>\n", " <td>12.856875</td>\n", " <td>-5.715375</td>\n", " <td>0.002190</td>\n", " <td>63.550156</td>\n", " <td>12.280437</td>\n", " <td>0.000691</td>\n", " <td>-11.053437</td>\n", " <td>1000.0</td>\n", " <td>NaN</td>\n", " <td>0.293834</td>\n", " <td>-0.531898</td>\n", " <td>0.130402</td>\n", " <td>-0.884716</td>\n", " <td>940.296</td>\n", " <td>0.055474</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " id time spd vn var_lon lon lat \\\n", "index \n", "3886 10206 2002-11-01 11.188906 6.509875 0.000996 67.351188 10.873656 \n", "4145 10206 2002-11-09 3.428062 1.562844 0.003551 67.108219 11.155719 \n", "5440 10206 2002-12-19 9.617437 4.556469 0.004192 64.896875 12.434812 \n", "5699 10206 2002-12-27 12.251438 -1.765500 0.001212 64.271031 12.549094 \n", "5958 10206 2003-01-04 12.856875 -5.715375 0.002190 63.550156 12.280437 \n", "\n", " var_lat ve var_tmp temp chlor_a chlor_a_log10 chl_rate \\\n", "index \n", "3886 0.000352 -6.823625 1000.0 NaN 0.132783 -0.876858 -0.017698 \n", "4145 0.000984 -0.786375 1000.0 NaN 0.135089 -0.869380 0.002306 \n", "5440 0.001140 -8.368125 1000.0 NaN 0.156649 -0.805072 0.019815 \n", "5699 0.000417 -11.493313 1000.0 NaN 0.163432 -0.786663 0.006783 \n", "5958 0.000691 -11.053437 1000.0 NaN 0.293834 -0.531898 0.130402 \n", "\n", " chl_rate_log10 dist chlor_a_logE_rate \n", "index \n", "3886 NaN 520.405 -0.016661 \n", "4145 -2.637141 545.197 0.002134 \n", "5440 -1.703006 795.611 0.015812 \n", "5699 -2.168578 866.410 0.005188 \n", "5958 -0.884716 940.296 0.055474 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# CSV CSV CSV CSV with specfic index\n", "# df_chl_out_3.csv -- {lat, lon, temp, chl_rate, dist}\n", "# df_chl_out_3.csv -- {lat, lon, temp, chl_rate, dist, chlor_a_log10_rate}\n", "\n", "# 3 represents 3 features: {temp, chl_rate, dist}\n", "# 4 represents 4 features: {temp, chl_rate, dist, chlor_a_log10_rate }\n", "print('out_filename:', out_filename)\n", "df_chl_out_3.to_csv(out_filename, sep=',', index_label = 'index')\n", "\n", "# load CSV output\n", "test = pd.read_csv(out_filename, index_col='index')\n", "\n", "# a check\n", "test.sort_values(by=['id', 'time']).head()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2002-11-01 is week 44\n", "2003-11-01 is week 44\n", "2004-11-01 is week 45\n", "2005-11-01 is week 44\n", "2006-11-01 is week 44\n", "2007-11-01 is week 44\n", "2008-11-01 is week 44\n", "2009-11-01 is week 44\n", "2010-11-01 is week 44\n", "2011-11-01 is week 44\n", "2012-11-01 is week 44\n", "2013-11-01 is week 44\n", "2014-11-01 is week 44\n", "2015-11-01 is week 44\n", "2016-11-01 is week 44\n", "----\n", "2002-3-31 is week 13\n", "2003-3-31 is week 14\n", "2004-3-31 is week 14\n", "2005-3-31 is week 13\n", "2006-3-31 is week 13\n", "2007-3-31 is week 13\n", "2008-3-31 is week 14\n", "2009-3-31 is week 14\n", "2010-3-31 is week 13\n", "2011-3-31 is week 13\n", "2012-3-31 is week 13\n", "2013-3-31 is week 13\n", "2014-3-31 is week 14\n", "2015-3-31 is week 14\n", "2016-3-31 is week 13\n" ] } ], "source": [ "## check the week numbers of the range from Nov-01-01 to Mar-01-01\n", "for year in range(2002, 2017):\n", " print(str(year)+'-11-01 is week', datetime.datetime(year, 11, 1).isocalendar()[1]) # 44, 45, \n", "\n", "print('----')\n", "for year in range(2002, 2017):\n", " print(str(year)+'-3-31 is week', datetime.datetime(year, 3, 31).isocalendar()[1]) # 13, 14\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "the min and max of the week index is 1, 53 :\n" ] }, { "data": { "image/png": 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iBaAflQIAh2y7T1i1lwsLC43/Lyoqwtq1a3H06FGrG0VERNRUnDkjw969Csyb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\nRYvK2LKlmF27Sli5spycHPj3v8t48EEHp04p/OEPZoYMCWX48BD+8Ac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s1wGe7+677+aX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WZm6j/+4z/UqlUrXbx4UaNHjza7jwAAAI+kWrUM/fSTg7KzKYwfBsWaSlGrVi1t2rRJUVFRio+P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\n8uVKTEzUunXrtH37d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"text/plain": [ "<matplotlib.figure.Figure at 0x118b97978>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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DiIiIiGTLrERaqVSiT58+Ot/7y1/+Ys6iiYiIiIjsmlkXG+bn52v+X1BQgJ07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\nVMkzunbtKnz55Zca7Xv27BHc3NyECxcuSJZVVFQkdO7cWVi0aJFG++j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"text/plain": [ "<matplotlib.figure.Figure at 0x117a872b0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "##### weekly plot on the Lagrangian rate of change of the chl-a\n", "#sns.set(style=\"white\")\n", "#sns.set(color_codes=True)\n", "\n", "### \n", "# Approach 1 depreciated\n", "#grouped = df_timed.chl_rate.groupby(df_timed.index.week)\n", "#grouped.plot.box()\n", "\n", "### \n", "# Approach 2\n", "# prepare data a. use index or columns to group\n", "\n", "###\n", "# select the corresponding weeks, prepare the data\n", "df_timed = df_chl_out_3.set_index('time')\n", "df_timed['week'] = df_timed.index.week\n", "\n", "mask_NovMar = (df_timed.week<=14) | (df_timed.week >=44)\n", "df_timed_NovMar = df_timed[mask_NovMar]\n", "#df_timed_NovMar.head()\n", "\n", "# now rotate the index to make Nov-01-01 the first month\n", "print('the min and max of the week index is %d, %d :' % (df_timed_NovMar.week.min(), df_timed_NovMar.week.max()) )\n", "# make the 44th week the 1st week\n", "df_timed_NovMar['week_rotate'] = (df_timed_NovMar.week + 10 ) % 53\n", "df_timed_NovMar.week_rotate.describe() # now from 1 to 24\n", "\n", "\n", "axes1=df_timed_NovMar.groupby(['week_rotate'])['chl_rate'].mean().plot(linestyle=\"-\",color='b', linewidth=1)\n", "df_timed_NovMar.groupby(['week_rotate'])['chl_rate'].quantile(.75).plot(linestyle=\"--\",color='g', linewidth=0.35)\n", "df_timed_NovMar.groupby(['week_rotate'])['chl_rate'].quantile(.50).plot(linestyle=\"--\",color='r', linewidth=0.75)\n", "df_timed_NovMar.groupby(['week_rotate'])['chl_rate'].quantile(.25).plot(linestyle=\"--\",color='g', linewidth=0.35)\n", "axes1.set_ylim(-3,2)\n", "axes1.set_title(\"Line plot of the weekly data on the rate of change of the $Chl_a$ Concentration\", fontsize=10)\n", "plt.xlabel('week', fontsize=10)\n", "plt.ylabel('rate of change of the $Chl_a$ in $mg/(m^3 day)$', fontsize=10)\n", "plt.yticks(np.arange(-3, 2, 0.5))\n", "plt.xticks(np.arange(1, 25, 1))\n", "plt.show()\n", "\n", "\n", "# http://pandas.pydata.org/pandas-docs/version/0.19.1/visualization.html\n", "#http://blog.bharatbhole.com/creating-boxplots-with-matplotlib/\n", "axes2 = df_timed_NovMar.boxplot(column='chl_rate', by='week_rotate')\n", "plt.suptitle(\"\") # equivalent\n", "axes2.set_ylim(-1.6,1.6)\n", "axes2.set_title(\"Box plot of the weekly data on the rate of change of the $Chl_a$ Concentration\", fontsize=10)\n", "plt.xlabel('week', fontsize=10)\n", "plt.ylabel('rate of change of the $Chl_a$ in $mg/(m^3 day)$', fontsize=10)\n", "plt.show()\n", "\n", "# the rate of change is slower on the regular scale\n", "\n", "#matplotlib.pyplot.close(\"all\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.text.Text at 0x11a01df60>" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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nnnkGL730Et5///1bejs6IiIiIqJbzabA+ebb0bm7u2Pq1Kn2yhsRERERUZdh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\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11a0a2ba8>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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b2Rhzt4s55Vl6nLdq1Qo3b97UtrgCwLJly7RDhRm6kJqz7/R57LHHcOPGDRQXF2unnTt3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"text/plain": [ "<matplotlib.figure.Figure at 0x11a009cc0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# weekly plot on the Lagrangian rate of change of the log-scale chl-a\n", "# This is the rate of change on the exponential scale\n", "axes1=df_timed_NovMar.groupby(['week_rotate'])['chlor_a_logE_rate'].mean().plot(linestyle=\"-\",color='b', linewidth=1)\n", "df_timed_NovMar.groupby(['week_rotate'])['chlor_a_logE_rate'].quantile(.75).plot(linestyle=\"--\",color='g', linewidth=0.35)\n", "df_timed_NovMar.groupby(['week_rotate'])['chlor_a_logE_rate'].quantile(.50).plot(linestyle=\"--\",color='r', linewidth=0.75)\n", "df_timed_NovMar.groupby(['week_rotate'])['chlor_a_logE_rate'].quantile(.25).plot(linestyle=\"--\",color='g', linewidth=0.35)\n", "axes1.set_ylim(-1,0.5)\n", "axes1.set_title(\"Line plot of the weekly data on the rate of change of the log-scale $Chl_a$ Concentration\", fontsize=10)\n", "plt.xlabel('week', fontsize=10)\n", "plt.ylabel('rate of change of the log-scale $Chl_a$ in $mg/(m^3 day)$', fontsize=10)\n", "plt.yticks(np.arange(-1, 0.5, 0.25))\n", "plt.xticks(np.arange(1, 25, 1))\n", "#plt.show()\n", "\n", "\n", "# http://pandas.pydata.org/pandas-docs/version/0.19.1/visualization.html\n", "#http://blog.bharatbhole.com/creating-boxplots-with-matplotlib/\n", "axes2 = df_timed_NovMar.boxplot(column='chlor_a_logE_rate', by='week_rotate')\n", "plt.suptitle(\"\") # equivalent\n", "axes2.set_ylim(-1,0.5)\n", "axes2.set_title(\"Box plot of the weekly data on the rate of change of the log-scale $Chl_a$ Concentration\", fontsize=10)\n", "plt.xlabel('week', fontsize=10)\n", "plt.ylabel('rate of change of the log-scale $Chl_a$ in $mg/(m^3 day)$', fontsize=10)\n", "#plt.show()\n", "\n", "\n", "#plt.close('all')" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x118457e48>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.close('all')\n", "plt.cla() # axis\n", "plt.clf() # figure\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", " summary of the Chl_rate \n", " count 108.000000\n", "mean -0.012186\n", "std 0.115592\n", "min -0.372265\n", "25% -0.038421\n", "50% -0.000576\n", "75% 0.041588\n", "max 0.345365\n", "Name: chl_rate, dtype: float64\n" ] }, { "data": { "image/png": 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79ixat26NyMhIeDweKIoCh8OBadOmYe3atWjWrBlee+01nx78pvb888/j66+/RkFBgbft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iwMHDhQmX/06FEsXboUxcXFCAkJwUcffaRcydWC0ze1RERERET1cerbD4iIiIiIGoJNLRERERG5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\nRK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x118457ba8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", " summary of the Chl_rate \n", " count 109.000000\n", "mean 0.028671\n", "std 0.323727\n", "min -1.444104\n", "25% -0.044968\n", "50% 0.005496\n", "75% 0.062134\n", "max 1.688675\n", "Name: chl_rate, dtype: float64\n" ] }, { "data": { "image/png": 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669q2bRuSkpIQGxuLyZMno7Cw0F2HQUREREQe5PGe2qlTpyI4OBibNm1CUVER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"text/plain": [ "<matplotlib.figure.Figure at 0x11841ff60>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", " summary of the Chl_rate \n", " count 145.000000\n", "mean 0.130195\n", "std 0.598494\n", "min -0.535257\n", "25% -0.044103\n", "50% 0.006622\n", "75% 0.132662\n", "max 4.694123\n", "Name: chl_rate, dtype: float64\n" ] }, { "data": { "image/png": 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IoqIiZGVloXXr1tXqKyUlBT169LBuN2/eHKGhoTh27FhdD5uIiIiIFMalM7W+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ABjm7+/P4QQKC0ttficjRs3IiwsDE899ZTZMY1Gg3bt2uHf//43oqOjER8fj/z8fMnG3xi8ppaI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\niIjI6f0fCdooBrxkd9sAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x1177f8748>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", " summary of the Chl_rate \n", " count 158.000000\n", "mean -0.165618\n", "std 4.219963\n", "min -28.683290\n", "25% -0.118069\n", "50% -0.005606\n", "75% 0.063794\n", "max 35.780121\n", "Name: chl_rate, dtype: float64\n" ] }, { "data": { "image/png": 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yJVq2bImvv/4a27dvh6OjIyZMmIA333yzXPE/bu7cuWjcuDG+/PJLhIeHo2nTpli6dCle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bDD/UX1SVsGg3ShoP2LJEmQG/nvDNnD5w+/oiUiCl4saolINyQPVWVjz3OVARgUAUe9AloWAgYO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\nymQy4e9//7vHrwHru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"text/plain": [ "<matplotlib.figure.Figure at 0x118433cf8>" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "\n", " summary of the Chl_rate \n", " count 163.000000\n", "mean -0.255519\n", "std 3.027861\n", "min -17.483995\n", "25% -0.202936\n", "50% -0.025407\n", "75% 0.001883\n", "max 16.557178\n", "Name: chl_rate, dtype: float64\n" ] }, { "data": { "image/png": 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oPjj+LhBRtbB17wljQlsqJ1+DL3YcclBERERUKjAwEE5OToiPjzeWnThxAm3b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\nixahb9++6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"text/plain": [ "<matplotlib.figure.Figure at 0x119dc6f28>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# spatial plot for different months -- totally five months 1, 2, 3, 11, 12, \n", "for i in range(0,5,1):\n", " month_ind = np.array([11,12,1,2,3])\n", " month_names = ['November', 'December','January','February', 'March']\n", " aa = df_timed_NovMar[df_timed_NovMar.index.month == month_ind[i]]\n", " fig, ax = plt.subplots(figsize=(8,6))\n", " ##aa.plot(kind='scatter', x='lon', y='lat', c='chl_rate', cmap='RdBu_r', vmin=aa.chl_rate.median()-0.5*aa.chl_rate.std(), vmax=aa.chl_rate.median()-0.5*aa.chl_rate.std(), edgecolor='none', ax=ax, title = 'rate of change of the $Chl_a$')\n", " ##aa.plot(kind='scatter', x='lon', y='lat', c='chl_rate', cmap='RdBu_r', vmin=aa.chl_rate.mean()-0.5*aa.chl_rate.std(), vmax=aa.chl_rate.mean()+0.5*aa.chl_rate.std(), edgecolor='none', ax=ax, title = 'rate of change of the $Chl_a$')\n", " print('\\n\\n summary of the Chl_rate \\n', aa.chl_rate.describe())\n", " aa.plot(kind='scatter', x='lon', y='lat', c='chl_rate', cmap='RdBu_r', vmin=-0.6, vmax=0.6, edgecolor='none', ax=ax, title = 'Rate of change of the $Chl_a$ in %s' % (month_names[i]))\n", " plt.xticks(np.arange(45, 80, 2.5))\n", " plt.yticks(np.arange(0, 28, 2.5))\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "test\n" ] } ], "source": [ "print(\"test\")" ] }, { "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
geography-munich/sciprog
material/sub/koldunov/06 - Time series analysis.ipynb
1
275721
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Time series analysis (Pandas)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nikolay Koldunov\n", "\n", "[email protected]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is part of [**Python for Geosciences**](https://github.com/koldunovn/python_for_geosciences) notes." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "================" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here I am going to show just some basic [pandas](http://pandas.pydata.org/) stuff for time series analysis, as I think for the Earth Scientists it's the most interesting topic. If you find this small tutorial useful, I encourage you to watch [this video](http://pyvideo.org/video/1198/time-series-data-analysis-with-pandas), where Wes McKinney give extensive introduction to the time series data analysis with pandas.\n", "\n", "On the official website you can find explanation of what problems pandas solve in general, but I can tell you what problem pandas solve for me. It makes analysis and visualisation of 1D data, especially time series, MUCH faster. Before pandas working with time series in python was a pain for me, now it's fun. Ease of use stimulate in-depth exploration of the data: why wouldn't you make some additional analysis if it's just one line of code? Hope you will also find this great tool helpful and useful. So, let's begin." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an example we are going to use time series of [Arctic Oscillation (AO)](http://en.wikipedia.org/wiki/Arctic_oscillation) and [North Atlantic Oscillation (NAO)](http://en.wikipedia.org/wiki/North_Atlantic_oscillation) data sets." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Module import" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we have to import necessary modules:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "pd.set_option('max_rows',15) # this limit maximum numbers of rows" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And \"switch on\" inline graphic for the notebook:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas developing very fast, and while we are going to use only basic functionality, some details may still change in the newer versions." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'0.16.1'" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.__version__" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, when we are done with preparations, let's get some data. If you work on Windows download monthly AO data [from here](http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/monthly.ao.index.b50.current.ascii). If you on *nix machine, you can do it directly from ipython notebook using system call to wget command:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "!wget http://www.cpc.ncep.noaa.gov/products/precip/CWlink/daily_ao_index/monthly.ao.index.b50.current.ascii" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas has very good IO capabilities, but we not going to use them in this tutorial in order to keep things simple. For now we open the file simply with numpy loadtxt:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ao = np.loadtxt('monthly.ao.index.b50.current.ascii')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Every line in the file consist of three elements: year, month, value:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 1.95000000e+03, 1.00000000e+00, -6.03100000e-02],\n", " [ 1.95000000e+03, 2.00000000e+00, 6.26810000e-01]])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ao[0:2]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And here is the shape of our array (note that shape of the file might differ in your case, since data updated monthly):" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(785, 3)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ao.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Time Series" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We would like to convert this data in to time series, that can be manipulated naturally and easily. First step, that we have to do is to create the range of dates for our time series. From the file it is clear, that record starts at January 1950 and ends at September 2013 (at the time I am writing this, of course). **You have to adjust the last date according to values in your file!** Frequency of the data is one month (freq='M'). " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dates = pd.date_range('1950-01', '2014-01', freq='M')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you see syntax is quite simple, and this is one of the reasons why I love Pandas so much :) Another thing to mention, is that we put October 2003 instead of September because the interval is open on the right side. You can check if the range of dates is properly generated:" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['1950-01-31', '1950-02-28', '1950-03-31', '1950-04-30',\n", " '1950-05-31', '1950-06-30', '1950-07-31', '1950-08-31',\n", " '1950-09-30', '1950-10-31', \n", " ...\n", " '2013-03-31', '2013-04-30', '2013-05-31', '2013-06-30',\n", " '2013-07-31', '2013-08-31', '2013-09-30', '2013-10-31',\n", " '2013-11-30', '2013-12-31'],\n", " dtype='datetime64[ns]', length=768, freq='M', tz=None)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dates" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "(768,)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dates.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we are ready to create our first time series. Dates from the *dates* variable will be our index, and AO values will be our, hm... values. We are going to use data only untill the end of 2013:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [], "source": [ "AO = pd.Series(ao[:768,2], index=dates)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1950-01-31 -0.060310\n", "1950-02-28 0.626810\n", "1950-03-31 -0.008127\n", "1950-04-30 0.555100\n", "1950-05-31 0.071577\n", "1950-06-30 0.538570\n", "1950-07-31 -0.802480\n", " ... \n", "2013-06-30 0.548650\n", "2013-07-31 -0.011112\n", "2013-08-31 0.154250\n", "2013-09-30 -0.460880\n", "2013-10-31 0.262760\n", "2013-11-30 2.029000\n", "2013-12-31 1.474900\n", "Freq: M, dtype: float64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can plot complete time series:" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1071d07d0>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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bI/n0mKNvfHiEX3q2Pp9CMcrUooq4U942G8y//0LwxJ26fz4/a0WKBRhc57l/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\n3XWN22y3kErmY+Fj14Z4PeVnehPFsPDYvUlZ/M8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"text/plain": [ "<matplotlib.figure.Figure at 0x1071a2c90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AO.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or its part:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x107424450>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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nO3J4+fcDfxOzPHO5H/SvaK8lvOxvX4xtUbSG2vP8hoGrMJUaHl6mndsesRSp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\nTxA+4USQqjztJzEZ8FiSa7Q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"text/plain": [ "<matplotlib.figure.Figure at 0x10742c850>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AO['1980-05':'1981-03'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reference to the time periods is done in a very natural way. You, of course, can also get individual values. By number: " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "-2.4842" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AO[120]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or by index (date in our case):" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1960-01-31 -2.4842\n", "Freq: M, dtype: float64" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AO['1960-01']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And what if we choose only one year?" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1960-01-31 -2.484200\n", "1960-02-29 -2.212400\n", "1960-03-31 -1.624600\n", "1960-04-30 -0.297310\n", "1960-05-31 -0.857430\n", "1960-06-30 0.054978\n", "1960-07-31 -0.619060\n", "1960-08-31 -1.007900\n", "1960-09-30 -0.381640\n", "1960-10-31 -1.187000\n", "1960-11-30 -0.553230\n", "1960-12-31 -0.342950\n", "Freq: M, dtype: float64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AO['1960']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Isn't that great? :)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One bonus example :)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1950-02-28 0.626810\n", "1950-04-30 0.555100\n", "1950-05-31 0.071577\n", "1950-06-30 0.538570\n", "1950-09-30 0.357970\n", "1951-07-31 0.090023\n", "1951-12-31 1.987200\n", " ... \n", "2013-04-30 0.322210\n", "2013-05-31 0.494010\n", "2013-06-30 0.548650\n", "2013-08-31 0.154250\n", "2013-10-31 0.262760\n", "2013-11-30 2.029000\n", "2013-12-31 1.474900\n", "dtype: float64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "AO[AO > 0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Frame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's make live a bit more interesting and download more data. This will be NAO time series (Windowd users can get it [here](http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii))." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "!wget http://www.cpc.ncep.noaa.gov/products/precip/CWlink/pna/norm.nao.monthly.b5001.current.ascii" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create Series the same way as we did for AO:" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [], "source": [ "nao = np.loadtxt('norm.nao.monthly.b5001.current.ascii')\n", "dates_nao = pd.date_range('1950-01', '2014-01', freq='M')\n", "NAO = pd.Series(nao[:768,2], index=dates_nao)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Time period is the same:" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "DatetimeIndex(['1950-01-31', '1950-02-28', '1950-03-31', '1950-04-30',\n", " '1950-05-31', '1950-06-30', '1950-07-31', '1950-08-31',\n", " '1950-09-30', '1950-10-31', \n", " ...\n", " '2013-03-31', '2013-04-30', '2013-05-31', '2013-06-30',\n", " '2013-07-31', '2013-08-31', '2013-09-30', '2013-10-31',\n", " '2013-11-30', '2013-12-31'],\n", " dtype='datetime64[ns]', length=768, freq='M', tz=None)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "NAO.index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we create Data Frame, that will contain both AO and NAO data. It sort of an Excel table where the first row contain headers for the columns and firs column is an index:" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [], "source": [ "aonao = pd.DataFrame({'AO' : AO, 'NAO' : NAO})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One can plot the data straight away:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x107b44250>" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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cB7p9LHuzud7FJ7nYy99Ny47v2N7vVbb8GqXP+YXnet9z8SSagu14/Y8/J9f+\n/uJEHrkjs/ffccexbdKjy1O+VhERbcYqSnE3g1xDu/IStgP3JtaOO+LeRKdPBQX8EJEtMiqf5/iB\nL1F9fUeu/bAAt1LqSqXUVvPvLxzzoIRN+P7nNpH4W2S3wPpFk85zuJIbzFWolNrmhT+TR9lEmFIA\nJ26+h03A+PJa+v76RdlQfLLs5yweddK6jedzCwCBCuC5DTd+pdQ2k97QXnvfP4td5nlAdQw2+eZQ\nSqltnEiWnw01+1yD78nsz4f34n8ZN7A8UyyUUttYvVqlGvcmYPg0SIH7uuu999dywKRPP38n33WP\nMyt87xy2e/Vz/o/gyo85C0VbgLenznSUUts4hQOl5SNx4JSfns4/99/hrb/9BOD/AvBGvuduKS6k\nZxNwKo8TxJYOuJlTzt7lPV+X1m/6vnFT8EQvvubAIJuAZSdl9bFuBM65awT4HABrlsKyjVb7ibiU\nW9zyUEpt41QyjX0zk87zSTZUbfvWquNzucV8f6xQPtEkrKxOeOW9MYL1wwkSN4CA49FpHtqVff9s\nHsZq3LY/WY3bXtcObgCerZTaxsaI9DT5LHyYXp+3+G43f075D5e15w7XotvHhlvN9W5eyQ+9/C07\nbRVbeVTqYjRQdZNX3+uHbPlNufGLYOL8xnWF9mHbd9go9D/eznez7ysI33gXqzdnVO/aUx+14UVY\nDuoac21otS0t4OY0ghNo8Qxua5P/gE3AcWbclyRk1Sry7ceGV0pt4x3cyiun1nLZvo7YPL+oEi3F\nqZMK7Lwvb9rXjiqB/OECk0sGOP3TJoTz2QcuuJgd5+ddmup3Khc1yKa2L5G6/GEhTFG0XXDQCFDB\ndKxmLMfdfQqaN/WVWIia2Xs+5eKn82Qet2+VPi/5mvdnPOfgTxtB9Lo4mTePgvM+AtWxdnRN+/Jz\nOe69G/1EJVEZx30zWYezX7Icd0YnuRZbcaSQBFRk9w5UOqZpq9gwVtzFybyXPn3Asz7A4mU6xCRs\nHNpfiFcSIWhp4La+nIcfdUP4llRQ5Li1HEyftQb8qbjEIXc+B257IcSVdjxv15U1qcvxXrqW3g38\nyLpWHSdPlWg+/Trgf82d74hwPKAPv7b1oQpUiV1faddepCtVcsp/wjtX/qHHce847zWpnfyVT3sX\n5/+5PYRbhwlarh23Io6qTI2046v99h7EYVru7Xeeavw48etL2wWAOQBuEfk08D1gs4g8ICKvbhu2\nLi+SunxZTUQkAAAgAElEQVTYuVXGQZYAd+FYkG7mgJA2ZFMpjZEBXvCaYmhRMLF0g3tCOr5Via3o\nPwHeU/KdLKrfW/LfWN8r1YNRBw9qxVoLGhWziaa0TnyLm1wRBblFv97tuMueF1KmQyeIUCHOLQec\n+c/wkhf8Yu6d3hYni6e9ZN/T0q51mzTZMSXw20FSsXnMm13m4lPGP0fTWZhzokoqChQ5q5KyNLn3\nisCtPEC3+bOochbwLzr2SUjC4iKcKEFUE0kUtp0OPeaGyBYnAe6+FNbcXJG6/GcuJr3bRhJo5U7T\nkSRkfAU0RiCptAOiUuCWupxo/gbA3WlbPfOflvGbJ8LSu23aFAfW+D68NWXg1vkQmeVOBtyNkXbt\nVKQur5e6+Ebb//HJTxKVLk768dQOLHGObbNtSl+vv+FNPOe3bJoMcDcDZwMUTC1exg9f/+x2aQMc\nc8AkojloGpNqp6DpNbKw4N7Xk7mwKnmpUmqtUqqmlNqglPpEh+C/DbzVfb23j4TtgLu7xm09qjWG\nB7IR2PmsJNAY3QC4u6x0vK1v18gGh7x9bfHbA/uyOCrjFbsuWrLtvCiVCdeMsEwk/Zuvc8k53w86\nAPdt6WKOX4btDD7uv0BrNlqzvqbgy+Li98MTvpD3m5ECt9RlXboI1xtw96Jxh/ax5gdTH8q6U8Sp\nxp3/QL4nG427kaXL07griV6cTPOcAlpbXxPK03wTRGnf1f6hEZABbbbhI5qEuxslllVKgBZBHJtv\nxN5i9MFVS2kMa7BrDMH33gHVcUEfF+fKQZvIlCqx0hg9AxXq2VzcC3CbKq3LCuBOUx7+2aeR2a25\n9PLMa+X+9Tn/IRLi17k434nT+hhf0W6DWACcim9tlrWtJJ2tI8Kv8+UP+xtsJhc97gN3kqV/70a7\nPyEzQQ7iZbDVHL0mZkbWo+WZJEFhplOUQS/9PUV86CXfIHoE7oJta5j764RNd0FZT1z2episDp3v\nKms+l9nnfquuG7zvqyQP3J3LrjIRkoTlA8zEkrw3QogmbIftZloUFDXuln9DUyXTtCpp89mDx6wz\n3wA4rwDc5SZXkfPdrKNK4n9k5hq3GREVvIQPceq//wG3vWgM+BkAScWjSpw9ADlLCjOyttW4DVUS\neBp3J6UBXOsORaA7dphQ1LhtfR+Xho8mQQXlGje0ELMVXwWJ5+Lgjudfwa5TNEcctCAus6oFbBsW\nBc1Bv/C1tq+NsFTYDrjLvO3VnGe28AxwT+p4Vh3IwHJimb/LVmvcbrsIgKFU2cmAu51LhghS+i+r\nk2gSs9nFjfvvuefSN+TeVwTOgRB6dqWvJ5bbQnaAuwVs2ZqFTzAuEcrEt+NWMkCSbecUYUikYD0y\nf4Bb6vKYmUb1AtwlVEmY57jtYkCx80yN2tFaNyircYcNp8G4rENCwVD/0TNeCUD1giZ6q/P3gc1+\noC4UQzQRkUQF8BThSUwsy07msNN9q3HvOe50q6FKXc6QulySS7SncUtdnsuiB/20dKJKTkkdtNvn\n/u6uvNiZe9CC2r4WT8o57Ismy1qYS5VkhZundCSBuIAR09K4eYLx+ZCEgh2UklAD1Oc//moDAOeY\n93zgVinH3Z4qEZXXuMu2rrvpdABGKkhiDx1wvTJCBnjOFu1J2Lg4W+jMog9AtTIfKpJYjVvqIkgS\npgNo0CorUyuWAoTmkF+20SSpxt3OlrlVK+NdLfWxkrzGbcv1rKWakG9VIxrDvsatgrw5XYDWuNeb\n5/ru1OJOwP2Y81tLZRyaw2i/7W7sJUfDuRp3EMPlr1kjdXk9E2k3zQO3SbtooM/s/H2x/l+UIMJi\ngjhABdk6g3aYl8dgA9xtqRSdjE4P51CWoSuzAiB12S11+WN69VVipzvF4rHT8fdLXd6kbxmNe2yF\naVBWq4od51QuVZJq3M49Mwg+8ZNDjO6IgPNLUtVF456MUClwuz3pd0u122hSd+Sg5fKInwG+kfte\nkFuc/A3WX+d3tE5USaZx5zTHYppEuJipRStNuuCMT8GGnN/0aKrdBh37XSex+cXJGFqFGXApcJvT\n5tOt5yShv8CjwgCrUSoD3I+feCUaBOzgkdt0kgKMQ5U4VWU1bp/jDp3fZZJxuCqogAIVKid8XuPO\nTAm11psbXMRy3I7GLbGjbIRIbJQPBUHi58EX820FrcEicCcWuKOK1GWr1OXtXpip0TITNZvfFeRn\nFdZ2XBKtqW+/5BJag77WXq5xD2I309h1oiTqBNx2U1kWJmymMw9vk1mQd5srOOdtwuDjcPIXXgb8\nLUlk05UDbvd1r3348sJXP8252kRlbILWoBuB14akLt/Fzmrmg8ZtxF1AWY7WgnrluHXJNEbzngFt\nJ/pdMl8TurHsOd7QEWIL3Jmy5zju/OqzHZU3/PEIF/1ZuwNRU0AUQURyq/rRRJRSJdX9bgWV17Kd\nVpp+a7yo+avk6V8P/2qFFepOGvdPsVqTfT4odXl7G437Oxw4RjtPClrl2nW2OcXLjfNdZxpaQpUU\nQUaX2bN+Gy76E/fMwL3O74s1VZDa+GLK2nDApndJAlfJPuBXTagcDZEuTro757LHcSVh43fgqR9w\n8+Vp3CJ8gz2bMvtMtx0oq3FHLsedB+6VaVqjSbj7oA9sotDorZrGLzg0RoacNhvpNtzQ2qIF33LJ\nKKZOGrfe9n0VerdyJsMlk4HGkO1XK3GoEhF+yt6NepZ6++N6n0RcqRLXumncluOOWHGb9s742En3\nooIBD4BdVwIPXGD99qzynichJIFK873srvyBEpCnSp71OxBXTTsxnUJaI2kawybaNt0kNeigcVfG\nq2k4OIbKRINmOXCbNYILSZ2VzQ+NGxyN20iT3jluq2nkn0RSF7v5Q5tRJZF1OZoD7thtMGUcdybu\nCJoDG3OIAPhl9yvktbnz/+J0nvubWnsefchVK9sBt053JaXAKvgzEkfj9pJUJDQv/gCM7vDNITOx\ncdpIzgY+1LazWw1j07fg+G8U6aGwUdbAyjXTssXJdlTJhR+C8/5qlQiDIryDogdCfwD2gTtO4/cX\nhnOEvCpq3G45FE9Vd6kS+/cSxle0OZRWoh6oEj2Q6oERkpKTDCzHXRlXWVgHuIPYAHdLUz3tgTu1\nEKJRAtypxp1SJWm7TRdjJxc/ZtKkH0wtssqUq3GHwKmMr9B9M0wcbVn1xHEDIW8+FdbdCJNLdnPC\n184jc9/7K1yVNsWIO59nrZqyuCU2g1Cg0ny/9STYUvdNf6dGF7F/nX8vrul2Ytt27cASyjRuZRYn\ny5UX2HfsfpNHnc6gSaZxF3z92zIwM9z5o3FH+CAzDeAO263EhmgnV9Ac0OibmIWVxsgi+zIAEpcf\nY1TGcduFH70xoZyD8svuGFblnCNu/M4JbPyO/vbwo+40LykdTK2nvcq4vVNlfJm7iGlf+mVW/cR9\ns7javuJ2eNLH7bZ0H2xPSz3bvd9/3maAt2Vxxctg85eL3zILo/LeyhOlLj+wuaFU445717jBto6/\nB/6sPHEq28ygaSkD3HbkLTSvhggniKDtQi2ouxq3ckaDJMr3niqOzxGxR1BVxg5SJkoqxqoE2mvc\nukzFgO+mZQ+VRYQkLSrjQhI0CJvuLDFEEkU4ZYC7B40bBc3cifHhVKZxIxUUmaYKsG+9pi7iypj3\nXlx1bdz9xUkLfE8YzTa4BK0RHj77MX74WqO+S94cMAPuNPvBmMdVJ4G73hQRTlk0zWbHQcvJjxNX\n0PILJ65VyVtgtgY0cJ/0VX1d278YF7ifku63kY4cN4mwe/MexpfHQEjQkrRN6XS4fXNaWHz4gPtP\nHttLUeMuDitlCyNJwRzQSkhzQHea+y/W206txt2qGeBOqZI2wD09jZuscbiFvp83nemH2rsxa6y1\n/S7gJaUgGU5WiCuuBUOV8ZXu0Vu2rt7Lpm06aVoLKjchaIzaj+TLM5fZDouTFkw6iV1wvOEt/wZY\nzSUChqUup+D7gSgCd+fFSYBjeNr78mFMSHfiFATpCTG2IxV5wgaaVrMrrGb9w+3MjqqTVPId8gtk\n3v8itJYJkpS3z0dPP91QJTY8wB+Y02psm7BrG3oaXrAqUYZnVw2qY0ISjRE2MmXjzx65NtW4JQYk\n7k6VJBS81Lkatwqigr3+zjM3eXHYQTGu2pmsa3xgZzKGaoizjwWtEQ6sPcjOM1MNhaLGvQyXplHi\nD4xx1VXkIsJGCXB7GnfWBptDxQZdKPLA71O1/ZmpYdCCJ/6TTbYiSDosTiYhrVoLSYTLXncRq25d\nShzptOuy8R1/TUMOH3DrKUYvGnfZzsm4zaOQ7ZdoxJxcbADIaNxWE9i7UWuegdN4Vv94ndTlKzrK\npAjcluPeTlnnt/FkZbfiVn96mwTgnuRcGc8Bd4kMPzrI+HL3TtXT/spV4grtDkBoDJWbrf2YNcAH\nnDtB2+ifcRU85e9Koy+IClzHUxHwQuBW7/v5lfJyjVu8X9F4i2e+Jx/GfjTjuHUn1ZFZzrJYd2YF\nL/2+7jhhG407LgC3u95RweZNtfGmN7V4jZNH20mfjD5Szde4g5b+d9fjua2pZjqOahFNCElFN9bh\nR62zp9NBhQzsh6e97wBhY6I7VaL0kWyWz29VM45b88IRrZpfV3s3LjO2/n7kKuwA3EbjvvmYS9Lw\nYXOYVrVBUmmnjAlwAZklEIjKAXfN3/DUGrAz02xmKynfn1ElAM2hvBMvCho3yl8IrR50gLsJj1jr\nQ+u/pMRSBbRSo6IYUbD4Ps3DJwa4tbJg10kECEhCxcevpWTDYTHqbgHmTKRwbl+LXq1K0i3vBZwP\nOXCM7kyNUWsKFpl3RkUQdp36IgCCgiPc56Rx5hcnXY276B75ZfZJeufJH13vhWgNQOAgUnXcp0o8\nMXlavGOIfa5XSqr2odTl3/BPU8nCqDYad1K1QOE3wskVS3n47HXOHTPglTSFwceL99qJH9bVJNxp\nqr8ZqbsdNyy/o72bTV/jJvWlbK0E8ou2jaEQcE3arB23+82swuNq+/b5H//4fOCP02+XSTjVcjTu\nvD/sHHDHGhRUfr+AMlx90iKaDEiiJnEVBvZaAMjK8aIPjhI2Rwrp+fTn4eZXZvlFwe5Tmzxylr5s\nDfgaNyUad1IZ0ANQ7ugxFViqJMDW+1eM9810QKxmZR40h4hrUymAOTFJXb7P8f9dxZ5MYyWupJqV\n1CWkVXNnVC/mwFrrVKxM4wYIpS52Jl6kXfMatyS+QUJlfBBX407blVFE7r/4lSKcACDCsca9L5AE\nqCBBEmH/et1BlAfctg1ouiiJFA9c5FB97eVIatwt2nDcIvhah22Yxd3AIeMrdZyNYbswpU+Z0JpA\nkBZy0Cy3DinXuPXfTcCiHfk33ke2SLJS6nIcm7/0TC9Ea4D08FWAoFGTunzQ+DqJC1ZxwjpGHxxk\n/wb3dtVRPn+ZqZGtJamv0k7jHty9mGe8B+BrIqk3Rag968ncfvkr3NwCHlWSbukt9/VQLu2A+/o3\nZ0d66TaQbU1+0ZVl3/DNAZffUT4w/dffjPkct5BuvrLUR35gePhJ5wBXsPZGQzMpa7bmfrMTVZLJ\n7lPeCJhyLD1CD8JGDAqSSFAecAe0o0pOWHW/F4coa7nQJJoMiatN4kpmhRS0isqF276mRuD2y+0E\nMCuXn71gkmvfpcPEtZxViUQFM80krGnzOpUD7nRTTgCmnd1+2ddN/nVC1hmlfPkdMLxzDXF1MqUM\nsngS4HyOvbZKfiHacs5alpBEui/rg45XOvWc1YN2X26TG2I32AX5w5ilWH6iajSG/tfkOyaaqpLR\narAupwPq79vp8n+AcYInSUgSxnoxeEQPyJOLjAF+4g7eGrjtgFvccFiQwwjcBY270+LkF70rOxXJ\nL7jvPTbTLuwWXhVEtAYAGQazc01/v9wOVJTmC1ffPJJtzXamPid+DZLAekt7B9q22ibkfmA71YMe\nx6E1bseoP2zV0OaK76Sc497B8KOj7MsBtzttrx08Nv8S/mIZ3H8h3PgGzR1u2nYaT09pwsz+VpTC\nP/rOW5yUsPnUNH/lJ4iUS8WlLB3g/smLP5r+Lg7eUMv5VNJacSZLt5cbJSdRUNS4jc+S1Ld0rnlt\nvBbWXQ+vOxfgIso0buVw3HHb6by/Tb6txt2IU7vqySWu5YnVthLsjMhSJUjJeVsJSNwgmoqIqy3i\nKoRTxkg5pmBH7KYnAwPS/IrSg5x9FluqJEjactwqHDDrERndAiAecP9/+pdJT2CA254kv/JnsOH6\npxLXJomreYNo/Uc7MfOBe9O3bnGulpi+DbV9ALXcAK0vzvuI/m6SDlgGuJu5gUcowlAyyE9/VS+A\nxtUm0UR2iHfYzPJn17/097M9BlaCJMhA2Ax4N73mAf7+Bosxug4v+kAN2GA2kfltsI0cbqrE5RDb\nALcCM52VunyK8/88Wy/KN9AvfOxLBC3tn0FUBtxxDVSwCAjSTwTNNsBttjSv/aEznTPfsfzp2CpL\n9D6Grhjb6Eynaw55vj5aA/5uvCBVXzLLh7xUJipMeTPEqjMnO8DBVSUvUcMFwtag1oyAlEL53SVw\n1tUZcO96WDzgvu6tmkoS6zM4fkL6zC5M3vjG0iR7YupGBOX5bggSjyopyMA+/7oxGnkOv2r7y/nD\npBIUOG7rs8QOmn6H1q5nX/Qqe90k64zuVtqsfsZXZANyfvHWavVP/nsYfegYysSGSSLJaXqCrrsx\nXKuSoAm3P5rf5GIWJ5MmQSy0BmKSCkSNGs1B5VEl6RuB/z64AGaAOwhT5TmumIXRdGEzLNG4qyQV\nkMLxc8U1H1uEYVNoDsDunKFMqzrpOAKz8ei/4ysUeeAOEndKHKEMcOu2U/XynwTZwKfpErs4OZCm\nKS/58hNVY2rRCDtPh8nFB83muIwqeShVNgxwK8iwzC2HABXG+tCPJOK+i++jMdpChb7G/YytfwTc\nipqPwG07rUp7byeO2y54vJSz/wFSzSgX3PKCcdWZ7khIXLWr3dkpKWFzgKmcfxudID99UBwgGiMH\nzC97ConP2YTNQaactqY17gyw7nrWv9iYaZvnJPS3W4cpxw18ts1ZdSGeb4yA1P8GYs5T3Aebvulo\n68rf1r37Cc/TwY1lRNjINjlZGqMNy+SnxFMUHdtt10KnZMzK89BTiyqk9A1CZaJ8VhZXQvJrt52p\nEj1CjKYgMpb6oHY7s3Lqx/X50cqN+1ZLv+z1MLyrdFRFklBTOIFgN/sAfP5jv4OS1cSVnRSsSqTE\njjshPcC4OahSS5zKhPDWzSVUSXuNO3OOFYTp4mRlfA9hA5Q0U+A2SkS26SWoeRp3JvkdoVl69hz/\nUiaXFGdVYauVud61/c8kJq7krS3At7+PsPigLRN94J5alFVaEmVUSWw25knL17iRMuAeJK7C3/wY\nWoMTRFO+VYltx3ax3de43TWcIKVKIDSAnOhdvw5wS6zXBHTZP2522naUw02VuPxNJ6rEQVixIFYU\niXVBxjWQWInwDA4csxEVxMQVvU3VVookA47vAScOS6Xozi51EY/jBphaZG1Xp/A1bvNuc8jTlluD\n/vRb0k0HGrhLTe9U4AFqa2DQmZ3cUkKvQDbl1qICPHPCNJTjBGfNisTbHZhO+1I6ajGA1OXDqc/n\nlgHuB88pOwVeS+orowmuxuyCSpnGDTA1mnEyzUFnJ53YY9iKYqmSlOMOSBfOyhcn9WBbs2OwY6cc\neFpYlmDX4qUA3K3ufSdohWZDiyAOcB9c80waw6dz13NOzNJgqJLNa+7iI7f68Wjg1gXcHFIF7r2j\nxm2ypkI7sNm8Oq4TxHgblFYKdJnGrReylVRNeeQ07qDo2iDrU/DIWXDaLu8VBh+LiCuxF9ZK3p+N\nFt0+rD28KK3F6M1qVa+NtWp+2diBaHKJbsTN4dWF2PPKQ6u2OK37JGoaWioD7g22LShXQWgP3CjR\nMxXRwK1CnyoRtdyJbsJ4k+woh1/jjqu2k5bbcWsZ9qbLKihfgAsscFfsVP9b6I4QE1c0x23LU5Ia\nkyU+cmylVQ9aoKsWzHsml9g0tyjTuIPWAJPO4d9a43Y0kBSwBIg9EM4aja9xTy4ZJmsMzTY7YCPE\n0U5U4H4rQx3Pe1mO4w4bdkOAAe7YFtJb2XCd+boB7v95b/tFk+qY/zf7dvlvV/7yji+nvxvDmcYN\n4uwk9SWp5AGE1KGYde7jg0KeOw4QFRJHCWFD2HmGIemljcada4JhM6R6gI4SxEbjDgWX+6xMQDQ5\n5K1p2Nkj0mT3KX48eku7bnONkeKOzo4ct2RUiQZZMbReRpWooKUHXmmllFPYtK5+TzLxpIuTIp5l\njm5MezZltJJNTxDrQX+Xe8QrEDTjlOMurMN5vLEVM2gNxvp7BrgjA9xu2JY7O1SkJoxTi/Q7zSF3\nb4SmaPKDR1xblM5qkjAH3A7HnWGLc+EBd6ipEgWiIq8utLZuGpgH3JP59d8ymTVwi8hzRORnInKn\niLyrfUCT2bhqO1C5VYnOW0YB6IbfXuO2zmRSjlYBNDnpyyfx3ui6TONWVSaL3lSxju6r6TmUtQLH\nPbXI1lRi0pbbddYc9OLOc9yXpsUS0M4cUFSY07iHHFRvttG4fTVQBW4ndjXuLMyuRwKvYUTjGhnD\nlugBMHZGICc/+q+uk7EVRTS1G+qquU2E0oPGHVezBB37/e/yjjXPMC9DNFHOcVvKzee4rVZUZsdd\n5os7IIliwqaQRMYm2/mcC9ZFqiRkYC8dJWgFIEqnTWUD6Ybvwf71B7wFQEuV3PqQb1paHQupHYCg\nZYE7KGxIy1tF7Xei0BzvVdZ5FBCYthQ6NIoGbkXD3NOHTx9Y1wJWifBdmkNLSCJFXB0AHs+appnl\n3PG856TfTBcnzRb8B52F65tfeSP/9dGvkhjgzg86dg+Fb6JnaKLhBIgQpdG5UgbcuXqyM4jmkH6n\nsKGsBLiTaFGqcauwaYwLMo17x5h91U5n0v/wgFsJKmxlVIkkgO5/rYFR4GoTTlMBuu0N0hgu3yzo\nyKyAW0RC4CNom+hTgZeKyCmlgW2nzew3A9oDN6R0iUDSTuNuOVRJSokAqskxP1pOEJ/mxFfxeGhA\nhAjMZoTawcw8K78g9thJVpvQW1cL00VRXoNpDfhmbid9xQ3tt5K00RSokiFvIbdM424M5T2tZZ1m\n85dd/wsOUuc07sp4BsJhEwb3lkwlY71N3mqgN195n06jUy1W067kNW6nYy6/485iJoC46nOalTF9\ngr0SIZpssxc/1+N1+Vg77jKrkrwtqbYVS6KYoGF9neAd2tGJKhncU+lqdRO0AjMTULgzoJM/D7df\nvtPbNRq0KLfjts8T42htJEiVFCv+MWYarD5yW3ap+EOQlgHuf0/pA0uZKYmNtcRBVAAb/+cy1t9g\nNfsQuJCpxetIwrigISdhjSSAYfcE6xxwu21355kHGF95kLhSrnFbSw3f9FcH0us8EagajWEYeQS2\nyiltgVuUw3HXBkvitSWU/TywZgoVjKRtXWvc2SzQM78s5bgdu9o4QElsFpdDEzAgCfP5Nm0vAFiL\nCruq3LPVuM8F7lJK3auUagL/SvHUDS2WW8x8fwTsW1++qKNFA7de3OlClVTJtp0qEOUMq1ajjSuM\nrYJHnujGcA+S6OlV6smLAYYe04Vv+dPbrrBWJTEYm0tXksqk75yoEnfo1L4dd2auCMd+J3N2HVeH\nssQXQEfLD97wVCebCYt23Fmu1UoGjGtWJB5wV8czdW1sRcJ5Hz678Prw7pt0mio2bbpuDjoGEBaw\nh3bd5r7q4Wv1YAPYWYg/D9xx1e4KlII2aUUFuhw9jttaTZQuThY1bkn0qn/UcDRu55zQkUd+lP7O\nA/fA3mpXdwB6wU/voJGkmg500RSML/dnWEGrRdCCU9ffWh6ZAfTGaFjgZEceKYbebY2DTGBFQhIO\nAJeZZhWlM68kSvSWeaXbcWAS1hip8MgTdSVLUkGFSUE7bdWGSCoQNLMHeY17raMwaR9CUylwFxYG\nk4ySyERHqI0LIpAKU6OwxEy33DaSX0i31jRxdbAkXvvN7LcKE5QMpW1dhQ3Cps9xbzT1lppEtqNK\nVKB9w2vawCxOBobj9lKgv2VnQG10FUdmC9zrgAec6x3YxYy8DBpf59nGlJDmUDsfu+CettGJKrFW\nJeL4p1DSSrcrZ8BYoTkMn/ukG8OGdPtvBtw1Bnf7Jff4CbB/3RQZVeKXW1yZyvGKYyU7LtPQ3lW2\nOAg4A04SDmIbw0+v2FxKlSjJuEZhCokbfPOPS8J5jp5816XVA9k89tvvfoTKuM8BArSMtzSrhVh3\nAm6eqyaaZXf/sDR/Oo0RJMWRRYX+6QxxzXpVCwgb5dqHEn8AdIF7yQPGVaoHcEWOG0LiSlPvGoys\n1pO95PrGdoG7OQCDj1e6blDSnLWO0wXuoAlxreZp9Je/xmqD7UYDA16LIvIz1ep4SXD0CTB21qaC\nJHUHod2VZ4uTcdUOMi2vTlsD8JMXv1RfqApJYCwkrI4koMKaGYD8NgYa5JT4s8WDxzwFaKQcd54q\n6ahxG+BWUqExCovv10+HHV0gz3FbjTsJjDlg3kFkjirR5TWYLU4GTaSVadwXf+D3Moqsm1VJHHhU\niZ2xFzVuH7jbHoKVyWyBu+vqZyrDu3QrDxzgbu9TAVyqpHhyDiauosatC6mVgkxqDtjQh/fmD/C1\nDneyo8MGGHpcSMKMP51YDh/a8SUyqsSPJIkaXl6ETqtW5VRJEGvwtaLCjOO+7UVvKB2Fd1zwTudq\nCkli9h4HD56TJ5oz1W7nbt+OO5rMWtD+DcLITh2XKwXgrmRWP//01Q+z83QbR4uhXTlTyZyZoCQF\nlUcl4Wf97w3o7ysRglaVRgnlp4IWSFZHlrd1pZPGnYQhg4+PklSaRBOk+wDcTqPaAPfYajjj0zXO\n+etiulzR5n3KuP/MDloOm9AaGPCoktU/0W4/b7nv+DaxGeAerYDqrd+pkEzjDpPUl4tJRdoX4qrm\ntMcYvckAACAASURBVAvAXXPWJaSiQSjW5ZoYCkgFNh/+KTJDu/QAlYTwoNMc9TfXEQ8007CubKnD\nylvz7UbnQfvfiRClNe7TP6OfnvO3WcjmoN9REgPcezdpk9gCVSL+AK8HWadupGVmILpghncdx70u\nLZvG2WZxMjCLk0loBoUyjdt+2/w99MD9IOBu99uA1rp9+Ryw5xMR3wrg+skB09kCVChsx+l89vc2\nsMA9eRM0bqh6z214iWH3w/DgGOkCwSN74d7JMNW4F31Shw+aehV96of+9x6YhPtiiCYHTFov5PH7\nA8ZW5dPzMD943cncvPJE7sFPz91JNghtB3beN+49d7/H3x3rLdY8MKnjtxq3DR9NLkeh2A4cuKWV\nAop93qrpxZks/ikkacE2eNhxeL8duPeBrI727Ak4cHv2/NEdI2n69q8V9t0FfDPx3n/kZ1qzT6IW\n24H9dxr3AgJ337uYB9JOdpAD31zm5XfiJjf/IXy1kq9vfeCv873r9+pt8pIk7NizjDsHvPBsh9Sf\nwyP2OoDGyKjfPpIs/JQ5JdxeJ+E5HP+tC7gnFh4cyzTTh3dl6UvCrD4scG8H7qrGLN0uPPljxfp1\nr8MG3JsID+/VM75WTT97YBJaNa2puuGDFjy6b23mqJ/fcp7r/O56cJgHx7Ke3b7/aJri/oaY8k1Q\nUnHC6/6wHbh3akADZdBiz/YsvtYA7Ll1VLdPVUEFMfc3gGtI/YA8sGc59yb6uf3+5A/hnavgpC/D\nrkfhMae9j/0UOOUOJpbqRvPwHj/9e+6DJ71MpZTGduDr6DWPeCDks7/wSu6ZyhYP8/l/6IBk9a9g\nx4Tw8SvfweL79YHFux+Y8MI/OAa7HI39/qmAR/ZUUyXl3r2j7L59KRYr7zlwLrevs/Hrbx37R/C0\n95kV4a8NpOUvibBj5wj3N3V5E8Twb6tpXucMWF/fzXYDHg9MKTj3Rr7+KFxDR5ktcP8AOElEjhOR\nKvBitOtLX14E/NoPQi5YBBcGyvCSGuw2kfGUmN/HN8EC9ybgjEcD77kNH8RwzBJYudpq3A3WjsJx\ntf0pcF98ow4fNrSGEV3of+/YCqxaCeFUzaT1J5zQFMaXO9/aAjDF7Ze9muUnruV4Z6axCdg4fDAF\n7k3AccP3lKYXgNffw1rHTH1jpOPXFTnJJmD5Oqge2ACiy2r0rN2F+Fo1bQ6VxT+JqBZsgY21KS/8\nxo1Zyzz1uBZDjgvatSsaafoOrIOTDwKXTHjvL7lAz0njiknf+nRZHV7/AKuX2fLYy6lD93r5XbbR\nzX9EeMlYvr6VUtu87z3hDE3aikpYv0SxYWC/93wTWuNWov3I6WuA0C9vlYX/2YtekHtfn1qzftEY\nJzZJ1a7Va6Yy3jxspOGttrwJGDxTg04Slrdft31ujBLWLIsRFRDXzHMFcVU7bXLDH3vtdbx2zz+Y\n9gawk4lfutM81z195Axh/WCmrpV9376fhLChliiltpnt7FUnfAhqnE3A2uUTKVWyaHMWX2sAVh97\nkK3PgKFdw6iwySaB4Km2vBXrlzdYPwz7Njwx/f6w9rDM4B5YdmzC6Q57OnryPUrd9kXGVjf5ux/A\n+qFi+i/40RhvOz67fhaa908iiN/0Uo6vNVP7/nz+l20U7/qYRRBc+XKO+7bewn7sQOI9XzsCqx2P\nFRuGWmwYCNKB4bilu1h+ivDZT78FgFOSITbZdX+nfS2+15iVPXsctmiPf5IErF23k+Mwi5MkcEVC\n8DQyH+LPmmSTaXvrh4AbLuPSNfAMOsqsgFsp1QLeDHwN7cLz/1dK3db2BW394VIl5XOCVz4LvE04\nbWRgr9ZSWjXLcU/pxUma7kY1QC8IjTxyK8vufId3X5TmxcKmXQDV2kd+MQoqJBUx06Mcxx218Ezs\nGl3sxNzvO9v5JdGA+/gJsOyeX2L0YW17quRgYfoU11Ru4W4SlO7QYTO/4uNuQSe3KJblZWJZYPw/\n+LRCXNHqjwp0+uzUUtshh469806W3ZUVRHPA3akImirpPg+8/bLL9HeSBFG1lDrx89TwaQ2hwNy5\nVEne73cSaZ7emqdmpnFZJLGzwuy2h3suPch/fgImys7OLaRTpQVmyylsQlKt5nzGQNjK7xFQZNRa\nYuIYQHqkSpKIjCoRrXE7CQOlF56Ouelm9Bqav3++NQDHXqutjBbfvwgVtIyFhCkvUYDORxJlZqSW\nBtDUpPLarrt5KInaWHkU+r5x1Vyx1E2UWjH91c/8kF6/VaBy3yhsec+1G4U+lCJdnJQWk0tOZv+G\nX9PBk2MZW6lfcGmeLF+ZV069qa5hwtnFydU5jns9zUFNzOs2OHU4OG6UUl9RSp2slDpRKfX+joHj\namZjG0dRW+DW0tWWkUU7YO0PfmDsuAEapmE1CptooglA4nTHlhVJ9OJk2NDAnQTajjup5CgOKqhA\nTKPMcdwVnxtMOtqJ+enyOO5Yv7frNH1vZKcugyScLFSmpUoyyRZIi5YlWXoffsw3O0xN54C4UjGN\nPOcvw2zD1v5fAAV/+ih8+gs67myR7REWPVhjYinc+3SoTMJTvSbR8VytLE0ph5ggSY1WtbhgJ8l+\nlMNx+7tGTRinQ8a59W0VDJn7ZlBKF/GyMEk1GxldQGiM6K3h7TYHed8RlfpXtmkIYqM9FpZuKnwO\n90QOlQ7GFl3i2gCFEcrI+LJ97Dj3pdnboZuvGAL/g0FL+2+JpsZNusaQOOM1WgMwtFuDyhmfPgEl\nTQ+4NXhr4A4b0BiGe59+MK0HDeiKh50t79kak2hrlPabcR2J0viGdkM4NZQ6NctvVikM0KHPl9uN\nZqmo3FpIkFCZ0H1aSzPl9XWqB3nogFlPK21flfSvJAFJ1NI+U6SKIgbWOL5KckkxwH0YOO7pSez4\nTUqiTemJJWVS21uyWyYni3ZAbd+UA9wtHb9qFkZW6/0s79yGnMbdGhhG4qwB/N8D15uAuoHqRpk3\nB8wBd0c7MUlBODXtBLOtWQPFY5th/7rMOiOuFRcs4mpe426k2mJ+0FKOD+VUWzLiatxJFJkBxP9W\nYk5NT6LdNkLGV4J2axI6neVhBvaMsH8dXL2tLO9h6h+kk1h+U5IEiavEteJAKMm+nMatSkzLnDzk\nOvRdz9UHCKcHw5q4PKuSqBy4p0YbJGHeI2IbMYuT+ltOesJimiAi9uy4XVRJ0veag0WlJgnh9svv\n52PXX+Pdy86YTnKuZXHcT+hvPvX9/8SS+65Ln8e1TNEKm6KBO3aAWxSoauqkKgmhMTKSadxRmcbt\n/I7KzfOKkgH3898EQ4+v4OZX7eCOX7y2MGtx49d23OIDd7OoOOWtSqoHoTVgK7dZcOSVP/ZNp83e\nswmoIYmgwpgkAFF2s8kSY52TpcNiom4nh0fjnpa4DbcyeSlrb9rUNuyK2/Ne0oqy+H6FJKGzc9L4\nJpFWwewnmgRUk1bNH+LzGndcGzIakeaXrX0vrGFy6TkpVfK1D7r58oHbUgrlkh32m0TlVEkSwuMn\nHJe+Mb78tEIscU2lPJ/GwlYHjTtr3WuXtLyO5B/bFRpfz7nXJQaO48D6E1DyGW77JXeXjUuVPEKQ\nrGLRg9spl4jiGZ5FySyEEoI4Iq4WVVtJ9nl23NpWOh8m+53XuANjN5ZUfKpk5W03Ze+4wO0cl9UY\nbWitqTfGokCVgNYU86ADFa7gOudagbV2MVq2CiGa1PblH3wYdp6hDzv9yG3w+U/sxPO1kjMHRHLA\nHdrRSL9TmWiAypC0MYx3OhBBg6BAlVRSALYHFdsJpySgIsUah1JyNe64UvQOWS7fA77qlde9T/+c\n+pcvPrVgJeaVqTIat9Md8wqdMbMntsAriuoBaNWMKYwUgbv65Bbf/v1bPJvxjCqpoH3OV7XGHcSm\nn9fMzOutVCbe6AG37a+iUKqNL6OcHGbgLrfqK5XF9xftifMy+Bja30TVaokauKPJ8cKUKEhA1FTu\nvDrSDThBS/eq1sCgtnU2FZEB9/OcLeUhd/5iFkdcbTH6ULYrsHBuoJ+S9JcL3NpZ/qR5HyaXZism\nzeHVhVHY+lAGjFOaVupVLMgfpeQ4+hfla9zHf3OtE/A9QLNAAaigqa5S9ynFhNqavJiHn+KODJGn\nccMqrAb3lb/I570MuIsn+4itOxUTtCJUUDSvDFq+xk2Jxk3pVFaL3Zqf8fcmhRMTzjvZ78TxHz22\nenMKGKUeJ/NpsFRJTtsso0o0x32Jub4TwQfuJISBvRfx8Wt/mYNroFXTjWDDd38NeCMucCtH49ba\ntf/BuOLuCDZ/TVqve8sdRuMO+Omv2Ah1uKCFoYoqHPftlSQVYwduzG1t2a67EVq1xJ+NpknQlN3w\nrqJjqCz/+qtXqfvVVeq5rLl5m5M3XTf5wa9IlQjL78iuSzVuRXqIr5KEsAUtc5YtKgPuKeM4c2JZ\nzAMX7PQUpLiiRPhftEvaMSxwE8So0OCLxErxV7xz1dXZrFZlHHxmekk3OXIadzcZeqz7yk/tAKAi\nzZ0nEI2HrLn5UaLJsdJdbZI0CsdR2Q04VuMeW7lIa9yhMmZmWctwFxXcBjO1qAIqq0VVvuJixAFu\nlyqJM6pEe5TL3kgqRaqkVVMpwOrpfYsVP7sHeG2RKnE57sfDdg1DKT5E2BwraNyqIxHpeJljD/ok\nEJ3/xzbnw0ZIEppFvRsA1FXqPwDYvTkDnMxRfcLwozCxbE8+IiTe63PckvCVD8Ptz3fCdNC4ratR\nZWigtHwdnsjluCtjWRlMjWbcaqMLcEsiKcftu+0tp0o+yZOV4lvqKiXqKnVTgeNefse/qT/dNcYD\nF2lV1SoJL3r1d5XiLrLFzHs9jVvnK0+VWJUxA27rVlZU0zgtC9JFWLvoveF7ubWAUKVUyaIdt6fO\ntwb3wE2vHufhfW5Y+2tZmv+9G/Od9XrKxG2HlsY69yPaxcYNb4K/vckvY1GgIuGZ783uDe/2G8Ki\nBx7QdukWuA1V1hy0s8oMuO2ZsHdVYmAq5wo6BOyu4wy4kyAhCfUGrOwsyTg9tT5s6o1SkC4lzUON\nexrAXdvXneOuHhSEiNGHb0Fixa/+yiiiVoFqlk5jJZkqnLxhNW57ztw9lzwdiR2uM6ijzR7NApjx\nmuYC684zzvbmzarNFnUTYcZx5zRuJNP+3EWXuEKJxu1QJRFATGWioRQf970BAuIupuY07qI0C65U\ni8DtJiaEdJG5iT73T4fPn6KigXsgNd1zZVs983FjPxc29WLUwWOKwB209uKq1EoSfvQquO7t+nr/\num9w1tVZ+AJwGzBJzCiRlonKgDt2SOwl92V5boxm4cv4TldESdqWXKBuR5XE+YM2UqpE/73k3f9s\nU2fS3fSus79neBy3khjXuki/axOUOO+a95Xm8YNW5pTMtquXXe63IRXGDlWSOK5zYXxF4jWXjFJY\nnea/VctPldpsO3YHVaNxP+2PtaOWxzZrF7IFzjvsjIKSBB5wJ5EO3xi15ZoBd2vwbmADzaEYUQ0P\nY/yZ5BhwFklY0xufDHCb9KurTJ1qr4/+orV+0DHJcLiBu6hhZHJgTcabtmoxlfH17QMb0dxVjUU7\n7gSEzV+2fpjLgVOSKZpDORAyGrcF7rAZaKsGbUOtFP8OXKWDhtmU0AXW1oDhEK04p3DY+ty7cSLd\nOTX4mHZQnOe4lQHuZXd9LeW7obzcksjRuA1VYjtgZcLXYFyNe/1o4czLnDRKqJJOGvfVDn5a4Nb5\nyLtCBSGIh7zt6fYTP35JRjVZ9wXVg6OMr6TUHDCa3AMux205YBPt2Mqh1C0tFKfQqRfDoOW955vD\nOe4Afn8nn9RHKdIYyQbuqdEuvawHquQzn7kBDVZVXp3tvsmlxxayC7KdgNv6pMkogILPH6kW37VH\nGakpPcVvBmleg1ZWNr6LhzjtF0jiubtNooTVjh/8zNf0asf3TR6oy9ubS0EmqXZh6s/6XXHrWVGw\nOimP2AVunTF7RiQ0UuAefGy3ukrtgEsnyZvMtsyCcdCE6oEp4F8ZX6HPBkhCRdCqgLNrOgmU9m5a\nAtzzTuNuZ1Bw+/Mf5GcvzBa0WgOPUxkr93niip6mLiEJm4iT25Jt1fp+PEVroA3H3bROzQdLbIKz\nDRdhk8wUKk0vzjRoMAVgLTqiu579CKKE57/uXBbv0Py9cqiSIAbUVJpOO/KD7uD5ylSB4rTPQqtq\nO0OMbRhBMw90jhrSg8Z95idf492xViWZ2MR8VymudxZamsBAusBV1LhbBK2ayUv7RISpb/CIA2vJ\nTktxJJra65eJARzbxh4650IvfP5U8Wx9wPIyNjIHuGsZVbJ//Rjbnwmt6rdJot9LAaE12NmeTRJJ\nT9XxLCocqkTbkodoV8Z5Zxo+VZIH6OzEnKTwXIXZ+yqIaQz7s9hM43besb+lZajBosYNOUCUVkqV\nKIk9jVsFKqdx24slqXva2PRJVRh8fHFPB0rS2ZA/Y1p2p7/nsJsRkyi9NyMx54vGNV0mU6OWMsqA\nO/PWGSPK72MTy7S9++WvhXes0ZuRtBlgggoUQVzBPQRYGdrVA+68qWJ7ObzA3fZQ1aZvk9OqPcbo\nQ90NPBsjCbAUFTSJJl1wagPcqukDt9kb0RyCsGmONkqGTGUrx447ozCs/4Ukp3Fn6W/5i5POVDUJ\nFOtuPDZ7FBuGRRncEeu/YZK46nDhZTMVS10mthNlGndc9fddidPLHtrbluM20mD1j3O+WaUd9aMj\nWny/jTDjRyHTuLOy0h1cZ7p9j1r502xheu9xkJQs9tb2a43b5bhdyQ90iVl3uOtZH+T+Cx93LB+s\nCWUaMosjyCgaFWoN9H1TLwQ+l+ZJ4sfa5gM0MJRp3C5VottLDAzxQc7z33c06P/X3pnHWVaUd//7\n3Hu7p2dhZgDZZB0WFUWWGEXRaCPikqigxuU1iviqyesSTYJG1Jg7qEk0GhdcY4SgMaCAiLswwExA\nICDCsG8z9AzDMBuzT890912e94+qOqfOdpe+3bdv95zf59OfvuecOnXqqVPnqad+9dRTBjHl1sTi\nFqf4pUYlU3H7ebvvzu6NWPMtbp+a8nOqRKgSPya7FmGdtx4ttLhPYd7Gk81TA4vbBP0oZy0w8hR3\nLZh/iI6Y9nv0/jB5hpFy31u9A6u4175gPTsOhXrJNFo3Mpd6qLiLdhcirikRj+fj5g8OuRP6d5uH\nFqqKFmpUB0qMzT2EyMRxisXt2mD/rqauNr2huAvVOnV/d+1Zm5i3QagM7GbLMdEsyioEIS7nGXck\nLYyxYHU4S5RlcavUqQ6EL79gJ9Ers6Ew5hT3gKnUSNuxijvD4jYV7zV+T9G5Xlq0hhaVYiXsYPqH\nzQcgastiJzgL1RHPmyWd43ar54rV0KvElkE/O7oqJngxcl8zi3v2ltgCo0yL26R72sO+xQ2FqmnU\nzvc55Jdr/NkHYN4GoZHiPua6MJb45uPwFkOEmL15S0x5hLwhkJjjcHmsOv0mxuaNBi5izsop2h3J\n/T0nKYShBuol38ILg4qN7dM4tqtxT02ZnPSoEmNJVoEiI3GaIOYOmLC4A1rOKbKQE6/HLO7hA333\nWyX0NkpSJWZSzfpvpyjuemQQN2a+iyIgtYj7nRZjq4yNzKrcx0eOMUo2cLVM2W/Thz+SDemz2ByF\nVNh8rP2OKtUYVWKW8e709nYWNfvS3vOOVXz5CQwXDVAwy+ylZiYhF65awpzNrgeqQz363p1x5DtF\nBH7cJRd9Maq4A4t7lsvD/N931eqG9cBUKe7EjtmVeoQjrvWtY591RWr9FVa8mkwYxW0sltlb+gM+\nWTI4snqxjpbCxvfhY22kttlQdDuD16zirqnHn4a+voVKkuM2Xh91oK5lVVS8KH9Bg6pRLymFuOIu\nECwCcCsnpR6lSgrVbyfqzFdMxvk/pEriEO8rO2xec457YFt8Eiten79wJTNH/17jih+BU9zFMcM/\nLlxlZgrvfwssP2cTUOXYa1weHwX+ulFBANhyHNRLyY54/xUxjtsqx9AjJZre5WFol3pgcSfSS4Xr\nP+duCq3peuApVAdGg/c/ssB9qWcxdHpsc0UA9bxKPIt7/hM/DHdZKVSwilkrekMsgyyqxNFi7vnD\nAKoocJAqtYjFrW5fyQCj3kgo6VXSv2uzaZsajpgkgyoRHQ1Golqox5axCwd6nr3efcEkXUiFNRtl\nh9crc2KKOxjZVqnZyc7+XcMxquQz5t7I+iWxNIjJZ2CLG23+LTCPQtUo7lk7txJ8X68aJm5xu28s\norhr2MlJe95rlM7inrPJs7iDy00XCHRLcZv4i37830gpqoq/hXut/3H2ebJEvVTP4KiMYIHilgrF\nsaLnmpU1uVHDr5SFtmMz7oDW4q5lW9z1ollcYFaEhVdNTIg64bAttvrNSGXdpuJK0dw6sHUHiHnr\nUq9EO7L+5JL3SHyFGFUSh0RMPZpQJUew/4rvR86URiJ0gCofsD/NC73/reYvsLgtxz6ywJRny7Fw\n9fdXA8FQXcv6ay3rNxoVBIAdhxIE3fdx1NKtaMSrJPQxhgaKu1ABqQXv11nc/hygMxZKY5u8+30u\neSx4/yMLnaW7i7F5vsb6hc1XCCgsT3G/+a1fC61WqZD5LTaxuPt3GYVf1vCrV4ynhZlDCamSY67z\nMx6z7ppngjdadNTMrB2bOebai4J8IFwYFSkOgI4ajrtkDJhCTHH77S1tLYfbdchNEGbCs7jH5o6C\nR6sE32thLBzlEh/lm4PIpLkWIqOiAx5eBaBKVZVhipUxu9OWEqmnenwuImlxF2pGFziLO06VDGyH\n954Wjl56TXFrWQ2p5Cox7m1QqEYtbi2uZGB7gXpfPWNY7xbHuK+vQnGsYJdgQ2bPXUi3Ss2S96JN\n0m8mVNSPVRLOXBsf72iDcA02HLZ5vbGzBDBUSaES7Ymc4i5WlXAjhWpgpSlVxuZG3TwcvRI8v+Ce\nnd7w9380HBuu3dksDvr+iTPH//TejLTxjMyH5UY8e/Y3XIlzV4SWlslFMLKQYHWjj7lPxeNxW6ok\n0+K2E8ylqjcJRri7vf0vGnbah97mWdx9vqUbWtx79nWKO1zaDmhZX2/zk4BHj9Z7NaRKCoHijoS4\nBejb4yzAdI67f1d2L2xWDY5E0ocYBUHLel0kT7d1WqFewe0kVU9R3FFXuNHQ28pug+Zjvdfvp3t5\nmOdsO3K/tIvhvV6PMDo/xjHb8kQDZUnseUZDJtyS1Z8jiRpWxbERu61cneAd/GYWotHFDm6DmLjF\nXS/WU6mSelGZvcX9tumb9FseukuVOCWWsLgrGhFKaitMuj6NVXz4kQDBSsF6oUKhHq5sylLcx191\nG2m9mb90tTpwMPW++L5+phAB1ROnSooEi2BsjsE1n+Oul7ItbihSGrVvXceCGeiVr/w7tDQWoTeq\n3h6b9YKrh2yL+6D7FsmZHzcBxs2wMDVZAi7dCZcn/agNJPbfyW3qYWShUdzbjwCjHD5HuxhZmG5x\nGz42PCqNmDJGdhTyECjuvqo34ef5zgbcuAb3nnKJ737mJwwt7spcl6afzcftYoe/EBWj4KSWZkHV\nQqpE7FY5KVh0w3L7K93i7tud/TIPv/VGZm8x9RKft4h6ryQ5brQC9XBuB9JCKRgIIVVCIU7JSDQ2\nTiUZ9nn/R+4zJfIWM33nLljzwmg6f32ExjyNiqOmg9NCxe9AY209qbjFWtyhx0dMcY+OGC664lnc\nw2vpH47SYmKFTFjcluM21FzY7oqVEeatW2XKmFDcvWFxBwjohTjHXdOIxb1wtYnHXBzrDxR6ZeAm\n4Gs2hbO4XQamthx1kqW4+4fHSKsUP4BQaRRqfQWqs3yOuxQpf6rFXQif61vcYdCiGpXZ/dT6/Zn9\nXRHF7fhK0WpQH2bC8j8idVb1AsRdcbk/OZn9wrceY557+NxaE4s7RNihZZkChdj/KFc5ssBksOVY\nl0dLEYUiGF0A9b60++qRWCXH//RHQAOLu+h28anEjARrcQeMRN2LMujTVVGqJOzEXeJ5LPnSCh79\n02RJXX7Fij9yqoVeJWYXAyAam9yUqzFV0rc7e5K3WBkNlszP3eRzFFWiituz5tXzpqhH47hE3NW8\npjZ7yyYGtg/baIBVBrb50bcKHORx3OcdvjhRTrdmIRwxw/qTr2Qktng6OncV/cbnbrS7g0iF0uiu\n4I4oUixudaGaXR1E67Nvz2jc4lb985ewYPXamAxFznkFeKHjA+7cRSise/Nr/cM7+YvXmjmgQC8G\nvUyPKW7XsyQmJ6sa+dL69pgXWRzrCz6yR173QS3r+ZH76m5Fmh2bNVPcprE3VtzFUVPO4QN9k20I\nWBE04DSOO7R6nQWVeIrpeSMjrBVRxV118a4rgQUgtYoqT0ZchNaeqoHvdHXAcYTZVAnA7v32E+Eo\npKYc8btPZKaLlHggPjyPI50qMfXwM7T0awDrGeS7mrWOkYW+cvQR3XHc7Z351PFQK2lyN/I+p7ir\nELG4jQymPj/D3ef83LMs/cm4wFtDlXAkGPiBsw/x+ndtpDhq3tU+Tz7mXfWokgYWt+8lYhBV3KWR\nRo7KYWc+Z1OgrbSsfWRa3Pa31McCJd5sKN+/aztzntphXRwrHHpHNCZ/nCJKQJyXWHiL8mZmbY+7\nWmYr7oCOkgr7P/Io6bAGWIRnL1i3QVcHURK+b3jE47h9jjr+/CJHXx8Ty6NKChWo9/kVUQMW2rxs\n+pBpyyi/V+huImszzEJVI+6AbsbWLCM1FTTnKd+32L6koAdrR3EnW5+/UKQ0CnM3rmP4wKrjT1XZ\nCpwQdjwxi1uLWI8W51vrWdxBzMZaTHG/CXi1F460yILH7X31atBhid1c13+VV/2XN2RsgSoB2HDS\neymODvHEcIm+3Tsy0/moDsRX68WRqbhVOVuVm4D57Nnf5dG+xV0dINWPG+oUquE2VcWK6ZQ3PwM+\nW/l5QsnU+uw+loUKfvsIJjNraFnL3PjpJ1J9uu0b0GAxjVPcAYWy2aT3jVJbPX0jzjz336JHLzCU\npwAAIABJREFUlYTj6wTHHb75uMVt5R5rFLktbO+3nHd/7FoWVeJcUiv4brAAWZtgFMf2gJo9XU3Q\nLn/HXmFDRP+mdcIJxZ2BbMUd8O8yRrB/aIbFHV8Wb6iSdI67NGb9uGtg61JEBr1n2DxiO7ecfa6j\nSqoBVZJU3AsixewGVSIibxaR+0WkJiJ/1NJNgctO7LHzNmyNrCpyjapQKwYNab8VvuL+Crd9KLS4\nNaBKnOTNLe61zw/Pxi1utMrwwfE8qlGqJGFxh8/13QHDtmM+1FBxP6xl3RCxuOds8TjygvOSqMTy\nMY0s6A+Krh6yLe49C0ELh/PpAVi4Zg5+PI5GGFnYicVtildWt4yuRjuK+4lTvc4lbj7b/Hx9XhgL\nXOowAX6iqet9zre8GpHfKSOfHgnbZ/YHFLW45wG/xNE33lMBKDnFTUxxB9/6aMr1eBniits0WqEM\n/GVGKUOL+/GXbo15dGRQJW6CuTYWeKTUmyjuvt3DiJbMSDRFcUelSr5LTVrcADz4xo2seJWfsDWL\nO5RHYt5hScXtOO6wXPERTNUobi3if18a8zkv7Yl6XJz8fecOWAvcAWt9fv15ijuQIcidJujE4r4X\ns5vkjS3fccTvlgBJqmRg+x5qkTjZluutFYIKWhDuSKtl/RS/+TrB9mTOYgkt7qiC8KNyuUrxlXU1\nZnGLVnnsjGEOONDPJfygpT6GFi4Kr8Q47shH4UVni27V5Dod9Ybm4cyak7tQcyahV5KiBC/ZdBrO\nFTFdcddLI0jN7F5jOOHmlMWG58INn3WmUisWt5KiuD34VMlrU65HcduHnmTPQhMXVuMzjTa/QjXc\nX7AUV9yxW2r9RjlW5lTwF3q43Yr8j2b1S+Haf/149go+8DluLeuwTRutp2LF9NJFS2dEtxwLqZIX\nfMO5X9YTHHeYZ1xx7wDQst6lZf2PjFI2os/+DgK3Ts+rpOi8SsaIxwFBhRs/iZUlzKlYGaVQ29co\np9IYccV94EHBge+26ME8Z3Sf6Nlbz3uSH/7Wy6neSHHbBxRGyTY00izuAiamiUbS+M8x32eBgOPW\nZcR6BOavXUgcBUuV7P/w3VZxp1MlgQxdoEpU9SFVfaR5Sg/znzT+pUk6rxCL2mct7qp4vptJa80t\nn3WccmWue2Gxlxosga/jKiXqSRL+NhZ3hY3PHeNL4R67EV7TDKtCH2S304d7rvNgMGVz+Q7HGoxT\n6DCwFaL8ey2kSmrVSD4AWpwVJF1zGvzmwkdoSJVIjVMvPNo70cjvKPQgacxxrwJ+H5PH1Xtaep8q\naU7V7D5gjC9sLZs7Eys3TX4+u2Y2X3UnzkhxBzQKenSf0OK+6gdw5aX/HhbP/RC45WOxzQzjT09Q\nJeG9kWOgNFbwDy1CquQ5V24lJYFFnK6qAaiyRrVpGLnohLXXhlS5UZVvJ/Ie3cf8PvC+9cF341vc\nN/xT2nNMuqNuhHp/XHEXvOcmArTbjG1smybRQ6PB49It7uv/+Xbgb9zZWA5pitvGKik0VtzNLO6+\n3YnAPBSqZjJ83saNZnKyP9vijhqzPcZxB1Zmor0Vwy2kADd0LFbFGx4lP14JnO/NvaOZVImvUEya\naobiLo1i3cXqxAO11T2O23+GoU6EUGH5VIkp42G33xNrMI4rVf76WWCGaJ7itmZ4cSwptxZgweOP\nB2U3sZmzrSuVGqd+05g9Q5D0lYvApCtUod7nKikt3+OAc/ynECrm4WTyiMXdnDKpzg7TawZVAqEf\nt3GlDBt8/BYX22JkYSXwTthwIjz1bGNMFAIaIJ1Ljn9MmqW4IzDHxVFrcWdSJYGrYdscd2P4baKR\nkg8t7j37mULN27A7CB0RcNyZ+iRso7W+uOKG258Dm561Qsv6t6l310t7+Oye9FDMEVhdsO6UUeLf\nuOO415+yR8vqrTtoQpU4r5KQos6wuDWwuEVkMNEmCylzDYVKOBluLG6fhokp7miwzERe8awbXRSR\nJSJyb8rf65pl3BDJF1SgOhDtTYPwtpI9/FaPPwaozo4PK8P8wnRulv3h4Ko/OVkM/C1TlJV9Xj2i\npB1V4nmVpExOFqqVTIs7ea4actwVx3H7Hg4wb+MW7746hrqKzuaHiH/s5njbEd+JJ9SyutWPUJ01\nP+N+t7IsPnnn3lXaQhvf4m5O1VRmV4P0O45I7oCTpCWiHkPJyUmjuIcPCicnzfvYEUuf1vlfRFzx\nHXjPh2wefro6GyO7zFnFPVZKKVPV45zdhaTV1pnibjxhHcLluYdXnP+z8FzCq8SfkffvN+/popux\nBphvOguPvxS++WB0Na4PqdeoDfSl6KvoCWegLf3MDrKokqi88c7qQpZ8fmWCKolG/suyuAPFbcsS\nmySuJoKrm00S+k17M+6AGZOTTKziVtUzVfW5KX+/aHRfHCJyCUuBn3EitwI3vRBu/6C5OAQ8zL7B\nXpBDwGJeFix/3byyZK2qms1rMLRK1ETwu/+h40yKWXWGgFt4VvDwIeCx4AXW4G9OZAgojm0LrqtH\n06/fCo8wG1AYjD3Ppl+7K/R2GQKG7wuscBEZ5HfXh5O1q53ng/UqGcJZiXbSaDT0jMBGJFx57zHB\nUOzWtccnnl+/CYJGuQy4aoGW9TIt6+Wp5X1ia3Tl5eqlzwRAtO6Vx5wSGTT1U1G0WGQI+Bx/4l/3\n8ze/lwGolrXGEHAzT49e//b3gC8AxlPnKk7Oyi8oj1HcVVgGK685jNs/cG3kuq/AzLGlwZaZOnEf\nskvvdkzZ/cpTWbPezILV+4AznsMQ3iDkjFOtPOH7vYhfJ8o7d9MaADbfta9X/jq3nwAXLCE4HgLW\n7LGKzLZXV/56n/n9XU4FHvWfEch2G4si7eUSnh+v/wbHNR5ioT02S8+HovmLyCC3cIw93MlPd50Q\nlE/qFYaAHSvsZRVYZsps9NznGQJ+z+GAmVx8fOUBfnuC66O8RPx9P8JKltgA3qKJ8sEy71hN+9l1\nzyzs+wnaa7BgbvDUyP2bNgbtW8u6mpufPcKuB8Lshyjy5M56wHEPUYqU/6ecxPqtWLexcETkvs8V\n9puWap+9P/yeClVYt3QRD7DQRgEsePIbjnsIWLfdKO4hqyt/xDEspSEmiippyLWp6rmcDpzFPbwI\nkJfBzX9vLi4CnslwYHEvsuS/U9wHHrGLRQQxCVR1WWQCZxFwwnFm5VW1v8Yi4DRWRK4fjdsgsgZf\nXc4isDGQ7WTdYFjYI4vwDLbjTUQknvf0+YpTHIuAgec5q6Sqqst40Wv+J0h/RH/dPk+ol0x6M6Fm\nGsqRpWChj5bV/D7mhAeDCbTB+Xeq6rJgdLEI4HQI6nwQeGMQxS61vM+ob4scHzloh5J1vzzB/SwC\nSiNVXBCnfwibUTx/83swlGcR8GKWR9O//32q3AJUWAS8MdwQN7W8i4Dq7KoJmDQI/Nt9gWUbr7/w\neBRc+sFQEbvrWrTuPHfdxLFzVgEYi/f6JSzCs9iuv9XKE77f93BroryOIlnwovVe+etwOugrXHLT\nHo+ph8vpw/LW0MLVHIXyl/wOuBQYTtTHqayw6U0Bz+XmeP03OK7yLDbH6zfx/k6zpkVZK7yNm4Ly\niZr3Ne94uOxqePQ1fw+Dfnv5EouA59tOp9YPB5/0mN+e4Iy6/30lyvvfeiyP3nm5PYqXT2EwPJa6\nKc/s5wWj26C9BqtTl/3Ou1844ECi5XndDgY8J7ijgZPWrgxGFeb9hBbvG7iNwwYCizsov7PQd74D\ndr0N5j61wLvfoDgK+579IM9mPaVROPaa6z35jeJeBByywCjuRVZXvo1HzSeejU7cAd8gImuAFwK/\nEpHftHzzyL7EXJMKVGdHec/hA/dQGTChGbNL4WgI8xGZqGDn4/q8PQth25HDwM3eTeYDqKXEvwB/\naWqS4w6f69LYHEtRjtvfvcaVcc5TwzGZbdlTRr5SrwUcWmkkzaskdqLpcDis2yEch9+Is4Q5m7bw\n0s++EWgQHznAY4BnxrA7I10bVElkp6LEIpOgTKF1FOW4k4tF3DupM2ej8ZYxQ+adsfRpVElSHhcm\ntjHH/TXM1lfO/cmvx6oqb0BwsbgvABZMAsfddNgN+OHywqh7bmK8XoSHz4LLr4pzx1FngFo/VGfF\n5y/EjcialDOtPcaokrorT5HmVMnpwCtIQpJeJZEJVGJ5jxqdEOO4g52Tiubnob+PcGSAif5p9JqR\n7/Bbn/CuTh5V0giq+lNVPVxVZ6vqwar6mpZv3rNvfGa3SMUq7l99/UwALv7dTXxt5ViglBvB+TrX\n+uta1i/gGt9P/wu++thNnuIJJwDr/emK22wK27jBqwiRycmSHUkF3OlI4p55G3cxd+Mq74y1wtIU\nt9YDr5L+XXaCyG9YAtF316SONNrIw8U92bcU6o9x3G83ZCeI4GTgJd7xnox02RPNcYzNjSruqCvd\n+fHkBBa3RVy2gguCT535T1rF3YcGQZWCKnR5uOcXtaxJzt7FkI5z3B60rP8EzOH+P7+BpRfEFZN7\n4AhQ1bIaqimJ6zGb53Y6OZnmFOAwx/sddnDuuwrXLNSAj3hpo5PNtT6oJbaZa+b5QljGJvrKucaa\nWPUxxV2L1I+WdZmW9WaSkMCAqhe+gfmO4sGoooq7OOY2WvHOB3HOM5YZAH3Ddt1AcJ/fPqLugN1S\n3B1hZGHSl9JRJc6S2XnobnY9vb+xxW0FHNhmFEW4k4b12CgBBb8SSsE91VnJfQwBBnY4P9DIEC/6\nVIlNThadPK7snjtg8Py6t2zalQX6dyWtuVnbd4cW92iW/K1b3OIp7kUQrPpqHI3skoZ5elBlp2rE\nk6SZxd3cq6QSUdxh0K9Nz3qm7ZwNwmFwVHHHP6Ydh21n+ACAehBSwR8Bzd3g6Kao2105EVbVXp1l\nFXdfpuK291e56tJf8j//CEQCl7kCvshbpJSIVaJlvUfL+sJ4uVpE1B0wG2mKu0ahFnUHNKOEC11J\niXfEtX6ozE5R3IPNnt98BAhh56spFnehHn5nzeB0z5WXfcSUj4K3Mw9E2+cYxVF42kOzsOM7Q5UU\nwsVJWU5afSOuHWcp7rABTg/FnWZxz7G9e/DFmf/GW6AxFqwxSsN9TO5e0+DiytJ/we9LyW0WjRcu\nYKstanGbHtt87EsX10KvGG+oG9lQ2A7/Cymr0Y6/+omA4y/U7D0pvu8hmlnc8QVJpuzZirugZf1u\n4zwbYk3G+dYsbhMVMlZX1uL+5oND6TcxBiF3npBtz/67+eJGMO/VXJy/5g3B9VLgdhm3uNPhLO1a\nxOLOusfMMYTugB9xHYKW9YGMe+Lo1OJuZPmmUyXpFrfNTSFhcWcp7qbIsrhjVEnQkaRZ3MkyhuWE\n0EU1oEr0/re4uinGVKGfxyjzNmzj0NvrQOiJ5naBT7e4/43ReSbQ1djcMS8/P6Fvvfe04n498El+\n/l146pkkAr1U5rrYv9FFHAtXrWqQp+OP3XJm10jMC9GE4jY7LV95KVz/z0tJ/whKNOO4ExZ3YuVk\n2DGFbkZ1LyARhLxtWsP2lbyVpWH7b2Jxxzww3FJmSeeuPWqplY8ufq9oWR/LuNwax12dpURl8n3c\no2VeEbSjUeBPAUPZ+VaQsaz9HVPMxfefcm3sGXjPjZcxWhdu79IwTrf/jDi2+0XXsl6YkS4tVonD\neBS3b3FLg9cZWNxaDkZnEihuf8/QKKIcd72P4DsOIdlzRQFMnd/0Sfjxldmpwo6kSLQe/g/LFt8V\nySuJcAHazkjoXQWKsQ1bolSJaD/CM7GKW0QGGZkfdmoxi1vL+tEgoqWh/PwFgA6xmyLv5nfA+gw5\ngC4qbi3rL7Ssa7jzfUQ2SnXlGHOKuy86JOzfleSL43DO95XZzn/U3LtnP0gqbuW+/wMbn7uT7Jfc\nmOM2QfejirseX5QTBMn3qJJC3PqPl89Bgx2wg+uJjy6+CiuKL66HTc9yKxRjH1zM4h6bA//z6UtT\nyjHRaGZxH8pd7/5ZuuLOoiwCl+FRNfGa3S5CYRoTTjPkbtOVczyeSGMF6SZPa33RYXU6jOLO6Chb\nROccdzbuIrloqkRpxFETfn5hgcIO3vyv9ftbijm00vmbfHcdAg++yT9/Jz5tUay4+Z66v4ZAy/oj\nth7jyh+tn9JuFxpgY3Bu56GwOHgVdaDIIX+4FPgy8FfA+70cRjEd20L8keTOw1zQsnSO243IRhdU\nCFcj+wljijtUxVrWf9CyHkIDTA1VAn4vvge4Phh6xC3uYqVRw4vyWpXZsyLHuw7cRLTh7o7dk9Xr\nNea4TQzn8KMPN1bwV5DF76oHL7MysBmzZDwsaxTqxU4w15OhKv13l8xj+CCzStCWJji/CAIl6JTb\n6AJY+pl7UsrRtsXdBA0tbi3rk4zO201tAKIKuuCVJKr8Dg1G+eHkI0QVt6HffA7brezzlW4QkKNR\nGQMEirvfb0ONFTfNFXdKrBKHzjnujFGblvWrWtZ4aL4SxbFmFncUtT5SNr1wHHcj2bO+8U/hr8IM\nFqM1WEkbz+uQ5X9Ilifx7BJnv+ceVc7Tsn5Xy/oT77qT57GA3lJdxo7DzAI4qe5J5bidHhvdp0rY\n9qLx2H20GiPfYuoUd1h/p2pZP8jofNvog1ow/wuVVqwU87KqgeI2LW34oCHCBnM0Zh9A35J1v39p\nzpT8jyO7wzCeQGlUSfScQWiVONexi275f1pWt/LRPce3VHyO1/zvH45P+PkN8I7Uctb70pVQsOor\nYjC1Ypl1iignmgYt1Gw4Wb88I57Si7aH0fnBr8j1SKzu4W2AC2taB44H4m6OBe+6X9Z0jM2z9EBK\njJ0knMXdMMsmmEyLOw0limOmzcUt7rG5sOPwlNGjxCdr7cmWypmAKqr+NnOlUdeRpIWyjY1QLRas\nSdnAOXFfNA6JX4awjUQ2TtCtR5n2NnfT4+n9iLUcagN1TMjfeB7TVXEHMGVwE4v9wYjNWdzZrT0c\nesYV99MAbNRANwk0ZF9CmsVttlOqRiY3NYWbe6kpcWBxGz9Ms/MHpFEl4dcaKm43EevLGd3xPIUq\nScDumIKEM/0xhJ2HKdcdf+nmxd3SfGMJmDbWkVZpES1w3FKzsWOc3M8GrnZXVWPlfDQwyKKKu7TH\np9iCUJ/2/rTNB5pRJdHnVmbbtQMRxZ3VIbVscTfguE0O6dH1stCqV0ka+ijtMQrHKpWg7r+yGn6w\nZGckbfDEWfHOa0XLHHczuDUN6ftWjmdEAqZ+Si2UIbCWI+9I6uleJf7c1gxV3NFJIalGX0Chml2h\n4dDPufi54+XArzEWUPx+v0LdNTcr7ivutOeutGWK3l8vYuOXhB/xlmPi92pAlThrLSwHsdCc6m1z\nlM7ttmLJ1EpRJVSxc1BmcvIwrrj8QnvsP2cy0VxxG4s7KI8qDxo+M0Pp7Q72No5SJfs/6k+Q+iFf\nAc4FvP20gOaTk1GM7GuuV/vTOuE47FxDRxz3eL7VmFdJW8zXbkpjpsOpxxTlnv1hdIGT5Q5CKiDe\nkYFZoAfOsyYdrbU9R92kK7ksQycudBpV4k9+ZyF9NCW1QsZenInvVss6E6iSAOEH8+XH4Q//z1EI\nLVAlMYvbTlRpWddoWf8Mz9Ly4HNh7rfd1mpWTHEPxh9oXfQqzvowx7O3fJpjlvwG/2P/z5uSxQ0s\n7tn+m0772OvM2uHMZddbt6+4Q+Vvlcxsy3FLTcu6lsfOHOPby+Him7Pym2iO23VSDT5UqVMZEFr9\nmOec6H5FLe5VL3sL8A/2XKQdaFl3aVlDZWPQ3uTkmA1/Wi/5ijt9uO0W+WjqED+WNpPjbteShIRX\nScs4AliKGynM3py2kEUBtKzPx6f5YopblR0weAzQyL20NdkKtXDRSxJin9du59iQKvEQ7gfgvyOp\nh7SccQawi2oiFvdy4PZYfh0p7mREq+7DKe4aOw6HuMVj9nrLQNAWreJOBFdLs7jdENq3uK1HS4Tj\nTnuuVdxB2zSV/8ETvonh0B/PLisS7J0YXc6dPjn5+Is3c/sH0F99sxPFHS3nOhujYesit5lqnQ0n\nuTRp48+0qHyTC5WqDbkbrRfJmFl7Kogn5j4sU08/uP4xFov7WFqhDNpzB3TptNisE/bu6GuquLOg\nZd0lF8jhbd42Lo5by+q8JypygcA+T64lukgHovUZtp3q7KQztvJY/FwMrZaxkeJ+WsY9zd57q1RJ\nusW9aOktbDvqKKAPlGCVbbh5i9r6PLVxudqzkXrL4jaIWjyNOO64V8mpX3t67Hraru5uuOJb3Ha7\npuDdDZvry+LPiyvuQNFrWf+gZf1+dhEh8OOuDfgfeHqDefyl2/j1N1MvWbSguPujFve6U2DxtVtZ\n/m63hDtlgimElnU5cGjT57SOtDjdUew4bBtbjk2zuJPt4CtDj/Kg3W81XC7uK15N+Z2F9rxKAnpu\nIG3vxnTUmivuRhy3lvWJrGsZ8OVe0WQtQDYGtpdUE2EM0ttOZXaiPTfj7WklBIKB1QmjaR5Q8W8/\nC/FK2AejB5u1j2BUEZFnYMcYs3aadh2dfE6fTM8qx7YjM71+0tALFrcj7OMcVXOO248/AfCyz22G\nz/op0ixuX3FXvXR4e+pl+Xibc2Fn0hofauB7Svh5p33s/iSqQ3ziq3mnW4tZ3FoExF+84D87tS1o\nWZ9s+pwWoWUdplmHc9tHHrFpYvWf4pKx/agK8xOUlN/x+0q4UafxXpIfWrPhu/KlJ2HXIb63T+N2\n0AJVMsHwLe7P0b/ro5j9MdvDwPa0rWnSLe7o/E2raHUvUrtw6uQ3pejDLAMji+OOjzCbWdxZi6uK\nod7wjbS+qG5qVq5Lf2n2o13cpBQWU2px21V2q+xhuuLeuui/ga+n3H4sx/7WBT52NRZvAGkct6+4\nl2BW2rk9Ll2aHTThuGNlTWusF5s8/QYmaYo7q4OIt8xzY8ctWNwxd8B6CXiFP9JoaHFPEeqkKu4M\nw2XW8+JnnLeNX4c1Vbbhez/4OZf1Ii3rxd7zIflO4wWos+sQaLSoIo56qROOezwIOi9VauOasbjr\n3XD/m1emXElzpfS3DwwTNpGpDV463Vfb4BGc80ALiG0CkpWnj3SOGwp2XUeUzqsMuA69NdnG9oHd\n8fnybPQCVeKQrrj//a47tawfjidWZSWzdvl89WhK+NGGFreWtapl/S0hVeIqfgXpgZKsj7Cdawqf\nl9JYeU/slPi8l3c+neOOvXAt63pG9/Gf0w7HbRV3EYyl0cuKu9HHGYey7Ugw/vkO8Q2MIYjd3PLI\nKLinhXTNaa/gatct7irRMrXv1fKzi2HNi7fEzv4KuMI7vgIwUT3H5nbinfQj4tueRdHI5e9c8DZQ\nyUbWd9Os3PEFcA6FVIu7OtvpmazRhKT+bBG9pLjjvGvU46Mx6qQPZRKK2y6LhuhQyadKDgR+AOzM\n5rhrfk0fmBGOM3hk+FPSGl7avbtI+8i+d+ujfD2Ic/Mj3MKhLAy9fAewkQhVckOfO45ZOt1WKlnI\ncutKUzpK5fdoWV/vnStGrhtkhZlt9PxmVEmaZd7onrfz9DvSF0p5aIEPbgdPENnfaNyIf0OvVQ3X\nHWhZx7Ss15mDZP/fhkwFVRqFuMjs1FWpZXTMzbSi2+m+Uac2H7N9nckwKk+ouP3RdWWOM/yaR/ec\nt67duYuesbIgjLwVsUojK6eykaW44/vf+fBdUALFrWXdBCCLUz0q4pOTuPQtQD2n/EZW2tO0rJtl\ncUpD2vSc4MFa1k82feI979yhd7/zmXKBfM08KWFx++iVttCOxZ2GNMX9prSEGXDtrtnz095lZltV\n5TK54MnBNsrRMbSs1wDX+KfGmVU3fPyhuSHZadtIQ9pEfQR+2N0UFANDLl1xN7e49121BrOuIG3P\n0VR0ZHGLyBdF5EERuVtErhKRBc3vysQjseNFqanSsZ2knySkUyUOScUdtaR3pnLcKuNduuzztpkf\nu5Y1y/3P5QGx5bdNnhk+o14CXh4NkBWiVxR3Gp2UBU15RwmqxKvT5hkaQyGtLuKWW1uK2+LjwAsa\nP39COe6JQkeKsg2ZUpdEehiP4v4SoT8/JN+jH3ysJSQ57hSqZNNzVttfrU68tqVUOv1YrwU+rqp1\nEfk88AnSdydpClX2iPByglVmDJAdkN9hFHC+k69Nud6q4o5z3JDuw1xn7QtgzlPNyuVhXJOT8RuD\nDABUOazFhzsl5i8VdmFr4+gVxZ3+cbbeWaZZ3G2hjVEetKG4tazbgN+Pp0xTjG6EQ4BJsLi1rLdh\ndhDKQnThVvvwJyfDs9d8+Q6u+xdoieNu//kdWdyqukQ1WAV3G7SkUNJWYdn8WOrxru8GDmqS16eB\nP25wPc2rxCGVKvHOpXPclyyD79z1v/ELLUDR1I/9h0Qn18L0SbT7vpw8XpS3ZZBeJ72iuLM47jRo\nyjvqWHFnPyv1uPUFOC1ggjnuOMZbHx3VYxsyTYbF3QxtW9wJjnv9KevMz0g11e2CwFZ2sGp7dnIi\nP9b/C1zWQrpBTEEbOsynOPwn05R1BxAP2+ijdarkD++DWdv93Uh2xW8A6iaOxkAbFrePJMetZf0i\n8MWUxI2oklbhFL3xYXY7f6S7X/W44k6N8zERndt4kUaV/LZLz+42usVxt6q4O+kg49+Q75k2HhT5\n/g03sliOjq3udflNClXStJGLyBIRuTfl73Vemk8BY6qaGoxfRC4RkcUishjkQyAvVmW1vTbo92AT\nfDwG3396/Lq10raGx89/Pr/4Llz545u9+x+HwXh+1sK7Yr6fX6Py8Phoyc7rC0jN/D77eS3crynX\nBZbRhvwiIoP8Lweb0he9OnBYZv+M4p7k99G8vjj/2f6u4E3SB2nC+z9xomeFK0O0U1/tHNsP8+PP\ndtdV2Q5X3uSPAtqXf9LKa9BmfRhZLlzUevqPnGjb+w7gN2myNbi/2OR6nSHgG5w2bvn51bzIKO1n\nPIeh0LW3lfx8eXiAA+DSg/jBNbdw2dW+/rDt46QXZORnlPwQ8DDzsbrF6spLjK5sALXPMficAAAT\nzklEQVRBb8f7h/GfvBkYyLiunT5j/GXTk0BPTzl/AGjBO15oLDq9MJbu4Nix2HT/3dLzF6N87IDN\nLEZZzNt5wzt+zGIU9NgWyv4dU3WRc4/EzzW4X0HvteV4r32upuTpzl82Ve8pVp7X2PJ8OXL+de+9\nnMXRtgR6Z4o8Z7tzLOa0+D0dlOsK/1mg+9tyvjKW7ietvqOu1+1itrZbH1bGv20j7em2vX9hHM+5\nrkn5F9q853fwHpdH3uNiThlvG7Fl+QXoRaDXgF7pPefNVqbUsrKYn9r7lcX8D+jOZFvOLlenXiWv\nBj4GnKWqzbcY6zJUuVuVpSnnN2nU3StjeapEHPpVg+FM1vLX1GJ4/zMX7KTgdyRDYY6XKvFop2VZ\naVPCGU4JsobDGUPJZfETvwX+ovE940J6kKmWy9niQyaX4x4v2qER3OrFQLe0IVM33AHj97ZKZQSI\nyVOwecbjnbRKlZwIvLXdMnTKB34dE/tgiYjcJSLf6jC/qUKzuAJZ6dvDY2eutCv9mipuVX6oyr6x\n0+0qbpfe61Q1TbEsV6VX3l2bk5OxE8qIKpdmXZ9AZHXC3fLA6CYeaiWRmk09XITM8Syw74biPgv4\nI+94fN9yCBfLO0txN5yc1LLeq2VdT5vtpqMJKVU9rpP7ewipilSz/U/b6KW9CYu7z1nN3efA+Bte\nOx3t30EQTtOzuE/v5YlJaG8CKs2PO3Z9wtCKV0nHz2zQ5iYCbZdNddwx2cNdTqbWjzsCVdbgb/o7\nDsXtyXM+cCtmhBdX3Fntw6Ejd8Be+mCnDKqomGps1nAcxttLt7qcOgstf0SqfMU79D10el1xp9fR\nyjNXseiGdvOaTOt3UqiSGYTxKPypcAdsmypx0LJ+AUAW83aSirsIEXo1jqnz456BGPAPGnBz7Www\nkNYLT7rijsGjSpam5dFLijv943zgLU9yYSL428fgy19tkNcQk7cZxKRQJZPMcXezUwnaWRsyTYXi\nbrSlWipS5FGSu+i0q1tzi7sD7NNiuu3NkySQteS93TzGgx3e7+lpcadAlaUi56lhhVKumzgy81Mv\ndo5JoUpmEMZjFDa8R8uqcoHABCrulmLEt5ANGRZ3A+RUyQQi8pE34OZaV9zRpdpTpbgfAE4Glmdw\n3K1SRN1AllWV2rAnmRNuhEmhSqZQnonGeDjupspey+PdxmfikCJPneSGw7ni7iJatbh3NE+SgDJF\nitsuLrjbuvSnNZBeCekKndfRZCFLQU8njntKqJIW8WHCCKHTDeOxuDtCznFHEVHcDbi58VAlEL7Y\nbniVZCAxwfcQjcMGdBvtuAN20+/5r4EXecdZHUxHw/ge9eMeD9riuFX5uirXTmqJJggZHHdccTf7\nVnOLewIxCRx3T1AljXAivWUltrkApztQZRPgx17POe7GmHJKo4vIOe4pRkRxN+DmHss4H8VP/vs6\nKgP387Y33YuJAOhCz06h4n55NP63jt8dapLQzg44U8kJTwpV0mt+3B0gsDhnEG8PpMqT5lUyqVRJ\nrrhDfAEbeKoR2lqQcO/b1wHrtKwXAcjinrC4e90inIgIcN1A7sfdGLnF3Ri/B14ay6Nl5By3hSrn\nq/IF/9wE8I1pu7WjzbfFysJEcNy9/kH1KscdR7OVcePC3spxTydkcNzPoQ3FrWUta1nnxfJoGbni\nnlzEFXenSnMClG7PG4Q9yXHH4a2I6+lyxtDLXiXTGW7hXjuTk3HkHPdEYQK4uTpRy7EHFPcZE2oh\nTgLaopN6gD/NimEyvsymXp6Jwnj8uKcFUuRx1rX/7n8AbJmsMuSKe3LRgxZ3T1uEMDnLmicTLU2i\n7oXYm0bzTnF7nRXbMNsStoqcKpkoTALH3QOK+/pef+dtTfr1AH86oYq7B+SZCHwXuMQdzBCZAqTI\n4xawdfJ95lRJD6EHFXfPo60l7z2A6VJO6FLZtKx/1Y3n9BCcHu3a99nr1teUYoI47olU3BPwvs7o\nNb/tOKYbxx3fOLqXOe4p6VR64B1NKFLkcYq7k++zO1SJiHxWRO4WkeUicr2IHD7evGYwVhIN2p5b\n3M3RljvgVMLu+BLfxq6XLe4ck4Ppo7iBf1XVk1T1ZOBqoNxBXj2JTrk5Vb6iylV+lp2VaCIU93W9\nFAkwDW25A/Ygf9qRJ0EPytMxZppMvcBxj1txq6ofoH4e8NR489qL0AOKu+ctwmljcWfgAuDYqS5E\nBnr93U9XdN3i7mhyUkT+CXgnhud7YSd59SImgZvrAY77Fb1Ot7S15L3X+FNVRgh3Oh/H/b0lz0Rg\npsnU8xy3iCwRkXtT/l4HoKqfUtUjMK4/X2mQzyUistj+/Y0/1BCRwb3oWGAZU3j/CCzbp4fqI3EM\nx54Gy8Aqbu/6Q71Qvul+zFB0qD/V5ZkJx/CzA8LDceen9tu+xP4tpgFEtfPRk4gcAfxaVU9Iuaaq\nU79rxXggIoMTaS2I8B7ge+PdOVuEEWBWB/c/CsuOVR3s2fchwlxgF3CWKj9vnn5i39FUYzLlkQvk\nSeCQbu8iM9PfkQg3AS8BLlflrePLk1XAkf633Uh3jpsqEZHjVPVRe3gWcFeb908Lvk1k4tu4SGaI\n0mYP67QwT9C7/KtDr+6AkyNHFqYVx/0vIvJMzAe2Enh/uxlMV0t8vBDhvcB/pFnMLXZkndbXO2Cw\n1xV3W0veZ5IlBzNPHph5MvUCxz1uxa2qfz7ee/diTOnkpCprgbUdlmGykVvck4fl5PU6GZgId8C1\nwKJWE+crJxsgOmk2Iei0vseY+bEw2rK4p4E8bWGS5TkbeMYk5p+KveAdTYTF/Vrg4FYT57FKuotO\nLe4TJyCPnoYqaqcVpsUcyHSClnVsqsswQ9Gxxa3KdtrYyzZX3A0wCdxcR7GwVVnRaQH2Ar5xWmOm\nyQMzT6YGHHfXjKpccXcXPwAemOpCTBPkFneO6YKJoEraQs5xN4B1jl8mIltEpD927Vwxi5GGRWSd\niHxLRBY0yk+VMVVumdxSN8ZewDdOa8w0eWDmyTRJHHdbyBV3YxwMvADYCLzenRSR84DPA+cB8zHL\n/Y8ElohIX0o+OXLkmLlwO910jSqZkJWTDR+QsfpnOqyoFJF/BP4YuA14oaq+TkTmY1x33q2qV3pp\n5wJDwMdV9T/H8ayer49uwS5QOkOVG6a6LDlytALbZq9V5VUTl2e2Tsgt7sY4B/gxcDnwKhE5EDgN\ns6uzH64VVR0Gfg2c2e1C5siRoyeQ74ADphfr9G/8z5aXAIcDP7dL+x8A3g48DXhKVdP8jNfb6z2L\nvYBvnNaYafLAzJOpgTw5xw3BDiMd/XXw+HcBv/fijl9hz20CniYiaXV3iL2eI0eOvQ+5O+BUQkRm\nA28BCiKyzp6eBSwA1gGjwJswytzdMw94NfCJ7pa2PewFPrXTGjNNHph5MjWQp2uGcK6403E2ZrHM\nSZhl5mB608sxvPcFwNdFZAdwA3Ao8C3M/pL/1fXS5siRoxeQUyVTjHOAi4FjVXWj/dsAfAPDc/8b\n8EngS5hlqv8LrAbOUNWe3kV9L+IbpyVmmjww82RqIE9OlUwlVPU1kHxBqnoFIT1ysf3LkSNHDsj9\nuPc+5PURQoRfAOeqsnmqy5IjRyuwHmy3qPLiictzEnbAyZFjsqDK66a6DDlyjAPTx49bRM4TkbqI\n7DcRBeolzDRuDmaeTLk8vY+ZJtO09+MWkcMxKwVXT0xxcuTIkWPaomuKuyOOW0SuAD4L/Ax4nqpu\nSUmTc9wtIK+PHDmmLyzHfYcqz5+4PCchVomInAU8oar3jLtkOXLkyDFz0BvugCKyhPR90D6FWSH4\nSj95g3wuAVbZw22YTUvdtUEIVyP10rHPZU3287pYH38DLO+F+s3l2SvkGQROVtWv9lB5Jlweu+9H\nYQL0zbkmv0BfpmJcVImInABcD+y2pw7DhDp9gapujKWdtlSJiAx2a7lut+qjmzJ1A7k8vY+ZJlOa\nPJYqWa7KKRP4nEydMCF+3CIyRM5xd4S8PnLkmL6wivseVU6auDwnPx73jNofUERWicgGEZnjnXuv\niCz1jkVEHhOR+1PuFxH5mIg8IiK7RWS1iPyzxLY/y5Ejx4zC9HAHdFDVo9Os7WmOAvDVBtdfiokY\neICI/HHs2oXA+4B3AvOA1wBnYIJUTSninPp0Ry5P72OmydRAnumzAGeGQjEBpN4q2RsAvwv4CcYV\n8l3upIgcB7wfeLuq3qaqdVV9ABMG9tUicvrkFj1HjhxThFxx9wDuAK4DPhq/YCmUNxFua/Y2EXEe\nOmcAa1T1Dv8eVX0CE0VwSrc2m0mTRJDLMx0w02TKkGc1cHu3ytDTsUrkAumYO9fyuCf8FPhH4GYR\n+Vrs2huBHap6s4gU7bnXAldjti5bn5HnOmD/cZYnR44cvYtj6OJcX08r7g6U7kThAOCXwPnAg975\nd2E3C1bVmohcbc9dDTyF2cIsDU8HHpu00raAvcE1azpjpskDM0+mNHlUqXWzDDlV0hxlzETjoQAi\ncijwcuBdIrJOzNZmbwH+VET2x+yIc7iIRJa+ionrcirG/z1Hjhw5xo08HncKrF/6e1T1Bnv8XQyn\nfQ9wLfAOwJ9kFOAW4Cuq+g0R+SaGy34nhit/FvCfwAZVTQ1Z2sv1kSNHju6jG37cMx2fAeZgOKxz\ngG9puKWZ29bsO/YawIeA7wE/BHYCv8FY4m/qeslz5Mgx45Bb3A2QL3nvfeTy9D5mmkzdkie3uHPk\nyJFjBiG3uHsEeX3kyJHDR25x58iRI8cMQq64G2CmxViAmSdTLk/vY6bJ1Avy5Io7R44cOaYZco67\nR5DXR44cOXw00glTuuRdpPNYJDly5Mixt6GTzYIXi8gTInKX/Xt1O/erqvT6H3B6l5836egFfm4i\nkcvT+5hpMvWCPJ1w3Ap8WVVPsX+/nahC9RBOnuoCTAJmmky5PL2PmSbTlMvT6eTkTOdkF051ASYB\nM02mXJ7ex0yTacrl6VRx/7WI3C0iF4lI14Tp4lDlqC49Z8bJlMszbhzVpefMOJlmmjyN0FBxi8gS\nEbk35e/1wLeBRZhhwzrg37pQXofBLj2nm0OiwS49p1syDXbpObk848dgl56Tv6MJxoS4A4rIUcAv\nVPW5Kddyz5EcOXLkGAcm3B1QRA5R1XX28A3Ave08OEeOHDlyjA+d+HF/QUROxniXDAF/NTFFypEj\nR44cjTDpKydz5MiRI8fEYq+LVSIiF4vIBhG51zt3kojcKiL3iMjPRWQfe/4oEdnjLTL6lnfPW61H\nzX0i8vmpkMWWo2V57LUT7bX77PV+e37aySMif+G9m7tEpCYiJ/aSPLYs7cg0ICKX2fMPiMj53j09\nIVOb8vSLyH/a88tF5GXePb0iz+EislRE7rdl+bA9v5910HhERK71PedE5BMi8qiIPCQir/TOd0cm\nVd2r/oA/AU4B7vXO/R74E/v73cBn7O+j/HRe+v2B1cD+9vgS4OXTQJ4ScDfwXHu8L6bznpbyxO47\nAXi0197PON7RucBl9vdsDA15RC/J1KY8HwQusr8PAO7otXcEHAycbH/PAx4Gjgf+Ffh7e/7jwOft\n72cDy4E+qyNWYNa0dE2mvc7iVtWbgK2x08fZ8wDX0XxvyKMxSmKzPb6+hXsmBW3K80rgHlW91967\nVVXrTF95fLwd+JH93TPyQNsyrQPmikgRmAuMATvoIZnalOd4YKm9bxOwTUSeT2/Js15Vl9vfu4AH\ngUOB1wPft8m+D5xtf5+F6VwrqroKo7hPpYsy7XWKOwP3i8hZ9vebgcO9a4vsMHyZiLzEnlsBPFNE\njhSREuaF+vdMNbLkeQagIvJbEfmDiHzMnp+u8vh4C3CZ/d3r8kCGTKp6DUZRrwNWAV9U1W30vkxZ\n7+hu4PUiUhSRRcDzgMOAR+lBecS4Np8C3AYcpGYjcIANwEH299OBJ7zbnrDnuiZTrrgN/i/wARG5\nAzNUGrPnnwQOV9VTgL8DLhWReaq6FXg/8GPgRsxwttb9YmciS54S8BKMdfoS4A0i8vJpLA8AInIq\nsFtVHwAzkqC35YEMmUTkHRiK5BDMArePisiiaSBT1ju6GKPY7gC+AtwC1Gxn1FPyiMg84CfAR1R1\np39NDffR0JOjmzJNaVjXXoGqPgy8CkBEngH8mT0/hm2AqnqniKzEWK13quovgV/ae/4SqE5B0VOR\nJQ+wBrhRVbfYa78G/gi4YZrK4/A24NLYPT0rD6TK9Kf20mnAT1W1BmwSkZuBPwaGelmmBt9QDWP0\nYK/dDDxir/WMPCLSh1Ha/6WqV9vTG0TkYFVdLyKHABvt+bVELenD7LmuyZRb3ICIHGD/F4B/wCzn\nR0SeZrlGRORo4DjgMXt8oP2/L6aX/V73S56OLHmAa4DnishsO5R7GXC/TTsd5XHn3kzIb7vzPSsP\npMr0HXvpIeDl9tpc4IUYzrWnZWrwDc22ciAiZwIVVX3IHveEPCIiwEXAA6r6Ve/Sz4F32d/vAq72\nzr/NeswswuiF221e3ZFpKmZxp/IPw4M+ibGk12CGeB/GzCQ/DPyzl/aNwH3AXcAfgD/zrl2KUXr3\nA2+ZDvLY9H9hZboXO0s+zeUZBG5Jyacn5BlHm5sF/NC+n/uB83pNpjblOQrTGT0AXIuhHntNnpcA\ndYynyF3279XAfpiJ1kds2Rd693wSM+/wEPCqbsuUL8DJkSNHjmmGnCrJkSNHjmmGXHHnyJEjxzRD\nrrhz5MiRY5ohV9w5cuTIMc2QK+4cOXLkmGbIFXeOHDlyTDPkijtHjhw5phlyxZ0jR44c0wz/H6qn\nuXXS1uAQAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x107b39c50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "aonao.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or have a look at the first several rows:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/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>AO</th>\n", " <th>NAO</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1950-01-31</th>\n", " <td>-0.060310</td>\n", " <td>0.92</td>\n", " </tr>\n", " <tr>\n", " <th>1950-02-28</th>\n", " <td>0.626810</td>\n", " <td>0.40</td>\n", " </tr>\n", " <tr>\n", " <th>1950-03-31</th>\n", " <td>-0.008127</td>\n", " <td>-0.36</td>\n", " </tr>\n", " <tr>\n", " <th>1950-04-30</th>\n", " <td>0.555100</td>\n", " <td>0.73</td>\n", " </tr>\n", " <tr>\n", " <th>1950-05-31</th>\n", " <td>0.071577</td>\n", " <td>-0.59</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " AO NAO\n", "1950-01-31 -0.060310 0.92\n", "1950-02-28 0.626810 0.40\n", "1950-03-31 -0.008127 -0.36\n", "1950-04-30 0.555100 0.73\n", "1950-05-31 0.071577 -0.59" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can reference each column by its name:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1950-01-31 0.92000\n", "1950-02-28 0.40000\n", "1950-03-31 -0.36000\n", "1950-04-30 0.73000\n", "1950-05-31 -0.59000\n", "1950-06-30 -0.06000\n", "1950-07-31 -1.26000\n", " ... \n", "2013-06-30 0.52076\n", "2013-07-31 0.67216\n", "2013-08-31 0.97019\n", "2013-09-30 0.24060\n", "2013-10-31 -1.28010\n", "2013-11-30 0.90082\n", "2013-12-31 0.94566\n", "Freq: M, Name: NAO, dtype: float64" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao['NAO']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "or as method of the Data Frame variable (if name of the variable is a valid python name):" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1950-01-31 0.92000\n", "1950-02-28 0.40000\n", "1950-03-31 -0.36000\n", "1950-04-30 0.73000\n", "1950-05-31 -0.59000\n", "1950-06-30 -0.06000\n", "1950-07-31 -1.26000\n", " ... \n", "2013-06-30 0.52076\n", "2013-07-31 0.67216\n", "2013-08-31 0.97019\n", "2013-09-30 0.24060\n", "2013-10-31 -1.28010\n", "2013-11-30 0.90082\n", "2013-12-31 0.94566\n", "Freq: M, Name: NAO, dtype: float64" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.NAO" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can simply add column to the Data Frame:" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/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>AO</th>\n", " <th>NAO</th>\n", " <th>Diff</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1950-01-31</th>\n", " <td>-0.060310</td>\n", " <td>0.92</td>\n", " <td>-0.980310</td>\n", " </tr>\n", " <tr>\n", " <th>1950-02-28</th>\n", " <td>0.626810</td>\n", " <td>0.40</td>\n", " <td>0.226810</td>\n", " </tr>\n", " <tr>\n", " <th>1950-03-31</th>\n", " <td>-0.008127</td>\n", " <td>-0.36</td>\n", " <td>0.351872</td>\n", " </tr>\n", " <tr>\n", " <th>1950-04-30</th>\n", " <td>0.555100</td>\n", " <td>0.73</td>\n", " <td>-0.174900</td>\n", " </tr>\n", " <tr>\n", " <th>1950-05-31</th>\n", " <td>0.071577</td>\n", " <td>-0.59</td>\n", " <td>0.661577</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " AO NAO Diff\n", "1950-01-31 -0.060310 0.92 -0.980310\n", "1950-02-28 0.626810 0.40 0.226810\n", "1950-03-31 -0.008127 -0.36 0.351872\n", "1950-04-30 0.555100 0.73 -0.174900\n", "1950-05-31 0.071577 -0.59 0.661577" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao['Diff'] = aonao['AO'] - aonao['NAO']\n", "aonao.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And delete it:" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/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>AO</th>\n", " <th>NAO</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2013-08-31</th>\n", " <td>0.15425</td>\n", " <td>0.97019</td>\n", " </tr>\n", " <tr>\n", " <th>2013-09-30</th>\n", " <td>-0.46088</td>\n", " <td>0.24060</td>\n", " </tr>\n", " <tr>\n", " <th>2013-10-31</th>\n", " <td>0.26276</td>\n", " <td>-1.28010</td>\n", " </tr>\n", " <tr>\n", " <th>2013-11-30</th>\n", " <td>2.02900</td>\n", " <td>0.90082</td>\n", " </tr>\n", " <tr>\n", " <th>2013-12-31</th>\n", " <td>1.47490</td>\n", " <td>0.94566</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " AO NAO\n", "2013-08-31 0.15425 0.97019\n", "2013-09-30 -0.46088 0.24060\n", "2013-10-31 0.26276 -1.28010\n", "2013-11-30 2.02900 0.90082\n", "2013-12-31 1.47490 0.94566" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "del aonao['Diff']\n", "aonao.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Slicing will also work:" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/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>AO</th>\n", " <th>NAO</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>1981-01-31</th>\n", " <td>-0.11634</td>\n", " <td>0.37</td>\n", " </tr>\n", " <tr>\n", " <th>1981-02-28</th>\n", " <td>-0.33158</td>\n", " <td>0.92</td>\n", " </tr>\n", " <tr>\n", " <th>1981-03-31</th>\n", " <td>-1.64470</td>\n", " <td>-1.19</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " AO NAO\n", "1981-01-31 -0.11634 0.37\n", "1981-02-28 -0.33158 0.92\n", "1981-03-31 -1.64470 -1.19" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao['1981-01':'1981-03']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "even in some crazy combinations:" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x107cf7810>" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x107cf7250>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import datetime\n", "aonao.ix[(aonao.AO > 0) & (aonao.NAO < 0) \n", " & (aonao.index > datetime.datetime(1980,1,1)) \n", " & (aonao.index < datetime.datetime(1989,1,1)),\n", " 'NAO'].plot(kind='barh')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we use special [advanced indexing attribute .ix](http://pandas.pydata.org/pandas-docs/stable/indexing.html#advanced-indexing-with-labels). We choose all NAO values in the 1980s for months where AO is positive and NAO is negative, and then plot them. Magic :)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Statistics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Back to simple stuff. We can obtain statistical information over elements of the Data Frame. Default is column wise:" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "AO -0.129191\n", "NAO -0.028639\n", "dtype: float64" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.mean()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "AO 3.4953\n", "NAO 3.0400\n", "dtype: float64" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.max()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "AO -4.2657\n", "NAO -3.1800\n", "dtype: float64" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.min()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can also do it row-wise:" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "1950-01-31 0.429845\n", "1950-02-28 0.513405\n", "1950-03-31 -0.184064\n", "1950-04-30 0.642550\n", "1950-05-31 -0.259211\n", "1950-06-30 0.239285\n", "1950-07-31 -1.031240\n", " ... \n", "2013-06-30 0.534705\n", "2013-07-31 0.330524\n", "2013-08-31 0.562220\n", "2013-09-30 -0.110140\n", "2013-10-31 -0.508670\n", "2013-11-30 1.464910\n", "2013-12-31 1.210280\n", "Freq: M, dtype: float64" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.mean(1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or get everything at once:" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/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>AO</th>\n", " <th>NAO</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>count</th>\n", " <td>768.000000</td>\n", " <td>768.000000</td>\n", " </tr>\n", " <tr>\n", " <th>mean</th>\n", " <td>-0.129191</td>\n", " <td>-0.028639</td>\n", " </tr>\n", " <tr>\n", " <th>std</th>\n", " <td>1.008903</td>\n", " <td>1.000970</td>\n", " </tr>\n", " <tr>\n", " <th>min</th>\n", " <td>-4.265700</td>\n", " <td>-3.180000</td>\n", " </tr>\n", " <tr>\n", " <th>25%</th>\n", " <td>-0.670015</td>\n", " <td>-0.762500</td>\n", " </tr>\n", " <tr>\n", " <th>50%</th>\n", " <td>-0.051368</td>\n", " <td>0.000000</td>\n", " </tr>\n", " <tr>\n", " <th>75%</th>\n", " <td>0.467525</td>\n", " <td>0.670000</td>\n", " </tr>\n", " <tr>\n", " <th>max</th>\n", " <td>3.495300</td>\n", " <td>3.040000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " AO NAO\n", "count 768.000000 768.000000\n", "mean -0.129191 -0.028639\n", "std 1.008903 1.000970\n", "min -4.265700 -3.180000\n", "25% -0.670015 -0.762500\n", "50% -0.051368 0.000000\n", "75% 0.467525 0.670000\n", "max 3.495300 3.040000" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By the way getting correlation coefficients for members of the Data Frame is as simple as:" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/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>AO</th>\n", " <th>NAO</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>AO</th>\n", " <td>1.000000</td>\n", " <td>0.611757</td>\n", " </tr>\n", " <tr>\n", " <th>NAO</th>\n", " <td>0.611757</td>\n", " <td>1.000000</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " AO NAO\n", "AO 1.000000 0.611757\n", "NAO 0.611757 1.000000" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "aonao.corr()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Resampling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas provide easy way to resample data to different time frequency. Two main parameters for resampling is time period you resemple to and the method that you use. By default the method is mean. Following example calculates annual ('A') mean:" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x107de2750>" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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5H+66FM07jzIVeEBzFuuCkYyexWSv0FRElplDeyP3PLJM1LmfAxwRCmz2BK7L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\nEwdDrpmPq4GncY/xpejRdRq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R4xuBfw21fSNwPXANcBVwaGjfPrjywiuAr9XV30Hb5Nu/\n2du1BD8y3gOb5gOXxxynkzYBm+CeppYAN+BKaXTdpjm4H62lwEU4ybONNu0HPIvLgLnG/x0EbIMb\nIL7J93+r0Gc+7vu+HHh9j+xaBdwHPOyv77yqdtkkJsMwjB5iy+wZhmH0EHPuhmEYPcScu2EYRg8x\n524YhtFDzLkbhmH0EHPuhmEYPcScu2EYRg8x524YhtFD/j8xcryVrAQysgAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x107cdca50>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AO_mm = AO.resample(\"A\")\n", "AO_mm.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "median:" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10810a550>" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gd4H/lXOwSBOkLWx8K/kGVhzqf8pQh7gnZu4hfgsnRGdTLHPfA9iedfcSog3f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\n9R5hUUsGnGCfBKzN8SWWNZA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"text/plain": [ "<matplotlib.figure.Figure at 0x108113fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AO_mm = AO.resample(\"A\", how='median')\n", "AO_mm.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can use your methods for resampling, for example np.max (in this case we change resampling frequency to 3 years):" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x10824f650>" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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RTAK+T4nTBUPzSYRBwAMiTEl3nmR+iTBYhOs67bfg3Z5eI+/fAeNgmw0KsseIzlTg1x0q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\n2EC11FQpgjF9ugv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"text/plain": [ "<matplotlib.figure.Figure at 0x108259a90>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AO_mm = AO.resample(\"3A\", how=np.max)\n", "AO_mm.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can specify several functions at once as a list:" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x1084c3210>" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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6xPMm8SPKQBmkSSA/x/1HjDtsFAYDvTBNJwxJRtjpaBrHKDIbIvsjcjN2X08EPsfu+1Eh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\nuWxbVrQm1U0Fa9iTwFbCis4hxBCpf0Hw1TtOCuQHmgrJf4v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"text/plain": [ "<matplotlib.figure.Figure at 0x1084d7fd0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "AO_mm = AO.resample(\"A\", how=['mean', np.min, np.max])\n", "#AO_mm['1900':'2020'].plot(subplots=True)\n", "AO_mm['1900':'2020'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's it. I hope you at least get a rough impression of what pandas can do for you. Comments are very welcome (below). If you have intresting examples of pandas usage in Earth Science, we would be happy to put them on [EarthPy](http://earthpy.org)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Links" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Time Series Data Analysis with pandas (Video)](http://www.youtube.com/watch?v=0unf-C-pBYE)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Data analysis in Python with pandas (Video)](http://www.youtube.com/watch?v=w26x-z-BdWQ)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Python for Data Analysis](http://shop.oreilly.com/product/0636920023784.do)" ] } ], "metadata": { "Author": "Nikolay Koldunov", "Category": "Data processing", "Date": "2013-3-30", "Tags": "pandas, data analysis, visualization", "Title": "Time series analysis with pandas", "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" }, "slug": "pandas-basics" }, "nbformat": 4, "nbformat_minor": 0 }
apache-2.0
napjon/ds-nd
p5-introml/final_project/code-analysis.ipynb
1
23490
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Goal: \n", "\n", "* Based on the email and financial dataset, predict whether Enron employee is fraud.\n", "* Use two evaluation metrics, precision and recall above 0.3\n", "\n", "Machine Learning\n", "\n", "* Use Naive Bayes for person classification, \n", "* preprocess with tools/email_preprocess. word_data contain list of words, email_authors contain the authors in numeric. Possibily we extract the name and the email, put it to preprocess.\n", "* Use SVM, using 1% features can generate 88-99% accuracy.\n", "* Naive Bayes is better at classifying text than SVM. Faster and gives better performance.\n", "* Decision Tree:\n", " * Min sample split: Minimum sample to split. >> to avoid overfitting\n", " * Feature selection based on information gain.\n", " * SelectPercentile to limit features, more features, more complex fit.\n", "* Use RandomForest has built-in overfitting(Randomly samples, bootstrap, majority votes(ensemble))\n", "* Use feature_format to convert dictionary into list of features2\n", "\n", " feature_list = [\"poi\", \"salary\", \"bonus\"] \n", " data_array = featureFormat( data_dictionary, feature_list )\n", " label, features = targetFeatureSplit(data_array)\n", "\n", " the line above (targetFeatureSplit) assumes that the\n", " label is the _first_ item in feature_list--very important\n", " that poi is listed first!\n", " \n", "* LinearRegression, long_term_incentive will do more to bonus than saalary\n", "* Outlier can greatly affected slope in linear regression\n", "* Visualize online outliers in outlier/enron_outliers.py\n", "* After remove outliers, Salary and Bonus is important numerical feature detecting POI!!!\n", "\n", "* Not using clustering since we already have labeled data.\n", "\n", "* Feature scaling not in SVM or kMeans, they both scale the features that dependent. Linear Regression and Decision tree are those models that have independent variable. (DT: split on 1 var, LR: coef every variable)\n", "\n", "* SelectPercentile: percent best features. SelectKBest: based on K\n", "* Use TfIdfVectorizer in words that frequent need to be filtered.\n", "* LasoRegression to avoid overfitting (regularization)\n", "* call `clf.feature_importances_` to see which features drive the most performance.\n", "* Use vectorizer and the method above to find what words that are signature of POI-s\n", "* Use stemmer (SnowballStemmer) to stem words.\n", "* Use ../tools/parse_out_email_text.py to parse out text from email.\n", "* Use text_learning/vectorize_text.py to parse out stemmed words, label 1 if from poi, 0 otherwise\n", "* use PCA to find latent features.\n", "* F! score best of both world\n", "* Use train_test_split, GridSearchCV to get the best parameter.\n", "\n", "Methods:\n", "\n", "* Use poi_names for labelling, connect it to enron dataset dictionary\n", "* The dictionary has email, if we want to see from-to enron email, crawl that dataset.\n", "* the dataset has the form\n", " enron_data[\"LASTNAME FIRSTNAME MIDDLEINITIAL\"] = { features_dict }\n", "* Knowing best features with human understanding about the dataset.\n", "* No training points would have \"NaN\" for total_payments when the class label is \"POI\"\n", "* Now there are 156 folks in dataset, 31 of whom have \"NaN\" total_payments. This makes for 20% of them with a \"NaN\" overall.\n", "* Added new number from dataset, there are 36% of POI have NaN for total payment\n", "* Don't try to remove that outliers that we want to pay attention\n", "* Remove outliers by removing 10% highest residual erros, and try again (outliers/outlier_cleaner.py)\n", "* Remove ['TOTAL'] outlier (it's not a person) in the final_project_dataset.\n", "* Two out of 4 ourliers next visualization(after remove total, outlier/enron_outliers.py) is poi indentified. Not sure about the rest, check again whether or it's mistypo.\n", "* feature scale all of your numerical features, including salary and exercised stock options.\n", "* Critical to feature scale n-message and salary! first one can be counted, later will have the order of thousands.\n", "\n", "* Intuition: POI sends another POI higher email rate than general\n", "* visualize: what features that makes POI stands out than general?\n", "* Use feature_selection/poi_flag_email.py to identify whether poi, based on given email file\n", "* Use feature_selection/new_enron_feature.py to create new feature, *count emails from poi*\n", "* Use feature_selection/visualize_new_feature.py to create fraction of from_poi/from_general ratio\n", "\n", "\n", "Problems\n", "\n", "* POI could end up not being in the dataset (Michael Krautz)\n", "* Inconsistent naming format in text emails.\n", "* Total payments could be the feature. NaN for non POI. Use isTotalPaymentExist for NaN categorical. (THIS INFORMATION CAN BE PICKED BY THE MODEL TO IDENTIFY POI). This is wrong, and as we added new dataset, this feature is normalized. \n", "* DIfferent data sources can introduce difference bias and mistake. \n", "* Financial features have some bias, you have to handle it. It doesn't if you just use email data.\n", "* Feature scale salary and stock might be an argument." ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SGDClassifier(alpha=1e-06, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', n_iter=31, n_jobs=-1,\n", " penalty='l2', power_t=0.5, random_state=42, shuffle=True, verbose=0,\n", " warm_start=False)\n", "\tAccuracy: 0.79058\tPrecision: 0.36853\tRecall: 0.35950\tF1: 0.36396\tF2: 0.36127\n", "\tTotal predictions: 12000\tTrue positives: 719\tFalse positives: 1232\tFalse negatives: 1281\tTrue negatives: 8768\n", "\n" ] } ], "source": [ "%run tester.py" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GridSearchCV(cv=None, error_score='raise',\n", " estimator=SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1,\n", " penalty='l2', power_t=0.5, random_state=42, shuffle=True, verbose=0,\n", " warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'loss': ['hinge', 'log', 'squared_hinge'], 'n_iter': [30, 35], 'alpha': [0.01, 0.0001, 1e-06]},\n", " pre_dispatch='2*n_jobs', refit=True, scoring='f1', verbose=0)\n", "\tAccuracy: 0.78658\tPrecision: 0.35534\tRecall: 0.34450\tF1: 0.34983\tF2: 0.34661\n", "\tTotal predictions: 12000\tTrue positives: 689\tFalse positives: 1250\tFalse negatives: 1311\tTrue negatives: 8750\n", "\n" ] } ], "source": [ "%run tester.py" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "GridSearchCV(cv=10, error_score='raise',\n", " estimator=SGDClassifier(alpha=0.0001, average=False, class_weight=None, epsilon=0.1,\n", " eta0=0.0, fit_intercept=True, l1_ratio=0.15,\n", " learning_rate='optimal', loss='hinge', n_iter=5, n_jobs=1,\n", " penalty='l2', power_t=0.5, random_state=42, shuffle=True, verbose=0,\n", " warm_start=False),\n", " fit_params={}, iid=True, n_jobs=1,\n", " param_grid={'loss': ['hinge', 'log', 'squared_hinge'], 'n_iter': [30, 35], 'alpha': [0.01, 0.0001, 1e-06]},\n", " pre_dispatch='2*n_jobs', refit=True, scoring='f1', verbose=0)\n", "\tAccuracy: 0.78225\tPrecision: 0.34924\tRecall: 0.35500\tF1: 0.35210\tF2: 0.35383\n", "\tTotal predictions: 12000\tTrue positives: 710\tFalse positives: 1323\tFalse negatives: 1290\tTrue negatives: 8677\n", "\n" ] } ], "source": [ "%run tester.py" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['deferred_income',\n", " 'bonus',\n", " 'total_stock_value',\n", " 'salary',\n", " 'exercised_stock_options']" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "features_percentiled" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pickle" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "clf = pickle.load(open('my_classifier.pkl','r'))" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Overwriting poi_id.py\n" ] } ], "source": [ "# %%writefile poi_id.py\n", "#!/usr/bin/python\n", "\n", "import sys\n", "import pickle\n", "import pandas as pd\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.feature_selection import SelectPercentile,f_classif\n", "from sklearn.feature_extraction.text import TfidfVectorizer,CountVectorizer\n", "\n", "sys.path.append(\"../tools\")\n", "\n", "from feature_format import featureFormat, targetFeatureSplit\n", "from tester import dump_classifier_and_data\n", "from visualize_new_feature import update_with_fraction_poi\n", "from vectorize_text import get_word_data_from_email\n", "\n", "\n", "### Load the dictionary containing the dataset\n", "data_dict = pickle.load(open('final_project_dataset.pkl', \"r\") )\n", "\n", "## Task 2: Remove outliers\n", "data_dict.pop('TOTAL')\n", "\n", "### Task 3: Create new feature(s)\n", "data_with_fraction_poi = update_with_fraction_poi(data_dict)#Update dataset with fraction poi\n", "\n", "\n", "full_df = pd.DataFrame.from_dict(data_with_fraction_poi,orient='index') #create pandas Dataframe from dataset\n", "df = full_df[full_df.email_address != 'NaN']\n", "\n", "cols = df.columns.tolist() # get the list of features\n", "cols.remove('email_address')#remove non numeric features\n", "cols.remove('poi')# remove labels\n", "impute = df[cols].copy().applymap(lambda x: 0 if x == 'NaN' else x) #replace NaN features as 0\n", "\n", "scaled = impute.apply(MinMaxScaler().fit_transform) #Scaled each of the feature \n", "\n", "selPerc = SelectPercentile(f_classif,percentile=21) # Built the SelectPercentile, 21 Selected Based on the Performance\n", "selPerc.fit(scaled,df['poi']) # Learn the Features, knowing which features to use\n", "\n", "features_percentiled = scaled.columns[selPerc.get_support()].tolist() #Filter columns based on what Percentile support\n", "scaled['poi'] = df['poi'] #rejoin the label\n", "\n", "###### Add Text Learning####\n", "word_data = df['email_address'].apply(get_word_data_from_email) # Extract words given email\n", "vect = TfidfVectorizer(stop_words='english',max_df=0.4,min_df=0.33) # Build the vectorizer\n", "vect.fit(word_data) # Vectorizer Learn from data\n", "words = vect.vocabulary_.keys() # what words to used\n", "vectorized_words = vect.transform(word_data).toarray()# The values of vectorized words\n", "df_docs = pd.DataFrame(vectorized_words, \n", " columns=words,\n", " index=df.index) # Using same index, person\n", "df_with_data = pd.concat([df_docs,scaled],axis=1) # Concat emails with numerical features\n", "############################\n", "\n", "### Store to my_dataset for easy export below.\n", "my_dataset = df_with_data.to_dict(orient='index') #change the dataframe back to dictionary\n", "# my_dataset = scaled.to_dict(orient='index') #change the dataframe back to dictionary\n", "\n", "### Task 1: Select what features you'll use.\n", "### features_list is a list of strings, each of which is a feature name.\n", "### The first feature must be \"poi\".\n", "features_list = ['poi'] + features_percentiled + words# You will need to use more features\n", "\n", "### Extract features and labels from dataset for local testing\n", "data = featureFormat(my_dataset, features_list, sort_keys = True)\n", "labels, features = targetFeatureSplit(data)\n", "\n", "### Task 4: Try a varity of classifiers\n", "### Please name your classifier clf for easy export below.\n", "### Note that if you want to do PCA or other multi-stage operations,\n", "### you'll need to use Pipelines. For more info:\n", "### http://scikit-learn.org/stable/modules/pipeline.html\n", "\n", "# Provided to give you a starting point. Try a variety of classifiers.\n", "from sklearn.naive_bayes import GaussianNB ##Default(Tuned): Precision: 0.29453\tRecall: 0.43650\n", "from sklearn.tree import DecisionTreeClassifier ##Default: Precision: 0.14830\tRecall: 0.05450\n", "from sklearn.ensemble import RandomForestClassifier ##Default: Precision: 0.47575\tRecall: 0.20600, Longer time\n", "from sklearn.linear_model import SGDClassifier ##Tuned: Precision: 0.36853\tRecall: 0.35950, BEST!\n", "\n", "\n", "\n", "# clf = SGDClassifier(loss='hinge',\n", "# penalty='l2',\n", "# alpha=1e-6,\n", "# n_iter=31,\n", "# n_jobs=-1,\n", "# random_state=42)\n", "\n", "# clf = GaussianNB()\n", "\n", "### Task 5: Tune your classifier to achieve better than .3 precision and recall \n", "### using our testing script. Check the tester.py script in the final project\n", "### folder for details on the evaluation method, especially the test_classifier\n", "### function. Because of the small size of the dataset, the script uses\n", "### stratified shuffle split cross validation. For more info: \n", "### http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.StratifiedShuffleSplit.html\n", "\n", "# Example starting point. Try investigating other evaluation techniques!\n", "from sklearn.cross_validation import StratifiedShuffleSplit\n", "from sklearn.grid_search import GridSearchCV\n", "from sklearn.metrics import f1_score\n", "\n", "folds = 1000\n", "\n", "# StratifiedShuffleSplit is used when we take advantage of skew data but still keeping proportion of labels\n", "# If we using usual train test split, it could be there's no POI labels in the test set, or even worse in train set\n", "# which would makes the model isn't good enough. If for example the StratifiedShuffleSplit have 10 folds, then every folds\n", "# will contains equal proportions of POI vs non-POI\n", "\n", "cv = StratifiedShuffleSplit(df.poi,10,random_state=42)\n", "\n", "sgd = SGDClassifier(penalty='l2',random_state=42)\n", "parameters = {'loss': ['hinge','log','squared_hinge'],\n", " 'n_iter': [30,35],\n", " 'alpha': [1e-2, 1e-4 ,1e-6],\n", " }\n", "\n", "clf = GridSearchCV(sgd,parameters,scoring='f1',cv=10)\n", "\n", "\n", "### Task 6: Dump your classifier, dataset, and features_list so anyone can\n", "### check your results. You do not need to change anything below, but make sure\n", "### that the version of poi_id.py that you submit can be run on its own and\n", "### generates the necessary .pkl files for validating your results.\n", "\n", "dump_classifier_and_data(clf, my_dataset, features_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "vectorize email py\n", "\n", "Given an email, parse the text from the email\n", "\n", "Look up vectorize_text.py\n", "\n", "if the email in poi_email_address.py/poiEmails(), then from_poi append 1 otherwise 0\n", "\n" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([[ 0. , 0.12080033, 0.521875 , ..., 0. ,\n", " 0. , 0. ],\n", " [ 0. , 0.99854354, 0. , ..., 0. ,\n", " 0.03904287, 0. ],\n", " [ 0. , 0.94246039, 0.05 , ..., 0. ,\n", " 0. , 0. ],\n", " ..., \n", " [ 0. , 1. , 0.05625 , ..., 0. ,\n", " 0. , 0. ],\n", " [ 0. , 1. , 0. , ..., 0. ,\n", " 0. , 0. ],\n", " [ 1. , 1. , 0. , ..., 0. ,\n", " 0. , 0. ]])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load ../tools/" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from vectorize_text import get_word_data_from_email" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pickle" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data_dict = pickle.load(open('final_project_dataset.pkl','r'))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "df = pd.DataFrame.from_dict(data_dict,orient='index')" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df['word_data'] = df['email_address'].apply(get_word_data_from_email)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vect = TfidfVectorizer(stop_words='english',max_df=0.8,min_df=0.1)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<145x518 sparse matrix of type '<type 'numpy.float64'>'\n", "\twith 13491 stored elements in Compressed Sparse Row format>" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vect.fit_transform(df['word_data'])" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_docs = pd.DataFrame(vect.fit_transform(df['word_data']).toarray(),\n", " columns=vect.vocabulary_.keys(),\n", " index=df.index)" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.naive_bayes import GaussianNB, MultinomialNB" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.naive_bayes" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "GaussianNB()" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "GaussianNB().fit(vect.fit_transform(df['word_data']).toarray(), df.poi)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "Index([u'BADUM JAMES P', u'BAXTER JOHN C', u'BAZELIDES PHILIP J',\n", " u'BELFER ROBERT', u'BLAKE JR. NORMAN P', u'CHAN RONNIE',\n", " u'CLINE KENNETH W', u'CUMBERLAND MICHAEL S', u'DUNCAN JOHN H',\n", " u'FUGH JOHN L', u'GAHN ROBERT S', u'GATHMANN WILLIAM D', u'GILLIS JOHN',\n", " u'GRAMM WENDY L', u'GRAY RODNEY', u'JAEDICKE ROBERT',\n", " u'LEMAISTRE CHARLES', u'LOCKHART EUGENE E', u'LOWRY CHARLES P',\n", " u'MENDELSOHN JOHN', u'MEYER JEROME J', u'NOLES JAMES L',\n", " u'PEREIRA PAULO V. FERRAZ', u'REYNOLDS LAWRENCE', u'SAVAGE FRANK',\n", " u'SULLIVAN-SHAKLOVITZ COLLEEN', u'THE TRAVEL AGENCY IN THE PARK',\n", " u'URQUHART JOHN A', u'WAKEHAM JOHN', u'WALTERS GARETH W',\n", " u'WHALEY DAVID A', u'WINOKUR JR. HERBERT S', u'WROBEL BRUCE',\n", " u'YEAP SOON'],\n", " dtype='object')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[df.email_address == 'NaN'].index" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "df_modified = pd.DataFrame.from_dict(pickle.load(open('../dataset/final_project_dataset_modified.pkl','r')),orient='index')" ] }, { "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 }
mit
DamienIrving/ocean-analysis
development/dask_iris.ipynb
1
7748
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using dask with iris\n", "\n", "Below I'm attempting to calculate the annual mean timeseries for data sufficiently large that without using dask I get a memory error." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prepare cube" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')\n", "\n", "import glob\n", "import iris\n", "from iris.experimental.equalise_cubes import equalise_attributes\n", "import iris.coord_categorisation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "infiles = glob.glob('/g/data/ua6/DRSv3/CMIP5/CCSM4/historical/mon/ocean/r1i1p1/thetao/latest/thetao_Omon_CCSM4_historical_r1i1p1_??????-??????.nc')\n", "infiles.sort()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "cube_list = iris.load(infiles)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 120; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>,\n", "<iris 'Cube' of sea_water_potential_temperature / (K) (time: 72; depth: 60; cell index along second dimension: 384; cell index along first dimension: 320)>]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cube_list" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "equalise_attributes(cube_list)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "cube = cube_list.concatenate_cube()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "iris.coord_categorisation.add_year(cube, 'time')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Using dask for the memory intensive calculation" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from dask.distributed import LocalCluster\n", "from dask.distributed import Client" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "bcf0cb914dcf4ac9ae742b760f1be7ff", "version_major": 2, "version_minor": 0 }, "text/plain": [ "VBox(children=(HTML(value='<h2>LocalCluster</h2>'), HBox(children=(HTML(value='\\n<div>\\n <style scoped>\\n …" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "cluster = LocalCluster(n_workers=4)\n", "cluster" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<table style=\"border: 2px solid white;\">\n", "<tr>\n", "<td style=\"vertical-align: top; border: 0px solid white\">\n", "<h3>Client</h3>\n", "<ul>\n", " <li><b>Scheduler: </b>tcp://127.0.0.1:40042\n", " <li><b>Dashboard: </b><a href='http://127.0.0.1:8787/status' target='_blank'>http://127.0.0.1:8787/status</a>\n", "</ul>\n", "</td>\n", "<td style=\"vertical-align: top; border: 0px solid white\">\n", "<h3>Cluster</h3>\n", "<ul>\n", " <li><b>Workers: </b>4</li>\n", " <li><b>Cores: </b>8</li>\n", " <li><b>Memory: </b>33.67 GB</li>\n", "</ul>\n", "</td>\n", "</tr>\n", "</table>" ], "text/plain": [ "<Client: scheduler='tcp://127.0.0.1:40042' processes=4 cores=8>" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "client = Client(cluster)\n", "client" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "test = cube.aggregated_by(['year'], iris.analysis.MEAN)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Whn I try this `test = ...` calculation without using dask, the kernel always dies, which I'm assuming is due to insufficient RAM to do the calculation.\n", "\n", "Whn I try it with dask, it just never finishes." ] }, { "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 }
mit
bekbote/project_repository
0417_OverfittingAndRegularisation_1555486774539.ipynb
1
3011004
null
apache-2.0
probml/pyprobml
notebooks/book1/08/smooth-vs-nonsmooth-1d.ipynb
1
919
{ "cells": [ { "cell_type": "code", "execution_count": null, "id": "0c6aa80a", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "try:\n", " import probml_utils as pml\n", "except ModuleNotFoundError:\n", " %pip install -qq git+https://github.com/probml/probml-utils.git\n", " import probml_utils as pml\n", "\n", "x = np.linspace(-1, 1, 100)\n", "y = np.power(x, 2)\n", "plt.figure()\n", "plt.plot(x, y, \"-\", lw=3)\n", "plt.title(\"Smooth function\")\n", "pml.savefig(\"smooth-fn.pdf\")\n", "\n", "y = np.abs(x)\n", "plt.figure()\n", "plt.plot(x, y, \"-\", lw=3)\n", "plt.title(\"Non-smooth function\")\n", "pml.savefig(\"nonsmooth-fn.pdf\")\n", "\n", "plt.show()" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }
mit
javoweb/deep-learning
sentiment-network/Sentiment_Classification_Projects.ipynb
1
100583
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sentiment Classification & How To \"Frame Problems\" for a Neural Network\n", "\n", "by Andrew Trask\n", "\n", "- **Twitter**: @iamtrask\n", "- **Blog**: http://iamtrask.github.io" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### What You Should Already Know\n", "\n", "- neural networks, forward and back-propagation\n", "- stochastic gradient descent\n", "- mean squared error\n", "- and train/test splits\n", "\n", "### Where to Get Help if You Need it\n", "- Re-watch previous Udacity Lectures\n", "- Leverage the recommended Course Reading Material - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) (Check inside your classroom for a discount code)\n", "- Shoot me a tweet @iamtrask\n", "\n", "\n", "### Tutorial Outline:\n", "\n", "- Intro: The Importance of \"Framing a Problem\" (this lesson)\n", "\n", "- [Curate a Dataset](#lesson_1)\n", "- [Developing a \"Predictive Theory\"](#lesson_2)\n", "- [**PROJECT 1**: Quick Theory Validation](#project_1)\n", "\n", "\n", "- [Transforming Text to Numbers](#lesson_3)\n", "- [**PROJECT 2**: Creating the Input/Output Data](#project_2)\n", "\n", "\n", "- Putting it all together in a Neural Network (video only - nothing in notebook)\n", "- [**PROJECT 3**: Building our Neural Network](#project_3)\n", "\n", "\n", "- [Understanding Neural Noise](#lesson_4)\n", "- [**PROJECT 4**: Making Learning Faster by Reducing Noise](#project_4)\n", "\n", "\n", "- [Analyzing Inefficiencies in our Network](#lesson_5)\n", "- [**PROJECT 5**: Making our Network Train and Run Faster](#project_5)\n", "\n", "\n", "- [Further Noise Reduction](#lesson_6)\n", "- [**PROJECT 6**: Reducing Noise by Strategically Reducing the Vocabulary](#project_6)\n", "\n", "\n", "- [Analysis: What's going on in the weights?](#lesson_7)" ] }, { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "56bb3cba-260c-4ebe-9ed6-b995b4c72aa3" } }, "source": [ "# Lesson: Curate a Dataset<a id='lesson_1'></a>\n", "The cells from here until Project 1 include code Andrew shows in the videos leading up to mini project 1. We've included them so you can run the code along with the videos without having to type in everything." ] }, { "cell_type": "code", "execution_count": null, "metadata": { <<<<<<< HEAD "collapsed": true, ======= >>>>>>> 993180e8fbabe9a0f202443c158945c6834735af "nbpresent": { "id": "eba2b193-0419-431e-8db9-60f34dd3fe83" } }, "outputs": [], "source": [ "def pretty_print_review_and_label(i):\n", " print(labels[i] + \"\\t:\\t\" + reviews[i][:80] + \"...\")\n", "\n", "g = open('reviews.txt','r') # What we know!\n", "reviews = list(map(lambda x:x[:-1],g.readlines()))\n", "g.close()\n", "\n", "g = open('labels.txt','r') # What we WANT to know!\n", "labels = list(map(lambda x:x[:-1].upper(),g.readlines()))\n", "g.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Note:** The data in `reviews.txt` we're using has already been preprocessed a bit and contains only lower case characters. If we were working from raw data, where we didn't know it was all lower case, we would want to add a step here to convert it. That's so we treat different variations of the same word, like `The`, `the`, and `THE`, all the same way." ] }, { "cell_type": "code", "execution_count": null, <<<<<<< HEAD "metadata": { "collapsed": true }, ======= "metadata": {}, >>>>>>> 993180e8fbabe9a0f202443c158945c6834735af "outputs": [], "source": [ "len(reviews)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { <<<<<<< HEAD "collapsed": true, ======= >>>>>>> 993180e8fbabe9a0f202443c158945c6834735af "nbpresent": { "id": "bb95574b-21a0-4213-ae50-34363cf4f87f" } }, "outputs": [], "source": [ "reviews[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { <<<<<<< HEAD "collapsed": true, ======= >>>>>>> 993180e8fbabe9a0f202443c158945c6834735af "nbpresent": { "id": "e0408810-c424-4ed4-afb9-1735e9ddbd0a" } }, "outputs": [], "source": [ "labels[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Lesson: Develop a Predictive Theory<a id='lesson_2'></a>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { <<<<<<< HEAD "collapsed": true, ======= >>>>>>> 993180e8fbabe9a0f202443c158945c6834735af "nbpresent": { "id": "e67a709f-234f-4493-bae6-4fb192141ee0" } }, "outputs": [], "source": [ "print(\"labels.txt \\t : \\t reviews.txt\\n\")\n", "pretty_print_review_and_label(2137)\n", "pretty_print_review_and_label(12816)\n", "pretty_print_review_and_label(6267)\n", "pretty_print_review_and_label(21934)\n", "pretty_print_review_and_label(5297)\n", "pretty_print_review_and_label(4998)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 1: Quick Theory Validation<a id='project_1'></a>\n", "\n", "There are multiple ways to implement these projects, but in order to get your code closer to what Andrew shows in his solutions, we've provided some hints and starter code throughout this notebook.\n", "\n", "You'll find the [Counter](https://docs.python.org/2/library/collections.html#collections.Counter) class to be useful in this exercise, as well as the [numpy](https://docs.scipy.org/doc/numpy/reference/) library." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from collections import Counter\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll create three `Counter` objects, one for words from postive reviews, one for words from negative reviews, and one for all the words." ] }, { "cell_type": "code", "execution_count": null, <<<<<<< HEAD "metadata": { "collapsed": true }, ======= "metadata": {}, >>>>>>> 993180e8fbabe9a0f202443c158945c6834735af "outputs": [], "source": [ "# Create three Counter objects to store positive, negative and total counts\n", "positive_counts = Counter()\n", "negative_counts = Counter()\n", "total_counts = Counter()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TODO:** Examine all the reviews. For each word in a positive review, increase the count for that word in both your positive counter and the total words counter; likewise, for each word in a negative review, increase the count for that word in both your negative counter and the total words counter.\n", "\n", "**Note:** Throughout these projects, you should use `split(' ')` to divide a piece of text (such as a review) into individual words. If you use `split()` instead, you'll get slightly different results than what the videos and solutions show." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: Loop over all the words in all the reviews and increment the counts in the appropriate counter objects" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following two cells to list the words used in positive reviews and negative reviews, respectively, ordered from most to least commonly used. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Examine the counts of the most common words in positive reviews\n", "positive_counts.most_common()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Examine the counts of the most common words in negative reviews\n", "negative_counts.most_common()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As you can see, common words like \"the\" appear very often in both positive and negative reviews. Instead of finding the most common words in positive or negative reviews, what you really want are the words found in positive reviews more often than in negative reviews, and vice versa. To accomplish this, you'll need to calculate the **ratios** of word usage between positive and negative reviews.\n", "\n", "**TODO:** Check all the words you've seen and calculate the ratio of postive to negative uses and store that ratio in `pos_neg_ratios`. \n", ">Hint: the positive-to-negative ratio for a given word can be calculated with `positive_counts[word] / float(negative_counts[word]+1)`. Notice the `+1` in the denominator – that ensures we don't divide by zero for words that are only seen in positive reviews." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create Counter object to store positive/negative ratios\n", "pos_neg_ratios = Counter()\n", "\n", "# TODO: Calculate the ratios of positive and negative uses of the most common words\n", "# Consider words to be \"common\" if they've been used at least 100 times" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Examine the ratios you've calculated for a few words:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(\"Pos-to-neg ratio for 'the' = {}\".format(pos_neg_ratios[\"the\"]))\n", "print(\"Pos-to-neg ratio for 'amazing' = {}\".format(pos_neg_ratios[\"amazing\"]))\n", "print(\"Pos-to-neg ratio for 'terrible' = {}\".format(pos_neg_ratios[\"terrible\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking closely at the values you just calculated, we see the following:\n", "\n", "* Words that you would expect to see more often in positive reviews – like \"amazing\" – have a ratio greater than 1. The more skewed a word is toward postive, the farther from 1 its positive-to-negative ratio will be.\n", "* Words that you would expect to see more often in negative reviews – like \"terrible\" – have positive values that are less than 1. The more skewed a word is toward negative, the closer to zero its positive-to-negative ratio will be.\n", "* Neutral words, which don't really convey any sentiment because you would expect to see them in all sorts of reviews – like \"the\" – have values very close to 1. A perfectly neutral word – one that was used in exactly the same number of positive reviews as negative reviews – would be almost exactly 1. The `+1` we suggested you add to the denominator slightly biases words toward negative, but it won't matter because it will be a tiny bias and later we'll be ignoring words that are too close to neutral anyway.\n", "\n", "Ok, the ratios tell us which words are used more often in postive or negative reviews, but the specific values we've calculated are a bit difficult to work with. A very positive word like \"amazing\" has a value above 4, whereas a very negative word like \"terrible\" has a value around 0.18. Those values aren't easy to compare for a couple of reasons:\n", "\n", "* Right now, 1 is considered neutral, but the absolute value of the postive-to-negative rations of very postive words is larger than the absolute value of the ratios for the very negative words. So there is no way to directly compare two numbers and see if one word conveys the same magnitude of positive sentiment as another word conveys negative sentiment. So we should center all the values around netural so the absolute value fro neutral of the postive-to-negative ratio for a word would indicate how much sentiment (positive or negative) that word conveys.\n", "* When comparing absolute values it's easier to do that around zero than one. \n", "\n", "To fix these issues, we'll convert all of our ratios to new values using logarithms.\n", "\n", "**TODO:** Go through all the ratios you calculated and convert them to logarithms. (i.e. use `np.log(ratio)`)\n", "\n", "In the end, extremely positive and extremely negative words will have positive-to-negative ratios with similar magnitudes but opposite signs." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: Convert ratios to logs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Examine the new ratios you've calculated for the same words from before:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "print(\"Pos-to-neg ratio for 'the' = {}\".format(pos_neg_ratios[\"the\"]))\n", "print(\"Pos-to-neg ratio for 'amazing' = {}\".format(pos_neg_ratios[\"amazing\"]))\n", "print(\"Pos-to-neg ratio for 'terrible' = {}\".format(pos_neg_ratios[\"terrible\"]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If everything worked, now you should see neutral words with values close to zero. In this case, \"the\" is near zero but slightly positive, so it was probably used in more positive reviews than negative reviews. But look at \"amazing\"'s ratio - it's above `1`, showing it is clearly a word with positive sentiment. And \"terrible\" has a similar score, but in the opposite direction, so it's below `-1`. It's now clear that both of these words are associated with specific, opposing sentiments.\n", "\n", "Now run the following cells to see more ratios. \n", "\n", "The first cell displays all the words, ordered by how associated they are with postive reviews. (Your notebook will most likely truncate the output so you won't actually see *all* the words in the list.)\n", "\n", "The second cell displays the 30 words most associated with negative reviews by reversing the order of the first list and then looking at the first 30 words. (If you want the second cell to display all the words, ordered by how associated they are with negative reviews, you could just write `reversed(pos_neg_ratios.most_common())`.)\n", "\n", "You should continue to see values similar to the earlier ones we checked – neutral words will be close to `0`, words will get more positive as their ratios approach and go above `1`, and words will get more negative as their ratios approach and go below `-1`. That's why we decided to use the logs instead of the raw ratios." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# words most frequently seen in a review with a \"POSITIVE\" label\n", "pos_neg_ratios.most_common()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# words most frequently seen in a review with a \"NEGATIVE\" label\n", "list(reversed(pos_neg_ratios.most_common()))[0:30]\n", "\n", "# Note: Above is the code Andrew uses in his solution video, \n", "# so we've included it here to avoid confusion.\n", "# If you explore the documentation for the Counter class, \n", "# you will see you could also find the 30 least common\n", "# words like this: pos_neg_ratios.most_common()[:-31:-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# End of Project 1. \n", "## Watch the next video to see Andrew's solution, then continue on to the next lesson.\n", "\n", "# Transforming Text into Numbers<a id='lesson_3'></a>\n", "The cells here include code Andrew shows in the next video. We've included it so you can run the code along with the video without having to type in everything." ] }, { "cell_type": "code", "execution_count": null, <<<<<<< HEAD "metadata": { "collapsed": true }, ======= "metadata": {}, >>>>>>> 993180e8fbabe9a0f202443c158945c6834735af "outputs": [], "source": [ "from IPython.display import Image\n", "\n", "review = \"This was a horrible, terrible movie.\"\n", "\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": null, <<<<<<< HEAD "metadata": { "collapsed": true }, ======= "metadata": {}, >>>>>>> 993180e8fbabe9a0f202443c158945c6834735af "outputs": [], "source": [ "review = \"The movie was excellent\"\n", "\n", "Image(filename='sentiment_network_pos.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 2: Creating the Input/Output Data<a id='project_2'></a>\n", "\n", "**TODO:** Create a [set](https://docs.python.org/3/tutorial/datastructures.html#sets) named `vocab` that contains every word in the vocabulary." ] }, { "cell_type": "code", "execution_count": null, <<<<<<< HEAD "metadata": { "collapsed": true }, ======= "metadata": {}, >>>>>>> 993180e8fbabe9a0f202443c158945c6834735af "outputs": [], "source": [ "# TODO: Create set named \"vocab\" containing all of the words from all of the reviews\n", "vocab = None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to check your vocabulary size. If everything worked correctly, it should print **74074**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "vocab_size = len(vocab)\n", "print(vocab_size)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take a look at the following image. It represents the layers of the neural network you'll be building throughout this notebook. `layer_0` is the input layer, `layer_1` is a hidden layer, and `layer_2` is the output layer." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "image/png": 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9M+3mYEl15p1MumgWi24VOcyStAwavngEdCikeIwbmwM96LvuusuarSETc+fO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\nOHz4cLNgwYLQxbKSWgAIL4teQUqwqOTdyPeqPvJFklhleqV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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network_2.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TODO:** Create a numpy array called `layer_0` and initialize it to all zeros. You will find the [zeros](https://docs.scipy.org/doc/numpy/reference/generated/numpy.zeros.html) function particularly helpful here. Be sure you create `layer_0` as a 2-dimensional matrix with 1 row and `vocab_size` columns. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: Create layer_0 matrix with dimensions 1 by vocab_size, initially filled with zeros\n", "layer_0 = None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell. It should display `(1, 74074)`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0.shape" ] }, { "cell_type": "code", "execution_count": null, <<<<<<< HEAD "metadata": { "collapsed": true }, ======= "metadata": {}, >>>>>>> 993180e8fbabe9a0f202443c158945c6834735af "outputs": [], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`layer_0` contains one entry for every word in the vocabulary, as shown in the above image. We need to make sure we know the index of each word, so run the following cell to create a lookup table that stores the index of every word." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Create a dictionary of words in the vocabulary mapped to index positions\n", "# (to be used in layer_0)\n", "word2index = {}\n", "for i,word in enumerate(vocab):\n", " word2index[word] = i\n", " \n", "# display the map of words to indices\n", "word2index" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TODO:** Complete the implementation of `update_input_layer`. It should count \n", " how many times each word is used in the given review, and then store\n", " those counts at the appropriate indices inside `layer_0`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def update_input_layer(review):\n", " \"\"\" Modify the global layer_0 to represent the vector form of review.\n", " The element at a given index of layer_0 should represent\n", " how many times the given word occurs in the review.\n", " Args:\n", " review(string) - the string of the review\n", " Returns:\n", " None\n", " \"\"\"\n", " global layer_0\n", " # clear out previous state by resetting the layer to be all 0s\n", " layer_0 *= 0\n", " \n", " # TODO: count how many times each word is used in the given review and store the results in layer_0 " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to test updating the input layer with the first review. The indices assigned may not be the same as in the solution, but hopefully you'll see some non-zero values in `layer_0`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "update_input_layer(reviews[0])\n", "layer_0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TODO:** Complete the implementation of `get_target_for_labels`. It should return `0` or `1`, \n", " depending on whether the given label is `NEGATIVE` or `POSITIVE`, respectively." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_target_for_label(label):\n", " \"\"\"Convert a label to `0` or `1`.\n", " Args:\n", " label(string) - Either \"POSITIVE\" or \"NEGATIVE\".\n", " Returns:\n", " `0` or `1`.\n", " \"\"\"\n", " # TODO: Your code here" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following two cells. They should print out`'POSITIVE'` and `1`, respectively." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "labels[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "get_target_for_label(labels[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following two cells. They should print out `'NEGATIVE'` and `0`, respectively." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "labels[1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "get_target_for_label(labels[1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# End of Project 2. \n", "## Watch the next video to see Andrew's solution, then continue on to the next lesson." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 3: Building a Neural Network<a id='project_3'></a>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**TODO:** We've included the framework of a class called `SentimentNetork`. Implement all of the items marked `TODO` in the code. These include doing the following:\n", "- Create a basic neural network much like the networks you've seen in earlier lessons and in Project 1, with an input layer, a hidden layer, and an output layer. \n", "- Do **not** add a non-linearity in the hidden layer. That is, do not use an activation function when calculating the hidden layer outputs.\n", "- Re-use the code from earlier in this notebook to create the training data (see `TODO`s in the code)\n", "- Implement the `pre_process_data` function to create the vocabulary for our training data generating functions\n", "- Ensure `train` trains over the entire corpus" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Where to Get Help if You Need it\n", "- Re-watch earlier Udacity lectures\n", "- Chapters 3-5 - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) - (Check inside your classroom for a discount code)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time\n", "import sys\n", "import numpy as np\n", "\n", "# Encapsulate our neural network in a class\n", "class SentimentNetwork:\n", " def __init__(self, reviews, labels, hidden_nodes = 10, learning_rate = 0.1):\n", " \"\"\"Create a SentimenNetwork with the given settings\n", " Args:\n", " reviews(list) - List of reviews used for training\n", " labels(list) - List of POSITIVE/NEGATIVE labels associated with the given reviews\n", " hidden_nodes(int) - Number of nodes to create in the hidden layer\n", " learning_rate(float) - Learning rate to use while training\n", " \n", " \"\"\"\n", " # Assign a seed to our random number generator to ensure we get\n", " # reproducable results during development \n", " np.random.seed(1)\n", "\n", " # process the reviews and their associated labels so that everything\n", " # is ready for training\n", " self.pre_process_data(reviews, labels)\n", " \n", " # Build the network to have the number of hidden nodes and the learning rate that\n", " # were passed into this initializer. Make the same number of input nodes as\n", " # there are vocabulary words and create a single output node.\n", " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", "\n", " def pre_process_data(self, reviews, labels):\n", " \n", " review_vocab = set()\n", " # TODO: populate review_vocab with all of the words in the given reviews\n", " # Remember to split reviews into individual words \n", " # using \"split(' ')\" instead of \"split()\".\n", " \n", " # Convert the vocabulary set to a list so we can access words via indices\n", " self.review_vocab = list(review_vocab)\n", " \n", " label_vocab = set()\n", " # TODO: populate label_vocab with all of the words in the given labels.\n", " # There is no need to split the labels because each one is a single word.\n", " \n", " # Convert the label vocabulary set to a list so we can access labels via indices\n", " self.label_vocab = list(label_vocab)\n", " \n", " # Store the sizes of the review and label vocabularies.\n", " self.review_vocab_size = len(self.review_vocab)\n", " self.label_vocab_size = len(self.label_vocab)\n", " \n", " # Create a dictionary of words in the vocabulary mapped to index positions\n", " self.word2index = {}\n", " # TODO: populate self.word2index with indices for all the words in self.review_vocab\n", " # like you saw earlier in the notebook\n", " \n", " # Create a dictionary of labels mapped to index positions\n", " self.label2index = {}\n", " # TODO: do the same thing you did for self.word2index and self.review_vocab, \n", " # but for self.label2index and self.label_vocab instead\n", " \n", " \n", " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Store the number of nodes in input, hidden, and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Store the learning rate\n", " self.learning_rate = learning_rate\n", "\n", " # Initialize weights\n", " \n", " # TODO: initialize self.weights_0_1 as a matrix of zeros. These are the weights between\n", " # the input layer and the hidden layer.\n", " self.weights_0_1 = None\n", " \n", " # TODO: initialize self.weights_1_2 as a matrix of random values. \n", " # These are the weights between the hidden layer and the output layer.\n", " self.weights_1_2 = None\n", " \n", " # TODO: Create the input layer, a two-dimensional matrix with shape \n", " # 1 x input_nodes, with all values initialized to zero\n", " self.layer_0 = np.zeros((1,input_nodes))\n", " \n", " \n", " def update_input_layer(self,review):\n", " # TODO: You can copy most of the code you wrote for update_input_layer \n", " # earlier in this notebook. \n", " #\n", " # However, MAKE SURE YOU CHANGE ALL VARIABLES TO REFERENCE\n", " # THE VERSIONS STORED IN THIS OBJECT, NOT THE GLOBAL OBJECTS.\n", " # For example, replace \"layer_0 *= 0\" with \"self.layer_0 *= 0\"\n", " pass\n", " \n", " def get_target_for_label(self,label):\n", " # TODO: Copy the code you wrote for get_target_for_label \n", " # earlier in this notebook. \n", " pass\n", " \n", " def sigmoid(self,x):\n", " # TODO: Return the result of calculating the sigmoid activation function\n", " # shown in the lectures\n", " pass\n", " \n", " def sigmoid_output_2_derivative(self,output):\n", " # TODO: Return the derivative of the sigmoid activation function, \n", " # where \"output\" is the original output from the sigmoid fucntion \n", " pass\n", "\n", " def train(self, training_reviews, training_labels):\n", " \n", " # make sure out we have a matching number of reviews and labels\n", " assert(len(training_reviews) == len(training_labels))\n", " \n", " # Keep track of correct predictions to display accuracy during training \n", " correct_so_far = 0\n", " \n", " # Remember when we started for printing time statistics\n", " start = time.time()\n", "\n", " # loop through all the given reviews and run a forward and backward pass,\n", " # updating weights for every item\n", " for i in range(len(training_reviews)):\n", " \n", " # TODO: Get the next review and its correct label\n", " \n", " # TODO: Implement the forward pass through the network. \n", " # That means use the given review to update the input layer, \n", " # then calculate values for the hidden layer,\n", " # and finally calculate the output layer.\n", " # \n", " # Do not use an activation function for the hidden layer,\n", " # but use the sigmoid activation function for the output layer.\n", " \n", " # TODO: Implement the back propagation pass here. \n", " # That means calculate the error for the forward pass's prediction\n", " # and update the weights in the network according to their\n", " # contributions toward the error, as calculated via the\n", " # gradient descent and back propagation algorithms you \n", " # learned in class.\n", " \n", " # TODO: Keep track of correct predictions. To determine if the prediction was\n", " # correct, check that the absolute value of the output error \n", " # is less than 0.5. If so, add one to the correct_so_far count.\n", " \n", " # For debug purposes, print out our prediction accuracy and speed \n", " # throughout the training process. \n", "\n", " elapsed_time = float(time.time() - start)\n", " reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) \\\n", " + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", " if(i % 2500 == 0):\n", " print(\"\")\n", " \n", " def test(self, testing_reviews, testing_labels):\n", " \"\"\"\n", " Attempts to predict the labels for the given testing_reviews,\n", " and uses the test_labels to calculate the accuracy of those predictions.\n", " \"\"\"\n", " \n", " # keep track of how many correct predictions we make\n", " correct = 0\n", "\n", " # we'll time how many predictions per second we make\n", " start = time.time()\n", "\n", " # Loop through each of the given reviews and call run to predict\n", " # its label. \n", " for i in range(len(testing_reviews)):\n", " pred = self.run(testing_reviews[i])\n", " if(pred == testing_labels[i]):\n", " correct += 1\n", " \n", " # For debug purposes, print out our prediction accuracy and speed \n", " # throughout the prediction process. \n", "\n", " elapsed_time = float(time.time() - start)\n", " reviews_per_second = i / elapsed_time if elapsed_time > 0 else 0\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \" #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) \\\n", " + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", " \n", " def run(self, review):\n", " \"\"\"\n", " Returns a POSITIVE or NEGATIVE prediction for the given review.\n", " \"\"\"\n", " # TODO: Run a forward pass through the network, like you did in the\n", " # \"train\" function. That means use the given review to \n", " # update the input layer, then calculate values for the hidden layer,\n", " # and finally calculate the output layer.\n", " #\n", " # Note: The review passed into this function for prediction \n", " # might come from anywhere, so you should convert it \n", " # to lower case prior to using it.\n", " \n", " # TODO: The output layer should now contain a prediction. \n", " # Return `POSITIVE` for predictions greater-than-or-equal-to `0.5`, \n", " # and `NEGATIVE` otherwise.\n", " pass\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to create a `SentimentNetwork` that will train on all but the last 1000 reviews (we're saving those for testing). Here we use a learning rate of `0.1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to test the network's performance against the last 1000 reviews (the ones we held out from our training set). \n", "\n", "**We have not trained the model yet, so the results should be about 50% as it will just be guessing and there are only two possible values to choose from.**" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to actually train the network. During training, it will display the model's accuracy repeatedly as it trains so you can see how well it's doing." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That most likely didn't train very well. Part of the reason may be because the learning rate is too high. Run the following cell to recreate the network with a smaller learning rate, `0.01`, and then train the new network." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.01)\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That probably wasn't much different. Run the following cell to recreate the network one more time with an even smaller learning rate, `0.001`, and then train the new network." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001)\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With a learning rate of `0.001`, the network should finall have started to improve during training. It's still not very good, but it shows that this solution has potential. We will improve it in the next lesson." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# End of Project 3. \n", "## Watch the next video to see Andrew's solution, then continue on to the next lesson." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Understanding Neural Noise<a id='lesson_4'></a>\n", "\n", "The following cells include includes the code Andrew shows in the next video. We've included it here so you can run the cells along with the video without having to type in everything." ] }, { "cell_type": "code", "execution_count": null, <<<<<<< HEAD "metadata": { "collapsed": true }, ======= "metadata": {}, >>>>>>> 993180e8fbabe9a0f202443c158945c6834735af "outputs": [], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def update_input_layer(review):\n", " \n", " global layer_0\n", " \n", " # clear out previous state, reset the layer to be all 0s\n", " layer_0 *= 0\n", " for word in review.split(\" \"):\n", " layer_0[0][word2index[word]] += 1\n", "\n", "update_input_layer(reviews[0])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "review_counter = Counter()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for word in reviews[0].split(\" \"):\n", " review_counter[word] += 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "review_counter.most_common()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 4: Reducing Noise in Our Input Data<a id='project_4'></a>\n", "\n", "**TODO:** Attempt to reduce the noise in the input data like Andrew did in the previous video. Specifically, do the following:\n", "* Copy the `SentimentNetwork` class you created earlier into the following cell.\n", "* Modify `update_input_layer` so it does not count how many times each word is used, but rather just stores whether or not a word was used. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: -Copy the SentimentNetwork class from Projet 3 lesson\n", "# -Modify it to reduce noise, like in the video " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to recreate the network and train it. Notice we've gone back to the higher learning rate of `0.1`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That should have trained much better than the earlier attempts. It's still not wonderful, but it should have improved dramatically. Run the following cell to test your model with 1000 predictions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# End of Project 4. \n", "## Andrew's solution was actually in the previous video, so rewatch that video if you had any problems with that project. Then continue on to the next lesson.\n", "# Analyzing Inefficiencies in our Network<a id='lesson_5'></a>\n", "The following cells include the code Andrew shows in the next video. We've included it here so you can run the cells along with the video without having to type in everything." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Image(filename='sentiment_network_sparse.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0 = np.zeros(10)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0[4] = 1\n", "layer_0[9] = 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "weights_0_1 = np.random.randn(10,5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_0.dot(weights_0_1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "indices = [4,9]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_1 = np.zeros(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for index in indices:\n", " layer_1 += (1 * weights_0_1[index])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Image(filename='sentiment_network_sparse_2.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_1 = np.zeros(5)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for index in indices:\n", " layer_1 += (weights_0_1[index])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "layer_1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 5: Making our Network More Efficient<a id='project_5'></a>\n", "**TODO:** Make the `SentimentNetwork` class more efficient by eliminating unnecessary multiplications and additions that occur during forward and backward propagation. To do that, you can do the following:\n", "* Copy the `SentimentNetwork` class from the previous project into the following cell.\n", "* Remove the `update_input_layer` function - you will not need it in this version.\n", "* Modify `init_network`:\n", ">* You no longer need a separate input layer, so remove any mention of `self.layer_0`\n", ">* You will be dealing with the old hidden layer more directly, so create `self.layer_1`, a two-dimensional matrix with shape 1 x hidden_nodes, with all values initialized to zero\n", "* Modify `train`:\n", ">* Change the name of the input parameter `training_reviews` to `training_reviews_raw`. This will help with the next step.\n", ">* At the beginning of the function, you'll want to preprocess your reviews to convert them to a list of indices (from `word2index`) that are actually used in the review. This is equivalent to what you saw in the video when Andrew set specific indices to 1. Your code should create a local `list` variable named `training_reviews` that should contain a `list` for each review in `training_reviews_raw`. Those lists should contain the indices for words found in the review.\n", ">* Remove call to `update_input_layer`\n", ">* Use `self`'s `layer_1` instead of a local `layer_1` object.\n", ">* In the forward pass, replace the code that updates `layer_1` with new logic that only adds the weights for the indices used in the review.\n", ">* When updating `weights_0_1`, only update the individual weights that were used in the forward pass.\n", "* Modify `run`:\n", ">* Remove call to `update_input_layer` \n", ">* Use `self`'s `layer_1` instead of a local `layer_1` object.\n", ">* Much like you did in `train`, you will need to pre-process the `review` so you can work with word indices, then update `layer_1` by adding weights for the indices used in the review." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: -Copy the SentimentNetwork class from Project 4 lesson\n", "# -Modify it according to the above instructions " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to recreate the network and train it once again." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That should have trained much better than the earlier attempts. Run the following cell to test your model with 1000 predictions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# End of Project 5. \n", "## Watch the next video to see Andrew's solution, then continue on to the next lesson.\n", "# Further Noise Reduction<a id='lesson_6'></a>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Image(filename='sentiment_network_sparse_2.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# words most frequently seen in a review with a \"POSITIVE\" label\n", "pos_neg_ratios.most_common()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# words most frequently seen in a review with a \"NEGATIVE\" label\n", "list(reversed(pos_neg_ratios.most_common()))[0:30]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from bokeh.models import ColumnDataSource, LabelSet\n", "from bokeh.plotting import figure, show, output_file\n", "from bokeh.io import output_notebook\n", "output_notebook()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hist, edges = np.histogram(list(map(lambda x:x[1],pos_neg_ratios.most_common())), density=True, bins=100, normed=True)\n", "\n", "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n", " toolbar_location=\"above\",\n", " title=\"Word Positive/Negative Affinity Distribution\")\n", "p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], line_color=\"#555555\")\n", "show(p)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "frequency_frequency = Counter()\n", "\n", "for word, cnt in total_counts.most_common():\n", " frequency_frequency[cnt] += 1" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "hist, edges = np.histogram(list(map(lambda x:x[1],frequency_frequency.most_common())), density=True, bins=100, normed=True)\n", "\n", "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n", " toolbar_location=\"above\",\n", " title=\"The frequency distribution of the words in our corpus\")\n", "p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], line_color=\"#555555\")\n", "show(p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project 6: Reducing Noise by Strategically Reducing the Vocabulary<a id='project_6'></a>\n", "\n", "**TODO:** Improve `SentimentNetwork`'s performance by reducing more noise in the vocabulary. Specifically, do the following:\n", "* Copy the `SentimentNetwork` class from the previous project into the following cell.\n", "* Modify `pre_process_data`:\n", ">* Add two additional parameters: `min_count` and `polarity_cutoff`\n", ">* Calculate the positive-to-negative ratios of words used in the reviews. (You can use code you've written elsewhere in the notebook, but we are moving it into the class like we did with other helper code earlier.)\n", ">* Andrew's solution only calculates a postive-to-negative ratio for words that occur at least 50 times. This keeps the network from attributing too much sentiment to rarer words. You can choose to add this to your solution if you would like. \n", ">* Change so words are only added to the vocabulary if they occur in the vocabulary more than `min_count` times.\n", ">* Change so words are only added to the vocabulary if the absolute value of their postive-to-negative ratio is at least `polarity_cutoff`\n", "* Modify `__init__`:\n", ">* Add the same two parameters (`min_count` and `polarity_cutoff`) and use them when you call `pre_process_data`" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TODO: -Copy the SentimentNetwork class from Project 5 lesson\n", "# -Modify it according to the above instructions " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to train your network with a small polarity cutoff." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=20,polarity_cutoff=0.05,learning_rate=0.01)\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And run the following cell to test it's performance. It should be " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Run the following cell to train your network with a much larger polarity cutoff." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=20,polarity_cutoff=0.8,learning_rate=0.01)\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And run the following cell to test it's performance." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# End of Project 6. \n", "## Watch the next video to see Andrew's solution, then continue on to the next lesson." ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Analysis: What's Going on in the Weights?<a id='lesson_7'></a>" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp_full = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=0,polarity_cutoff=0,learning_rate=0.01)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "mlp_full.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "Image(filename='sentiment_network_sparse.png')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_most_similar_words(focus = \"horrible\"):\n", " most_similar = Counter()\n", "\n", " for word in mlp_full.word2index.keys():\n", " most_similar[word] = np.dot(mlp_full.weights_0_1[mlp_full.word2index[word]],mlp_full.weights_0_1[mlp_full.word2index[focus]])\n", " \n", " return most_similar.most_common()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "get_most_similar_words(\"excellent\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "get_most_similar_words(\"terrible\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.colors as colors\n", "\n", "words_to_visualize = list()\n", "for word, ratio in pos_neg_ratios.most_common(500):\n", " if(word in mlp_full.word2index.keys()):\n", " words_to_visualize.append(word)\n", " \n", "for word, ratio in list(reversed(pos_neg_ratios.most_common()))[0:500]:\n", " if(word in mlp_full.word2index.keys()):\n", " words_to_visualize.append(word)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pos = 0\n", "neg = 0\n", "\n", "colors_list = list()\n", "vectors_list = list()\n", "for word in words_to_visualize:\n", " if word in pos_neg_ratios.keys():\n", " vectors_list.append(mlp_full.weights_0_1[mlp_full.word2index[word]])\n", " if(pos_neg_ratios[word] > 0):\n", " pos+=1\n", " colors_list.append(\"#00ff00\")\n", " else:\n", " neg+=1\n", " colors_list.append(\"#000000\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn.manifold import TSNE\n", "tsne = TSNE(n_components=2, random_state=0)\n", "words_top_ted_tsne = tsne.fit_transform(vectors_list)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n", " toolbar_location=\"above\",\n", " title=\"vector T-SNE for most polarized words\")\n", "\n", "source = ColumnDataSource(data=dict(x1=words_top_ted_tsne[:,0],\n", " x2=words_top_ted_tsne[:,1],\n", " names=words_to_visualize,\n", " color=colors_list))\n", "\n", "p.scatter(x=\"x1\", y=\"x2\", size=8, source=source, fill_color=\"color\")\n", "\n", "word_labels = LabelSet(x=\"x1\", y=\"x2\", text=\"names\", y_offset=6,\n", " text_font_size=\"8pt\", text_color=\"#555555\",\n", " source=source, text_align='center')\n", "p.add_layout(word_labels)\n", "\n", "show(p)\n", "\n", "# green indicates positive words, black indicates negative words" ] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { <<<<<<< HEAD "display_name": "Python [default]", ======= "display_name": "Python 3", >>>>>>> 993180e8fbabe9a0f202443c158945c6834735af "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
xiongzhenggang/xiongzhenggang.github.io
data-science/24-simple_liner.ipynb
1
656360
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 简单线图\n", " 先设置ipython notebook 作图环境" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "#使用seaborn-whitegrid风格\n", "plt.style.use('seaborn-whitegrid')\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对于所有Matplotlib图，我们首先创建一个图形和一个轴。以最简单的形式，可以如下创建图形和轴：" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"248.064219pt\" version=\"1.1\" viewBox=\"0 0 373.55 248.064219\" width=\"373.55pt\" 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}, "metadata": {} } ], "source": [ "fig = plt.figure()\n", "ax = plt.axes()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "在Matplotlib中，图形（类plt.Figure的一个实例）可以被认为是一个包含所有代表轴，图形，文本和标签的对象的容器。轴（类plt.Axes的实例）就是我们在上面看到的：一个带有刻度和标签的边界框，该边界框最终将包含构成我们可视化的绘图元素。在本书中，我们通常使用变量名fig来引用图形实例，并使用ax来引用轴实例或一组轴实例。\n", "创建轴后，可以使用ax.plot函数绘制一些数据。让我们从一个简单的正弦波开始：" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"244.485312pt\" version=\"1.1\" viewBox=\"0 0 378.001562 244.485312\" width=\"378.001562pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <defs>\r\n <style 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}, "metadata": {} } ], "source": [ "fig = plt.figure()\n", "ax = plt.axes()\n", "\n", "x = np.linspace(0, 10, 1000)\n", "ax.plot(x, np.sin(x));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "或者，我们可以使用pylab界面，并在后台为我们创建图形和轴：" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"244.485312pt\" version=\"1.1\" viewBox=\"0 0 378.001562 244.485312\" width=\"378.001562pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <defs>\r\n <style type=\"text/css\">\r\n*{stroke-linecap:butt;stroke-linejoin:round;}\r\n </style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n <g 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\n" }, "metadata": {} } ], "source": [ "plt.plot(x, np.sin(x));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "如果想在一个图中，画出多条曲线，可以调用多次plot()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"244.485312pt\" version=\"1.1\" viewBox=\"0 0 378.001562 244.485312\" width=\"378.001562pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <defs>\r\n <style type=\"text/css\">\r\n*{stroke-linecap:butt;stroke-linejoin:round;}\r\n </style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n <g id=\"patch_1\">\r\n <path d=\"M 0 244.485312 \r\nL 378.001562 244.485312 \r\nL 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}, "metadata": {} } ], "source": [ "plt.plot(x, np.sin(x))\n", "plt.plot(x, np.cos(x));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这就是在Matplotlib中绘制简单函数！现在，我们将深入探讨有关如何控制轴和线的外观的更多详细信息。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 调整图：线条颜色和样式" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "您可能希望对图形进行的第一个调整是控制线条的颜色和样式。 plt.plot（）函数采用其他可用于指定这些参数的参数。要调整颜色，可以使用color关键字，该关键字接受一个表示几乎任何可想象的颜色的字符串参数。可以通过多种方式指定颜色：" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"244.485312pt\" version=\"1.1\" viewBox=\"0 0 378.001562 244.485312\" width=\"378.001562pt\" 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}, "metadata": {} } ], "source": [ "plt.plot(x, np.sin(x - 0), color='blue') # specify color by name\n", "plt.plot(x, np.sin(x - 1), color='g') # short color code (rgbcmyk)\n", "plt.plot(x, np.sin(x - 2), color='0.75') # Grayscale between 0 and 1\n", "plt.plot(x, np.sin(x - 3), color='#FFDD44') # Hex code (RRGGBB from 00 to FF)\n", "plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 to 1\n", "plt.plot(x, np.sin(x - 5), color='chartreuse'); # all HTML color names supporte" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "如果未指定颜色，则Matplotlib将自动在一组默认颜色中循环显示多行。\n", "\n", "同样，可以使用linestyle关键字调整线条样式：" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n 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}, "metadata": {} } ], "source": [ "plt.plot(x, x + 0, linestyle='solid')\n", "plt.plot(x, x + 1, linestyle='dashed')\n", "plt.plot(x, x + 2, linestyle='dashdot')\n", "plt.plot(x, x + 3, linestyle='dotted');\n", "\n", "# 也可以使用符合来替代单词\n", "plt.plot(x, x + 4, linestyle='-') # solid\n", "plt.plot(x, x + 5, linestyle='--') # dashed\n", "plt.plot(x, x + 6, linestyle='-.') # dashdot\n", "plt.plot(x, x + 7, linestyle=':'); # dotted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "如果您想更简洁，可以将这些线型和颜色代码组合到plt.plot（）函数的单个非关键字参数中：" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"244.485312pt\" 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\n" }, "metadata": {} } ], "source": [ "plt.plot(x, x + 0, '-g') # solid green\n", "plt.plot(x, x + 1, '--c') # dashed cyan\n", "plt.plot(x, x + 2, '-.k') # dashdot black\n", "plt.plot(x, x + 3, ':r'); # dotted red" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "这些单字符颜色代码反映了通常用于数字彩色图形的RGB（红色/绿色/蓝色）和CMYK（青色/品红色/黄色/黑色）颜色系统中的标准缩写。\n", "还有许多其他关键字参数可用于微调图的外观。有关更多详细信息，建议使用IPython的帮助工具查看plt.plot（）函数的文档字符串（请参阅[IPython中的帮助和文档](https://jakevdp.github.io/PythonDataScienceHandbook/01.01-help-and-documentation.html)）。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 调整图：轴极限\n", "Matplotlib在为绘图选择默认轴限制方面做得不错，但是有时候更好的控制会很好。调整轴限制的最基本方法是使用plt.xlim（）和plt.ylim（）方法：" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n 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}, "metadata": {} } ], "source": [ "plt.plot(x, np.sin(x))\n", "# 我们取x轴范围和y轴范围为(-1, 11)和（-1.5，1.5）\n", "plt.xlim(-1, 11)\n", "plt.ylim(-1.5, 1.5);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "一个有用的相关方法是plt.axis（）（在此注意带有e的轴和带有i的轴之间的潜在混淆）。通过传递一个指定[xmin，xmax，ymin，ymax]的列表，可以使用plt.axis（）方法通过一次调用来设置x和y限制：" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "(-1.0, 11.0, -1.5, 1.5)" }, "metadata": {}, "execution_count": 25 }, { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"248.064219pt\" version=\"1.1\" viewBox=\"0 0 372.440625 248.064219\" width=\"372.440625pt\" 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}, "metadata": {} } ], "source": [ "plt.plot(x, np.cos(x),'--y')\n", "plt.axis([-1, 11, -1.5, 1.5])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "plt.axis（）方法甚至超越了此范围，允许您执行诸如自动收紧当前图的边界之类的操作：" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"244.485312pt\" version=\"1.1\" viewBox=\"0 0 378.001562 244.485312\" width=\"378.001562pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <defs>\r\n <style type=\"text/css\">\r\n*{stroke-linecap:butt;stroke-linejoin:round;}\r\n </style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n <g id=\"patch_1\">\r\n <path 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}, "metadata": {} } ], "source": [ "plt.plot(x, np.sin(x))\n", "plt.axis('tight');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "它甚至允许更高级别的规格，例如确保长宽比相等，以便在屏幕上x中的一个单位等于y中的一个单位：" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"244.485313pt\" version=\"1.1\" viewBox=\"0 0 364.101562 244.485313\" width=\"364.101562pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <defs>\r\n <style type=\"text/css\">\r\n*{stroke-linecap:butt;stroke-linejoin:round;}\r\n </style>\r\n </defs>\r\n <g id=\"figure_1\">\r\n <g id=\"patch_1\">\r\n <path d=\"M 0 244.485313 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}, "metadata": {} } ], "source": [ "plt.plot(x, np.sin(x))\n", "plt.axis('equal');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "有关轴限制和plt.axis方法的其他功能的更多信息，可以参考plt.axis文档字符串。" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 标记图\n", "作为本节的最后一部分，我们将简要介绍绘图的标签：标题，轴标签和简单图例。\n", "\n", "标题和轴标签是最简单的此类标签-有些方法可用于快速设置它们：" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"272.22pt\" version=\"1.1\" viewBox=\"0 0 391.146875 272.22\" width=\"391.146875pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n <defs>\r\n <style 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}, "metadata": {} } ], "source": [ "plt.plot(x,np.sin(x),':r')\n", "plt.title('sin 函数图像')\n", "plt.xlabel('x轴')\n", "plt.ylabel('y轴');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以使用函数的可选参数来调整这些标签的位置，大小和样式。有关更多信息，请参见Matplotlib文档和每个函数的文档字符串。\n", "\n", "当在单个轴上显示多条线时，创建标记每个线型的打印图例可能很有用。同样，Matplotlib具有快速创建此类图例的内置方法。通过plt.legend（）方法完成。尽管有几种有效的用法，但我发现使用plot函数的label关键字指定每条线的标签最简单：" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n<!-- Created with matplotlib (https://matplotlib.org/) -->\r\n<svg height=\"244.485313pt\" version=\"1.1\" viewBox=\"0 0 364.101562 244.485313\" width=\"364.101562pt\" xmlns=\"http://www.w3.org/2000/svg\" 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}, "metadata": {} } ], "source": [ "\n", "plt.plot(x, np.sin(x), '-g', label='sin(x)')\n", "plt.plot(x, np.cos(x), ':b', label='cos(x)')\n", "plt.axis('equal')\n", "plt.legend();\n", "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "如您所见，plt.legend（）函数跟踪线条样式和颜色，并将它们与正确的标签匹配。在plt.legend文档字符串中可以找到有关指定和格式化图例的更多信息。此外，我们将在“自定义绘图图例”中介绍一些更高级的图例选项。\n", "### Matplotlib陷阱\n", "尽管大多数plt函数直接转换为ax方法（例如plt.plot（）→ax.plot（），plt.legend（）→ax.legend（）等），但并非所有命令都如此。特别是，用于设置限制，标签和标题的功能略有修改。为了在MATLAB样式的函数和面向对象的方法之间转换，请进行以下更改：\n", "\n", " plt.xlabel() → ax.set_xlabel()\n", " plt.ylabel() → ax.set_ylabel()\n", " plt.xlim() → ax.set_xlim()\n", " plt.ylim() → ax.set_ylim()\n", " plt.title() → ax.set_title()\n", "在绘图的面向对象的界面中，而不是单独调用这些函数，使用ax.set（）方法一次设置所有这些属性通常更为方便：" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "<Figure size 432x288 with 1 Axes>", "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" 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\n" }, "metadata": {} } ], "source": [ "ax = plt.axes()\n", "ax.plot(x, np.sin(x))\n", "ax.set(xlim=(0, 10), ylim=(-2, 2),\n", " xlabel='x', ylabel='sin(x)',\n", " title='A Simple Plot');" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.2 64-bit", "language": "python", "name": "python38264bit2876e2a279924e5480800c750f5add25" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.2-final" } }, "nbformat": 4, "nbformat_minor": 4 }
gpl-3.0
CLandauGWU/group_e
data/cdl_links.ipynb
2
1429
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "links = ['http://opendata.dc.gov/datasets/highway-plan-lines',\n", " 'http://opendata.dc.gov/datasets/historic-data-on-dc-buildings',\n", " 'http://opendata.dc.gov/datasets/district-government-land-owned-operated-and-or-managed',\n", " 'http://opendata.dc.gov/datasets/anacostia-waterfront-initiative'\n", " 'http://opendata.dc.gov/datasets/zoning-regulations-of-1958',\n", " 'http://opendata.dc.gov/datasets/real-estate-portfolio-at-dmped',\n", " 'http://opendata.dc.gov/datasets/high-technology-development-zones',\n", " 'http://opendata.dc.gov/datasets/nonprofit-tax-abatement-zones',\n", " 'http://opendata.dc.gov/datasets/economic-development-zones',\n", " 'http://opendata.dc.gov/datasets/street-vending-zones'\n", " ]" ] } ], "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
yugangzhang/CHX_Pipelines
.ipynb_checkpoints/XPCS_SAXS_pipeline-Update-Copy1-checkpoint.ipynb
1
4292545
null
bsd-3-clause
ebu/cpa-tutorial
docker/tutorial/00_CPA_Tutorial_Introduction.ipynb
1
3152
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cross Platform Authentication\n", "\n", "## Device flow\n", "\n", "The device flow is designed for limited input, limited display devices, e.g., media devices:\n", "\n", "* Hybrid radios\n", "* Connected TVs\n", "\n", "## Broadcaster requirements\n", "\n", "* Single sign-on across multiple services using a common authorization service.\n", "* Client mode, so that devices work \"out of the box\" without user registration.\n", "* User mode, to associate a device with a user account.\n", "\n", "## Based on existing standards\n", "\n", "* OAuth 2.0 (RFC 6749)\n", "* OAuth 2.0 Bearer Token Usage (RFC 6750)\n", "* OAuth 2.0 Device Profile (draft)\n", "* OAuth 2.0 Dynamic Client Registration (draft)\n", "* HTTP over TLS (RFC 2818)\n", "\n", "## Components\n", "\n", "The diagram below shows the main parties involved.\n", "\n", "* **Client**: Represents the use of a service provider by an application on a device.\n", "* **Service Provider**: An online service which requires authorization to access its protected resources.\n", "* **Authorization Provider**: Manages client identities, the association of client identities with authenticated user identities, and the issuing of access tokens to clients.\n", "\n", "Also shown, but not itself a participant in the CPA protocol:\n", "\n", "* **Identity Provider**: Authenticates the end user and provides user identities to the Authorization Provider.\n", "\n", "![CPA components](/notebooks/tutorial/images/cpa.png)\n", "\n", "## Single sign-on\n", "\n", "CPA optionally supports single sign-on between multiple services that share a common Authorization Provider. \n", "\n", "Depending on business rules reflecting the relationship between service providers, the Authorization Provider can:\n", "\n", "* Require the user to sign in and enter a pairing code.\n", "* Require the user to sign in and give confirmation, without entering a pairing code.\n", "* Automatically grant an access token, without requiring any user action.\n", "\n", "![Single sign-on](/notebooks/tutorial/images/cpa-single-sign-on.png)\n", "\n", "## Users, clients, and devices\n", "\n", "![Users, clients, and devices](/notebooks/tutorial/images/cpa-client-device.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Next\n", "Go to [Tutorial Setup](01_CPA_Tutorial_Setup.ipynb)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "NodeJS", "language": "javascript", "name": "nodejs" }, "language_info": { "codemirror_mode": "javascript", "file_extension": "js", "mimetype": "text/javascript", "name": "nodejs", "pygments_lexer": "javascript", "version": "0.10" } }, "nbformat": 4, "nbformat_minor": 0 }
bsd-3-clause
unique-horn/ppap-experiments
dfn/dfn-mnist.ipynb
1
1832306
null
gpl-3.0
fluxcapacitor/source.ml
jupyterhub.ml/notebooks/train_deploy/tensorflow/tensorflow_linear/04_PredictModel.ipynb
1
3052
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Predict with Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Init Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%bash \n", "\n", "pio init-model \\\n", " --model-server-url=http://prediction-tensorflow.community.pipeline.io \\\n", " --model-type=tensorflow \\\n", " --model-namespace=default \\\n", " --model-name=tensorflow_linear \\\n", " --model-version=1 \\\n", " --model-path=." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predict with Model (CLI)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "%%bash\n", "\n", "pio predict \\\n", " --model-test-request-path ./data/test_request.json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predict Many\n", "This is a mini load test to provide instant feedback on relative performance. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%%bash\n", "\n", "pio predict_many \\\n", " --model-test-request-path ./data/test_request.json \\\n", " --num-iterations 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Predict with Model (REST)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "\n", "model_type = 'tensorflow'\n", "model_namespace = 'default'\n", "model_name = 'tensorflow_linear'\n", "model_version = '1'\n", "\n", "deploy_url = 'http://prediction-%s.community.pipeline.io/api/v1/model/predict/%s/%s/%s/%s' % (model_type, model_type, model_namespace, model_name, model_version)\n", "\n", "\n", "with open('./data/test_request.json', 'rb') as fh:\n", " model_input_binary = fh.read()\n", "\n", "response = requests.post(url=deploy_url,\n", " data=model_input_binary,\n", " timeout=30)\n", " \n", "print(\"Success! %s\" % response.text)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "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": 2 }
apache-2.0
ahwillia/MethylClust
1_demo.ipynb
1
222239
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Description of Data\n", "\n", "Our goal is to construct a low-rank approximation of a data matrix $A$ of DNA methylation patterns (with many missing data points). Each row of $A$ contains a binary sequence of methylation, $A_{ij} = 1$ for methylated sites, $A_{ij} = 0$ for unmethylated sites, and $A_{ij} = \\text{NA}$ when site $j$ is unobserved in read $i$.\n", "\n", "We suspect that $A$ is low-rank, since similar methylation patterns should be observed across reads originating from the same cell type. The code below plots some synthetic data with 2 underlying cell types (red and blue)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Loading help data...\n" ] }, { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x10f36d350>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x113832090>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "2" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "using LowRankModels\n", "import MethylClust: generate_reads\n", "include(\"addons/plots.jl\")\n", "\n", "## Plot an example data matrix \"A\"\n", "A,P,c,obs = generate_reads(N=50,W=15,L=40)\n", "figure(figsize=(3*1.3,4*1.3))\n", "plot_meth(A,c)\n", "savefig(\"demo1.png\",dpi=500)\n", "figure(figsize=(3*1.3,4*1.3))\n", "plot_meth(A[randperm(50),:],ones(50))\n", "savefig(\"demo2.png\",dpi=500)\n", "\n", "# Get matrix dimensions and indices of observed datapoints A_ij\n", "m,n = size(A)\n", "k = 2 # Assume we know there are two cell types" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Matrix Factorization/Completion Approach\n", "\n", "To obtain a low-rank approximation, we factor $A$ as a product of two matrices $X$ and $Y$:\n", "\n", "$$\n", "A = XY \\\\ A \\in \\mathbf{R}^{m \\times n},~~ X \\in \\mathbf{R}^{m \\times k},~~ Y \\in \\mathbf{R}^{k \\times n}\n", "$$\n", "\n", "That is, we have a dataset $m$ reads that provide partial information over $n$ methylation-eligible sites of interest (e.g. over a selected number of genes, or, more ambitiously, over the entire genome). We suppose that there are no more than $k$ types of cells with distinct methylation patterns.\n", "\n", "We restrict the rows of $X$ to be nonnegative, so that each row of $A$ is approximated as a weighted sum of the $k$ different methylation patterns shown in the rows of $Y$. An interesting question is whether we believe the rows of $X$ to be sparse. An $X$ with sparse rows would correspond to there being distinct methylation patterns across cell types, with little to no mixed patterns. An $X$ with non-sparse rows would allow for a spectrum of different DNA methylation profiles.\n", "\n", "For the purposes of this preliminary analysis, we focus on the case where the rows of $X$ are sparse. This matches the synthetic data plotted above." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Optimization Framework\n", "\n", "We use $x_i$ to denote row $i$ of $X$, and $y_j$ to denote column $j$ of $Y$, so that $x_i y_j$ is a vector dot product. Let $(i,j) \\in \\Omega$ denote the indices of observed entries in $A$. Then a low-rank approximation can be achieved by solving the unconstrained optimization problem (see [Udell et al., 2014](http://web.stanford.edu/~udell/doc/glrm.pdf)):\n", "\n", "$$\n", "\\begin{aligned}\n", "& \\underset{X,Y}{\\text{minimize}}\n", "& & \\sum_{(i,j) \\in \\Omega} L(A_{ij},x_i y_j) - \\gamma_x \\sum_i r_x(x_i) - \\gamma_x \\sum_j r_y(y_j) \n", "\\end{aligned}\n", "$$\n", "\n", "Here $L(\\cdot)$ is the loss function of choice; $r_x(\\cdot)$ and $r_y(\\cdot)$ regularize the rows and columns of $X$ and $Y$ respectively; the set $\\Omega$ holds the observed entries of $A$ (i.e. we ignore all $A_{ij} = \\text{NA}$). We will use the [LowRankModels package](https://github.com/madeleineudell/LowRankModels.jl) in Julia to solve problems in this format.\n", "\n", "###The functions below will help us fit these models:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "fit (generic function with 1 method)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "function fit_once(loss,rx,ry,initX,initY)\n", " ## Fits a low-rank model given:\n", " ## loss() -- the loss function\n", " ## rx(),ry() -- regularization functions\n", " ## initX,initY -- initial guesses for X,Y\n", " losses = fill(loss,n)\n", " glrm = GLRM(A,losses,rx,ry,k;obs=obs)\n", " \n", " if initX != nothing\n", " glrm.X = initX\n", " end\n", " if initY != nothing\n", " glrm.Y = initY\n", " end\n", " X,Y,ch = fit!(glrm,verbose=false)\n", " return X,Y,ch\n", "end\n", "\n", "function fit_batch(loss_function,rx,ry,initX,initY,n_repeats)\n", " ## Fits a specified low-rank model n_repeats times, returns best fit\n", " best = Inf\n", " X,Y,ch = 0,0,0\n", " for i = 1:n_repeats\n", " Xest,Yest,ch_ = fit_once(loss_function,rx,ry,initX,initY)\n", " if ch_.objective[end] < best\n", " best = ch_.objective[end]\n", " X,Y,ch = Xest,Yest,ch_\n", " end\n", " end\n", " println(\"Best Objective: \",ch.objective[end])\n", " return X,Y,ch\n", "end\n", "\n", "function fit(loss_function,rx,ry;n_repeats=20,initX=nothing,initY=nothing)\n", " ## Fits a low-rank model n_repeats times, and compares output to ground truth\n", " X,Y,ch = fit_batch(loss_function,rx,ry,initX,initY,n_repeats);\n", " \n", " # reconstruct \"true\" x\n", " x_real = zeros(size(X))\n", " for i = 1:m\n", " x_real[c[i],i] = 1\n", " end\n", " \n", " # re-sort the rows and columns of X so that the largest cluster is first\n", " #println(X)\n", " c_order = sortperm(squeeze(sum(X,2),2),rev=true)\n", " X = X[c_order,:]\n", " Y = Y[c_order,:]\n", " \n", " # Compare true and inferred cluster assignments\n", " figure()\n", " imshow(X,cmap=ColorMap(\"Greys\"),interpolation=\"none\")\n", " xlabel(\"Inferred Cluster Assignments\")\n", " yticks([])\n", " \n", " figure()\n", " imshow(x_real,cmap=ColorMap(\"Greys\"),interpolation=\"none\")\n", " xlabel(\"True Cluster Assignments\")\n", " yticks([])\n", " show()\n", " return X,Y,ch\n", "end" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Encouraging sparsity in $X$\n", "\n", "To enforce hard clustering, we can regularize each row of $X$ to have unit length and have only one nonzero element. This constraint set is clearly ***not convex**.\n", "\n", "$$\n", "r_x(x) = \\left\\{\\begin{matrix}\n", "\\infty & \\mathbf{Card}(x) \\neq 1 ~~\\text{ OR }~~ \\sum_p x_p \\neq 1 \\\\ \n", "0 & \\text{otherwise}\n", "\\end{matrix}\\right.\n", "$$" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "Best Objective: " ] }, { "data": { "image/png": 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tWbJEewVw+/btiIiIQHBwMFavXo3Hjx9j06ZNCAgIwPnz59GqVSsAwIEDBzBixAh07NgRH330EXJzczFx4kS4ublV+ZzZsWPHcO/ePcyZM6faz6RV9Dzb8+1bt27FrFmzEBISgqioKBQWFuLChQs4c+YMxowZgxEjRiAjIwM7duzAunXr4OjoqD3XALBixQosWrQIoaGhmDx5Mm7fvo0NGzagd+/eOH/+vPYKrSRJyMvLw8CBAzFmzBiMHz8eTk5OlR5DUVER4uPjMWXKFEiShLCwMMycORO3bt3SWbeqYwCAKVOmID4+HpGRkfDy8kJubi6OHz+O9PR0dO7cWef8lNFoNBgyZAhSUlIwbdo0dOjQAYmJiQgPD9frC1Q9bp8VGxuLp0+fYvbs2cjLy8Pq1asRGhqK119/HSdPnsT777+PjIwMbNiwAX/84x/x17/+VbuuoWPQkJiaNWuGTZs2YerUqRg+fDiGDx8OAOjUqRMAYMSIEfj5558xc+ZMqNVq3Lp1C0lJSbh27ZrOfojoGYKIKrVt2zYhSZI4e/asti08PFxIkiSWL1+u09fPz0907dpVp61Vq1ZiyJAhOm3bt28XDRo0EMePH9dp/+KLL4QkSeLEiRPaNkmShJmZmUhLSys3rt69ewuNRqNtz8/PF3Z2duK9997T6X/r1i1hZ2cnJk+erG3z9fUVzZs3Fw8fPtS2HTx4UEiSJNzd3Ss9L5999pmQJEn8/e9/r7RfmeTkZCFJkvjhhx+0bWq1WkyYMEGvb58+fUTfvn21v7/55pvCx8en0u2vWbNGSJIkrly5otOelZUlGjRoID766COd9p9++kmYm5uLlStX6uxXkiSxZcsWg45JCCF2794tJEkSP/30kxBCiNzcXNGwYUOxbt06nX6GHEPjxo1FZGRkpX3Cw8OFWq3W/h4fHy8kSRLr16/Xtmk0GtGvXz8hSZKIiYnRWdeQcfvbb78JSZKEk5OTzthYuHChkCRJ+Pr6itLSUm3722+/LSwsLERxcbEQwrgxaGhMd+7cEZIkiaVLl+r0u3fvnpAkSURHR1dy1ojoebwFTPQCnr9iEhAQgP/85z9Vrrdr1y54enqiffv2yM3N1f6U3fZ8/tOzffr0QYcOHcrd1qRJk3Su8hw8eBAPHjzA6NGjdbYtyzK6d++u3XZOTg4uXLiA8PBw2NjYaNd/44034OXlVeUxPHz4EAB01q0t9vb2uHbtGlJTU41ed8+ePRBCYOTIkTrnw8nJCR4eHnrnWqVSlXtbuiJxcXH43e9+B29vbwCAg4MDgoOD9W4DG3IM9vb2OHXqFHJycgze/759+9CwYUNMmjRJ2yZJEqZPn17hOoaO25CQEJ38du/eHQAwbtw4yLKs015cXIzs7GwAho/B6sT0PEtLSzRs2BDJycm4f/9+lf2J6L94C5iomiwtLeHg4KDTZm9vX+6zUM/LyMhAeno6mjZtqrdMkiTcuXNHp62yed+eX5aRkQEA+P3vf19u/7LbnWXPR7Vt21avT7t27fDjjz9WcgSAra0tgP8+t1bbFixYgKSkJHTv3h0eHh7o378/3n77bfj7+1e5bkZGBoQQ5R4nAFhYWOj83rx5c4M/uHL//n3861//wvTp05GZmalt7927N+bNm4eMjAztfg05htWrVyM8PBwtWrRAly5dMHDgQIwfP77S/F+5cgUuLi5QqVQ67W3atCm3vzHjtmXLljq/l42dZ581fbb93r17UKvVBo/B6sT0PAsLC3z88ceYO3cunJyc0KNHDwwePNig2/dESsYCkKianr0CYiyNRoNOnTrh008/LXe5m5ubzu+WlpYVbuv5ZWUPzsfGxsLZ2Vmvf019KtfT0xMAcPHiRQwdOrRa26jo2cHS0lKd89uhQwf88ssv2Lt3L/bt24f4+Hj85S9/waJFi7BkyZJK96HRaCBJEvbt24cGDRroLX9+upbKzvXzdu3aheLiYqxduxZr167VWx4XF6eNz5BjCAkJQa9evZCQkIADBw5gzZo1+Pjjj7Fnzx4EBwdXGIcw4pOxxozb8s5XZe1lcRg7Bl/ktQQAs2bNwpAhQ5CYmIj9+/fjww8/xKpVq3Do0CH4+vq+0LaJTBULQKJXwMPDAxcuXKjwCsmLbhsAmjZtWun2yx6Ov3z5st6yX375pcr9BAQEwN7eHjt27MDChQur9Z94RVd5rly5oj2OMo0aNcKoUaMwatQolJSUYPjw4VixYgUWLlyIhg0bVlhMenh4QAgBtVpd4VXA6oqLi4OPjw8WL16s0y6EwBdffIGvv/5ap0Ct6hgAwNnZGVOnTsXUqVNx584d+Pn5YcWKFRUWgK1atcLhw4dRUFCgU7w+e0XyZSu7+ljVGDRGVR80at26NebMmYM5c+YgMzMTvr6+iI6Oxvbt22tk/0Smhs8AElXTi3wbw6hRo5CdnY2tW7fqLSsoKMCTJ0+qve2goCDY2tpi5cqVePr0qd7y3NxcAICLiwt8fX0RExOjfZ4P+O/zW2lpaVXux9LSEgsWLEBaWhoWLFhQbp/Y2FikpKRUuI02bdrg1KlTKCkp0bbt3btXb3qWvLw8nd/Nzc21VyDL1rWysgIAvYJy+PDhaNCggXbKkGcJIXD37t0K46vMtWvXcPToUYwaNUr7ydSynxEjRmDChAnIzMzEmTNnDDoGjUaj9y0mTZs2hYuLi97UQM+OveDgYJSUlOiMJY1Gg88//7zcuF/Gt4gEBwcbNAaNialRo0YA9PNbUFCAwsJCnbbWrVvD2tpa77wR0f/wCiBRNRlz2+1548aNw86dOzFlyhQkJyfD398fpaWlSE9Px65du3DgwAH4+flVa9s2NjbYtGkTxo0bBz8/P4wePRqOjo64evUqvv32WwQEBGDDhg0AgFWrVmHQoEEICAjAhAkTcPfuXWzcuBHe3t549OhRlfuaN28eLl26hOjoaCQnJ2PkyJFwcnLCzZs3kZiYiJSUFJw8ebLC9d99913s3r0bwcHBCAkJwa+//oq4uDi0adNG5/z2798fLi4u8Pf3h5OTE9LS0vD5559j0KBB2sKva9euAIAPPvgAoaGhMDc3x9ChQ9G6dWssX74c77//PrKysvDmm2/CxsYGv/32GxITEzF58mTtFD2A4Xn9+uuvIYSo8Pb3gAEDYGZmhri4OHTv3r3KY7h//z7c3NwQEhKCTp06wdraGklJSUhNTdV7VODZGIcNG4bu3btj7ty5yMzMRPv27fGPf/xDWyg9X1y9yLg1lDFj0NCYLC0t4eXlhb/97W9o164d7O3t4ePjg5KSEvTr1w+hoaHw9PSEmZkZEhIScOfOnVr/Wj6ieu1VfPSYqD7Ztm2bkGVZZxqYiIgIYWNjo9d3yZIlQpZlnTa1Wq03DYwQQpSUlIjVq1eLjh07CpVKJZo0aSK6desmli1bpjP1hiRJ5U4NUl5czzp8+LAIDg4WdnZ2wtLSUrRt21ZMnDhRnDt3Tqffnj17hJeXl1CpVKJjx44iMTFRREREVDkNzLPi4+NFUFCQcHBwEObm5sLV1VWEhISIw4cPa/skJycLWZZ1poERQohPP/1UuLm5CZVKJXr16iXOnTsnAgMDdaaB2bJli+jTp49wdHQUKpVKtG3bVixYsEDk5+frbGv58uXCzc1NNGjQQMiyrDMlzJ49e0SvXr2EtbW1sLa2Fl5eXiIyMlJkZGRo+wQGBlY5VUuZTp066UzHUp6+ffsKZ2dn8fTp0yqPobi4WMyfP1/4+voKW1tbYW1tLTp37iw2b96ss83ycpObmyvGjh0rbG1thZ2dnQgPDxfHjh0TkiSJnTt36qxryLgtmwbm+alVynIYHx+v017RWDRkDBrzWjp58qTo2rWrsLCwELIsi6VLl4q8vDwxY8YM4enpKaytrYWdnZ3o2bOn2L17t942ieh/JCH4vTpERKYmMTERw4cPf2nfxUxE9QsLQCKieq6wsFBnGpjS0lL0798f586dw82bN/WmuiEi4jOARET13IwZM1BYWIgePXqgqKgIe/bswcmTJ7Fq1SoWf0RULl4BJCKq53bs2IHo6GhkZmaisLAQbdu2xdSpUzFt2rRXHRoR1VEsAImIiIgUhvMAEhERESkMC0AiIiIihWEBSERERKQwLACJiIiIFIYFIBEREZHCsAAkIiIiUhgWgEREREQKwwKQiIiISGFYABIREREpzP8DY0p1gunAnZcAAAAASUVORK5CYII=", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x115d72050>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x114dce110>)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "300.66892023652684\n" ] } ], "source": [ "## Low-rank model with hard clustering regularization\n", "using LowRankModels\n", "fit(logistic(),unitonesparse(),zeroreg());" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This doesn't seem to work well, since solving this optimization problem is NP-hard. Instead, we first consider the following relaxation:\n", "\n", "$$\n", "r_x(x) = \\left\\{\\begin{matrix}\n", "\\infty & x \\nsucceq 0 \\\\ \n", "\\sum_p x_p & \\text{otherwise}\n", "\\end{matrix}\\right.\n", "$$\n", "\n", "That is, we restrict all elements of $X$ to be nonnegative and we introduce an $\\ell_1$ norm penalty on the rows of $X$. This encourages the rows of $X$ to be sparse." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "Best Objective: " ] }, { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x115f93d50>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x115ee3590>)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "445.1240790972315\n" ] } ], "source": [ "## Low-rank model with non-negative constraint and L1 norm penalty\n", "X1,Y1,ch1 = fit(logistic(),nonneg_onereg(1),quadreg());" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that this does a pretty good job of finding the correct clusters for each read. We can use this solution to initialize the hard clustering problem, and recover the ground truth clusters in the synthetic dataset:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams" ] }, { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x115f48290>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x11697ef50>)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "Best Objective: 338.0960367083144\n" ] } ], "source": [ "## Low-rank model with hard clustering regularization, with better initialization\n", "X2,Y2,ch2 = fit(logistic(),unitonesparse(),zeroreg(),initX=deepcopy(X1),initY=deepcopy(Y1));" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The matrix $Y$ (whose rows correspond to the methylation pattern) also appears to do a good job of matching the ground truth probability of methylation:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x116864590>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x116b32450>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "figure(), imshow(Y2,cmap=ColorMap(\"Greys\"),interpolation=\"none\")\n", "xlabel(\"Reconstructed methylation profiles\"), yticks([])\n", "figure()\n", "imshow(P,cmap=ColorMap(\"Greys\"),interpolation=\"none\")\n", "xlabel(\"True methylation profiles\"), yticks([])\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below we plot a histogram of the absolute residuals (i.e., subtract the two matrices plotted above, vectorize the result, and plot a histogram)." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x116b32a50>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "resid = vec((1+Y2)/2 - P)\n", "figure(figsize=(8,3)),PyPlot.hist(resid,50)\n", "xlabel(\"Residuals (for methylation profiles)\",fontweight=\"bold\")\n", "show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Logistic loss function doesn't seem work well\n", "\n", "In the above analysis, I used a quadratic loss function. However, the logistic loss function would be more natural for our problem, since $A$ is composed of have binary data:\n", "\n", "$$L(A_{ij},x_i,y_j) = \\log(1+\\exp(-A_{ij} (x_i y_j))$$\n", "\n", "Surprisingly, a quadratic loss function seems to work better (see results below). This is still really puzzling to me, and I'm trying to figure out why this would be the case." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams" ] }, { "data": { "image/png": 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zs4ODgwMGDRqEn376yWhf0tPT0b59e1hbWyMoKAhpaWkmHYPCwkKsXLkS/v7+WLNmTYUxY8aMQZcuXSrdhlarRUxMjFF7aGgo+vTpY9C2YcMGBAYGwtbWFk2bNkWXLl2wfft2AMDixYsxb948AICPj4/RsQaAxMREPPXUU7CxsYGzszNGjx6NixcvGvUbFBSEY8eOoVevXrC1tcWCBQuqPRZJSUlo0aIFJkyYgO7duyMpKanCuKr2AQAKCgowa9YsaLVaaDQauLm5oV+/fjhx4oQ+pqJ7APPz8zFu3Dg4ODjAyckJ0dHROHnyJGRZRkJCgsG61c1bAMjNzYUsy4iPj8eGDRvg4+MDW1tb9OvXDxcuXIBOp8PSpUvh5eUFGxsbPP/887h586bR/poyB00ZU25uLpo1awYAWLJkiT6/S5YsAQBcvXoVMTEx8PLygkajgaenJ4YOHYpz585VmzsiteIlYKIaKisrQ1hYGLp164b4+Hjs27cP8fHx8PX1xeTJkxEQEIBt27YhLi4OLVq0wJw5cwAALi4u0Ol0GDJkCA4ePIiXX34Z/v7++P7777F27VqcPXvWqAjbv38/duzYgdjYWLi4uECr1er/4JYXf4sXL9afAdy2bRuio6MRHh6OVatW4e7du3jvvffQo0cPnDhxAt7e3gCAvXv3Yvjw4Wjfvj3efPNN5OXlYcKECfDy8qr2PrOvv/4aN2/exOzZs2t8T1pl97M93L5lyxbMnDkTERERiIuLQ1FREU6ePIkjR45g9OjRGD58OLKzs7F9+3asW7cOLi4u+mMNAMuXL8fChQsRGRmJSZMm4fr169iwYQN69eqFEydO6M/QSpKE/Px8DBgwAKNHj8b48ePh5uZW5T4UFxcjJSUFkydPhiRJGDt2LGbMmIFr164ZrFvdPgDA5MmTkZKSgtjYWAQEBCAvLw8HDx7EmTNn0LFjR4PjU06n02Hw4MHIysrC1KlT0a5dO6SnpyMqKsooFqh+3j4oMTER9+/fx6xZs5Cfn49Vq1YhMjISzzzzDA4dOoTXXnsN2dnZ2LBhA1555RX84x//0K9r6hw0ZUzNmjXDe++9hylTpmDYsGEYNmwYAKBDhw4AgOHDh+Onn37CjBkzoNVqce3aNWRkZODChQsG/RDRAwQRVWnr1q1CkiRx7NgxfVtUVJSQJEksW7bMILZTp06ic+fOBm3e3t5i8ODBBm3btm0TjRo1EgcPHjRo37Rpk5AkSXzzzTf6NkmShIWFhTh9+nSF4+rVq5fQ6XT69oKCAuHo6Chefvllg/hr164JR0dHMWnSJH1bcHCwaN68ufjjjz/0bfv27ROSJAkfH58qj8s777wjJEkS//rXv6qMK5eZmSkkSRJffPGFvk2r1YqYmBij2N69e4s+ffrov3/uuedEUFBQldtfvXq1kCRJnDt3zqA9NzdXNGrUSLz55psG7T/++KOwtLQUK1asMOhXkiSxefNmk/ZJCCGSk5OFJEnixx9/FEIIkZeXJxo3bizWrVtnEGfKPjRp0kTExsZWGRMVFSW0Wq3++5SUFCFJkli/fr2+TafTib59+wpJkkRCQoLBuqbM299++01IkiTc3NwM5saCBQuEJEkiODhYlJWV6dtfeOEFYWVlJUpKSoQQyuagqWO6ceOGkCRJLFmyxCDu5s2bQpIkER8fX8VRI6KH8RIw0SN4+IxJjx498Ouvv1a73s6dO+Hv74+2bdsiLy9P/1V+2fPhT8/27t0b7dq1q3BbEydONDjLs2/fPty+fRujRo0y2LYsy+jatat+21euXMHJkycRFRUFe3t7/frPPvssAgICqt2HP/74AwAM1q0rTk5OuHDhAo4ePap43dTUVAghMGLECIPj4ebmBj8/P6NjrdFoKrwsXZmkpCT86U9/QmBgIADA2dkZ4eHhRpeBTdkHJycnfPvtt7hy5YrJ/e/evRuNGzfGxIkT9W2SJGHatGmVrmPqvI2IiDDIb9euXQEA48aNgyzLBu0lJSW4dOkSANPnYE3G9DBra2s0btwYmZmZuHXrVrXxRPQ/vARMVEPW1tZwdnY2aHNycqrwXqiHZWdn48yZM3B1dTVaJkkSbty4YdBW1XPfHl6WnZ0NAPjzn/9cYXz55c7y+6Nat25tFNOmTRt89913VewB4ODgAOB/963Vtfnz5yMjIwNdu3aFn58f+vXrhxdeeAEhISHVrpudnQ0hRIX7CQBWVlYG3zdv3tzkD67cunULn376KaZNm4acnBx9e69evTB37lxkZ2fr+zVlH1atWoWoqCi0aNECTz31FAYMGIDx48dXmf9z587Bw8MDGo3GoN3X17fCeCXztmXLlgbfl8+dB+81fbD95s2b0Gq1Js/BmozpYVZWVnjrrbcwZ84cuLm5oVu3bhg0aJBJl++J1IwFIFENPXgGRCmdTocOHTrg7bffrnC5l5eXwffW1taVbuvhZeU3zicmJsLd3d0ovrY+levv7w8A+P777zFkyJAabaOyewfLysoMjm+7du3w888/Y9euXdi9ezdSUlLw97//HQsXLsTixYur7EOn00GSJOzevRuNGjUyWv7w41qqOtYP27lzJ0pKSrB27VqsXbvWaHlSUpJ+fKbsQ0REBHr27Im0tDTs3bsXq1evxltvvYXU1FSEh4dXOg6h4JOxSuZtRcerqvbycSidg4/yswQAM2fOxODBg5Geno49e/bgjTfewMqVK7F//34EBwc/0raJzBULQKLHwM/PDydPnqz0DMmjbhsAXF1dq9x++c3xZ8+eNVr2888/V9tPjx494OTkhO3bt2PBggU1+iNe2Vmec+fO6fejnI2NDUaOHImRI0eitLQUw4YNw/Lly7FgwQI0bty40mLSz88PQghotdpKzwLWVFJSEoKCgrBo0SKDdiEENm3ahI8++sigQK1uHwDA3d0dU6ZMwZQpU3Djxg106tQJy5cvr7QA9Pb2xoEDB1BYWGhQvD54RrK+lZ99rG4OKlHdB41atWqF2bNnY/bs2cjJyUFwcDDi4+Oxbdu2WumfyNzwHkCiGnqUtzGMHDkSly5dwpYtW4yWFRYW4t69ezXedlhYGBwcHLBixQrcv3/faHleXh4AwMPDA8HBwUhISNDfzwf87/6t06dPV9uPtbU15s+fj9OnT2P+/PkVxiQmJiIrK6vSbfj6+uLbb79FaWmpvm3Xrl1Gj2fJz883+N7S0lJ/BrJ8XVtbWwAwKiiHDRuGRo0a6R8Z8iAhBH7//fdKx1eVCxcu4KuvvsLIkSP1n0wt/xo+fDhiYmKQk5ODI0eOmLQPOp3O6C0mrq6u8PDwMHo00INzLzw8HKWlpQZzSafT4d13361w3PXxFpHw8HCT5qCSMdnY2AAwzm9hYSGKiooM2lq1agU7Ozuj40ZE/8czgEQ1pOSy28PGjRuHHTt2YPLkycjMzERISAjKyspw5swZ7Ny5E3v37kWnTp1qtG17e3u89957GDduHDp16oRRo0bBxcUF58+fxyeffIIePXpgw4YNAICVK1di4MCB6NGjB2JiYvD7779j48aNCAwMxJ07d6rta+7cuTh16hTi4+ORmZmJESNGwM3NDVevXkV6ejqysrJw6NChStd/6aWXkJycjPDwcEREROCXX35BUlISfH19DY5vv3794OHhgZCQELi5ueH06dN49913MXDgQH3h17lzZwDA66+/jsjISFhaWmLIkCFo1aoVli1bhtdeew25ubl47rnnYG9vj99++w3p6emYNGmS/hE9gOl5/eijjyCEqPTyd//+/WFhYYGkpCR07dq12n24desWvLy8EBERgQ4dOsDOzg4ZGRk4evSo0a0CD45x6NCh6Nq1K+bMmYOcnBy0bdsW//73v/WF0sPF1aPMW1MpmYOmjsna2hoBAQH45z//iTZt2sDJyQlBQUEoLS1F3759ERkZCX9/f1hYWCAtLQ03btyo89fyETVoj+Ojx0QNydatW4UsywaPgYmOjhb29vZGsYsXLxayLBu0abVao8fACCFEaWmpWLVqlWjfvr3QaDSiadOmokuXLmLp0qUGj96QJKnCR4NUNK4HHThwQISHhwtHR0dhbW0tWrduLSZMmCCOHz9uEJeamioCAgKERqMR7du3F+np6SI6Orrax8A8KCUlRYSFhQlnZ2dhaWkpPD09RUREhDhw4IA+JjMzU8iybPAYGCGEePvtt4WXl5fQaDSiZ8+e4vjx4yI0NNTgMTCbN28WvXv3Fi4uLkKj0YjWrVuL+fPni4KCAoNtLVu2THh5eYlGjRoJWZYNHgmTmpoqevbsKezs7ISdnZ0ICAgQsbGxIjs7Wx8TGhpa7aNaynXo0MHgcSwV6dOnj3B3dxf379+vdh9KSkrEvHnzRHBwsHBwcBB2dnaiY8eO4v333zfYZkW5ycvLE2PGjBEODg7C0dFRREVFia+//lpIkiR27NhhsK4p87b8MTAPP1qlPIcpKSkG7ZXNRVPmoJKfpUOHDonOnTsLKysrIcuyWLJkicjPzxfTp08X/v7+ws7OTjg6Ooru3buL5ORko20S0f9JQvC9OkRE5iY9PR3Dhg2rt3cxE1HDwgKQiKiBKyoqMngMTFlZGfr164fjx4/j6tWrRo+6ISLiPYBERA3c9OnTUVRUhG7duqG4uBipqak4dOgQVq5cyeKPiCrEM4BERA3c9u3bER8fj5ycHBQVFaF169aYMmUKpk6d+riHRkRPKBaARERERCrD5wASERERqQwLQCIiIiKVYQFIREREpDIsAImIiIhUhgUgERERkcqwACQiIiJSGRaARERERCrDApCIiIhIZVgAEhEREanMfwHUwcURl4TBbwAAAABJRU5ErkJggg==", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x116bbed10>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x116e757d0>)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "Best Objective: 450.56585383284397\n" ] } ], "source": [ "## Low-rank model with non-negative constraint and L1 norm penalty\n", "X3,Y3,ch3 = fit(logistic(),nonneg_onereg(),quadreg());" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams" ] }, { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x116f27510>)" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x11701a890>)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "ProxGradParams(1.0,100,1,1.0e-5,0.01)\n", "Best Objective: 412.4891081109101\n" ] } ], "source": [ "## Low-rank model with hard clustering regularization, with better initialization\n", "X4,Y4,ch4 = fit(logistic(),unitonesparse(),quadreg(),initX=X3,initY=Y3);" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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tW7eWJB04cKDI8efPn9fkyZM1bdo0jqICAACgSF6H1PT0dEVGRuZrd7cdP368yPFz5sxRSEiIJkyYUMwSAQAAUNn4edsxIyNDAQEB+doDAwPt5YU5dOiQFi1apHfffVf+/v4lKBMAAACViddHUoOCgpSVlZWvPTMz015emEcffVTt27dXnz59SlAiAAAAKhuvj6RGRkYW+JF+enq6JCkqKqrAcVu2bNHGjRu1atUqpaWl2e05OTm6ePGijhw5opo1ayosLKzQbU+YMEHVqlXzaBs8eLAGDx7sbfkAAAAopeTkZCUnJ3u0nTt3rky25XVIbdGihbZu3arz5897BMrdu3dLkpo3b17guKNHj0qS7rvvvnzLjh8/roYNG2rhwoUaP358odtOSEhQy5YtvS0VAAAAZaCgg4T79u1Tq1atfL4tr0Nqv379tGDBAi1dulRPPPGEpMvfQJWYmKj4+HjVr19fknTixAmdPXtWjRo1kp+fn7p06aLU1FSPdRljNGbMGMXExGj69Olq2rSpD3cJAAAAv3Zeh9Q2bdqof//+mjp1qk6ePKnY2FgtW7ZMR48eVWJiot1vypQpSkpKUlpamho0aKDrr79e119/fb71Pfroo6pbt6569erlmz0BAABAheF1SJWkpKQkzZgxQ8uXL9eZM2fUrFkzrVu3Th06dLD7WJbl1ded8pWoAAAAKIxlHPzVT+5zHPbu3cs5qQAAAA5UVnnN61tQAQAAAOWFkAoAAADHIaQCAADAcQipAAAAcBxCKgAAAByHkAoAAADHIaQCAADAcQipAAAAcBxCKgAAAByHkAoAAADHIaQCAADAcQipAAAAcBxCKgAAAByHkAoAAADHIaQCAADAcQipAAAAcBxCKgAAAByHkAoAAADHIaQCAADAcQipAAAAcBxCKgAAAByHkAoAAADHIaQCAADAcQipAAAAcBxCKgAAAByHkAoAAADHIaQCAADAcQipAAAAcBxCKgAAAByHkAoAAADHIaQCAADAcQipAAAAcBxCKgAAAByHkAoAAADHKXZIzcrK0uTJkxUVFaXg4GDFx8dr8+bNVx330UcfaeTIkYqLi1NISIhiY2M1evRonThxokSFAwAAoOIqdkgdMWKEEhISNGzYMC1atEhVqlRR9+7dtXPnziLHTZ48Wdu2bVPfvn21ePFiDRo0SCtXrlSLFi30n//8p8Q7AAAAgIrHrzid9+zZoxUrVmjBggV6/PHHJUnDhg1T06ZNNWnSpCKD6sKFC9WhQwePtnvuuUd33XWXXnrpJc2dO7cE5QMAAKAiKtaR1JSUFPn5+WnMmDF2W0BAgEaNGqVdu3bp2LFjhY69MqBK0p133qmaNWvq4MGDxSkDAAAAFVyxQur+/fsVFxen0NBQj/bWrVtLkg4cOFCsjV+4cEHnz59XREREscYBAACgYitWSE1PT1dkZGS+dnfb8ePHi7XxhQsXKjs7WwMHDizWOAAAAFRsxQqpGRkZCggIyNceGBhoL/fWtm3bNHv2bA0cOFAdO3YsThkAAACo4IoVUoOCgpSVlZWvPTMz017ujYMHD6pPnz667bbb9PrrrxenBAAAAFQCxbq6PzIyssCP9NPT0yVJUVFRV13Hd999p65du6pGjRpav369QkJCrjpmwoQJqlatmkfb4MGDNXjwYC8rBwAAQGklJycrOTnZo+3cuXNlsq1ihdQWLVpo69atOn/+vMLCwuz23bt3S5KaN29e5PjTp0+ra9euys7O1scff6y6det6td2EhAS1bNmyOKUCAADAxwo6SLhv3z61atXK59sq1sf9/fr1U25urpYuXWq3ZWVlKTExUfHx8apfv74k6cSJEzp48KBycnLsfj///LO6d++u9PR0rV+/XrGxsT7aBQAAAFQ0xTqS2qZNG/Xv319Tp07VyZMnFRsbq2XLluno0aNKTEy0+02ZMkVJSUlKS0tTgwYNJEn333+/PvnkE40cOVJffPGFvvjiC7t/WFiYevfu7aNdAgAAwK9dsUKqJCUlJWnGjBlavny5zpw5o2bNmmndunUeN+u3LEuWZXmM++yzz2RZlt544w298cYbHstiYmIIqQAAALBZxhhzrYsojPsch71793JOKgAAgAOVVV4r1jmpAAAAQHkgpAIAAMBxCKkAAABwHEIqAAAAHIeQCgAAAMchpAIAAMBxCKkAAABwHEIqAAAAHIeQCgAAAMchpAIAAMBxCKkAAABwHEIqAAAAHIeQCgAAAMchpAIAAMBxCKkAAABwHEIqAAAAHIeQCgAAAMchpAIAAMBxCKkAAABwHEIqAAAAHIeQCgAAAMchpAIAAMBxCKkAAABwHEIqAAAAHIeQCgAAAMchpAIAAMBxCKkAAABwHEIqAAAAHIeQCgAAAMchpAIAAMBxCKkAAABwHEIqAAAAHIeQCgAAAMchpAIAAMBxihVSs7KyNHnyZEVFRSk4OFjx8fHavHmzV2PPnj2rMWPGqHbt2goNDVXnzp21f//+EhUNAACAiq1YIXXEiBFKSEjQsGHDtGjRIlWpUkXdu3fXzp07ixx36dIl9ejRQ8nJyRo/frzmz5+vkydPqmPHjvrmm29KtQMAAACoeLwOqXv27NGKFSv03HPP6fnnn9eDDz6oLVu2KDo6WpMmTSpybEpKinbt2qVly5ZpxowZ+sMf/qCtW7eqSpUqmjlzZql3AhVHcnLytS4B5Yj5rlyY78qF+UZpeR1SU1JS5OfnpzFjxthtAQEBGjVqlHbt2qVjx44VObZevXq677777LaIiAgNGDBAa9asUXZ2dgnLR0XDm1rlwnxXLsx35cJ8o7S8Dqn79+9XXFycQkNDPdpbt24tSTpw4ECRY1u2bJmvvXXr1rp48aIOHTrkbRkAAACoBLwOqenp6YqMjMzX7m47fvx4mYwFAABA5eN1SM3IyFBAQEC+9sDAQHt5YTIzM0s8FgAAAJWPn7cdg4KClJWVla89MzPTXu7rse7w+tVXX3lbJn7lzp07p3379l3rMlBOmO/KhfmuXJjvysOd03x90NHrkBoZGVngx/Lp6emSpKioKJ+PTUtLkyQNHTrU2zJRAbRq1epal4ByxHxXLsx35cJ8Vy5paWlq3769z9bndUht0aKFtm7dqvPnzyssLMxu3717tySpefPmhY5t3ry5tm/fLmOMLMvyGBsSEqK4uLgCx3Xr1k1vvfWWYmJiijxSCwAAgGsjIyNDaWlp6tatm0/XaxljjDcd9+zZo/j4eP3pT3/SE088IenyN1A1bdpUtWvX1j/+8Q9J0okTJ3T27Fk1atRIfn6XM/DKlSs1aNAgvffee+rbt68k6dSpU7rxxht177336p133vHpTgEAAODXzeuQKkkDBw7U6tWrNWHCBMXGxmrZsmX69NNP9dFHH6lDhw6SLn8rVVJSktLS0tSgQQNJl79xqkOHDvr88881ceJE1apVS6+88oq+//57ffLJJ7rxxhvLZu8AAADwq+T1x/2SlJSUpBkzZmj58uU6c+aMmjVrpnXr1tkBVZIsy/L4SF+SXC6X1q9fr4kTJ2rRokXKyMhQmzZtlJSUREAFAABAPsU6kgoAAACUB6/vkwoAAACUl2sSUrOysjR58mRFRUUpODhY8fHx2rx5s1djz549qzFjxqh27doKDQ1V586dtX///jKuGKVR0vn+6KOPNHLkSMXFxSkkJESxsbEaPXq0Tpw4UQ5Vo6RK8/rOa/To0XK5XOrZs2cZVAlfKe18b968WZ07d1b16tUVHh6u22+/XStXrizDilEapZnvzZs3q0uXLqpTp47CwsLUrFkzLV68WJcuXSrjqlESP//8s2bOnKl77rlHNWvWlMvl0rJly7we75O8Zq6BQYMGGX9/fzNp0iTz2muvmXbt2hl/f3+zY8eOIsfl5uaadu3amdDQUDNnzhzz8ssvm1tuucWEh4ebr7/+upyqR3GVdL5btWplYmNjzZQpU8xf/vIXM23aNBMeHm7q1atnTpw4UU7Vo7hKOt95ffLJJ8bf398EBQWZnj17lmG1KK3SzPcbb7xhXC6Xueeee8wrr7xilixZYiZMmGD+/Oc/l0PlKImSzvcHH3xgLMsyt956q1m4cKFZunSp+f3vf28syzKPPvpoOVWP4jh8+LCxLMvExMSYTp06GcuyzLJly7wa66u8Vu4hdffu3cayLI83oczMTNOoUSPTrl27IseuWLHCWJZl3n//fbvthx9+MDVq1DBDhgwps5pRcqWZ7+3bt+dr27Ztm7Esyzz55JM+rxWlV5r5drt06ZJp27atefDBB01MTAwh1cFKM9+HDx82QUFB5rHHHivrMuEjpZnvIUOGmMDAQHPmzBmP9rvuustUq1atTOpF6WRlZZn//Oc/xhhjPv3002KFVF/ltXL/uD8lJUV+fn4aM2aM3RYQEKBRo0Zp165dOnbsWJFj69Wrp/vuu89ui4iI0IABA7RmzRplZ2eXae0ovtLMd967Rrjdeeedqlmzpg4ePFgm9aJ0SjPfbsuXL9eXX36pefPmyXBdp6OVZr5fffVVGWM0Z84cSdKFCxeYb4crzXwHBQUpICBA1apV82ivV6+egoODy6xmlFzVqlVVp04dSSr2a9NXea3cQ+r+/fsVFxen0NBQj/bWrVtLkg4cOFDk2JYtW+Zrb926tS5evKhDhw75tliUWmnmuyAXLlzQ+fPnFRER4bMa4Tulne/z589r8uTJmjZtmurWrVtmdcI3SjPfmzdvVuPGjbVu3Tpdd911Cg8PV0REhJ566inCqkOVZr7HjRunS5cu6aGHHtLBgwd15MgRvfrqq1q9erWmTp1apnWj/Pkqr5V7SE1PT1dkZGS+dnfb8ePHy2Qsrg1fz9nChQuVnZ2tgQMH+qQ++FZp53vOnDkKCQnRhAkTyqQ++FZp5vvrr7/W0aNHNXLkSD344IN6//33de+992revHmaPn16mdWMkivNfDdr1kxbtmzRX//6V918881q2LChxo0bp8WLF2vcuHFlVjOuDV/97S/Wzfx9ISMjQwEBAfnaAwMD7eWFyczMLPFYXBulme8rbdu2TbNnz9bAgQPVsWNHX5UIHyrNfB86dEiLFi3Su+++K39//zKrEb5Tmvl2f7z//PPPa+LEiZKkPn366Mcff9SLL76oadOm5Ttih2urNPN98OBB9ejRQ9HR0frTn/6kwMBAvfPOO3rkkUdUt25d9e7du8zqRvnzVV4r9yOpQUFBysrKyteemZlpLy+Lsbg2fDVnBw8eVJ8+fXTbbbfp9ddf92mN8J3SzPejjz6q9u3bq0+fPmVWH3yrtO/nlmVp8ODBHu2DBg1SRkZGsU8FQtkrzXz/8Y9/lJ+fn7Zu3aqhQ4eqX79+WrVqlTp06KCHH35Yubm5ZVY3yp+v/vaXe0iNjIws8DBvenq6JCkqKqpMxuLa8MWcfffdd+ratatq1Kih9evXKyQkxOd1wjdKOt9btmzRxo0bNX78eKWlpdk/OTk5unjxoo4cOaLz58+Xae0ovtK8vt3Lrjz32H2hxpkzZ3xVJnykNPO9Y8cOde7cOd9FUj179tTx48d15MgR3xaLa8pXea3cQ2qLFi106NChfH9wdu/eLUlq3rx5oWObN2+uffv25Tupfvfu3QoJCVFcXJzvC0aplGa+Jen06dPq2rWrsrOztXHjRi6mcbiSzvfRo0clSffdd59uuOEG++f48ePasmWLGjZsqMTExLItHsVWmtf37bffLmOMvv/+e4929x+22rVr+7halFZp5jsnJ6fAo6Xuq7xzcnJ8WCmuNZ/lNa9vVuUj7vusLViwwG5z32etbdu2dlt6err56quvTHZ2tt3mvu9WSkqK3fbDDz+Y6tWrm8GDB5fPDqBYSjPfFy5cMG3atDHVqlUz+/btK9e6UTIlne+jR4+aNWvWePykpqaaOnXqmDZt2pg1a9aYb7/9ttz3B0Urzes7NTXVWJZlpk+fbrfl5uaaDh06mIiICPPLL7+Uz07Aa6WZ7w4dOphatWqZ06dP2205OTmmVatWplq1aib6qkolAAAXAUlEQVQnJ6d8dgIl8sknnxR6n9SyzGvX5BunBgwYYH9jxZIlS0y7du1M1apVPW7ePnz4cGNZljly5Ijdlpuba9q2bWvCwsI8vsGgWrVq5tChQ9diV+CFks537969jWVZZtSoUWb58uUeP6mpqddiV+CFks53QaKjo7mZv8OVZr5/+9vfGpfLZR566CHz8ssvm7vvvttYlmVee+218t4NeKmk8/3BBx8Yl8tlGjVqZObPn28WLVpk2rZtayzLMs8888y12BV4YfHixWbu3Llm7NixxrIs07dvXzN37lwzd+5cc+7cOWNM2ea1axJSMzMzzcSJE01kZKQJDAw0d9xxh9m0aZNHnxEjRhiXy5XvTe3MmTPmwQcfNBERESYkJMR06tTJ7N27tzzLRzGVdL5jYmKMy+UylmXl+2nYsGF57wa8VJrX95X4xinnK818X7hwwTz22GMmMjLSBAQEmGbNmpl33nmnPMtHMZVmvjds2GDuvPNOExISYs/30qVLy7N8FFNMTIz9d9flctl/k/POb1nmNcsY7poMAAAAZyn3C6cAAACAqyGkAgAAwHEIqQAAAHAcQioAAAAch5AKAAAAxyGkAgAAwHEIqQAAAHAcQioAAAAch5AKAAAAxyGkAgAAwHEIqUA5GTFihFwul1wul/7+979ftX/Hjh3t/kePHi3z+rZu3Wpv74EHHvDpuo0xuvXWW+VyufTQQw95LPvpp5/0yCOPKDo6WlWqVJHL5dKECRN8uv1fG/c8NGzY0KfrffPNN+11z54926frdivu87wi++WXXzRjxgzFxsbK399fLpdLffr00ZEjR+zHqFOnTnb/WbNm2e3Lli0r09qefvppuVwu1atXTxcvXizTbQElRUhFhZf3jd/94+fnpzp16ujee+/Vhg0byqUOy7Ls/7p/97Z/efP1NlesWKEvvvhClmXpscce81g2adIkvfLKK/ruu+9kjPH68fk1S01N1axZszR79mwdOXKk0H5l+TiUdN1HjhzRrFmzNGvWLK1Zs6bQ9VaGebyaF154QU8//bQOHz6s3NzcfI9JYY9ReTx2Y8eOVVBQkE6ePKmXXnqpTLcFlJTftS4AKG+WZckYo1OnTmnjxo3atGmTVq9erV69epXpdqdPn67Ro0dLkpo2bXrV/saYMq2nPC1YsECSFB8fryZNmngsW7dunSSpatWqeuuttxQVFaX69euXe43lKTU1VUlJSZKkTp06KTo6+hpX5L3Dhw9rzpw5ki4fNe3du7fH8uI+zysy93Nbkv7nf/5Ht956q2rVqqXIyEjt2LFDklStWrV848rjtV+zZk39/ve/V3JyshYuXKgnnnhCVapUKfPtAsVBSEWl0r17d02bNk0//PCDZs2apc8++0zGGC1evLjMQ2qjRo3UqFGjMt2GE/2///f/tG/fPklS37598y0/fvy4JCkyMlL9+vXz+fYvXLig0NBQn6/XF9z/w/RrVVDtFfF5/vPPPyskJKTY49zPbcuy8p3m0q5dO5/UVhr33XefkpOTdeLECW3YsEE9evS41iUBHvi4H5VKnTp11K5dO/Xu3VtPPfWU3f7999/n6/vPf/5TgwcPVmRkpKpWrar69etr9OjROnbsmEe/jIwMTZw4UTfeeKMCAgIUEhKihg0bqm/fvkpNTbX7FXauXm5urmbNmqX69esrODhYnTt31meffVZg/UWdN1rQeYzHjh3TyJEj1axZM0VERMjf3181a9ZUly5dCvyotiCnT5/Wf//3fys6OlpVq1ZVWFiY4uLiNGTIEG3btu2q492PgWVZ6tq1q93uPg3DLe95eu7z8YwxWrp0qeLj4xUWFqbAwEA1adJE06dP108//eSxnbzn8O7fv18jR45URESEwsPDi6wv77h9+/Zp6NChCgsLU7169fTkk0/q0qVL2rt3r+666y4FBQUpOjpaixcvzree7OxsvfDCC2rVqpVCQkIUEhKi+Ph4vf3223aftLQ0uVwu+yiqMUadOnWyt1/Q43nkyBH16dNHYWFhqlWrlsaOHausrCxJ0kcffWSPHTFihMe4zz77zF52tf8BS01NVa9evdSwYUOFhYWpatWqio6O1siRIz1OR+jYsaM6d+5s/3vZsmX5no9FnZO6ZcsW9ejRQxEREapataoaNGigBx54QN98841Hv7yn6Lz55ptauHChGjVqpICAADVv3lwff/xxkfvjlvc18c033+h3v/udQkNDVbt2bT3yyCMe52K658Z9nui2bdvUtm1bBQUF6ZFHHrH77du3T/3791e9evVUtWpV1atXT/3797f/R0z6v3N/09LSJF2e57znAhd2TmpRfP1+JEm//e1v7d9Xr17tVR1AuTJABTdz5kxjWZaxLMs88MADdntKSord3rlzZ48x69evNwEBAfZyl8tl/x4ZGWkOHz5s9x05cqS97Mq+Q4cOtfsNHz7cXv73v//dbn/44Yc9xluWZapVq2YaNmxo9z9y5IgxxpiPP/64wH0xxtjtDRs2tNt27dplryPvj7tvUlKS3bewdXfu3LnAfbMsyzz55JNXffy7detmLMsywcHBJjc3126fNWtWvv12b2PZsmXGGGMGDRpUYB/LskyTJk3MmTNn7PXddddd9rIbbrjBY31FyTuuUaNG+bYzbNgwExoamq998+bN9jp++eUX06VLl0KfB5MnTzbGGHP48OFC9yfv88LdVqNGDVOvXr18ffM+7u59DQ8PNxkZGXb7nDlz7PW+++67xhhjEhMT7XXMnj3b7vvQQw/le564+9WrV8+cPHnSGGNMx44dC6zb5XLZz5nCnucvv/xyoY9PeHi4+eSTT+y+eV+zsbGx+bYZHh7uMfeFyfs41q1bN9967r33Xrtv3rmpX7++CQwMtOt079uaNWuMv79/gftQtWpVs3btWmOMMW+++Wahczx79myTlpZmt3Xq1KnA/Xa/Bowpm/cjN/fj26RJk6s+nkB540gqKpX//Oc/2rlzp1JTUzV37lxJ+T+Ku3jxooYPH65ffvlF/v7+euaZZ7Rp0yZNmjRJknTixAn94Q9/sPu7j0jGxMTo/fff16ZNm/TGG29o+PDhqlmzZr4aTJ6PSA8ePKhXXnlFkuyjLOvWrVPbtm3tozClERkZqeeee04pKSn68MMPtWXLFr355puqXbu2JGnevHlFjj9//rx91Kply5Zau3atNmzYoCVLlqhfv35efYz+5ZdfSpKio6M9jpyOHDlS27dv96h1x44d2r59u+69916tWLFCK1askHT5/LnXXntNq1ev1m233Sbp8mM3bdq0Arf5/fffa9asWdq0aZMSEhKuWqPbhQsX9O677+qZZ56x29566y01aNBAqampGjt2rN2+ZMkS+/cXX3xRW7ZskSS1bdtWqampeu+993TTTTdJkubPn689e/YoKirK3j+3xYsX2/vdvHlzj3rOnj2rmjVratWqVfbz9cptu49gnj9/XmvXrrXb3b8HBwdf9Uhqt27dtGTJEq1du1Yff/yxPvjgAz3xxBOSLr9mXn/9dbvWRYsW2eO6d++u7du3a/v27Zo+fbrHOvM+z7/77jv7jg0ul0szZszQ3/72N/Xv39+u/cojwW7//ve/NWXKFK1du1bNmjWz+7/zzjtF7lNeZ8+eVYMGDbRmzRotXrxYwcHBkqQNGzZ4nDfqdvz4cTVo0EBvv/221q9fr9///vf6+eefNWrUKOXk5EiS/vCHP2j9+vX2e0F2drZGjRqlixcv2o9LvXr1JF1+j3HP8ciRI4t1ikdZvx+5T804dOjQr/rUE1RQ1zYjA2Uv79GJK3/q1q1rli9f7tF/9erV9vLu3bubHTt2mB07dpjt27d7HN08ffq0McaYyMhIY1mWad68uTlw4IDJysoqsA73ESbLsuwjTM8//7zdNnDgQLvvuXPnTEhISKmPpBpz+ajOnXfeaapXr17gkZ3z588Xuu6LFy+aKlWqGMuyTNeuXc1XX31lcnJyivX4BwUFGcuyTLt27QpcXljdvXr1spe9/PLLdvvnn39ut9esWdNuz3tE1JsjvAWNe/311+32vEdPP/74Y2OMMadOnbLbWrZsafdt1qyZ3f7ee+/Zzxf30UzLssy4cePs/gU9Fwp6TFwul/nss8/s9iZNmtjtP/30kzHGmO+++86eo169ehljjDl+/Li9jiFDhtjjCzuSevr0afP444+bm266yZ6vvD99+/a1+xb1HCxs31544QW7rX///nbf7Oxs+/VjWZY5cOCAMcbzNdunTx+7/4oVK+z2xx9/vMD5LOxx/Pbbb+32J5980l42atQoY4znkVQ/Pz9z6NAhj3WtWrXKXt66dWuPZbfffru9LDU11W6Pjo62t59X3m1d7UhqWb0fuQ0cONBex6lTp676mALliSOpqHSsPLd3+eGHH/T55597LD906JD9+wcffKA777xTd955p37zm994nGN28OBBSdKoUaMkXT4HsEWLFgoODtbNN9+sJ554QidOnCiyln//+9/2761bt7Z/Dw8Pt4/ClUZCQoIeeOAB7dixQ+fOnSvw1jZnz54tdHxQUJAGDx4sSfrwww918803KygoSC1bttTMmTPznRdaFFPMozR55+GOO+6wf7/lllsUFBRk137q1Kl8Y3v27FmsbUmXnxdt2rSx/12jRg27/fbbb5ck1apVy16e93HLW+uAAQPs58vMmTPt9q+++qrYNYWHh9tHjiV5HAlzb/+6666zz/XduHGjfvzxR/31r3+1+7nnrzC5ubn67W9/q4SEBB06dEiZmZnFeo54o7C59PPzU4sWLex/f/311/nG3nXXXfbvBe2/N2rWrKkbbrjB/nfe19rhw4fz9b/xxht14403erQVtg+SPJ43Be1DaZT1+1He12VxX6NAWSOkolIZMWKEsrOz9cEHHyg4OFjGGM2fP9/jI78r36jdwe7Kn59//lmSNHfuXCUnJ6t///5q3LixqlSpooMHDyohIUFdu3ZVbm6uz+rPGx7yrregoCbJ4wKfyZMn66OPPtK2bdt066232u2XLl0qcpuJiYlasmSJevXqpUaNGskYowMHDmju3LkaOHDgVWt2n1pw5syZq/b1BcuyVLdu3RKNzXs7oLynJhR0WkNhf9wLe76U5Ibp7qDs5uf3fzdkybtNdzDJzs7WypUr7Y/6a9asqXvuuafIbezcuVMHDhyQJEVFRSkpKUnbtm1TcnKy3edqz5GylPcxKGz/i+vKEH6lqz1/yjPMlfX7kft1aVmWx/+EAU5ASEWl43K51LVrV/ucLkmaMWOG/XveI5gjR45Ubm5uvp8LFy7o7rvvlnT5j8jAgQO1YsUKffnll/rpp5/sWyl98cUXRR5ZiY2NtX//5JNP7N/PnTunf/3rX/n65w1R6enp9u+FfSGB+8rfiIgIPfvss+rYsaOaN29e4N0MClOlShWNHj1aqampOnTokH788Uf79jkffvihMjIyihzvvi/qkSNHihV28s7D7t277d8///xze5s1atRQREREvrFXCyG+5q7VsiylpaUV+JzZvHmz3T9vAPbF/8T06tVLERERMsboL3/5i31+bN++fT2CXUHyXh0+ZMgQDR06VO3bty80iJWk9sLmMjs7W/v377f/HRcX59X6iuv06dP69ttvC6wh7xFWt4KeP3n3Yc+ePR7L8v7b1/vg6/ejvEdmJdl3VoiLi6v0X74A5+E+qai0xo0bp/nz5+vixYv67LPP9OGHH+ruu+9W165dFRERoVOnTikxMVE1atRQly5dlJOTo7S0NG3fvl1ff/21fcuZ9u3bq2XLlmrdurXq16+v8+fP64svvpB0+Q+G+3ZBbnn/EPTs2VOTJ0+WJL3//vuaN2+eWrZsqZdeeqnAI28NGzaUy+XSpUuXtGXLFk2fPl2hoaF67rnnCtzHmJgYff311zp16pSef/553XrrrXrxxReLdVQzNjZW/fr102233aaoqCidPHnSPk3BvX/uj98L0r59e23atElZWVn6/PPPPT6+LsqQIUPsI4JPPfWUAgICVKtWLY+v8/TmSG55GDJkiP75z3/KGKMePXpo4sSJioqKUnp6ur766iutXr1aM2fOtOvN+7H1W2+9ZX8dbIcOHUq0fX9/fw0bNkwJCQnau3ev3X61j/qly88Rt5SUFLVv314//vijpkyZUmD/vLXv2LFDGzZsUGhoqG666Sb7qLnk+Tzv16+fJk+erOzsbK1atUqzZs3SHXfcoWXLltkfQd9yyy1ePzdKYsiQIXryySf13XffaeHChXb7lV9GUJiuXbuqVq1aOn36tD799FONGzdO3bt31/r16+3HvHbt2nZY9BVfvh9Jl7+q1e3s2bP2KQPt27f3ad2AT5TrGbDANVDYLaiMMeaRRx6xl9199912+7p16+xb0BT0k/d2LQXdtsj907RpU3Pp0iVjTOEXy4wdOzbfuODgYHPdddfZ/3ZfOGWMMUOGDMnX/5ZbbrF/j4mJsfsuWLAgX986deqYxo0b51t33gtiRowYYa/Dz8+v0P3LewufwuS90OnPf/5zvuUF1e1W1C2obr75ZnP27Fm7b94LoPI+XldT2Dj3RS+WZV213l9++cV06tSp0FpdLpdZsWKF3X/dunUF9rvaY1LUPn7xxRce66pfv36+fS3owqnc3FyPC7/cP3feeWeBF/fk5OR4XOzk/nnzzTeNMYU/z1955ZV8tzBz/1SrVs18+umndt/CbsV0tYu2ruTuW6tWLXP99dfn2263bt3svoVdzJTXmjVrTNWqVQvch4CAAPPXv/7Vo39hz6G82+rYseNV97ss3o+MMea9996zl61fv/6qjydQ3vi4HxWelee7xK/02GOPyeVyybIsffTRR/ZN9Hv06KE9e/bo/vvv13XXXaeqVauqdu3aatmypSZOnOhx+5upU6eqd+/eiomJUUhIiKpWraqGDRtq7Nix2rJlS77tX1nH4sWLNWPGDEVGRiooKEgdOnTQli1b7FvDFNS/f//+Cg0NVfXq1TV8+HD7punWFRdGTZgwQfPmzVN0dLRCQkLUqVMnbdmyxePWOAU9Tnnbn3nmGXXr1k3XX3+9AgMDFRgYqMaNG2vSpEl67733rvr433LLLfaFR6tWrSqwz5XbdHvnnXf06quvqk2bNgoNDVVgYKBuuukmTZ06Vf/7v//rcfpDUfNclMLGuWsqaH1Xtvv7+2vTpk1auHChWrdurbCwMAUFBSk2NlY9e/ZUYmKiunfvbvfv0aOHFixYoNjYWPn7+xe4ncLaCtvHm2++2eOCngEDBni1ry6XS3/729/Uu3dvVa9eXXXq1NFjjz2m1157rcBtValSRWvXrlWHDh0UHh6e73EqrMaxY8fqww8/1L333qtatWrJ399f9evX1/Dhw7V37161atXqqvtZ0jkODw/Xjh071LNnT4WGhtpfilDQ87Godffq1Uu7du1Sv379VLduXfn7+6tOnTrq27ev/vGPf+h3v/tdvnUV9hzKu/xq+1cW70fS/70eIyMjr3ruMnAtWMZwOR+AsrVy5UoNGjRI0uXz4tznqcK35syZo1mzZsmyLO3evdv+n4PKyn3+bExMjMedNHD5PN3rr79emZmZmj9/vv74xz9e65KAfDiSCqDMDRgwQE2bNpVlWR7nA8I3Lly4oG+++UbvvvuuJKlx48aVPqCiaK+++qoyMzNVt25dj699BZyEI6kA8CuX94p7y7L09ttv20euKzP34xIdHV3g/VABOBtHUgGgAnC5XIqJidGLL75IQM2jqHNCATgbR1IBAADgOBxJBQAAgOMQUgEAAOA4hFQAAAA4DiEVAAAAjkNIBQAAgOMQUgEAAOA4hFQAAAA4DiEVAAAAjvP/AVAQ+g8aAEBiAAAAAElFTkSuQmCC", "text/plain": [ "Figure(PyObject <matplotlib.figure.Figure object at 0x11701ae90>)" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "resid = vec((1./(1+exp(-Y4))) - P)\n", "figure(figsize=(8,3)),PyPlot.hist(resid,50)\n", "xlabel(\"Residuals (for methylation profiles)\",fontweight=\"bold\")\n", "show()" ] }, { "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": "Julia 0.3.9", "language": "julia", "name": "julia-0.3" }, "language_info": { "name": "julia", "version": "0.3.9" } }, "nbformat": 4, "nbformat_minor": 0 }
mit
SuperCowPowers/workbench
workbench/notebooks/PCAP_to_Graph.ipynb
2
6211
{ "metadata": { "name": "", "signature": "sha256:2dbb2f1330b2cb8ff4d81035e42c352fa78ae99b51ca379a2f1ea1aa0793a25f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div style=\"float: right; margin: 0px 0px 0px 0px\"><img src=\"files/images/workbench.jpg\" width=\"400px\"></div>\n", "# PCAP to Graph\n", "This notebook demonstrates how short and sweet a workbench python script can be. :)\n", "\n", "Here we're using the workbench server to look at a specific case captured by [ThreatGlass](http://www.threatglass.com/). The exploited website for this exercise is gold-xxx.net [ThreatGlass_Info](http://www.threatglass.com/malicious_urls/141deabbc8741175d9f51559cf4ef3dd?process_date=2014-05-29).\n", "\n", "**Tools in this Notebook:**\n", "\n", "- Workbench: Open Source Security Framework [Workbench GitHub](https://github.com/SuperCowPowers/workbench)\n", "- Bro Network Security Monitor (http://www.bro.org)\n", "\n", "**More Info:** \n", "\n", "- See [PCAP_to_Dataframe](http://nbviewer.ipython.org/github/SuperCowPowers/workbench/blob/master/workbench/notebooks/PCAP_to_Dataframe.ipynb) for a short notebook on turning this PCAP into a Pandas Dataframe.\n", "- See [Workbench Demo Notebook](http://nbviewer.ipython.org/github/SuperCowPowers/workbench/blob/master/workbench/notebooks/Workbench_Demo.ipynb) for a lot more info on using workbench.\n", "\n", "## Lets start up the workbench server...\n", "Run the workbench server (from somewhere, for the demo we're just going to start a local one)\n", "<pre>\n", "$ workbench_server\n", "</pre>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Lets start to interact with workbench, please note there is NO specific client to workbench,\n", "# Just use the ZeroRPC Python, Node.js, or CLI interfaces.\n", "import zerorpc\n", "c = zerorpc.Client(timeout=120)\n", "c.connect(\"tcp://127.0.0.1:4242\")" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 5, "text": [ "[None]" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div style=\"float: left; margin: 0px 30px 0px 0px\"><img src=\"files/images/disk.jpg\" width=\"250px\"></div>\n", "# Read in the Data\n", "<font size=4> The data is pulled from [ThreatGlass](http://www.threatglass.com/), the exploited website for this exercise is gold-xxx.net [ThreatGlass_Info](http://www.threatglass.com/malicious_urls/141deabbc8741175d9f51559cf4ef3dd?process_date=2014-05-29).\n", "</font>" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Load in the PCAP file\n", "with open('../data/pcap/gold_xxx.pcap','rb') as f:\n", " pcap_md5 = c.store_sample(f.read(), 'gold_xxx', 'pcap')" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Now give us a HTTP graph of all the activities within that PCAP.\n", "# Workbench also has DNS and CONN graphs, but for now we're just interested in HTTP.\n", "c.work_request('pcap_http_graph', pcap_md5)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 7, "text": [ "{'pcap_http_graph': {'md5': 'c8e58ff22b9a8e48838373fbb1692bdd',\n", " 'output': 'go to http://localhost:7474/browser and execute this query \"match (s:origin), (t:file), p=allShortestPaths((s)--(t)) return p\"'}}" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "<div style=\"margin: 0px 30px 0px 0px\"><img src=\"files/images/gold_xxx.png\" width=\"900px\"></div>\n", "# Workbench + Neo4j = Awesome\n", "<font size=4> The HTTP graph has quite a bit of info, but you can see that we've conducted a shortest paths search from all nodes of type 'origin' (any node originating http communications) to any node of type 'file'. So for this use case we're interested in all of the various files that got downloaded from our network tap in the last few minutes.</font>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#Wrap Up\n", "Well for this short notebook we used 3 lines of python to go from PCAP file to Neo4j graph. We hope this exercise showed some neato functionality using [Workbench](https://github.com/SuperCowPowers/workbench), we encourage you to check out the GitHub repository and our other notebooks:\n", "\n", "- [PCAP_to_Dataframe](http://nbviewer.ipython.org/github/SuperCowPowers/workbench/blob/master/workbench/notebooks/PCAP_to_Dataframe.ipynb) for a short notebook on turning this PCAP into a Pandas Dataframe.\n", "- [Workbench Demo](http://nbviewer.ipython.org/url/raw.github.com/SuperCowPowers/workbench/master/workbench/notebooks/Workbench_Demo.ipynb) general introduction to Workbench.\n", "- [PCAP_DriveBy](http://nbviewer.ipython.org/url/raw.github.com/SuperCowPowers/workbench/master/workbench/notebooks/PCAP_DriveBy.ipynb) a detail look at a Web DriveBy from the [ThreatGlass](http://www.threatglass.com) repository.\n", "- [PE File Sim Graph](http://nbviewer.ipython.org/url/raw.github.com/SuperCowPowers/workbench/master/workbench/notebooks/PE_SimGraph.ipynb) using Neo4j to generate a similarity graph using PE File features.\n", "- [Generator Pipelines](http://nbviewer.ipython.org/url/raw.github.com/SuperCowPowers/workbench/master/workbench/notebooks/Generator_Pipelines.ipynb) using the client/server streaming generators to demonstrate 'chaining' generators." ] } ], "metadata": {} } ] }
mit
danresende/deep-learning
sentiment_network/Sentiment Classification - What's Going on in the Weights.ipynb
1
898336
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Sentiment Classification & How To \"Frame Problems\" for a Neural Network\n", "\n", "by Andrew Trask\n", "\n", "- **Twitter**: @iamtrask\n", "- **Blog**: http://iamtrask.github.io" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### What You Should Already Know\n", "\n", "- neural networks, forward and back-propagation\n", "- stochastic gradient descent\n", "- mean squared error\n", "- and train/test splits\n", "\n", "### Where to Get Help if You Need it\n", "- Re-watch previous Udacity Lectures\n", "- Leverage the recommended Course Reading Material - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) (40% Off: **traskud17**)\n", "- Shoot me a tweet @iamtrask\n", "\n", "\n", "### Tutorial Outline:\n", "\n", "- Intro: The Importance of \"Framing a Problem\"\n", "\n", "\n", "- Curate a Dataset\n", "- Developing a \"Predictive Theory\"\n", "- **PROJECT 1**: Quick Theory Validation\n", "\n", "\n", "- Transforming Text to Numbers\n", "- **PROJECT 2**: Creating the Input/Output Data\n", "\n", "\n", "- Putting it all together in a Neural Network\n", "- **PROJECT 3**: Building our Neural Network\n", "\n", "\n", "- Understanding Neural Noise\n", "- **PROJECT 4**: Making Learning Faster by Reducing Noise\n", "\n", "\n", "- Analyzing Inefficiencies in our Network\n", "- **PROJECT 5**: Making our Network Train and Run Faster\n", "\n", "\n", "- Further Noise Reduction\n", "- **PROJECT 6**: Reducing Noise by Strategically Reducing the Vocabulary\n", "\n", "\n", "- Analysis: What's going on in the weights?" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true, "nbpresent": { "id": "56bb3cba-260c-4ebe-9ed6-b995b4c72aa3" } }, "source": [ "# Lesson: Curate a Dataset" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true, "nbpresent": { "id": "eba2b193-0419-431e-8db9-60f34dd3fe83" } }, "outputs": [], "source": [ "def pretty_print_review_and_label(i):\n", " print(labels[i] + \"\\t:\\t\" + reviews[i][:80] + \"...\")\n", "\n", "g = open('reviews.txt','r') # What we know!\n", "reviews = list(map(lambda x:x[:-1],g.readlines()))\n", "g.close()\n", "\n", "g = open('labels.txt','r') # What we WANT to know!\n", "labels = list(map(lambda x:x[:-1].upper(),g.readlines()))\n", "g.close()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "25000" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(reviews)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false, "deletable": true, "editable": true, "nbpresent": { "id": "bb95574b-21a0-4213-ae50-34363cf4f87f" } }, "outputs": [ { "data": { "text/plain": [ "'bromwell high is a cartoon comedy . it ran at the same time as some other programs about school life such as teachers . my years in the teaching profession lead me to believe that bromwell high s satire is much closer to reality than is teachers . the scramble to survive financially the insightful students who can see right through their pathetic teachers pomp the pettiness of the whole situation all remind me of the schools i knew and their students . when i saw the episode in which a student repeatedly tried to burn down the school i immediately recalled . . . . . . . . . at . . . . . . . . . . high . a classic line inspector i m here to sack one of your teachers . student welcome to bromwell high . i expect that many adults of my age think that bromwell high is far fetched . what a pity that it isn t '" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reviews[0]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "deletable": true, "editable": true, "nbpresent": { "id": "e0408810-c424-4ed4-afb9-1735e9ddbd0a" } }, "outputs": [ { "data": { "text/plain": [ "'POSITIVE'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[0]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Lesson: Develop a Predictive Theory" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "deletable": true, "editable": true, "nbpresent": { "id": "e67a709f-234f-4493-bae6-4fb192141ee0" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "labels.txt \t : \t reviews.txt\n", "\n", "NEGATIVE\t:\tthis movie is terrible but it has some good effects . ...\n", "POSITIVE\t:\tadrian pasdar is excellent is this film . he makes a fascinating woman . ...\n", "NEGATIVE\t:\tcomment this movie is impossible . is terrible very improbable bad interpretat...\n", "POSITIVE\t:\texcellent episode movie ala pulp fiction . days suicides . it doesnt get more...\n", "NEGATIVE\t:\tif you haven t seen this it s terrible . it is pure trash . i saw this about ...\n", "POSITIVE\t:\tthis schiffer guy is a real genius the movie is of excellent quality and both e...\n" ] } ], "source": [ "print(\"labels.txt \\t : \\t reviews.txt\\n\")\n", "pretty_print_review_and_label(2137)\n", "pretty_print_review_and_label(12816)\n", "pretty_print_review_and_label(6267)\n", "pretty_print_review_and_label(21934)\n", "pretty_print_review_and_label(5297)\n", "pretty_print_review_and_label(4998)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Project 1: Quick Theory Validation" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from collections import Counter\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "positive_counts = Counter()\n", "negative_counts = Counter()\n", "total_counts = Counter()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "for i in range(len(reviews)):\n", " if(labels[i] == 'POSITIVE'):\n", " for word in reviews[i].split(\" \"):\n", " positive_counts[word] += 1\n", " total_counts[word] += 1\n", " else:\n", " for word in reviews[i].split(\" \"):\n", " negative_counts[word] += 1\n", " total_counts[word] += 1" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[('', 550468),\n", " ('the', 173324),\n", " ('.', 159654),\n", " ('and', 89722),\n", " ('a', 83688),\n", " ('of', 76855),\n", " ('to', 66746),\n", " ('is', 57245),\n", " ('in', 50215),\n", " ('br', 49235),\n", " 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('however', 1795),\n", " ('actually', 1790),\n", " ('action', 1789),\n", " ('going', 1783),\n", " ('bit', 1757),\n", " ('comedy', 1742),\n", " ('down', 1740),\n", " ('music', 1738),\n", " ('must', 1728),\n", " ('take', 1709),\n", " ('saw', 1692),\n", " ('long', 1690),\n", " ('right', 1688),\n", " ('fun', 1686),\n", " ('fact', 1684),\n", " ('excellent', 1683),\n", " ('around', 1674),\n", " ('didn', 1672),\n", " ('without', 1671),\n", " ('thing', 1662),\n", " ('thought', 1639),\n", " ('got', 1635),\n", " ('each', 1630),\n", " ('day', 1614),\n", " ('feel', 1597),\n", " ('seems', 1596),\n", " ('come', 1594),\n", " ('done', 1586),\n", " ('beautiful', 1580),\n", " ('especially', 1572),\n", " ('played', 1571),\n", " ('almost', 1566),\n", " ('want', 1562),\n", " ('yet', 1556),\n", " ('give', 1553),\n", " ('pretty', 1549),\n", " ('last', 1543),\n", " ('since', 1519),\n", " ('different', 1504),\n", " ('although', 1501),\n", " ('gets', 1490),\n", " ('true', 1487),\n", " ('interesting', 1481),\n", " ('job', 1470),\n", " ('enough', 1455),\n", " ('our', 1454),\n", " ('shows', 1447),\n", " ('horror', 1441),\n", " ('woman', 1439),\n", " ('tv', 1400),\n", " ('probably', 1398),\n", " ('father', 1395),\n", " ('original', 1393),\n", " ('girl', 1390),\n", " ('point', 1379),\n", " ('plays', 1378),\n", " ('wonderful', 1372),\n", " ('far', 1358),\n", " ('course', 1358),\n", " ('john', 1350),\n", " ('rather', 1340),\n", " ('isn', 1328),\n", " ('ll', 1326),\n", " ('later', 1324),\n", " ('dvd', 1324),\n", " ('whole', 1310),\n", " ('war', 1310),\n", " ('d', 1307),\n", " ('found', 1306),\n", " ('away', 1306),\n", " ('screen', 1305),\n", " ('nothing', 1300),\n", " ('year', 1297),\n", " ('once', 1296),\n", " ('hard', 1294),\n", " ('together', 1280),\n", " ('set', 1277),\n", " ('am', 1277),\n", " ('having', 1266),\n", " ('making', 1265),\n", " ('place', 1263),\n", " ('might', 1260),\n", " ('comes', 1260),\n", " ('sure', 1253),\n", " ('american', 1248),\n", " ('play', 1245),\n", " ('kind', 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" ('fans', 797),\n", " ('city', 792),\n", " ('reason', 789),\n", " ('written', 787),\n", " ('production', 787),\n", " ('several', 784),\n", " ('school', 783),\n", " ('based', 781),\n", " ('rest', 781),\n", " ('try', 780),\n", " ('dead', 776),\n", " ('hope', 775),\n", " ('strong', 768),\n", " ('white', 765),\n", " ('tell', 759),\n", " ('itself', 758),\n", " ('half', 753),\n", " ('person', 749),\n", " ('sometimes', 746),\n", " ('past', 744),\n", " ('start', 744),\n", " ('genre', 743),\n", " ('beginning', 739),\n", " ('final', 739),\n", " ('town', 738),\n", " ('art', 734),\n", " ('humor', 732),\n", " ('game', 732),\n", " ('yes', 731),\n", " ('idea', 731),\n", " ('late', 730),\n", " ('becomes', 729),\n", " ('despite', 729),\n", " ('able', 726),\n", " ('case', 726),\n", " ('money', 723),\n", " ('child', 721),\n", " ('completely', 721),\n", " ('side', 719),\n", " ('camera', 716),\n", " ('getting', 714),\n", " ('instead', 712),\n", " ('soon', 702),\n", " ('under', 700),\n", " ('viewer', 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321),\n", " ('imagine', 321),\n", " ('kept', 320),\n", " ('office', 320),\n", " ('uses', 319),\n", " ('pure', 318),\n", " ('wait', 318),\n", " ('stunning', 318),\n", " ('review', 317),\n", " ('previous', 317),\n", " ('copy', 317),\n", " ('seriously', 317),\n", " ('reading', 316),\n", " ('create', 316),\n", " ('hot', 316),\n", " ('created', 316),\n", " ('magic', 316),\n", " ('somehow', 316),\n", " ('stay', 315),\n", " ('attempt', 315),\n", " ('escape', 315),\n", " ('crazy', 315),\n", " ('air', 315),\n", " ('frank', 315),\n", " ('hands', 314),\n", " ('filled', 313),\n", " ('expected', 312),\n", " ('average', 312),\n", " ('surprisingly', 312),\n", " ('complex', 311),\n", " ('quickly', 310),\n", " ('successful', 310),\n", " ('studio', 310),\n", " ('plus', 309),\n", " ('male', 309),\n", " ('co', 307),\n", " ('images', 306),\n", " ('casting', 306),\n", " ('following', 306),\n", " ('minute', 306),\n", " ('exciting', 306),\n", " ('members', 305),\n", " ('follows', 305),\n", " ('themes', 305),\n", " ('german', 305),\n", " ('reasons', 305),\n", " ('e', 305),\n", " ('touch', 304),\n", " ('edge', 304),\n", " ('free', 304),\n", " ('cute', 304),\n", " ('genius', 304),\n", " ('outside', 303),\n", " ('reviews', 302),\n", " ('admit', 302),\n", " ('ok', 302),\n", " ('younger', 302),\n", " ('fighting', 301),\n", " ('odd', 301),\n", " ('master', 301),\n", " ('recent', 300),\n", " ('thanks', 300),\n", " ('break', 300),\n", " ('comment', 300),\n", " ('apart', 299),\n", " ('emotions', 298),\n", " ('lovely', 298),\n", " ('begin', 298),\n", " ('doctor', 297),\n", " ('party', 297),\n", " ('italian', 297),\n", " ('la', 296),\n", " ('missed', 296),\n", " ...]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "positive_counts.most_common()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "pos_neg_ratios = Counter()\n", "\n", "for term,cnt in list(total_counts.most_common()):\n", " if(cnt > 100):\n", " pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)\n", " pos_neg_ratios[term] = pos_neg_ratio\n", "\n", "for word,ratio in pos_neg_ratios.most_common():\n", " if(ratio > 1):\n", " pos_neg_ratios[word] = np.log(ratio)\n", " else:\n", " pos_neg_ratios[word] = -np.log((1 / (ratio+0.01)))" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[('edie', 4.6913478822291435),\n", " ('paulie', 4.0775374439057197),\n", " ('felix', 3.1527360223636558),\n", " ('polanski', 2.8233610476132043),\n", " ('matthau', 2.8067217286092401),\n", " ('victoria', 2.6810215287142909),\n", " ('mildred', 2.6026896854443837),\n", " ('gandhi', 2.5389738710582761),\n", " ('flawless', 2.451005098112319),\n", " ('superbly', 2.2600254785752498),\n", " ('perfection', 2.1594842493533721),\n", " ('astaire', 2.1400661634962708),\n", 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('presents', 0.44973793178615257),\n", " ('years', 0.44919197032104968),\n", " ('chief', 0.44895022004790319),\n", " ('shadows', 0.44802472252696035),\n", " ('closely', 0.44701411102103689),\n", " ('segments', 0.44701411102103689),\n", " ('lose', 0.44658335503763702),\n", " ('caine', 0.44628710262841953),\n", " ('caught', 0.44610275383999071),\n", " ('hamlet', 0.44558510189758965),\n", " ('chinese', 0.44507424620321018),\n", " ('welcome', 0.44438052435783792),\n", " ('birth', 0.44368632092836219),\n", " ('represents', 0.44320543609101143),\n", " ('puts', 0.44279106572085081),\n", " ('visuals', 0.44183275227903923),\n", " ('fame', 0.44183275227903923),\n", " ('closer', 0.44183275227903923),\n", " ('web', 0.44183275227903923),\n", " ('criminal', 0.4412745608048752),\n", " ('minor', 0.4409224199448939),\n", " ('jon', 0.44086703515908027),\n", " ('liked', 0.44074991514020723),\n", " ('restaurant', 0.44031183943833246),\n", " ('de', 0.43983275161237217),\n", " ('flaws', 0.43983275161237217),\n", " ('searching', 0.4393666597838457),\n", " ('rap', 0.43891304217570443),\n", " ('light', 0.43884433018199892),\n", " ('elizabeth', 0.43872232986464682),\n", " ('marry', 0.43861731542506488),\n", " ('learned', 0.43825493093115531),\n", " ('controversial', 0.43825493093115531),\n", " ('oz', 0.43825493093115531),\n", " ('slowly', 0.43785660389939979),\n", " ('comedic', 0.43721380642274466),\n", " ('wayne', 0.43721380642274466),\n", " ('thrilling', 0.43721380642274466),\n", " ('bridge', 0.43721380642274466),\n", " ('married', 0.43658501682196887),\n", " ('nazi', 0.4361020775700542),\n", " ('murder', 0.4353180712578455),\n", " ('physical', 0.4353180712578455),\n", " ('johnny', 0.43483971678806865),\n", " ('michelle', 0.43445264498141672),\n", " ('wallace', 0.43403848055222038),\n", " ('comedies', 0.43395706390247063),\n", " ('silent', 0.43395706390247063),\n", " ('played', 0.43387244114515305),\n", " ('international', 0.43363598507486073),\n", " ('vision', 0.43286408229627887),\n", " ('intelligent', 0.43196704885367099),\n", " ('shop', 0.43078291609245434),\n", " ('also', 0.43036720209769169),\n", " ('levels', 0.4302451371066513),\n", " ('miss', 0.43006426712153217),\n", " ('movement', 0.4295626596872249),\n", " ...]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# words most frequently seen in a review with a \"POSITIVE\" label\n", "pos_neg_ratios.most_common()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[('boll', -4.0778152602708904),\n", " ('uwe', -3.9218753018711578),\n", " ('seagal', -3.3202501058581921),\n", " ('unwatchable', -3.0269848170580955),\n", " ('stinker', -2.9876839403711624),\n", " ('mst', -2.7753833211707968),\n", " ('incoherent', -2.7641396677532537),\n", " ('unfunny', -2.5545257844967644),\n", " ('waste', -2.4907515123361046),\n", " ('blah', -2.4475792789485005),\n", " ('horrid', -2.3715779644809971),\n", " ('pointless', -2.3451073877136341),\n", " ('atrocious', -2.3187369339642556),\n", " ('redeeming', -2.2667790015910296),\n", " ('prom', -2.2601040980178784),\n", " ('drivel', -2.2476029585766928),\n", " ('lousy', -2.2118080125207054),\n", " ('worst', -2.1930856334332267),\n", " ('laughable', -2.172468615469592),\n", " ('awful', -2.1385076866397488),\n", " ('poorly', -2.1326133844207011),\n", " ('wasting', -2.1178155545614512),\n", " ('remotely', -2.111046881095167),\n", " ('existent', -2.0024805005437076),\n", " ('boredom', -1.9241486572738005),\n", " ('miserably', -1.9216610938019989),\n", " ('sucks', -1.9166645809588516),\n", " ('uninspired', -1.9131499212248517),\n", " ('lame', -1.9117232884159072),\n", " ('insult', -1.9085323769376259)]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# words most frequently seen in a review with a \"NEGATIVE\" label\n", "list(reversed(pos_neg_ratios.most_common()))[0:30]" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Transforming Text into Numbers" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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id8YZZzAf0/mkzF6r5Ox2SvChtsiRRx7Z2E3gGLj5QB7SSC/iAkEhYBgDBVYJ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\nAH0AAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "\n", "review = \"This was a horrible, terrible movie.\"\n", "\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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4nb799ttmyZIlLeslHQN9WpN91nx5boSslh0hMlL33XffbX19Jk2aJKcq/U1f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\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "review = \"The movie was excellent\"\n", "\n", "Image(filename='sentiment_network_pos.png')" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "# Project 2: Creating the Input/Output Data" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "74074\n" ] } ], "source": [ "vocab = set(total_counts.keys())\n", "vocab_size = len(vocab)\n", "print(vocab_size)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "['',\n", " 'stage',\n", " 'yuen',\n", " 'balder',\n", " 'timers',\n", " 'mask',\n", " 'muro',\n", " 'abromowitz',\n", " 'partly',\n", " 'joies',\n", " 'azar',\n", " 'ddr',\n", " 'germane',\n", " 'bllsosopher',\n", " 'dissolve',\n", " 'breathing',\n", " 'tableau',\n", " 'prosthetic',\n", " 'taurus',\n", " 'gleamed',\n", " 'diverge',\n", " 'nighttime',\n", " 'homelessness',\n", " 'thanatopsis',\n", " 'untreated',\n", " 'doctrines',\n", " 'goodloe',\n", " 'rhythm',\n", " 'substandard',\n", " 'tentatively',\n", " 'underlying',\n", " 'whittier',\n", " 'pico',\n", " 'peopled',\n", " 'bullsh',\n", " 'pesky',\n", " 'yale',\n", " 'foulata',\n", " 'hyperkinetic',\n", " 'scholl',\n", " 'laughometer',\n", " 'oren',\n", " 'suprising',\n", " 'cans',\n", " 'lecturing',\n", " 'umber',\n", " 'forgery',\n", " 'autonomous',\n", " 'indigestible',\n", " 'chides',\n", " 'reclamation',\n", " 'wardens',\n", " 'footed',\n", " 'unilaterally',\n", " 'affter',\n", " 'ferber',\n", " 'portrayals',\n", " 'allows',\n", " 'extracurricular',\n", " 'neo',\n", " 'washing',\n", " 'ukraine',\n", " 'miryang',\n", " 'annick',\n", " 'reckless',\n", " 'blissfully',\n", " 'tsu',\n", " 'denison',\n", " 'headache',\n", " 'paypal',\n", " 'louque',\n", " 'traced',\n", " 'relegates',\n", " 'loiret',\n", " 'ropers',\n", " 'unwinds',\n", " 'aito',\n", " 'dashingly',\n", " 'racist',\n", " 'fondly',\n", " 'frostbite',\n", " 'vampiros',\n", " 'repulsed',\n", " 'predicated',\n", " 'forsa',\n", " 'flitty',\n", " 'sunekosuri',\n", " 'vampyr',\n", " 'oless',\n", " 'nuke',\n", " 'punky',\n", " 'sawney',\n", " 'upsets',\n", " 'expels',\n", " 'dena',\n", " 'kiva',\n", " 'squeazy',\n", " 'penal',\n", " 'dartboard',\n", " 'boarders',\n", " 'mnm',\n", " 'mrquez',\n", " 'perversions',\n", " 'aggrandizing',\n", " 'brokovich',\n", " 'dependent',\n", " 'pursuing',\n", " 'familiarized',\n", " 'marchal',\n", " 'raju',\n", " 'bogarts',\n", " 'panes',\n", " 'caitlin',\n", " 'paarthale',\n", " 'recur',\n", " 'warping',\n", " 'bradycardia',\n", " 'arcadia',\n", " 'intergender',\n", " 'subterranean',\n", " 'assistant',\n", " 'unscheduled',\n", " 'ozporns',\n", " 'liner',\n", " 'aragorn',\n", " 'lonliness',\n", " 'tashy',\n", " 'corleone',\n", " 'bombshell',\n", " 'companionship',\n", " 'ricci',\n", " 'solves',\n", " 'isint',\n", " 'underflowing',\n", " 'pransky',\n", " 'internalist',\n", " 'liaison',\n", " 'teletype',\n", " 'wile',\n", " 'programmation',\n", " 'applause',\n", " 'unmated',\n", " 'hassett',\n", " 'achterbusch',\n", " 'irk',\n", " 'bloodbath',\n", " 'explorations',\n", " 'dearies',\n", " 'rocco',\n", " 'homework',\n", " 'addresses',\n", " 'scales',\n", " 'yul',\n", " 'engine',\n", " 'unchoreographed',\n", " 'talented',\n", " 'ruler',\n", " 'maude',\n", " 'preferences',\n", " 'punsley',\n", " 'reentered',\n", " 'ditches',\n", " 'skis',\n", " 'tribbiani',\n", " 'normal',\n", " 'bryans',\n", " 'varhola',\n", " 'seam',\n", " 'coates',\n", " 'clavell',\n", " 'harping',\n", " 'chipped',\n", " 'sages',\n", " 'abolition',\n", " 'medias',\n", " 'megalomania',\n", " 'masina',\n", " 'peeves',\n", " 'bohlen',\n", " 'disdainful',\n", " 'cucumbers',\n", " 'vehicles',\n", " 'excepting',\n", " 'fizzly',\n", " 'treads',\n", " 'stopovers',\n", " 'kumai',\n", " 'carabiners',\n", " 'reconnoitering',\n", " 'psychoanalytical',\n", " 'novarro',\n", " 'squirmish',\n", " 'carfully',\n", " 'spruced',\n", " 'reid',\n", " 'esha',\n", " 'unknowns',\n", " 'communicable',\n", " 'poundage',\n", " 'cartwright',\n", " 'homoeroticism',\n", " 'peyote',\n", " 'neutrality',\n", " 'reefer',\n", " 'premedical',\n", " 'alekos',\n", " 'schnook',\n", " 'quotation',\n", " 'rashly',\n", " 'ingenue',\n", " 'keenan',\n", " 'hagia',\n", " 'studding',\n", " 'amusements',\n", " 'critic',\n", " 'worshiper',\n", " 'psychokinetic',\n", " 'braking',\n", " 'capo',\n", " 'whisking',\n", " 'mc',\n", " 'hou',\n", " 'basis',\n", " 'aniston',\n", " 'screwee',\n", " 'followings',\n", " 'breakaway',\n", " 'gharlie',\n", " 'reichskanzler',\n", " 'pebble',\n", " 'discotheque',\n", " 'huntsbery',\n", " 'grueling',\n", " 'wilmington',\n", " 'insurgency',\n", " 'gaa',\n", " 'personifies',\n", " 'poodles',\n", " 'er',\n", " 'solutions',\n", " 'larraz',\n", " 'em',\n", " 'slouches',\n", " 'raducanu',\n", " 'avenues',\n", " 'magnified',\n", " 'pear',\n", " 'swamps',\n", " 'braslia',\n", " 'wrinkling',\n", " 'bernal',\n", " 'giza',\n", " 'craig',\n", " 'hof',\n", " 'giordano',\n", " 'munchkin',\n", " 'dough',\n", " 'leery',\n", " 'crucifixion',\n", " 'posturing',\n", " 'riveting',\n", " 'defectives',\n", " 'transpose',\n", " 'cajoling',\n", " 'combines',\n", " 'livery',\n", " 'mining',\n", " 'wong',\n", " 'poldi',\n", " 'perdition',\n", " 'daw',\n", " 'bloopers',\n", " 'defacing',\n", " 'euthanasiarist',\n", " 'outrages',\n", " 'gfx',\n", " 'goodluck',\n", " 'pnico',\n", " 'asks',\n", " 'honored',\n", " 'doofuses',\n", " 'indigineous',\n", " 'bldy',\n", " 'paint',\n", " 'weeny',\n", " 'dailey',\n", " 'wolfpack',\n", " 'supplanted',\n", " 'kiera',\n", " 'hairbrained',\n", " 'teleportation',\n", " 'sense',\n", " 'yiiii',\n", " 'inject',\n", " 'flamboyant',\n", " 'ahlberg',\n", " 'puszta',\n", " 'lorean',\n", " 'fiers',\n", " 'shallow',\n", " 'charteris',\n", " 'glitxy',\n", " 'sinclair',\n", " 'kindegarden',\n", " 'refusals',\n", " 'leonidas',\n", " 'undeserved',\n", " 'jensen',\n", " 'sabretooth',\n", " 'vitriolic',\n", " 'bereaved',\n", " 'fishtail',\n", " 'camaraderie',\n", " 'questmaster',\n", " 'adverse',\n", " 'impostor',\n", " 'coaxing',\n", " 'videotaping',\n", " 'orchidea',\n", " 'hedaya',\n", " 'bell',\n", " 'delpy',\n", " 'brit',\n", " 'lawnmowerman',\n", " 'calculating',\n", " 'phoned',\n", " 'container',\n", " 'resistant',\n", " 'proprietress',\n", " 'vodyanoi',\n", " 'leashes',\n", " 'benzedrine',\n", " 'lenghts',\n", " 'painkillers',\n", " 'dreams',\n", " 'zabriskie',\n", " 'harleys',\n", " 'foundationally',\n", " 'lassie',\n", " 'trustees',\n", " 'ducks',\n", " 'workers',\n", " 'cough',\n", " 'sizing',\n", " 'cardos',\n", " 'dong',\n", " 'uniforms',\n", " 'acquitted',\n", " 'bohnen',\n", " 'slightyly',\n", " 'surfaced',\n", " 'diced',\n", " 'lashley',\n", " 'shotgunning',\n", " 'submerges',\n", " 'centrepiece',\n", " 'perron',\n", " 'fundamental',\n", " 'sizzling',\n", " 'undefeated',\n", " 'sprinkle',\n", " 'speckle',\n", " 'teller',\n", " 'moviefreak',\n", " 'skaal',\n", " 'raindeer',\n", " 'ironhead',\n", " 'uncompromizing',\n", " 'lamonte',\n", " 'laguna',\n", " 'cryptozoology',\n", " 'mohamed',\n", " 'sllskapsresan',\n", " 'pesce',\n", " 'walder',\n", " 'espionage',\n", " 'seams',\n", " 'necklace',\n", " 'reviles',\n", " 'provisions',\n", " 'butter',\n", " 'fledgling',\n", " 'revamped',\n", " 'xvid',\n", " 'transmits',\n", " 'bronsan',\n", " 'swirls',\n", " 'mindy',\n", " 'tethered',\n", " 'redid',\n", " 'gathered',\n", " 'griffen',\n", " 'sabrian',\n", " 'jurking',\n", " 'swindlers',\n", " 'bettering',\n", " 'triviata',\n", " 'dread',\n", " 'wilding',\n", " 'mojo',\n", " 'disrepair',\n", " 'ruptured',\n", " 'circuits',\n", " 'analyzing',\n", " 'wirsching',\n", " 'escaping',\n", " 'sickingly',\n", " 'splitting',\n", " 'gft',\n", " 'licencing',\n", " 'frock',\n", " 'lyoko',\n", " 'males',\n", " 'franklin',\n", " 'vaitongi',\n", " 'sightless',\n", " 'bmx',\n", " 'viewability',\n", " 'conditional',\n", " 'burstingly',\n", " 'chauvinistic',\n", " 'bergerac',\n", " 'operetta',\n", " 'grungy',\n", " 'broadbent',\n", " 'levens',\n", " 'eaves',\n", " 'expansionist',\n", " 'casablanka',\n", " 'oneself',\n", " 'excessiveness',\n", " 'keitel',\n", " 'honolulu',\n", " 'horrifying',\n", " 'stupefying',\n", " 'weekdays',\n", " 'eyebrow',\n", " 'gratefulness',\n", " 'mere',\n", " 'finals',\n", " 'cannible',\n", " 'dozing',\n", " 'salaries',\n", " 'prescience',\n", " 'bashings',\n", " 'liken',\n", " 'lenoire',\n", " 'americaness',\n", " 'staunchly',\n", " 'gruff',\n", " 'silliest',\n", " 'bleek',\n", " 'circumlocution',\n", " 'fearlessly',\n", " 'hit',\n", " 'vays',\n", " 'randolph',\n", " 'long',\n", " 'matarazzo',\n", " 'dorsey',\n", " 'rediculas',\n", " 'gao',\n", " 'doones',\n", " 'iglesia',\n", " 'torin',\n", " 'songwriters',\n", " 'plentiful',\n", " 'horsecocky',\n", " 'dreufuss',\n", " 'dicky',\n", " 'esq',\n", " 'besco',\n", " 'underused',\n", " 'forerunner',\n", " 'dreamgirl',\n", " 'gaining',\n", " 'rereads',\n", " 'platters',\n", " 'franciosa',\n", " 'legacy',\n", " 'carlita',\n", " 'repartees',\n", " 'decimation',\n", " 'borel',\n", " 'poach',\n", " 'aces',\n", " 'reorganized',\n", " 'purrs',\n", " 'shockers',\n", " 'campesinos',\n", " 'rohal',\n", " 'volunteered',\n", " 'pathedic',\n", " 'sayings',\n", " 'putty',\n", " 'isham',\n", " 'iwas',\n", " 'wretched',\n", " 'lovelier',\n", " 'cartooned',\n", " 'depressive',\n", " 'sissily',\n", " 'moe',\n", " 'infringement',\n", " 'fairview',\n", " 'artificial',\n", " 'plotholes',\n", " 'konchalovsky',\n", " 'himbut',\n", " 'correspondence',\n", " 'imagination',\n", " 'bancroft',\n", " 'outpost',\n", " 'sbardellati',\n", " 'scob',\n", " 'timeshifts',\n", " 'tenacity',\n", " 'labourer',\n", " 'unclever',\n", " 'deniers',\n", " 'narrtor',\n", " 'marathan',\n", " 'peculating',\n", " 'bridges',\n", " 'quinnn',\n", " 'chewed',\n", " 'doghi',\n", " 'savanna',\n", " 'hulbert',\n", " 'sarde',\n", " 'valenti',\n", " 'manson',\n", " 'glib',\n", " 'strays',\n", " 'when',\n", " 'annoyingly',\n", " 'andrei',\n", " 'anxiety',\n", " 'mlc',\n", " 'ears',\n", " 'paine',\n", " 'rummaged',\n", " 'musa',\n", " 'inspected',\n", " 'hopelessly',\n", " 'assassinate',\n", " 'relished',\n", " 'joke',\n", " 'warmhearted',\n", " 'undefined',\n", " 'une',\n", " 'incorporates',\n", " 'chee',\n", " 'takeko',\n", " 'ghosthouse',\n", " 'homebase',\n", " 'unlikley',\n", " 'unambiguous',\n", " 'dearest',\n", " 'preforming',\n", " 'group',\n", " 'selects',\n", " 'wrestles',\n", " 'moravia',\n", " 'mears',\n", " 'gaita',\n", " 'completest',\n", " 'joel',\n", " 'highlights',\n", " 'ooooohhhh',\n", " 'launching',\n", " 'snorting',\n", " 'cruiser',\n", " 'weingartner',\n", " 'beans',\n", " 'brion',\n", " 'deadlier',\n", " 'couldve',\n", " 'descents',\n", " 'inferno',\n", " 'vining',\n", " 'westwood',\n", " 'gibs',\n", " 'gundam',\n", " 'pining',\n", " 'mates',\n", " 'tickling',\n", " 'appoint',\n", " 'overabundance',\n", " 'mnica',\n", " 'deadfall',\n", " 'aspires',\n", " 'twinned',\n", " 'bitsmidohio',\n", " 'vctor',\n", " 'peak',\n", " 'gamers',\n", " 'interactive',\n", " 'decree',\n", " 'formosa',\n", " 'undressed',\n", " 'individuation',\n", " 'cabo',\n", " 'seboipepe',\n", " 'ryoko',\n", " 'friels',\n", " 'unbounded',\n", " 'rajnikant',\n", " 'freaky',\n", " 'ompuri',\n", " 'hallmark',\n", " 'glamourous',\n", " 'klok',\n", " 'calmly',\n", " 'attracted',\n", " 'powermaster',\n", " 'lyricists',\n", " 'dissing',\n", " 'portfolios',\n", " 'shakily',\n", " 'stair',\n", " 'document',\n", " 'unforgettable',\n", " 'sociable',\n", " 'vrsel',\n", " 'backlash',\n", " 'skitters',\n", " 'crapo',\n", " 'nicholls',\n", " 'alta',\n", " 'violation',\n", " 'bedevils',\n", " 'potion',\n", " 'italia',\n", " 'seiing',\n", " 'torpedos',\n", " 'tirith',\n", " 'templates',\n", " 'limbs',\n", " 'solver',\n", " 'stationary',\n", " 'malfique',\n", " 'denys',\n", " 'coulthard',\n", " 'schygulla',\n", " 'emannuelle',\n", " 'bunuel',\n", " 'xu',\n", " 'mon',\n", " 'xd',\n", " 'pb',\n", " 'consider',\n", " 'pianist',\n", " 'risks',\n", " 'dahl',\n", " 'beachcomber',\n", " 'repairs',\n", " 'jing',\n", " 'strobes',\n", " 'crediblity',\n", " 'canvas',\n", " 'torments',\n", " 'despicable',\n", " 'philbin',\n", " 'histrionics',\n", " 'awsomeness',\n", " 'bleed',\n", " 'bickering',\n", " 'finishing',\n", " 'von',\n", " 'motormouth',\n", " 'leclerc',\n", " 'dharmendra',\n", " 'globally',\n", " 'exhooker',\n", " 'illuminations',\n", " 'showiest',\n", " 'norris',\n", " 'seselj',\n", " 'denominator',\n", " 'il',\n", " 'spanishness',\n", " 'vandalizing',\n", " 'mch',\n", " 'trample',\n", " 'cleve',\n", " 'litters',\n", " 'lifeblood',\n", " 'entrusted',\n", " 'cc',\n", " 'coroner',\n", " 'lahaye',\n", " 'deludes',\n", " 'wishbone',\n", " 'sari',\n", " 'withdrawal',\n", " 'accentuate',\n", " 'klan',\n", " 'tain',\n", " 'bronco',\n", " 'jovan',\n", " 'lidsville',\n", " 'dodesukaden',\n", " 'lexus',\n", " 'snyder',\n", " 'raves',\n", " 'striped',\n", " 'pupi',\n", " 'bravo',\n", " 'uno',\n", " 'saving',\n", " 'empathized',\n", " 'goetter',\n", " 'regimental',\n", " 'sprawling',\n", " 'aranoa',\n", " 'floundered',\n", " 'trifecta',\n", " 'powerglove',\n", " 'hifi',\n", " 'franfreako',\n", " 'goodnik',\n", " 'gillette',\n", " 'byronic',\n", " 'pollak',\n", " 'polution',\n", " 'grammatically',\n", " 'insurgents',\n", " 'apaches',\n", " 'gall',\n", " 'sneaking',\n", " 'pout',\n", " 'gull',\n", " 'siddons',\n", " 'zavet',\n", " 'knockdown',\n", " 'supports',\n", " 'hampeita',\n", " 'tripods',\n", " 'hito',\n", " 'philanthropic',\n", " 'punks',\n", " 'clytemenstra',\n", " 'kinski',\n", " 'cherri',\n", " 'mantis',\n", " 'smartest',\n", " 'uninjured',\n", " 'seagoing',\n", " 'faustino',\n", " 'hig',\n", " 'simpons',\n", " 'ethan',\n", " 'gumshoe',\n", " 'sunnydale',\n", " 'youknowwhat',\n", " 'piece',\n", " 'compelling',\n", " 'instigator',\n", " 'pollyanna',\n", " 'sirbossman',\n", " 'quayle',\n", " 'rissole',\n", " 'gaslit',\n", " 'vomited',\n", " 'roadster',\n", " 'plastic',\n", " 'salkow',\n", " 'thad',\n", " 'rosenstrasse',\n", " 'yall',\n", " 'tamo',\n", " 'herod',\n", " 'vivacious',\n", " 'rhinos',\n", " 'applewhite',\n", " 'originators',\n", " 'hypnotising',\n", " 'bulgakov',\n", " 'tottering',\n", " 'vilifies',\n", " 'gnash',\n", " 'sophisticate',\n", " 'spheres',\n", " 'sprocket',\n", " 'weeks',\n", " 'citizenx',\n", " 'ist',\n", " 'viren',\n", " 'compute',\n", " 'deteriorate',\n", " 'popularize',\n", " 'enterntainment',\n", " 'at',\n", " 'proposition',\n", " 'filmstiftung',\n", " 'assael',\n", " 'terribly',\n", " 'normand',\n", " 'ritual',\n", " 'tame',\n", " 'threateningly',\n", " 'classrooms',\n", " 'shite',\n", " 'flimsily',\n", " 'artists',\n", " 'sandbag',\n", " 'horowitz',\n", " 'removes',\n", " 'hoofer',\n", " 'biggest',\n", " 'anathema',\n", " 'shattering',\n", " 'twists',\n", " 'comas',\n", " 'parameters',\n", " 'berliner',\n", " 'vaticani',\n", " 'dolly',\n", " 'crypts',\n", " 'squirrels',\n", " 'flubbing',\n", " 'yeccch',\n", " 'findlay',\n", " 'personae',\n", " 'rectitude',\n", " 'dnouement',\n", " 'indisputably',\n", " 'arithmetic',\n", " 'nebot',\n", " 'geeeee',\n", " 'rampantly',\n", " 'fickleness',\n", " 'natassia',\n", " 'jellybean',\n", " 'formulae',\n", " 'scorning',\n", " 'robald',\n", " 'lurching',\n", " 'petter',\n", " 'ivanek',\n", " 'zombiefest',\n", " 'hunnicutt',\n", " 'contrived',\n", " 'sags',\n", " 'israelis',\n", " 'earner',\n", " 'zaara',\n", " 'booker',\n", " 'bergre',\n", " 'plaudits',\n", " 'gubra',\n", " 'plex',\n", " 'lecter',\n", " 'hurrrts',\n", " 'zapp',\n", " 'police',\n", " 'pocketbooks',\n", " 'doctoral',\n", " 'yabba',\n", " 'speeds',\n", " 'shauvians',\n", " 'juxtaposed',\n", " 'eastman',\n", " 'integrates',\n", " 'starfucker',\n", " 'pursuant',\n", " 'authority',\n", " 'shlocky',\n", " 'swooshes',\n", " 'shovel',\n", " 'cannavale',\n", " 'avjo',\n", " 'assess',\n", " 'stucco',\n", " 'completetly',\n", " 'waved',\n", " 'irrepressible',\n", " 'distractive',\n", " 'interiors',\n", " 'alps',\n", " 'scorer',\n", " 'tetsukichi',\n", " 'dried',\n", " 'micah',\n", " 'patient',\n", " 'emminently',\n", " 'arrgh',\n", " 'trickling',\n", " 'aimanov',\n", " 'farily',\n", " 'deitrich',\n", " 'whorde',\n", " 'orca',\n", " 'leaped',\n", " 'linguistically',\n", " 'extreamely',\n", " 'fbl',\n", " 'prem',\n", " 'blanc',\n", " 'rearrange',\n", " 'salgueiro',\n", " 'channels',\n", " 'chris',\n", " 'feij',\n", " 'lapsed',\n", " 'sensible',\n", " 'boyum',\n", " 'bases',\n", " 'haywood',\n", " 'chikatilo',\n", " 'apollonia',\n", " 'contactable',\n", " 'clenched',\n", " 'aborigines',\n", " 'negativistic',\n", " 'mochrie',\n", " 'piggy',\n", " 'twoooooooo',\n", " 'suchet',\n", " 'looping',\n", " 'dasilva',\n", " 'privilege',\n", " 'sooooo',\n", " 'juliana',\n", " 'chapin',\n", " 'depreciative',\n", " 'lomas',\n", " 'bop',\n", " 'jetee',\n", " 'pausing',\n", " 'peephole',\n", " 'hassadeevichit',\n", " 'intoxication',\n", " 'babied',\n", " 'greengrass',\n", " 'steelcrafts',\n", " 'astrogators',\n", " 'ensure',\n", " 'pandora',\n", " 'excution',\n", " 'handmade',\n", " 'kikabidze',\n", " 'fetching',\n", " 'liferaft',\n", " 'transpires',\n", " 'stroh',\n", " 'hillman',\n", " 'jembs',\n", " 'deco',\n", " 'biased',\n", " 'fassbinder',\n", " 'envelopes',\n", " 'mumford',\n", " 'fugace',\n", " 'blinds',\n", " 'formats',\n", " 'roscoe',\n", " 'yokels',\n", " 'kirsty',\n", " 'crossfire',\n", " 'mistaken',\n", " 'captivating',\n", " 'replies',\n", " 'fratelli',\n", " 'sarafina',\n", " 'mn',\n", " 'plod',\n", " 'daines',\n", " 'cheeni',\n", " 'conquerors',\n", " 'budding',\n", " 'exterminating',\n", " 'carefully',\n", " 'corporation',\n", " 'ideologically',\n", " 'halpin',\n", " 'vfx',\n", " 'conaughey',\n", " 'floating',\n", " 'belivably',\n", " 'adoption',\n", " 'sweaters',\n", " 'favourably',\n", " 'readable',\n", " 'female',\n", " 'western',\n", " 'infinity',\n", " 'uncharismatic',\n", " 'idiotized',\n", " 'ronnie',\n", " 'examined',\n", " 'atmospheres',\n", " 'perspiring',\n", " 'cookers',\n", " 'courtesan',\n", " 'mostof',\n", " 'format',\n", " 'polonius',\n", " 'asphyxiated',\n", " ...]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(vocab)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "\n", "layer_0 = np.zeros((1,vocab_size))\n", "layer_0" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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WU0gI3cKUzJIlS+xKGkiJpFkIXHzxxeakk04apjTnRo0aZX75y18OO1/kDzZmpI4bbrih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\nAH0AAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "{'': 0,\n", " 'inhabitants': 1,\n", " 'goku': 2,\n", " 'stunts': 3,\n", " 'catepillar': 4,\n", " 'kristensen': 5,\n", " 'goddess': 7,\n", " 'offing': 49797,\n", " 'distroy': 8,\n", " 'unexplainably': 9,\n", " 'concoctions': 10,\n", " 'petite': 11,\n", " 'paramilitary': 24759,\n", " 'scribe': 12,\n", " 'stevson': 13,\n", " 'senegal': 6,\n", " 'sctv': 14,\n", " 'soundscape': 15,\n", " 'rana': 16,\n", " 'immortalizer': 18,\n", " 'rene': 67354,\n", " 'eko': 23,\n", " 'planning': 20,\n", " 'akiva': 21,\n", " 'plod': 22,\n", " 'orderly': 24,\n", " 'zeleznice': 25,\n", " 'critize': 29,\n", " 'baguettes': 25649,\n", " 'jefferies': 30,\n", " 'uncertainties': 61695,\n", " 'mountainbillies': 31,\n", " 'steinbichler': 32,\n", " 'vowel': 33,\n", " 'rafe': 34,\n", " 'donig': 68719,\n", " 'tulipe': 36,\n", " 'clot': 37,\n", " 'hack': 12526,\n", " 'distended': 38,\n", " 'cornered': 37116,\n", " 'impatiently': 40,\n", " 'batrice': 12525,\n", " 'unfortuntly': 41,\n", " 'lung': 42,\n", " 'scapegoats': 43,\n", " 'pscychosexual': 45,\n", " 'outbid': 46,\n", " 'obit': 47,\n", " 'sideshows': 48,\n", " 'jugde': 49,\n", " 'kevloun': 51,\n", " 'quartier': 53,\n", " 'harp': 61948,\n", " 'unravelling': 54,\n", " 'antiques': 56,\n", " 'strutts': 57,\n", " 'tilts': 58,\n", " 'disconcert': 59,\n", " 'dossiers': 60,\n", " 'sorriest': 61,\n", " 'craftsman': 49412,\n", " 'blart': 62,\n", " 'dependence': 37120,\n", " 'sated': 61698,\n", " 'iberia': 63,\n", " 'sagan': 72,\n", " 'frmann': 65,\n", " 'daniell': 66,\n", " 'rays': 67,\n", " 'pried': 68,\n", " 'khoobsurat': 69,\n", " 'leavitt': 70,\n", " 'caiano': 71,\n", " 'attractiveness': 73,\n", " 'kitaparaporn': 74,\n", " 'hamilton': 75,\n", " 'massages': 76,\n", " 'horgan': 78,\n", " 'chemist': 79,\n", " 'audrey': 80,\n", " 'yeow': 55655,\n", " 'jana': 81,\n", " 'dutch': 82,\n", " 'pinchot': 24773,\n", " 'override': 83,\n", " 'dwervick': 63223,\n", " 'spasms': 84,\n", " 'resumed': 85,\n", " 'tamale': 66259,\n", " 'calibanian': 49636,\n", " 'stinson': 86,\n", " 'widows': 87,\n", " 'stonewall': 88,\n", " 'palatial': 89,\n", " 'neuman': 90,\n", " 'abandon': 91,\n", " 'lemmings': 65314,\n", " 'anglophile': 92,\n", " 'ertha': 61706,\n", " 'chevette': 94,\n", " 'unscary': 95,\n", " 'spoilerific': 97,\n", " 'neworleans': 67639,\n", " 'metamorphose': 17,\n", " 'brigand': 99,\n", " 'cheating': 41603,\n", " 'clued': 101,\n", " 'dermatonecrotic': 102,\n", " 'grady': 103,\n", " 'mulligan': 104,\n", " 'ol': 105,\n", " 'incubation': 107,\n", " 'plaintiffs': 110,\n", " 'snden': 109,\n", " 'fk': 111,\n", " 'deply': 112,\n", " 'franchot': 113,\n", " 'henstridge': 19,\n", " 'cyhper': 114,\n", " 'verbose': 26,\n", " 'mazovia': 116,\n", " 'elizabeth': 117,\n", " 'palestine': 118,\n", " 'robby': 119,\n", " 'wongo': 120,\n", " 'moshing': 121,\n", " 'mstified': 12543,\n", " 'eeeee': 122,\n", " 'doltish': 123,\n", " 'bree': 124,\n", " 'postponed': 125,\n", " 'debacles': 127,\n", " 'amplify': 27,\n", " 'kamm': 128,\n", " 'phantom': 18893,\n", " 'boylen': 136,\n", " 'rolando': 131,\n", " 'premises': 133,\n", " 'bruck': 134,\n", " 'loosely': 135,\n", " 'wodehousian': 139,\n", " 'onishi': 70389,\n", " 'encapsuling': 140,\n", " 'partly': 141,\n", " 'stadling': 144,\n", " 'calms': 143,\n", " 'darkie': 148,\n", " 'wheeling': 147,\n", " 'ursla': 15875,\n", " 'subsidized': 49420,\n", " 'mckellar': 149,\n", " 'ooookkkk': 151,\n", " 'milky': 152,\n", " 'unfolded': 153,\n", " 'degrades': 154,\n", " 'authenticating': 155,\n", " 'writeup': 12548,\n", " 'rotheroe': 156,\n", " 'beart': 157,\n", " 'intoxicants': 160,\n", " 'grispin': 159,\n", " 'cannes': 61718,\n", " 'antithetical': 70398,\n", " 'nnette': 161,\n", " 'tsukamoto': 163,\n", " 'antwones': 44205,\n", " 'stows': 164,\n", " 'suddenness': 165,\n", " 'vol': 61720,\n", " 'waqt': 166,\n", " 'camazotz': 168,\n", " 'paps': 55042,\n", " 'shakher': 170,\n", " 'terminate': 63868,\n", " 'kotex': 56419,\n", " 'delinquency': 171,\n", " 'bromwell': 25214,\n", " 'insecticide': 173,\n", " 'charlton': 174,\n", " 'nakada': 177,\n", " 'titted': 24791,\n", " 'urbane': 178,\n", " 'depicted': 54491,\n", " 'sadomasochistic': 179,\n", " 'hyping': 181,\n", " 'yr': 182,\n", " 'hebert': 183,\n", " 'waxwork': 12990,\n", " 'deathrow': 185,\n", " 'nourishes': 24792,\n", " 'unmediated': 187,\n", " 'tamper': 37143,\n", " 'soad': 190,\n", " 'alphabet': 189,\n", " 'donen': 191,\n", " 'lord': 192,\n", " 'recess': 193,\n", " 'watchably': 61023,\n", " 'handsome': 194,\n", " 'vignettes': 196,\n", " 'pairings': 198,\n", " 'uselful': 199,\n", " 'sanders': 200,\n", " 'outbursts': 72891,\n", " 'nots': 201,\n", " 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'acomplication': 241,\n", " 'contrasted': 243,\n", " 'muzzle': 44,\n", " 'amphibians': 72141,\n", " 'springs': 246,\n", " 'reformatted': 49443,\n", " 'toolbox': 247,\n", " 'contacting': 248,\n", " 'washrooms': 250,\n", " 'raving': 251,\n", " 'dynamism': 252,\n", " 'mae': 253,\n", " 'disharmony': 255,\n", " 'molls': 72979,\n", " 'dewaere': 12569,\n", " 'untutored': 256,\n", " 'icarus': 257,\n", " 'taint': 258,\n", " 'kargil': 259,\n", " 'captain': 260,\n", " 'paucity': 261,\n", " 'fits': 262,\n", " 'tumbles': 263,\n", " 'amer': 264,\n", " 'bueller': 265,\n", " 'cleansed': 267,\n", " 'shara': 269,\n", " 'humma': 270,\n", " 'outa': 272,\n", " 'piglets': 273,\n", " 'gombell': 274,\n", " 'supermen': 275,\n", " 'superlow': 276,\n", " 'kubanskie': 280,\n", " 'goode': 278,\n", " 'disorganised': 45570,\n", " 'zenith': 281,\n", " 'ananda': 282,\n", " 'matlin': 284,\n", " 'particolare': 50,\n", " 'presumptuous': 286,\n", " 'rerun': 287,\n", " 'toyko': 288,\n", " 'bilb': 291,\n", " 'sundry': 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936,\n", " 'dashboard': 12667,\n", " 'unknowingly': 937,\n", " 'flatmates': 51897,\n", " 'unnerve': 938,\n", " 'caning': 939,\n", " 'shortland': 146,\n", " 'recluse': 941,\n", " 'dcreasy': 942,\n", " 'scratchiness': 24911,\n", " 'pms': 30930,\n", " 'chipmunk': 943,\n", " 'tkachenko': 49537,\n", " 'dipper': 944,\n", " 'europeans': 61601,\n", " 'berserkers': 948,\n", " 'shys': 947,\n", " 'monte': 68505,\n", " 'eve': 949,\n", " 'luxury': 61828,\n", " 'conflagration': 950,\n", " 'water': 46389,\n", " 'irks': 951,\n", " 'positronic': 954,\n", " 'cushy': 150,\n", " 'swiftness': 957,\n", " 'underimpressed': 964,\n", " 'imprint': 959,\n", " 'sundance': 961,\n", " 'aida': 31951,\n", " 'thematically': 963,\n", " 'uno': 965,\n", " 'expressly': 966,\n", " 'russkies': 967,\n", " 'discos': 968,\n", " 'shaping': 969,\n", " 'verson': 970,\n", " 'blushed': 61831,\n", " 'prototype': 971,\n", " 'lifewell': 976,\n", " 'trafficker': 973,\n", " 'crucifixions': 62188,\n", " 'unrealistically': 975,\n", " 'rivas': 977,\n", " 'consequent': 978,\n", " 'katsu': 979,\n", " 'titantic': 980,\n", " 'jalees': 981,\n", " 'ranee': 982,\n", " 'gambles': 984,\n", " 'dispenses': 985,\n", " 'disfigurement': 986,\n", " 'bright': 987,\n", " 'cristian': 988,\n", " 'subculture': 37268,\n", " 'capta': 991,\n", " 'jewel': 992,\n", " 'erect': 993,\n", " 'avoide': 996,\n", " 'inconnu': 997,\n", " 'headquarters': 998,\n", " 'babbling': 1000,\n", " 'pac': 1001,\n", " 'performace': 1003,\n", " 'dorrit': 1004,\n", " 'runners': 1005,\n", " 'sentimentality': 1006,\n", " 'marred': 1007,\n", " 'commemorative': 1008,\n", " 'helpers': 1012,\n", " 'chiles': 1011,\n", " 'snowy': 1013,\n", " 'cheddar': 1014,\n", " 'neath': 158,\n", " 'outshine': 1016,\n", " 'nadu': 1019,\n", " 'wellbeing': 1020,\n", " 'envisioned': 43779,\n", " 'fanaticism': 1021,\n", " 'morrisette': 12687,\n", " 'sesame': 1024,\n", " 'gran': 1023,\n", " 'marlina': 1025,\n", " 'artificiality': 1030,\n", " 'coinsidence': 1027,\n", " 'founders': 1028,\n", " 'dismissably': 1029,\n", " 'dracht': 66299,\n", " 'scavengers': 1031,\n", " 'neese': 12685,\n", " 'pangborn': 1034,\n", " 'elmore': 1039,\n", " 'bristol': 71162,\n", " 'lillies': 1035,\n", " 'parkers': 1036,\n", " 'skipped': 1038,\n", " 'clipboard': 1042,\n", " 'jucier': 1041,\n", " 'haifa': 1043,\n", " ...}" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "word2index = {}\n", "\n", "for i,word in enumerate(vocab):\n", " word2index[word] = i\n", "word2index" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def update_input_layer(review):\n", " \n", " global layer_0\n", " \n", " # clear out previous state, reset the layer to be all 0s\n", " layer_0 *= 0\n", " for word in review.split(\" \"):\n", " layer_0[0][word2index[word]] += 1\n", "\n", "update_input_layer(reviews[0])" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 18., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_target_for_label(label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "'POSITIVE'" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[0]" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_target_for_label(labels[0])" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "'NEGATIVE'" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels[1]" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_target_for_label(labels[1])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Project 3: Building a Neural Network" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true, "deletable": true, "editable": true }, "source": [ "- Start with your neural network from the last chapter\n", "- 3 layer neural network\n", "- no non-linearity in hidden layer\n", "- use our functions to create the training data\n", "- create a \"pre_process_data\" function to create vocabulary for our training data generating functions\n", "- modify \"train\" to train over the entire corpus" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Where to Get Help if You Need it\n", "- Re-watch previous week's Udacity Lectures\n", "- Chapters 3-5 - [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning) - (40% Off: **traskud17**)" ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import time\n", "import sys\n", "import numpy as np\n", "\n", "# Let's tweak our network from before to model these phenomena\n", "class SentimentNetwork:\n", " def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):\n", " \n", " # set our random number generator \n", " np.random.seed(1)\n", " \n", " self.pre_process_data(reviews, labels)\n", " \n", " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", " \n", " \n", " def pre_process_data(self, reviews, labels):\n", " \n", " review_vocab = set()\n", " for review in reviews:\n", " for word in review.split(\" \"):\n", " review_vocab.add(word)\n", " self.review_vocab = list(review_vocab)\n", " \n", " label_vocab = set()\n", " for label in labels:\n", " label_vocab.add(label)\n", " \n", " self.label_vocab = list(label_vocab)\n", " \n", " self.review_vocab_size = len(self.review_vocab)\n", " self.label_vocab_size = len(self.label_vocab)\n", " \n", " self.word2index = {}\n", " for i, word in enumerate(self.review_vocab):\n", " self.word2index[word] = i\n", " \n", " self.label2index = {}\n", " for i, label in enumerate(self.label_vocab):\n", " self.label2index[label] = i\n", " \n", " \n", " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", " \n", " self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " \n", " self.learning_rate = learning_rate\n", " \n", " self.layer_0 = np.zeros((1,input_nodes))\n", " \n", " \n", " def update_input_layer(self,review):\n", "\n", " # clear out previous state, reset the layer to be all 0s\n", " self.layer_0 *= 0\n", " for word in review.split(\" \"):\n", " if(word in self.word2index.keys()):\n", " self.layer_0[0][self.word2index[word]] += 1\n", " \n", " def get_target_for_label(self,label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0\n", " \n", " def sigmoid(self,x):\n", " return 1 / (1 + np.exp(-x))\n", " \n", " \n", " def sigmoid_output_2_derivative(self,output):\n", " return output * (1 - output)\n", " \n", " def train(self, training_reviews, training_labels):\n", " \n", " assert(len(training_reviews) == len(training_labels))\n", " \n", " correct_so_far = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(training_reviews)):\n", " \n", " review = training_reviews[i]\n", " label = training_labels[i]\n", " \n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", "\n", " # Input Layer\n", " self.update_input_layer(review)\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", "\n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # TODO: Output error\n", " layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n", " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n", "\n", " # TODO: Backpropagated error\n", " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n", " layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n", "\n", " # TODO: Update the weights\n", " self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n", " self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step\n", "\n", " if(np.abs(layer_2_error) < 0.5):\n", " correct_so_far += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", " if(i % 2500 == 0):\n", " print(\"\")\n", " \n", " def test(self, testing_reviews, testing_labels):\n", " \n", " correct = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(testing_reviews)):\n", " pred = self.run(testing_reviews[i])\n", " if(pred == testing_labels[i]):\n", " correct += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \"% #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", " \n", " def run(self, review):\n", " \n", " # Input Layer\n", " self.update_input_layer(review.lower())\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", " \n", " if(layer_2[0] > 0.5):\n", " return \"POSITIVE\"\n", " else:\n", " return \"NEGATIVE\"\n", " " ] }, { "cell_type": "code", "execution_count": 87, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):587.5% #Correct:500 #Tested:1000 Testing Accuracy:50.0%" ] } ], "source": [ "# evaluate our model before training (just to show how horrible it is)\n", "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):89.58 #Correct:1250 #Trained:2501 Training Accuracy:49.9%\n", "Progress:20.8% Speed(reviews/sec):95.03 #Correct:2500 #Trained:5001 Training Accuracy:49.9%\n", "Progress:27.4% Speed(reviews/sec):95.46 #Correct:3295 #Trained:6592 Training Accuracy:49.9%" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-62-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_1_2\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mlayer_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update hidden-to-output weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_0_1\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_1_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update input-to-hidden weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.5\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 the network\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.01)" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):96.39 #Correct:1247 #Trained:2501 Training Accuracy:49.8%\n", "Progress:20.8% Speed(reviews/sec):99.31 #Correct:2497 #Trained:5001 Training Accuracy:49.9%\n", "Progress:22.8% Speed(reviews/sec):99.02 #Correct:2735 #Trained:5476 Training Accuracy:49.9%" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-64-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_1_2\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mlayer_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update hidden-to-output weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_0_1\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_1_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update input-to-hidden weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.5\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 the network\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.001)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):98.77 #Correct:1267 #Trained:2501 Training Accuracy:50.6%\n", "Progress:20.8% Speed(reviews/sec):98.79 #Correct:2640 #Trained:5001 Training Accuracy:52.7%\n", "Progress:31.2% Speed(reviews/sec):98.58 #Correct:4109 #Trained:7501 Training Accuracy:54.7%\n", "Progress:41.6% Speed(reviews/sec):93.78 #Correct:5638 #Trained:10001 Training Accuracy:56.3%\n", "Progress:52.0% Speed(reviews/sec):91.76 #Correct:7246 #Trained:12501 Training Accuracy:57.9%\n", "Progress:62.5% Speed(reviews/sec):92.42 #Correct:8841 #Trained:15001 Training Accuracy:58.9%\n", "Progress:69.4% Speed(reviews/sec):92.58 #Correct:9934 #Trained:16668 Training Accuracy:59.5%" ] }, { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-66-d0f5d85ad402>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# train the network\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mmlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreviews\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;32m<ipython-input-59-6334c4ec4642>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, training_reviews, training_labels)\u001b[0m\n\u001b[1;32m 117\u001b[0m \u001b[0;31m# TODO: Update the weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 118\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_1_2\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mlayer_1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update hidden-to-output weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweights_0_1\u001b[0m \u001b[0;34m-=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer_0\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_1_delta\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;31m# update input-to-hidden weights with gradient descent step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 120\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlayer_2_error\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0.5\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 the network\n", "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Understanding Neural Noise" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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id8YZZzAf0/mkzF6r5Ox2SvChtsiRRx7Z2E3gGLj5QB7SSC/iAkEhYBgDBVYJ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\nAH0AAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.display import Image\n", "Image(filename='sentiment_network.png')" ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def update_input_layer(review):\n", " \n", " global layer_0\n", " \n", " # clear out previous state, reset the layer to be all 0s\n", " layer_0 *= 0\n", " for word in review.split(\" \"):\n", " layer_0[0][word2index[word]] += 1\n", "\n", "update_input_layer(reviews[0])" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([[ 18., 0., 0., ..., 0., 0., 0.]])" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 79, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "review_counter = Counter()" ] }, { "cell_type": "code", "execution_count": 80, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "for word in reviews[0].split(\" \"):\n", " review_counter[word] += 1" ] }, { "cell_type": "code", "execution_count": 81, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[('.', 27),\n", " ('', 18),\n", " ('the', 9),\n", " ('to', 6),\n", " ('i', 5),\n", " ('high', 5),\n", " ('is', 4),\n", " ('of', 4),\n", " ('a', 4),\n", " ('bromwell', 4),\n", " ('teachers', 4),\n", " ('that', 4),\n", " ('their', 2),\n", " ('my', 2),\n", " ('at', 2),\n", " ('as', 2),\n", " ('me', 2),\n", " ('in', 2),\n", " ('students', 2),\n", " ('it', 2),\n", " ('student', 2),\n", " ('school', 2),\n", " ('through', 1),\n", " ('insightful', 1),\n", " ('ran', 1),\n", " ('years', 1),\n", " ('here', 1),\n", " ('episode', 1),\n", " ('reality', 1),\n", " ('what', 1),\n", " ('far', 1),\n", " ('t', 1),\n", " ('saw', 1),\n", " ('s', 1),\n", " ('repeatedly', 1),\n", " ('isn', 1),\n", " ('closer', 1),\n", " ('and', 1),\n", " ('fetched', 1),\n", " ('remind', 1),\n", " ('can', 1),\n", " ('welcome', 1),\n", " ('line', 1),\n", " ('your', 1),\n", " ('survive', 1),\n", " ('teaching', 1),\n", " ('satire', 1),\n", " ('classic', 1),\n", " ('who', 1),\n", " ('age', 1),\n", " ('knew', 1),\n", " ('schools', 1),\n", " ('inspector', 1),\n", " ('comedy', 1),\n", " ('down', 1),\n", " ('about', 1),\n", " ('pity', 1),\n", " ('m', 1),\n", " ('all', 1),\n", " ('adults', 1),\n", " ('see', 1),\n", " ('think', 1),\n", " ('situation', 1),\n", " ('time', 1),\n", " ('pomp', 1),\n", " ('lead', 1),\n", " ('other', 1),\n", " ('much', 1),\n", " ('many', 1),\n", " ('which', 1),\n", " ('one', 1),\n", " ('profession', 1),\n", " ('programs', 1),\n", " ('same', 1),\n", " ('some', 1),\n", " ('such', 1),\n", " ('pettiness', 1),\n", " ('immediately', 1),\n", " ('expect', 1),\n", " ('financially', 1),\n", " ('recalled', 1),\n", " ('tried', 1),\n", " ('whole', 1),\n", " ('right', 1),\n", " ('life', 1),\n", " ('cartoon', 1),\n", " ('scramble', 1),\n", " ('sack', 1),\n", " ('believe', 1),\n", " ('when', 1),\n", " ('than', 1),\n", " ('burn', 1),\n", " ('pathetic', 1)]" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "review_counter.most_common()" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Project 4: Reducing Noise in our Input Data" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import time\n", "import sys\n", "import numpy as np\n", "\n", "# Let's tweak our network from before to model these phenomena\n", "class SentimentNetwork:\n", " def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):\n", " \n", " # set our random number generator \n", " np.random.seed(1)\n", " \n", " self.pre_process_data(reviews, labels)\n", " \n", " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", " \n", " \n", " def pre_process_data(self, reviews, labels):\n", " \n", " review_vocab = set()\n", " for review in reviews:\n", " for word in review.split(\" \"):\n", " review_vocab.add(word)\n", " self.review_vocab = list(review_vocab)\n", " \n", " label_vocab = set()\n", " for label in labels:\n", " label_vocab.add(label)\n", " \n", " self.label_vocab = list(label_vocab)\n", " \n", " self.review_vocab_size = len(self.review_vocab)\n", " self.label_vocab_size = len(self.label_vocab)\n", " \n", " self.word2index = {}\n", " for i, word in enumerate(self.review_vocab):\n", " self.word2index[word] = i\n", " \n", " self.label2index = {}\n", " for i, label in enumerate(self.label_vocab):\n", " self.label2index[label] = i\n", " \n", " \n", " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", " \n", " self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " \n", " self.learning_rate = learning_rate\n", " \n", " self.layer_0 = np.zeros((1,input_nodes))\n", " \n", " \n", " def update_input_layer(self,review):\n", "\n", " # clear out previous state, reset the layer to be all 0s\n", " self.layer_0 *= 0\n", " for word in review.split(\" \"):\n", " if(word in self.word2index.keys()):\n", " self.layer_0[0][self.word2index[word]] = 1\n", " \n", " def get_target_for_label(self,label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0\n", " \n", " def sigmoid(self,x):\n", " return 1 / (1 + np.exp(-x))\n", " \n", " \n", " def sigmoid_output_2_derivative(self,output):\n", " return output * (1 - output)\n", " \n", " def train(self, training_reviews, training_labels):\n", " \n", " assert(len(training_reviews) == len(training_labels))\n", " \n", " correct_so_far = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(training_reviews)):\n", " \n", " review = training_reviews[i]\n", " label = training_labels[i]\n", " \n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", "\n", " # Input Layer\n", " self.update_input_layer(review)\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", "\n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # TODO: Output error\n", " layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n", " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n", "\n", " # TODO: Backpropagated error\n", " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n", " layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n", "\n", " # TODO: Update the weights\n", " self.weights_1_2 -= layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n", " self.weights_0_1 -= self.layer_0.T.dot(layer_1_delta) * self.learning_rate # update input-to-hidden weights with gradient descent step\n", "\n", " if(np.abs(layer_2_error) < 0.5):\n", " correct_so_far += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", " if(i % 2500 == 0):\n", " print(\"\")\n", " \n", " def test(self, testing_reviews, testing_labels):\n", " \n", " correct = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(testing_reviews)):\n", " pred = self.run(testing_reviews[i])\n", " if(pred == testing_labels[i]):\n", " correct += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \"% #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", " \n", " def run(self, review):\n", " \n", " # Input Layer\n", " self.update_input_layer(review.lower())\n", "\n", " # Hidden layer\n", " layer_1 = self.layer_0.dot(self.weights_0_1)\n", "\n", " # Output layer\n", " layer_2 = self.sigmoid(layer_1.dot(self.weights_1_2))\n", " \n", " if(layer_2[0] > 0.5):\n", " return \"POSITIVE\"\n", " else:\n", " return \"NEGATIVE\"\n", " " ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false, "deletable": true, "editable": true, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:0.0% Speed(reviews/sec):0.0 #Correct:0 #Trained:1 Training Accuracy:0.0%\n", "Progress:10.4% Speed(reviews/sec):91.50 #Correct:1795 #Trained:2501 Training Accuracy:71.7%\n", "Progress:20.8% Speed(reviews/sec):95.25 #Correct:3811 #Trained:5001 Training Accuracy:76.2%\n", "Progress:31.2% Speed(reviews/sec):93.74 #Correct:5898 #Trained:7501 Training Accuracy:78.6%\n", "Progress:41.6% Speed(reviews/sec):93.69 #Correct:8042 #Trained:10001 Training Accuracy:80.4%\n", "Progress:52.0% Speed(reviews/sec):95.27 #Correct:10186 #Trained:12501 Training Accuracy:81.4%\n", "Progress:62.5% Speed(reviews/sec):98.19 #Correct:12317 #Trained:15001 Training Accuracy:82.1%\n", "Progress:72.9% Speed(reviews/sec):98.56 #Correct:14440 #Trained:17501 Training Accuracy:82.5%\n", "Progress:83.3% Speed(reviews/sec):99.74 #Correct:16613 #Trained:20001 Training Accuracy:83.0%\n", "Progress:93.7% Speed(reviews/sec):100.7 #Correct:18794 #Trained:22501 Training Accuracy:83.5%\n", "Progress:99.9% Speed(reviews/sec):101.9 #Correct:20115 #Trained:24000 Training Accuracy:83.8%" ] } ], "source": [ "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):832.7% #Correct:851 #Tested:1000 Testing Accuracy:85.1%" ] } ], "source": [ "# evaluate our model before training (just to show how horrible it is)\n", "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Analyzing Inefficiencies in our Network" ] }, { "cell_type": "code", "execution_count": 88, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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kXFTbwvXm0k/K0NchI8Tp5BOdPl86uRb3F2dRbZa6Cu3Jq/u4UPq0XIfglrLm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\nEDAEykSgaYlXsXhBrCAwEKcwyeIc15588sm8xZGmEGmCdEHSCpWVqyI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SVSxyls4QMATi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\nAghYEyB4WesY9SKAAAIIIICAWYFnu/zIiInRwogAAAAASUVORK5CYII=\n", "text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='sentiment_network_sparse.png')" ] }, { "cell_type": "code", "execution_count": 89, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "layer_0 = np.zeros(10)" ] }, { "cell_type": "code", "execution_count": 90, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 91, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "layer_0[4] = 1\n", "layer_0[9] = 1" ] }, { "cell_type": "code", "execution_count": 92, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([ 0., 0., 0., 0., 1., 0., 0., 0., 0., 1.])" ] }, "execution_count": 92, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "weights_0_1 = np.random.randn(10,5)" ] }, { "cell_type": "code", "execution_count": 94, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([-0.10503756, 0.44222989, 0.24392938, -0.55961832, 0.21389503])" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_0.dot(weights_0_1)" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "indices = [4,9]" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "layer_1 = np.zeros(5)" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "for index in indices:\n", " layer_1 += (weights_0_1[index])" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "array([-0.10503756, 0.44222989, 0.24392938, -0.55961832, 0.21389503])" ] }, "execution_count": 104, "metadata": {}, "output_type": "execute_result" } ], "source": [ "layer_1" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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apXPHaKqJjr744WSiQNCj0ezqd865210y9hI/u3m/vfdKvdUE4BGUGvC09FTb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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='sentiment_network_sparse_2.png')" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Project 5: Making our Network More Efficient" ] }, { "cell_type": "code", "execution_count": 105, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import time\n", "import sys\n", "\n", "# Let's tweak our network from before to model these phenomena\n", "class SentimentNetwork:\n", " def __init__(self, reviews,labels,hidden_nodes = 10, learning_rate = 0.1):\n", " \n", " np.random.seed(1)\n", " \n", " self.pre_process_data(reviews)\n", " \n", " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", " \n", " \n", " def pre_process_data(self,reviews):\n", " \n", " review_vocab = set()\n", " for review in reviews:\n", " for word in review.split(\" \"):\n", " review_vocab.add(word)\n", " self.review_vocab = list(review_vocab)\n", " \n", " label_vocab = set()\n", " for label in labels:\n", " label_vocab.add(label)\n", " \n", " self.label_vocab = list(label_vocab)\n", " \n", " self.review_vocab_size = len(self.review_vocab)\n", " self.label_vocab_size = len(self.label_vocab)\n", " \n", " self.word2index = {}\n", " for i, word in enumerate(self.review_vocab):\n", " self.word2index[word] = i\n", " \n", " self.label2index = {}\n", " for i, label in enumerate(self.label_vocab):\n", " self.label2index[label] = i\n", " \n", " \n", " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", " \n", " self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " \n", " self.learning_rate = learning_rate\n", " \n", " self.layer_0 = np.zeros((1,input_nodes))\n", " self.layer_1 = np.zeros((1,hidden_nodes))\n", " \n", " def sigmoid(self,x):\n", " return 1 / (1 + np.exp(-x))\n", " \n", " \n", " def sigmoid_output_2_derivative(self,output):\n", " return output * (1 - output)\n", " \n", " def update_input_layer(self,review):\n", "\n", " # clear out previous state, reset the layer to be all 0s\n", " self.layer_0 *= 0\n", " for word in review.split(\" \"):\n", " self.layer_0[0][self.word2index[word]] = 1\n", "\n", " def get_target_for_label(self,label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0\n", " \n", " def train(self, training_reviews_raw, training_labels):\n", " \n", " training_reviews = list()\n", " for review in training_reviews_raw:\n", " indices = set()\n", " for word in review.split(\" \"):\n", " if(word in self.word2index.keys()):\n", " indices.add(self.word2index[word])\n", " training_reviews.append(list(indices))\n", " \n", " assert(len(training_reviews) == len(training_labels))\n", " \n", " correct_so_far = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(training_reviews)):\n", " \n", " review = training_reviews[i]\n", " label = training_labels[i]\n", " \n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", "\n", " # Input Layer\n", "\n", " # Hidden layer\n", "# layer_1 = self.layer_0.dot(self.weights_0_1)\n", " self.layer_1 *= 0\n", " for index in review:\n", " self.layer_1 += self.weights_0_1[index]\n", " \n", " # Output layer\n", " layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))\n", "\n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # Output error\n", " layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n", " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n", "\n", " # Backpropagated error\n", " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n", " layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n", "\n", " # Update the weights\n", " self.weights_1_2 -= self.layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n", " \n", " for index in review:\n", " self.weights_0_1[index] -= layer_1_delta[0] * self.learning_rate # update input-to-hidden weights with gradient descent step\n", "\n", " if(np.abs(layer_2_error) < 0.5):\n", " correct_so_far += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", " \n", " \n", " def test(self, testing_reviews, testing_labels):\n", " \n", " correct = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(testing_reviews)):\n", " pred = self.run(testing_reviews[i])\n", " if(pred == testing_labels[i]):\n", " correct += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \"% #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", " \n", " def run(self, review):\n", " \n", " # Input Layer\n", "\n", "\n", " # Hidden layer\n", " self.layer_1 *= 0\n", " unique_indices = set()\n", " for word in review.lower().split(\" \"):\n", " if word in self.word2index.keys():\n", " unique_indices.add(self.word2index[word])\n", " for index in unique_indices:\n", " self.layer_1 += self.weights_0_1[index]\n", " \n", " # Output layer\n", " layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))\n", " \n", " if(layer_2[0] > 0.5):\n", " return \"POSITIVE\"\n", " else:\n", " return \"NEGATIVE\"\n", " " ] }, { "cell_type": "code", "execution_count": 106, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000], learning_rate=0.1)" ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):1581.% #Correct:857 #Tested:1000 Testing Accuracy:85.7%" ] } ], "source": [ "# evaluate our model before training (just to show how horrible it is)\n", "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Further Noise Reduction" ] }, { "cell_type": "code", "execution_count": 112, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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apXPHaKqJjr744WSiQNCj0ezqd865210y9hI/u3m/vfdKvdUE4BGUGvC09FTb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('segments', 0.44701411102103689),\n", " ('lose', 0.44658335503763702),\n", " ('caine', 0.44628710262841953),\n", " ('caught', 0.44610275383999071),\n", " ('hamlet', 0.44558510189758965),\n", " ('chinese', 0.44507424620321018),\n", " ('welcome', 0.44438052435783792),\n", " ('birth', 0.44368632092836219),\n", " ('represents', 0.44320543609101143),\n", " ('puts', 0.44279106572085081),\n", " ('fame', 0.44183275227903923),\n", " ('closer', 0.44183275227903923),\n", " ('visuals', 0.44183275227903923),\n", " ('web', 0.44183275227903923),\n", " ('criminal', 0.4412745608048752),\n", " ('minor', 0.4409224199448939),\n", " ('jon', 0.44086703515908027),\n", " ('liked', 0.44074991514020723),\n", " ('restaurant', 0.44031183943833246),\n", " ('flaws', 0.43983275161237217),\n", " ('de', 0.43983275161237217),\n", " ('searching', 0.4393666597838457),\n", " ('rap', 0.43891304217570443),\n", " ('light', 0.43884433018199892),\n", " ('elizabeth', 0.43872232986464677),\n", " ('marry', 0.43861731542506488),\n", " ('oz', 0.43825493093115531),\n", " ('controversial', 0.43825493093115531),\n", " ('learned', 0.43825493093115531),\n", " ('slowly', 0.43785660389939979),\n", " ('bridge', 0.43721380642274466),\n", " ('thrilling', 0.43721380642274466),\n", " ('wayne', 0.43721380642274466),\n", " ('comedic', 0.43721380642274466),\n", " ('married', 0.43658501682196887),\n", " ('nazi', 0.4361020775700542),\n", " ('murder', 0.4353180712578455),\n", " ('physical', 0.4353180712578455),\n", " ('johnny', 0.43483971678806865),\n", " ('michelle', 0.43445264498141672),\n", " ('wallace', 0.43403848055222038),\n", " ('silent', 0.43395706390247063),\n", " ('comedies', 0.43395706390247063),\n", " ('played', 0.43387244114515305),\n", " ('international', 0.43363598507486073),\n", " ('vision', 0.43286408229627887),\n", " ('intelligent', 0.43196704885367099),\n", " ('shop', 0.43078291609245434),\n", " ('also', 0.43036720209769169),\n", " ('levels', 0.4302451371066513),\n", " ('miss', 0.43006426712153217),\n", " ('ocean', 0.4295626596872249),\n", " ...]" ] }, "execution_count": 113, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# words most frequently seen in a review with a \"POSITIVE\" label\n", "pos_neg_ratios.most_common()" ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[('boll', -4.0778152602708904),\n", " ('uwe', -3.9218753018711578),\n", " ('seagal', -3.3202501058581921),\n", " ('unwatchable', -3.0269848170580955),\n", " ('stinker', -2.9876839403711624),\n", " ('mst', -2.7753833211707968),\n", " ('incoherent', -2.7641396677532537),\n", " ('unfunny', -2.5545257844967644),\n", " ('waste', -2.4907515123361046),\n", " ('blah', -2.4475792789485005),\n", " ('horrid', -2.3715779644809971),\n", " ('pointless', -2.3451073877136341),\n", " ('atrocious', -2.3187369339642556),\n", " ('redeeming', -2.2667790015910296),\n", " ('prom', -2.2601040980178784),\n", " ('drivel', -2.2476029585766928),\n", " ('lousy', -2.2118080125207054),\n", " ('worst', -2.1930856334332267),\n", " ('laughable', -2.172468615469592),\n", " ('awful', -2.1385076866397488),\n", " ('poorly', -2.1326133844207011),\n", " ('wasting', -2.1178155545614512),\n", " ('remotely', -2.111046881095167),\n", " ('existent', -2.0024805005437076),\n", " ('boredom', -1.9241486572738005),\n", " ('miserably', -1.9216610938019989),\n", " ('sucks', -1.9166645809588516),\n", " ('uninspired', -1.9131499212248517),\n", " ('lame', -1.9117232884159072),\n", " ('insult', -1.9085323769376259)]" ] }, "execution_count": 114, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# words most frequently seen in a review with a \"NEGATIVE\" label\n", "list(reversed(pos_neg_ratios.most_common()))[0:30]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-root\">\n", " <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n", " <span id=\"9600c2ee-4684-4193-ab58-01f39912be62\">Loading BokehJS ...</span>\n", " </div>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": [ "\n", "(function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", "\n", " var force = true;\n", "\n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n", " window._bokeh_onload_callbacks = [];\n", " window._bokeh_is_loading = undefined;\n", " }\n", "\n", "\n", " \n", " if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n", " window._bokeh_timeout = Date.now() + 5000;\n", " window._bokeh_failed_load = false;\n", " }\n", "\n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"</p>\\n\"+\n", " \"<ul>\\n\"+\n", " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", " \"</ul>\\n\"+\n", " \"<code>\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"</code>\\n\"+\n", " \"</div>\"}};\n", "\n", " function display_loaded() {\n", " if (window.Bokeh !== undefined) {\n", " document.getElementById(\"9600c2ee-4684-4193-ab58-01f39912be62\").textContent = \"BokehJS successfully loaded.\";\n", " } else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", "\n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " window._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " window._bokeh_is_loading--;\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", " }\n", " };var element = document.getElementById(\"9600c2ee-4684-4193-ab58-01f39912be62\");\n", " if (element == null) {\n", " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '9600c2ee-4684-4193-ab58-01f39912be62' but no matching script tag was found. \")\n", " return false;\n", " }\n", "\n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.js\"];\n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " \n", " function(Bokeh) {\n", " \n", " document.getElementById(\"9600c2ee-4684-4193-ab58-01f39912be62\").textContent = \"BokehJS is loading...\";\n", " },\n", " function(Bokeh) {\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.4.min.css\");\n", " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n", " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.12.4.min.css\");\n", " }\n", " ];\n", "\n", " function run_inline_js() {\n", " \n", " if ((window.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!window._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " window._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"9600c2ee-4684-4193-ab58-01f39912be62\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", "\n", " }\n", "\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(this));" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from bokeh.models import ColumnDataSource, LabelSet\n", "from bokeh.plotting import figure, show, output_file\n", "from bokeh.io import output_notebook\n", "output_notebook()" ] }, { "cell_type": "code", "execution_count": 116, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/html": [ "\n", "\n", " <div class=\"bk-root\">\n", " <div class=\"plotdiv\" id=\"1f2bbb42-b317-4ca2-bf06-3978b9b7bf10\"></div>\n", " </div>\n", "<script type=\"text/javascript\">\n", " \n", " (function(global) {\n", " function now() {\n", " return new Date();\n", " }\n", " \n", " var force = \"\";\n", " \n", " if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force !== \"\") {\n", " window._bokeh_onload_callbacks = [];\n", " window._bokeh_is_loading = undefined;\n", " }\n", " \n", " \n", " \n", " if (typeof (window._bokeh_timeout) === \"undefined\" || force !== \"\") {\n", " window._bokeh_timeout = Date.now() + 0;\n", " window._bokeh_failed_load = false;\n", " }\n", " \n", " var NB_LOAD_WARNING = {'data': {'text/html':\n", " \"<div style='background-color: #fdd'>\\n\"+\n", " \"<p>\\n\"+\n", " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", " \"</p>\\n\"+\n", " \"<ul>\\n\"+\n", " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n", " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n", " \"</ul>\\n\"+\n", " \"<code>\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"</code>\\n\"+\n", " \"</div>\"}};\n", " \n", " function display_loaded() {\n", " if (window.Bokeh !== undefined) {\n", " Bokeh.$(\"#1f2bbb42-b317-4ca2-bf06-3978b9b7bf10\").text(\"BokehJS successfully loaded.\");\n", " } else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(display_loaded, 100)\n", " }\n", " }\n", " \n", " function run_callbacks() {\n", " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", " delete window._bokeh_onload_callbacks\n", " console.info(\"Bokeh: all callbacks have finished\");\n", " }\n", " \n", " function load_libs(js_urls, callback) {\n", " window._bokeh_onload_callbacks.push(callback);\n", " if (window._bokeh_is_loading > 0) {\n", " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", " return null;\n", " }\n", " if (js_urls == null || js_urls.length === 0) {\n", " run_callbacks();\n", " return null;\n", " }\n", " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " window._bokeh_is_loading = js_urls.length;\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var s = document.createElement('script');\n", " s.src = url;\n", " s.async = false;\n", " s.onreadystatechange = s.onload = function() {\n", " window._bokeh_is_loading--;\n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", " run_callbacks()\n", " }\n", " };\n", " s.onerror = function() {\n", " console.warn(\"failed to load library \" + url);\n", " };\n", " console.log(\"Bokeh: injecting script tag for BokehJS 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cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " \n", " }\n", " \n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }(this));\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist, edges = np.histogram(list(map(lambda x:x[1],pos_neg_ratios.most_common())), density=True, bins=100, normed=True)\n", "\n", "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n", " toolbar_location=\"above\",\n", " title=\"Word Positive/Negative Affinity Distribution\")\n", "p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], line_color=\"#555555\")\n", "show(p)" ] }, { "cell_type": "code", "execution_count": 117, "metadata": { "collapsed": true, "deletable": true, "editable": true }, 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cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " \n", " }\n", " \n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }(this));\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "hist, edges = np.histogram(list(map(lambda x:x[1],frequency_frequency.most_common())), density=True, bins=100, normed=True)\n", "\n", "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n", " toolbar_location=\"above\",\n", " title=\"The frequency distribution of the words in our corpus\")\n", "p.quad(top=hist, bottom=0, left=edges[:-1], right=edges[1:], line_color=\"#555555\")\n", "show(p)" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Reducing Noise by Strategically Reducing the Vocabulary" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import time\n", "import sys\n", "import numpy as np\n", "\n", "# Let's tweak our network from before to model these phenomena\n", "class SentimentNetwork:\n", " def __init__(self, reviews,labels,min_count = 10,polarity_cutoff = 0.1,hidden_nodes = 10, learning_rate = 0.1):\n", " \n", " np.random.seed(1)\n", " \n", " self.pre_process_data(reviews, polarity_cutoff, min_count)\n", " \n", " self.init_network(len(self.review_vocab),hidden_nodes, 1, learning_rate)\n", " \n", " \n", " def pre_process_data(self,reviews, polarity_cutoff,min_count):\n", " \n", " positive_counts = Counter()\n", " negative_counts = Counter()\n", " total_counts = Counter()\n", "\n", " for i in range(len(reviews)):\n", " if(labels[i] == 'POSITIVE'):\n", " for word in reviews[i].split(\" \"):\n", " positive_counts[word] += 1\n", " total_counts[word] += 1\n", " else:\n", " for word in reviews[i].split(\" \"):\n", " negative_counts[word] += 1\n", " total_counts[word] += 1\n", "\n", " pos_neg_ratios = Counter()\n", "\n", " for term,cnt in list(total_counts.most_common()):\n", " if(cnt >= 50):\n", " pos_neg_ratio = positive_counts[term] / float(negative_counts[term]+1)\n", " pos_neg_ratios[term] = pos_neg_ratio\n", "\n", " for word,ratio in pos_neg_ratios.most_common():\n", " if(ratio > 1):\n", " pos_neg_ratios[word] = np.log(ratio)\n", " else:\n", " pos_neg_ratios[word] = -np.log((1 / (ratio + 0.01)))\n", " \n", " review_vocab = set()\n", " for review in reviews:\n", " for word in review.split(\" \"):\n", " if(total_counts[word] > min_count):\n", " if(word in pos_neg_ratios.keys()):\n", " if((pos_neg_ratios[word] >= polarity_cutoff) or (pos_neg_ratios[word] <= -polarity_cutoff)):\n", " review_vocab.add(word)\n", " else:\n", " review_vocab.add(word)\n", " self.review_vocab = list(review_vocab)\n", " \n", " label_vocab = set()\n", " for label in labels:\n", " label_vocab.add(label)\n", " \n", " self.label_vocab = list(label_vocab)\n", " \n", " self.review_vocab_size = len(self.review_vocab)\n", " self.label_vocab_size = len(self.label_vocab)\n", " \n", " self.word2index = {}\n", " for i, word in enumerate(self.review_vocab):\n", " self.word2index[word] = i\n", " \n", " self.label2index = {}\n", " for i, label in enumerate(self.label_vocab):\n", " self.label2index[label] = i\n", " \n", " \n", " def init_network(self, input_nodes, hidden_nodes, output_nodes, learning_rate):\n", " # Set number of nodes in input, hidden and output layers.\n", " self.input_nodes = input_nodes\n", " self.hidden_nodes = hidden_nodes\n", " self.output_nodes = output_nodes\n", "\n", " # Initialize weights\n", " self.weights_0_1 = np.zeros((self.input_nodes,self.hidden_nodes))\n", " \n", " self.weights_1_2 = np.random.normal(0.0, self.output_nodes**-0.5, \n", " (self.hidden_nodes, self.output_nodes))\n", " \n", " self.learning_rate = learning_rate\n", " \n", " self.layer_0 = np.zeros((1,input_nodes))\n", " self.layer_1 = np.zeros((1,hidden_nodes))\n", " \n", " def sigmoid(self,x):\n", " return 1 / (1 + np.exp(-x))\n", " \n", " \n", " def sigmoid_output_2_derivative(self,output):\n", " return output * (1 - output)\n", " \n", " def update_input_layer(self,review):\n", "\n", " # clear out previous state, reset the layer to be all 0s\n", " self.layer_0 *= 0\n", " for word in review.split(\" \"):\n", " self.layer_0[0][self.word2index[word]] = 1\n", "\n", " def get_target_for_label(self,label):\n", " if(label == 'POSITIVE'):\n", " return 1\n", " else:\n", " return 0\n", " \n", " def train(self, training_reviews_raw, training_labels):\n", " \n", " training_reviews = list()\n", " for review in training_reviews_raw:\n", " indices = set()\n", " for word in review.split(\" \"):\n", " if(word in self.word2index.keys()):\n", " indices.add(self.word2index[word])\n", " training_reviews.append(list(indices))\n", " \n", " assert(len(training_reviews) == len(training_labels))\n", " \n", " correct_so_far = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(training_reviews)):\n", " \n", " review = training_reviews[i]\n", " label = training_labels[i]\n", " \n", " #### Implement the forward pass here ####\n", " ### Forward pass ###\n", "\n", " # Input Layer\n", "\n", " # Hidden layer\n", "# layer_1 = self.layer_0.dot(self.weights_0_1)\n", " self.layer_1 *= 0\n", " for index in review:\n", " self.layer_1 += self.weights_0_1[index]\n", " \n", " # Output layer\n", " layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))\n", "\n", " #### Implement the backward pass here ####\n", " ### Backward pass ###\n", "\n", " # Output error\n", " layer_2_error = layer_2 - self.get_target_for_label(label) # Output layer error is the difference between desired target and actual output.\n", " layer_2_delta = layer_2_error * self.sigmoid_output_2_derivative(layer_2)\n", "\n", " # Backpropagated error\n", " layer_1_error = layer_2_delta.dot(self.weights_1_2.T) # errors propagated to the hidden layer\n", " layer_1_delta = layer_1_error # hidden layer gradients - no nonlinearity so it's the same as the error\n", "\n", " # Update the weights\n", " self.weights_1_2 -= self.layer_1.T.dot(layer_2_delta) * self.learning_rate # update hidden-to-output weights with gradient descent step\n", " \n", " for index in review:\n", " self.weights_0_1[index] -= layer_1_delta[0] * self.learning_rate # update input-to-hidden weights with gradient descent step\n", "\n", " if(layer_2 >= 0.5 and label == 'POSITIVE'):\n", " correct_so_far += 1\n", " if(layer_2 < 0.5 and label == 'NEGATIVE'):\n", " correct_so_far += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(training_reviews)))[:4] + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] + \" #Correct:\" + str(correct_so_far) + \" #Trained:\" + str(i+1) + \" Training Accuracy:\" + str(correct_so_far * 100 / float(i+1))[:4] + \"%\")\n", " \n", " \n", " def test(self, testing_reviews, testing_labels):\n", " \n", " correct = 0\n", " \n", " start = time.time()\n", " \n", " for i in range(len(testing_reviews)):\n", " pred = self.run(testing_reviews[i])\n", " if(pred == testing_labels[i]):\n", " correct += 1\n", " \n", " reviews_per_second = i / float(time.time() - start)\n", " \n", " sys.stdout.write(\"\\rProgress:\" + str(100 * i/float(len(testing_reviews)))[:4] \\\n", " + \"% Speed(reviews/sec):\" + str(reviews_per_second)[0:5] \\\n", " + \"% #Correct:\" + str(correct) + \" #Tested:\" + str(i+1) + \" Testing Accuracy:\" + str(correct * 100 / float(i+1))[:4] + \"%\")\n", " \n", " def run(self, review):\n", " \n", " # Input Layer\n", "\n", "\n", " # Hidden layer\n", " self.layer_1 *= 0\n", " unique_indices = set()\n", " for word in review.lower().split(\" \"):\n", " if word in self.word2index.keys():\n", " unique_indices.add(self.word2index[word])\n", " for index in unique_indices:\n", " self.layer_1 += self.weights_0_1[index]\n", " \n", " # Output layer\n", " layer_2 = self.sigmoid(self.layer_1.dot(self.weights_1_2))\n", " \n", " if(layer_2[0] >= 0.5):\n", " return \"POSITIVE\"\n", " else:\n", " return \"NEGATIVE\"\n", " " ] }, { "cell_type": "code", "execution_count": 123, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=20,polarity_cutoff=0.05,learning_rate=0.01)" ] }, { "cell_type": "code", "execution_count": 124, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):1371. #Correct:20461 #Trained:24000 Training Accuracy:85.2%" ] } ], "source": [ "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 125, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):1708.% #Correct:859 #Tested:1000 Testing Accuracy:85.9%" ] } ], "source": [ "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "code", "execution_count": 126, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=20,polarity_cutoff=0.8,learning_rate=0.01)" ] }, { "cell_type": "code", "execution_count": 127, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):7089. #Correct:20552 #Trained:24000 Training Accuracy:85.6%" ] } ], "source": [ "mlp.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 128, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\r", "Progress:0.0% Speed(reviews/sec):0.0% #Correct:0 #Tested:1 Testing Accuracy:0.0%\r", "Progress:0.1% Speed(reviews/sec):2123.% #Correct:1 #Tested:2 Testing Accuracy:50.0%\r", "Progress:0.2% Speed(reviews/sec):3623.% #Correct:2 #Tested:3 Testing Accuracy:66.6%\r", "Progress:0.3% Speed(reviews/sec):4477.% #Correct:3 #Tested:4 Testing Accuracy:75.0%\r", "Progress:0.4% Speed(reviews/sec):5488.% #Correct:3 #Tested:5 Testing Accuracy:60.0%\r", "Progress:0.5% Speed(reviews/sec):5995.% #Correct:4 #Tested:6 Testing Accuracy:66.6%\r", "Progress:0.6% Speed(reviews/sec):5698.% #Correct:5 #Tested:7 Testing Accuracy:71.4%\r", "Progress:0.7% Speed(reviews/sec):5448.% #Correct:6 #Tested:8 Testing Accuracy:75.0%\r", "Progress:0.8% Speed(reviews/sec):5041.% 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#Correct:757 #Tested:924 Testing Accuracy:81.9%\r", "Progress:92.4% Speed(reviews/sec):3945.% #Correct:758 #Tested:925 Testing Accuracy:81.9%\r", "Progress:92.5% Speed(reviews/sec):3910.% #Correct:759 #Tested:926 Testing Accuracy:81.9%\r", "Progress:92.6% Speed(reviews/sec):3883.% #Correct:760 #Tested:927 Testing Accuracy:81.9%\r", "Progress:92.7% Speed(reviews/sec):3883.% #Correct:761 #Tested:928 Testing Accuracy:82.0%\r", "Progress:92.8% Speed(reviews/sec):3885.% #Correct:761 #Tested:929 Testing Accuracy:81.9%\r", "Progress:92.9% Speed(reviews/sec):3851.% #Correct:762 #Tested:930 Testing Accuracy:81.9%\r", "Progress:93.0% Speed(reviews/sec):3808.% #Correct:763 #Tested:931 Testing Accuracy:81.9%\r", "Progress:93.1% Speed(reviews/sec):3775.% #Correct:764 #Tested:932 Testing Accuracy:81.9%\r", "Progress:93.2% Speed(reviews/sec):3777.% #Correct:765 #Tested:933 Testing Accuracy:81.9%\r", "Progress:93.3% Speed(reviews/sec):3776.% #Correct:766 #Tested:934 Testing Accuracy:82.0%\r", 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Speed(reviews/sec):3809.% #Correct:782 #Tested:956 Testing Accuracy:81.7%\r", "Progress:95.6% Speed(reviews/sec):3811.% #Correct:783 #Tested:957 Testing Accuracy:81.8%\r", "Progress:95.7% Speed(reviews/sec):3813.% #Correct:784 #Tested:958 Testing Accuracy:81.8%\r", "Progress:95.8% Speed(reviews/sec):3814.% #Correct:785 #Tested:959 Testing Accuracy:81.8%\r", "Progress:95.9% Speed(reviews/sec):3817.% #Correct:786 #Tested:960 Testing Accuracy:81.8%\r", "Progress:96.0% Speed(reviews/sec):3812.% #Correct:787 #Tested:961 Testing Accuracy:81.8%\r", "Progress:96.1% Speed(reviews/sec):3814.% #Correct:788 #Tested:962 Testing Accuracy:81.9%\r", "Progress:96.2% Speed(reviews/sec):3816.% #Correct:789 #Tested:963 Testing Accuracy:81.9%\r", "Progress:96.3% Speed(reviews/sec):3819.% #Correct:790 #Tested:964 Testing Accuracy:81.9%\r", "Progress:96.4% Speed(reviews/sec):3821.% #Correct:791 #Tested:965 Testing Accuracy:81.9%\r", "Progress:96.5% Speed(reviews/sec):3823.% #Correct:792 #Tested:966 Testing Accuracy:81.9%\r", "Progress:96.6% Speed(reviews/sec):3826.% #Correct:793 #Tested:967 Testing Accuracy:82.0%\r", "Progress:96.7% Speed(reviews/sec):3829.% #Correct:794 #Tested:968 Testing Accuracy:82.0%\r", "Progress:96.8% Speed(reviews/sec):3831.% #Correct:795 #Tested:969 Testing Accuracy:82.0%\r", "Progress:96.9% Speed(reviews/sec):3833.% #Correct:796 #Tested:970 Testing Accuracy:82.0%\r", "Progress:97.0% Speed(reviews/sec):3832.% #Correct:797 #Tested:971 Testing Accuracy:82.0%\r", "Progress:97.1% Speed(reviews/sec):3833.% #Correct:798 #Tested:972 Testing Accuracy:82.0%\r", "Progress:97.2% Speed(reviews/sec):3835.% #Correct:798 #Tested:973 Testing Accuracy:82.0%\r", "Progress:97.3% Speed(reviews/sec):3833.% #Correct:799 #Tested:974 Testing Accuracy:82.0%\r", "Progress:97.4% Speed(reviews/sec):3825.% #Correct:800 #Tested:975 Testing Accuracy:82.0%\r", "Progress:97.5% Speed(reviews/sec):3825.% #Correct:801 #Tested:976 Testing Accuracy:82.0%\r", "Progress:97.6% Speed(reviews/sec):3821.% #Correct:802 #Tested:977 Testing Accuracy:82.0%\r", "Progress:97.7% Speed(reviews/sec):3815.% #Correct:803 #Tested:978 Testing Accuracy:82.1%\r", "Progress:97.8% Speed(reviews/sec):3817.% #Correct:804 #Tested:979 Testing Accuracy:82.1%\r", "Progress:97.9% Speed(reviews/sec):3816.% #Correct:805 #Tested:980 Testing Accuracy:82.1%\r", "Progress:98.0% Speed(reviews/sec):3817.% #Correct:806 #Tested:981 Testing Accuracy:82.1%\r", "Progress:98.1% Speed(reviews/sec):3793.% #Correct:807 #Tested:982 Testing Accuracy:82.1%\r", "Progress:98.2% Speed(reviews/sec):3787.% #Correct:808 #Tested:983 Testing Accuracy:82.1%\r", "Progress:98.3% Speed(reviews/sec):3779.% #Correct:809 #Tested:984 Testing Accuracy:82.2%\r", "Progress:98.4% Speed(reviews/sec):3781.% #Correct:809 #Tested:985 Testing Accuracy:82.1%\r", "Progress:98.5% Speed(reviews/sec):3784.% #Correct:810 #Tested:986 Testing Accuracy:82.1%\r", "Progress:98.6% Speed(reviews/sec):3786.% #Correct:811 #Tested:987 Testing Accuracy:82.1%\r", "Progress:98.7% Speed(reviews/sec):3787.% #Correct:812 #Tested:988 Testing Accuracy:82.1%\r", "Progress:98.8% Speed(reviews/sec):3789.% #Correct:813 #Tested:989 Testing Accuracy:82.2%\r", "Progress:98.9% Speed(reviews/sec):3790.% #Correct:814 #Tested:990 Testing Accuracy:82.2%\r", "Progress:99.0% Speed(reviews/sec):3792.% #Correct:815 #Tested:991 Testing Accuracy:82.2%\r", "Progress:99.1% Speed(reviews/sec):3793.% #Correct:816 #Tested:992 Testing Accuracy:82.2%\r", "Progress:99.2% Speed(reviews/sec):3796.% #Correct:817 #Tested:993 Testing Accuracy:82.2%\r", "Progress:99.3% Speed(reviews/sec):3798.% #Correct:818 #Tested:994 Testing Accuracy:82.2%\r", "Progress:99.4% Speed(reviews/sec):3798.% #Correct:818 #Tested:995 Testing Accuracy:82.2%\r", "Progress:99.5% Speed(reviews/sec):3798.% #Correct:819 #Tested:996 Testing Accuracy:82.2%\r", "Progress:99.6% Speed(reviews/sec):3800.% #Correct:820 #Tested:997 Testing Accuracy:82.2%\r", "Progress:99.7% Speed(reviews/sec):3802.% #Correct:821 #Tested:998 Testing Accuracy:82.2%\r", "Progress:99.8% Speed(reviews/sec):3803.% #Correct:821 #Tested:999 Testing Accuracy:82.1%\r", "Progress:99.9% Speed(reviews/sec):3805.% #Correct:822 #Tested:1000 Testing Accuracy:82.2%" ] } ], "source": [ "mlp.test(reviews[-1000:],labels[-1000:])" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Analysis: What's Going on in the Weights?" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "mlp_full = SentimentNetwork(reviews[:-1000],labels[:-1000],min_count=0,polarity_cutoff=0,learning_rate=0.01)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Progress:99.9% Speed(reviews/sec):717.6 #Correct:20335 #Trained:24000 Training Accuracy:84.7%" ] } ], "source": [ "mlp_full.train(reviews[:-1000],labels[:-1000])" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "image/png": 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kXFTbwvXm0k/K0NchI8Tp5BOdPl86uRb3F2dRbZa6Cu3Jq/u4UPq0XIfglrLm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\nEDAEykSgaYlXsXhBrCAwEKcwyeIc15588sm8xZGmEGmCdEHSCpWVqyI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"text/plain": [ "<IPython.core.display.Image object>" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Image(filename='sentiment_network_sparse.png')" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def get_most_similar_words(focus = \"horrible\"):\n", " most_similar = Counter()\n", "\n", " for word in mlp_full.word2index.keys():\n", " most_similar[word] = np.dot(mlp_full.weights_0_1[mlp_full.word2index[word]],\n", " mlp_full.weights_0_1[mlp_full.word2index[focus]])\n", " \n", " return most_similar.most_common()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "data": { "text/plain": [ "[('excellent', 0.13672950757352462),\n", " ('perfect', 0.12548286087225946),\n", " ('amazing', 0.091827633925999672),\n", " ('today', 0.090223662694414217),\n", " ('wonderful', 0.089355976962214562),\n", " ('fun', 0.087504466674206888),\n", " ('great', 0.087141758882292031),\n", " ('best', 0.085810885617880611),\n", " ('liked', 0.077697629123843398),\n", " ('definitely', 0.076628781406965982),\n", " ('brilliant', 0.073423858769279038),\n", " ('loved', 0.073285428928122121),\n", " ('favorite', 0.072781136036160779),\n", " ('superb', 0.07173620717850504),\n", " ('fantastic', 0.070922191916266197),\n", " ('job', 0.069160617207634015),\n", " ('incredible', 0.066424077952614402),\n", " ('enjoyable', 0.065632560502888765),\n", " ('rare', 0.064819212662615075),\n", " ('highly', 0.063889453350970501),\n", " ('enjoyed', 0.062127546101812925),\n", " ('wonderfully', 0.062055178604090155),\n", " ('perfectly', 0.061093208811887373),\n", " ('fascinating', 0.060663547937493852),\n", " ('bit', 0.059655427045653034),\n", " ('gem', 0.059510859296156772),\n", " ('outstanding', 0.058860808147083013),\n", " ('beautiful', 0.058613934703162063),\n", " ('surprised', 0.058273314482562975),\n", " ('worth', 0.057657484236471213),\n", " ('especially', 0.057422020781760771),\n", " ('refreshing', 0.057310532092265755),\n", " ('entertaining', 0.056612033835629197),\n", " ('hilarious', 0.05616854103228662),\n", " ('masterpiece', 0.054993988649431565),\n", " ('simple', 0.054484083134924075),\n", " ('subtle', 0.054368883033508605),\n", " ('funniest', 0.05345716487130267),\n", " ('solid', 0.052903564743620644),\n", " ('awesome', 0.05248919420277038),\n", " ('always', 0.052260328525345262),\n", " ('noir', 0.05153019472640688),\n", " ('guys', 0.051109413645642678),\n", " ('sweet', 0.05081893031752599),\n", " ('unique', 0.050670162263589169),\n", " ('very', 0.050132994948528464),\n", " ('heart', 0.049948058498243582),\n", " ('moving', 0.04942460116437912),\n", " ('atmosphere', 0.048842500895912841),\n", " ('strong', 0.04857088063175919),\n", " ('remember', 0.048479036942291255),\n", " ('believable', 0.04841538439160379),\n", " ('shows', 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0.009349942694876422),\n", " ('hell', 0.0093400360705317275),\n", " ('inducing', 0.0093382129792553489),\n", " ('stomach', 0.0093378286385158559),\n", " ('shambles', 0.0093335457329829768),\n", " ('virgin', 0.0093312001339055928),\n", " ('extraneous', 0.0093250413800351293),\n", " ('cameras', 0.0093229460267977154),\n", " ('suffers', 0.0093204929924830034),\n", " ('justified', 0.009316321747936316),\n", " ('plummer', 0.0092948273285103945),\n", " ('ponderous', 0.0092880344237223338),\n", " ('player', 0.0092802296345443642),\n", " ('survivor', 0.0092767026472125712),\n", " ('rainy', 0.009269703421813753),\n", " ('graces', 0.0092620944963291291),\n", " ...]" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_most_similar_words(\"terrible\")" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "import matplotlib.colors as colors\n", "\n", "words_to_visualize = list()\n", "for word, ratio in pos_neg_ratios.most_common(500):\n", " if(word in mlp_full.word2index.keys()):\n", " words_to_visualize.append(word)\n", " \n", "for word, ratio in list(reversed(pos_neg_ratios.most_common()))[0:500]:\n", " if(word in mlp_full.word2index.keys()):\n", " words_to_visualize.append(word)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "pos = 0\n", "neg = 0\n", "\n", "colors_list = list()\n", "vectors_list = list()\n", "for word in words_to_visualize:\n", " if word in pos_neg_ratios.keys():\n", " vectors_list.append(mlp_full.weights_0_1[mlp_full.word2index[word]])\n", " if(pos_neg_ratios[word] > 0):\n", " pos+=1\n", " colors_list.append(\"#00ff00\")\n", " else:\n", " neg+=1\n", " colors_list.append(\"#ff0000\")\n", " " ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "from sklearn.manifold import TSNE\n", "tsne = TSNE(n_components=2, random_state=0)\n", "words_top_ted_tsne = tsne.fit_transform(vectors_list)" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/envs/dlnd/lib/python3.6/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: \n", "Supplying a user-defined data source AND iterable values to glyph methods is deprecated.\n", "\n", "See https://github.com/bokeh/bokeh/issues/2056 for more information.\n", "\n", " warn(message)\n", "/opt/conda/envs/dlnd/lib/python3.6/site-packages/bokeh/util/deprecation.py:34: BokehDeprecationWarning: \n", "Supplying a user-defined data source AND iterable values to glyph methods is deprecated.\n", "\n", "See https://github.com/bokeh/bokeh/issues/2056 for more information.\n", "\n", " warn(message)\n" ] }, { "data": 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J9p72bPDLApmv9uF6iMsAm0h3ABY8rwFLImZfoHizAAnKtO09SKkClVbylQioPQE9yRAtFrCzAB02jnYdKKkCM7rHTl1s0wKzd43DiXivAQNY54ObPKUBjhwZ7LfYbQAU206zVciJAb71l7sqPNMCCHqhF24gDQLnY5boRZ++/Q2zkj/J9EECmEnCNqlUxwNQ2dmI6sydAVGHhCdpOCUDSXnt+E6QewHdUb7ZYdArAky55Hl2ZKsDYrzGYY5QmQCkOBuEF0xnAXljslpVSLcB4hvzLYvoTQL/c9fqyfy3AJi1dCeB1AkCJgUywslUVQO69WzhySDXAmEDuxBOSGkDz7VxT1kgqQMFUcmwoaSzADSmEh6umBcCAk/XfyScVwEP0OmirJyHAXh6MftiFG8A1UkWu9jwFwHSE3fsB3RRASaYJhWzFJUCHKm21BAkfQDRvPZ5/YhZAXL6dALq0KUAI3rff7KIZQPZNqVv3FDTA8TNCMSNNNcCA7qsWEEoTwCk826zViBRAiSH2vqxNNcAicCBdMx8jQOclwElx/CZAcId77/quLsBVFgMIZ9QdwJhdVxG8xxhA2DMFNJGPA0CzSDPCbU81wPXNguAv/SRALFJq8FRFJkBiekROTyU1wGK6Jh6UECdAPhXc8dLRK8DTwhG0h24ewEu0AuxfJChAdSo5GB7P8T/NuGqRcRwYQC6fsYhvOSpAsiZhPPQHJ0DQf+MaWssKQCJGDTA+sw9AssGfaIRAAsDlMbSOC+QswGICJxS87AJAn0SPxiOKBED55E2gRjIswOFsoOud6wTA8M6EKi62EED+JWsPiqgHwFb0URPYQipAfzPfJyhPHcAUq3tFcQcTwDvR5TusGCvA1o4zzEoqG0DXbtp2yVoXQMyA/QOHHzTAAYaA2w9NBsCZ5YCerCoJwD73AKfb+A1A2zBKp/fYFcCq9JmdsjI0wJBzdkMtMwDA/09S0eIcNcDK4PXIf5smQMruW+EzKzPAN95JwSBBKkDT4tlYUiwcQLmsWA5apCRAhUAcTz7bKEDFKtsRwj8qQArlpF9enB9A5YTCqjInIUD4ch0fMuO1v34CVDupxRRAiyRWtUQTEkDDIKKRhMsuwFUPPKTxJipA0PDO3SnLEkCz5GunragkQGbrflAS/CVAtc5rfROIHMC9Y+eHcIYfQKTEMusXujPA8HUyJimNGkCOfCdlOYIVwNBaAVvxYAVAnr7JGVk8A0BNY0V3eE8JQB/XTbrqXC3A3l5nLrJ2KsBbfLVNdLPmv02xCu8SkRpAa7MguB3lLMD3+tV+9ssawMHF7KSV7iZADd9V0WtKJUCmj4gHzqciwMJOdpkQVypASktdLfGnEECzb4r5yEcnQBseHZ4dXjTAkgLKYJIFJkBpKFYr/eUrwIhOu+vCwxpA4twBQmSZF8A9FDRX6KM0wEbvINuX+B/ACk2ckSypIsAbkd/fqBUoQI9tCc4uqylAXa1fTgLkIcAIq6gO2BELQEFd2gXCqivAIDhUOmUIIEBbHX/P8+4QQDQgN1j7Ifu/5IQ4Tn4kIUAUcTwL70QUwAvPzeGhsSlAnBMHbPc6M8DjVCmQMVINQFWHbUkAeBhABsDdFAHLHcA98QovH4wdwHHPxgHS/SZAQa5YwnUHIsAHr1kc7roiwOlA+lz1WypAufr4SPX/CcCR9x7rUP7+P+7R8P6MtyhAA1kdNXi1LUDm8DyZSbwwQKCj8UHMryrAtzeSbOB7GkCIDdbA14IrwLCBT9ujrSvANtU7NjmgIsD2uQl4OawXwMgjimNyE/g/tizMZygWIkDEAHjTHz0dwEvgVUTRQPq/uKZYZ5h7HsBWlt52r9kDQNL05vAGQhHAP7i3/1WaK8DuhWBfwV8XwKIQvW+wuhvAp2xrElqpF8ChA1ouMgEZwArv/WbI9x/A1XpMcEN8KMAhsC0iMxX+P9QW4+mEIBTAZRB5nwSLF8A5T8mOSgYpwCNbYrW8KybA1T38qhZ5HMBX0Qe80G4rwBPpdMKAPjBAEN4D0CE1K8BJw/Fw59gbQDNLIeKg+x7AgQyt+VrBK8AalgicJOcCwLSMEkOVbRvAl1ecgp5DKsDMxELs3vkfwMYQz+3e1jBAJsodtyYUKsCMenG/1C4dQJqJN5nwLxvAZ8qGsTXcG8BEP2HzOm0YQAx1jog7qivA16PVAztHKsBdj7elAX8kwPMVB944SxrA9CNJ6w4nAUBfT5hn8NMZwBsnFe7LlBjACuX5pk1ZK8BW1oYZwtQZwDytdGRq8iHA7YoMPkEwEMCLOOFEOPMZwJXZ9RfUR+Q//wSMtSG6IUASsBAQqJYkQOUkAoWx3xjAw9DIlsD0H8A640yfH30cwMk9O9z7YB7AmHnuYOoNIcAmK1wvldocQEI4+UteWCPAJP9RSitdLkCD+svKX4ErQHuKsS6avh3AdK0oJhftIsDy5kL+e54pwCOaCXyeIhzAd+n0S08sEMBOHNDrbp0fwPeMMIijORnAo/fKv+9+K8C3Na6OPDUsQOSoMa3yiyRAfJX4DAC+AMCW8wgvUf7wv27ArCxrTypAwX/fREo2FEAEWBgnUygXQBem+lq49RlAU8leyFG5L0CEhyJutRMWQGC3s2CezzBASqDxsvcPGMDRyFyB45IPQF/V/UYvUhDAKtsrZYTA3r9paPgFTLAwQLW24yBnFCHANBcmwItRKsAjguDPUawhQHVXkYQf5CfAWP9W3kvSKcCnRnv+g0oiwOzj2QNjFi1A29uF0AB4GsBP6QuHCcQwQFRkIVWT4RrARaeT1ZcR/z+TKBDG6tkcwLmoD9rtIxnAid2eD6brIcBpmf4YMBUtQF8uH/zGgxRA+kNIvGlyKsBYya9a5+wvQLkD8L6VDxnAy5rcmoS8K8A2aKzix+chQIcNgEXQqCbAlztw46RKEMB+5q6SjC0mwM52eaJMNhnAw3umMczsL0BOzBMhVEMbwLd+9FcPqyFA820VTv2GGMByxHD+bwQuQGlLuI7IeCDAnuod/GH/KsDwYb8kaF7+P4pazXmivTBA0UgQ2P9WDMD6seZhXVP3P3sa7lHbn/g/1ppl6cDdIsC+IlbaWO8CQF0U7BWQshlAO72j3xDXKcACyfZVSKIkQGuyt/cylOm/V0Lak2hcK8Ci1GTucvkowFQzJqXV/P0/AC38/Ys9CMCGaQGeMLsiQF8+XdkVBChARdFIM4NXEECFMdG1a6QtQARlDg6r0y1AXaKU78X0LkCJsHNCoQ0MQHZqnXrBYSpAPdUmExojIEDeMIETVRQRwBeVHC1M/R3AIKKntfNlLUAca6xj8eQcwFyarsp3byNAlsleSZqmHkBe/LI9tUEnwEgYXLFWUiDA3jWu54lWK8AzfFk5SD8OwOW3jxLPwSFA+pXJ44kQBUCNdHZfb5IMwIDUwwxrliVA10OtNq/XKkBpBsjbPvQjQNwTR2kiKBFAaDva6c3kJMDqGK/taEUdwC7BpcqXpytATlGXAQAjEkD6x6sbohMdwBNE0bLDsiNA6WZm7dvwMECRsgmgzGwYQKPQEmpSfSrAAqm2RqLELECEb9EKoqYqwLNpihe1eibAaKyy55iyLEAGdFmGBrkhwGwhfeA2jivAPZIGu/KsIkAsijYEnKQqwHQWdw2T9TBAjYuy9gpk8r/zdfOkE8ojQBDbY75lqyPApOOFhIxpIcDQ0hk/Z7AVwNfqPmhMuhhA5MctbbRDB0B550RX47kOwA93yYm6kiHApj+TgenGI8DhaoFq+d0FQKoFQXSHiALArK9Av26wMECIL7p8PQYuQOm17LB2UR9A/so06g1fGEDKvoSDKeoawBv9/Y36G+E/PrQtKL/XMEDxDpycdZPhP7jndeuYaxzAO0+YSwQhEkBvPfYDy3AqwF660w/fNyDAu5GvbRPuGkAtq2pEEjUqwNjqGDOXMg1AxCuTtaY7KsBz5H0AlMf5v59SHgSmaQBAOxE7NKpK/b8YqRZ7eposQNdZ01ZDwiJAj57sAzqlKcBaleUdzEAewPZ/KyVG8DBA9e9fwClIB0BATYA6DS0gQE2yjKhBLRxAU42UsCe3IcDcfeMK6D4uQCYbP6LK+RZAOpReLgwsGUAxaZIQovrsv4b4gGfgAQLAygwoB12eAcAQzuoqmiILQMKE0nArcARAV/1xf1EZBMDuNvc/kwsiQDyk1QG98CtAOVBhA3ohKkDhkWNiaDMqQNAdKck3CR3AKiYHxe0iKUBfTA6hUKQdQLz86mUary5Am6FrGTTN9T/Hbz7acisTwEf7xOGDViDAxcYGH0AxGUApzgSKh8wrwJgqWpMknSvACE8OecBwKsAPxpiHN0QfQCb4tSZBH/c/9vyn73YD87/roA+4Ojn2P1MXDpOaRCvAaBSTjw/6F0BAgy4YZLUgQFz9CZvdJixAqKgkCSTnMEBD5ejK/4ovQPuUN/8lXSHAqlGt1i26H8A2nIJFOVgmQIQGpX7AuiNASEecgUyKF8B72GZ0cNAqwGfh9XscwyvAbuJtZHtOGEDQ62FHYxQcwCo8o1k6pSDAz6vq5loz7D+UuP7i3NcqQIj2zo1XZS9Ax/4gUua6+b95HvjeXnz5P0pdAxgGTiLAqH582r0NIsC0jiqsi7oRQKaWmYMIcClAT+nJKyh6L0C9d1uX+gQWQB0zGTKK4inAk9kcnG9sKUDaoSS6E4oYQEpBcgtH+v0/PdtvNbFwK8BfToGLeJUwQHvBrA45TBNAgC7wTMbACcALPGLYRVoPwEf//oRTVRnAO/7aHgrsMEB+DIzWq7DTv5GlOA4stANAVO84ppkrE0ChXCvdOKMdQAIY8qilMwZA33JhIJ2CGUBjjWubkO4cwIKDi+9uHxDA4wkRLnYzJMDOh4vg3iMpwIJCnzS7kyjAWJrN0fHuL0CWnaws/u4rQPqWkUD/3gDAaJWrN3WEFkBzPNHD6cDsP/qXZh16FA5ABRAAcPKABMD+gX3QoEcrwFrbwSVG7zBAt1zYyVVLH8Cvj8LMv6skQH8tObmA+jBAMiErPqkyGUBZT+ie6G4tQGjGsDwb3hBAgVygCKUSKUDaiSK+9PIrQBXhkjLGCh7AE/Kg/HcXDUAu8KbRj4kpQHx+ARdWiyNAsrlLrmOpDsA6q5iK2KglwOY3HGNm9zBAuPRVH92sAED8W/57WDghwM0NMHHLcClAKgyS2JKNKsABsf+ZpP4ewBaptnGUaQfAKymXAY5+CECN2ZfNNnofQBP46pEOcyhA0t4V3CEqLkAG323yYO4hQCK0ZE2Ajy9ACFlDocX3MEBk2SARG+kdwNasEfAVwR3AplqpKYCUA0AbjftbvRTlP6zdNRBOkhtADfjmFaU4IsAczbRrF9wvQIabYVKy8RJAXvssayjiMECyDGSoRxstQDMEPUY9Zy5AAXtmKgUBKUDkjKvUp68nwAhX4exgh/4/xRF9e3K/GkD13JwxTOYUQDyK7KD18itAHAx7xaLCIcC7SXlu7lsqQPe/jiWKSypAIlV9dGKnIEC4UwhhHxAkQOLskmByaBBAXtYAmnL5EsCPXt19AvcuQJ9n9Oerp+a/hnqLAlWPAUCFToxgtZIAQHbmbxvpvRtAQbLopyNbE0D/RKE7PdkwQLkoE2Q/YgfAmT7ehNb/IkDxch/XDTASQOpGBfE+sStAoK8eIAEHIkCe7cZDG4buPyOYckLztCtAyL8pqFOyGEAQuwN/dMkjQI0me5fEDRxAjzPxlflOHECcmpIK2L0pQIfM6H2wLwTAh8jbVdwHF0A0Fu5WgBkoQG4vuwvGQCvA8ck7yIrKI0ALBdvrw5EXQOyI3cgkpv4/5LTSsTw8/z/6agjfJ/IgwJ2cihgOnA3AssfknB3BIkB2G+73ahsTQExGXLJhnhrA8eG7kz5SEkAaycBB5jYrQNBKSOAfGgRAFemn6xQl/j/BnHw3pjr8v5O3Yl4cbjBAWaI/rDQlAUCRr0ATfbkvQNX6xuef5vw/fxBaYopyLEBNJckG+CcjQPKpY1FDIgXAhDqP/cyQFUBaMjx6WZgnQPcRFbkraSZA41HJRMBU8b9B4jRUircuQFekIEAq5xRASKhNWL17IMCtfigpFt4TwEeftoRWjDBAiODq9X/uKUBXY6+O8VsCwHqBaEom6BjAPs5/hm7uKcDBplK1iB8jQMT9O5isDCRAte1wWABv8j+YoYr+he39P9KuAG45Ox9AeHSPxXIbI0DCx1sX8McewPN0ZF0F9zBAxFOeDWFGKkDV5jreMswgQBmhALSJJQLA1VaAMb0nLkBWfwpRT5ISQKuPO6chyh5AuAKpYqXKIUCv75T81ecwQIZ1YZ1BGQnAkki3FlIAEUCg/rhM790kQBFIgwZxHSVAU06v/vGk7b8xSijbJuQwQF2VLOCGSiJA0J5L8yjHLUDjVbHcFmwuQGGwlIkOqg3A8T/rwQHQFEAnaswfVSUqQPge3yYo9xdALH7vMo0IKsCJToaDBpwgQKsFPDMtjTBArkpZsUKlKEDRtwdyltkqwGanTSS1LP4/dwVSPlHvHUC49Jl+EvEvQLy8PHV1wwRA2u+/lnI7JkB0i/bkWfowQHEJO/wamylAx6cg1nzcKUDgOMB/0OAqwJwvaaZmTQlA1Ie8mNm5FUCci0Dlsjf+P36NJQ+z1irAxzQgBGU9GECY0e3CnTQEQGagPBW1nwZANGfGOsIdFUC3n8ozAiocQMUaRxUbShZAKnSnB9B8FkBiZxt4x3AvQGKKoBTOqyvAXi3CWRG+KUDszXt+kGYrwE2ePFhsdRBAY3UT7sZXLUDgIAUiG0oBQMsB5/5yqwJA6YxskO17CUAnXbwaKEMgQFwJo6C+IBdAk5gMtpcyHsB6xtznVN4hQNTYqNB6XBBAD237F7hMAsAHSMM1WUQwQIKmfmwYaiHAe7Ufg49XGEDatZYspnohQBrKgFXJIitAleck9nG3K8Cqgv0JGXnUP2Zt4MKMLBLAuQztOrwuLEClAfFjxTMvQC69zDbpqyjAvGOqAoXKK8A=\",\"dtype\":\"float64\",\"shape\":[1000]},\"x2\":{\"__ndarray__\":\"G+XQve1QMEDyxMOfiOUkwMwmDLruKS5Az7AKAX5qKEBYpe9FXTsUwAOWJjPH9DFAMoWitR5cMkAc9wqK2askwA3iCTXZMTLApe8owY3jKcDjZk7f4goawJ2g6nwBaSTA+7G/lQsnMcBiy9MER+MgwIDAwH/7xjJAVksjFkaAL0BQfmwIfewkwK2YOLrW8CTApqkhzaT2JMCi54fvfC0hwFHZc0K5eDJAb3lijGr0A8D6dibowVwyQFgcYJ7eihLALqGFZ/SPMECWHEnk3UIpQGHXDQ7+G/u/Rh0M8D/MA8CIcewNZVcxQNxYT9lSiAPAPyMxWdKyMUBnokKfa64xwJx2sdBIeSnAFM0P30oQM0CCyww1sY8xQJCDQsCmRilAmCXZ8pn8GcDmpojuPvEkwM31/IgBRDFA2fXo34TYL0DVbrRUl9cjwLEHuQrP9zBAAdC2KMKQC8Ctbnr+K7EZwEusuyixpvW/Yep2j4y7LMCCQpU4C/UgwHOQsMsyNjLAyOKvBv/CMMAmu68DLw4zQFwutNJ37STANbF91dLQ+L8oFGFgNTQxwCrwPe0xERTA8mBcXZzfJMDRnP0fjS4ywKeOY8cpNTLAtU7WLCUNM0CjdyJhmkcxQG6ppji25zFAGUXS+BgbGcDqRI8rgJYyQCsa12iYIDLA5ITrHAA3MECTDE1eaVUyQD4V+pE7MxrAS+2kl1JHDMAe2x1jR+YyQNbyVkoB9yTAVNclDxcwMECFfecT+y8fwCUxpCJ23CxApGmNaMYyGsBSjCblxgQawDn3tMc0VCpAYxWBZxbpKEDkzQ09QvULwEg6TUYfojJAQ5bgMYoMM0AklxHuWTYrwFeUrSFM8ytAOWrL3/dTJMDIEDCmjDwWwMuRkOHcKylAwCnkSP8oMUCIKsn+HZUYwMv6wBTd9RjA7ZZgu3KfFMB06j2i08grwOi989i4DR7AShuY4KFyGcCjv9pb9TMJwNbi6eU7IRDAtC9IUevnMEByh/syl/ErwFsHzk7rqNG/sctig41E1j9AbYOp1vYkwLcAEWl8fSlAj4X91gIPMsDhwekw6BAGQOi1SKMNQTFAyIErILgGxL+XHsyQRkMWwA1nQiGUEzNAKKxbtjHlK0A9E+QtKQAxwKY53539BiTARDmZEHjIBEDNuUNvYTYywC2DEf0d8yPANa3srHdUIsB8Iecg/+kkwO+qZOyRKCTAhc6DQoTpMkDLuqkrCQ0xwKkHTVE/Md+/1zaEoGrWGcAWzAYbZzsZwGh0F9ChBDJAjYHvTzB7K0DAgm5iLRUzQOU7UvcM3jFAzQoimXBdIMA78JOCCSkxQOfNLhYmOQvATyWPdhGg/L/Kg9lSgbkyQBOGrMFiIei/V0wQTGCnMEBVI0lmWZspwP7Rze1dlyHABZ1GzeQdLMCvpm/gW14kwJvpRE23piTAqsQ0KbwuMsBoYnWuJV/9v4EEKQobNTJAQwVnChTg6r/Y6aNAHuQyQO9HaFnWFDNA6Mk9EHVeK0DFJRBT0xIkwLxCA07rPgVAzuwBs+PaMkAUBbSZGW4WwNPE7xa77DHAG1p2nz03MkAlJqy+y0gyQAVasYw70hnA4F4nKxsq4L++oQJz890rwDmKZJsCJyPAtm5nN3sVIMCO4/pKo6chwIUZyO+knxTAfml1TPOG+L+ATiEk7CLtv0+ycU6UDTNAcTgBJSoOK8Bu9SWvaKYpwHJk41XTKBrACbcJsB0CMkC9ZiYXpM0xQKAwT48ROTHAIuA0vnazMcCroRAvDy8ywATcWvx/6STAp7QHDc06AMD7JOyxntwqQA9IQPEZFwDAWlgNTDApBcD+UgpRcqgdwOx8Ou8S8iTA0p4JnLbCI8B3jd5qAzAkwJbNuLK2XDHAk0sPaaGTFsC+2WlK6zUkwM2QAENFRyLAGK9Ze+CoKkDFT+OHcD0jwBe/QFvvjjBAOQWWjwfpMMCkUUG9Z50oQNlA4fQ+bRbAhirHcNp+LUA6fwTPN+YPwMjlVn+QszJAb07gwAd+J0BL8JNtsZz6vzlhBCy2NyzACiN4DCFkKMBajxNjK5kxwARj+6VcRxXAwpGBuz50FcDUr/H41gMawAxm4Br7MzLAReVaIS8rMsCcdDGY6o8kwOs1wvZe1iTA1EFe3PnDI8BeEEIuD84xwN5J9IR28jJAzEJPnIwMHsC/HyXP4csoQKACN3JRKRDAAQHiGOMU8j+KU+6eAGwWwEWc2PVmNi1AT8KZdyHSMUB2kBZWXFEdwHSveZDeWg7AeC+btQKL+b9f2A5T4AcawJcmEewHO8m/BW2T8atoAMDj9ZDAsxcqQBv4lUU8MzLAa1metDE2MsAmkiFB2IwxwPk/AnT8+CTAqnlrQIP1McCwkce5oCciwCGW4jpoExTAaN+eOkzxJMD6zuHYlA0yQNOHfKL2kTJAplestF5iFsCMYRHWMjYYwNrL0cTthDHAZ6BStc0lKcAPuia5w0AZwMYb+gIHXPu/PGukPvkJIcAVmP+YuHwjwJIvOe/YhQfAkbxV0gfqKkBgvxR3t3gxwCkMcSjWfjFAKtfJRC6w378nb78E9k0FQPz/wiNuezFAeZzCNP3kH8CBi3mbGP4XwOos+AzmpRbAI/+h13u3MUBHyIItPloxwCLzokxriwvA7eW78ATqI8A68DQpYkUyQB4IOJgNVCvAdpeqqvtdHcAd0Txf8irDvxfEWcLvE/8/Mx7kRDcaKsBzuDCQfR0ywMfm4L0v4xtAB9U14QO3I8DQqjiyuqYxQOejZeGnOBxAtUlM57DMGcDqXz6CP98awNW8doElCzLAXKTfXZnuGcC3Fz0xnksyQNzqb0v2UBXApWG46egvGsDAXXyjZCcywKuyaNTnDBrAbzd2A36YGcCLOARRKlUyQHrt0m1XMjLAN1oK4BuiMcAlpnUANagawG81WD01fBnAmt878hSyEcCpffOtnxwyQIzVLvsoNBrAvXKT/qvKBkCRCyUZRCkiwMd56wb4gzHAbDiOnHDDAcDRpehSKK8hwAmqIbr7eiTAL2UC8YE1McCcLqT8Sy8kwDtgBv66IxrA9vzJwAnK+79vPJ5Nlw8zQKF64rPhcSlA/O5xRwc0MsCwRv+W9r0xQOp2eUhxKhrA3CTa+UIuLMD83WDfcOMgwJKdcob8zhnA/7KhHG0R9L+ERgHegUAGwHDthJO5EBrAFe4wQ2ryJMCsuF0gCXgGwL7YSepWsjHAOYWhWuPHMkAQBLCYiBckwJ1lWD6PhADAOKrMvWwwMsA88jhR/2v8PzR8MEbb9zHAkC9o02FQMECS2SYHyugkwHdSKROoRAnAXFNFuGxpJMCbC3GAcaYxQJDzGZpQ3Q/Asmrh9YkvHED9m4seLRHzv69qJuS1dCPAga6zZTtvEcAbbTy7Y1cyQD9RRD4pGAJA4IHdNEoKMUD4RJkcTYsxQHa9hiP2ESTAb8j3KWqy/r+9F6kPfTUywKJYiSHK7StAxOzCCj6kGcBLMmrTL8QrQGL1NSCuFjBA/wKMFRb2HMD9eiEvqzEtQNssIQCQvTHAv7oUsKL1EcDpsgf072AVwKipCOKiiBfABYR9xd3sJMCDEKruUwIhwNsBxrOD+h/AqTs1mfy1MMBCPG3rtCgqQOlO0s8owBjA3+VULsoQGMBN6QCXNSMrwEpFcnktfCrA30HHnxhJHcCuGZg/3dMwQHL6O/nWdRnAB7P8wX6YHcCfZ+RwgP/Wv69PkyEbWSvAxj5G2zsXL0CvE+ETPvMkwB1oNAVxSDJA7uAfFbNVEcB890fGMmf5Pyi3blgmxiDAkLTAdSuNMEBZYlSoU84ZwHhW6s6akSLAp48OHRkeAcBe1iq1B4X1v3fEYGVNPBrAmXrybz0XLMAH8639D2cjwEstLGD4JAvAAXtTJPnXKMAw98ztSikywCMQrxOO0RnA3xcaTmo39j9n6pJeyyAywJerQR3+eQTAqOQnYBYbBkAj7WUA3eosQCl5D5CfICrAtl/9nQdWHMD1hlERZdgSwDY5EZfE0CpAPEuVY2ikBsAPBnSy730cwKK5e6yu3CTAmrEy0SbMMUDbDAKLaLsxwJ+c61pe6yTAmwXBgSddL0AatH/ZSv0xwCb/rb8E0RPA89myyzjl/b/dLWlOEqsUwP2LV4D+VxxAYuWI4VZaA8AQ6MgpT3IkwBW4orQmazFAXe6kWp4eGsC1+PKb0L39vzFtZq7XrgZAlGQ7alIcJMDJgjaM+0H/v+v9g+xr8jFAv2verrmQI8A60F/TNg8JwCB68vDVYjFAiWiaIHjwGMCaKL4PFEH4v+1qsydHHSLA6mnhonvrMkAraja6/sYGQDikCUFEki1Axi5XDG1mMUBTt9adhjMywGaaqI4ewDBA59jObTexHcB8otWwXXopwPM6NkbPNzJAEPxI1x0sJMBm1dtLEjEZwLAYFzLZoBzAsAw3Rlg1MsDXgx3fSnobwEfENSFQ0B3AC1KTPL74AMDPn8ds4+sUwK+G7kucoSvAhduVNLH2JMDE3jlLEEQKwK8RxF2moBPAFYAEa8IMHMANqGjw/NIawARIGvuYjyTASW9MSw4eEsDbZay9S/XQv7/XL/o2hB7AJvOidKZh9L/GXIVAJI8xQMjSRZHL6hbAqG8QV7FpLEAUVKaFaEAKwDdUHHc5JSTADSoIwqdrMEA83Hm1fWUtQPWfnM0ZsjHAsC6hr017McAPjIVYYBAzQCwtOBi92yTAar0md+cxMsBkpDJUpiIawOzCq7tx8i1ATqG5O1gvMsAzT6K8HiQkwMqCYejCsSPAnyaa/2GGA8D+67JvRqoBQCb5YXkzrMK/SSpqa4T4JMDg4exNEH8SwODO0naKOCTAIo8kh4QWJMDwR34HXJQxQLNOeoajxyjAOBaxEcM0LMCcBpqs7CIhwBwJJxaG+CTA57+bONx4KcAx7/JlsJ8IwIVupxKhWQLATnBkcfzkMUDu97I+q7gnQM4klfonFh3AfyHmLiN/MEBz44aSREsxQGNI3W4Xa9e/tdMlGMS7MUATBIV5RcEqwCJSrYIBAAZAEv6WNR8tEMDJ3KdEy9wQwK61Q2blASxATrR3BwixJ0DchT/sGS0iwE6nmlGwKyDAPAD475LAKUAec9mpu14xQBYUU7kfBSvAj0DNcsneAEBdi5iZG2cwQOnnhS9wgx/AEOIbhMx7McALx/HCLbkTwIkdte0yZS9AkCfb/uzYzr/wzspH2DUywJRxoK9ZCDLAa0jnLkMb/r/XPm8tVQUAwB9GmSyyJSPAQZtcgMh9KUBI4R9TMVAnQN/WiWPK1BzAyR3MfO5uGsDMCe72pq3iv86U+K4gEwDAzGHPHR+F7L/89+Jb62sNQBqVnjUsHRRAzVifpQnHJEAn+eJ3xhj7PyfgWFXUCBBAVK3AXyVkIcDs13+71sQvwAPmH3iVOyVAZS4CC7xMI0CwMutWgs4owLiyn2eBViFAU+a7CJaiJ8D2/Zy2ABX4v1z35OhfcxVAhS3C+qlG/z/Md1SK+BEwwB04RTXvbSrA1+Xc0b7IL8Ck1jhu8wouwLz6knQZUxRAue8BAN/3FkB3XrqZL1klQLBIPerkehRAI/urOSLlE0AH47nuT5AWQL750Z4sgxdAT/oYLfGfKcB83oCZTSj4P9NKEg71/t8/iIYfDqTl8D+S2ACaB08KwH6e4nhIGRRA/hbhabNbBUAgNPoiDfweQAiR0FNrxirAh1J+eNf3FEAyOgI0Y0QmwIn9Ihm/xfc/FQR0Vh9IFUDRdZY6ZmckQGPgGTDWFSvABuOguMFIKsAP5bD32QUlQGiIJk+PLRBAc73aep7C1b8i91uMqxgXQN7IPid6NizAUKR4S1xlJUDYto7XudoswACDKKaNoS7Aw36VGuwWEkAtJQkjbdkswPIFXeHOtyLAY0SYOXkSFkC61JXXZq0swFo6GHtepSRAwtwhEotsI0AdTkW/XlwiQLAbTE3jOi7A6xroa0lSFEAag5/td5cpwD+lXDVkuifAyLpmsZtYJMDExh6nTRoKwNlITVqohBZAQFrMbhUZ5L9FsjZOc54eQBazrSqe6BNAgeyUc5VCFkDyy7wJnfMVQGR1IzRT+inAMqLHmUQVFkD4BEAC/zsUQF81O4YPuS3At/0JIwlgEUBex87dZ2P1v9ek4SESQwXApgyNPzJCIEDNyr48J4EiQKFTtBvyZR3ATKmxOg5aJUAE/ujuQSUlQPVGz5LmXgZADDMPwArKFkDOyzPQkTslQH+gdGezTAtAYvvQqIzZE0CqsurU1XolQHhFpoEx+xVAaGL2AZRgI0B+BljaWp3yP2uOtyzfBxVAK984aNhw0r8m66poQXEjQDQNbO2SRBdAF+Dgh7KZ7L96HkacZA4iwNKjt6bUpPG/NKICME39K8BRSLF2Hgf1P9XOjxJfdCvAB1ka0CI6479fxj/l0jQpwPPm0zyt2C3AXy4B1XzEIsBnKdhVRnccQC/cLFLNLwnAKUFrXXSKvr+xNXlTWA8WQNTn5nuq9S3ArWvzf+a0DkA5Q+sj8lwjQKqxDSi+hRdA5ldL/UYAFkBp7bKQCYMXQBCOu731vS3ABIQ96JwPFkDI0gtqdv4qwI9iBsmvcSNA3vCQAKmzLsDsh9Slwejov5HqGNFcoRRAvkH2+U8z5j9kIfeEvjzbv44LDYtSMvQ/IVKEjrEUGEAMQwehuTYlQOz/eJDVPiVA4rfkEuLkIMDwB2Nifsr3P5dsB9BZ4CRABTAf9X2pFUDkIbEejycFwOmtxhAp7yJA7E09KuKu9T89z0A4LZsWQEePVvXkiZU/xHIq/EjzGkDgo3IYcC8HwM6oRity/SBAapdrtwjyBcAA9Jvlm44bQP1QQG2UK+u/FTviAa70GEB3mpS4mR4DwB8qmNsu4RvA4Lem0WjIJMB2EDIMNYkVQApuXiKR8RNAZB+MWKIS8L9jXmGB2dQTQB7RTEOcziJA+2e8YhEwJEB6elKc6HUXQBHiz9vNhxRAVm53hVIcEkD52+nds/gWQEdrkxv5aSNAbJ0N6y5zJUBRY9ANnO8XQCKkHLnSBATAECuQGf5iH0C/kQHaxJsiQLFZKrV6diVAJbFYih0/F0Bk1aCWccUowCqw4tZfYve/vcPdMm9wJUDK8TmGWPsowJSGrZ0cNwbAX4fb4rYr/T8CdMJunWwKwChBoT87XLC//EasYsDwHEC35tEigDAUQCZrLLFrhRdAEJdHv3Jq87+MQzk8JyMjwMJJ5U7uAf0/ZfGGpO4WI0BuMtMnjXHDP92oLqwdJ/8/wTq9mqJZIkBSVhtsAawiQDx1Yh5ttBZAm5yLsGq1I8DQVH707SEUQL96KhURsQZAzrNVMNd2JUB0lAR+TrwWQKfO9YKfZiPAPsSlGDDDFkDLaYiIp0/8v5gpaN+0QB9A+5/5M8GLDkCLapRa0+EaQMdmYmlBcQnAq2jGLzdrCsCDJvJh/2krwIZgvZ6xjSRAH6D8+sHp9z/49tefcJEkQAlQ2jZVqinAJnpBgEw7BUDg16z+0aYUQAaw6SggdxRA+Ug7ndy3JEBMQLxDuJbbv5Y0tqU45gPA1F/eKOUDFUCKz5tEzWYhQNBpMCSSYSVAoceLDIrpIEC1BiYUB9DzvyKwnHriKAfARWPegCaC8b9KyDBc/9gnwDD5hP6OTgZAydKHdSMgAEBv4whkwe0jQHeP60GNFgVAW+l7EsZ2FUDq8Z+9YXcaQPpaXCnzxwZAMNn5vx18CsCLbSF5i8UiQEQzHTiPoB9A3vBxwovmH0DBZ/2bOxgCQIBz5EEkivs/qUvhyNkXHkB7CWELblAjQGLgFCVQZva/7ohM638RIECjxDOYoAggQFkQzMJt1xNAVyYWL0YH/79V/tI4R1YkQAXDNl/mk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Application\",\"version\":\"0.12.4\"}};\n", " var render_items = [{\"docid\":\"9eeda085-a786-41e0-b4ea-3e44fd6a9f4c\",\"elementid\":\"3e21663a-f2f8-475a-a8dc-0f2140652719\",\"modelid\":\"5ecaec60-7de9-433d-851b-9b0daa985378\"}];\n", " \n", " Bokeh.embed.embed_items(docs_json, render_items);\n", " };\n", " if (document.readyState != \"loading\") fn();\n", " else document.addEventListener(\"DOMContentLoaded\", fn);\n", " })();\n", " },\n", " function(Bokeh) {\n", " }\n", " ];\n", " \n", " function run_inline_js() {\n", " \n", " if ((window.Bokeh !== undefined) || (force === true)) {\n", " for (var i = 0; i < inline_js.length; i++) {\n", " inline_js[i](window.Bokeh);\n", " }if (force === true) {\n", " display_loaded();\n", " }} else if (Date.now() < window._bokeh_timeout) {\n", " setTimeout(run_inline_js, 100);\n", " } else if (!window._bokeh_failed_load) {\n", " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " window._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", " var cell = $(document.getElementById(\"3e21663a-f2f8-475a-a8dc-0f2140652719\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " \n", " }\n", " \n", " if (window._bokeh_is_loading === 0) {\n", " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(js_urls, function() {\n", " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", " }(this));\n", "</script>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p = figure(tools=\"pan,wheel_zoom,reset,save\",\n", " toolbar_location=\"above\",\n", " title=\"vector T-SNE for most polarized words\")\n", "\n", "source = ColumnDataSource(data=dict(x1=words_top_ted_tsne[:,0],\n", " x2=words_top_ted_tsne[:,1],\n", " names=words_to_visualize))\n", "\n", "p.scatter(x=\"x1\", y=\"x2\", size=8, source=source,color=colors_list)\n", "\n", "word_labels = LabelSet(x=\"x1\", y=\"x2\", text=\"names\", y_offset=6,\n", " text_font_size=\"8pt\", text_color=\"#555555\",\n", " source=source, text_align='center')\n", "#p.add_layout(word_labels)\n", "\n", "show(p)\n", "\n", "# green indicates positive words, black indicates negative words" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "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": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 1 }
mit
feststelltaste/software-analytics
notebooks/Tracking Reengineerings under the Hood.ipynb
1
109511
{ "cells": [ { "cell_type": "code", "execution_count": 1, "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>additions</th>\n", " <th>deletions</th>\n", " <th>file</th>\n", " <th>timestamp</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>19</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2017-12-31 19:41:29</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>55</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2017-12-30 12:48:20</td>\n", " </tr>\n", " <tr>\n", " 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2017-12-30 12:48:20 \n", "3 2017-12-30 00:38:54 \n", "4 2017-12-30 00:38:54 " ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "log = pd.read_csv(\"datasets/git_log_refactoring.gz\")\n", "log.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<class 'pandas.core.frame.DataFrame'>\n", "RangeIndex: 31487 entries, 0 to 31486\n", "Data columns (total 4 columns):\n", "additions 31487 non-null int64\n", "deletions 31487 non-null int64\n", "file 31487 non-null object\n", "timestamp 31487 non-null object\n", "dtypes: int64(2), object(2)\n", "memory usage: 984.0+ KB\n" ] } ], "source": [ "log.info()" ] }, { "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", " 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<td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2013-05-15 17:36:46</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>0</td>\n", " <td>139</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2013-05-15 17:36:46</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " additions deletions file \\\n", "0 250 0 src/main/java/org/springframework/samples/petc... \n", "1 0 90 src/main/java/org/springframework/samples/petc... \n", "2 93 0 src/main/java/org/springframework/samples/petc... \n", "3 39 0 src/main/java/org/springframework/samples/petc... \n", "4 0 139 src/main/java/org/springframework/samples/petc... \n", "\n", " timestamp \n", "0 2013-05-15 03:35:33 \n", "1 2013-05-15 17:36:46 \n", "2 2013-05-15 17:36:46 \n", "3 2013-05-15 17:36:46 \n", "4 2013-05-15 17:36:46 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log['timestamp'] = pd.to_datetime(log['timestamp'])\n", "log = log.sort_values(by='timestamp').reset_index(drop=True)\n", "log.head()" ] }, { "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>additions</th>\n", " <th>deletions</th>\n", " <th>file</th>\n", " <th>timestamp</th>\n", " <th>type</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>250</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2013-05-15 03:35:33</td>\n", " <td>jdbc</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>50</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2013-05-16 02:15:44</td>\n", " <td>jdbc</td>\n", " </tr>\n", " <tr>\n", " <th>55</th>\n", " <td>78</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2013-05-17 15:12:26</td>\n", " <td>jdbc</td>\n", " </tr>\n", " <tr>\n", " <th>186</th>\n", " <td>142</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2013-05-24 05:52:31</td>\n", " <td>jdbc</td>\n", " </tr>\n", " <tr>\n", " <th>243</th>\n", " <td>123</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2013-05-28 08:15:35</td>\n", " <td>jdbc</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " additions deletions file \\\n", "0 250 0 src/main/java/org/springframework/samples/petc... \n", "24 50 0 src/main/java/org/springframework/samples/petc... \n", "55 78 0 src/main/java/org/springframework/samples/petc... \n", "186 142 0 src/main/java/org/springframework/samples/petc... \n", "243 123 0 src/main/java/org/springframework/samples/petc... \n", "\n", " timestamp type \n", "0 2013-05-15 03:35:33 jdbc \n", "24 2013-05-16 02:15:44 jdbc \n", "55 2013-05-17 15:12:26 jdbc \n", "186 2013-05-24 05:52:31 jdbc \n", "243 2013-05-28 08:15:35 jdbc " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log.loc[log['file'].str.contains(\"/jdbc/\"), 'type'] = 'jdbc'\n", "log.loc[log['file'].str.contains(\"/jpa/\"), 'type'] = 'jpa'\n", "log = log.dropna(subset=['type'])\n", "log.head()" ] }, { "cell_type": "code", "execution_count": 5, "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>additions</th>\n", " <th>deletions</th>\n", " <th>file</th>\n", " <th>timestamp</th>\n", " <th>type</th>\n", " <th>lines</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>250</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2013-05-15 03:35:33</td>\n", " <td>jdbc</td>\n", " <td>250</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>50</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2013-05-16 02:15:44</td>\n", " <td>jdbc</td>\n", " <td>50</td>\n", " </tr>\n", " <tr>\n", " <th>55</th>\n", " <td>78</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2013-05-17 15:12:26</td>\n", " <td>jdbc</td>\n", " <td>78</td>\n", " </tr>\n", " <tr>\n", " <th>186</th>\n", " <td>142</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2013-05-24 05:52:31</td>\n", " <td>jdbc</td>\n", " <td>142</td>\n", " </tr>\n", " <tr>\n", " <th>243</th>\n", " <td>123</td>\n", " <td>0</td>\n", " <td>src/main/java/org/springframework/samples/petc...</td>\n", " <td>2013-05-28 08:15:35</td>\n", " <td>jdbc</td>\n", " <td>123</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ " additions deletions file \\\n", "0 250 0 src/main/java/org/springframework/samples/petc... \n", "24 50 0 src/main/java/org/springframework/samples/petc... \n", "55 78 0 src/main/java/org/springframework/samples/petc... \n", "186 142 0 src/main/java/org/springframework/samples/petc... \n", "243 123 0 src/main/java/org/springframework/samples/petc... \n", "\n", " timestamp type lines \n", "0 2013-05-15 03:35:33 jdbc 250 \n", "24 2013-05-16 02:15:44 jdbc 50 \n", "55 2013-05-17 15:12:26 jdbc 78 \n", "186 2013-05-24 05:52:31 jdbc 142 \n", "243 2013-05-28 08:15:35 jdbc 123 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log['lines'] = log['additions'] - log['deletions']\n", "log.head()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "timestamp type\n", "2013-05-15 03:35:33 jdbc 250\n", "2013-05-16 02:15:44 jdbc 50\n", "2013-05-17 15:12:26 jdbc 78\n", "2013-05-24 05:52:31 jdbc 142\n", "2013-05-28 08:15:35 jdbc 123\n", "Name: lines, dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log_timed = log.groupby(['timestamp', 'type']).lines.sum()\n", "log_timed.head()" ] }, { "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", " 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378 0\n", "2013-05-24 05:52:31 520 0\n", "2013-05-28 08:15:35 643 0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "log_progess = log_timed.unstack(fill_value=0).cumsum()\n", "log_progess.head()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\r\n", "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\r\n", " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\r\n", "<!-- Created with matplotlib (http://matplotlib.org/) -->\r\n", "<svg height=\"267pt\" version=\"1.1\" viewBox=\"0 0 405 267\" width=\"405pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\r\n", " <defs>\r\n", " <style type=\"text/css\">\r\n", "*{stroke-linecap:butt;stroke-linejoin:round;}\r\n", " </style>\r\n", " </defs>\r\n", " <g id=\"figure_1\">\r\n", " <g id=\"patch_1\">\r\n", " <path d=\"M 0 267.142752 \r\n", "L 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technology\")\n", "ax.set_xlabel(\"time\")\n", "ax.set_ylabel(\"changes\");" ] }, { "cell_type": "code", "execution_count": 45, "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>type</th>\n", " <th>jdbc</th>\n", " <th>jpa</th>\n", " </tr>\n", " <tr>\n", " <th>year</th>\n", " <th></th>\n", " <th></th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>2013</th>\n", " <td>5940</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2014</th>\n", " <td>12997</td>\n", " <td>0</td>\n", " </tr>\n", " <tr>\n", " <th>2015</th>\n", " <td>7424</td>\n", " <td>2655</td>\n", " </tr>\n", " <tr>\n", " <th>2016</th>\n", " <td>3614</td>\n", " <td>4769</td>\n", " </tr>\n", " <tr>\n", " <th>2017</th>\n", " <td>3320</td>\n", " <td>6761</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "</div>" ], "text/plain": [ "type jdbc jpa\n", "year \n", "2013 5940 0\n", "2014 12997 0\n", "2015 7424 2655\n", "2016 3614 4769\n", "2017 3320 6761" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "progress_per_year = log_progess.groupby(log_progess.index.year).last()\n", "progress_per_year.index.name = \"year\"\n", "progress_per_year" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x21f699c19e8>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.savefig(\"reengineering.svg\", format=\"svg\")" ] } ], "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 }
gpl-3.0
mne-tools/mne-tools.github.io
0.12/_downloads/plot_megsim_data_single_trial.ipynb
1
2161
{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n# MEGSIM single trial simulation dataset\n\n\nThe MEGSIM consists of experimental and simulated MEG data\nwhich can be useful for reproducing research results.\n\nThe MEGSIM files will be dowloaded automatically.\n\nThe datasets are documented in:\nAine CJ, Sanfratello L, Ranken D, Best E, MacArthur JA, Wallace T,\nGilliam K, Donahue CH, Montano R, Bryant JE, Scott A, Stephen JM\n(2012) MEG-SIM: A Web Portal for Testing MEG Analysis Methods using\nRealistic Simulated and Empirical Data. Neuroinformatics 10:141-158\n\n" ], "cell_type": "markdown", "metadata": {} }, { "execution_count": null, "cell_type": "code", "source": [ "from mne import read_evokeds\nfrom mne.datasets.megsim import load_data\n\nprint(__doc__)\n\ncondition = 'visual' # or 'auditory' or 'somatosensory'\n\n# Load experimental RAW files for the visual condition\nepochs_fnames = load_data(condition=condition, data_format='single-trial',\n data_type='simulation', verbose=True)\n\n# Take only 10 trials from the same simulation setup.\nepochs_fnames = [f for f in epochs_fnames if 'sim6_trial_' in f][:10]\n\nevokeds = [read_evokeds(f)[0] for f in epochs_fnames]\nmean_evoked = sum(evokeds[1:], evokeds[0])\n\n# Visualize the average\nmean_evoked.plot()" ], "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": "2.7.11", "pygments_lexer": "ipython2", "codemirror_mode": { "version": 2, "name": "ipython" } } } }
bsd-3-clause
twosigma/beakerx
test/ipynb/scala/SparkUITest.ipynb
2
2799
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "%%classpath add mvn\n", "org.apache.spark spark-sql_2.12 2.4.4" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%spark\n", "SparkSession.builder()\n", " .appName(\"Simple Application\")\n", " .master(\"local[4]\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import scala.math.random\n", "val NUM_SAMPLES = 10000000\n", "\n", "val count2 = spark.sparkContext.parallelize(1 to NUM_SAMPLES).map{i =>\n", " val x = random\n", " val y = random\n", " if (x*x + y*y < 1) 1 else 0\n", "}.reduce(_ + _)\n", "\n", "println(\"Pi is roughly \" + 4.0 * count2 / NUM_SAMPLES)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "val tornadoesPath = java.nio.file.Paths.get(\"../../resources/data/tornadoes_2014.csv\").toAbsolutePath()\n", "\n", "val ds = spark.read.format(\"csv\").option(\"header\", \"true\").load(tornadoesPath.toString())\n", "ds" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "// cell 4 expected result\n", "Image(\"../../resources/img/scala/sparkgui/cell4_case1.png\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ds.display(1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "// cell 5 expected result\n", "Image(\"../../resources/img/scala/sparkgui/cell5_case1.png\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### AutoConnect: use --start or -s" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%spark --start" ] } ], "metadata": { "kernelspec": { "display_name": "Scala", "language": "scala", "name": "scala" }, "language_info": { "codemirror_mode": "text/x-scala", "file_extension": ".scala", "mimetype": "", "name": "Scala", "nbconverter_exporter": "", "version": "2.11.12" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": false, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": false, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }
apache-2.0
waltherg/notebooks
2014-10-24-Gmail_PDF_HTML_Data_Pipeline.ipynb
1
23600
{ "metadata": { "name": "", "signature": "sha256:36b6e90945207bc790ddbd40c314a984158569046f8e670ff553e63eea1f83ff" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Extracting Text From Heterogeneous Sources" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I consume online media through different apps on different devices (plain browsers, Feedly, Twitter, etc.)\n", "and to archive interesting reads for future reference I developed a habit of\n", "e-mailing links to myself.\n", "\n", "Gmail addresses are great for this purpose since an e-mail sent to *[email protected]* still\n", "ends up in the inbox of *[email protected]*.\n", "Combining this with [Gmail filters](https://support.google.com/mail/answer/6579?hl=en) allows you to set up\n", "a rule so that all e-mails received on *[email protected]* are archived automatically and\n", "are assigned a label that you can use to collect all blog posts you want to archive (I use the label *Links*).\n", "\n", "I already collected a couple of hundred links to blog articles, webpages, pdfs, etc. that I feel compelled to\n", "do something interesting with (such as topic modelling and automatic summarization).\n", "\n", "In this blog article I describe a pipeline that automatically accesses my Gmail account and extracts the readable\n", "text from all links I have sent myself over time.\n", "Of course I am opening a can of worms here since these e-mails come in lots of different\n", "formats and point to different materials:\n", "\n", "- some e-mails are just plain text with one or multiple links\n", "- many e-mails come straight out of Feedly on my iPad which tends to send entire blog articles with html formatting\n", "- many e-mails contain direct links to PDF files which are generally not as easy to parse as plain HTML\n", "\n", "Here I will use a rule-based approach to distinguish between these different formats." ] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Import Python Libraries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are the Python imports I will need throughout the pipeline:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import httplib2\n", "\n", "from apiclient.discovery import build\n", "from oauth2client.client import flow_from_clientsecrets\n", "from oauth2client.file import Storage\n", "from oauth2client.tools import run\n", "\n", "import base64\n", "import re\n", "import requests\n", "from lxml import etree\n", "from StringIO import StringIO\n", "import itertools as it\n", "\n", "import urllib2\n", "from pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\n", "from pdfminer.converter import TextConverter\n", "from pdfminer.layout import LAParams\n", "from pdfminer.pdfpage import PDFPage\n", "from cStringIO import StringIO\n", "\n", "from collections import defaultdict\n", "\n", "from lxml.etree import fromstring\n", "\n", "import sqlite3\n", "from datetime import datetime\n", "import zlib" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Set Up Access to Gmail" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Google offer an API to many of their services including Gmail.\n", "The free of charge quota of the Gmail API is very generous and you will essentially not need to worry about\n", "going over the free allowance.\n", "\n", "To access Gmail through Python you need to followe the steps outlined here\n", "[here](https://developers.google.com/gmail/api/quickstart/quickstart-python)\n", "(all steps assume that you are logged into your Gmail account in the browser):\n", "\n", "- create a new project in the Google Developer Console\n", "- on the page of your new project, make certain to set a product name in the APIs&auth/consent screen tab\n", "- download the JSON from the APIs&auth/credentials tab and store it in a location accessible from this Python script (I called mine *client_secret.json*)\n", "\n", "Get the Google API Python package\n", "\n", " pip install google-api-python-client\n", " \n", "and the following code ([source](https://developers.google.com/gmail/api/quickstart/quickstart-python)) will\n", "connect to the Gmail API and expose an API resource object pointed to by `gmail_service`." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# Path to the client_secret.json file downloaded from the Developer Console\n", "CLIENT_SECRET_FILE = 'client_secret.json'\n", "\n", "# Check https://developers.google.com/gmail/api/auth/scopes for all available scopes\n", "OAUTH_SCOPE = 'https://www.googleapis.com/auth/gmail.readonly'\n", "\n", "# Location of the credentials storage file\n", "STORAGE = Storage('gmail.storage')\n", "\n", "# Start the OAuth flow to retrieve credentials\n", "flow = flow_from_clientsecrets(CLIENT_SECRET_FILE, scope=OAUTH_SCOPE)\n", "http = httplib2.Http()\n", "\n", "# Try to retrieve credentials from storage or run the flow to generate them\n", "credentials = STORAGE.get()\n", "if credentials is None or credentials.invalid:\n", " credentials = run(flow, STORAGE, http=http)\n", "\n", "# Authorize the httplib2.Http object with our credentials\n", "http = credentials.authorize(http)\n", "\n", "# Build the Gmail service from discovery\n", "gmail_service = build('gmail', 'v1', http=http)" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Retrieve Bodies of Relevant E-Mail Messages" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Gmail API resource `gmail_service` is the only object we need to query to eventually download\n", "all e-mail bodies of interest.\n", "The [Gmail API reference](https://developers.google.com/gmail/api/v1/reference/) explains how to use the different\n", "resource types (labels, message lists, messages) that we need to work with." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Gmail label of interest is known to me as *Links* but internally Gmail identifies all labels by a unique ID.\n", "The following code identifies the label ID that corresponds to the label name *Links*:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "labels = gmail_service.users().labels().list(userId='me').execute()['labels'] # 'me' is the currently logged-in user\n", "label_id = filter(lambda x: x['name'] == 'Links', labels)[0]['id']" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now query `gmail_serice` for all e-mail messages with the label *Links* (as identified by `label_id`).\n", "The API returns messages in pages of at most 100 messages so that we will need to track a pointer to the\n", "next page (`nextPageToken`) until all pages are consumed." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def get_message_ids():\n", " \"\"\" Page through all messages in `label_id` \"\"\"\n", " next_page = None\n", "\n", " while True:\n", " if next_page is not None:\n", " response = gmail_service.users().messages().list(userId='me', labelIds=[label_id], pageToken=next_page).execute()\n", " else:\n", " response = gmail_service.users().messages().list(userId='me', labelIds=[label_id]).execute()\n", "\n", " messages = response.get('messages')\n", " next_page = response.get('nextPageToken')\n", "\n", " for el in messages:\n", " yield el['id']\n", "\n", " if next_page is None:\n", " break" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To extract message bodies we need to distinguish between `text/plain` and `MIME` e-mails (to my understanding the latter allows\n", "embedded images and such).\n", "When an e-mail is just plain text then the body is found directly in the `payload`, however when the e-mail is MIME I will\n", "extract the body as the first of potentially many `parts`.\n", "See the [message API reference](https://developers.google.com/gmail/api/v1/reference/users/messages) for more detail." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def message_bodies():\n", " for ctr, message_id in enumerate(get_message_ids()):\n", " message = gmail_service.users().messages().get(userId='me', id=message_id, format='full').execute()\n", " \n", " try:\n", " body = message['payload']['parts'][0]['body']['data'] # MIME\n", " except KeyError:\n", " body = message['payload']['body']['data'] # text/plain\n", " \n", " body = base64.b64decode(str(body), '-_')\n", " \n", " yield body" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Parse Links (URLs) to Material of Interest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I think of every e-mail with the *Links* label as a pointer to a resource on the Internet (be it a link to a webpage, PDF, etc.).\n", "Generally to extract URLs from plain text I will make use of the following regular expression formulated by\n", "[John Gruber](http://daringfireball.net/2010/07/improved_regex_for_matching_urls):" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pattern = (r'(?i)\\b((?:[a-z][\\w-]+:(?:/{1,3}|[a-z0-9%])|'\n", " r'www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)'\n", " r'(?:[^\\s()<>]+|\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\))'\n", " r'+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))* \\)|'\n", " r'[^\\s`!()\\[\\]{};:\\'\\\".,<>?\\\u00ab\\\u00bb\\\u201c\\\u201d\\\u2018\\\u2019]))')" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When I sent myself a link through the Feedly iPad app, Feedly sometimes sends the entire article in the e-mail body\n", "but it does not do so consistently as far as I can judge.\n", "\n", "Some Feedly-originating e-mail bodies will contain multiple URLs (e.g. links as part of a blog article) but they mostly\n", "provide a link to the original story as the first URL in the e-mail.\n", "\n", "Other times I may type an e-mail myself in which I copy and paste multiple links to interesting resources - in which\n", "case I want to extract all URLs from the body and not just the first one." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def is_feedly(body):\n", " return 'feedly.com' in body" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def urls():\n", " for body in message_bodies():\n", " matches = re.findall(pattern, body)\n", " if is_feedly(body):\n", " match = matches[0]\n", " yield match[0] # Feedly e-mail: first URL is link to original story\n", " else:\n", " for match in matches:\n", " yield match[0]" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I noticed a number of URLs in `urls()` that did not match my expectation - such as links to disqus comments and an online retailer.\n", "This is where a rule-based filter comes in:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "exclude = ['packtpub.com', 'disqus', '@', 'list-manage', 'utm_', 'ref=', 'campaign-archive']\n", "def urls_filtered():\n", " for url in urls():\n", " if not any([pattern in url.lower() for pattern in exclude]):\n", " yield url" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I quite often send myself a link to the Hacker News message thread on an article of interest.\n", "\n", "To extract the URL to the actual article we need to parse the HTML of the Hacker News discussion\n", "and locate the relevant URL in a `td` element with `class=\"title\"` - a rule that works for these particular Hacker News pages." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def is_hn(url):\n", " return 'news.ycombinator.com' in url" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "parser = etree.HTMLParser()\n", "def urls_hn_filtered():\n", " for url in urls_filtered():\n", " if is_hn(url) and (re.search(r'item\\?id=', url) is None):\n", " continue # do not keep HN links that do not point to an article\n", " elif is_hn(url):\n", " r = requests.get(url)\n", " if r.status_code != 200:\n", " continue # download of HN html failed, skip\n", " root = etree.parse(StringIO(r.text), parser).getroot()\n", " title = root.find(\".//td[@class='title']\")\n", " \n", " try:\n", " a = [child for child in title.getchildren() if child.tag == 'a'][0]\n", " except AttributeError:\n", " continue # title is None\n", "\n", " story_url = a.get('href')\n", " yield story_url\n", " else:\n", " yield url" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Poor memory gets the better of me quite often and I end up sending myself the same link multiple times.\n", "To avoid downloading the same resource twice and skewing follow-on work with duplicates I will track\n", "seen URLs with the following code:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "def unique_urls():\n", " seen = defaultdict(bool)\n", " for url in urls_hn_filtered():\n", " key = hash(url)\n", " if seen[key]:\n", " continue\n", " else:\n", " seen[key] = True\n", " yield url" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Extract Readable Text from PDF and HTML Documents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have a stream of unique URLs of interesting articles provided by `unique_urls` we can\n", "download each of these resources and extract their readable text.\n", "\n", "To extract readable text I will here distinguish between HTML and PDF.\n", "PDF text extraction is handled with `pdfminer` and I am making use of\n", "[this code](http://stackoverflow.com/a/23840353) shared in a StackOverflow answer.\n", "HTML text extraction is handled with `lxml` and [this code snippet](http://stackoverflow.com/a/23929292)." ] }, { "cell_type": "code", "collapsed": false, "input": [ "def pdf_from_url_to_txt(url):\n", " rsrcmgr = PDFResourceManager()\n", " retstr = StringIO()\n", " codec = 'utf-8'\n", " laparams = LAParams()\n", " device = TextConverter(rsrcmgr, retstr, codec=codec, laparams=laparams)\n", " # Open the url provided as an argument to the function and read the content\n", " f = urllib2.urlopen(urllib2.Request(url)).read()\n", " # Cast to StringIO object\n", " fp = StringIO(f)\n", " interpreter = PDFPageInterpreter(rsrcmgr, device)\n", " password = \"\"\n", " maxpages = 0\n", " caching = True\n", " pagenos = set()\n", " for page in PDFPage.get_pages(fp,\n", " pagenos,\n", " maxpages=maxpages,\n", " password=password,\n", " caching=caching,\n", " check_extractable=True):\n", " interpreter.process_page(page)\n", " fp.close()\n", " device.close()\n", " string = retstr.getvalue()\n", " retstr.close()\n", " return string" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "code", "collapsed": false, "input": [ "def resource_text():\n", " headers = {'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64; rv:33.0) Gecko/20100101 Firefox/33.0'}\n", " html_parser = etree.HTMLParser(recover=True, encoding='utf-8')\n", " \n", " for url in unique_urls():\n", " if url.endswith('.pdf'):\n", " try:\n", " text = pdf_from_url_to_txt(url)\n", " except:\n", " continue # something went wrong, just skip ahead\n", " \n", " yield url, text\n", " \n", " else:\n", " try:\n", " r = requests.get(url, headers=headers)\n", " except:\n", " continue # something went wrong with HTTP GET, just skip ahead\n", " \n", " if r.status_code != 200:\n", " continue\n", " if not 'text/html' in r.headers.get('content-type', ''):\n", " continue\n", " \n", " # from: http://stackoverflow.com/a/23929292 and http://stackoverflow.com/a/15830619\n", " try:\n", " document = fromstring(r.text.encode('utf-8'), html_parser)\n", " except:\n", " continue # error parsing document, just skip ahead\n", " \n", " yield url, '\\n'.join(etree.XPath('//text()')(document))" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Consume Pipeline and Store Extracted Text" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This concludes our pipeline as the generator exposed by `readable_text` returns our current best representation of\n", "useful text for each resource.\n", "\n", "All that remains now is to consume this generator and store the extracted text together with the source URL for future reference.\n", "\n", "Since this pipeline consists of a number of failure-prone steps (communicating with APIs, extracting text, etc.) I will also\n", "want to make certain to catch errors and deal with them appropriately:\n", "In the generator `resource_text` I already included a number of places where we just skip ahead to the next resource in case of failure - however failure may still happen and it will be best to catch those errors right here where we consume the pipeline.\n", "\n", "I will be lazy here and just skip all resources that generate an error during text extraction.\n", "Hence I will consume the generator `readable_text` until it throws the `StopIteration` exception and still skip all other exceptions.\n", "Using the `with` statement here makes certain that we do not need to worry about closing the file that we write to once `StopIteration` is thrown." ] }, { "cell_type": "code", "collapsed": false, "input": [ "text_generator = resource_text()\n", "\n", "try:\n", " db = sqlite3.connect('gmail_extracted_text.db')\n", " db.execute('CREATE TABLE gmail (date text, url text, compression text, extracted blob)')\n", " db.commit()\n", " db.close()\n", "except sqlite3.OperationalError:\n", " pass # table gmail already exists\n", "\n", "while True:\n", " try:\n", " db = sqlite3.connect('gmail_extracted_text.db')\n", " \n", " url, text = text_generator.next()\n", " \n", " now = datetime.now().__str__()\n", " if isinstance(text, unicode):\n", " text = zlib.compress(text.encode('utf-8')) # to decompress: zlib.decompress(text).decode('utf-8')\n", " else:\n", " text = zlib.compress(text)\n", " db.execute('INSERT INTO gmail VALUES (?, ?, ?, ?)', (unicode(now), unicode(url), u'zlib', sqlite3.Binary(text)))\n", " db.commit()\n", " except StopIteration:\n", " break # generator consumed, stop calling .next() on it\n", " except Exception, e:\n", " print e\n", " continue # some other exception was thrown by the pipeline, just skip ahead\n", " finally:\n", " db.close() # tidy up" ], "language": "python", "metadata": {}, "outputs": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In my case the above pipeline extracted text from 571 resources and the resultant database is 7.9 MB in size." ] } ], "metadata": {} } ] }
bsd-3-clause
xtr33me/deep-learning
gan_mnist/Intro_to_GANs_Solution.ipynb
1
207147
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Generative Adversarial Network\n", "\n", "In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\n", "\n", "GANs were [first reported on](https://arxiv.org/abs/1406.2661) in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out:\n", "\n", "* [Pix2Pix](https://affinelayer.com/pixsrv/) \n", "* [CycleGAN](https://github.com/junyanz/CycleGAN)\n", "* [A whole list](https://github.com/wiseodd/generative-models)\n", "\n", "The idea behind GANs is that you have two networks, a generator $G$ and a discriminator $D$, competing against each other. The generator makes fake data to pass to the discriminator. The discriminator also sees real data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks _as close as possible_ to real data. And the discriminator is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistiguishable from real data to the discriminator.\n", "\n", "![GAN diagram](assets/gan_diagram.png)\n", "\n", "The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector the generator uses to contruct it's fake images. As the generator learns through training, it figures out how to map these random vectors to recognizable images that can foold the discriminator.\n", "\n", "The output of the discriminator is a sigmoid function, where 0 indicates a fake image and 1 indicates an real image. If you're interested only in generating new images, you can throw out the discriminator after training. Now, let's see how we build this thing in TensorFlow." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import pickle as pkl\n", "import numpy as np\n", "import tensorflow as tf\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Successfully downloaded train-labels-idx1-ubyte.gz 28881 bytes.\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", "Successfully downloaded t10k-images-idx3-ubyte.gz 1648877 bytes.\n", "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", "Successfully downloaded t10k-labels-idx1-ubyte.gz 4542 bytes.\n", "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" ] } ], "source": [ "from tensorflow.examples.tutorials.mnist import input_data\n", "mnist = input_data.read_data_sets('MNIST_data')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Model Inputs\n", "\n", "First we need to create the inputs for our graph. We need two inputs, one for the discriminator and one for the generator. Here we'll call the discriminator input `inputs_real` and the generator input `inputs_z`. We'll assign them the appropriate sizes for each of the networks." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def model_inputs(real_dim, z_dim):\n", " inputs_real = tf.placeholder(tf.float32, (None, real_dim), name='input_real') \n", " inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')\n", " \n", " return inputs_real, inputs_z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generator network\n", "\n", "![GAN Network](assets/gan_network.png)\n", "\n", "Here we'll build the generator network. To make this network a universal function approximator, we'll need at least one hidden layer. We should use a leaky ReLU to allow gradients to flow backwards through the layer unimpeded. A leaky ReLU is like a normal ReLU, except that there is a small non-zero output for negative input values.\n", "\n", "#### Variable Scope\n", "Here we need to use `tf.variable_scope` for two reasons. Firstly, we're going to make sure all the variable names start with `generator`. Similarly, we'll prepend `discriminator` to the discriminator variables. This will help out later when we're training the separate networks.\n", "\n", "We could just use `tf.name_scope` to set the names, but we also want to reuse these networks with different inputs. For the generator, we're going to train it, but also _sample from it_ as we're training and after training. The discriminator will need to share variables between the fake and real input images. So, we can use the `reuse` keyword for `tf.variable_scope` to tell TensorFlow to reuse the variables instead of creating new ones if we build the graph again.\n", "\n", "To use `tf.variable_scope`, you use a `with` statement:\n", "```python\n", "with tf.variable_scope('scope_name', reuse=False):\n", " # code here\n", "```\n", "\n", "Here's more from [the TensorFlow documentation](https://www.tensorflow.org/programmers_guide/variable_scope#the_problem) to get another look at using `tf.variable_scope`.\n", "\n", "#### Leaky ReLU\n", "TensorFlow doesn't provide an operation for leaky ReLUs, so we'll need to make one . For this you can use take the outputs from a linear fully connected layer and pass them to `tf.maximum`. Typically, a parameter `alpha` sets the magnitude of the output for negative values. So, the output for negative input (`x`) values is `alpha*x`, and the output for positive `x` is `x`:\n", "$$\n", "f(x) = max(\\alpha * x, x)\n", "$$\n", "\n", "#### Tanh Output\n", "The generator has been found to perform the best with $tanh$ for the generator output. This means that we'll have to rescale the MNIST images to be between -1 and 1, instead of 0 and 1." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def generator(z, out_dim, n_units=128, reuse=False, alpha=0.01):\n", " with tf.variable_scope('generator', reuse=reuse):\n", " # Hidden layer\n", " h1 = tf.layers.dense(z, n_units, activation=None)\n", " # Leaky ReLU\n", " h1 = tf.maximum(alpha * h1, h1)\n", " \n", " # Logits and tanh output\n", " logits = tf.layers.dense(h1, out_dim, activation=None)\n", " out = tf.tanh(logits)\n", " \n", " return out" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discriminator\n", "\n", "The discriminator network is almost exactly the same as the generator network, except that we're using a sigmoid output layer." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def discriminator(x, n_units=128, reuse=False, alpha=0.01):\n", " with tf.variable_scope('discriminator', reuse=reuse):\n", " # Hidden layer\n", " h1 = tf.layers.dense(x, n_units, activation=None)\n", " # Leaky ReLU\n", " h1 = tf.maximum(alpha * h1, h1)\n", " \n", " logits = tf.layers.dense(h1, 1, activation=None)\n", " out = tf.sigmoid(logits)\n", " \n", " return out, logits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Hyperparameters" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Size of input image to discriminator\n", "input_size = 784\n", "# Size of latent vector to generator\n", "z_size = 100\n", "# Sizes of hidden layers in generator and discriminator\n", "g_hidden_size = 128\n", "d_hidden_size = 128\n", "# Leak factor for leaky ReLU\n", "alpha = 0.01\n", "# Smoothing \n", "smooth = 0.1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build network\n", "\n", "Now we're building the network from the functions defined above.\n", "\n", "First is to get our inputs, `input_real, input_z` from `model_inputs` using the sizes of the input and z.\n", "\n", "Then, we'll create the generator, `generator(input_z, input_size)`. This builds the generator with the appropriate input and output sizes.\n", "\n", "Then the discriminators. We'll build two of them, one for real data and one for fake data. Since we want the weights to be the same for both real and fake data, we need to reuse the variables. For the fake data, we're getting it from the generator as `g_model`. So the real data discriminator is `discriminator(input_real)` while the fake discriminator is `discriminator(g_model, reuse=True)`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "tf.reset_default_graph()\n", "# Create our input placeholders\n", "input_real, input_z = model_inputs(input_size, z_size)\n", "\n", "# Build the model\n", "g_model = generator(input_z, input_size)\n", "# g_model is the generator output\n", "\n", "d_model_real, d_logits_real = discriminator(input_real)\n", "d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discriminator and Generator Losses\n", "\n", "Now we need to calculate the losses, which is a little tricky. For the discriminator, the total loss is the sum of the losses for real and fake images, `d_loss = d_loss_real + d_loss_fake`. The losses will by sigmoid cross-entropys, which we can get with `tf.nn.sigmoid_cross_entropy_with_logits`. We'll also wrap that in `tf.reduce_mean` to get the mean for all the images in the batch. So the losses will look something like \n", "\n", "```python\n", "tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))\n", "```\n", "\n", "For the real image logits, we'll use `d_logits_real` which we got from the discriminator in the cell above. For the labels, we want them to be all ones, since these are all real images. To help the discriminator generalize better, the labels are reduced a bit from 1.0 to 0.9, for example, using the parameter `smooth`. This is known as label smoothing, typically used with classifiers to improve performance. In TensorFlow, it looks something like `labels = tf.ones_like(tensor) * (1 - smooth)`\n", "\n", "The discriminator loss for the fake data is similar. The logits are `d_logits_fake`, which we got from passing the generator output to the discriminator. These fake logits are used with labels of all zeros. Remember that we want the discriminator to output 1 for real images and 0 for fake images, so we need to set up the losses to reflect that.\n", "\n", "Finally, the generator losses are using `d_logits_fake`, the fake image logits. But, now the labels are all ones. The generator is trying to fool the discriminator, so it wants to discriminator to output ones for fake images." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Calculate losses\n", "d_loss_real = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, \n", " labels=tf.ones_like(d_logits_real) * (1 - smooth)))\n", "d_loss_fake = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, \n", " labels=tf.zeros_like(d_logits_real)))\n", "d_loss = d_loss_real + d_loss_fake\n", "\n", "g_loss = tf.reduce_mean(\n", " tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake,\n", " labels=tf.ones_like(d_logits_fake)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Optimizers\n", "\n", "We want to update the generator and discriminator variables separately. So we need to get the variables for each part build optimizers for the two parts. To get all the trainable variables, we use `tf.trainable_variables()`. This creates a list of all the variables we've defined in our graph.\n", "\n", "For the generator optimizer, we only want to generator variables. Our past selves were nice and used a variable scope to start all of our generator variable names with `generator`. So, we just need to iterate through the list from `tf.trainable_variables()` and keep variables to start with `generator`. Each variable object has an attribute `name` which holds the name of the variable as a string (`var.name == 'weights_0'` for instance). \n", "\n", "We can do something similar with the discriminator. All the variables in the discriminator start with `discriminator`.\n", "\n", "Then, in the optimizer we pass the variable lists to `var_list` in the `minimize` method. This tells the optimizer to only update the listed variables. Something like `tf.train.AdamOptimizer().minimize(loss, var_list=var_list)` will only train the variables in `var_list`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "# Optimizers\n", "learning_rate = 0.002\n", "\n", "# Get the trainable_variables, split into G and D parts\n", "t_vars = tf.trainable_variables()\n", "g_vars = [var for var in t_vars if var.name.startswith('generator')]\n", "d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n", "\n", "d_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(d_loss, var_list=d_vars)\n", "g_train_opt = tf.train.AdamOptimizer(learning_rate).minimize(g_loss, var_list=g_vars)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mkdir: cannot create directory ‘checkpoints’: File exists\r\n" ] } ], "source": [ "!mkdir checkpoints" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "batch_size = 100\n", "epochs = 100\n", "samples = []\n", "losses = []\n", "# Only save generator variables\n", "saver = tf.train.Saver(var_list=g_vars)\n", "with tf.Session() as sess:\n", " sess.run(tf.global_variables_initializer())\n", " for e in range(epochs):\n", " for ii in range(mnist.train.num_examples//batch_size):\n", " batch = mnist.train.next_batch(batch_size)\n", " \n", " # Get images, reshape and rescale to pass to D\n", " batch_images = batch[0].reshape((batch_size, 784))\n", " batch_images = batch_images*2 - 1\n", " \n", " # Sample random noise for G\n", " batch_z = np.random.uniform(-1, 1, size=(batch_size, z_size))\n", " \n", " # Run optimizers\n", " _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z})\n", " _ = sess.run(g_train_opt, feed_dict={input_z: batch_z})\n", " \n", " # At the end of each epoch, get the losses and print them out\n", " train_loss_d = sess.run(d_loss, {input_z: batch_z, input_real: batch_images})\n", " train_loss_g = g_loss.eval({input_z: batch_z})\n", " \n", " print(\"Epoch {}/{}...\".format(e+1, epochs),\n", " \"Discriminator Loss: {:.4f}...\".format(train_loss_d),\n", " \"Generator Loss: {:.4f}\".format(train_loss_g)) \n", " # Save losses to view after training\n", " losses.append((train_loss_d, train_loss_g))\n", " \n", " # Sample from generator as we're training for viewing afterwards\n", " sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n", " gen_samples = sess.run(\n", " generator(input_z, input_size, reuse=True),\n", " feed_dict={input_z: sample_z})\n", " samples.append(gen_samples)\n", " saver.save(sess, './checkpoints/generator.ckpt')\n", "\n", "# Save training generator samples\n", "with open('train_samples.pkl', 'wb') as f:\n", " pkl.dump(samples, f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Training loss\n", "\n", "Here we'll check out the training losses for the generator and discriminator." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.legend.Legend at 0x7f4e8947fbe0>" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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PTB4BBt/J7vUFRmWRu22eeyHJPY1yl8kdINI0K/doigpVkQJ59Bm2efI6OCd1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\nyus7eODSbD74z+X8vyVTeG77ce5+dS99JrNN8spLDic3OZyE8ABuWZb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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4e7fda4be0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "losses = np.array(losses)\n", "plt.plot(losses.T[0], label='Discriminator')\n", "plt.plot(losses.T[1], label='Generator')\n", "plt.title(\"Training Losses\")\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generator samples from training\n", "\n", "Here we can view samples of images from the generator. First we'll look at images taken while training." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def view_samples(epoch, samples):\n", " fig, axes = plt.subplots(figsize=(7,7), nrows=4, ncols=4, sharey=True, sharex=True)\n", " for ax, img in zip(axes.flatten(), samples[epoch]):\n", " ax.xaxis.set_visible(False)\n", " ax.yaxis.set_visible(False)\n", " im = ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n", " \n", " return fig, axes" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load samples from generator taken while training\n", "with open('train_samples.pkl', 'rb') as f:\n", " samples = pkl.load(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are samples from the final training epoch. You can see the generator is able to reproduce numbers like 1, 7, 3, 2. Since this is just a sample, it isn't representative of the full range of images this generator can make." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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4jl9++aXpWrVqmebYsYDR9/mqqo3G7CRmXvoYOXKk6cro5ZfWRuPzxTEt1LOT\nhnHjxsVuZ7Yk+86xwJOFxizU5H0YZYzyGvDZ5vXgPr7nmZZaMdzvinCEEEIEQROOEEKIIKS21Fj0\nWAiYDTF69GjTXLlz8ODBpuMyNfbcc0/bdu2115pmtocPHo+ZUb59SL169XIev1BkKVzk6pvMGKNd\nyTbnzMjZb7/9Ys+Bq6Vec801zjnn3nvvPdvGDDTaX7QiuVph7969TT/99NOmuSSBryV+sfXmygot\nkyTP44knnlih9/FZNj77Zs011zTdrFkz0xz3JPD5qiwb7dRTTzXNnmmke/fupp999lnTvG7s40d7\njdcwV7ZZkn5oPtu4ffv2sftU1jOhCEcIIUQQUkc4/Mtlzpw5ptluhJ2A08IZnsc/7LDDTHPdexLV\n2Tz//POx/z/Jj+O+dhGEEQEXt0oC6yYqE15ndtdu1aqV6f333980I5aDDjrINBeBYu0Lf6hetGiR\nc678X2ocC95TvLasK3nrrbdMs+WQr7s3Cd1myAejkYsuusg0ozSeHyNMXrtPPvnENJMmGCmybozX\ni89G9Fcu69MYUTA5g22DHnjgAdObb765adZWvf7666b/+9//mr7qqqtcGrLUl2XBF9WQUaNGmeb9\nzDE888wzc+4Td08yUYD3dVqHiVEnxypt1JkvFOEIIYQIgiYcIYQQQUhtqdHmIllsNMLwskGDBqZZ\nw+FjwYIFzrnyP+YNGTIk5+toSyT5MS2tjUb4o3xofD/80nJi52jWFtx4442mmzdvbpptZqZMmWKa\n69tH9gDroGgX0dJh0ghtWloDPBfeL7RDmUDAfUJbNLRRWFfUp08f0z6bj2PUunVr06x9or3MpBVe\nL9oztOai5Jrhw4fbNtZEscv2c889Z5r3y+OPP276wAMPNM06qyhppCIUQ+1NEnzfG+yETt2lSxfT\ntKWje4H2KG3Ir776yvQjjzyS87zeeOMN05VloxFFOEIIIYKgCUcIIUQQUltqIWGGhy/Dh9ZJZCM8\n+uijts3XIZU2RsiOtMVeE7J06VLTzAycOnWq6SjrzDnnJk6caPqxxx4zzS7OkU2z1VZb2TbaP6x3\n8q31Tivou+++M83r6avD8bXOCQHPj/cis+uIr5vzRx99ZJqdzpnBxK7fvg7gtB2jFiw+S/mEE04w\nzS7qRx99tGladzvuuKPpzp07x36OmgDrntq2bWuaNTm0zLh4G223CF/9TJJF0Y466ijTxZCtqQhH\nCCFEEDT/1DcZAAAgAElEQVThCCGECEJRW2rnnXeeaWZGMcRk99Z27do55/w2GsNYdj+uKTAE9xWT\nLVu2zDQLP2mR9e3b1/Qrr7ximuE+LZjddtvNOVc+jO/YsaNpdtnmMZiR06tXL9O08fg5fAuq0W6o\nzPYeSbr1suM2Czx79uxpmvamz5rj52Rn4h49epju16/fcudFe42Ft1zIkG2maAHdeeedpm+77bac\n55Xk+hdDO5a058Dssdtvv910hw4dTHNsc+F7zyT3E9+HzweLt2mFFxpFOEIIIYKgCUcIIUQQMllq\nabMeWFTILBofDO8JQ9z333/f9DbbbLPcvgxHQ9poxWAF/B2G4DynRo0amT7ggANMX3nllaYPP/xw\n0+yrxjCdmplM0bhvv/32tm3dddc1HRXsOufcrbfeavrBBx80/dprr5n2ZZoVwwJT/0SS82OxLXus\nsVt2kvuJ+9Cmo9UVHZ9ZUtFiec6Vf6ZZtEprlgt80TLisz5v3rxU506K4dlJew5cgM33HZYPOA4+\nfvvtN9P8TgppoxFFOEIIIYKgCUcIIUQQMllqDLlZyMesI0IbLUtPq+uvv9706aef/o/7xi1oFIJi\nsAKSwmJAWi7s2cX29Bxr2mgsSOQCa1FfLWYacvxZ1MjF1WjFpC1U81mavB9CW3CTJ082TRuL8Flo\n2bKl6ST3Ez8bCzK5VAD700VjcNZZZ9k2jlHTpk1N77PPPqZpoy1evNg0sxpp2dD64fvTvhPp8dl1\nvFf4DA8dOtQ0sx5DoghHCCFEEDThCCGECELestR8NhpJsuJmktfSAoijkJkh/0QxZqYR3znNmDHD\nNIskuVLj2LFjTT/zzDOm77//ftMTJkwwzZbr0fIDPluM2Wi0XbP0e8pSLFcofDYa4ZIcXFHynHPO\nMc1lIPg5eb1odTFjjGO3ZMkS55xz//nPf2wb7+HBgwebHjZsWOx78ly4oqTvWahpNloh+5dF4/d3\neO0HDBhgOmTPSB+KcIQQQgRBE44QQogg5C1LLQkMrZmNs8suu5h+6aWXYl87bdo0075eaZHtU1mh\nYzHaaEnwFYQyNOeKj7z+rVq1Mj1p0iTTI0aMMB21uZ89e7Zt42qSXHEyX5aXr69ascMCSxbKcqVY\n333ms7FYWMvizGhZCGamcamINm3amGb/PGaJ0jKilc2Cwyyceuqppu+44468HDNf+K43vxe//PJL\n01wpmEs5xI0nexHWqVPHNHsXMrvUB5cb4XEqC0U4QgghgqAJRwghRBCKYnkCn43GflsM7wnD0S5d\nuuT3xDLAfkosgiwUWTLkaAEwlOcKms8//7xpWjAs2qSNRUslyl5jr7uRI0eaZtifL2ij8TP5MntC\ns95665nmMgwcO66aecopp5hmjzXyyy+/mKb9xBVyaWU/9NBDzjnnrrvuOtvGZ4jXkPeXz7JOYqPR\nsqNNy5VeeR8Vm41GaCf6rOhmzZrF6kKuuMklK0J896RBEY4QQoggaMIRQggRhKKw1HxEPbic84f3\nXM2wmAgdyqa10XwFaSuvvLJprpBKZs6caZo2ClcLZQFjlB3D92FGUxY7MEk2WmXaaBdccIHpG2+8\n0TRtNB9vvfWW6aOOOsq07xqxZxkLRQ855BDTXIoiyjZk4a1v9U/eLwMHDsx57j5Y2EuLlVYfrSdm\nRPK+KwZ4Lfm5fPZaIWF2I5dhKbbMWUU4QgghgqAJRwghRBAqzVLzZXgceuihptlO22edFCLDKR9E\n/cOcK194Vyz4smRYYOiDywZwXGgxMFNm+vTpzrnylhKL4LLYaIXM9skHjzzyiGmuTsusLF/GGmEB\nJDWhfcPrwswwjsu+++7rnHPu2WeftW18trgvz8v3/kngefmWJZkzZ06Fjx8S33OdxEbLVbzLLFf2\nQ3vzzTdNt23b1vTo0aNzHrsYUIQjhBAiCJpwhBBCBKHSLDWfFTJkyBDTvtU6k/QQykWSFUqzUIw2\nWlpoXbENPQtCffszu6i0tNQ559zUqVNtW6dOnUyPGzcu57lkWSG2MmH/ONpohNcqSzt7XqPVV1/d\n9NZbb23aV2QdB7MUu3XrlupcCHt4+TIfSceOHU0zU6/YYDadz0bjZ99iiy1McxXWisJi7KqCIhwh\nhBBB0IQjhBAiCAW31NjKvFevXqa5yiD7Jb3xxhumoywa55x78cUXTeej9TltNFoXtDSKfQXPQsMe\nZCzC9cHryGynKDuLhYkvv/xyqnOpSjZaWmgzZcm64zXi/ZrGRqMtykwpHmPDDTc0/d133+U8Jj8f\n+7DxfiC00Yp5mYkk2XT87O+++24hT6dKoAhHCCFEEDThCCGECELBLTXaaKRPnz6maWP961//Ms0Q\netttt835XpEFlqWvGKmJNhpJYqMRXi8WDVZ0XGoKSa5zEmspH9eXVnO7du1Mc+VKjq0PFrOymJg2\n2iabbGKay1yQYrPRsuDL7qxJKMIRQggRBE04QgghglCSJgwvKSlZ5JybnXNHkYbmZWVlmdZY0LgU\nhEzjojEpCHpWipPE45JqwhFCCCEqiiw1IYQQQdCEI4QQIgiacIQQQgRBE44QQoggaMIRQggRBE04\nQgghgqAJRwghRBA04QghhAiCJhwhhBBB0IQjhBAiCJpwhBBCBEETjhBCiCBowhFCCBEETThCCCHC\nUFZWlvhfw4YNy5xz+penf+3bty9zzi1KMwYal6oxLg0bNoyOo3/5+6dnpcj+pX1WUkU4paWlaXYX\nOZg4caJzeVgMKum4rLDCCvZP+MnHuJSWlkbHEfkj2LNSUykpKbF/SfZJ+6zom0cIIUQQVqrsExD5\nh3+dcEXXP//8szJOJxW+cxeiOlHI+3zFFVc0/ccff6R6bZJzyXK+inCEEEIEQROOEEKIIBTEUpMt\nUrn4rnmWUDsUIe8X3aciX/juJW6vVauW6enTp5tu37696W+++cY559zpp59u22699dZU51Ksz7Zz\ninCEEEIEQhOOEEKIIBTEUqtse4J1JlUhMysUWULt6nhNK/s+FdUH3kt169Y1/f3335u+8sorTW+3\n3Xamv/32W9OzZ/9fSct5551XkPPMxQYbbGD6s88+y/vxFeEIIYQIgiYcIYQQQaiWhZ9JLJ/atWub\n7t69u+knn3wy1XGqElmyskJdC46LL9vn119/DXIu+YA2JrMEs5B2HH2tjKJr/fPPP+flvKoKt99+\nu+nTTjstdp8szwpttIULF5pesmSJ6TXXXNP0RRddZDqytCor06wQNhpRhCOEECIImnCEEEIEoVpa\naj622WYb088//7zpNm3aVMbpBKfQWVm0IVZeeWXTkQW22Wabxe5Lq2GVVVYxXadOHdPz58+P1b6u\ntsWSgZYvG434MqJOPvlk0yeccILppk2bml5ttdVMjx8/3jnn3HrrrWfbaAHNmDHD9LHHHhv7/mnZ\na6+9TD/zzDMVPk4WfDYaSfIZaf/+9ttvphs2bGj6iCOOMH3//feb7tq1q+kDDzzQdGSpfffddznf\nPwu+rNNDDjnE9MMPP5z/9837EYUQQogYNOEIIYQIQpW01HJlkPD/b7jhhqYffPBB0wwjL7vsMtNn\nn3127HGKuT9RZbLSSn/dQrQS1l13XdM333yzc658NtSNN95ompYax4Vjt/7665tee+21TQ8bNsx0\nTcm2otXIosG0C+vtvPPOy23jNW/btq3pJ554wvTIkSNNp81erCwbjdxyyy2mzzzzzJz783uAun79\n+qZ53TbaaCPTzIijXUyrdcGCBabHjBnjnHOuY8eOOc8rLUky7wphoxFFOEIIIYKgCUcIIUQQisJS\n82VMMOz0he5xYSIzcTp37mya2ToMKbfYYgvTTZo0MR31NRLl4biwgG3PPfc0zWK2efPmOeecu+22\n22wbC8x4zb/44gvTHKNDDz3UNLPUGjRosNz7OOe3D2gB/v77764qQgsyrY0WB6/Pxx9/bHrLLbc0\n/csvv8S+5z777GP66aefjt3+wgsvmC4G2zOJjUZ4ffr27Wv64osvNv3qq6+avueee0wzW5P777TT\nTqY7depkms9NvuH34o8//mg6ZJ9ERThCCCGCoAlHCCFEEIraUvP1ofLZJdH+DP8POOAA07RTyLJl\ny0zPnTs31blXZ3idef2ZJbXpppuaptVzwQUXmH7jjTecc+UzqjgWS5cuNc0+aWeddZZpWme+Ysfr\nr78+9tx/+OEH07ynqtKKnzxXXjtfv7kkRK9lkSGPR/tr1VVXNf3TTz+Zpl3WsmVL0+eff75pWm1V\nEWaXXXvttaZ5L/G6ffXVV6Zfeukl01y5k5lpvP49e/Z0zpXPynzllVdMf/7556nPP4LPAe+VkD0j\nFeEIIYQIgiYcIYQQQSgKS43ZQr7MtDjrzMcaa6xhmgVUtO5ou73//vux71kT8RW51apVyzRXBWSx\nJy21jz76yHQUyvPasvcUs9RouzVu3Ng0C+toqQ0cONA0M4LWWmst0+wJRng+ae2o0PDenTx5sukv\nv/zS9L777ms6KrZ1rvw1vfTSS01HvdTIJptsYprFu8wAJLxutJKoqzq0FtdZZx3T7Dt3+OGHmx43\nbpxpZlRyDO+8807TfBaifoPM/mTB+o477mg6bZYln2GfHVhoFOEIIYQIgiYcIYQQQag0S43WGcM7\nZsNw5bw0YR9bf0+bNs309ttvb5rh6IQJE2LPpSbCsL9FixamacuwV9Rrr71mmtdu8eLFyx3Tt3wB\n27wzM433AjMJo6w355z75JNPTNP64Pny3mHGEbOtijFLjWPBa7vffvuZ7tatm2n2KTvvvPNM0468\n8MILTZ9xxhnOOeeaN29u2x555BHTvCa+TNJRo0aZbtSokelWrVrFf6gE8Di8jyoLflexfxrPkz0Y\nmY12zjnnmGaWGOH1PPfcc51zzh199NG2jcXQaW00niN/XuC4+ShEFqciHCGEEEHQhCOEECIIRWep\n0UZLSxQCMvzfdtttTTMbZMqUKaZHjx5d4fesSjADiVlkpLS01DSLzOrVq2eaFqSvr1dcViEtMvaS\nYr87Fuqy99N//vMf02yPv/nmm8eeLwvrCG20YseXMUm78KGHHjLNAkKfHdK/f3/T33zzjXOuvBXJ\nHmhcrZLH4LPLbECunMt9Dj74YNOPPvpo7Gei1VkMNhpp1qyZaWa0cuVQLnHCwtyhQ4fmPD7Heaut\ntnLOlV/WIEnGH5fs6NWrl+nIonOu/PcsbWyOLTXt2nx9RyrCEUIIEQRNOEIIIYJQaZYae2bli6iw\nibYBw9WZM2eaZiabL3ukuuGz0QiXDWB4TauNdiSzZnJlsqy++uqmd9llF9NceoC97FhsyJbvtMV4\nvlko9sJPss0225hmEajvM/i2RxlXHDceu0OHDqZPOeUU0ywqpY1Gy5r2ahI7phiWLSA9evQwzcw9\nWoW0vfr162faV8jugxbYDjvs4JwrX6TJ4mbadd27dzd9//33m2avQ56L7z7g+PMZLcRPDYpwhBBC\nBEETjhBCiCAURS+1fBFlXrCVPenTp0/I06mS+PqLffDBB6bT2GjO/WWvMDPm1FNPNU0bjXbNiBEj\nTBfCgiXFWPhJWPjapUsX077i1TQWIfdlVhWtGWZq7bHHHqZpnTHTjBlR7LeXJQs1JLTRCO9hrppJ\nS5BWMAuc2YeN14rP0x133LHcNo4Ply14+OGHTX/44YemuYIxX8tz5PjwmHzO+LPDY4895vKBIhwh\nhBBBqFYRThTBsE5h4sSJpvkXSU1JFMgC/2Jmd+0k0UDcgmFss/Hee++Z/vTTT03zL8uQnbuLPWmA\nHdDbtm1rmi1VfONFzdqm6Idp/uV79dVXmx42bFjsufAvdd+9wB/LL7nkEtMnnXRS7P7FDO8NRpRs\nc/Pqq6+aZksoRnS+BSBJXPIE35/JCexKzVo2X6TL6IwRFBMFmITC6CxfKMIRQggRBE04QgghglDl\nLTWGqdEiSGzxMWjQINPPPvus6WL/kbgQ+NoJJYHXizUCvjoDtrGJrIfrrrvOtrVu3dr0m2++mepc\nCkFVuh/YXoXJHF9//bXpOXPmmKalxqSAaAExX/sntgfacsstTXMcfePP69m0aVPfR6kS+O4NWvf8\noZ7WMb+LkhB9n9Hyop3KeijWGyZJGPEtQPnOO++YfuGFF1Kdb1oU4QghhAiCJhwhhBBBqJKWGrMq\nWMMR1Q0sWrTItr377rumq5JtUgjS2mi+jsO0URo3bmw66j7sXHkLpm/fvs45f/0O26NwbGlZiHiO\nPfZY08xgGjt2rGnWn/FaR7U9vufixRdfjD0eNZ8vwnvnoIMO8n+AKobvmaDm9w9bODGjcM8994w9\nZjQm119/vW1j12Yufrh06VLTtMuSnDvrgHhPsP2V77NmyehUhCOEECIImnCEEEIEoUpaai+//LLp\nunXrLvf/mcUze/bsIOdUHfFZLbzmDM179+5t+uSTT15unzfeeMO2nX766bHHY5aUiIeFhW+//bZp\nXseWLVuapu2cxqb0jf+0adMSH8O58kXWSayfYsZ3TWgzsWCZFjEXC+T+1FEH6tdff922HXjggaaf\nfvpp04MHDzb91ltvJfsAMbzyyiumWdTrK7zO8tNE1R59IYQQVQZNOEIIIYJQZSy1evXqmW7Xrt0/\n7svFump6ZloSaHP4wmiG/Sxs23vvvU1Hhbd/5+6773bOOffkk0/attdeey32eIXun8ZCYWbNVSVm\nzZplmllFV1xxhen999/fNAtCc5HkXqClx8JG2kcsSGVRZHXFd62ef/5507z3uP8XX3xh+pBDDnHO\nOXfDDTfYtlatWplmV+itt97aNK/xmDFjTLNTN9+f99CGG24Ye+6FQBGOEEKIIGjCEUIIEYS8WWq+\nIqF8cdFFF5n29W+KChuZuVPTSTIuvu2+jCJmHdHqZLt2ZqQ99dRTzrny2YM8hq+VflqS9IqrqjYa\noU2y2267maa9tdZaa5lO85mTZCZ+/PHHsfvQJuKCcWkLjiuLQnyHcbmBJk2axO5DizK6brTO2Evt\ngAMOMM2iW2ap8Xqz7yFttE6dOiX7AHlGEY4QQoggaMIRQggRhLxZaoXOBtt9991z7hOtVvf5558X\n9FyqEknGJYnVRk3rZvz48aY7duxomv2ZouLbL7/8MvZ98mW5VBXrJitLliwxzQwm9rJj9uC9995r\netKkSabjxn3nnXc2zeypGTNm5DyvESNGmD733HP/8X2KkWI4z2hZD2b8ERZac0VOfuc99thjpo87\n7jjTX331lWkuPRESRThCCCGCoAlHCCFEEIq68JNZUptttplpn9Vz8803O+fKrzbJ3lyVFTIXOoMv\nxPvxmjIDihYMM9Ooo+wY2gHMRktyjqGvYVVh9OjRptddd13TvO+ZwcTrGI2Bb1xo2SSBmWxccbQm\njx2/w3h9mMXnW003gkW048aNM/3jjz+a/vTTT01H34POOffvf//b9E477WSaGYUh+9spwhFCCBEE\nTThCCCGCUNSWGi0CttBmxhozpqZOnbrctppio5Es78drPn/+fNPs27TddtuZZrbLDjvsYHr48OGm\nf/311+XOy7eyZ9qVBWvXrr3c+9QkLrjgAtMsvB00aJBpn2VCmzSC9loSNtlkE9PTp0+P3aeYbTSu\nWMv7PV/QumKxM+9b3udR7zU+V8cff3zs8S6++GLTRx55pGmO4frrr2+aRdq+e4L3ELMeCXuypUUR\njhBCiCBowhFCCBGEorbUmIXBVQuZtcE299H2Qre4T0JoGyFfFt68efNMszcZ7YChQ4eapjXAVuyr\nrbbacufGDBzaX3wfjp3PXqsOSwzki5EjR5pmYV8hM49oqfpstKpCIWy0JPiKlNkbL4K2GF/XpUsX\n07vuuqvpzp07xx47yWqvPhuNZHnmFOEIIYQIgiYcIYQQQSg6S432CsO7c845xzTtGFo9NZlCWHgM\n36M+dc45V6dOHdNcfZX7v/fee6YjC4x2GS23JGPIz1fTbTQ+IyzwXGeddUyfccYZpr///nvT0eqr\nzv1lU3LpD/Zda968uek333zTNAtJRX6JrFA+Kz6rlEXXfCYaNGhgmn330tKzZ0/Tw4YNq/BxiCIc\nIYQQQdCEI4QQIghFZ6n5sjeWLl0a+EzEmmuuaZorF9LGmTlzpuk0Lc+7du1qmplWxZBhWOz4nhHa\njrfcckvsPkOGDEn8PhxP3yq7VRFmPI4aNcr0XnvtVRmnU464+5/jnWQJjnx9V/pstCwZkIpwhBBC\nBEETjhBCiCAUnaUmwpCkUPTrr7/OeZw0hXN8T2Y9hSySrcmt8sXyFIONlg+aNWtmmktD+Lb7oHXq\ns++y2N6KcIQQQgRBE44QQoggVJqlxr5azHoSYfDZSYXsU8b3XLhwYV6PXZFzEDWTqnwP0BKm9tll\nSWw04rPR8mVFK8IRQggRBE04QgghglCSJjwqKSlZ5JybXbjTqZE0LysrWyvLATQuBSHTuGhMCoKe\nleIk8bikmnCEEEKIiiJLTQghRBA04Qg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zE3mPHHTQQaZHjx5tulmzZqZp+/gWkiqWbKi0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\nCCGECML/A9aD2hgAgC7ZAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11d1d1550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "_ = view_samples(-1, samples)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below I'm showing the generated images as the network was training, every 10 epochs. With bonus optical illusion!" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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33BBIdlGPi9wu5EK7OOyww2q4sH/D7nrP0/yKs7jkYp111uGPf/zjkHBRb420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maM1nK+rzkqIP3TKpwzzzwTSApFBWy2khXkdlvw/J933nkAzD777EDK7HrppZdGuP9V6p2WZ9yZ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\nnEQikUg0gnzgJBKJRKIR5AMnkUgkEo0gHziJRCKRaAT5wEkkE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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4e7c13e0f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rows, cols = 10, 6\n", "fig, axes = plt.subplots(figsize=(7,12), nrows=rows, ncols=cols, sharex=True, sharey=True)\n", "\n", "for sample, ax_row in zip(samples[::int(len(samples)/rows)], axes):\n", " for img, ax in zip(sample[::int(len(sample)/cols)], ax_row):\n", " ax.imshow(img.reshape((28,28)), cmap='Greys_r')\n", " ax.xaxis.set_visible(False)\n", " ax.yaxis.set_visible(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It starts out as all noise. Then it learns to make only the center white and the rest black. You can start to see some number like structures appear out of the noise like 1s and 9s." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sampling from the generator\n", "\n", "We can also get completely new images from the generator by using the checkpoint we saved after training. We just need to pass in a new latent vector $z$ and we'll get new samples!" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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x6bj3EO8/fg5FqQkhhCgXaMIRQggRhLS1J0iFZJZx3IfWVaK6ZuPHjzcdtSxw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\n37+/6VWrVmXqFHMO3sc+qlWrZvqiiy4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"text/plain": [ "<matplotlib.figure.Figure at 0x7f4e80057c18>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "saver = tf.train.Saver(var_list=g_vars)\n", "with tf.Session() as sess:\n", " saver.restore(sess, tf.train.latest_checkpoint('checkpoints'))\n", " sample_z = np.random.uniform(-1, 1, size=(16, z_size))\n", " gen_samples = sess.run(\n", " generator(input_z, input_size, reuse=True),\n", " feed_dict={input_z: sample_z})\n", "_ = view_samples(0, [gen_samples])" ] } ], "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": 2 }
mit
mitliagkas/graphs
LJ Spectrum.ipynb
1
40211
{ "metadata": { "name": "", "signature": "sha256:44806a376ec76a0172093c5e65dbe1fc077241d35cf398ed7b07ac2bab5a7f16" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 12 }, { "cell_type": "code", "collapsed": false, "input": [ "import scipy as sp" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "import networkx as nx" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 7 }, { "cell_type": "code", "collapsed": false, "input": [ "fn = '/var/datasets/livejournal/small-soc-LiveJournal1.txt'" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 52 }, { "cell_type": "code", "collapsed": false, "input": [ "!head {fn}" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "# Directed graph (each unordered pair of nodes is saved once): soc-LiveJournal1.txt \r", "\r\n", "# Directed LiveJournal friednship social network\r", "\r\n", "# Nodes: 4847571 Edges: 68993773\r", "\r\n", "# FromNodeId\tToNodeId\r", "\r\n", "0\t1\r", "\r\n", "0\t2\r", "\r\n", "0\t3\r", "\r\n", "0\t4\r", "\r\n", "0\t5\r", "\r\n", "0\t6\r", "\r\n" ] } ], "prompt_number": 53 }, { "cell_type": "code", "collapsed": false, "input": [ "import csv\n", "from scipy import sparse" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 99 }, { "cell_type": "code", "collapsed": false, "input": [ "nodes = 100000 \n", "matrix = sparse.lil_matrix((nodes,nodes), dtype=float)\n", "\n", "csvreader = csv.reader(open(fn))" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 106 }, { "cell_type": "code", "collapsed": false, "input": [ "with open(fn,'r') as f:\n", " next(f);next(f);next(f);next(f)\n", " reader = csv.reader(f,delimiter='\\t')\n", " for column,row in reader:\n", " column = int(column)\n", " row = int(row)\n", " if column >= nodes or row >= nodes:\n", " continue\n", " #matrix.data[row].append(column)\n", " matrix[row,column] = 1\n", "\n" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 107 }, { "cell_type": "code", "collapsed": false, "input": [ "csc = matrix.tocsc()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 108 }, { "cell_type": "code", "collapsed": false, "input": [ "k=200" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 178 }, { "cell_type": "code", "collapsed": false, "input": [ "res = sp.sparse.linalg.svds(csc, k=k)" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 163 }, { "cell_type": "code", "collapsed": false, "input": [ "semilogy(abs(res[0][:,-1]))" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 164, "text": [ "[<matplotlib.lines.Line2D 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"collapsed": false, "input": [ "plot(range(k-1,-1,-1), res[1]/sum(res[1]),range(k-1,-1,-1), cumsum( res[1]/sum(res[1]) ) )\n", "#axis([0, k-1, 0, max(res[1]/sum(res[1]) ) ])\n", "axis([0, k-1, 0, 1 ])" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 177, "text": [ "[0, 199, 0, 1]" ] }, { "metadata": {}, "output_type": "display_data", "png": 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"cell_type": "code", "collapsed": false, "input": [ "reversed(res[1].array())" ], "language": "python", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'numpy.ndarray' object has no attribute 'array'", "output_type": "pyerr", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m<ipython-input-147-a8030afeac81>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mreversed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\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[0m", "\u001b[1;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'array'" ] } ], "prompt_number": 147 }, { "cell_type": "code", "collapsed": false, "input": [ "range(5,-1,1)" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 140, "text": [ "[]" ] } ], "prompt_number": 140 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
mit
dolittle007/dolittle007.github.io
notebooks/convolutional_vae_keras_advi.ipynb
1
148331
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Convolutional variational autoencoder with PyMC3 and Keras" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this document, I will show how autoencoding variational Bayes (AEVB) works in PyMC3's automatic differentiation variational inference (ADVI). The example here is borrowed from [Keras example](https://github.com/fchollet/keras/blob/master/examples/variational_autoencoder_deconv.py), where convolutional variational autoencoder is applied to the MNIST dataset. The network architecture of the encoder and decoder are completely same. However, PyMC3 allows us to define the probabilistic model, which combines the encoder and decoder, in the way by which other general probabilistic models (e.g., generalized linear models), rather than directly implementing of Monte Carlo sampling and the loss function as done in the Keras example. Thus I think the framework of AEVB in PyMC3 can be extended to more complex models such as [latent dirichlet allocation](https://taku-y.github.io/notebook/20160928/lda-advi-ae.html). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- Notebook Written by Taku Yoshioka (c) 2016" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For using Keras with PyMC3, we need to choose [Theano](http://deeplearning.net/software/theano/) as the backend of Keras. \n", "\n", "Install required packages, including pymc3, if it is not already available:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#!pip install --upgrade git+https://github.com/Theano/Theano.git#egg=Theano" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#!pip install --upgrade keras" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "#!pip install --upgrade pymc3" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#!conda install -y mkl-service" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "IPython.notebook.set_autosave_interval(0)" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Autosave disabled\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n" ] } ], "source": [ "%autosave 0\n", "%matplotlib inline\n", "import sys, os\n", "os.environ['KERAS_BACKEND'] = 'theano'\n", "\n", "from theano import config\n", "config.floatX = 'float32'\n", "config.optimizer = 'fast_run'\n", "\n", "from collections import OrderedDict\n", "from keras.layers import InputLayer, BatchNormalization, Dense, Conv2D, Deconv2D, Activation, Flatten, Reshape\n", "import numpy as np\n", "import pymc3 as pm\n", "from pymc3.variational import advi_minibatch\n", "from theano import shared, config, function, clone, pp\n", "import theano.tensor as tt\n", "import keras\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "import seaborn as sns\n", "\n", "from keras import backend as K\n", "K.set_image_dim_ordering('th')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.1.rc2\n", "0.9.0\n", "2.0.2\n" ] } ], "source": [ "import pymc3, theano\n", "print(pymc3.__version__)\n", "print(theano.__version__)\n", "print(keras.__version__)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load images\n", "MNIST dataset can be obtained by [scikit-learn API](http://scikit-learn.org/stable/datasets/) or from [Keras datasets](https://keras.io/datasets/). The dataset contains images of digits. " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# from sklearn.datasets import fetch_mldata\n", "# mnist = fetch_mldata('MNIST original')\n", "# print(mnist.keys())\n", "\n", "from keras.datasets import mnist\n", "(x_train, y_train), (x_test, y_test) = mnist.load_data()\n", "data = x_train.reshape(-1, 1, 28, 28).astype('float32')\n", "data /= np.max(data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Use Keras\n", "We define a utility function to get parameters from Keras models. Since we have set the backend to Theano, parameter objects are obtained as shared variables of Theano. \n", "\n", "In the code, 'updates' are expected to include update objects (dictionary of pairs of shared variables and update equation) of scaling parameters of batch normalization. While not using batch normalization in this example, if we want to use it, we need to pass these update objects as an argument of `theano.function()` inside the PyMC3 ADVI function. The current version of PyMC3 does not support it, it is easy to modify (I want to send PR in future). \n", "\n", "The learning phase below is used for Keras to known the learning phase, training or test. This information is important also for batch normalization. " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from keras.models import Sequential\n", "from keras.layers import Dense, BatchNormalization\n", "\n", "def get_params(model):\n", " \"\"\"Get parameters and updates from Keras model\n", " \"\"\"\n", " shared_in_updates = list()\n", " params = list()\n", " updates = dict()\n", " \n", " for l in model.layers:\n", " attrs = dir(l)\n", " # Updates\n", " if 'updates' in attrs:\n", " updates.update(l.updates)\n", " shared_in_updates += [e[0] for e in l.updates]\n", " \n", " # Shared variables\n", " for attr_str in attrs:\n", " attr = getattr(l, attr_str)\n", " if type(attr) is tt.sharedvar.TensorSharedVariable:\n", " if attr is not model.get_input_at(0):\n", " params.append(attr)\n", " \n", " return list(set(params) - set(shared_in_updates)), updates\n", "\n", "# This code is required when using BatchNormalization layer\n", "keras.backend.theano_backend._LEARNING_PHASE = \\\n", " shared(np.uint8(1), name='keras_learning_phase')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Encoder and decoder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we define the convolutional neural network for encoder using Keras API. This function returns a CNN model given the shared variable representing observations (images of digits), the dimension of latent space, and the parameters of the model architecture. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def cnn_enc(xs, latent_dim, nb_filters=64, nb_conv=3, intermediate_dim=128):\n", " \"\"\"Returns a CNN model of Keras.\n", " \n", " Parameters\n", " ----------\n", " xs : theano.tensor.sharedvar.TensorSharedVariable\n", " Input tensor.\n", " latent_dim : int\n", " Dimension of latent vector.\n", " \"\"\"\n", " input_layer = InputLayer(input_tensor=xs, \n", " batch_input_shape=xs.get_value().shape)\n", " model = Sequential()\n", " model.add(input_layer)\n", " \n", " cp1 = {'padding': 'same', 'activation': 'relu'}\n", " cp2 = {'padding': 'same', 'activation': 'relu', 'strides': (2, 2)}\n", " cp3 = {'padding': 'same', 'activation': 'relu', 'strides': (1, 1)}\n", " cp4 = cp3\n", " \n", " model.add(Conv2D(1, (2, 2), **cp1))\n", " model.add(Conv2D(nb_filters, (2, 2), **cp2))\n", " model.add(Conv2D(nb_filters, (nb_conv, nb_conv), **cp3))\n", " model.add(Conv2D(nb_filters, (nb_conv, nb_conv), **cp4))\n", " model.add(Flatten())\n", " model.add(Dense(intermediate_dim, activation='relu'))\n", " model.add(Dense(2 * latent_dim))\n", "\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we define a utility class for encoders. This class does not depend on the architecture of the encoder except for input shape (`tensor4` for images), so we can use this class for various encoding networks. " ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Encoder:\n", " \"\"\"Encode observed images to variational parameters (mean/std of Gaussian).\n", "\n", " Parameters\n", " ----------\n", " xs : theano.tensor.sharedvar.TensorSharedVariable\n", " Placeholder of input images. \n", " dim_hidden : int\n", " The number of hidden variables. \n", " net : Function\n", " Returns \n", " \"\"\"\n", " def __init__(self, xs, dim_hidden, net):\n", " model = net(xs, dim_hidden)\n", " \n", " self.model = model\n", " self.xs = xs\n", " self.out = model.get_output_at(-1)\n", " self.means = self.out[:, :dim_hidden]\n", " self.lstds = self.out[:, dim_hidden:]\n", " self.params, self.updates = get_params(model)\n", " self.enc_func = None\n", " self.dim_hidden = dim_hidden\n", " \n", " def _get_enc_func(self):\n", " if self.enc_func is None:\n", " xs = tt.tensor4()\n", " means = clone(self.means, {self.xs: xs})\n", " lstds = clone(self.lstds, {self.xs: xs})\n", " self.enc_func = function([xs], [means, lstds])\n", " \n", " return self.enc_func\n", " \n", " def encode(self, xs):\n", " # Used in test phase\n", " keras.backend.theano_backend._LEARNING_PHASE.set_value(np.uint8(0))\n", " \n", " enc_func = self._get_enc_func()\n", " means, _ = enc_func(xs)\n", " \n", " return means\n", "\n", " def draw_samples(self, xs, n_samples=1):\n", " \"\"\"Draw samples of hidden variables based on variational parameters encoded.\n", " \n", " Parameters\n", " ----------\n", " xs : numpy.ndarray, shape=(n_images, 1, height, width)\n", " Images.\n", " \"\"\"\n", " # Used in test phase\n", " keras.backend.theano_backend._LEARNING_PHASE.set_value(np.uint8(0))\n", "\n", " enc_func = self._get_enc_func()\n", " means, lstds = enc_func(xs)\n", " means = np.repeat(means, n_samples, axis=0)\n", " lstds = np.repeat(lstds, n_samples, axis=0)\n", " ns = np.random.randn(len(xs) * n_samples, self.dim_hidden)\n", " zs = means + np.exp(lstds) * ns\n", " \n", " return ns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a similar way, we define the decoding network and a utility class for decoders. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def cnn_dec(zs, nb_filters=64, nb_conv=3, output_shape=(1, 28, 28)):\n", " \"\"\"Returns a CNN model of Keras.\n", " \n", " Parameters\n", " ----------\n", " zs : theano.tensor.var.TensorVariable\n", " Input tensor.\n", " \"\"\"\n", " minibatch_size, dim_hidden = zs.tag.test_value.shape\n", " input_layer = InputLayer(input_tensor=zs, \n", " batch_input_shape=zs.tag.test_value.shape)\n", " model = Sequential()\n", " model.add(input_layer)\n", " \n", " model.add(Dense(dim_hidden, activation='relu'))\n", " model.add(Dense(nb_filters * 14 * 14, activation='relu'))\n", " \n", " cp1 = {'padding': 'same', 'activation': 'relu', 'strides': (1, 1)}\n", " cp2 = cp1\n", " cp3 = {'padding': 'valid', 'activation': 'relu', 'strides': (2, 2)}\n", " cp4 = {'padding': 'same', 'activation': 'sigmoid'}\n", "\n", " output_shape_ = (minibatch_size, nb_filters, 14, 14)\n", " model.add(Reshape(output_shape_[1:]))\n", " model.add(Deconv2D(nb_filters, (nb_conv, nb_conv), data_format='channels_first', **cp1))\n", " model.add(Deconv2D(nb_filters, (nb_conv, nb_conv), data_format='channels_first', **cp2))\n", " output_shape_ = (minibatch_size, nb_filters, 29, 29)\n", " model.add(Deconv2D(nb_filters, (2, 2), data_format='channels_first', **cp3))\n", " model.add(Conv2D(1, (2, 2), **cp4))\n", "\n", " return model" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Decoder:\n", " \"\"\"Decode hidden variables to images.\n", " \n", " Parameters\n", " ----------\n", " zs : Theano tensor\n", " Hidden variables.\n", " \"\"\"\n", " def __init__(self, zs, net):\n", " model = net(zs)\n", " self.model = model\n", " self.zs = zs\n", " self.out = model.get_output_at(-1)\n", " self.params, self.updates = get_params(model)\n", " self.dec_func = None\n", " \n", " def _get_dec_func(self):\n", " if self.dec_func is None:\n", " zs = tt.matrix()\n", " xs = clone(self.out, {self.zs: zs})\n", " self.dec_func = function([zs], xs)\n", " \n", " return self.dec_func\n", " \n", " def decode(self, zs):\n", " \"\"\"Decode hidden variables to images. \n", " \n", " An image consists of the mean parameters of the observation noise.\n", " \n", " Parameters\n", " ----------\n", " zs : numpy.ndarray, shape=(n_samples, dim_hidden)\n", " Hidden variables. \n", " \"\"\" \n", " # Used in test phase\n", " keras.backend.theano_backend._LEARNING_PHASE.set_value(np.uint8(0))\n", "\n", " return self._get_dec_func()(zs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generative model\n", "We can construct the generative model with PyMC3 API and the functions and classes defined above. We set the size of mini-batches to 100 and the dimension of the latent space to 2 for visualization. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Constants\n", "minibatch_size = 100\n", "dim_hidden = 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A placeholder of images is required to which mini-batches of images will be placed in the ADVI inference. It is also the input to the encoder. In the below, `enc.model` is a Keras model of the encoder network, thus we can check the model architecture using the method `summary()`. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_1 (InputLayer) (100, 1, 28, 28) 0 \n", "_________________________________________________________________\n", "conv2d_1 (Conv2D) (100, 1, 28, 28) 5 \n", "_________________________________________________________________\n", "conv2d_2 (Conv2D) (100, 64, 14, 14) 320 \n", "_________________________________________________________________\n", "conv2d_3 (Conv2D) (100, 64, 14, 14) 36928 \n", "_________________________________________________________________\n", "conv2d_4 (Conv2D) (100, 64, 14, 14) 36928 \n", "_________________________________________________________________\n", "flatten_1 (Flatten) (100, 12544) 0 \n", "_________________________________________________________________\n", "dense_1 (Dense) (100, 128) 1605760 \n", "_________________________________________________________________\n", "dense_2 (Dense) (100, 4) 516 \n", "=================================================================\n", "Total params: 1,680,457.0\n", "Trainable params: 1,680,457.0\n", "Non-trainable params: 0.0\n", "_________________________________________________________________\n" ] } ], "source": [ "# Placeholder of images\n", "xs_t = shared(np.zeros((minibatch_size, 1, 28, 28)).astype('float32'), name='xs_t')\n", "\n", "# Encoder\n", "enc = Encoder(xs_t, dim_hidden, net=cnn_enc)\n", "enc.model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The probabilistic model involves only two random variables; latent variable $\\mathbf{z}$ and observation $\\mathbf{x}$. We put a Normal prior on $\\mathbf{z}$, decode the variational parameters of $q(\\mathbf{z}|\\mathbf{x})$ and define the likelihood of the observation $\\mathbf{x}$. " ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "with pm.Model() as model:\n", " # Hidden variables\n", " zs = pm.Normal('zs', mu=0, sd=1, shape=(minibatch_size, dim_hidden), dtype='float32')\n", "\n", " # Decoder and its parameters\n", " dec = Decoder(zs, net=cnn_dec)\n", " \n", " # Observation model\n", " xs_ = pm.Normal('xs_', mu=dec.out.ravel(), sd=0.1, observed=xs_t.ravel(), dtype='float32')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above definition of the generative model, we do not know how the decoded variational parameters are passed to $q(\\mathbf{z}|\\mathbf{x})$. To do this, we will set the argument `local_RVs` in the ADVI function of PyMC3. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "local_RVs = OrderedDict({zs: ((enc.means, enc.lstds), len(data) / float(minibatch_size))})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This argument is a `OrderedDict` whose keys are random variables to which the decoded variational parameters are set, `zs` in this model. Each value of the dictionary contains two theano expressions representing variational mean (`enc.means`) and log of standard deviations (`enc.lstds`). In addition, a scaling constant (`len(data) / float(minibatch_size)`) is required to compensate for the size of mini-batches of the corresponding log probability terms in the evidence lower bound (ELBO), the objective of the variational inference. \n", "\n", "The scaling constant for the observed random variables is set in the same way. " ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "observed_RVs = OrderedDict({xs_: len(data) / float(minibatch_size)})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also check the architecture of the decoding network as for the encoding network. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "input_2 (InputLayer) (100, 2) 0 \n", "_________________________________________________________________\n", "dense_3 (Dense) (100, 2) 6 \n", "_________________________________________________________________\n", "dense_4 (Dense) (100, 12544) 37632 \n", "_________________________________________________________________\n", "reshape_1 (Reshape) (100, 64, 14, 14) 0 \n", "_________________________________________________________________\n", "conv2d_transpose_1 (Conv2DTr (100, 64, 14, 14) 36928 \n", "_________________________________________________________________\n", "conv2d_transpose_2 (Conv2DTr (100, 64, 14, 14) 36928 \n", "_________________________________________________________________\n", "conv2d_transpose_3 (Conv2DTr (100, 64, 28, 28) 16448 \n", "_________________________________________________________________\n", "conv2d_5 (Conv2D) (100, 1, 28, 28) 257 \n", "=================================================================\n", "Total params: 128,199.0\n", "Trainable params: 128,199.0\n", "Non-trainable params: 0.0\n", "_________________________________________________________________\n" ] } ], "source": [ "dec.model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To perform inference, we need to create generators of mini-batches and define the optimizer used for ADVI. The optimizer is a function that returns Theano parameter update object (dictionary). " ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Mini-batches\n", "def create_minibatch(data, minibatch_size):\n", " rng = np.random.RandomState(0)\n", " start_idx = 0\n", " while True:\n", " # Return random data samples of set size batchsize each iteration\n", " ixs = rng.randint(data.shape[0], size=minibatch_size)\n", " yield data[ixs]\n", "\n", "minibatches = zip(create_minibatch(data, minibatch_size))\n", "\n", "def rmsprop(loss, param):\n", " adam_ = keras.optimizers.RMSprop()\n", " return adam_.get_updates(param, [], loss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us execute ADVI function of PyMC3. " ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Average ELBO = -54,641,644.54: 100%|██████████| 1000/1000 [18:59<00:00, 1.19s/it]\n", "Finished minibatch ADVI: ELBO = -53,421,131.77\n" ] } ], "source": [ "with model:\n", " v_params = pm.variational.advi_minibatch(\n", " n=1000, minibatch_tensors=[xs_t], minibatches=minibatches,\n", " local_RVs=local_RVs, observed_RVs=observed_RVs, \n", " encoder_params=(enc.params + dec.params), \n", " optimizer=rmsprop\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`v_params`, the returned value of the ADVI function, has the trace of ELBO during inference (optimization). We can see the convergence of the inference. " ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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c9FgcLtMjKkyLM9N6IFinRrBOjadm5CEiRIOiEtvJKsN9GGNzDrjBmbEe93uO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\nYacNbpQAAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x144d2e550>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(v_params.elbo_vals);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we see the distribution of the images in the latent space. To do this, we make 2-dimensional points in a grid and feed them into the decoding network. The mean of $p(\\mathbf{x}|\\mathbf{z})$ is the image corresponding to the samples on the grid. " ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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Ihzz57BcpZZehO7x0xiiMG145MSPctx3ZsCfiNYgNgQGBfaAtsGHYG3Mf88fs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\nCAQCgYsgmKJAoAS0YsszgeFqlL1rFJPJPJ390Kh89TIH68W1TWedsHr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bIhbIdunuFFKy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CqTE2Uync/HbMPZB69Txe6Z3fSnn35ayq9S5bl4VXB5uDpj4rmgnP2aDb8ukMoi7T4p00WreW4g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"text/plain": [ "<matplotlib.figure.Figure at 0x1366727f0>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nn = 10\n", "zs = np.array([(z1, z2) \n", " for z1 in np.linspace(-2, 2, nn) \n", " for z2 in np.linspace(-2, 2, nn)]).astype('float32')\n", "xs = dec.decode(zs)[:, 0, :, :]\n", "xs = np.bmat([[xs[i + j * nn] for i in range(nn)] for j in range(nn)])\n", "matplotlib.rc('axes', **{'grid': False})\n", "plt.figure(figsize=(10, 10))\n", "plt.imshow(xs, interpolation='none', cmap='gray')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.5.1" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 }, "nav_menu": {}, "toc": { "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 6, "toc_cell": false, "toc_section_display": "block", "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 1 }
gpl-3.0
NumCosmo/NumCosmo
notebooks/MagDustBounce.ipynb
1
264877
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "**License**\n", "\n", " MagDustBounce\n", "\n", " Mon Jun 01 09:20:00 2020\\\n", " Copyright 2020\\\n", " Sandro Dias Pinto Vitenti <[email protected]>\n", "\n", "---\n", "---\n", "\n", " MagDustBounce\\\n", " Copyright (C) 2020 Sandro Dias Pinto Vitenti <[email protected]>\n", "\n", " numcosmo is free software: you can redistribute it and/or modify it\n", " under the terms of the GNU General Public License as published by the\n", " Free Software Foundation, either version 3 of the License, or\n", " (at your option) any later version.\n", "\n", " numcosmo is distributed in the hope that it will be useful, but\n", " WITHOUT ANY WARRANTY; without even the implied warranty of\n", " MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n", " See the GNU General Public License for more details.\n", "\n", " You should have received a copy of the GNU General Public License along\n", " with this program. If not, see <http://www.gnu.org/licenses/>.\n", " \n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Magnetic field in dust dominated bouncing cosmology\n", "\n", "In this notebook we develop the necessary objects to compute the power spectrum (and other observables) for magnetic field production in the contraction phase of a bouncing cosmology dominated by a dust-like fluid.\n", "\n", "The scale factor for this model is given by:\n", "\\begin{equation}\n", "\\frac{a(t)}{a_0} = \\frac{1}{x_b}\\left[1 + \\left(\\frac{t}{t_b}\\right)^2\\right]^{1/3}, \\quad x \\equiv \\frac{a_0}{a}, \\quad x_b \\equiv \\frac{a_0}{a_b},\n", "\\end{equation}\n", "where $a_b$ gives the value of the scale factor at the bounce ($t = 0$), and $t_b$ the bounce time-scale. Solving the time $t$ in terms of the scale factor and substituting back to the Hubble function we can relate the free parameter $t_b$ with the dimensionless density $\\Omega_m$, i.e.,\n", "\\begin{equation}\n", "H^2 = \\left(\\frac{\\dot a}{a}\\right)^2 = \\frac{4}{9}\\frac{x^3}{t_b^2x_b^3} - \\frac{4}{9}\\frac{x^6}{t_b^2x_b^6} = \\frac{1}{R_H^2}\\left(\\Omega_m x^3 + \\Omega_Q x^6\\right) \\quad\\Rightarrow\\quad t_b = \\frac{2}{3}\\frac{R_H}{\\sqrt{\\Omega_m x_b^3}}, \\quad \\Omega_Q = - \\frac{\\Omega_m}{x_b^3}.\n", "\\end{equation}\n", "where $R_H \\equiv 1/H_0$ is the Hubble radius today and $\\Omega_m$ is the dimensionless matter density today.\n", "\n", "From here on we will express everything in unit of the Hubble radius, $t \\to t/R_H$ and $t_b \\to t_b / R_H$.\n", "\n", "The electromagnetic vector field satisfy the following Hamilton equations:\n", "\\begin{equation}\n", "\\dot{A} = \\frac{\\Pi_A}{m}, \\qquad \\dot{\\Pi}_A = -m\\nu^2 A, \\qquad m \\equiv a F,\\quad \\nu = \\frac{k}{a},\\qquad F \\equiv \\frac{1}{4}\\left(1+l_*^2 R\\right), \\qquad R = 6\\left[\\frac{\\ddot{a}}{a}+\\left(\\frac{\\dot{a}}{a}\\right)^2\\right].\n", "\\end{equation}\n", "for the mode $k$. It is convenient to rewrite $F$ as\n", "\\begin{equation}\n", "F = \\frac{1}{4}\\left\\{1+c_*^2\\frac{\\left(t/t_b\\right)^2+3}{\\left[\\left(t/t_b\\right)^2+1\\right]^2}\\right\\}, \\qquad c_* \\equiv \\frac{2}{\\sqrt3}\\frac{l_*}{t_b}.\n", "\\end{equation}\n", "\n", "\n", "## Loading NumCosmo\n", "\n", "The first step is to load both NumCosmo and NumCosmoMath libraries." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "try:\n", " import gi\n", " gi.require_version('NumCosmo', '1.0')\n", " gi.require_version('NumCosmoMath', '1.0')\n", "except:\n", " pass\n", "\n", "import sys\n", "import math\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from gi.repository import NumCosmo as Nc\n", "from gi.repository import NumCosmoMath as Ncm\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# A helper function to write latex formatted numbers" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def latex_float(f):\n", " float_str = \"{0:.2g}\".format(f)\n", " if \"e\" in float_str:\n", " base, exponent = float_str.split(\"e\")\n", " if base == 1.0:\n", " return r\"10^{{{1}}}\".format (int(exponent))\n", " else:\n", " return r\"{0} \\times 10^{{{1}}}\".format(base, int(exponent))\n", " else:\n", " return float_str" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Initializing the library" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "__name__ = \"NcContext\"\n", "\n", "Ncm.cfg_init ()\n", "Ncm.cfg_set_log_handler (lambda msg: sys.stdout.write (msg) and sys.stdout.flush ())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Complex structure quantization object\n", "\n", "Below we define our object as a child of the complex structure quantization object Ncm.CSQ1D" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "class PyCSQ1DMagDust (Ncm.CSQ1D):\n", " def __init__ (self, Omega_m = 0.3, xb = 1.0e25, cc = 1.0, h = 0.7, a_ns = 1.0e-12):\n", " Ncm.CSQ1D.__init__ (self)\n", "\n", " self.h = h\n", " self.Omega_m = Omega_m\n", " self.xb = xb\n", " self.tb = (2.0 / 3.0) * (1.0 / math.sqrt (Omega_m * xb**3))\n", " self.cc = cc\n", " self.RH = (Ncm.C.c () / (1.0e5 * h)) # Hubble radius in units of Mpc\n", " self.a_ns = a_ns\n", "\n", " # Scale factor over scale factor today\n", " def y_t (self, t):\n", " tbar = t / self.tb\n", " tbar2 = tbar * tbar\n", "\n", " return np.cbrt (1.0 + tbar2) / self.xb\n", "\n", " def Omega_m_t (self, t):\n", " return self.Omega_m * self.y_t (t)**(-3)\n", " \n", " def t_y (self, y):\n", " return self.tb * math.sqrt ((self.xb * y)**3 - 1.0 if (self.xb * y)**3 > 1.0 else 0.0)\n", "\n", " # The Hubble function\n", " def H_t (self, t):\n", " tbar = t / self.tb\n", " tbar2 = tbar * tbar\n", " \n", " return (2.0 / 3.0 * tbar / (1.0 + tbar2)) / self.tb\n", "\n", " # The function $F$ defined above\n", " def F_t (self, t):\n", " cc = self.cc\n", " cc2 = cc**2\n", " tbar = t / self.tb\n", " tbar2 = tbar * tbar\n", " \n", " return 0.25 * (1.0 + cc2 * (tbar2 + 3.0) / (tbar2 + 1.0)**2)\n", "\n", " def Fconst_time (self):\n", " cc = self.cc\n", " cc2 = cc**2\n", " tbar = math.sqrt (0.5 * (- 2.0 + cc2 + cc * math.sqrt (8.0 + cc2)))\n", " return tbar * self.tb\n", "\n", " def set_max_c (self, f):\n", " w = self.a_ns * self.xb\n", " self.cc = math.sqrt (f) * w**(3.0 / 2.0) / math.sqrt (1.0 + 2.0 / w**3)\n", " return self.cc\n", "\n", " def do_eval_xi (self, model, t, k):\n", " return math.log (k) + math.log (self.F_t (t))\n", "\n", " def do_eval_nu (self, model, t, k):\n", " return k / self.y_t (t)\n", "\n", " def do_eval_nu2 (self, model, t, k):\n", " return (k / self.y_t (t))**2\n", "\n", " def do_eval_m (self, model, t, k):\n", " return self.y_t (t) * self.F_t (t)\n", "\n", " def do_eval_F1 (self, model, t, k):\n", " cc = self.cc\n", " cc2 = cc**2\n", " tbar = t / self.tb\n", " tbar2 = tbar * tbar\n", " tbar3 = tbar2 * tbar\n", " tbar4 = tbar2 * tbar2\n", "\n", " if tbar2 < 1.0:\n", " T1 = -2.0 * cc2 * tbar * (tbar2 + 5.0)\n", " T2 = tbar2 + 1.0\n", " T3 = T2**2\n", " T4 = cc2 * (tbar2 + 3.0)\n", " return (T1 / (T2 * (T3 + T4) * self.tb)) / (2.0 * self.do_eval_nu (model, t, k))\n", " else:\n", " T1 = -2.0 * cc2 * tbar * (1.0 + 5.0 / tbar2)\n", " T2 = (1.0 + 1.0 / tbar2)\n", " T3 = T2**2\n", " T4 = cc2 * (1.0 + 3.0 / tbar2)\n", " return (T1 / (T2 * (T3 + T4 / tbar2) * tbar4 * self.tb)) / (2.0 * self.do_eval_nu (model, t, k))\n", "\n", " def do_eval_F2 (self, model, t, k):\n", " cc = self.cc\n", " cc2 = cc**2\n", " tbar = t / self.tb\n", " tbar2 = tbar * tbar\n", " tbar4 = tbar2 * tbar2\n", " tbar6 = tbar4 * tbar2\n", " \n", " if tbar2 < 1.0:\n", " T1 = (2.0 / 3.0) * cc2 / self.tb**2\n", " T2 = cc2 * (-45.0 + 3.0 * tbar2 + 17.0 * tbar4 + tbar6)\n", " T3 = (1.0 + tbar2)**2\n", " T4 = (-15.0 + 7.0 * tbar2 * (8.0 + tbar2))\n", " T5 = (T3 + cc2 * (3.0 + tbar2))**2\n", " return (T1 * (T2 + T3 * T4) / (T3 * T5)) / (2.0 * self.do_eval_nu (model, t, k))**2\n", " else:\n", " T1 = (2.0 / 3.0) * cc2 / self.tb**2\n", " T2 = cc2 * (-45.0 / tbar6 + 3.0 / tbar4 + 17.0 / tbar2 + 1.0)\n", " T3 = (1.0 / tbar2 + 1.0)**2\n", " T4 = (-15.0 / tbar4 + 7.0 * (8.0 / tbar2 + 1.0))\n", " T5 = (T3 + cc2 * (3.0 / tbar4 + 1.0 / tbar2))**2\n", " return (T1 * (T2 / tbar2 + T3 * T4) / (T3 * T5 * tbar4)) / (2.0 * self.do_eval_nu (model, t, k))**2\n", "\n", " def do_prepare (self, model):\n", " pass \n", "\n", " def eval_PB_PE (self, t, c_A2 = 0.0, c_PiA2 = 0.0):\n", " if (c_A2 == 0.0) or (c_PiA2 == 0.0):\n", " (J11, J12, J22) = self.get_J_at (None, t) \n", " A2 = 0.5 * J11 if c_A2 == 0.0 else c_A2\n", " PiA2 = 0.5 * J22 if c_PiA2 == 0.0 else c_PiA2\n", " F = self.F_t (t)\n", " xb = self.xb\n", " k = self.get_k ()\n", " y = self.y_t (t)\n", " RH = self.RH\n", " m = self.eval_m (None, t, k)\n", " PE = PiA2 * (k / y)**3 / (2.0 * math.pi**2 * RH**4 * m)\n", " PB = m * A2 * (k / y)**5 / (2.0 * math.pi**2 * RH**4)\n", "\n", " return PB, PE\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "c = 1e+26\n", "t_F 1.21716123890037e-19 y_F 2.15443469003188e-13\n", "True 2.983105403634846e-11\n" ] } ], "source": [ "csq1d = PyCSQ1DMagDust (cc = 1.0e20, xb = 1.0e30)\n", "\n", "print (\"c = % 22.15g\" % csq1d.set_max_c (1.0e-2))\n", "csq1d.cc = 1.0e26\n", "\n", "conv_rhoc = 5.0e-108\n", "conv_g2 = 3.5e-115\n", "t_F = csq1d.Fconst_time ()\n", "y_F = csq1d.y_t (t_F)\n", "\n", "print (\"t_F % 22.15g y_F % 22.15g\" % (t_F, y_F))\n", "\n", "ki = 1.0\n", "kf = 4.0e3\n", "k_a = np.geomspace (ki, kf, 3)\n", "\n", "csq1d.set_k (kf)\n", "(Found2, tf) = csq1d.find_adiab_time_limit (None, +1.0e-25, +1.0e15, 1.0e0)\n", "\n", "print (Found2, tf)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x504 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure (figsize=(14, 7))\n", "\n", "max_tf = -1.0e300\n", "min_ti = +1.0e300\n", "\n", "colors = ['r','g','b']\n", "i = 0\n", "\n", "for k in k_a:\n", " csq1d.set_k (k)\n", " csq1d.set_reltol (1.0e-12)\n", " csq1d.set_save_evol (True)\n", "\n", " #print (csq1d.eval_adiab_at (None, -5))\n", "\n", " (Found1, ti) = csq1d.find_adiab_time_limit (None, -1.0e15, -1.0e-25, 1.0e-3)\n", " #print (Found1, ti)\n", "\n", " (Found2, tfa) = csq1d.find_adiab_time_limit (None, +1.0e-25, +1.0e15, 1.0e0)\n", " tf = tfa * 20\n", "\n", " min_ti = min (ti, min_ti)\n", " max_tf = max (tf, max_tf)\n", "\n", " csq1d.set_ti (ti)\n", " csq1d.set_tf (tf)\n", " csq1d.prepare ()\n", "\n", " t_a, t_s = csq1d.get_time_array ()\n", " y_a = []\n", " Abs_phi2_a = []\n", " Abs_Pphi2_a = []\n", "\n", " t_a = np.array (t_a)\n", "\n", " for t in t_a:\n", " (J11, J12, J22) = csq1d.get_J_at (None, t)\n", " Abs_phi2_a.append (0.5 * J11)\n", " Abs_Pphi2_a.append (0.5 * J22)\n", "\n", " y_a = np.array (y_a)\n", " Abs_phi2_a = np.array (Abs_phi2_a)\n", " Abs_Pphi2_a = np.array (Abs_Pphi2_a)\n", "\n", " mylw = 1\n", " t_ar = (t_a > tfa)\n", " ampPhi = math.sqrt (max (Abs_phi2_a[t_ar]))\n", " ampPphi = math.sqrt (max (Abs_Pphi2_a[t_ar]))\n", "\n", " plt.plot (t_a, np.sqrt (Abs_phi2_a), lw=mylw, label = r'$|A_{sk}|(k_s = %s)$' % latex_float (k), linestyle='--', color=colors[i])\n", " #plt.plot (t_a[t_ar], [ampPhi] * len (t_a[t_ar]), lw=mylw, label = r'Amp of $\\mathrm{Abs}(\\phi)(k_s = %s)$' % latex_float (k), linestyle='--', color=colors[i])\n", " plt.plot (t_a, np.sqrt (Abs_Pphi2_a), lw=mylw, label = r'$|\\Pi_A|(k_s = %s)$' % latex_float (k), color=colors[i])\n", " #plt.plot (t_a[t_ar], [ampPphi] * len (t_a[t_ar]), lw=mylw, label = r'Amp of $\\mathrm{Abs}(\\Pi_\\phi)(k_s = %s)$' % latex_float (k), color=colors[i])\n", "\n", " i=i+1\n", " \n", "tc_a = np.geomspace (min_ti, -t_s, 1000)\n", "te_a = np.geomspace (t_s, max_tf, 1000)\n", "t_a = np.concatenate ((tc_a, te_a))\n", "\n", "#plt.plot (t_a, [csq1d.y_t (t) for t in t_a], lw=mylw, label = r'$a(t)$')\n", "#plt.plot (t_a, [csq1d.do_eval_F1 (None, t, k) for t in t_a], lw=mylw, label = r'$F_1(t)$')\n", "#plt.plot (t_a, [csq1d.do_eval_m (None, t, k) for t in t_a], lw=mylw, label = r'$m(t)$')\n", "#plt.plot (t_a, [csq1d.F_t (t) for t in t_a], lw=mylw, label = r'$F(t)$')\n", "\n", "plt.grid (b=True, which='both', linestyle=':', color='0.75', linewidth=0.5)\n", "leg = plt.legend (loc=\"best\", ncol = 2, fontsize=13)\n", "plt.xscale('symlog', linthreshx = t_s)\n", "plt.yscale('log')\n", "\n", "plt.xlabel('$t_s$',fontsize=16)\n", "plt.xticks(size=14)\n", "plt.yticks(size=14)\n", "\n", "# Erase overlapping ticks labels\n", "ticks=plt.xticks()[0]\n", "ticks[7]=0\n", "ticks[9]=0\n", "plt.xticks(ticks)\n", "\n", "plt.savefig('MagDustModeEvol.pdf')\n", "plt.show ()\n", "\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1008x504 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure (figsize=(14, 7))\n", "\n", "max_tf = -1.0e300\n", "min_ti = +1.0e300\n", "\n", "colors = ['r','g','b']\n", "i = 0\n", "\n", "for k in k_a:\n", " csq1d.set_k (k)\n", " csq1d.set_reltol (1.0e-12)\n", " csq1d.set_save_evol (True)\n", "\n", " #print (csq1d.eval_adiab_at (None, -5))\n", "\n", " (Found1, ti) = csq1d.find_adiab_time_limit (None, -1.0e15, -1.0e-25, 1.0e-3)\n", " #print (Found1, ti)\n", "\n", " (Found2, tfa) = csq1d.find_adiab_time_limit (None, +1.0e-25, +1.0e15, 1.0e0)\n", " tf = tfa * 20\n", "\n", " min_ti = min (ti, min_ti)\n", " max_tf = max (tf, max_tf)\n", "\n", " csq1d.set_ti (ti)\n", " csq1d.set_tf (tf)\n", " csq1d.prepare ()\n", "\n", " t_a, t_s = csq1d.get_time_array ()\n", " y_a = []\n", " PB_a = []\n", " PE_a = []\n", "\n", " t_a = np.array (t_a)\n", "\n", " for t in t_a:\n", " (PB, PE) = csq1d.eval_PB_PE (t)\n", " y_a.append (csq1d.y_t (t))\n", " PB_a.append (PB)\n", " PE_a.append (PE)\n", "\n", " y_a = np.array (y_a)\n", " PB_a = np.array (PB_a) * conv_g2\n", " PE_a = np.array (PE_a) * conv_g2\n", "\n", " mylw = 1\n", "\n", " #plt.plot (t_a, PB_a, lw=mylw, label = r'$P_B(k_s = %s)$' % latex_float (k),linestyle='--',color=colors[i])\n", " #plt.plot (t_a, PE_a, lw=mylw, label = r'$P_E(k_s = %s)$' % latex_float (k),color=colors[i])\n", " #plt.plot (t_a, PE_a+PB_a, lw=mylw, label = r'$P_B+P_E(k_s = %s)$' % latex_float (k),color=colors[i])\n", " plt.plot (t_a, ((PE_a+PB_a)[-1]) * y_a[-1]**4 / y_a**4, lw=mylw, label = r'$\\rho_r(k_s = %s)$' % latex_float (k)) \n", "\n", " if i>2:\n", " i=0\n", " else:\n", " i=i+1\n", " \n", "tc_a = np.geomspace (min_ti, -t_s, 1000)\n", "te_a = np.geomspace (t_s, max_tf, 1000)\n", "t_a = np.concatenate ((tc_a, te_a))\n", "\n", "#plt.plot (t_a, [csq1d.y_t (t) for t in t_a], lw=mylw, label = r'$a(t)$')\n", "plt.plot (t_a, [csq1d.Omega_m_t (t) for t in t_a], lw=mylw, label = r'$\\rho_m$')\n", "#plt.axhline (y = 1.0)\n", "\n", "plt.grid (b=True, which='both', linestyle=':', color='0.75', linewidth=0.5)\n", "leg = plt.legend (loc=\"best\", ncol=2, fontsize=13)\n", "plt.xscale('symlog', linthreshx = t_s)\n", "plt.yscale('log')\n", "\n", "plt.xlabel('$t_s$',fontsize=16)\n", "plt.xticks(size=14)\n", "plt.yticks(size=14)\n", "\n", "# Erase overlapping ticks labels\n", "ticks=plt.xticks()[0]\n", "ticks[7]=0\n", "ticks[9]=0\n", "plt.xticks(ticks)\n", "\n", "\n", "plt.savefig('MagDustModeEvolPBPE.pdf')\n", "plt.show ()\n", "\n" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/lib/python3.8/site-packages/numpy/ma/core.py:6759: RuntimeWarning: overflow encountered in power\n", " result = np.where(m, fa, umath.power(fa, fb)).view(basetype)\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "<Figure size 1152x576 with 1 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "nE = 6.00000006752166\n", "nB = 6.00000029154731\n", "Ab[G2] = 1.984265330144050e-55\n", "B1[nG] = 3.499192709772306e-08\n", "Ab[rc] = 2.834664757348644e-48\n", "kcut = 8.405869903844148e+07\n", "Or0 = 1.935135726347594e-27\n", "x_eq = 1.550278855975776e+26\n", "t_eq = 6.305701059664368e-40\n" ] } ], "source": [ "fig = plt.figure (figsize=(16, 8))\n", "ax = fig.add_subplot(111)\n", "\n", "k_a = np.geomspace (ki, kf, 10)\n", "PB1_a = []\n", "PE1_a = []\n", "PB2_a = []\n", "PE2_a = []\n", "\n", "tf1 = 1.0e-35\n", "tf2 = csq1d.t_y (1.0 / (1.0 + 0.0))\n", "\n", "for k in k_a:\n", " csq1d.set_k (k)\n", " csq1d.set_reltol (1.0e-12)\n", " csq1d.set_save_evol (True)\n", "\n", " (Found1, ti) = csq1d.find_adiab_time_limit (None, -1.0e15, -1.0e-25, 1.0e-3)\n", " (Found2, tfa) = csq1d.find_adiab_time_limit (None, +1.0e-25, +1.0e15, 1.0e0)\n", " tf = tfa * 20\n", " \n", " csq1d.set_ti (ti)\n", " csq1d.set_tf (tf)\n", " csq1d.prepare ()\n", " \n", " t_a, t_s = csq1d.get_time_array ()\n", " t_a = np.array (t_a)\n", " t_ra = t_a > tfa\n", "\n", " JM = np.array ([np.array (csq1d.get_J_at (None, t)) * conv_g2 for t in t_a[t_ra]])\n", " phiAmp = 0.5 * max (JM[:,0])\n", " PphiAmp = 0.5 * max (JM[:,2])\n", "\n", " #(PE1, PB1) = csq1d.eval_PB_PE (tf1, c_A2 = phiAmp, c_PiA2 = PphiAmp)\n", " #PB1_a.append (PB1)\n", " #PE1_a.append (PE1)\n", "\n", " (PE2, PB2) = csq1d.eval_PB_PE (tf2, c_A2 = phiAmp, c_PiA2 = PphiAmp)\n", " PB2_a.append (PB2)\n", " PE2_a.append (PE2)\n", "\n", " #print (phiAmp, PphiAmp, PB2, PE2)\n", "\n", "PE2_a = np.array (PE2_a)\n", "PB2_a = np.array (PB2_a)\n", "\n", "mylw = 1\n", "\n", "#plt.plot (k_a, PE1_a, lw=mylw, label = r'$P_E(%s)$' % latex_float (tf1))\n", "#plt.plot (k_a, PB1_a, lw=mylw, label = r'$P_B(%s)$' % latex_float (tf1))\n", "\n", "#plt.plot (k_a, PE2_a, lw=mylw, label = r'$P_E(%s)$' % latex_float (tf2))\n", "#plt.plot (k_a, PB2_a, lw=mylw, label = r'$P_B(%s)$' % latex_float (tf2))\n", "#plt.plot (k_a, PB2_a[0] * (k_a / k_a[0] )**4, lw=mylw, label = r'$P_{B0}(%s)k^4$' % latex_float (tf2))\n", "#plt.plot (k_a, PB2_a[0] * (k_a / k_a[0] )**6, lw=mylw, label = r'$P_{B0}(%s)k^6$' % latex_float (tf2))\n", "\n", "plt.plot (k_a, np.sqrt (PB2_a * csq1d.RH**6) * 1.0e9, lw=mylw, label = r'$P_B(t = %s)$' % latex_float (tf2))\n", "plt.plot (k_a, math.sqrt (PB2_a[0] * csq1d.RH**6) * (k_a / k_a[0])**6 * 1.0e9, lw=mylw, label = r'$P_{B0}(t = %s)k^6$' % latex_float (tf2))\n", "\n", "plt.grid (b=True, which='both', linestyle=':', color='0.75', linewidth=0.5)\n", "leg = plt.legend (loc=\"best\", fontsize=13)\n", "\n", "plt.xscale('log')\n", "plt.yscale('log')\n", "\n", "# Top x-axis\n", "\n", "def forward(x):\n", " return [csq1d.RH / xi if xi > 0.0 else 1.0e30 for xi in x]\n", "\n", "def inverse(x):\n", " return [csq1d.RH / xi if xi > 0.0 else 1.0e30 for xi in x]\n", "\n", "ax2=ax.secondary_xaxis('top', functions=(forward,inverse))\n", "ax2.set_xlabel('$\\lambda (Mpc)$',fontsize=16)\n", "\n", "plt.xlabel('$k R_H$',fontsize=16)\n", "plt.ylabel('$B_{\\lambda} (nG)$',fontsize=16)\n", "plt.xticks(size=14)\n", "plt.yticks(size=14)\n", "\n", "\n", "plt.savefig('MagDustPS.pdf')\n", "plt.show ()\n", "\n", "nE = math.log (PE2_a[1] / PE2_a[0]) / math.log (k_a[1] / k_a[0])\n", "nB = math.log (PB2_a[1] / PB2_a[0]) / math.log (k_a[1] / k_a[0])\n", "\n", "Abg = ((PE2_a[0] + PB2_a[0]) / k_a[0]**nB)\n", "Abr = Abg / conv_g2 * conv_rhoc\n", "Or0 = Abr * kf**nB / nB\n", "xeq = csq1d.Omega_m / Or0\n", "teq = csq1d.t_y (1.0 / xeq)\n", "\n", "print ( \"nE = % 22.15g\" % (nE) )\n", "print ( \"nB = % 22.15g\" % (nB) )\n", "print ( \"Ab[G2] = % 22.15e\" % (Abg) )\n", "print ( \"B1[nG] = % 22.15e\" % (math.sqrt (Abg * csq1d.RH**6) * 1.0e9) )\n", "print ( \"Ab[rc] = % 22.15e\" % (Abr) )\n", "print ( \"kcut = % 22.15e\" % ((1.0 / Abr)**(1.0 / nB)) )\n", "print ( \"Or0 = % 22.15e\" % (Or0) )\n", "print ( \"x_eq = % 22.15e\" % (xeq) )\n", "print ( \"t_eq = % 22.15e\" % (teq) )\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 2 }
gpl-3.0
jonathanmorgan/msu_phd_work
data/article_loading/proquest_hnp/proquest_hnp-article_loading.ipynb
1
34358
{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Introduction\" data-toc-modified-id=\"Introduction-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Introduction</a></span></li><li><span><a href=\"#Setup\" data-toc-modified-id=\"Setup-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Setup</a></span><ul class=\"toc-item\"><li><span><a href=\"#Setup---Debug\" data-toc-modified-id=\"Setup---Debug-2.1\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Setup - Debug</a></span></li><li><span><a href=\"#Setup---Imports\" data-toc-modified-id=\"Setup---Imports-2.2\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Setup - Imports</a></span></li><li><span><a href=\"#Setup---working-folder-paths\" data-toc-modified-id=\"Setup---working-folder-paths-2.3\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Setup - working folder paths</a></span></li><li><span><a href=\"#Setup---logging\" data-toc-modified-id=\"Setup---logging-2.4\"><span class=\"toc-item-num\">2.4&nbsp;&nbsp;</span>Setup - logging</a></span></li><li><span><a href=\"#Setup---virtualenv-jupyter-kernel\" data-toc-modified-id=\"Setup---virtualenv-jupyter-kernel-2.5\"><span class=\"toc-item-num\">2.5&nbsp;&nbsp;</span>Setup - virtualenv jupyter kernel</a></span></li><li><span><a href=\"#Setup---Initialize-Django\" data-toc-modified-id=\"Setup---Initialize-Django-2.6\"><span class=\"toc-item-num\">2.6&nbsp;&nbsp;</span>Setup - Initialize Django</a></span></li><li><span><a href=\"#Setup---Initialize-LoggingHelper\" data-toc-modified-id=\"Setup---Initialize-LoggingHelper-2.7\"><span class=\"toc-item-num\">2.7&nbsp;&nbsp;</span>Setup - Initialize LoggingHelper</a></span></li><li><span><a href=\"#Setup---initialize-ProquestHNPNewspaper\" data-toc-modified-id=\"Setup---initialize-ProquestHNPNewspaper-2.8\"><span class=\"toc-item-num\">2.8&nbsp;&nbsp;</span>Setup - initialize ProquestHNPNewspaper</a></span><ul class=\"toc-item\"><li><span><a href=\"#load-from-database\" data-toc-modified-id=\"load-from-database-2.8.1\"><span class=\"toc-item-num\">2.8.1&nbsp;&nbsp;</span>load from database</a></span></li><li><span><a href=\"#set-up-manually\" data-toc-modified-id=\"set-up-manually-2.8.2\"><span class=\"toc-item-num\">2.8.2&nbsp;&nbsp;</span>set up manually</a></span></li></ul></li></ul></li><li><span><a href=\"#Find-articles-to-be-loaded\" data-toc-modified-id=\"Find-articles-to-be-loaded-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Find articles to be loaded</a></span><ul class=\"toc-item\"><li><span><a href=\"#Uncompress-files\" data-toc-modified-id=\"Uncompress-files-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Uncompress files</a></span></li><li><span><a href=\"#Work-with-uncompressed-files\" data-toc-modified-id=\"Work-with-uncompressed-files-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Work with uncompressed files</a></span></li><li><span><a href=\"#parse-and-load-XML-files\" data-toc-modified-id=\"parse-and-load-XML-files-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>parse and load XML files</a></span></li><li><span><a href=\"#build-list-of-all-ObjectTypes\" data-toc-modified-id=\"build-list-of-all-ObjectTypes-3.4\"><span class=\"toc-item-num\">3.4&nbsp;&nbsp;</span>build list of all ObjectTypes</a></span></li><li><span><a href=\"#map-files-to-types\" data-toc-modified-id=\"map-files-to-types-3.5\"><span class=\"toc-item-num\">3.5&nbsp;&nbsp;</span>map files to types</a></span></li></ul></li><li><span><a href=\"#XML-analysis\" data-toc-modified-id=\"XML-analysis-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>XML analysis</a></span></li><li><span><a href=\"#TODO\" data-toc-modified-id=\"TODO-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>TODO</a></span></li></ul></div>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "This is a notebook that expands on the OpenCalais code in the file `article_coding.py`, also in this folder. It includes more sections on selecting publications you want to submit to OpenCalais as an example. It is intended to be copied and re-used." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup - Debug\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "debug_flag = False" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup - Imports\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import datetime\n", "import glob\n", "import logging\n", "import lxml\n", "import os\n", "import six\n", "import xml\n", "import xmltodict\n", "import zipfile" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup - working folder paths\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "What data are we looking at?" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# paper identifier\n", "paper_identifier = \"BostonGlobe\"\n", "archive_identifier = \"BG_20171002210239_00001\"\n", "\n", "# source\n", "source_paper_folder = \"/mnt/hgfs/projects/phd/proquest_hnp/proquest_hnp/data\"\n", "source_paper_path = \"{}/{}\".format( source_paper_folder, paper_identifier )\n", "\n", "# uncompressed\n", "uncompressed_paper_folder = \"/mnt/hgfs/projects/phd/proquest_hnp/uncompressed\"\n", "uncompressed_paper_path = \"{}/{}\".format( uncompressed_paper_folder, paper_identifier )\n", "\n", "# make sure an identifier is set before you make a path here.\n", "if ( ( archive_identifier is not None ) and ( archive_identifier != \"\" ) ):\n", " \n", " # identifier is set.\n", " source_archive_file = \"{}.zip\".format( archive_identifier )\n", " source_archive_path = \"{}/{}\".format( source_paper_path, source_archive_file )\n", " uncompressed_archive_path = \"{}/{}\".format( uncompressed_paper_path, archive_identifier )\n", "\n", "#-- END check to see if archive_identifier present. --#" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%pwd" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# current working folder\n", "current_working_folder = \"/home/jonathanmorgan/work/django/research/work/phd_work/data/article_loading/proquest_hnp/{}\".format( paper_identifier )\n", "current_datetime = datetime.datetime.now()\n", "current_date_string = current_datetime.strftime( \"%Y-%m-%d-%H-%M-%S\" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup - logging\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "configure logging for this notebook's kernel (If you do not run this cell, you'll get the django application's logging configuration." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "logging_file_name = \"{}/research-data_load-{}-{}.log.txt\".format( current_working_folder, paper_identifier, current_date_string )\n", "logging.basicConfig(\n", " level = logging.DEBUG,\n", " format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',\n", " filename = logging_file_name,\n", " filemode = 'w' # set to 'a' if you want to append, rather than overwrite each time.\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup - virtualenv jupyter kernel\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "If you are using a virtualenv, make sure that you:\n", "\n", "- have installed your virtualenv as a kernel.\n", "- choose the kernel for your virtualenv as the kernel for your notebook (Kernel --> Change kernel).\n", "\n", "Since I use a virtualenv, need to get that activated somehow inside this notebook. One option is to run `../dev/wsgi.py` in this notebook, to configure the python environment manually as if you had activated the `sourcenet` virtualenv. To do this, you'd make a code cell that contains:\n", "\n", " %run ../dev/wsgi.py\n", " \n", "This is sketchy, however, because of the changes it makes to your Python environment within the context of whatever your current kernel is. I'd worry about collisions with the actual Python 3 kernel. Better, one can install their virtualenv as a separate kernel. Steps:\n", "\n", "- activate your virtualenv:\n", "\n", " workon research\n", "\n", "- in your virtualenv, install the package `ipykernel`.\n", "\n", " pip install ipykernel\n", "\n", "- use the ipykernel python program to install the current environment as a kernel:\n", "\n", " python -m ipykernel install --user --name <env_name> --display-name \"<display_name>\"\n", " \n", " `sourcenet` example:\n", " \n", " python -m ipykernel install --user --name sourcenet --display-name \"research (Python 3)\"\n", " \n", "More details: [http://ipython.readthedocs.io/en/stable/install/kernel_install.html](http://ipython.readthedocs.io/en/stable/install/kernel_install.html)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup - Initialize Django\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "First, initialize my dev django project, so I can run code in this notebook that references my django models and can talk to the database using my project's settings." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# init django\n", "django_init_folder = \"/home/jonathanmorgan/work/django/research/work/phd_work\"\n", "django_init_path = \"django_init.py\"\n", "if( ( django_init_folder is not None ) and ( django_init_folder != \"\" ) ):\n", " \n", " # add folder to front of path.\n", " django_init_path = \"{}/{}\".format( django_init_folder, django_init_path )\n", " \n", "#-- END check to see if django_init folder. --#" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%run $django_init_path" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# context_text imports\n", "from context_text.article_coding.article_coding import ArticleCoder\n", "from context_text.article_coding.article_coding import ArticleCoding\n", "from context_text.article_coding.open_calais_v2.open_calais_v2_article_coder import OpenCalaisV2ArticleCoder\n", "from context_text.collectors.newsbank.newspapers.GRPB import GRPB\n", "from context_text.collectors.newsbank.newspapers.DTNB import DTNB\n", "from context_text.models import Article\n", "from context_text.models import Article_Subject\n", "from context_text.models import Newspaper\n", "from context_text.shared.context_text_base import ContextTextBase\n", "\n", "# context_text_proquest_hnp\n", "from context_text_proquest_hnp.proquest_hnp_newspaper_helper import ProquestHNPNewspaperHelper" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup - Initialize LoggingHelper\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "Create a LoggingHelper instance to use to log debug and also print at the same time.\n", "\n", "Preconditions: Must be run after Django is initialized, since `python_utilities` is in the django path." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# python_utilities\n", "from python_utilities.logging.logging_helper import LoggingHelper\n", "\n", "# init\n", "my_logging_helper = LoggingHelper()\n", "my_logging_helper.set_logger_name( \"proquest_hnp-article-loading-{}\".format( paper_identifier ) )\n", "log_message = None" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup - initialize ProquestHNPNewspaper\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "Create an initialize an instance of ProquestHNPNewspaper for this paper." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### load from database\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_paper = ProquestHNPNewspaperHelper()\n", "paper_instance = my_paper.initialize_from_database( paper_identifier )\n", "my_paper.source_all_papers_folder = source_paper_folder\n", "my_paper.destination_all_papers_folder = uncompressed_paper_folder" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print( my_paper )\n", "print( paper_instance )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### set up manually\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "my_paper = ProquestHNPNewspaperHelper()\n", "my_paper.paper_identifier = paper_identifier\n", "my_paper.source_all_papers_folder = source_paper_folder\n", "my_paper.source_paper_path = source_paper_path\n", "my_paper.destination_all_papers_folder = uncompressed_paper_folder\n", "my_paper.destination_paper_path = uncompressed_paper_path\n", "my_paper.paper_start_year = 1872\n", "my_paper.paper_end_year = 1985\n", "\n", "my_newspaper = Newspaper.objects.get( id = 6 )\n", "my_paper.newspaper = my_newspaper" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If desired, add to database." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "phnp_newspaper_instance = my_paper.create_PHNP_newspaper()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print( phnp_newspaper_instance )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Find articles to be loaded\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "Specify which folder of XML files should be loaded into system, then process all files within the folder.\n", "\n", "The compressed archives from proquest_hnp just contain publication XML files, no containing folder.\n", "\n", "To process:\n", "\n", "- **uncompresed paper folder ( `<paper_folder>` )** - make a folder in `/mnt/hgfs/projects/phd/proquest_hnp/uncompressed` for the paper whose data you are working with, named the same as the paper's folder in `/mnt/hgfs/projects/phd/proquest_hnp/proquest_hnp/data`.\n", "\n", " - for example, for the Boston Globe, name it \"`BostonGlobe`\".\n", "\n", "- **uncompressed archive folder ( `<archive_folder>` )** - inside a given paper's folder in uncompressed, for each archive file, create a folder named the same as the archive file, but with no \".zip\" at the end.\n", "\n", " - For example, for the file \"`BG_20171002210239_00001.zip`\", make a folder named \"`BG_20171002210239_00001`\".\n", " - path should be \"`<paper_folder>/<archive_name_no_zip>`.\n", "\n", "- unzip the archive into this folder:\n", "\n", " unzip <path_to_zip> -d <archive_folder>\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Uncompress files\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "See if the uncompressed paper folder exists. If not, set flag and create it." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# create folder to hold the results of decompressing paper's zip files.\n", "did_uncomp_paper_folder_exist = my_paper.make_dest_paper_folder()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For each *.zip file in the paper's source folder:\n", "\n", "- parse file name from path returned by glob.\n", "- parse the part before \".zip\" from the file name. This is referred to subsequently as the \"archive identifier\".\n", "- check if folder named the same as the \"archive identifier\" is present.\n", "\n", " - If no:\n", " \n", " - create it.\n", " - then, uncompress the archive into it.\n", " \n", " - If yes:\n", " \n", " - output a message. Don't want to uncompress if it was already uncompressed once." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# decompress the files\n", "my_paper.uncompress_paper_zip_files()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Work with uncompressed files\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "Change working directories to the uncompressed paper path." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%cd $uncompressed_paper_path" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%ls" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## parse and load XML files\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "Load one of the files into memory and see what we can do with it. Beautiful Soup?\n", "\n", "Looks like the root element is \"Record\", then the high-level type of the article is \"ObjectType\".\n", "\n", "ObjectType values:\n", "\n", "- Advertisement\n", "- ...\n", "\n", "Good options for XML parser:\n", "\n", "- `lxml.etree` - [https://stackoverflow.com/questions/12290091/reading-xml-file-and-fetching-its-attributes-value-in-python](https://stackoverflow.com/questions/12290091/reading-xml-file-and-fetching-its-attributes-value-in-python)\n", "- `xmltodict` - [https://docs.python-guide.org/scenarios/xml/](https://docs.python-guide.org/scenarios/xml/)\n", "- `beautifulsoup` using `lxml`" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# loop over files in the current archive folder path.\n", "object_type_to_count_map = my_paper.process_archive_object_types( uncompressed_archive_path )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Processing 5752 files in /mnt/hgfs/projects/phd/proquest_hnp/uncompressed/BostonGlobe/BG_20171002210239_00001\n", " ----> XML file count: 5752\n", "\n", " Counters:\n", " - Processed 5752 files\n", " - No Record: 0\n", " - No ObjectType: 0\n", " - No ObjectType value: 0\n", "\n", " ObjectType values and occurrence counts:\n", " - A|d|v|e|r|t|i|s|e|m|e|n|t: 1902\n", " - Article|Feature: 1792\n", " - N|e|w|s: 53\n", " - Commentary|Editorial: 36\n", " - G|e|n|e|r|a|l| |I|n|f|o|r|m|a|t|i|o|n: 488\n", " - S|t|o|c|k| |Q|u|o|t|e: 185\n", " - Advertisement|Classified Advertisement: 413\n", " - E|d|i|t|o|r|i|a|l| |C|a|r|t|o|o|n|/|C|o|m|i|c: 31\n", " - Correspondence|Letter to the Editor: 119\n", " - Front Matter|Table of Contents: 193\n", " - O|b|i|t|u|a|r|y: 72\n", " - F|r|o|n|t| |P|a|g|e|/|C|o|v|e|r| |S|t|o|r|y: 107\n", " - I|m|a|g|e|/|P|h|o|t|o|g|r|a|p|h: 84\n", " - Marriage Announcement|News: 6\n", " - I|l|l|u|s|t|r|a|t|i|o|n: 91\n", " - R|e|v|i|e|w: 133\n", " - C|r|e|d|i|t|/|A|c|k|n|o|w|l|e|d|g|e|m|e|n|t: 30\n", " - News|Legal Notice: 17" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## build list of all ObjectTypes\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "Loop over all folders in the paper path. For each folder, grab all files in the folder. For each file, parse XML, then get the ObjectType value and if it isn't already in map of obect types to counts, add it. Increment count.\n", "\n", "From command line, in the uncompressed BostonGlobe folder:\n", "\n", " find . -type f -iname \"*.xml\" | wc -l\n", "\n", "resulted in 11,374,500 articles. That is quite a few." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "xml_folder_list = glob.glob( \"{}/*\".format( uncompressed_paper_path ) )\n", "print( \"folder_list: {}\".format( xml_folder_list ) )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# build map of all object types for a paper to the overall counts of each\n", "paper_object_type_to_count_map = my_paper.process_paper_object_types()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Example output:**\n", "\n", "XML file count: 5752\n", "Counters:\n", "- Processed 5752 files\n", "- No Record: 0\n", "- No ObjectType: 0\n", "- No ObjectType value: 0\n", "\n", "ObjectType values and occurrence counts:\n", "- A|d|v|e|r|t|i|s|e|m|e|n|t: 2114224\n", "- Feature|Article: 5271887\n", "- I|m|a|g|e|/|P|h|o|t|o|g|r|a|p|h: 249942\n", "- O|b|i|t|u|a|r|y: 625143\n", "- G|e|n|e|r|a|l| |I|n|f|o|r|m|a|t|i|o|n: 1083164\n", "- S|t|o|c|k| |Q|u|o|t|e: 202776\n", "- N|e|w|s: 140274\n", "- I|l|l|u|s|t|r|a|t|i|o|n: 106925\n", "- F|r|o|n|t| |P|a|g|e|/|C|o|v|e|r| |S|t|o|r|y: 386421\n", "- E|d|i|t|o|r|i|a|l| |C|a|r|t|o|o|n|/|C|o|m|i|c: 78993\n", "- Editorial|Commentary: 156342\n", "- C|r|e|d|i|t|/|A|c|k|n|o|w|l|e|d|g|e|m|e|n|t: 68356\n", "- Classified Advertisement|Advertisement: 291533\n", "- R|e|v|i|e|w: 86889\n", "- Table of Contents|Front Matter: 69798\n", "- Letter to the Editor|Correspondence: 202071\n", "- News|Legal Notice: 24053\n", "- News|Marriage Announcement: 41314\n", "- B|i|r|t|h| |N|o|t|i|c|e: 926\n", "- News|Military/War News: 3\n", "- U|n|d|e|f|i|n|e|d: 5\n", "- Article|Feature: 137526\n", "- Front Matter|Table of Contents: 11195\n", "- Commentary|Editorial: 3386\n", "- Marriage Announcement|News: 683\n", "- Correspondence|Letter to the Editor: 7479\n", "- Legal Notice|News: 1029\n", "- Advertisement|Classified Advertisement: 12163\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## map files to types\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "Choose a directory, then loop over the files in the directory to build a map of types to lists of file names." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# directory to work in.\n", "uncompressed_archive_folder = \"BG_20151211054235_00003\"\n", "uncompressed_archive_path = \"{}/{}\".format( uncompressed_paper_path, uncompressed_archive_folder )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# build map of file types to lists of files of that type in specified folder.\n", "object_type_to_file_path_map = my_paper.map_archive_folder_files_to_types( uncompressed_archive_path )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# which types do we want to preview?\n", "types_to_output = master_object_type_list\n", "#types_to_output = [ 'Advertisement|Classified Advertisement' ]\n", "\n", "# declare variables\n", "xml_file_path_list = None\n", "xml_file_path_example_list = None\n", "xml_file_path = None\n", "xml_file = None\n", "xml_dict = None\n", "xml_string = None\n", "\n", "# loop over types\n", "for object_type in types_to_output:\n", " \n", " # print type and count\n", " xml_file_path_list = object_type_to_file_path_map.get( object_type, [] )\n", " xml_file_path_example_list = xml_file_path_list[ : 10 ]\n", " print( \"\\n- {}:\".format( object_type ) )\n", " for xml_file_path in xml_file_path_example_list:\n", " \n", " print( \"----> {}\".format( xml_file_path ) )\n", "\n", " # try to parse the file\n", " with open( xml_file_path ) as xml_file:\n", "\n", " # parse XML\n", " xml_dict = xmltodict.parse( xml_file.read() )\n", " \n", " #-- END with open( xml_file_path ) as xml_file: --#\n", " \n", " # pretty-print\n", " xml_string = xmltodict.unparse( xml_dict, pretty = True )\n", "\n", " # output\n", " print( xml_string )\n", " \n", " #-- END loop over example file paths. --#\n", " \n", "#-- END loop over object types. --#" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# XML analysis\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "IDs:\n", "\n", " <RecordID>1821311973</RecordID>\n", " <URLDocView>http://search.proquest.com/docview/1821311973/</URLDocView>\n", "\n", "- and, the file name is the ID, also.\n", "\n", "Object Types:\n", "\n", " <ObjectType>Feature</ObjectType>\n", "\t<ObjectType>Article</ObjectType>\n", "\n", "- So, order is probably arbitrary, that is why they can be in either order.\n", "\n", "Action code:\n", "\n", " <ActionCode>change</ActionCode>\n", " \n", "- will you ever get the same aritcle with different action codes? Hopefully just get last time it was sent.\n", "\n", "Publication Date:\n", "\n", " <AlphaPubDate>Nov 20, 1985</AlphaPubDate>\n", "\t<NumericPubDate>19851120</NumericPubDate>\n", "\n", "Headline:\n", "\n", " <RecordTitle>Ulster pact rapped in Irish Parliament</RecordTitle>\n", "\n", "Author:\n", "\n", " <Contributor>\n", "\t\t<ContribRole>Author</ContribRole>\n", "\t\t<OriginalForm>Bob O'Connor Special to the Globe</OriginalForm>\n", "\t</Contributor>\n", " \n", " From /mnt/hgfs/projects/phd/proquest_hnp/uncompressed/BostonGlobe/BG_20151210230044_00004/367105818.xml:\n", " <Contributor>\n", "\t\t<ContribRole>Author</ContribRole>\n", "\t\t<LastName>McCain</LastName>\n", "\t\t<FirstName>Nina</FirstName>\n", "\t\t<PersonName>Nina McCain</PersonName>\n", "\t\t<OriginalForm>Nina McCain</OriginalForm>\n", "\t</Contributor>\n", " (\"Globe Staff\" is still in the body text).\n", " \n", "- Looks like you can count on the person's name being in the \"Contributor\" element, sometimes parsed into name parts, sometimes not. Looks like it will not parse if the author string includes a suffix. Example:\n", "\n", " from /mnt/hgfs/projects/phd/proquest_hnp/uncompressed/ChristianScienceMonitor/CSM_20170929191926_00001/513134635.xml:\n", " <Contributor>\n", " <ContribRole>Author</ContribRole>\n", " <OriginalForm>John Dillin Staff writer of The Christian Science Monitor</OriginalForm>\n", " </Contributor>\n", "\n", "- If no \"Contributor\" element, then they are asserting that there is no byline.\n", "- Shared bylines = Multiple Contributor elements:\n", "\n", " from /mnt/hgfs/projects/phd/proquest_hnp/uncompressed/Newsday/Newsday_20171006231925_00050/1000174750.xml\n", " <Contributor>\n", " <ContribRole>Author</ContribRole>\n", " <LastName>Nash</LastName>\n", " <MiddleName>M</MiddleName>\n", " <FirstName>Bruce</FirstName>\n", " <PersonName>Bruce M Nash</PersonName>\n", " <OriginalForm>Bruce M Nash</OriginalForm>\n", " </Contributor>\n", " <Contributor>\n", " <ContribRole>Author</ContribRole>\n", " <LastName>Monchick</LastName>\n", " <MiddleName>B</MiddleName>\n", " <FirstName>Randolph</FirstName>\n", " <PersonName>Randolph B Monchick</PersonName>\n", " <OriginalForm>Randolph B Monchick</OriginalForm>\n", " </Contributor>\n", "\n", "- **Boston Globe**\n", "\n", " - byline suffixes (loop and compile all from the \"OriginalForm\" strings):\n", "\n", " - Contributing Reporter\n", " - Globe Staff\n", " - Special to the Globe\n", " - ...\n", " - if no identifier?\n", " \n", " - `/mnt/hgfs/projects/phd/proquest_hnp/uncompressed/BostonGlobe/BG_20151210230044_00004/367091933.xml` - in this case, person is not a staff journalist.\n", "\n", "- **Newsday**\n", "\n", " - looks like it doesn't have any suffixes, and it has opinion mixed in with hard news (Globe probably does, also). Could start a list of columnists... They'd likely not have many sources, also.\n", " - might be some suffixes (but not many):\n", " \n", " - \"Miriam Pawel Newsday Staff Correspondent\" from `/mnt/hgfs/projects/phd/proquest_hnp/uncompressed/Newsday/Newsday_20171006231925_00050/1000247974.xml`\n", " - \"Susan Page Newsday Washington Bureau\" from `/mnt/hgfs/projects/phd/proquest_hnp/uncompressed/Newsday/Newsday_20171006231925_00050/1002490977.xml`\n", "\n", "- **Christian Science Monitor**\n", "\n", " - looks like news includes a suffix. Example:\n", "\n", " from /mnt/hgfs/projects/phd/proquest_hnp/uncompressed/ChristianScienceMonitor/CSM_20170929191926_00001/513134635.xml:\n", " <Contributor>\n", " <ContribRole>Author</ContribRole>\n", " <OriginalForm>John Dillin Staff writer of The Christian Science Monitor</OriginalForm>\n", " </Contributor>\n", "\n", "Body text:\n", "\n", "- `<Abstract>` is first sentence/lead, and `<FullText>` is the full text (so could look for contents of abstract in full text to see where article itself begins? In first example I looked at, the abstract had a period at the end and the sentence in the full text did not. Hmmm.). Headline and byline are in the full text. For Globe, looks like the good way to split is on the \"Original Form\" of the \"Contributor\". That is what I'd try first.\n", "- Punctuation is often missing, might really screw with parser. Would have to test this out.\n", "- all papers, they also include page furniture in the text, including page numbers, text that explains that the story jumped from a previous page, etc.\n", "- and, scan quality generally looks about as good as you'd expect (not good).\n", "- Newsday has lots of different types of content mixed into Article|Feature and front page. Globe looked like it was more hard news, but also might just have not been a representative sample.\n", "- Christian Science Monitor is a national news paper, shouldn't include it - no \"local\" coverage. Also, they have a lot of garbage mixed in with their news, might be hard to confirm that you got all the news, none of the opinion pieces." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# TODO\n", "\n", "- Back to [Table of Contents](#Table-of-Contents)\n", "\n", "TODO:\n", "\n", "- figure out which ObjectTypes to explore, pick a folder and just eyeball a few, to see what they look like.\n", "- run summarize on all articles, store the results, so we can start to look at article quality in different eras." ] } ], "metadata": { "kernelspec": { "display_name": "research_virtualenv", "language": "python", "name": "research" }, "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" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "215px" }, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 2 }
lgpl-3.0
davidthomas5412/PanglossNotebooks
MassLuminosityProject/RegenerateDataSet_2017_02_21.ipynb
1
36480
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import matplotlib.pyplot as plt\n", "from matplotlib import rc\n", "from scipy.stats import norm, lognorm\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import itertools\n", "\n", "rc('text', usetex=True)\n", "sns.set_style(\"whitegrid\")\n", "\n", "# we will work with datasets of 2000 samples\n", "\n", "tmp = pd.read_csv('mock_data.csv')\n", "mass = tmp.mass_h.as_matrix()\n", "z = tmp.z.as_matrix()\n", "\n", "# np.random.seed(0)\n", "# alpha1 = norm(10.709, 0.022).rvs()\n", "# alpha2 = norm(0.359, 0.009).rvs()\n", "# alpha3 = 2.35e14\n", "# alpha4 = norm(1.10, 0.06).rvs()\n", "# S = norm(0.155, 0.0009).rvs()\n", "# sigma_L = 0.05\n", "# mu_li = np.exp(alpha1) * ((mass / alpha3) ** (alpha2))* ((1+z) ** (alpha4))\n", "# li = lognorm(S, scale=mu_li).rvs()\n", "# observed = lognorm(sigma_L, scale=li).rvs()\n", "\n", "# tmp['lum'] = " ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "tmp.drop('Unnamed: 0', axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "np.random.seed(0)\n", "alpha1 = norm(10.709, 0.022).rvs()\n", "alpha2 = norm(0.359, 0.009).rvs()\n", "alpha3 = 2.35e14\n", "alpha4 = norm(1.10, 0.06).rvs()\n", "S = norm(0.155, 0.0009).rvs()\n", "sigma_L = 0.05\n", "mu_li = np.exp(alpha1) * ((mass / alpha3) ** (alpha2))* ((1+z) ** (alpha4))\n", "li = lognorm(S, scale=mu_li).rvs()\n", "observed = lognorm(sigma_L, scale=li).rvs()\n", "\n", "tmp['mass'] = tmp['mass_h']\n", "tmp['lum'] = li\n", "tmp['lum_obs'] = observed\n", "tmp.drop('mass_h', axis=1).to_csv('mock_data.csv')" ] }, { "cell_type": "code", "execution_count": 4, "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>gal_id</th>\n", " <th>z</th>\n", " <th>mass_h</th>\n", " <th>ra</th>\n", " <th>dec</th>\n", " <th>lum</th>\n", " <th>lum_obs</th>\n", " <th>mass</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " <tr>\n", " <th>0</th>\n", " <td>22005657000031</td>\n", " <td>2.07709</td>\n", " <td>1.005246e+11</td>\n", " <td>-108.623172</td>\n", " <td>-101.696113</td>\n", " <td>13777.036439</td>\n", " <td>13748.793557</td>\n", " <td>1.005246e+11</td>\n", " </tr>\n", " <tr>\n", " <th>1</th>\n", " <td>23003396000121</td>\n", " <td>2.00452</td>\n", " <td>9.989612e+10</td>\n", " <td>-106.131150</td>\n", " <td>-104.863309</td>\n", " <td>8553.898485</td>\n", " <td>8259.738500</td>\n", " <td>9.989612e+10</td>\n", " </tr>\n", " <tr>\n", " <th>2</th>\n", " <td>23003339006170</td>\n", " <td>1.99264</td>\n", " <td>3.945599e+11</td>\n", " <td>-105.764686</td>\n", " <td>-102.987674</td>\n", " <td>18963.368661</td>\n", " <td>18382.304954</td>\n", " <td>3.945599e+11</td>\n", " </tr>\n", " <tr>\n", " <th>3</th>\n", " <td>23003385000769</td>\n", " <td>1.99573</td>\n", " <td>1.432479e+11</td>\n", " <td>-105.287527</td>\n", " <td>-101.737709</td>\n", " <td>11060.429382</td>\n", " <td>11510.690346</td>\n", " <td>1.432479e+11</td>\n", " </tr>\n", " <tr>\n", " <th>4</th>\n", " <td>22000106000031</td>\n", " <td>2.04182</td>\n", " <td>1.344521e+11</td>\n", " <td>-105.776374</td>\n", " <td>-102.895886</td>\n", " <td>11085.593094</td>\n", " <td>12059.614847</td>\n", " <td>1.344521e+11</td>\n", " </tr>\n", " <tr>\n", " <th>5</th>\n", " <td>489020438001227</td>\n", " <td>2.12403</td>\n", " <td>3.062861e+12</td>\n", " <td>-107.538907</td>\n", " <td>-104.584507</td>\n", " <td>38500.816256</td>\n", " <td>35905.873192</td>\n", " <td>3.062861e+12</td>\n", " </tr>\n", " <tr>\n", " <th>6</th>\n", " <td>23005411000031</td>\n", " <td>2.01065</td>\n", " <td>6.471282e+10</td>\n", " 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<th>14</th>\n", " <td>23003339014535</td>\n", " <td>1.98944</td>\n", " <td>1.124623e+11</td>\n", " <td>-105.593142</td>\n", " <td>-102.989737</td>\n", " <td>10871.301611</td>\n", " <td>11056.979460</td>\n", " <td>1.124623e+11</td>\n", " </tr>\n", " <tr>\n", " <th>15</th>\n", " <td>23003331037591</td>\n", " <td>1.97385</td>\n", " <td>5.283835e+11</td>\n", " <td>-105.580423</td>\n", " <td>-103.554902</td>\n", " <td>15765.466881</td>\n", " <td>16591.973900</td>\n", " <td>5.283835e+11</td>\n", " </tr>\n", " <tr>\n", " <th>16</th>\n", " <td>489020517000263</td>\n", " <td>2.10828</td>\n", " <td>1.589546e+11</td>\n", " <td>-106.462205</td>\n", " <td>-101.378808</td>\n", " <td>8221.382866</td>\n", " <td>8353.804189</td>\n", " <td>1.589546e+11</td>\n", " </tr>\n", " <tr>\n", " <th>17</th>\n", " <td>22004397000031</td>\n", " <td>2.08523</td>\n", " <td>1.652377e+11</td>\n", " <td>-106.450516</td>\n", " <td>-102.725718</td>\n", " <td>13676.265269</td>\n", " <td>14017.038305</td>\n", " <td>1.652377e+11</td>\n", " </tr>\n", " <tr>\n", " <th>18</th>\n", " <td>23003675000174</td>\n", " <td>2.01170</td>\n", " <td>1.093204e+11</td>\n", " <td>-108.069007</td>\n", " <td>-102.400163</td>\n", " <td>11834.539246</td>\n", " <td>12092.872682</td>\n", " <td>1.093204e+11</td>\n", " </tr>\n", " <tr>\n", " <th>19</th>\n", " <td>23005110000031</td>\n", " <td>2.01723</td>\n", " <td>1.200010e+11</td>\n", " <td>-108.045975</td>\n", " <td>-101.793401</td>\n", " <td>9532.293118</td>\n", " <td>8826.989692</td>\n", " <td>1.200010e+11</td>\n", " </tr>\n", " <tr>\n", " <th>20</th>\n", " <td>489020448001768</td>\n", " <td>2.14908</td>\n", " <td>7.979119e+10</td>\n", " <td>-108.037036</td>\n", " <td>-101.556540</td>\n", " <td>13862.595120</td>\n", " <td>14071.799044</td>\n", " <td>7.979119e+10</td>\n", " </tr>\n", " <tr>\n", " <th>21</th>\n", " <td>23003331013748</td>\n", " <td>1.97491</td>\n", " <td>6.408451e+10</td>\n", " <td>-108.739368</td>\n", " <td>-103.838516</td>\n", " <td>6679.421052</td>\n", " <td>6892.031867</td>\n", " <td>6.408451e+10</td>\n", " </tr>\n", " <tr>\n", " <th>22</th>\n", " <td>23004504000031</td>\n", " <td>1.98738</td>\n", " <td>2.877521e+11</td>\n", " <td>-107.932529</td>\n", " <td>-102.690996</td>\n", " <td>14643.738159</td>\n", " <td>14536.083254</td>\n", " <td>2.877521e+11</td>\n", " </tr>\n", " <tr>\n", " <th>23</th>\n", " <td>23003334023784</td>\n", " <td>2.02952</td>\n", " <td>9.612713e+10</td>\n", " <td>-107.996127</td>\n", " <td>-103.134122</td>\n", " <td>9641.738331</td>\n", " <td>9188.549870</td>\n", " <td>9.612713e+10</td>\n", " </tr>\n", " <tr>\n", " <th>24</th>\n", " <td>22004259000031</td>\n", " <td>2.09194</td>\n", " <td>2.381180e+11</td>\n", " <td>-108.011941</td>\n", " <td>-102.476825</td>\n", " <td>17970.112504</td>\n", " <td>19243.524955</td>\n", " <td>2.381180e+11</td>\n", " </tr>\n", " <tr>\n", " <th>25</th>\n", " <td>23003339008071</td>\n", " <td>1.99263</td>\n", " <td>1.671226e+11</td>\n", " <td>-105.009757</td>\n", " <td>-101.750085</td>\n", " <td>15067.634606</td>\n", " <td>16431.649986</td>\n", " <td>1.671226e+11</td>\n", " </tr>\n", " <tr>\n", " <th>26</th>\n", " <td>489020440007032</td>\n", " <td>2.14665</td>\n", " <td>1.413630e+11</td>\n", " <td>-105.275494</td>\n", " <td>-103.993215</td>\n", " <td>12226.647968</td>\n", " <td>12885.988070</td>\n", " <td>1.413630e+11</td>\n", " </tr>\n", " <tr>\n", " <th>27</th>\n", " <td>23003334015778</td>\n", " <td>2.03251</td>\n", " <td>9.926832e+10</td>\n", " <td>-105.723777</td>\n", " <td>-104.821024</td>\n", " <td>10672.525773</td>\n", " <td>11567.896090</td>\n", " <td>9.926832e+10</td>\n", " </tr>\n", " <tr>\n", " <th>28</th>\n", " <td>23003339007672</td>\n", " <td>1.98870</td>\n", " <td>1.086926e+11</td>\n", " <td>-106.592151</td>\n", " <td>-104.371367</td>\n", " <td>8889.961199</td>\n", " <td>9025.298145</td>\n", " <td>1.086926e+11</td>\n", " </tr>\n", " <tr>\n", " <th>29</th>\n", " <td>489020492001498</td>\n", " <td>2.14307</td>\n", " <td>1.162321e+11</td>\n", " <td>-106.609684</td>\n", " <td>-104.005591</td>\n", " <td>8133.363440</td>\n", " <td>7537.693881</td>\n", " <td>1.162321e+11</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", " </tr>\n", " <tr>\n", " <th>115889</th>\n", " <td>127016819000036</td>\n", " <td>3.03286</td>\n", " <td>1.086926e+11</td>\n", " <td>-119.645276</td>\n", " <td>-110.286698</td>\n", " <td>14315.341869</td>\n", " <td>13571.901017</td>\n", " <td>1.086926e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115890</th>\n", " <td>154012642001049</td>\n", " <td>3.17510</td>\n", " <td>2.167567e+11</td>\n", " <td>-118.837062</td>\n", " <td>-110.060150</td>\n", " <td>19845.531114</td>\n", " <td>19001.082643</td>\n", " <td>2.167567e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115891</th>\n", " <td>131003555070522</td>\n", " <td>3.08358</td>\n", " <td>6.785423e+10</td>\n", " <td>-119.661433</td>\n", " <td>-108.975198</td>\n", " <td>9258.001351</td>\n", " <td>9493.968473</td>\n", " <td>6.785423e+10</td>\n", " </tr>\n", " <tr>\n", " <th>115892</th>\n", " <td>127016102000104</td>\n", " <td>3.04269</td>\n", " <td>1.470176e+11</td>\n", " <td>-119.478889</td>\n", " <td>-112.091171</td>\n", " <td>15191.511017</td>\n", " <td>16528.606424</td>\n", " <td>1.470176e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115893</th>\n", " <td>131003555040466</td>\n", " <td>3.09984</td>\n", " <td>6.911078e+10</td>\n", " <td>-119.005511</td>\n", " <td>-106.677751</td>\n", " <td>10290.137419</td>\n", " <td>10297.270659</td>\n", " <td>6.911078e+10</td>\n", " </tr>\n", " <tr>\n", " <th>115894</th>\n", " <td>131003555039564</td>\n", " <td>3.10047</td>\n", " <td>1.137187e+11</td>\n", " <td>-119.000698</td>\n", " <td>-106.456360</td>\n", " <td>12579.171357</td>\n", " <td>11963.993162</td>\n", " <td>1.137187e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115895</th>\n", " <td>128000002009906</td>\n", " <td>2.99064</td>\n", " <td>6.892229e+11</td>\n", " <td>-119.020294</td>\n", " <td>-105.916290</td>\n", " <td>32551.998886</td>\n", " <td>31005.010183</td>\n", " <td>6.892229e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115896</th>\n", " <td>289000366000035</td>\n", " <td>2.84549</td>\n", " <td>1.137187e+11</td>\n", " <td>-89.779851</td>\n", " <td>-113.219783</td>\n", " <td>17078.054226</td>\n", " <td>15657.830180</td>\n", " <td>1.137187e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115897</th>\n", " <td>289000147000179</td>\n", " <td>2.87487</td>\n", " <td>1.652377e+11</td>\n", " <td>-89.396542</td>\n", " <td>-113.230440</td>\n", " <td>13001.474551</td>\n", " <td>12855.690953</td>\n", " <td>1.652377e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115898</th>\n", " <td>289000953000035</td>\n", " <td>2.87505</td>\n", " <td>1.206296e+11</td>\n", " <td>-89.440202</td>\n", " <td>-116.122961</td>\n", " <td>15638.008391</td>\n", " <td>16159.031398</td>\n", " <td>1.206296e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115899</th>\n", " <td>294001497000086</td>\n", " <td>2.89482</td>\n", " <td>3.267057e+10</td>\n", " <td>-89.833824</td>\n", " <td>-115.281056</td>\n", " <td>8643.156429</td>\n", " <td>8533.088527</td>\n", " <td>3.267057e+10</td>\n", " </tr>\n", " <tr>\n", " <th>115900</th>\n", " <td>294001194000468</td>\n", " <td>2.93028</td>\n", " <td>1.306824e+11</td>\n", " <td>-86.980837</td>\n", " <td>-115.130827</td>\n", " <td>15230.707827</td>\n", " <td>13504.234213</td>\n", " <td>1.306824e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115901</th>\n", " <td>289000147000035</td>\n", " <td>2.87565</td>\n", " <td>2.814690e+11</td>\n", " <td>-89.169307</td>\n", " <td>-112.778033</td>\n", " <td>22596.962193</td>\n", " <td>23415.017152</td>\n", " <td>2.814690e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115902</th>\n", " <td>294001150002253</td>\n", " <td>2.94287</td>\n", " <td>6.973902e+10</td>\n", " <td>-86.572089</td>\n", " 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<th>115910</th>\n", " <td>289000193000147</td>\n", " <td>2.81032</td>\n", " <td>3.769676e+10</td>\n", " <td>-87.295391</td>\n", " <td>-111.342430</td>\n", " <td>11300.310106</td>\n", " <td>10480.906947</td>\n", " <td>3.769676e+10</td>\n", " </tr>\n", " <tr>\n", " <th>115911</th>\n", " <td>289000037005179</td>\n", " <td>2.88232</td>\n", " <td>3.361300e+11</td>\n", " <td>-88.427098</td>\n", " <td>-110.789984</td>\n", " <td>23981.064809</td>\n", " <td>22403.320152</td>\n", " <td>3.361300e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115912</th>\n", " <td>294002054000035</td>\n", " <td>2.92923</td>\n", " <td>2.525683e+11</td>\n", " <td>-87.772207</td>\n", " <td>-118.405968</td>\n", " <td>14234.588200</td>\n", " <td>14285.396718</td>\n", " <td>2.525683e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115913</th>\n", " <td>294001194000115</td>\n", " <td>2.93078</td>\n", " <td>1.878553e+11</td>\n", " <td>-87.795240</td>\n", " <td>-116.497331</td>\n", " <td>14470.491858</td>\n", " <td>14279.630544</td>\n", " <td>1.878553e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115914</th>\n", " <td>289000217000035</td>\n", " <td>2.84022</td>\n", " <td>3.644021e+11</td>\n", " <td>-89.583899</td>\n", " <td>-116.869983</td>\n", " <td>19098.012484</td>\n", " <td>18561.440351</td>\n", " <td>3.644021e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115915</th>\n", " <td>294002202000035</td>\n", " <td>2.94070</td>\n", " <td>9.612713e+10</td>\n", " <td>-87.618883</td>\n", " <td>-119.025794</td>\n", " <td>13678.508971</td>\n", " <td>13842.939986</td>\n", " <td>9.612713e+10</td>\n", " </tr>\n", " <tr>\n", " <th>115916</th>\n", " <td>131004218000036</td>\n", " <td>3.08036</td>\n", " <td>1.671226e+11</td>\n", " <td>-119.445543</td>\n", " <td>-110.464086</td>\n", " <td>18504.595929</td>\n", " <td>19165.509454</td>\n", " <td>1.671226e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115917</th>\n", " <td>131003555070726</td>\n", " <td>3.08397</td>\n", " <td>6.106881e+11</td>\n", " <td>-119.474076</td>\n", " <td>-109.414542</td>\n", " <td>26433.179858</td>\n", " <td>28743.105812</td>\n", " <td>6.106881e+11</td>\n", " </tr>\n", " <tr>\n", " <th>115918</th>\n", " <td>154012673000342</td>\n", " <td>3.17980</td>\n", " <td>1.589546e+11</td>\n", " <td>-116.335070</td>\n", " <td>-111.939910</td>\n", " <td>17296.712819</td>\n", " <td>17474.887424</td>\n", " <td>1.589546e+11</td>\n", " </tr>\n", " </tbody>\n", "</table>\n", "<p>115919 rows × 8 columns</p>\n", "</div>" ], "text/plain": [ " gal_id z mass_h ra dec \\\n", "0 22005657000031 2.07709 1.005246e+11 -108.623172 -101.696113 \n", "1 23003396000121 2.00452 9.989612e+10 -106.131150 -104.863309 \n", "2 23003339006170 1.99264 3.945599e+11 -105.764686 -102.987674 \n", "3 23003385000769 1.99573 1.432479e+11 -105.287527 -101.737709 \n", "4 22000106000031 2.04182 1.344521e+11 -105.776374 -102.895886 \n", "5 489020438001227 2.12403 3.062861e+12 -107.538907 -104.584507 \n", "6 23005411000031 2.01065 6.471282e+10 -107.512780 -102.343440 \n", "7 489020448004718 2.14909 1.262842e+11 -108.392843 -102.238933 \n", "8 22000106000306 2.04232 9.738346e+10 -106.649906 -103.290196 \n", "9 489020438013554 2.12085 1.011532e+11 -106.629967 -103.928585 \n", "10 23003331032688 1.97194 3.706852e+10 -105.134891 -102.794129 \n", "11 23003339009419 1.99453 7.514255e+11 -105.130422 -101.295615 \n", "12 23003339012562 1.99308 1.865990e+11 -107.446432 -101.609138 \n", "13 23003334013157 2.03242 1.206296e+11 -107.873056 -101.492942 \n", "14 23003339014535 1.98944 1.124623e+11 -105.593142 -102.989737 \n", "15 23003331037591 1.97385 5.283835e+11 -105.580423 -103.554902 \n", "16 489020517000263 2.10828 1.589546e+11 -106.462205 -101.378808 \n", "17 22004397000031 2.08523 1.652377e+11 -106.450516 -102.725718 \n", "18 23003675000174 2.01170 1.093204e+11 -108.069007 -102.400163 \n", "19 23005110000031 2.01723 1.200010e+11 -108.045975 -101.793401 \n", "20 489020448001768 2.14908 7.979119e+10 -108.037036 -101.556540 \n", "21 23003331013748 1.97491 6.408451e+10 -108.739368 -103.838516 \n", "22 23004504000031 1.98738 2.877521e+11 -107.932529 -102.690996 \n", "23 23003334023784 2.02952 9.612713e+10 -107.996127 -103.134122 \n", "24 22004259000031 2.09194 2.381180e+11 -108.011941 -102.476825 \n", "25 23003339008071 1.99263 1.671226e+11 -105.009757 -101.750085 \n", "26 489020440007032 2.14665 1.413630e+11 -105.275494 -103.993215 \n", "27 23003334015778 2.03251 9.926832e+10 -105.723777 -104.821024 \n", "28 23003339007672 1.98870 1.086926e+11 -106.592151 -104.371367 \n", "29 489020492001498 2.14307 1.162321e+11 -106.609684 -104.005591 \n", "... ... ... ... ... ... \n", "115889 127016819000036 3.03286 1.086926e+11 -119.645276 -110.286698 \n", "115890 154012642001049 3.17510 2.167567e+11 -118.837062 -110.060150 \n", "115891 131003555070522 3.08358 6.785423e+10 -119.661433 -108.975198 \n", "115892 127016102000104 3.04269 1.470176e+11 -119.478889 -112.091171 \n", "115893 131003555040466 3.09984 6.911078e+10 -119.005511 -106.677751 \n", "115894 131003555039564 3.10047 1.137187e+11 -119.000698 -106.456360 \n", "115895 128000002009906 2.99064 6.892229e+11 -119.020294 -105.916290 \n", "115896 289000366000035 2.84549 1.137187e+11 -89.779851 -113.219783 \n", "115897 289000147000179 2.87487 1.652377e+11 -89.396542 -113.230440 \n", "115898 289000953000035 2.87505 1.206296e+11 -89.440202 -116.122961 \n", "115899 294001497000086 2.89482 3.267057e+10 -89.833824 -115.281056 \n", "115900 294001194000468 2.93028 1.306824e+11 -86.980837 -115.130827 \n", "115901 289000147000035 2.87565 2.814690e+11 -89.169307 -112.778033 \n", "115902 294001150002253 2.94287 6.973902e+10 -86.572089 -114.226012 \n", "115903 289000451000035 2.86855 3.700567e+11 -87.598601 -115.240835 \n", "115904 294001497000120 2.89598 1.972796e+11 -89.906360 -115.581172 \n", "115905 289000212000035 2.87128 2.525683e+11 -87.654292 -114.474561 \n", "115906 289001462000035 2.84460 1.030380e+11 -87.700014 -113.171999 \n", "115907 294001522000035 2.93074 1.451328e+11 -86.291225 -113.679754 \n", "115908 294001150002370 2.94288 1.137187e+11 -86.789011 -114.943813 \n", "115909 294001422000035 2.90749 9.801126e+10 -88.278931 -113.928647 \n", "115910 289000193000147 2.81032 3.769676e+10 -87.295391 -111.342430 \n", "115911 289000037005179 2.88232 3.361300e+11 -88.427098 -110.789984 \n", "115912 294002054000035 2.92923 2.525683e+11 -87.772207 -118.405968 \n", "115913 294001194000115 2.93078 1.878553e+11 -87.795240 -116.497331 \n", "115914 289000217000035 2.84022 3.644021e+11 -89.583899 -116.869983 \n", "115915 294002202000035 2.94070 9.612713e+10 -87.618883 -119.025794 \n", "115916 131004218000036 3.08036 1.671226e+11 -119.445543 -110.464086 \n", "115917 131003555070726 3.08397 6.106881e+11 -119.474076 -109.414542 \n", "115918 154012673000342 3.17980 1.589546e+11 -116.335070 -111.939910 \n", "\n", " lum lum_obs mass \n", "0 13777.036439 13748.793557 1.005246e+11 \n", "1 8553.898485 8259.738500 9.989612e+10 \n", "2 18963.368661 18382.304954 3.945599e+11 \n", "3 11060.429382 11510.690346 1.432479e+11 \n", "4 11085.593094 12059.614847 1.344521e+11 \n", "5 38500.816256 35905.873192 3.062861e+12 \n", "6 8735.401794 8726.686673 6.471282e+10 \n", "7 14405.812397 14915.361990 1.262842e+11 \n", "8 11297.504338 11609.498789 9.738346e+10 \n", "9 10670.604639 9932.444944 1.011532e+11 \n", "10 7370.070293 7135.366056 3.706852e+10 \n", "11 21759.307213 22347.824551 7.514255e+11 \n", "12 15745.834820 13544.178225 1.865990e+11 \n", "13 10451.207326 10514.498134 1.206296e+11 \n", "14 10871.301611 11056.979460 1.124623e+11 \n", "15 15765.466881 16591.973900 5.283835e+11 \n", "16 8221.382866 8353.804189 1.589546e+11 \n", "17 13676.265269 14017.038305 1.652377e+11 \n", "18 11834.539246 12092.872682 1.093204e+11 \n", "19 9532.293118 8826.989692 1.200010e+11 \n", "20 13862.595120 14071.799044 7.979119e+10 \n", "21 6679.421052 6892.031867 6.408451e+10 \n", "22 14643.738159 14536.083254 2.877521e+11 \n", "23 9641.738331 9188.549870 9.612713e+10 \n", "24 17970.112504 19243.524955 2.381180e+11 \n", "25 15067.634606 16431.649986 1.671226e+11 \n", "26 12226.647968 12885.988070 1.413630e+11 \n", "27 10672.525773 11567.896090 9.926832e+10 \n", "28 8889.961199 9025.298145 1.086926e+11 \n", "29 8133.363440 7537.693881 1.162321e+11 \n", "... ... ... ... \n", "115889 14315.341869 13571.901017 1.086926e+11 \n", "115890 19845.531114 19001.082643 2.167567e+11 \n", "115891 9258.001351 9493.968473 6.785423e+10 \n", "115892 15191.511017 16528.606424 1.470176e+11 \n", "115893 10290.137419 10297.270659 6.911078e+10 \n", "115894 12579.171357 11963.993162 1.137187e+11 \n", "115895 32551.998886 31005.010183 6.892229e+11 \n", "115896 17078.054226 15657.830180 1.137187e+11 \n", "115897 13001.474551 12855.690953 1.652377e+11 \n", "115898 15638.008391 16159.031398 1.206296e+11 \n", "115899 8643.156429 8533.088527 3.267057e+10 \n", "115900 15230.707827 13504.234213 1.306824e+11 \n", "115901 22596.962193 23415.017152 2.814690e+11 \n", "115902 11099.456197 11325.810672 6.973902e+10 \n", "115903 22057.698120 20356.659578 3.700567e+11 \n", "115904 17143.196571 18003.320684 1.972796e+11 \n", "115905 20107.707367 18876.353496 2.525683e+11 \n", "115906 17422.167163 17759.578980 1.030380e+11 \n", "115907 13600.906702 14261.624933 1.451328e+11 \n", "115908 15530.533164 15623.516584 1.137187e+11 \n", "115909 14151.860591 13839.037172 9.801126e+10 \n", "115910 11300.310106 10480.906947 3.769676e+10 \n", "115911 23981.064809 22403.320152 3.361300e+11 \n", "115912 14234.588200 14285.396718 2.525683e+11 \n", "115913 14470.491858 14279.630544 1.878553e+11 \n", "115914 19098.012484 18561.440351 3.644021e+11 \n", "115915 13678.508971 13842.939986 9.612713e+10 \n", "115916 18504.595929 19165.509454 1.671226e+11 \n", "115917 26433.179858 28743.105812 6.106881e+11 \n", "115918 17296.712819 17474.887424 1.589546e+11 \n", "\n", "[115919 rows x 8 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tmp" ] } ], "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 }
mit
rethore/windIO
examples/wind_plants/middelgrunden.ipynb
1
5331495
null
apache-2.0
russellclarke82/CV
Pi/Dictionaries.ipynb
1
10662
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "dict = {'key':'a', 'key1':'2', 'key2':'2.34'}" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'key': 'a', 'key1': '2', 'key2': '2.34'}" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dict" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2'" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dict['key1']" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#good exmaple of dictionary usecase would be items in a store.\n", "items = {'apples':1.76, 'bananas':1.00, 'toy car':2.99}" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "car = str(items['toy car']) #to call an integer as a string this must be cast inside parenthises." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'2.99'" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "car" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The cost of this item is: $2.99\n" ] } ], "source": [ "print('The cost of this item is: $' + car) # Alas after several attempts, this now prints out nicely." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "out = 'some string $'" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "car = items['toy car']" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "must be str, not float", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m<ipython-input-34-3962787f3896>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mout\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mcar\u001b[0m \u001b[0;31m# See the difference.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: must be str, not float" ] } ], "source": [ "out + car # See the difference." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "some string $2.99\n" ] } ], "source": [ "print(out + str(car)) # Integers or float datatypes have to be cast to string. " ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "dtypes = {'k1':23, 'k2':[1, 2, 3], 'k3':{'key':'value1', 'key1':23.4, 'key2':['a', 'b', 'c']}}\n", "#Examples of nesting and multiple datatypes including nesting lists within dictionaries and dictionaries inside dictionaries." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'key': 'value1', 'key1': 23.4, 'key2': ['a', 'b', 'c']}" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dtypes['k3']" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "d = dtypes['k3']" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'key': 'value1', 'key1': 23.4, 'key2': ['a', 'b', 'c']}" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "list = d['key2']" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['a', 'b', 'c']" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'b'" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list[1]" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'k1': 23,\n", " 'k2': [1, 2, 3],\n", " 'k3': {'key': 'value1', 'key1': 23.4, 'key2': ['a', 'b', 'c']}}" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dtypes" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'b'" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The proper syntax to call nested values is as follows\n", "dtypes['k3']['key2'][1]" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'B'" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Can then add methods for example:\n", "dtypes['k3']['key2'][1].upper()" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [], "source": [ "nd = {'k1':100, 'k2':200}" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'k1': 100, 'k2': 200}" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-59-8870947e74f8>, line 1)", "output_type": "error", "traceback": [ "\u001b[0;36m File \u001b[0;32m\"<ipython-input-59-8870947e74f8>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m nd.append('k3':300) # Bad, very bad.\u001b[0m\n\u001b[0m ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "nd.append('k3':300) # Bad, very bad." ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "nd['k3'] = 300" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'k1': 100, 'k2': 200, 'k3': 300}" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['k1', 'k2', 'k3'])" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd.keys()" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_items([('k1', 100), ('k2', 200), ('k3', 300)])" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd.items()" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'2': None, 'k': None}" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd.fromkeys('k2')" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'0': None, '2': None}" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd.fromkeys('200')" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_values([100, 200, 300])" ] }, "execution_count": 70, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd.values()" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "300" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nd.get('k3')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "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
Vvkmnn/books
ThinkBayes/12_Evidence.ipynb
1
36038
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So the real Dean of Admission\n", "would probably suggest that we choose the candidate who best\n", "demonstrates the other skills and attitudes we look for in students. But\n", "as an example of Bayesian hypothesis testing, let’s stick with a\n", "narrower question: “How strong is the evidence that Alice is better\n", "prepared than Bob?”\n", "\n", "To answer that question, we need to make some modeling decisions. I’ll\n", "start with a simplification I know is wrong; then we’ll come back and\n", "improve the model. I pretend, temporarily, that all SAT questions are\n", "equally difficult. Actually, the designers of the SAT choose questions\n", "with a range of difficulty, because that improves the ability to measure\n", "statistical differences between test-takers.\n", "\n", "But if we choose a model where all questions are equally difficult, we\n", "can define a characteristic, `p_correct`, for each test-taker, which is\n", "the probability of answering any question correctly. This simplification\n", "makes it easy to compute the likelihood of a given score." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The scale\n", "\n", "In order to understand SAT scores, we have to understand the scoring and\n", "scaling process. Each test-taker gets a raw score based on the number of\n", "correct and incorrect questions. The raw score is converted to a scaled\n", "score in the range 200–800.\n", "\n", "In 2009, there were 54 questions on the math SAT. The raw score for each\n", "test-taker is the number of questions answered correctly minus a penalty\n", "of $1/4$ point for each question answered incorrectly.\n", "\n", "The College Board, which administers the SAT, publishes the map from raw\n", "scores to scaled scores. I have downloaded that data and wrapped it in\n", "an Interpolator object that provides a forward lookup (from raw score to\n", "scaled) and a reverse lookup (from scaled score to raw).\n", "\n", "You can download the code for this example from\n", "<http://thinkbayes.com/sat.py>. For more information see\n", "Section [download]." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The prior\n", "\n", "The College Board also publishes the distribution of scaled scores for\n", "all test-takers. If we convert each scaled score to a raw score, and\n", "divide by the number of questions, the result is an estimate of\n", "`p_correct`. So we can use the distribution of raw scores to model the\n", "prior distribution of `p_correct`.\n", "\n", "Here is the code that reads and processes the data:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "class Exam(object):\n", "\n", " def __init__(self):\n", " self.scale = ReadScale()\n", " scores = ReadRanks()\n", " score_pmf = thinkbayes.MakePmfFromDict(dict(scores))\n", " self.raw = self.ReverseScale(score_pmf)\n", " self.prior = DivideValues(raw, 54)\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`Exam` encapsulates the information we have about the exam.\n", "`ReadScale` and `ReadRanks` read files and return\n", "objects that contain the data: `self.scale` is the\n", "`Interpolator` that converts from raw to scaled scores and\n", "back; `scores` is a list of (score, frequency) pairs.\n", "\n", "`score_pmf` is the Pmf of scaled scores. `self.raw` is the\n", "Pmf of raw scores, and `self.prior` is the Pmf of\n", "`p_correct`.\n", "\n", "![Prior distribution of `p\\_correct` for SAT\n", "test-takers.](figs/sat_prior.pdf)\n", "\n", "[fig.satprior]\n", "\n", "Figure [fig.satprior] shows the prior distribution of `p_correct`. This\n", "distribution is approximately Gaussian, but it is compressed at the\n", "extremes. By design, the SAT has the most power to discriminate between\n", "test-takers within two standard deviations of the mean, and less power\n", "outside that range.\n", "\n", "For each test-taker, I define a Suite called `Sat` that\n", "represents the distribution of `p_correct`. Here’s the definition:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```python\n", "class Sat(thinkbayes.Suite):\n", "\n", " def __init__(self, exam, score):\n", " thinkbayes.Suite.__init__(self)\n", "\n", " self.exam = exam\n", " self.score = score\n", "\n", " # start with the prior distribution\n", " for p_correct, prob in exam.prior.Items():\n", " self.Set(p_correct, prob)\n", "\n", " # update based on an exam score\n", " self.Update(score)\n", " ```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`__init__` takes an Exam object and a scaled score. It makes a copy of\n", "the prior distribution and then updates itself based on the exam score.\n", "\n", "As usual, we inherit `Update` from `Suite` and\n", "provide `Likelihood`:\n", "```python\n", " def Likelihood(self, data, hypo):\n", " p_correct = hypo\n", " score = data\n", "\n", " k = self.exam.Reverse(score)\n", " n = self.exam.max_score\n", " like = thinkbayes.EvalBinomialPmf(k, n, p_correct)\n", " return like\n", "```\n", "\n", "`hypo` is a hypothetical value of `p_correct`, and\n", "`data` is a scaled score.\n", "\n", "To keep things simple, I interpret the raw score as the number of\n", "correct answers, ignoring the penalty for wrong answers. With this\n", "simplification, the likelihood is given by the binomial distribution,\n", "which computes the probability of $k$ correct responses out of $n$\n", "questions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Posterior\n", "\n", "![Posterior distributions of `p\\_correct` for Alice and\n", "Bob.](figs/sat_posteriors_p_corr.pdf)\n", "\n", "[fig.satposterior1]\n", "\n", "Figure [fig.satposterior1] shows the posterior distributions of\n", "`p_correct` for Alice and Bob based on their exam scores. We can see\n", "that they overlap, so it is possible that `p_correct` is actually higher\n", "for Bob, but it seems unlikely.\n", "\n", "Which brings us back to the original question, “How strong is the\n", "evidence that Alice is better prepared than Bob?” We can use the\n", "posterior distributions of `p_correct` to answer this question.\n", "\n", "To formulate the question in terms of Bayesian hypothesis testing, I\n", "define two hypotheses:\n", "\n", "- $A$: `p_correct` is higher for Alice than for Bob.\n", "\n", "- $B$: `p_correct` is higher for Bob than for Alice.\n", "\n", "To compute the likelihood of $A$, we can enumerate all pairs of values\n", "from the posterior distributions and add up the total probability of the\n", "cases where `p_correct` is higher for Alice than for Bob. And we already\n", "have a function, `thinkbayes.PmfProbGreater`, that does that.\n", "\n", "So we can define a Suite that computes the posterior probabilities of\n", "$A$ and $B$:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import thinkbayes\n", "\n", "class TopLevel(thinkbayes.Suite):\n", "\n", " def Update(self, data):\n", " a_sat, b_sat = data\n", "\n", " a_like = thinkbayes.PmfProbGreater(a_sat, b_sat)\n", " b_like = thinkbayes.PmfProbLess(a_sat, b_sat)\n", " c_like = thinkbayes.PmfProbEqual(a_sat, b_sat)\n", "\n", " a_like += c_like / 2\n", " b_like += c_like / 2\n", "\n", " self.Mult('A', a_like)\n", " self.Mult('B', b_like)\n", "\n", " self.Normalize()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Usually when we define a new Suite, we inherit `Update` and\n", "provide `Likelihood`. In this case I override\n", "`Update`, because it is easier to evaluate the likelihood of\n", "both hypotheses at the same time.\n", "\n", "The data passed to `Update` are Sat objects that represent\n", "the posterior distributions of `p_correct`.\n", "\n", "`a_like` is the total probability that `p_correct` is higher for Alice;\n", "`b_like` is that probability that it is higher for Bob.\n", "\n", "`c_like` is the probability that they are “equal,” but this equality is\n", "an artifact of the decision to model `p_correct` with a set of discrete\n", "values. If we use more values, `c_like` is smaller, and in the extreme,\n", "if `p_correct` is continuous, `c_like` is zero. So I treat `c_like` as a\n", "kind of round-off error and split it evenly between `a_like` and\n", "`b_like`.\n", "\n", "Here is the code that creates `TopLevel` and updates it:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A 0.793123245041\n", "B 0.206876754959\n" ] } ], "source": [ "from sat import Exam, Sat\n", "\n", "exam = Exam()\n", "a_sat = Sat(exam, 780)\n", "b_sat = Sat(exam, 740)\n", "\n", "top = TopLevel('AB')\n", "top.Update((a_sat, b_sat))\n", "top.Print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The likelihood of $A$ is 0.79 and the likelihood of $B$ is 0.21. The\n", "likelihood ratio (or Bayes factor) is 3.8, which means that these test\n", "scores are evidence that Alice is better than Bob at answering SAT\n", "questions. If we believed, before seeing the test scores, that $A$ and\n", "$B$ were equally likely, then after seeing the scores we should believe\n", "that the probability of $A$ is 79%, which means there is still a 21%\n", "chance that Bob is actually better prepared." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## A better model\n", "\n", "Remember that the analysis we have done so far is based on the\n", "simplification that all SAT questions are equally difficult. In reality,\n", "some are easier than others, which means that the difference between\n", "Alice and Bob might be even smaller.\n", "\n", "But how big is the modeling error? If it is small, we conclude that the\n", "first model—based on the simplification that all questions are equally\n", "difficult—is good enough. If it’s large, we need a better model.\n", "\n", "In the next few sections, I develop a better model and discover (spoiler\n", "alert!) that the modeling error is small. So if you are satisfied with\n", "the simple mode, you can skip to the next chapter. If you want to see\n", "how the more realistic model works, read on...\n", "\n", "- Assume that each test-taker has some degree of\n", " `efficacy`, which measures their ability to answer SAT\n", " questions.\n", "\n", "- Assume that each question has some level of `difficulty`.\n", "\n", "- Finally, assume that the chance that a test-taker answers a question\n", " correctly is related to `efficacy` and\n", " `difficulty` according to this function:\n", "\n", " def ProbCorrect(efficacy, difficulty, a=1):\n", " return 1 / (1 + math.exp(-a * (efficacy - difficulty)))\n", "\n", "This function is a simplified version of the curve used in `**item\n", "response theory**`, which you can read about at\n", "<http://en.wikipedia.org/wiki/Item_response_theory>.\n", "`efficacy` and `difficulty` are considered to be\n", "on the same scale, and the probability of getting a question right\n", "depends only on the difference between them.\n", "\n", "When `efficacy` and `difficulty` are equal, the\n", "probability of getting the question right is 50%. As\n", "`efficacy` increases, this probability approaches 100%. As it\n", "decreases (or as `difficulty` increases), the probability\n", "approaches 0%.\n", "\n", "Given the distribution of `efficacy` across test-takers and\n", "the distribution of `difficulty` across questions, we can\n", "compute the expected distribution of raw scores. We’ll do that in two\n", "steps. First, for a person with given `efficacy`, we’ll\n", "compute the distribution of raw scores." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def PmfCorrect(efficacy, difficulties):\n", " pmf0 = thinkbayes.Pmf([0])\n", "\n", " ps = [ProbCorrect(efficacy, diff) for diff in difficulties]\n", " pmfs = [BinaryPmf(p) for p in ps]\n", " dist = sum(pmfs, pmf0)\n", " return dist" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`difficulties` is a list of difficulties, one for each\n", "question. `ps` is a list of probabilities, and\n", "`pmfs` is a list of two-valued Pmf objects; here’s the\n", "function that makes them:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def BinaryPmf(p):\n", " pmf = thinkbayes.Pmf()\n", " pmf.Set(1, p)\n", " pmf.Set(0, 1-p)\n", " return pmf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`dist` is the sum of these Pmfs. Remember from\n", "Section [addends] that when we add up Pmf objects, the result is the\n", "distribution of the sums. In order to use Python’s `sum` to\n", "add up Pmfs, we have to provide `pmf0` which is the identity\n", "for Pmfs, so `pmf + pmf0` is always `pmf`.\n", "\n", "If we know a person’s efficacy, we can compute their distribution of raw\n", "scores. For a group of people with a different efficacies, the resulting\n", "distribution of raw scores is a mixture. Here’s the code that computes\n", "the mixture:\n", "\n", "```python\n", "# class Exam:\n", " def MakeRawScoreDist(self, efficacies):\n", " pmfs = thinkbayes.Pmf()\n", " for efficacy, prob in efficacies.Items():\n", " scores = PmfCorrect(efficacy, self.difficulties)\n", " pmfs.Set(scores, prob)\n", "\n", " mix = thinkbayes.MakeMixture(pmfs)\n", " return mix\n", "```\n", "`MakeRawScoreDist` takes `efficacies`, which is a\n", "Pmf that represents the distribution of efficacy across test-takers. I\n", "assume it is Gaussian with mean 0 and standard deviation 1.5. This\n", "choice is mostly arbitrary. The probability of getting a question\n", "correct depends on the difference between efficacy and difficulty, so we\n", "can choose the units of efficacy and then calibrate the units of\n", "difficulty accordingly.\n", "\n", "`pmfs` is a meta-Pmf that contains one Pmf for each level of\n", "efficacy, and maps to the fraction of test-takers at that level.\n", "`MakeMixture` takes the meta-pmf and computes the\n", "distribution of the mixture (see Section [mixture])." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Calibration\n", "\n", "If we were given the distribution of difficulty, we could use\n", "`MakeRawScoreDist` to compute the distribution of raw scores. But for us\n", "the problem is the other way around: we are given the distribution of\n", "raw scores and we want to infer the distribution of difficulty.\n", "\n", "![Actual distribution of raw scores and a model to fit\n", "it.](figs/sat_calibrate.pdf)\n", "\n", "[fig.satcalibrate]\n", "\n", "I assume that the distribution of difficulty is uniform with parameters\n", "`center` and `width`.\n", "`MakeDifficulties` makes a list of difficulties with these\n", "parameters." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def MakeDifficulties(center, width, n):\n", " low, high = center-width, center+width\n", " return numpy.linspace(low, high, n)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By trying out a few combinations, I found that `center=-0.05`\n", "and `width=1.8` yield a distribution of raw scores similar to\n", "the actual data, as shown in Figure [fig.satcalibrate].\n", "\n", "So, assuming that the distribution of difficulty is uniform, its range\n", "is approximately `-1.85` to `1.75`, given that\n", "efficacy is Gaussian with mean 0 and standard deviation 1.5.\n", "\n", "The following table shows the range of `ProbCorrect` for\n", "test-takers at different levels of efficacy:\n", "\n", " Difficulty\n", " Efficacy -1.85 -0.05 1.75\n", " 3.00 0.99 0.95 0.78\n", " 1.50 0.97 0.82 0.44\n", " 0.00 0.86 0.51 0.15\n", " -1.50 0.59 0.19 0.04\n", " -3.00 0.24 0.05 0.01\n", "\n", "Someone with efficacy 3 (two standard deviations above the mean) has a\n", "99% chance of answering the easiest questions on the exam, and a 78%\n", "chance of answering the hardest. On the other end of the range, someone\n", "two standard deviations below the mean has only a 24% chance of\n", "answering the easiest questions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Posterior distribution of efficacy\n", "\n", "![Posterior distributions of efficacy for Alice and\n", "Bob.](figs/sat_posteriors_eff.pdf)\n", "\n", "[fig.satposterior2]\n", "\n", "Now that the model is calibrated, we can compute the posterior\n", "distribution of efficacy for Alice and Bob. Here is a version of the Sat\n", "class that uses the new model:\n", "\n", "```python\n", "class Sat2(thinkbayes.Suite):\n", "\n", " def __init__(self, exam, score):\n", " self.exam = exam\n", " self.score = score\n", "\n", " # start with the Gaussian prior\n", " efficacies = thinkbayes.MakeGaussianPmf(0, 1.5, 3)\n", " thinkbayes.Suite.__init__(self, efficacies)\n", "\n", " # update based on an exam score\n", " self.Update(score)\n", "```\n", "\n", "`Update` invokes `Likelihood`, which computes the likelihood of a given\n", "test score for a hypothetical level of efficacy.\n", "\n", "```python\n", " def Likelihood(self, data, hypo):\n", " efficacy = hypo\n", " score = data\n", " raw = self.exam.Reverse(score)\n", "\n", " pmf = self.exam.PmfCorrect(efficacy)\n", " like = pmf.Prob(raw)\n", " return like\n", "```\n", "\n", "`pmf` is the distribution of raw scores for a test-taker with\n", "the given efficacy; `like` is the probability of the observed\n", "score.\n", "\n", "Figure [fig.satposterior2] shows the posterior distributions of efficacy\n", "for Alice and Bob. As expected, the location of Alice’s distribution is\n", "farther to the right, but again there is some overlap.\n", "\n", "Using `TopLevel` again, we compare $A$, the hypothesis that\n", "Alice’s efficacy is higher, and $B$, the hypothesis that Bob’s is\n", "higher. The likelihood ratio is 3.4, a bit smaller than what we got from\n", "the simple model (3.8). So this model indicates that the data are\n", "evidence in favor of $A$, but a little weaker than the previous\n", "estimate.\n", "\n", "If our prior belief is that $A$ and $B$ are equally likely, then in\n", "light of this evidence we would give $A$ a posterior probability of 77%,\n", "leaving a 23% chance that Bob’s efficacy is higher." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predictive distribution\n", "\n", "The analysis we have done so far generates estimates for Alice and Bob’s\n", "efficacy, but since efficacy is not directly observable, it is hard to\n", "validate the results.\n", "\n", "To give the model predictive power, we can use it to answer a related\n", "question: “If Alice and Bob take the math SAT again, what is the chance\n", "that Alice will do better again?”\n", "\n", "We’ll answer this question in two steps:\n", "\n", "- We’ll use the posterior distribution of efficacy to generate a\n", " predictive distribution of raw score for each test-taker.\n", "\n", "- We’ll compare the two predictive distributions to compute the\n", " probability that Alice gets a higher score again.\n", "\n", "We already have most of the code we need. To compute the predictive\n", "distributions, we can use `MakeRawScoreDist` again:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "exam = Exam()\n", "a_sat = Sat(exam, 780)\n", "b_sat = Sat(exam, 740)\n", "\n", "a_pred = exam.MakeRawScoreDist(a_sat)\n", "b_pred = exam.MakeRawScoreDist(b_sat)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then we can find the likelihood that Alice does better on the second\n", "test, Bob does better, or they tie:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a_like = thinkbayes.PmfProbGreater(a_pred, b_pred)\n", "b_like = thinkbayes.PmfProbLess(a_pred, b_pred)\n", "c_like = thinkbayes.PmfProbEqual(a_pred, b_pred)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The probability that Alice does better on the second exam is 63%, which\n", "means that Bob has a 37% chance of doing as well or better.\n", "\n", "Notice that we have more confidence about Alice’s efficacy than we do\n", "about the outcome of the next test. The posterior odds are 3:1 that\n", "Alice’s efficacy is higher, but only 2:1 that Alice will do better on\n", "the next exam." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Discussion\n", "\n", "![Joint posterior distribution of `p\\_correct` for Alice and\n", "Bob.](figs/sat_joint.pdf)\n", "\n", "[fig.satjoint]\n", "\n", "We started this chapter with the question, “How strong is the evidence\n", "that Alice is better prepared than Bob?” On the face of it, that sounds\n", "like we want to test two hypotheses: either Alice is more prepared or\n", "Bob is.\n", "\n", "But in order to compute likelihoods for these hypotheses, we have to\n", "solve an estimation problem. For each test-taker we have to find the\n", "posterior distribution of either `p_correct` or `efficacy`.\n", "\n", "Values like this are called `**nuisance parameters**` because\n", "we don’t care what they are, but we have to estimate them to answer the\n", "question we care about.\n", "\n", "One way to visualize the analysis we did in this chapter is to plot the\n", "space of these parameters. `thinkbayes.MakeJoint` takes two Pmfs,\n", "computes their joint distribution, and returns a joint pmf of each\n", "possible pair of values and its probability." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def MakeJoint(pmf1, pmf2):\n", " joint = Joint()\n", " for v1, p1 in pmf1.Items():\n", " for v2, p2 in pmf2.Items():\n", " joint.Set((v1, v2), p1 * p2)\n", " return joint" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This function assumes that the two distributions are independent.\n", "\n", "Figure [fig.satjoint] shows the joint posterior distribution of\n", "`p_correct` for Alice and Bob. The diagonal line indicates the part of\n", "the space where `p_correct` is the same for Alice and Bob. To the right\n", "of this line, Alice is more prepared; to the left, Bob is more prepared.\n", "\n", "In `TopLevel.Update`, when we compute the likelihoods of $A$\n", "and $B$, we add up the probability mass on each side of this line. For\n", "the cells that fall on the line, we add up the total mass and split it\n", "between $A$ and $B$.\n", "\n", "The process we used in this chapter—estimating nuisance parameters in\n", "order to evaluate the likelihood of competing hypotheses—is a common\n", "Bayesian approach to problems like this." ] } ], "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 }
gpl-3.0
kit-cel/lecture-examples
sigNT/tutorial/filter_design.ipynb
2
124141
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Content and Objective\n", "\n", "+ Show design of a filter using IFFT" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# importing\n", "import numpy as np\n", "from scipy import signal\n", "import scipy as sp\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "\n", "# showing figures inline\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# plotting options \n", "font = {'size' : 30}\n", "plt.rc('font', **font)\n", "plt.rc('text', usetex=True)\n", "\n", "matplotlib.rc('figure', figsize=(30, 8) )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### parameters" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# sampling time\n", "t_s = 1.\n", "f_s = 1. / t_s\n", "\n", "N_fft = 2048\n", "f = np.arange( -f_s/2, f_s/2, f_s/N_fft) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### define frequency response and get impulse response" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# define ideal lowpass in the frequency regime\n", "# end of passband and filter length\n", "\n", "# ideal lowpass\n", "f_g = f_s/3\n", "K_1 = 51\n", "H_w = np.zeros( N_fft )\n", "H_w[ np.where( np.abs(f)<f_g) ] = 1\n", "\n", "\n", "# triang. filter\n", "f_g = f_s / 7\n", "N_samples_tri = int( f_g / f_s * N_fft )\n", "H_w = np.zeros(N_fft)\n", "H_w[ 0 : N_samples_tri ] = 1 - np.arange( 0, N_samples_tri, 1 ) /N_samples_tri\n", "H_w[ - N_samples_tri : ] = 1 - np.arange( N_samples_tri, 0, -1 ) /N_samples_tri" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# find impulse response by IFFT and restricting to K values\n", "h_1_part = np.fft.ifft( H_w*np.exp(-1j*2*np.pi*f*(K_1-1)/2), N_fft)[:(K_1+1)//2]\n", "h_1 = np.append( h_1_part, (h_1_part[::-1])[1:])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### get (more accurate) frequency response" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "freq, H_1 = signal.freqz(h_1, worN=f*2*np.pi, whole=True) \n", "\n", "H_w = np.fft.fftshift( H_w )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### plotting" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\jaekel\\Anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py:85: ComplexWarning: Casting complex values to real discards the imaginary part\n", " return array(a, dtype, copy=False, order=order)\n" ] }, { "data": { "text/plain": [ "Text(0.5, 1.0, '$|H_{designed}(f)| \\\\; (dB)$')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "<Figure size 2160x576 with 3 Axes>" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.subplot(131)\n", "plt.plot( np.arange(len(h_1)), h_1)\n", "plt.grid(True)\n", "plt.xlabel('$n$')\n", "plt.title('$h_{designed}[n]$')\n", "\n", "plt.subplot(132)\n", "plt.plot( f, np.abs(H_1), label='$|H_{designed}(f)|$')\n", "plt.plot( f, np.abs(H_w), label='$|H_{intended}(f)|$')\n", "plt.grid(True) \n", "plt.legend(loc='upper right') \n", "plt.xlabel('$f/\\mathrm{Hz}$')\n", "\n", "plt.subplot(133)\n", "plt.plot( f, 10*np.log10(np.abs(H_1)))\n", "plt.grid(True) \n", "plt.xlabel('$f/\\mathrm{Hz}$')\n", "plt.title('$|H_{designed}(f)| \\\\; (dB)$') " ] }, { "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 4 }
gpl-2.0
richstoner/postmortem-processing-tools
notebooks/1. Registration example.ipynb
1
903839
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#### This notebook takes you through a registration example\n", "\n", "Sequeunce:\n", "\n", "1. Query the Allen Institute data api for a list of images to work with\n", "2. Download each image at the appropriate level of downsampling\n", "3. Create a high-contrast version of each image\n", "4. Pass each image into a sequential registration tool, saving the transform at each step\n", "5. Generate a summary video of the result" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we need to set the path to the python modules" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "import sys, os\n", "print 'Working in %s' % os.path.abspath(os.path.curdir)\n", "\n", "# adding path to python modules\n", "sys.path.append('../src/python')" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Populating the interactive namespace from numpy and matplotlib\n", "Working in /vagrant/notebooks\n" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We import the aibs module, a small wrapper created to query parts of the Allen Institute Data api [api.brain-map.org](http://api.brain-map.org) easily" ] }, { "cell_type": "code", "collapsed": false, "input": [ "import aibs;\n", "api = aibs.api()" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 2 }, { "cell_type": "markdown", "metadata": {}, "source": [ "The specimen name and marker list can be found by browsing [human.brain-map.org](http://human.brain-map.org)" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# list of all experiments belonging to this specimen name\n", "explist = api.getValidSpecimentsWithName('H08-0083.01')\n", "\n", "# we want the first\n", "e = explist[0]\n", "\n", "# we then specify which markers to filter by\n", "e.markersOfInterest = ['PCP4']\n", "\n", "# and filter the available marker list\n", "e.getMarkerList(verbose=False)\n", "\n", "# finally, we query the api for the list of images that match our search criteria\n", "e.getSectionImages()\n", "\n", "# just confirming the name\n", "print e.subjectName" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "H08-0083.01\n" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then import the processing module. This module was written to wrap various shell scripts & command line commands initially, but has since been expanded to include image processing steps itself, implemented in scikit image and numpy." ] }, { "cell_type": "code", "collapsed": false, "input": [ "import pmip;\n", "reload(pmip); # in case any development has occured since last import\n", "\n", "# we create an instance of the class, passing it the experiment we defined above\n", "pe = pmip.Processing(e)\n", "\n", "# initializing the environment then creates the necessary directories for derived data to go\n", "pe.initEnv();\n", "\n", "# this is a utility command to see the total file counts in each directory\n", "pe.listSubjectDirectory();" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "* initEnv\n", "--------------------------------------------------------------------------------\n", "found : /data/reconstruction\n", "found : /data/reconstruction/specimens\n", "directories for H08-0083_01 created\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/detect_points\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/detect_raw\n", "[20 files] /data/reconstruction/specimens/H08-0083_01/register_contrast\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_density\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_points\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/register_raw\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/register_source\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_stack\n", "[28 files] /data/reconstruction/specimens/H08-0083_01/register_target\n", "[2 files] /data/reconstruction/specimens/H08-0083_01/video\n" ] } ], "prompt_number": 4 }, { "cell_type": "code", "collapsed": false, "input": [ "# Set this parameter to clear all of the subject folder (careful!)\n", "shouldClear = False\n", "\n", "if shouldClear:\n", " pe.clearSubjectDirs()\n", " pe.clearRegisterSourceDirectory()\n", "\n", "pe.listSubjectDirectory();" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0 files] /data/reconstruction/specimens/H08-0083_01/detect_points\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/detect_raw\n", "[20 files] /data/reconstruction/specimens/H08-0083_01/register_contrast\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_density\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_points\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/register_raw\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/register_source\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_stack\n", "[28 files] /data/reconstruction/specimens/H08-0083_01/register_target\n", "[2 files] /data/reconstruction/specimens/H08-0083_01/video\n" ] } ], "prompt_number": 5 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We request all of the images in the section image list via the API" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# This command retrieve the images in the section list and stores them in register_raw\n", "pe.collectImagesForRegistration()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "* collectRaw, downsample by 2^4\n", "--------------------------------------------------------------------------------\n", "-> collecting images from remote source\n", "http://api.brain-map.org/cgi-bin/imageservice?path=/external/aibssan/production30/prod1/0400061699/0400061699.aff&top=1280&left=320&width=1472&height=1056&downsample=4\n", "http://api.brain-map.org/cgi-bin/imageservice?path=/external/aibssan/production30/prod1/0400061699/0400061699.aff&top=448&left=24128&width=1516&height=1164&downsample=4\n", "http://api.brain-map.org/cgi-bin/imageservice?path=/external/aibssan/production30/prod1/0400061843/0400061843.aff&top=64&left=64&width=1516&height=1289&downsample=4\n", "http://api.brain-map.org/cgi-bin/imageservice?path=/external/aibssan/production30/prod1/0400061843/0400061843.aff&top=128&left=24704&width=1628&height=1285&downsample=4\n", "http://api.brain-map.org/cgi-bin/imageservice?path=/external/aibssan/production30/prod1/0400061987/0400061987.aff&top=64&left=512&width=1584&height=1335&downsample=4\n", "http://api.brain-map.org/cgi-bin/imageservice?path=/external/aibssan/production30/prod1/0400061987/0400061987.aff&top=512&left=24640&width=1584&height=1304&downsample=4\n", "http://api.brain-map.org/cgi-bin/imageservice?path=/external/aibssan/production30/prod1/0400062131/0400062131.aff&top=128&left=64&width=1596&height=1332&downsample=4\n", "http://api.brain-map.org/cgi-bin/imageservice?path=/external/aibssan/production30/prod1/0400062131/0400062131.aff&top=64&left=25664&width=1581&height=1312&downsample=4\n", "http://api.brain-map.org/cgi-bin/imageservice?path=/external/aibssan/production30/prod1/0400062275/0400062275.aff&top=0&left=64&width=1576&height=1251&downsample=4\n", "http://api.brain-map.org/cgi-bin/imageservice?path=/external/aibssan/production30/prod1/0400062275/0400062275.aff&top=448&left=24320&width=1604&height=1223&downsample=4\n" ] } ], "prompt_number": 6 }, { "cell_type": "code", "collapsed": false, "input": [ "# Checking our work\n", "pe.listSubjectDirectory()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0 files] /data/reconstruction/specimens/H08-0083_01/detect_points\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/detect_raw\n", "[20 files] /data/reconstruction/specimens/H08-0083_01/register_contrast\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_density\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_points\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/register_raw\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/register_source\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_stack\n", "[28 files] /data/reconstruction/specimens/H08-0083_01/register_target\n", "[2 files] /data/reconstruction/specimens/H08-0083_01/video\n" ] } ], "prompt_number": 7 }, { "cell_type": "markdown", "metadata": {}, "source": [ "In order to register ISH images stained with different labels together into a single stack, we need a label-invariant, high contrast version of the image.\n", "\n", "Using an ImageJ / FIJI macro, we convert each input image into a high contrast image. For details, see the script called REG-filter-red50.ijm in src/fijimacros" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# This command takes the raw source as input and creates high-contrast versions using imageJ\n", "# Specifically, it uses the script called REG-filter-red50.ijm in src/fijimacros\n", "pe.createContrast()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "* createContrast\n", "--------------------------------------------------------------------------------\n", "Executing REG-filter.ijm on 0 files\n" ] } ], "prompt_number": 9 }, { "cell_type": "code", "collapsed": false, "input": [ "# checking our work\n", "pe.listSubjectDirectory()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0 files] /data/reconstruction/specimens/H08-0083_01/detect_points\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/detect_raw\n", "[20 files] /data/reconstruction/specimens/H08-0083_01/register_contrast\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_density\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_points\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/register_raw\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/register_source\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_stack\n", "[28 files] /data/reconstruction/specimens/H08-0083_01/register_target\n", "[2 files] /data/reconstruction/specimens/H08-0083_01/video\n" ] } ], "prompt_number": 9 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our registration code expects are sequential list of input files. This section of code reads in the list of constrast images and generates the needed list." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# This command takes a sorted list of the files from the high-contrast folder and renames them\n", "pe.createFrames()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "* createFrames\n", "--------------------------------------------------------------------------------\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# checking our work\n", "pe.listSubjectDirectory()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "[0 files] /data/reconstruction/specimens/H08-0083_01/detect_points\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/detect_raw\n", "[20 files] /data/reconstruction/specimens/H08-0083_01/register_contrast\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_density\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_points\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/register_raw\n", "[10 files] /data/reconstruction/specimens/H08-0083_01/register_source\n", "[0 files] /data/reconstruction/specimens/H08-0083_01/register_stack\n", "[28 files] /data/reconstruction/specimens/H08-0083_01/register_target\n", "[2 files] /data/reconstruction/specimens/H08-0083_01/video\n" ] } ], "prompt_number": 11 }, { "cell_type": "markdown", "metadata": {}, "source": [ "To register the image stack, we modified an ITK example to register a series of images sequentially and return the transform for each. For sequential registration, we use the previously registered image in sequence as the current registration target. This method is not ideal, but one of the few available without external fiducials." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# This command registers images seqeuentially, saving the transforms to a text file for each step \n", "pe.register()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "* register\n", "--------------------------------------------------------------------------------\n", "/vagrant/bin/RigidBodyImageRegistration /data/reconstruction/specimens/H08-0083_01/register_source/frame%04d.jpg /data/reconstruction/specimens/H08-0083_01/register_target/register%04d.jpg 10 0\n", "Rigid registration, type: 0\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Registering /data/reconstruction/specimens/H08-0083_01/register_source/frame0001.jpg to /data/reconstruction/specimens/H08-0083_01/register_source/frame0000.jpg, saving as /data/reconstruction/specimens/H08-0083_01/register_target/register0001.jpg\n", "\n", "Registration: RegStepGradient Descent + MeanSquaresImageToMetric\n", "\n", "0\t4186.62\t[-0.00795543, 448.871, 332.054, 7.94613, -4.20155]\n", "\n", "1\t4123.7\t[-0.0112972, 448.871, 332.055, 7.85808, -4.24883]\n", "\n", "2\t4109.12\t[-0.0139631, 448.87, 332.056, 7.77445, -4.30359]\n", "\n", "3\t4102.2\t[-0.0177486, 448.869, 332.057, 7.68787, -4.35346]\n", "\n", "4\t4089.94\t[-0.0216302, 448.868, 332.058, 7.60189, -4.40435]\n", "\n", "5\t4081.04\t[-0.0252843, 448.867, 332.06, 7.51303, -4.45001]\n", "\n", "6\t4071.77\t[-0.0309169, 448.866, 332.062, 7.42807, -4.50239]\n", "\n", "7\t4062.88\t[-0.0337147, 448.864, 332.065, 7.33945, -4.54853]\n", "\n", "8\t4056.18\t[-0.0359105, 448.863, 332.068, 7.25098, -4.59497]\n", "\n", "9\t4052.49\t[-0.0373772, 448.861, 332.071, 7.16188, -4.64022]\n", "\n", "10\t4048.15\t[-0.0363817, 448.859, 332.075, 7.07233, -4.68455]\n", "\n", "11\t4041.71\t[-0.0364937, 448.858, 332.078, 6.98228, -4.72788]\n", "\n", "12\t4036.31\t[-0.0365407, 448.856, 332.081, 6.89144, -4.76954]\n", "\n", "13\t4030.81\t[-0.0364983, 448.855, 332.084, 6.8003, -4.81053]\n", "\n", "14\t4025.19\t[-0.0364841, 448.853, 332.088, 6.70855, -4.85014]\n", "\n", "15\t4019.68\t[-0.0367447, 448.852, 332.091, 6.61675, -4.88962]\n", "\n", "16\t4014.73\t[-0.0369286, 448.85, 332.094, 6.52585, -4.93113]\n", "\n", "17\t4009.8\t[-0.037191, 448.848, 332.098, 6.43587, -4.97461]\n", "\n", "18\t4005.44\t[-0.0373435, 448.847, 332.101, 6.34662, -5.01956]\n", "\n", "19\t4001.01\t[-0.0379178, 448.845, 332.104, 6.25721, -5.06419]\n", "\n", "20\t3997.99\t[-0.0370695, 448.843, 332.108, 6.16705, -5.10727]\n", "\n", "21\t3990.09\t[-0.0374648, 448.841, 332.111, 6.07869, -5.15395]\n", "\n", "22\t3986.68\t[-0.0364719, 448.84, 332.114, 5.98918, -5.19836]\n", "\n", "23\t3979.18\t[-0.0361612, 448.838, 332.117, 5.90092, -5.24524]\n", "\n", "24\t3974.06\t[-0.0353999, 448.836, 332.12, 5.81288, -5.29252]\n", "\n", "25\t3968.09\t[-0.0340281, 448.835, 332.124, 5.72449, -5.33913]\n", "\n", "26\t3960.73\t[-0.0347331, 448.833, 332.127, 5.63585, -5.38528]\n", "\n", "27\t3957.37\t[-0.0344238, 448.831, 332.13, 5.54538, -5.42774]\n", "\n", "28\t3951.88\t[-0.0340353, 448.83, 332.133, 5.45439, -5.46908]\n", "\n", "29\t3946.42\t[-0.0333935, 448.828, 332.136, 5.36339, -5.5104]\n", "\n", "30\t3941.14\t[-0.0331793, 448.827, 332.139, 5.27434, -5.55579]\n", "\n", "31\t3936.57\t[-0.0324608, 448.825, 332.142, 5.18528, -5.60113]\n", "\n", "32\t3930.9\t[-0.0337008, 448.824, 332.145, 5.09786, -5.64957]\n", "\n", "33\t3929.15\t[-0.0310468, 448.822, 332.148, 5.00804, -5.69332]\n", "\n", "34\t3919.32\t[-0.0309875, 448.821, 332.15, 4.92024, -5.74108]\n", "\n", "35\t3914.75\t[-0.0306059, 448.819, 332.153, 4.83238, -5.78873]\n", "\n", "36\t3909.88\t[-0.0308194, 448.818, 332.156, 4.74462, -5.83658]\n", "\n", "37\t3905.82\t[-0.0303741, 448.816, 332.158, 4.65592, -5.88264]\n", "\n", "38\t3900.54\t[-0.0310259, 448.815, 332.161, 4.56735, -5.92896]\n", "\n", "39\t3897.97\t[-0.0301599, 448.813, 332.164, 4.47736, -5.97245]\n", "\n", "40\t3891.09\t[-0.0307536, 448.812, 332.166, 4.3895, -6.02011]\n", "\n", "41\t3888.79\t[-0.0295948, 448.81, 332.169, 4.29972, -6.06403]\n", "\n", "42\t3881.36\t[-0.0288868, 448.809, 332.172, 4.21224, -6.11239]\n", "\n", "43\t3875.81\t[-0.0290364, 448.807, 332.174, 4.12561, -6.16226]\n", "\n", "44\t3872.5\t[-0.0283384, 448.806, 332.177, 4.03723, -6.20895]\n", "\n", "45\t3866.49\t[-0.0281615, 448.805, 332.179, 3.94682, -6.25158]\n", "\n", "46\t3862.09\t[-0.0280113, 448.804, 332.182, 3.85609, -6.29354]\n", "\n", "47\t3857.86\t[-0.0283379, 448.802, 332.184, 3.76475, -6.33415]\n", "\n", "48\t3855.23\t[-0.0286911, 448.801, 332.187, 3.67473, -6.37759]\n", "\n", "49\t3852.69\t[-0.0278869, 448.8, 332.189, 3.5871, -6.42568]\n", "\n", "Optimizer stop condition: RegularStepGradientDescentOptimizer: Maximum number of iterations (50) exceeded.\n", "\n", "Result = \n", "\n", " Angle (radians) = -0.0278869\n", "\n", " Angle (degrees) = -1.5978\n", "\n", " Center X = 448.8\n", "\n", " Center Y = 332.189\n", "\n", " Translation X = 3.5871\n", "\n", " Translation Y = -6.42568\n", "\n", " Iterations = 50\n", "\n", " Metric value = 3852.69\n", "\n", "/dat\n", "\n", "Registering /data/reconstruction/specimens/H08-0083_01/register_source/frame0002.jpg to /data/reconstruction/specimens/H08-0083_01/register_target/register0001.jpg, saving as /data/reconstruction/specimens/H08-0083_01/register_target/register0002.jpg\n", "\n", "Registration: RegStepGradient Descent + MeanSquaresImageToMetric\n", "\n", "0\t3813.42\t[0.000708147, 453.243, 334.293, 4.09491, -33.6087]\n", "\n", "1\t3809.07\t[0.0105613, 453.243, 334.293, 4.18109, -33.6585]\n", "\n", "2\t3788.61\t[0.0126044, 453.242, 334.294, 4.27429, -33.6223]\n", "\n", "3\t3780.33\t[0.0129239, 453.242, 334.295, 4.37033, -33.5945]\n", "\n", "4\t3774.53\t[0.0146835, 453.242, 334.297, 4.46588, -33.565]\n", "\n", "5\t3769.74\t[0.0151483, 453.241, 334.298, 4.56098, -33.5342]\n", "\n", "6\t3763.77\t[0.0148108, 453.241, 334.299, 4.65532, -33.501]\n", "\n", "7\t3758.56\t[0.0153279, 453.24, 334.301, 4.75104, -33.4721]\n", "\n", "8\t3752.45\t[0.0157865, 453.24, 334.302, 4.84547, -33.4393]\n", "\n", "9\t3746.52\t[0.0164777, 453.239, 334.304, 4.93932, -33.4048]\n", "\n", "10\t3741.48\t[0.017167, 453.239, 334.305, 5.03508, -33.376]\n", "\n", "11\t3736.05\t[0.0171463, 453.238, 334.307, 5.13148, -33.3495]\n", "\n", "12\t3730.91\t[0.0169556, 453.238, 334.309, 5.22768, -33.3223]\n", "\n", "13\t3725.4\t[0.0171546, 453.238, 334.31, 5.32375, -33.2945]\n", "\n", "14\t3719.47\t[0.017675, 453.237, 334.312, 5.4198, -33.2668]\n", "\n", "15\t3713.66\t[0.0177065, 453.237, 334.314, 5.51587, -33.2391]\n", "\n", "16\t3707.91\t[0.0184288, 453.236, 334.315, 5.61185, -33.2111]\n", "\n", "17\t3702.94\t[0.0193214, 453.236, 334.317, 5.70745, -33.1818]\n", "\n", "18\t3698.16\t[0.0188591, 453.235, 334.319, 5.80245, -33.1506]\n", "\n", "19\t3693.25\t[0.0180789, 453.234, 334.321, 5.8981, -33.1215]\n", "\n", "20\t3687.54\t[0.0190765, 453.234, 334.322, 5.99431, -33.0943]\n", "\n", "21\t3683.32\t[0.0188029, 453.233, 334.324, 6.0898, -33.0647]\n", "\n", "22\t3678.27\t[0.0186724, 453.233, 334.326, 6.18567, -33.0363]\n", "\n", "23\t3673.34\t[0.0174366, 453.232, 334.328, 6.28236, -33.0109]\n", "\n", "24\t3668.61\t[0.017002, 453.232, 334.33, 6.37839, -32.9831]\n", "\n", "25\t3663.4\t[0.0167774, 453.232, 334.331, 6.47415, -32.9543]\n", "\n", "26\t3658.48\t[0.0156724, 453.231, 334.333, 6.57003, -32.926]\n", "\n", "27\t3655.87\t[0.0153555, 453.231, 334.334, 6.66726, -32.9026]\n", "\n", "28\t3651.65\t[0.0154241, 453.23, 334.336, 6.76519, -32.8825]\n", "\n", "29\t3646.87\t[0.0162995, 453.23, 334.337, 6.8627, -32.8604]\n", "\n", "30\t3641.63\t[0.0158287, 453.23, 334.339, 6.9606, -32.84]\n", "\n", "31\t3637.5\t[0.016573, 453.229, 334.341, 7.05866, -32.8205]\n", "\n", "32\t3633.7\t[0.0170251, 453.229, 334.342, 7.15561, -32.7961]\n", "\n", "33\t3629.77\t[0.0164484, 453.229, 334.344, 7.25211, -32.7699]\n", "\n", "34\t3625.28\t[0.0177943, 453.228, 334.345, 7.34839, -32.743]\n", "\n", "35\t3621.54\t[0.0154197, 453.228, 334.347, 7.44489, -32.7169]\n", "\n", "36\t3617.47\t[0.0138111, 453.228, 334.349, 7.54336, -32.6996]\n", "\n", "37\t3615.09\t[0.0163648, 453.227, 334.35, 7.64173, -32.6819]\n", "\n", "38\t3609.36\t[0.0156776, 453.227, 334.352, 7.73936, -32.6603]\n", "\n", "39\t3606.25\t[0.0145937, 453.227, 334.353, 7.83852, -32.6475]\n", "\n", "40\t3604.08\t[0.0113935, 453.227, 334.355, 7.93667, -32.6287]\n", "\n", "41\t3603.19\t[0.00927782, 453.226, 334.356, 8.03303, -32.6021]\n", "\n", "42\t3601.32\t[0.0102544, 453.226, 334.357, 8.13232, -32.5903]\n", "\n", "43\t3597\t[0.0120379, 453.226, 334.358, 8.23148, -32.5775]\n", "\n", "44\t3591.14\t[0.0100023, 453.226, 334.359, 8.33034, -32.5626]\n", "\n", "45\t3589.38\t[0.0133373, 453.226, 334.36, 8.43, -32.5551]\n", "\n", "46\t3583.72\t[0.0163407, 453.226, 334.361, 8.52992, -32.5529]\n", "\n", "47\t3580.55\t[0.015343, 453.226, 334.363, 8.62891, -32.5388]\n", "\n", "48\t3578.37\t[0.0108787, 453.225, 334.364, 8.72865, -32.5334]\n", "\n", "49\t3579.82\t[0.0103165, 453.225, 334.365, 8.82817, -32.5238]\n", "\n", "Optimizer stop condition: RegularStepGradientDescentOptimizer: Maximum number of iterations (50) exceeded.\n", "\n", "Result = \n", "\n", " Angle (radians) = 0.0103165\n", "\n", " Angle (degrees) = 0.59109\n", "\n", " Center X = 453.225\n", "\n", " Center Y = 334.365\n", "\n", " Translation X = 8.82817\n", "\n", " Translation Y = -32.5238\n", "\n", " Iterations = 50\n", "\n", " Metric value = 3579.82\n", "\n", "/dat\n", "\n", "Registering /data/reconstruction/specimens/H08-0083_01/register_source/frame0003.jpg to /data/reconstruction/specimens/H08-0083_01/register_target/register0002.jpg, saving as /data/reconstruction/specimens/H08-0083_01/register_target/register0003.jpg\n", "\n", "Registration: RegStepGradient Descent + MeanSquaresImageToMetric\n", "\n", "0\t4117.62\t[0.0326999, 448.394, 333.165, 1.12054, -2.0623]\n", "\n", "1\t3806.83\t[0.0507117, 448.395, 333.166, 1.15395, -2.09481]\n", "\n", "2\t3731.32\t[0.0600354, 448.397, 333.168, 1.18585, -2.13209]\n", "\n", "3\t3708.98\t[0.0656872, 448.399, 333.17, 1.22323, -2.16467]\n", "\n", "4\t3702.68\t[0.0697128, 448.401, 333.172, 1.265, -2.19167]\n", "\n", "5\t3700.24\t[0.0725076, 448.403, 333.175, 1.30828, -2.21629]\n", "\n", "6\t3699\t[0.074617, 448.404, 333.179, 1.3551, -2.23333]\n", "\n", "7\t3699.46\t[0.0757936, 448.406, 333.182, 1.40053, -2.25385]\n", "\n", "8\t3698.53\t[0.0766317, 448.407, 333.186, 1.44742, -2.27079]\n", "\n", "9\t3698.04\t[0.0782814, 448.409, 333.189, 1.49469, -2.28655]\n", "\n", "10\t3699.18\t[0.078431, 448.41, 333.193, 1.54228, -2.30137]\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "11\t3697.57\t[0.0785319, 448.411, 333.196, 1.58976, -2.31654]\n", "\n", "12\t3695.9\t[0.0783907, 448.413, 333.2, 1.63715, -2.33201]\n", "\n", "13\t3693.98\t[0.0784559, 448.414, 333.204, 1.68441, -2.34783]\n", "\n", "14\t3692.23\t[0.0785384, 448.416, 333.207, 1.73144, -2.36437]\n", "\n", "15\t3690.52\t[0.0785736, 448.417, 333.211, 1.77866, -2.38032]\n", "\n", "16\t3688.72\t[0.0788548, 448.418, 333.215, 1.8257, -2.3968]\n", "\n", "17\t3687.19\t[0.0793507, 448.42, 333.218, 1.87289, -2.41286]\n", "\n", "18\t3686.07\t[0.080356, 448.421, 333.222, 1.91976, -2.42979]\n", "\n", "19\t3685.89\t[0.0806918, 448.423, 333.226, 1.96651, -2.44707]\n", "\n", "20\t3684.5\t[0.0807786, 448.424, 333.229, 2.01325, -2.46437]\n", "\n", "21\t3682.75\t[0.0807379, 448.426, 333.233, 2.05977, -2.48224]\n", "\n", "22\t3680.88\t[0.0808989, 448.427, 333.237, 2.10652, -2.4995]\n", "\n", "23\t3679.18\t[0.0808671, 448.429, 333.241, 2.15305, -2.51737]\n", "\n", "24\t3677.3\t[0.0810985, 448.431, 333.244, 2.19937, -2.53575]\n", "\n", "25\t3675.65\t[0.0815031, 448.432, 333.248, 2.24568, -2.55417]\n", "\n", "26\t3674.15\t[0.0819365, 448.434, 333.252, 2.29161, -2.57348]\n", "\n", "27\t3672.8\t[0.0828316, 448.436, 333.255, 2.33774, -2.59233]\n", "\n", "28\t3672.39\t[0.0838292, 448.437, 333.259, 2.38396, -2.61091]\n", "\n", "29\t3671.68\t[0.0841, 448.439, 333.263, 2.42983, -2.63037]\n", "\n", "30\t3670.07\t[0.0841135, 448.441, 333.267, 2.47562, -2.65001]\n", "\n", "31\t3668.19\t[0.0841614, 448.443, 333.27, 2.52171, -2.66893]\n", "\n", "32\t3666.36\t[0.0843278, 448.445, 333.274, 2.56776, -2.68796]\n", "\n", "33\t3664.68\t[0.0844149, 448.446, 333.278, 2.61391, -2.70674]\n", "\n", "34\t3662.91\t[0.0847224, 448.448, 333.282, 2.66011, -2.72539]\n", "\n", "35\t3661.36\t[0.0851737, 448.45, 333.286, 2.70615, -2.74442]\n", "\n", "36\t3660\t[0.0855403, 448.452, 333.29, 2.75197, -2.76396]\n", "\n", "37\t3658.64\t[0.0855991, 448.454, 333.293, 2.79757, -2.78404]\n", "\n", "38\t3656.82\t[0.0857582, 448.456, 333.297, 2.84275, -2.80501]\n", "\n", "39\t3655.11\t[0.0859996, 448.457, 333.301, 2.8881, -2.82563]\n", "\n", "40\t3653.45\t[0.0861518, 448.459, 333.305, 2.93307, -2.84707]\n", "\n", "41\t3651.69\t[0.0865273, 448.461, 333.309, 2.97809, -2.8684]\n", "\n", "42\t3650.13\t[0.0867691, 448.463, 333.312, 3.02294, -2.89008]\n", "\n", "43\t3648.5\t[0.0870067, 448.466, 333.316, 3.0679, -2.91151]\n", "\n", "44\t3646.76\t[0.0871367, 448.468, 333.32, 3.11236, -2.93398]\n", "\n", "45\t3644.94\t[0.0873408, 448.47, 333.324, 3.15682, -2.95643]\n", "\n", "46\t3643.18\t[0.087412, 448.472, 333.328, 3.20088, -2.97966]\n", "\n", "47\t3641.28\t[0.0875025, 448.474, 333.331, 3.24506, -3.00267]\n", "\n", "48\t3639.4\t[0.087518, 448.476, 333.335, 3.28904, -3.02606]\n", "\n", "49\t3637.43\t[0.0875534, 448.479, 333.339, 3.333, -3.04948]\n", "\n", "Optimizer stop condition: RegularStepGradientDescentOptimizer: Maximum number of iterations (50) exceeded.\n", "\n", "Result = \n", "\n", " Angle (radians) = 0.0875534\n", "\n", " Angle (degrees) = 5.01644\n", "\n", " Center X = 448.479\n", "\n", " Center Y = 333.339\n", "\n", " Translation X = 3.333\n", "\n", " Translation Y = -3.04948\n", "\n", " Iterations = 50\n", "\n", " Metric value = 3637.43\n", "\n", "/dat\n", "\n", "Registering /data/reconstruction/specimens/H08-0083_01/register_source/frame0004.jpg to /data/reconstruction/specimens/H08-0083_01/register_target/register0003.jpg, saving as /data/reconstruction/specimens/H08-0083_01/register_target/register0004.jpg\n", "\n", "Registration: RegStepGradient Descent + MeanSquaresImageToMetric\n", "\n", "0\t3986.2\t[-0.00886067, 446.238, 334.22, 13.3534, -15.9764]\n", "\n", "1\t3839.71\t[-0.0594223, 446.239, 334.22, 13.3392, -15.8913]\n", "\n", "2\t3572.87\t[-0.056879, 446.245, 334.221, 13.3312, -15.7918]\n", "\n", "3\t3562.26\t[-0.0568433, 446.25, 334.221, 13.327, -15.692]\n", "\n", "4\t3556.78\t[-0.0567467, 446.256, 334.222, 13.3243, -15.5922]\n", "\n", "5\t3551.3\t[-0.05671, 446.262, 334.222, 13.3224, -15.4924]\n", "\n", "6\t3545.83\t[-0.0568827, 446.267, 334.222, 13.3202, -15.3926]\n", "\n", "7\t3540.66\t[-0.0571681, 446.273, 334.222, 13.3171, -15.2928]\n", "\n", "8\t3535.98\t[-0.0578021, 446.279, 334.223, 13.3124, -15.1931]\n", "\n", "9\t3532.38\t[-0.057472, 446.284, 334.223, 13.3054, -15.0935]\n", "\n", "10\t3526.56\t[-0.0579817, 446.29, 334.224, 13.2985, -14.9939]\n", "\n", "11\t3522.75\t[-0.0572976, 446.296, 334.225, 13.2907, -14.8944]\n", "\n", "12\t3515.76\t[-0.057578, 446.301, 334.225, 13.2842, -14.7948]\n", "\n", "13\t3511.54\t[-0.0573874, 446.307, 334.226, 13.278, -14.6951]\n", "\n", "14\t3506.56\t[-0.0571769, 446.313, 334.226, 13.2712, -14.5955]\n", "\n", "15\t3501.81\t[-0.0571263, 446.319, 334.227, 13.2659, -14.4958]\n", "\n", "16\t3497.48\t[-0.0572617, 446.324, 334.227, 13.2598, -14.3962]\n", "\n", "17\t3493.52\t[-0.0573675, 446.33, 334.228, 13.2552, -14.2964]\n", "\n", "18\t3489.64\t[-0.0578304, 446.336, 334.228, 13.247, -14.1969]\n", "\n", "19\t3486.88\t[-0.0569207, 446.341, 334.229, 13.2382, -14.0975]\n", "\n", "20\t3480.81\t[-0.0576866, 446.347, 334.23, 13.2264, -13.9984]\n", "\n", "21\t3478.74\t[-0.057475, 446.353, 334.231, 13.2159, -13.8991]\n", "\n", "22\t3474.28\t[-0.0574738, 446.358, 334.231, 13.2062, -13.7997]\n", "\n", "23\t3470.63\t[-0.056833, 446.364, 334.232, 13.196, -13.7004]\n", "\n", "24\t3466.64\t[-0.0560065, 446.37, 334.233, 13.1807, -13.6018]\n", "\n", "25\t3462.69\t[-0.0578726, 446.375, 334.234, 13.1664, -13.503]\n", "\n", "26\t3461.67\t[-0.0567985, 446.381, 334.235, 13.157, -13.4036]\n", "\n", "27\t3457.41\t[-0.057019, 446.386, 334.236, 13.1365, -13.3059]\n", "\n", "28\t3454.87\t[-0.0563251, 446.392, 334.237, 13.1167, -13.208]\n", "\n", "29\t3451.16\t[-0.0559414, 446.397, 334.239, 13.0974, -13.11]\n", "\n", "30\t3448.45\t[-0.0577104, 446.403, 334.24, 13.0834, -13.0112]\n", "\n", "31\t3448.06\t[-0.0562675, 446.409, 334.241, 13.0622, -12.9137]\n", "\n", "32\t3444.02\t[-0.058237, 446.414, 334.242, 13.0437, -12.8156]\n", "\n", "33\t3444.2\t[-0.057681, 446.42, 334.243, 13.0318, -12.7164]\n", "\n", "34\t3440.88\t[-0.0536629, 446.425, 334.244, 13.0081, -12.6196]\n", "\n", "35\t3437.11\t[-0.0624121, 446.431, 334.245, 12.9938, -12.5211]\n", "\n", "36\t3448.94\t[-0.052264, 446.437, 334.247, 12.9726, -12.4241]\n", "\n", "37\t3434.49\t[-0.0680633, 446.442, 334.248, 12.9619, -12.3261]\n", "\n", "38\t3471.43\t[-0.0462853, 446.448, 334.249, 12.9373, -12.2319]\n", "\n", "39\t3450.14\t[-0.0838958, 446.452, 334.25, 12.9207, -12.1408]\n", "\n", "40\t3588.9\t[-0.0616972, 446.46, 334.251, 12.917, -12.0437]\n", "\n", "41\t3437.38\t[-0.0501228, 446.466, 334.253, 12.8822, -11.9509]\n", "\n", "42\t3431.26\t[-0.0795187, 446.471, 334.253, 12.8859, -11.8555]\n", "\n", "43\t3548.66\t[-0.0527802, 446.478, 334.255, 12.8705, -11.7607]\n", "\n", "44\t3424.53\t[-0.0758262, 446.483, 334.257, 12.8383, -11.669]\n", "\n", "45\t3513.9\t[-0.0472786, 446.49, 334.259, 12.8155, -11.5762]\n", "\n", "46\t3437.29\t[-0.104272, 446.494, 334.26, 12.7886, -11.4986]\n", "\n", "47\t3727.43\t[-0.0812422, 446.504, 334.258, 12.8118, -11.4047]\n", "\n", "48\t3550.39\t[-0.0569228, 446.511, 334.259, 12.805, -11.3082]\n", "\n", "49\t3420.16\t[-0.0766124, 446.517, 334.259, 12.8026, -11.2104]\n", "\n", "Optimizer stop condition: RegularStepGradientDescentOptimizer: Maximum number of iterations (50) exceeded.\n", "\n", "Result = \n", "\n", " Angle (radians) = -0.0766124\n", "\n", " Angle (degrees) = -4.38957\n", "\n", " Center X = 446.517\n", "\n", " Center Y = 334.259\n", "\n", " Translation X = 12.8026\n", "\n", " Translation Y = -11.2104\n", "\n", " Iterations = 50\n", "\n", " Metric value = 3420.16\n", "\n", "/dat\n", "\n", "Registering /data/reconstruction/specimens/H08-0083_01/register_source/frame0005.jpg to /data/reconstruction/specimens/H08-0083_01/register_target/register0004.jpg, saving as /data/reconstruction/specimens/H08-0083_01/register_target/register0005.jpg\n", "\n", "Registration: RegStepGradient Descent + MeanSquaresImageToMetric\n", "\n", "0\t4758.54\t[-0.0136218, 447.224, 329.376, -8.21331, -0.786536]\n", "\n", "1\t4595.12\t[-0.0473664, 447.224, 329.375, -8.11923, -0.783627]\n", "\n", "2\t4350.98\t[-0.0551509, 447.224, 329.37, -8.01975, -0.779055]\n", "\n", "3\t4334.67\t[-0.0596053, 447.224, 329.364, -7.92005, -0.782361]\n", "\n", "4\t4329.63\t[-0.0624187, 447.224, 329.359, -7.82107, -0.794949]\n", "\n", "5\t4326.38\t[-0.0645241, 447.223, 329.352, -7.72293, -0.812989]\n", "\n", "6\t4322.12\t[-0.0673726, 447.222, 329.346, -7.62565, -0.835096]\n", "\n", "7\t4317.71\t[-0.070034, 447.22, 329.34, -7.52996, -0.863199]\n", "\n", "8\t4312.52\t[-0.0723104, 447.218, 329.333, -7.43452, -0.892142]\n", "\n", "9\t4307.87\t[-0.0740563, 447.217, 329.326, -7.33838, -0.918629]\n", "\n", "10\t4304.58\t[-0.0754714, 447.215, 329.319, -7.24313, -0.948139]\n", "\n", "11\t4300.22\t[-0.0765473, 447.213, 329.311, -7.14745, -0.976226]\n", "\n", "12\t4296.26\t[-0.0770356, 447.211, 329.304, -7.0518, -1.00438]\n", "\n", "13\t4291.07\t[-0.0767128, 447.209, 329.297, -6.95598, -1.03191]\n", "\n", "14\t4283.95\t[-0.0767994, 447.207, 329.289, -6.86024, -1.05979]\n", "\n", "15\t4277.84\t[-0.0772398, 447.205, 329.282, -6.76444, -1.0874]\n", "\n", "16\t4272.73\t[-0.0768833, 447.204, 329.274, -6.66841, -1.11421]\n", "\n", "17\t4265.63\t[-0.0766328, 447.202, 329.267, -6.57221, -1.14043]\n", "\n", "18\t4258.88\t[-0.076355, 447.2, 329.259, -6.47571, -1.16549]\n", "\n", "19\t4252.19\t[-0.0765166, 447.199, 329.252, -6.37952, -1.19175]\n", "\n", "20\t4246.6\t[-0.0766562, 447.197, 329.244, -6.28311, -1.21721]\n", "\n", "21\t4240.85\t[-0.0765872, 447.195, 329.237, -6.18671, -1.24265]\n", "\n", "22\t4234.38\t[-0.0766252, 447.193, 329.23, -6.09049, -1.26881]\n", "\n", "23\t4228.17\t[-0.0766795, 447.192, 329.222, -5.99445, -1.29561]\n", "\n", "24\t4222.08\t[-0.076363, 447.19, 329.215, -5.89842, -1.32244]\n", "\n", "25\t4215.15\t[-0.0762455, 447.188, 329.207, -5.80245, -1.34947]\n", "\n", "26\t4208.81\t[-0.0760177, 447.186, 329.2, -5.70626, -1.37576]\n", "\n", "27\t4202.29\t[-0.0756884, 447.185, 329.192, -5.61006, -1.40197]\n", "\n", "28\t4195.85\t[-0.0753551, 447.183, 329.185, -5.51402, -1.4288]\n", "\n", "29\t4189.5\t[-0.0756756, 447.181, 329.178, -5.4184, -1.45708]\n", "\n", "30\t4184.5\t[-0.0760555, 447.179, 329.171, -5.32251, -1.48444]\n", "\n", "31\t4179.75\t[-0.0763112, 447.178, 329.163, -5.22639, -1.51096]\n", "\n", "32\t4174.4\t[-0.0761689, 447.176, 329.156, -5.12984, -1.53587]\n", "\n", "33\t4168.19\t[-0.0762198, 447.174, 329.148, -5.03392, -1.56311]\n", "\n", "34\t4162.47\t[-0.0758705, 447.172, 329.141, -4.93777, -1.58952]\n", "\n", "35\t4156.23\t[-0.0758009, 447.171, 329.134, -4.84168, -1.61614]\n", "\n", "36\t4150.55\t[-0.0757031, 447.169, 329.126, -4.7455, -1.64245]\n", "\n", "37\t4144.93\t[-0.0756308, 447.167, 329.119, -4.649, -1.66755]\n", "\n", "38\t4139.5\t[-0.0759667, 447.166, 329.111, -4.55244, -1.69245]\n", "\n", "39\t4134.64\t[-0.0759444, 447.164, 329.104, -4.45562, -1.71629]\n", "\n", "40\t4129.42\t[-0.0763767, 447.163, 329.097, -4.35906, -1.74115]\n", "\n", "41\t4124.72\t[-0.0762064, 447.161, 329.089, -4.2622, -1.76484]\n", "\n", "42\t4119.08\t[-0.0765146, 447.16, 329.082, -4.16534, -1.78852]\n", "\n", "43\t4113.99\t[-0.0765381, 447.158, 329.074, -4.06862, -1.81272]\n", "\n", "44\t4108.69\t[-0.0768069, 447.156, 329.067, -3.97201, -1.83741]\n", "\n", "45\t4103.91\t[-0.0776534, 447.155, 329.059, -3.87556, -1.86266]\n", "\n", "46\t4100.68\t[-0.0787135, 447.153, 329.052, -3.77858, -1.88578]\n", "\n", "47\t4097.7\t[-0.0782, 447.152, 329.044, -3.68097, -1.90602]\n", "\n", "48\t4091.23\t[-0.0782651, 447.151, 329.036, -3.58328, -1.92593]\n", "\n", "49\t4086.12\t[-0.0788524, 447.149, 329.029, -3.48545, -1.9451]\n", "\n", "Optimizer stop condition: RegularStepGradientDescentOptimizer: Maximum number of iterations (50) exceeded.\n", "\n", "Result = \n", "\n", " Angle (radians) = -0.0788524\n", "\n", " Angle (degrees) = -4.51791\n", "\n", " Center X = 447.149\n", "\n", " Center Y = 329.029\n", "\n", " Translation X = -3.48545\n", "\n", " Translation Y = -1.9451\n", "\n", " Iterations = 50\n", "\n", " Metric value = 4086.12\n", "\n", "/dat\n", "\n", "Registering /data/reconstruction/specimens/H08-0083_01/register_source/frame0006.jpg to /data/reconstruction/specimens/H08-0083_01/register_target/register0005.jpg, saving as /data/reconstruction/specimens/H08-0083_01/register_target/register0006.jpg\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "Registration: RegStepGradient Descent + MeanSquaresImageToMetric\n", "\n", "0\t4119.01\t[-0.0157956, 442.25, 330.108, -22.1092, -14.725]\n", "\n", "1\t3874.97\t[-0.0620925, 442.25, 330.107, -22.0225, -14.7064]\n", "\n", "2\t3404.9\t[-0.0872673, 442.25, 330.101, -21.9261, -14.7111]\n", "\n", "3\t3404.01\t[-0.07374, 442.254, 330.1, -21.9214, -14.6634]\n", "\n", "4\t3389.85\t[-0.0768127, 442.256, 330.097, -21.8775, -14.64]\n", "\n", "5\t3390.76\t[-0.0799767, 442.258, 330.094, -21.8345, -14.615]\n", "\n", "6\t3392.32\t[-0.0830819, 442.261, 330.092, -21.8044, -14.5753]\n", "\n", "7\t3394.3\t[-0.082631, 442.265, 330.091, -21.7965, -14.5261]\n", "\n", "8\t3393.49\t[-0.0832023, 442.269, 330.091, -21.7867, -14.4773]\n", "\n", "9\t3393.61\t[-0.0788299, 442.273, 330.09, -21.7706, -14.4303]\n", "\n", "10\t3389.87\t[-0.0821731, 442.277, 330.087, -21.7391, -14.3919]\n", "\n", "11\t3392.27\t[-0.0832503, 442.281, 330.087, -21.7368, -14.3421]\n", "\n", "12\t3392.75\t[-0.0780245, 442.285, 330.087, -21.7274, -14.2934]\n", "\n", "13\t3388.06\t[-0.0811857, 442.288, 330.084, -21.6945, -14.2561]\n", "\n", "14\t3390.29\t[-0.0830558, 442.292, 330.084, -21.6925, -14.2064]\n", "\n", "15\t3391.81\t[-0.0771466, 442.296, 330.085, -21.7, -14.1575]\n", "\n", "16\t3386.06\t[-0.0812236, 442.298, 330.082, -21.6598, -14.1284]\n", "\n", "17\t3389.5\t[-0.071022, 442.302, 330.083, -21.6707, -14.0808]\n", "\n", "18\t3382.06\t[-0.0758085, 442.302, 330.081, -21.6466, -14.0767]\n", "\n", "19\t3383.84\t[-0.0768876, 442.303, 330.079, -21.622, -14.0731]\n", "\n", "20\t3384.68\t[-0.078322, 442.304, 330.078, -21.602, -14.0582]\n", "\n", "21\t3386.4\t[-0.0783133, 442.306, 330.077, -21.5908, -14.0359]\n", "\n", "22\t3386.2\t[-0.0777291, 442.307, 330.076, -21.5794, -14.0138]\n", "\n", "23\t3385.15\t[-0.0792029, 442.309, 330.075, -21.5647, -13.9938]\n", "\n", "24\t3386.91\t[-0.0733291, 442.311, 330.075, -21.5561, -13.9711]\n", "\n", "25\t3380.83\t[-0.0782824, 442.312, 330.073, -21.5331, -13.9628]\n", "\n", "26\t3385.35\t[-0.0754366, 442.313, 330.072, -21.5277, -13.9386]\n", "\n", "27\t3382.03\t[-0.0754206, 442.314, 330.071, -21.5028, -13.9384]\n", "\n", "28\t3381.87\t[-0.0752786, 442.314, 330.069, -21.4779, -13.9373]\n", "\n", "29\t3381.58\t[-0.0754678, 442.314, 330.067, -21.453, -13.9362]\n", "\n", "30\t3381.64\t[-0.0751064, 442.314, 330.065, -21.4281, -13.9352]\n", "\n", "31\t3381.13\t[-0.0752964, 442.314, 330.063, -21.4033, -13.9328]\n", "\n", "32\t3381.18\t[-0.0745692, 442.314, 330.061, -21.3784, -13.9311]\n", "\n", "33\t3380.32\t[-0.0759664, 442.315, 330.059, -21.3537, -13.9283]\n", "\n", "34\t3381.53\t[-0.0732616, 442.316, 330.058, -21.3326, -13.9152]\n", "\n", "35\t3378.53\t[-0.0781572, 442.316, 330.056, -21.3094, -13.9074]\n", "\n", "36\t3384.06\t[-0.0684165, 442.317, 330.057, -21.315, -13.9019]\n", "\n", "37\t3378.97\t[-0.069939, 442.317, 330.056, -21.3094, -13.9043]\n", "\n", "38\t3378.27\t[-0.0713263, 442.317, 330.056, -21.3033, -13.9044]\n", "\n", "39\t3378.18\t[-0.0719206, 442.317, 330.055, -21.2972, -13.9053]\n", "\n", "40\t3377.82\t[-0.0722212, 442.317, 330.055, -21.2911, -13.9067]\n", "\n", "41\t3377.75\t[-0.0728139, 442.316, 330.055, -21.285, -13.9074]\n", "\n", "42\t3377.82\t[-0.0739133, 442.317, 330.054, -21.2789, -13.9063]\n", "\n", "43\t3378.92\t[-0.0746012, 442.317, 330.054, -21.2728, -13.9055]\n", "\n", "44\t3379.73\t[-0.0749068, 442.317, 330.053, -21.2666, -13.9057]\n", "\n", "45\t3379.98\t[-0.0748917, 442.317, 330.053, -21.2603, -13.9058]\n", "\n", "46\t3379.95\t[-0.0749453, 442.317, 330.052, -21.2541, -13.906]\n", "\n", "47\t3379.96\t[-0.0748736, 442.317, 330.052, -21.2479, -13.9064]\n", "\n", "48\t3379.86\t[-0.0749469, 442.317, 330.051, -21.2417, -13.9067]\n", "\n", "49\t3379.93\t[-0.0747146, 442.317, 330.051, -21.2355, -13.9075]\n", "\n", "Optimizer stop condition: RegularStepGradientDescentOptimizer: Maximum number of iterations (50) exceeded.\n", "\n", "Result = \n", "\n", " Angle (radians) = -0.0747146\n", "\n", " Angle (degrees) = -4.28083\n", "\n", " Center X = 442.317\n", "\n", " Center Y = 330.051\n", "\n", " Translation X = -21.2355\n", "\n", " Translation Y = -13.9075\n", "\n", " Iterations = 50\n", "\n", " Metric value = 3379.93\n", "\n", "/dat\n", "\n", "Registering /data/reconstruction/specimens/H08-0083_01/register_source/frame0007.jpg to /data/reconstruction/specimens/H08-0083_01/register_target/register0006.jpg, saving as /data/reconstruction/specimens/H08-0083_01/register_target/register0007.jpg\n", "\n", "Registration: RegStepGradient Descent + MeanSquaresImageToMetric\n", "\n", "0\t3979.82\t[-0.00920415, 441.34, 329.209, -1.29141, 28.7802]\n", "\n", "1\t3878.48\t[-0.0282893, 441.339, 329.208, -1.22545, 28.7075]\n", "\n", "2\t3733.22\t[-0.0365018, 441.337, 329.207, -1.16408, 28.6291]\n", "\n", "3\t3698.39\t[-0.0413441, 441.335, 329.204, -1.08832, 28.5641]\n", "\n", "4\t3685.56\t[-0.0420542, 441.333, 329.2, -1.00255, 28.5128]\n", "\n", "5\t3679.62\t[-0.0428007, 441.331, 329.196, -0.917178, 28.4609]\n", "\n", "6\t3674.3\t[-0.0427006, 441.329, 329.193, -0.831123, 28.4102]\n", "\n", "7\t3668.9\t[-0.0429859, 441.327, 329.189, -0.745351, 28.3589]\n", "\n", "8\t3663.93\t[-0.0431103, 441.324, 329.185, -0.659161, 28.3084]\n", "\n", "9\t3658.96\t[-0.0425626, 441.322, 329.182, -0.572926, 28.258]\n", "\n", "10\t3653.35\t[-0.0426705, 441.32, 329.178, -0.487139, 28.2068]\n", "\n", "11\t3648.26\t[-0.0422808, 441.318, 329.174, -0.401452, 28.1554]\n", "\n", "12\t3642.92\t[-0.0418189, 441.316, 329.17, -0.316726, 28.1024]\n", "\n", "13\t3637.71\t[-0.0414581, 441.314, 329.167, -0.233236, 28.0476]\n", "\n", "14\t3632.64\t[-0.0422236, 441.312, 329.163, -0.149596, 27.9929]\n", "\n", "15\t3627.68\t[-0.0411496, 441.309, 329.16, -0.06435, 27.9408]\n", "\n", "16\t3622.89\t[-0.0412192, 441.307, 329.156, 0.0175708, 27.8836]\n", "\n", "17\t3617.85\t[-0.0406255, 441.305, 329.153, 0.0995609, 27.8265]\n", "\n", "18\t3612.89\t[-0.0420538, 441.303, 329.15, 0.180126, 27.7674]\n", "\n", "19\t3607.97\t[-0.0411931, 441.3, 329.146, 0.264535, 27.714]\n", "\n", "20\t3602.87\t[-0.0417546, 441.298, 329.143, 0.347719, 27.6586]\n", "\n", "21\t3598.26\t[-0.0416926, 441.296, 329.139, 0.433023, 27.6066]\n", "\n", "22\t3593.48\t[-0.04189, 441.294, 329.135, 0.518506, 27.5549]\n", "\n", "23\t3588.9\t[-0.041197, 441.292, 329.132, 0.603737, 27.5028]\n", "\n", "24\t3583.88\t[-0.0415218, 441.29, 329.128, 0.687607, 27.4485]\n", "\n", "25\t3579.44\t[-0.040545, 441.287, 329.125, 0.770707, 27.393]\n", "\n", "26\t3574.92\t[-0.0398485, 441.285, 329.121, 0.852211, 27.3352]\n", "\n", "27\t3571.25\t[-0.0404639, 441.283, 329.118, 0.931216, 27.274]\n", "\n", "28\t3566.51\t[-0.0402408, 441.281, 329.115, 1.01152, 27.2146]\n", "\n", "29\t3562.55\t[-0.0402583, 441.278, 329.112, 1.09051, 27.1534]\n", "\n", "30\t3558.37\t[-0.0407087, 441.276, 329.109, 1.16785, 27.0901]\n", "\n", "31\t3554.29\t[-0.0412325, 441.273, 329.105, 1.24679, 27.0289]\n", "\n", "32\t3550.57\t[-0.0412839, 441.271, 329.102, 1.32739, 26.9698]\n", "\n", "33\t3546.78\t[-0.0414289, 441.268, 329.099, 1.40715, 26.9096]\n", "\n", "34\t3543.22\t[-0.0414488, 441.266, 329.095, 1.48993, 26.8537]\n", "\n", "35\t3539.52\t[-0.0406032, 441.264, 329.092, 1.57291, 26.7981]\n", "\n", "36\t3534.49\t[-0.0427611, 441.261, 329.088, 1.65092, 26.7357]\n", "\n", "37\t3533.85\t[-0.0368371, 441.259, 329.085, 1.73726, 26.6857]\n", "\n", "38\t3531.08\t[-0.0446605, 441.256, 329.082, 1.7963, 26.6055]\n", "\n", "39\t3532.27\t[-0.0352434, 441.255, 329.078, 1.88678, 26.5642]\n", "\n", "40\t3530.23\t[-0.0446371, 441.252, 329.076, 1.94217, 26.4815]\n", "\n", "41\t3525.88\t[-0.0351682, 441.25, 329.072, 2.03247, 26.4399]\n", "\n", "42\t3523.87\t[-0.0463656, 441.247, 329.07, 2.08494, 26.3556]\n", "\n", "43\t3527.71\t[-0.0322669, 441.246, 329.066, 2.17897, 26.3249]\n", "\n", "44\t3529.43\t[-0.04685, 441.243, 329.065, 2.21682, 26.2336]\n", "\n", "45\t3524.67\t[-0.0318541, 441.242, 329.06, 2.31268, 26.2098]\n", "\n", "46\t3525.96\t[-0.0482122, 441.239, 329.059, 2.34791, 26.1177]\n", "\n", "47\t3527.84\t[-0.029346, 441.238, 329.054, 2.44337, 26.0952]\n", "\n", "48\t3534.74\t[-0.0521854, 441.235, 329.053, 2.47556, 26.0033]\n", "\n", "49\t3551.75\t[-0.0278183, 441.236, 329.048, 2.5721, 26.0111]\n", "\n", "Optimizer stop condition: RegularStepGradientDescentOptimizer: Maximum number of iterations (50) exceeded.\n", "\n", "Result = \n", "\n", " Angle (radians) = -0.0278183\n", "\n", " Angle (degrees) = -1.59387\n", "\n", " Center X = 441.236\n", "\n", " Center Y = 329.048\n", "\n", " Translation X = 2.5721\n", "\n", " Translation Y = 26.0111\n", "\n", " Iterations = 50\n", "\n", " Metric value = 3551.75\n", "\n", "/dat\n", "\n", "Registering /data/reconstruction/specimens/H08-0083_01/register_source/frame0008.jpg to /data/reconstruction/specimens/H08-0083_01/register_target/register0007.jpg, saving as /data/reconstruction/specimens/H08-0083_01/register_target/register0008.jpg\n", "\n", "Registration: RegStepGradient Descent + MeanSquaresImageToMetric\n", "\n", "0\t4247.62\t[-0.00732663, 437.237, 331.83, -15.725, 18.5779]\n", "\n", "1\t4155.39\t[-0.0240198, 437.237, 331.83, -15.7109, 18.5329]\n", "\n", "2\t3913.94\t[-0.034961, 437.236, 331.83, -15.7003, 18.4853]\n", "\n", "3\t3786.07\t[-0.0432151, 437.234, 331.83, -15.692, 18.4367]\n", "\n", "4\t3714.69\t[-0.049959, 437.232, 331.829, -15.6871, 18.3875]\n", "\n", "5\t3662.69\t[-0.0567359, 437.229, 331.829, -15.6834, 18.3381]\n", "\n", "6\t3608.75\t[-0.0638737, 437.227, 331.829, -15.6778, 18.289]\n", "\n", "7\t3557.5\t[-0.0688543, 437.223, 331.828, -15.6697, 18.24]\n", "\n", "8\t3532.83\t[-0.0721349, 437.22, 331.827, -15.6601, 18.1912]\n", "\n", "9\t3522.31\t[-0.0742984, 437.217, 331.826, -15.65, 18.1424]\n", "\n", "10\t3517.24\t[-0.0752758, 437.213, 331.826, -15.6402, 18.0935]\n", "\n", "11\t3514.54\t[-0.0755409, 437.209, 331.825, -15.6302, 18.0447]\n", "\n", "12\t3511.69\t[-0.0756087, 437.206, 331.824, -15.6201, 17.9959]\n", "\n", "13\t3508.73\t[-0.0755158, 437.202, 331.823, -15.6099, 17.9471]\n", "\n", "14\t3505.56\t[-0.0755646, 437.198, 331.822, -15.5992, 17.8984]\n", "\n", "15\t3502.6\t[-0.0754981, 437.195, 331.821, -15.5888, 17.8496]\n", "\n", "16\t3499.47\t[-0.0754725, 437.191, 331.82, -15.578, 17.8009]\n", "\n", "17\t3496.44\t[-0.0755008, 437.187, 331.819, -15.5672, 17.7523]\n", "\n", "18\t3493.51\t[-0.0756233, 437.184, 331.818, -15.5564, 17.7036]\n", "\n", "19\t3490.74\t[-0.0755639, 437.18, 331.817, -15.5458, 17.6549]\n", "\n", "20\t3487.66\t[-0.075479, 437.176, 331.816, -15.5351, 17.6062]\n", "\n", "21\t3484.55\t[-0.0753238, 437.173, 331.815, -15.5245, 17.5575]\n", "\n", "22\t3481.3\t[-0.0750614, 437.169, 331.814, -15.5142, 17.5087]\n", "\n", "23\t3477.97\t[-0.0747791, 437.166, 331.813, -15.5039, 17.4599]\n", "\n", "24\t3474.74\t[-0.07469, 437.162, 331.813, -15.4934, 17.4111]\n", "\n", "25\t3471.79\t[-0.0748648, 437.158, 331.812, -15.4833, 17.3623]\n", "\n", "26\t3469.19\t[-0.0748258, 437.155, 331.811, -15.473, 17.3135]\n", "\n", "27\t3466.38\t[-0.0749066, 437.151, 331.81, -15.4634, 17.2646]\n", "\n", "28\t3463.76\t[-0.0747808, 437.147, 331.809, -15.4539, 17.2157]\n", "\n", "29\t3460.88\t[-0.0746416, 437.144, 331.808, -15.4449, 17.1666]\n", "\n", "30\t3457.99\t[-0.074552, 437.14, 331.807, -15.4363, 17.1175]\n", "\n", "31\t3455.2\t[-0.0745281, 437.137, 331.807, -15.4279, 17.0684]\n", "\n", "32\t3452.53\t[-0.0745194, 437.133, 331.806, -15.4197, 17.0192]\n", "\n", "33\t3449.93\t[-0.0744734, 437.129, 331.805, -15.4112, 16.97]\n", "\n", "34\t3447.28\t[-0.0745809, 437.126, 331.804, -15.4022, 16.921]\n", "\n", "35\t3444.96\t[-0.07452, 437.122, 331.804, -15.3928, 16.872]\n", "\n", "36\t3442.36\t[-0.0742986, 437.118, 331.803, -15.3834, 16.8231]\n", "\n", "37\t3439.44\t[-0.0741331, 437.115, 331.802, -15.3737, 16.7741]\n", "\n", "38\t3436.61\t[-0.0740284, 437.111, 331.801, -15.365, 16.7251]\n", "\n", "39\t3433.91\t[-0.0738832, 437.108, 331.8, -15.3561, 16.676]\n", "\n", "40\t3431.15\t[-0.0739162, 437.104, 331.799, -15.3468, 16.627]\n", "\n", "41\t3428.75\t[-0.0738143, 437.1, 331.799, -15.3371, 16.5781]\n", "\n", "42\t3426.09\t[-0.0737325, 437.097, 331.798, -15.3279, 16.5291]\n", "\n", "43\t3423.49\t[-0.0735485, 437.093, 331.797, -15.3189, 16.48]\n", "\n", "44\t3420.73\t[-0.0735359, 437.09, 331.796, -15.3103, 16.4309]\n", "\n", "45\t3418.29\t[-0.0733907, 437.086, 331.795, -15.3015, 16.3818]\n", "\n", "46\t3415.63\t[-0.0733578, 437.082, 331.795, -15.2927, 16.3327]\n" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n", "47\t3413.21\t[-0.0731961, 437.079, 331.794, -15.284, 16.2836]\n", "\n", "48\t3410.6\t[-0.0730443, 437.075, 331.793, -15.2754, 16.2345]\n", "\n", "49\t3408.06\t[-0.0730359, 437.072, 331.792, -15.2665, 16.1855]\n", "\n", "Optimizer stop condition: RegularStepGradientDescentOptimizer: Maximum number of iterations (50) exceeded.\n", "\n", "Result = \n", "\n", " Angle (radians) = -0.0730359\n", "\n", " Angle (degrees) = -4.18465\n", "\n", " Center X = 437.072\n", "\n", " Center Y = 331.792\n", "\n", " Translation X = -15.2665\n", "\n", " Translation Y = 16.1855\n", "\n", " Iterations = 50\n", "\n", " Metric value = 3408.06\n", "\n", "/dat\n", "\n", "Registering /data/reconstruction/specimens/H08-0083_01/register_source/frame0009.jpg to /data/reconstruction/specimens/H08-0083_01/register_target/register0008.jpg, saving as /data/reconstruction/specimens/H08-0083_01/register_target/register0009.jpg\n", "\n", "Registration: RegStepGradient Descent + MeanSquaresImageToMetric\n", "\n", "0\t5500.84\t[-0.0107656, 436.582, 334.124, -5.60864, 45.4124]\n", "\n", "1\t5373.48\t[-0.0563529, 436.581, 334.124, -5.59704, 45.3242]\n", "\n", "2\t4725.59\t[-0.107046, 436.576, 334.125, -5.61319, 45.2396]\n", "\n", "3\t4281.15\t[-0.110892, 436.58, 334.128, -5.64391, 45.2785]\n", "\n", "4\t4283.16\t[-0.110164, 436.585, 334.13, -5.66089, 45.3252]\n", "\n", "5\t4281.68\t[-0.108975, 436.59, 334.133, -5.67866, 45.3716]\n", "\n", "6\t4280.62\t[-0.110871, 436.594, 334.136, -5.70831, 45.4115]\n", "\n", "7\t4281.81\t[-0.11075, 436.599, 334.138, -5.72606, 45.4579]\n", "\n", "8\t4281.23\t[-0.110235, 436.604, 334.141, -5.74649, 45.5032]\n", "\n", "9\t4280.37\t[-0.110119, 436.609, 334.144, -5.77016, 45.5469]\n", "\n", "10\t4279.99\t[-0.1101, 436.613, 334.147, -5.79525, 45.5898]\n", "\n", "11\t4279.74\t[-0.111162, 436.618, 334.15, -5.82099, 45.6323]\n", "\n", "12\t4280.22\t[-0.111475, 436.623, 334.152, -5.84251, 45.6771]\n", "\n", "13\t4280.35\t[-0.111878, 436.628, 334.155, -5.8611, 45.7232]\n", "\n", "14\t4280.71\t[-0.111357, 436.633, 334.157, -5.87656, 45.7704]\n", "\n", "15\t4279.65\t[-0.11159, 436.638, 334.159, -5.89619, 45.816]\n", "\n", "16\t4279.69\t[-0.111698, 436.643, 334.161, -5.91083, 45.8635]\n", "\n", "17\t4279.61\t[-0.111855, 436.648, 334.163, -5.92671, 45.9106]\n", "\n", "18\t4279.59\t[-0.111557, 436.653, 334.165, -5.93943, 45.9586]\n", "\n", "19\t4279.02\t[-0.111421, 436.659, 334.167, -5.95621, 46.0054]\n", "\n", "20\t4278.69\t[-0.111258, 436.663, 334.17, -5.97548, 46.0512]\n", "\n", "21\t4278.32\t[-0.111099, 436.668, 334.172, -5.99581, 46.0966]\n", "\n", "22\t4278.01\t[-0.111158, 436.673, 334.175, -6.02178, 46.1389]\n", "\n", "23\t4277.91\t[-0.111828, 436.677, 334.179, -6.04876, 46.1807]\n", "\n", "24\t4278.09\t[-0.109487, 436.682, 334.181, -6.07009, 46.2255]\n", "\n", "25\t4276.46\t[-0.115393, 436.686, 334.185, -6.09857, 46.2658]\n", "\n", "26\t4283.1\t[-0.111366, 436.692, 334.185, -6.10366, 46.315]\n", "\n", "27\t4277.35\t[-0.112934, 436.696, 334.189, -6.1358, 46.3529]\n", "\n", "28\t4278.21\t[-0.114209, 436.701, 334.192, -6.16065, 46.3959]\n", "\n", "29\t4279.89\t[-0.112459, 436.706, 334.194, -6.17532, 46.4433]\n", "\n", "30\t4277.26\t[-0.113416, 436.71, 334.198, -6.20747, 46.4812]\n", "\n", "31\t4278.2\t[-0.11482, 436.715, 334.201, -6.22897, 46.526]\n", "\n", "32\t4280.14\t[-0.111609, 436.721, 334.202, -6.23505, 46.5752]\n", "\n", "33\t4276.49\t[-0.116458, 436.725, 334.206, -6.26885, 46.6113]\n", "\n", "34\t4282.73\t[-0.110879, 436.73, 334.207, -6.27353, 46.6604]\n", "\n", "35\t4276.04\t[-0.120588, 436.735, 334.21, -6.30062, 46.701]\n", "\n", "36\t4292.97\t[-0.113071, 436.74, 334.209, -6.28999, 46.7489]\n", "\n", "37\t4277.28\t[-0.116084, 436.745, 334.212, -6.30947, 46.7945]\n", "\n", "38\t4280.69\t[-0.111913, 436.751, 334.213, -6.31935, 46.843]\n", "\n", "39\t4276.53\t[-0.125948, 436.755, 334.216, -6.34455, 46.8835]\n", "\n", "40\t4315.15\t[-0.116046, 436.761, 334.215, -6.33087, 46.9301]\n", "\n", "41\t4279.84\t[-0.112865, 436.767, 334.216, -6.34278, 46.9782]\n", "\n", "42\t4277.33\t[-0.120517, 436.771, 334.219, -6.36711, 47.0209]\n", "\n", "43\t4289.25\t[-0.111558, 436.777, 334.219, -6.36018, 47.0692]\n", "\n", "44\t4276.85\t[-0.134971, 436.78, 334.223, -6.39492, 47.0961]\n", "\n", "45\t4363.78\t[-0.120206, 436.786, 334.222, -6.38071, 47.1412]\n", "\n", "46\t4287.15\t[-0.111951, 436.792, 334.221, -6.3719, 47.1894]\n", "\n", "47\t4277.21\t[-0.125171, 436.792, 334.223, -6.39298, 47.1899]\n", "\n", "48\t4307.29\t[-0.122571, 436.794, 334.223, -6.39017, 47.2017]\n", "\n", "49\t4295.26\t[-0.120129, 436.795, 334.223, -6.38688, 47.2134]\n", "\n", "Optimizer stop condition: RegularStepGradientDescentOptimizer: Maximum number of iterations (50) exceeded.\n", "\n", "Result = \n", "\n", " Angle (radians) = -0.120129\n", "\n", " Angle (degrees) = -6.88291\n", "\n", " Center X = 436.795\n", "\n", " Center Y = 334.223\n", "\n", " Translation X = -6.38688\n", "\n", " Translation Y = 47.2134\n", "\n", " Iterations = 50\n", "\n", " Metric value = 4295.26\n", "\n", "/dat\n", "\n" ] } ], "prompt_number": 16 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we convert the registered frames into a video to quickly review the alignment." ] }, { "cell_type": "code", "collapsed": false, "input": [ "# generate videos of the result\n", "pe.generateSourceVideo()\n", "pe.generateRegisteredVideo()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/usr/bin/avconv -f image2 -i /data/reconstruction/specimens/H08-0083_01/register_source/frame%04d.jpg -r 12 -s hd1080 /data/reconstruction/specimens/H08-0083_01/video/source.mp4\n", "/usr/bin/avconv -f image2 -i /data/reconstruction/specimens/H08-0083_01/register_target/register%04d.jpg -r 12 -s hd1080 /data/reconstruction/specimens/H08-0083_01/video/register.mp4" ] }, { "output_type": "stream", "stream": "stdout", "text": [ "\n" ] } ], "prompt_number": 17 }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Additional capabilities\n", "\n", "In addition to the ImageJ-based contrast method, we've created a scikit-image method as well. It has been encapsulated in the pmip module, as createContrastUsingSK" ] }, { "cell_type": "code", "collapsed": false, "input": [ "pe.createContrastUsingSK()" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "\n", "* createContrast with scikit image\n", "--------------------------------------------------------------------------------\n", "/data/reconstruction/specimens/H08-0083_01/register_raw/006-100034525-DSx4.jpg\n", "/data/reconstruction/specimens/H08-0083_01/register_raw/036-100034525-DSx4.jpg\n", "/data/reconstruction/specimens/H08-0083_01/register_raw/066-100034525-DSx4.jpg\n", "/data/reconstruction/specimens/H08-0083_01/register_raw/096-100034525-DSx4.jpg\n", "/data/reconstruction/specimens/H08-0083_01/register_raw/126-100034525-DSx4.jpg\n", "/data/reconstruction/specimens/H08-0083_01/register_raw/156-100034525-DSx4.jpg\n", "/data/reconstruction/specimens/H08-0083_01/register_raw/186-100034525-DSx4.jpg\n", "/data/reconstruction/specimens/H08-0083_01/register_raw/216-100034525-DSx4.jpg\n", "/data/reconstruction/specimens/H08-0083_01/register_raw/246-100034525-DSx4.jpg\n", "/data/reconstruction/specimens/H08-0083_01/register_raw/276-100034525-DSx4.jpg\n" ] } ], "prompt_number": 12 }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can leverage IPython notebook's HTML display capability to review all of the images" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from IPython.core.display import HTML\n", "HTML(pe.generateSummaryTable())" ], "language": "python", "metadata": {}, "outputs": [ { "html": [ "<div>Original - Contrast - Contrast Scikit - Registered</div<div><h5>/data/reconstruction/specimens/H08-0083_01/register_raw/006-100034525-DSx4</h5><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_raw/006-100034525-DSx4.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/006-100034525-DSx4-c.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/006-100034525-DSx4-csk.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_target/register0000.jpg\"/></div><div><h5>/data/reconstruction/specimens/H08-0083_01/register_raw/036-100034525-DSx4</h5><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_raw/036-100034525-DSx4.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/036-100034525-DSx4-c.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/036-100034525-DSx4-csk.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_target/register0001.jpg\"/></div><div><h5>/data/reconstruction/specimens/H08-0083_01/register_raw/066-100034525-DSx4</h5><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_raw/066-100034525-DSx4.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/066-100034525-DSx4-c.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/066-100034525-DSx4-csk.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_target/register0002.jpg\"/></div><div><h5>/data/reconstruction/specimens/H08-0083_01/register_raw/096-100034525-DSx4</h5><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_raw/096-100034525-DSx4.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/096-100034525-DSx4-c.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/096-100034525-DSx4-csk.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_target/register0003.jpg\"/></div><div><h5>/data/reconstruction/specimens/H08-0083_01/register_raw/126-100034525-DSx4</h5><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_raw/126-100034525-DSx4.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/126-100034525-DSx4-c.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/126-100034525-DSx4-csk.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_target/register0004.jpg\"/></div><div><h5>/data/reconstruction/specimens/H08-0083_01/register_raw/156-100034525-DSx4</h5><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_raw/156-100034525-DSx4.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/156-100034525-DSx4-c.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/156-100034525-DSx4-csk.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_target/register0005.jpg\"/></div><div><h5>/data/reconstruction/specimens/H08-0083_01/register_raw/186-100034525-DSx4</h5><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_raw/186-100034525-DSx4.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/186-100034525-DSx4-c.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/186-100034525-DSx4-csk.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_target/register0006.jpg\"/></div><div><h5>/data/reconstruction/specimens/H08-0083_01/register_raw/216-100034525-DSx4</h5><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_raw/216-100034525-DSx4.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/216-100034525-DSx4-c.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/216-100034525-DSx4-csk.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_target/register0007.jpg\"/></div><div><h5>/data/reconstruction/specimens/H08-0083_01/register_raw/246-100034525-DSx4</h5><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_raw/246-100034525-DSx4.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/246-100034525-DSx4-c.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/246-100034525-DSx4-csk.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_target/register0008.jpg\"/></div><div><h5>/data/reconstruction/specimens/H08-0083_01/register_raw/276-100034525-DSx4</h5><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_raw/276-100034525-DSx4.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/276-100034525-DSx4-c.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_contrast/276-100034525-DSx4-csk.jpg\"/><img style=\"width: 150px; margin:3px;\" src=\"//192.2.2.2//data/reconstruction/specimens/H08-0083_01/register_target/register0009.jpg\"/></div>" ], "metadata": {}, "output_type": "pyout", "prompt_number": 13, "text": [ "<IPython.core.display.HTML at 0x2852d50>" ] } ], "prompt_number": 13 }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, we can load an image into the environment directly and present it via matplotlib" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# get a file to load\n", "\n", "img_to_load = pe.processing_status['regcontrast'][3]\n", "print img_to_load" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "/data/reconstruction/specimens/H08-0083_01/register_contrast/096-100034525-DSx4-c.jpg\n" ] } ], "prompt_number": 10 }, { "cell_type": "code", "collapsed": false, "input": [ "# bring in the scipy and pylab methods\n", "from scipy import ndimage\n", "from pylab import *\n", "\n", "# load the image from disk into memory\n", "img = ndimage.imread(img_to_load)\n", "\n", "# display its dimensions\n", "print img.shape" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(750, 1000)\n" ] } ], "prompt_number": 11 }, { "cell_type": "code", "collapsed": false, "input": [ "# Set the plot size\n", "fig, ax = subplots(figsize=(20, 20))\n", "\n", "# and show the image, applying a grayscale color map\n", "ax.imshow(img, cmap='gray')" ], "language": "python", "metadata": {}, "outputs": [ { "metadata": {}, "output_type": "pyout", "prompt_number": 16, "text": [ "<matplotlib.image.AxesImage at 0x483e6d0>" ] }, { "metadata": {}, "output_type": "display_data", "png": 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FBNm0my063OedRHUS+Jsgi78180BXs5Tk021U1EezOyqVSjq3Q/vYZywQUE8C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\nbm4mnYUua7fbdv36dXvnnXfs0qVLtr29bU+fPrXB4OWB3G+++aa12+0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PAAAg\nAElEQVSFW7duYTKZCMimky57vV54vV6EQiEkk0n5rvbKkdGmwxNmVTVj0u/j42PUajXE43G43W74\nfD4A54bT4eEhPv74Y+RyOVmDBObIqGCVsYWFBTHC2+22JDdmOOLe3h729vZQq9XgdDqRyWSQSCTQ\n6/Wwu7uLfr+PQqGARCIhCv5gMECxWMTjx4+l/Px4fF4KvtPpyPohyMb1SDCGxjeVWc06ASCJh8kc\n06w5KrB+vx93795FLBaDx+OB1WpFt9tFKpVCLpfDyckJ2u22gH+dTgdHR0fY3t6W87DT6SAcDsse\nCgaDiMViSKfTyGQyGAwGaDabePLkCWw2G1577TWsrq4iFAphMplIouyFhQVsbW0JC4VAYr/fR6VS\nmcotw/WbSCQkPNBms6FYLEqCaIIIwWBQcucQNCkUChLCxDOIPzPUDcAUAEHDhuyqZrP5Kdo/f56f\nn5eceUzk7Pf7xSgioN9oNHB4eAgAIjN5phLsoIyg7Mrn82g0GlL9j0Cz3X5eMdDlcqHT6ci6GY/H\nCIVCiEaj8Pl8AtjYbDZUq1UcHh7K3mKC7VarNZX7EDg3qILBIO7fvw+n0ynr4ODgQCrotVqtT52V\nJrBhOsYIbPBvDAElkKuBeu537oPLDOxZTjvthKJeovvEn3VIHwF/yiEC+hoIMM9XPusiZop+FnVL\nHT6or2N/CP7rsQQg3yHoqM9wGtba4abPPf3si4Ags896bC+7jv2hIc79ofvFswyAMOM1C5PjqPVA\n7kNdrY/5IU0Hmv6Z4dSU85SVGkww14zW4zlXZMFT/mnwTTPEtG6sHUUa6NLPoQ6kASD9zpxrXUDF\nXFuzxsYEpcx5elabwxwTHSrL+WIeOZ2HUI+fCZoBEFB1MpkIeE4GsgbWCNZQpzSjCmaBSm63W34m\nM5k5vLim+v2+VEJkM+9tAmQcYw0WmXtXt1my6Lp9+do1APQ1bybKSzo5D5lnpf3OEqoaSKJ3wywh\nawrK59H0vSgAydZgwmUaVlR49Pf4PwUnDx+tPFwF0nA8zEOWgpaHp6be/1/E2hPgYNUZHrw6t4FZ\nfcOc12cV7peBQSZIRyMilUrh9u3bWFtbQzgcluSjBICYu6RUKolnmP01Q6o4twRvGI7E8t92u10M\n8kajISFVmv2ijfxOpzN1cDIZeLPZlAo0OiE0k9lyTLkPtGHHw9tmO68W5Ha7ATxV3N1u91QidbIH\nyNAg8MH+UAEwDQVzf2oFTa977kkq39wzdrsdrVYL7XZb3qfVauHo6AhHR0cyhmQTAEChUBDGFY3D\nUCgkSV3L5bIkXy0UCmIo9vt9UZBsNpuwRk5OTiSRNd9RJ7bl3iZrzOv1wu/3y3rT65ZKUrPZRKPR\ngNPplCpUp6enAkyREUKDotlsSt4aKm5cJ16vF7dv30YsFoPT6cTNmzcRiUQk/xINPzOx+vz8PGKx\nmBiCrCrH+5tKMQCpINZqtSTx8pMnT1AoFDAej9Fut+Hz+ZDNZgGcG9GPHz/G/v6+JM9mRa5Go4Fe\nrydrh6GVnU5HAASG8R0eHgowWKlU0O/3EY1G4fF4kEwmAZyDTQSiWDbd7XajWCzigw8+wAcffIBG\noyHl6N1ut4Ae3DMEMWk4FItF7O7uolgsCmhBkIfz4fF4hIWigddarSbgFZluzHdEECeRSIjBVCqV\nMB6PpRoVS8p3u13Mz88Le5CGfyQSEcadVsRDoRDcbjdef/11hEIhMe4Z3udwOBAKhdBsNjE3Nydg\nKuUJzx+GKiaTSdy4cQNOp1NAs62tLVgs56GY3BtkYwUCAQGWNjc3BVCm8UWQi1R+9p2ywwQfOp2O\n5A3S8oOJtN1ut5RxZ2U6rkWCD5VKBRbLecgswTeGmjqdTqlwl06nhSFEcLPZbGIwGGBubk6eE41G\nMT8/j1KpJPt9NBrB6/XC5/PB7/fL2bKwsACLxYJqtSpydjgcot1uI5/PC3DFMDU+5+bNm4jH48jn\n81PAJMFm6jSzvP6UtVoXoGwlW6rb7QrbWOsYOveS/l+Hs/OcM6/RQArnm8/gOc9raFzS0KchbT7L\ndFbo88J8vr6O78Q1QxlNhp0+kyifNTigASetq/AagpR8T+qd/B7vCTzNU0XgZRagNqvNYpaYepDd\nbhcZoPvAxtx2HB/9jp1ORxyls55tghx87qy0CZxXMh8JKM1i0Mxilui55j8ycfQ78exleDgZ0xx3\nrnOON+UAv891QD1cA3Wcb4KxZg5O9pOgG508pv5s6jyzQK+LmjkGPI81W8l89iy9S4OlVqsVTqdT\n9JtZYLFe1zxjTOYRG9cGCxmQyc49QrmrwVvadxwPjj/7bzKWKLPICJ61d/i96/bVatcA0Ne4zULB\nSRfkQftFgBl9D4vlKauEyqZWGJ6n8DAVEh5cwFM2D0vgamXHVOA0ZVIj8BexdPjOGvxh0wctBSwB\nCyp8sw44fud5NRqy9OwxGScAScRLRfSiOfksgN2sdzC9GFQWotEo7t27h29/+9vY2NiQXDShUEjK\nljN8hAcQAJTLZezt7cHv909VqmKyW74Xw14KhYKEj8TjcTGgWY6bHk8aN2dnZ2g2m2i1WqJgdjod\nKWPcaDSkHDGrOTkcDmHNnJ2dodvtwul0imFGpYljo72GeuxYdjkUCgkQRMWPAA3voQ91rlEqUJfN\nj1ZCdOic1WqdCuOo1+uoVqsol8solUoytlQQq9UqPvnkE1QqFQnJ6na7UwmW6bksFovY2trC7u6u\nAEVkaDQaDcnddHp6ilKphL29PRwcHMBms8lYEEClR03nLdEKkfaocR8PBgN0u10BFAAI6MMKRLpy\nGllew+EQzWZTlGuuCwBIJpMIBAJot9uSb+nk5ETGzeVySeiLDlHQjB/mwgEgoU5UPjlHXMMff/wx\nDg4OhL3GhNIulwvVahWPHz9GrVbDZDLByckJyuWy5BsCICF6ZC8xpGk0Os95RrC01WoJ0FQsFiW/\nE9kgLpcLtVpNwLFgMCigF/eezWaT/DmPHz8WsHBlZQWpVEr6z3s5HOfluRm+0+l0cHJyguFwCJfL\nJaFlVJbJZpufn0c4HBZGYKFQQLVaRa1Ww/7+PiaTcxbN66+/LuuWCq7f75cxIUgGQFiI3AfMScU1\ny/w0BJoJDNjtdiSTSSQSCRlzzjlzUTG/Fllc8/PzkoOIBjn7cPPmTbzyyisShtfpdJDNZiUn2GAw\nwMLCggAYBEmcTqcAHNzXVORZoZIG9Xj8tCqlw+HAwsKCsMFqtZrkwqMcpDPB5/NJkuvFxUXcuHED\ngUAA/X4fxWJR5kGHCI5GI8mdBgB+vx/RaFTYOoFAQBLVcz0tLCxIQudEIiF5fgKBAGKxGHq9Hvr9\nvswTw4iZ2JxAFRNEd7tdVKtVnJycwGq1IpVKSeigBvr53XK5jMPDQ5Eb/X5fwuu07NbGkf5cs3oY\nckmQk4n3CSppw9E0RvmZ1ifMs5ZnK+UjwWdtrPFn6iA8h8iQ1iChrihHYEeDLFzf/BvvpYEKyjod\n+jNLn9KAD++lz0g93gTN6fDRzzNBf74Xn2GehXr89OecT3NOTR2POq9OFUAngQ5f4z0JBnAOOHb6\n2ew39UV+z8y9pRvnZTJ5WohBM/e04W42vb4I8vD9qP+yT9wrXD/Ma6a/Oz8/P1VIhWCPDk/TFfM4\nV5qRd5HOSVlGcEaPifk+5vhcpcNqBhPXHtehuR75Dpolxc81kEpdiH8jKEtbg+NCdpEGmbVOrkEp\nrge9XylH9JjzfU2nt95n5vrkGGsZYrLr9L57nrbKdXvx7RoA+po3c8NqQfBFN7P21rDkrEayadQ8\nb8GhwRZ9bx5QZm4XCnLt/dR9/TxAzCwgR3uRZnmTTMH7ohoVTtLO9XP1QTPrMNDvc1XTB5Sec443\njQyXywWv14t0Oo27d+/ijTfewNLSkuR0YY4Xfp9hT16vV5gBx8fHSKfTWFpaQiQSgcPhkPw0BH7I\naGBYVaVSmaqIRcWDRhOAqbwwFst5JbEnT56g1+tJkmWGOzEPDL0spVJJvP42mw2JRAKRSAQ+n088\nc8Cn1ykbD2cqiRx3HrYmdVpTn7WCbIKbl60vKlJa0SDQFAgEEI1GJSlru92We3MMCU7okAEyfgiw\nnJ2dYX9/H5ubmygUCmK0MUxpa2tLcqGUSiU8efIE+/v7KBaLwpLS1HEAYqSwxDgVoNPT0ylAhdc0\nGg0BshjOw3LW5XJZctMAEIORAFC5XBZadrlcRq1WExYKDctKpYJcLicgCsP13G431tfXxQjRoJ9e\ncwQUGo2GyCAy0U5OTvDxxx9jb28P1WpVwm24n6ik5fN5FAoFnJ6eShJj5i/SCiL/brfbBfghoEd2\nBvvKcWK45Hh8XgXu4OAAFosFyWQSTqcTi4uLcLlcCAQCktOH46NB1ZOTEwE6WZGNe3Jvbw/FYlH2\n+9LSkjB9yOagImu1WiXRtmYFkH3AhNWcq9u3b09V/6OsIrDHEDben8At+99sNnF2diasIzKNmLPr\n4OAAnU5HmCymc4F7luNA1hkTuRPAbrVaUh7+xo0bUlENgIzr/v4+fvnLX6Jerwt7Shvo2uHA9aG9\n79pgmkwmAoaFQiEsLy8jm81iOBxie3tb9izwtMLMwsICIpEIstks4vG4VDwLBAIS3lKr1WQ+CCaQ\nPUmQmFXaKH/I+GQydbfbjWAwiHA4jHA4PJW7KJFIYDgcwuv1ol6vy74lMJhMJpHJZATc1/Kg2+3C\nZjuv6EhnhA4l0kaSxfI08TtzkRGknSVfTd1BMxcvMtz5TzuuPk/TBirnn2CjPuO1rqMBHp0LkPoC\n1yz7yvvyOm3UayOV46cBLcoE3luHp2m5TtayNp41KEN9xjRCaUhzLICnRRs4NrzmKiBEj6meU/09\nyhyG2/F6bVgnEgm4XC6MRiO0Wi1hevE8M++rc5cRsKY+PYstZII7ev4ITnAe2LdZ9zAdUdqBYsoS\n7pWLADXz+3rtcI51nkzdP52rU8+TXl8aeKNsucx5qdfOZY1zyDVMmUnwSwM5fAcT5JsF2PC9GdbO\nd2O/uI6ob7Cy5aym9wkdeASNeEab7889o891PUc6DyjBPpfLJe9PnZj72gwx+zz20nX79bRrAOhr\n3ExvgP78i2xe8xAy6bmajvqiUONZAIb2/FPppIDTSTd5YOsD6Ys2CleCAewPBT6F5v9FGBgwzfag\nIcDxYijMRe1Zx8M8bEmx9/l8U+EfPFCSySTi8bjQ+Bl+cnp6Ksbh2dmZJLil9zafz0t+CB6kDEsB\nILlDGKpDhYKeaHqjY7EYkskklpaW4HQ60Ww2hWlDA4FJdsvlMvr9vhgioVBIEgnPzc1hfX0d0WhU\n8kzRGGalGWA6txTHi+NrKnGaxULDVbN1TKPjIubPVWCTzoGhn6HncGlpCZlMRp5tt9vFyD48PESt\nVpOwBoKMzO3k9XolFCcYDAro0W63cXR0hEqlgg8++EC8oQT48vk8Op2OfE5whkwHq/U8wWuxWJTQ\nAoaOLC0tSbge2Vq8J5VqKjwE8drtNlqtFiyW83xO8Xhc2EQ6VKtWq6HX68Fms0noENdlsVgUUIxr\nx+fzYX19fWqMORdcO/yduV1YoSqXy2Fvbw/7+/vSd53ngXR7zbhiSCJBVjLkOK9UvAn8NptN8fQS\nmKACSNYk2U1a8WV1PuZXSiQSSKfTwtayWCxIp9O4ffu2AGj9fh+tVkuSRW9sbGB5eRnz8/MCzpLF\n8vLLL0tiY673arWK4+NjAQ3I7iD40Wg0hE3DfcEKXXt7e5ibm8PCwoK8LwAJt+J8UaE3mWT0iDIM\n0+12YzAYIJfLIZ/P4+DgAPV6XYwunoHaEOIzCBZZLBakUin4/X5hcR0cHGA0GmFpaQmpVEoAuMlk\nIn3NZrNIpVI4PDycCuFjkvjJ5DzvUDgcFvnHufT5fJJ8fjQaCdtlbW0NDx48kPxFk8kEm5ub+I//\n+A/8+Mc/RrlcFjCOgA/DdLmXaDA0m00xILjOaZhw7TKM02q1otFooNvtSsgMAGHO6RAtzqndbpe1\ntri4KAAu2UnMneZ2u6eMSG2QESgMBoNwuVxydumQCg0iMj+clptcR2SsaJmunUmcf4LImiWtQW2e\nnbMA/IsAC/1uJluBY80+amCAuo7p/OP3CNjo72oDfTKZCFBDgJbVICnHaHBqMEPnGaPewTOF4CTz\npHD/0UnB/vF+ZD9oI5xjrVlt/IznupZ/+r01YEF5rQEHDTRotrCecy3bV1ZW8Id/+IeIxWIYDAZ4\n+PAhHj9+jE6nI2ud92VfKM+1ka4Bu8saZSUNegL+Wo49q65JkGoymUjIp67YOOvZei40e55Njy/H\nlnuRP3Ofs5nOKX3eacCF48S+zALzZvXbfAf+bzquuR61jsv3Mfcn30PvFw0c6ecA07q52fdZ+16P\nLe87K00Fx2sWKKSBLZ5pfJ623zQ4zO8QbKKz5Lp9ddo1APQ1b7OEBP9/VqT8onuZ9yVFkdeadOTn\n1bRXQwtm/j4YDNDpdCQMhwoo+0ihTOE36114r6saFRO+O4U/lS96nXmdKfyfd+Oc8nCmF1lX/KE3\nwaTRmgfVVU17NWw2G4LBoBh54XBY8tnYbDYJA2A+EBr/NPp1HhyWw65UKvIsv9+PTCaD9fV1ZDIZ\nAVqcTqckGKYyqQ1tnZvA7XYjnU5jZWVFQjHG4zECgYA8s1ar4ZNPPkE+n5fwlmAwCL/fj1QqBZvN\nBq/Xi0wmI2wQKrassmV6yi7ynHEt6Dh+0/NlfsecL5OCbO5vs2kDAnjqRTW9sGbjOr59+zYmk4kY\nlVSImfOCTMBwOIx0Oi3XcDz39vaws7MjBhbDd8hEYVgMv0dDhCALQRibzYZut4sPP/xQkjeTjt7r\n9XBycoJcLgePxyOgsM1mk3AthrdRwWRJc+b+IYOBAEq328Xu7i5KpZKsNc4/PX2sgETZw7xV3I8a\n3OPa7Ha7aDabqNVqyOVyOD4+Rr1el+pWNMra7TYATNHAdUU5XQKbYAaBXxovXCv1el2Ap8lkIrmB\n+O4EhyjDgOncGtyzZGjQ4PB6vUilUkin06hWqwKykXW3/P/ZJsyN4/V6US6XEQgEsLq6OuU9ZfJd\njs1oNEK1WhUgluynYrEo4Wlc241GA++++y6sVitu3LgBu92OdrstoGW325U1zb1D0EkbiGQ/JRIJ\nLC4uIhKJSM4ghpJtb29je3sboVBIqo7p+aXizLx0t2/fRjabhdvtxsHBgVyXzWYFtGH1Fo4Fq4nR\nW0xDl7ml/H4/bt26haWlJXz44YcCrvFenU5HGE0LCwtYXV3Fm2++id/+7d/GgwcPRHasrKxgPB5j\nb28P/X5fZF46nUYkEgFwniNLhxOxXzT2eZ5SFmsgi6F0zDV0dnYmzDCyDyORCBYWFgTw5M80Yjwe\nD8LhMOr1+pR8IPNHAyrD4VAA4Xq9LvnCNLjAee52u8jlcjg4OEAul0OtVpOzROdnoc6gAQPub541\nnB8Ck71eT8AkLavN8/Yi2a3ZW2z6jKBRqMF9rmXNXNB5CJm4X8t2vhv7bSbx5TpMJpNYXl5GKpXC\naDRCpVIRoFyziGg46jNGJ9Tm+aVzMeozj+ARwQg68ShryZTh3PAc5btTZpE1wYIPPOe4rgigs0/s\nA+eMZzTHXLMPqftEIhH81m/9Fn74wx8ik8lgOBzixz/+Md555x08fvxYWKtcfzrJrl5LfA6dEBfp\naASA+f4cIzoT2L+rdG/T8URQVSeO1+wwzS7jXBJ00kxDzQSn3sk9Z+rMWofX65rf5byORufJ9Kkj\n6P3wWXVXjqf+DgEbDRyzLzpfncn6YTg631M7aEzwSOt3vAfHf1b/uZfohFpYWPgUW0q/yyzWoQ5x\nZ448Vrccj8cin7QTQwOLfH8TZHsR9st1e77tGgC6bgCmAR9TCD3Ld6/a7DxIGFtvHl4vspl94+EI\nQA5vk9apQ3TMmOzPIth4cFLwA08Vl/F4LDR1ejqYJ+WLPPOyxvtQ6RmPx6JM68PNDN2apXA+6/Mm\nk/Pkvuvr63jrrbewuroqjARW46IR4nK5hKF1enoq3nC/3y9JMwFI7oV6vQ6r1YpEIoH19XUsLy/D\n6/XKYcqxZil1KpP0WurcBjRcCYwx1CmRSEhYBfNL7O7uotVqSagEPdM+n0/WONeP2+2e8gzO8hBd\nBMbo63S4hg5ZNJXAWcCQNiIu2tdUZnnQ67wlBA3MxOj0omrP9Xg8FmOA72DmbmAFNgIcjUZDmClk\nThA4YYJMAmB8FvPIOJ1OydsyGo0EaGPS2f39fTEkdT6E8XgsVc6azaawtZj7x2KxSKhbLpcT0IVs\nI5aBJptsPB5LyCIZMOPxGCcnJ3A4HCiXy6hUKtja2sL9+/clNEjLBc26IXOtWq2iUqmg2WzCYrHI\nPtDJJ6lQ6twxVAK1557gCpMYc437fD6cnp5KKBPp4zqxJPeNDiEiE4YKuNVqleS7mpqvWW/MNWSx\nWITRR6WYxvPc3JwkgZ+bmxN6v9PplPCsXC4neWXm5uYE6K3X65Iw2gR22I+joyN4vV5hivAasma6\n3a6sZ7vdLnm7CKQxmfLh4SE6nY7Ic80G6Xa72N7exk9+8hPcuXMHmUxG1g73rdvtxsbGhpw9TCB+\nenoq+agWFhbg8/kEbKR80QAxc2Bxrt1uN27duoU7d+4gmUzCZrNhZ2cHfr8f//u//yvgE89iGlKh\nUEiqmYXDYVgs50wlGmOBQADhcBiRSERKrTMpPUHD0WiEQqGAUqkkbCqdSJx5y9hnJn4maD8YDCTM\nLJvNYnl5WdYpAXTKGK4bAtQEKnmmM4wOwJRR3mg0sLu7i8ePH2N3dxe9Xg83btyA3+8XIIwgKoHK\n/f19bG1tyZqj00DrC5xXEwCisUe5qQ1k7jW+i2YSXHQWPAt7gf+4TnR/NNvTBIY4R2R4aBYRASOy\npqnbMDfTysoK7t+/L4mzCZyROUX2bLlclmpuLPTg8Xik4iTPHb2fTNY4DV8CcRoIMs9Jzr/WZ3gm\n8QzjmaBBDso9Mic0QMTzSgMbs0AV6r03btxAJpMRZnIoFBJgiYCYztGm9VFeQ3BoVk4hPX8EXvgd\ngvcaaGDfZjXeUzNdzDVI4EfbDnqOeM5Sl9Qhfhpcm+X8NN+JepnJ4KPOovUg7n1+xu9ogOKzOrWp\n+5ApyPWozy6uE4JhGuzSIbeUA7qZY0wwiWvuIp2b49put2Vt6gqZ5nuYz+VcEMjmftd65Kw1QtCH\nzGp9v+dpr1y3F9uuAaDrNrN9VsR81qY3PRIWi0UEDRXhF9G0l2cWGs3DkYokUWxTwZr12bM0fR0V\nGP2ZTpqm82PQ6zbrOc9LqJoghJ4TraBqmvUX6QMPikgkghs3buCll15CJpOR5K6j0Xl5cwIoPMxP\nT09htZ7n9WA1HR3qRI9/oVAQlgONHyqo2rNExggAYU6w2pLX6xUllrlqCGAw7we9IXa7XXJxkOWg\nvaBacdEePJ3AkWNpzvMswMc09AhcAZ9Opq3nSHvLLpqzWUaFNgY048ikxOvv6Gs1Q0IrdRpMpZHv\n9Xolyezp6SlisZiU4WaIF0EOMiW4dxmyx5ATfub1erG2toZUKoXBYIDNzU1hYvh8PlQqFRweHk4x\njOz285KpXAf0bGta//HxMebm5iQUkY0GIAEzhjeFw2FR5GlEt1oteDweHBwcYGVlRdazxXJemYg5\nfQg0cN3W63WUSiVJUs41S8OXIKdm7nHvsmLVaDRCrVbD3NwcFhcXpUw489bY7XZhTdAA4h6gfNQe\nbXrb9c+dTkdCKFdXV6dCBjqdDur1uoRbejweWQfhcFjmkeuVoUxaweZY0vAjgMGQE7IN+CyycQii\n0Vi028+TMxeLRWEycR50XhqGFxKkjkajWFpaQjwel3w9DCvc2dkR+cLQHjKxjo6O4HK5hI3U7XbF\nWF5eXsba2pqsxcFggKOjI5TLZXz00UfY3t7GysqKyDI6LnRC3ng8jtu3b+Phw4c4PT1FPp/H2dkZ\nXn/9dcmVZrFYEA6HkUqlkEgkUK/XxZikweFwnCd9jsViUhWNz+BadTqdiMViaDabknuK8z8ejwVs\nZw40hgCyAhANISbWTiaTWF1dxeLiouRGOzs7w/b2toAyoVBoyjChvDE96fwbz1t+p9VqSXJ0u92O\nRqOB7e1tvPfee8JmslrPk3AzWTZLy5O5MhwOUSgUJGeVZjSa/TIZOxxnU1ZStmsjWIMV3MOzdDH9\nvqZxSHlNg4731M8GIOe+LmXNPc2QPW2Uc6/T2CWbkEUKFhcX8Ru/8Rv4zne+I2zY4XCIUqkkY3t4\neIjd3V3Z31arVdh+zWYTh4eHUu1RM7G1nOEaYaNhznmZn58X8EHrNTzLTOYI5bRmABFQAp4CLwTX\n7Xa7ACoEhijLaLjzzNMgONcmn8OwVYKNdEIR0GZIL2UeAX7KeVOXMNeHDs0GIExzzVC6CFjgPQny\nUK8ia53jbgKhui8anNHrlNdwfev8Ndr5TJ2Ka1afa7wnwwMpBygLtA1gMuQ+ry7Ne9F2IACkdSad\n7JqNe0xX3dV7VDvU9Huznxqs0z/rcSZIpteWfo527M+yaThX1J21w00nttZhYLofs9bPdfvyt2sA\n6Lp96gCh8X+ZkNTXX2Zgsmkhrv+9CKRYG6H6d9O7pr1uWmHX3pyLQKRneT5/plKljXPdDx582kN5\n0f2+aDNBOTJgrFbrVNK7Wc83+2ECbSZjislEE4kEXn75ZXznO9/BN77xDczNzWF3dxfVahXj8VjK\nP9OzzQSbVNxZ5YfeF00Z56HPfCyNRkO8d51OB7lcDicnJ1PGUiaTkeoxTOJJZdHlciESiSAcDkuf\ntCGvKa+8px6vWYfrLA/dsxya+nf9nKvuddXvF/1t1nXak33Zvbh/zD6ZCoJ5PwJv2WxWlI+joyPx\nDs/NzWE0Os/nUiwWUa/XYbfb4fV6EY1GhX0wGo0kVOju3bvw+/1oNpv45JNPpFgNmQkAACAASURB\nVAIRmRX5fB4nJydSxpy5TAioAOfADtcRQSAaNGdnZ0LrJjBHZgKBRoaZ6UpgBBYCgQCKxaIAJN1u\nFycnJ9je3sbx8bGESnk8Hkk022q1JLcRlV0AUywFemA16EbjlslWWS2JVbR0Ek+Gwfn9fsTjcQm3\nYY4fVv3i+BCco8edbA6G22nZSXYTQ4QYruP1erG0tIT19XUEAoEpr7dWhNkIKAWDQQyHQ2GSEail\ncepyubCzsyNheWR2NZtNqS7l9XqnKo0xHxUrwDH3k9frRSwWw/LyMuLxuIzZ3bt38fjxY+zv78v8\nsDLgYDAQAIU5whwOB3K5HHK5HE5PT7G4uCjsKwI/NIDL5TJyuZwklad8ZR4bDcp6PB68+eabcDgc\n+OUvf4lCoYB6vS7rjtXMyFIjmzIej0v+ITJ8GALA/UAmlAZZg8EgFhcXZa4YPma326dyVvF7rApG\n5hPXJIGrVCo1lb9qMpkI60vnFJolV7ge9PrQYDWdDwQwGe7HSpAsPsBk5YlEAlardSpsiaGYlUpF\ndIalpSX5js1mQ61Ww5MnT9BoNOSM0uAaz3gafRaLRdYr1x0ZNzoUiveggcgKeA6HQ5LCV6vVqfAM\njgkT6evxIQjGvUs5QhnKBO7pdFpCKQlMasCJfSEAlEwmcevWLbz++utYXV0VZ4zD4UAmk0EqlZIc\nUfq97ty5g9u3b0v4HVlWm5ubePToEfb29mRdasYNgClnFd/97OxM9A7tgCEzgmtLn+0Oh0MASs6v\nDinSOXkoc3kdzzN+TpmuwSs6iH71q1/hJz/5CbLZLFqtFh49eiT6CueIgAv7TEcE9R2+k6ljms4c\njg/nmvvKvHaWTqHBQ86vTnCswQI60XgumICSBjDMz7SeTiCE764BIgKoGkDisyhneZ0GC/V3qe/P\n0qWfRb83r9HgCBlkWpfnd/g9vpdm1Gn2l9ajua44RiYbX8+5OaZkw7FpZx4b1xOZo9QTyC6lvOKY\n67Hnz3TwDYfDKYY7z9/r9tVo1wDQdZvauNzYGpEGZsehzzp0LmoUOmZZwqsM0M/bTIScCgJpi/yb\nDlvh4auT+2lgA/j0wXtV46Gg+6UVltFoJCEfJt1z1vh/0abnWh/kAGRutFI1a34um3tNw2Xlmtu3\nb+PBgwd4+eWXkU6nxRCp1+sSttPr9SQEgCWf8/m8GI4PHjxAOByeUs7oiaG3tlwuS+iJx+MRj5nb\n7cbq6ioWFhYQjUaRTCbh8/lkzM28Awz10cmxuYY0cHHR+M76+bLPrvq7OQ/Peq+r2ufpy1XXXtVP\nri3gqRJjtVqFuePz+VCtVmWf0htVKpUkeS9z6JB9wD3jcrkQi8UQi8VgtVollEeHG0wmE1QqFTGS\ntZLNcdYhTtrjRTYKm2bFANOJMsmAaDabYkQRYCkUCvjkk08k1IF9pOHPstIul0sYLAQpNKNN5yDS\n1HKGgNFTrtkE9GzzPSjb9D7wer1itM/Pz6NUKuHw8FCeSTlJpY+ACFkdWrHV3lkymRg+FYlEEAwG\ncePGDSwtLc3MjzVrPc3PzyMUCmF+fl7mVQMFNE4JurDimw5vrNVqAvIxqTLHiF7+eDyObDYLv9+P\nYDAoFcq4TrxerxiItVoNp6en8Pl8GI/HQsnntTpPiF77lGO1Wg35fF5AIOa04rzyXvTeayfC3Nwc\nMpmM7Jl3330XhUIB29vbcLlcWFxchNVqFdCTIXpkBLRarSlGEeUxWXgWi0WYTo1GQ/IVMRxI95Vn\nPADZjzdv3hRghSyt09NThEIhpFIp+Hy+KV2A4Ag/0+PIdco51h71i+QNy63TCCyVSigWi7DZbFhf\nX8fKyor0j2GgDENm2CD74XQ6pVrlnTt3JEzu6OgIP/vZz/Dzn/8c+/v7ok+wL/TMU54wAXkwGBTg\nmGAMK8sRvCF7xO/3Y2lpCYuLi5ibm0OtVsPu7i4++OADScxOeeR2uxEKheDz+aZyQiUSCakY2Ol0\nkM/n0W634XQ64fP5EI/H8dJLL2FpaQmNRgOPHz/G9va2gEwaoCWrweFwIBwOY3FxEYFAQPQDynoy\niZhjhAURAoEA3nzzTaytrQmQvLi4iPv37+PDDz+U8ujVanUKFOC4al1F61R05FCWMMn0ZDKR4gMs\nNkHjvVKpiPyjY8nhcAjATJkFTLOodGN/tL6gwzJ3d3fxzjvvSFU9Arzj8VjytbERRKKhbjoQzWbq\nYpwfsoap8+nQ4Fn3IvCjQ4L0dzUgbwJK+vNZeiv7r6/R80rd2Az1o5y8yDFqXs81x/tqxxQBNM3u\nv0q35nqfpfszdxTlnmbLaPYXr9GJswkMco613k8nkgbKLrM9dP9M+afHaTKZTDHo9PlNGaHBa22r\n6ftrlpyW1dcA0FerXQkA/dmf/Rn+/d//HbFYDB9++CEAoFqt4o//+I+xv7+P5eVl/Mu//ItQ1v/2\nb/8W//RP/wSbzYa///u/x+/93u+92De4bs+9zRLkbPzsswASGuzo9XqfApzMez+PNutw4sFATxLf\nQ8dVM9REHzhfBHzhAUBjSIcLUdkgRZco/Itss7xEVE757vogNsPALrqXeeC73W4sLy/jm9/8Jl59\n9VXcvHlTFFcmlaNix/Ght7ndbuP4+BgHBweSjNfv94s3jcpTPp/HYDCAx+NBJBIRZY3zabPZpOy6\nDikhbZf9NQ+vWUqOfsfr9sXarHEk84SJuHVZXIKAsVgM8XgcpVIJg8EAyWQSFst5KfZerzeVc4jr\nGYCEpdBAZWLpVqsl4VRUtDRAS0NgMplMha7S06aVHXp/aeRZLOfsiVKphJOTE0kaa7Vakc/nMTc3\nJww4Jg2mIU1GG4Cpkva6CpoGcZkbhca4xWKRkuiBQACJREIqQ1ksFqlKZ7GcJ3x+8uQJtra2UK/X\nYbFYhLWXSqUwHo+l72QBEQygF5x5urjPtMLJfhJYZ38DgQCSySRisdjUHr2qcX/qBNhURjkPACRs\ndDAY4Pj4GHt7e1Pec1YYNBPwE0yLx+O4d+8e1tbWBFjqdDoSHkPmIp83Gp1XLeS5whxlZ2dnMt8u\nl0vC7AaDAer1OgqFAnq9nrDSTk5OUCwWZR0x7KXZbApIYVL6aTxHIhFsbGxIYu3hcIjNzU0cHh5i\nPB5LLpxMJoN0Og2Px4NGoyHJ9MnMYjJ1svPIvCyXy8LO47ySWcXQRA1wkFnHXEEER4PBIPr9vjBU\nuFbYZgE6/J37mvNwmVzRclsDMQyzIei8trYGr9cr5x1DCLk3yboaDocIBAJ466238ODBA6ysrIjx\nk0qlcHZ2hp2dHXzyySdivPG9dHimBksZIkcAiGvb7XYLQETGYyqVwtramuR04t71er3Y2dlBoVDA\n2dmZ5NkiAMR5iEaj2NjYQCKRwNzcHAqFAh4+fCjV4+iwefXVVxEOh4Wt1263RUdg2BYAeQ+CWePx\nWBgtBFcJKNKpc3h4iFKphNPTU2HquFwu2Zsej0dytR0fH6NYLIqeRJZyu93+1J4lQM8QMFYaHY/H\nKJfLMi7M0+fz+UQucqwJcHMf872os5rN1A20PklDm+cY99TPf/5zPHr0aGqdM0dWv9+Hw+EQpg/f\nkXte5825qpEtqAF+HWo16z3IUNFnHc/SiyICTHDkItthFiDE99LAFK/RjPiLQBA+WwPHHDMdcq/H\njcAGr39WsOKiMSc7kLJDAyu8vwZ/ue5ph+hCDBrsMsPinqXpPcFmOrJ5P4LLBH513kodDjoLNASe\nJob3er1TOTepQ3HMvojtdN1efLsSAPrhD3+IP//zP8cPfvAD+eztt9/G7/7u7+Iv//Iv8Xd/93d4\n++238fbbb+PRo0f453/+Zzx69AjHx8f4nd/5HWxubn7Kq3fdvhxtFiL/LB6GWejzRZ9T8NAwoJfL\n9NhchPJ/3mbeh8+hcCPQoQUu6etURp4Xkk3BSEq09tZr5pFJcX2efWDTYAfvT4+qTpzIf+YBNMuj\nY84/k6aurKzg7t27uHfvHhKJhITMkLmglWQ+l96wcrmMer2OVquFXC6HJ0+eyPXNZhOFQgEHBwew\n2WyIxWJ4+eWXpcw4vZIWi2Uq1EX3UQM/mi1mKgVaubhuz6dpdqEJtGlvLhtzTFBZYZLiQCAguSKo\npGvPGa9hAmWCFwz7I/ODQI+uFERDgEwOt9uNaDQq+aB0BQzgaWlY9tHhcKDVakneGzIJzs7O0Gg0\nUCgUxLCuVquSdJrXUBbRcKEiSXaQ2+1GOBwWT7rFYpGQs2AwiFu3bmFjYwN+v18YFqyYwzw3/X4f\nm5ubePfdd3FycoJAIIDFxUUsLS0hnU7D5/NN5QWiUkjwhMoscy+53W5552AwKAY/ZS2NWbv9vFR8\nKBSaquL0LA4ArhMzNIx/00b2wsICUqkUVlZWsLW1JYAxcC73CKixTzwHmOvnzp07iMfjGI1GKBaL\nEr5FQODw8FCAAp1HhfPI0EWOt8/nmzISDg4OBHBjom/mLSLA4XQ6hbUSj8cF+NJKNmUYQ9Zeeukl\nRKNRdDodVCoVqTxHAINKO1kimUwGTqcTvV5PQEzm4uJZ2W63pRqf6dBhRTYa/jp8hAasDpfx+XyS\nO80EfvT86r9po0jnQtOOHODy8GXOC/vm8/kQiUQEKAAgAIzH4xHDnc6hVquFeDyO1157TQoZ0KAM\nBoNYXl6G0+mcki1m3zTbgXntHA4HEomEJBhnuBJwDmRGo1EJowqHwwJ8cf/oohGcU50Im+9KRls2\nm5U8TEzWTRBFzyvPaZ3cWydM1slfOT6FQkEqIHItECzd3NzExx9/jEKhgMlkAo/Hg1KpJIAWx4j6\nF+eBufm4b/mPY0tg2eVySbhlKpVCLBbDeDzG/v4+rFaryHiyRwmmRaNR5HI5AJD8cw6HQ8JcebZo\nlugsgJJ6DPVdnUeIzr1utyuAOtkpo9FIcpFRT+Za5/xxP8xyys3Su7nnAAigxHEynV56fRI80fop\nfzYN+ovGgfec5SBk0/Jbgz36O2ZKBv2+s84JLWd0H7Q8vgr00fd9FgcwHah8HseL3+P6JOhPRrPT\n6RTQhZ/xO5Q3BI4oD5/FNmIIKZuWa5SnHo9H8liy4hfPNACS50sDcSbrjXKSwPl//dd/CcvuWlf+\n6rQrAaBvfetb2Nvbm/rsnXfewU9/+lMAwJ/+6Z/iO9/5Dt5++23827/9G77//e/D4XBg+f8nN/zF\nL36BN99884V0/rp98aYVbwoIJnTTgscUzheh/vrvmklC5ViHIzD8ghRtHYrzvN+RhzETsVGJpJKq\nD0SdfO55PFsrBnymRsw1RZsKLsdbC97nIVi1cs2DkY3zoePF2Xd9GJrjwjHjmmAIydraGtLptHj4\nqODQiGVFH/4j84prjmEKzNuQy+XQaDRwcnKCarUKq/W8jPMbb7whXlwdZsH+amVkltdOf27So2eF\nbVwD2l+s6fE315UGgvg3yhGWFQ+HwxKv3u12xZNMTznZJgCQTqclpwvzXVERphygcsYk7MPhUECa\ncrmM4XCIaDQqa5vrnXsFeOoRS6VSWFhYmDKkGObIz/meWtbwncli6na7aLVaYlzSc03PcCgUws2b\nN5HNZrGwsIBarYaPPvpIcpq89tpr2NjYECPTLPUMAOVyGY8fP8bOzg4mkwk2Njbw0ksvIZvNShLr\ndrsNn8+HpaUl+Hw+lEol5HI5AWKpIDLM4uTkBDs7OxIOQgCBoUj01DMvjbk/2S6SdSbwoxVcLcMp\nbwOBAG7fvo1arYbJZIJcLofhcCi5W+jd1qwrl8uF5eVlAUaYC+X4+BiFQkGeX6/XhY2igUH2keE7\nTHzNNcD5KBaLGI1G8Pv96Pf7kjyZXmuXyyWy8/DwEFarVQA9nYCXzySL7ubNm5JzZ39/H48fP0Y+\nn8dkMpG8WQyd9Hg8yGQy0gfuw1qthpOTE3GIcO8wd5NOBjyZTKbYFTzzWKHPBPgInppzrQEd04Aj\nsAtAWCfa46yNSd5TrwXei7lxmMBZh4Pr52kDrdvtivxYWlqSHFfsl17TNPJ1f3iu8t/CwoKUvXe7\n3YhEIlheXsbNmzcRiUTQ6/Wkwh0Bwng8LvOmdQIyg7LZLHw+H1ZWVmSf1Wo1VCoVqeJGuVatViW8\nlMnUOb7VahX7+/uIRqPo9Xqo1+tShXEyOc/fxNAsnbtEsweA8ygBfm6z2UQ2FItFtFotCWHb2trC\n0tISotGonP3NZlMqhzEvEUEIymjOn54DMgszmQxu3LiBdDotQA7DiAm8rays4M0338TKygrC4bD0\n9/DwEEdHR7IORqORsCV0wmFTbvM9KVOAp+FUNOqZO46gAEMvtTzj75o9qUPddEJ+fYaaZ4rOIcPz\nkPNlgjb6Z/1dE6SaJaPNf9qxyz09CzzTYWkm84ffoT44S9/k9fyOBn/MfEemLaKfcRHb22wXAU6X\n6ZKz7CM9B9rx2O/35ToyfnUlr2e1i7hm9XO13eF2u3H//n18+9vfxq1bt+Q8oVyhLJs1J6Z9x/XY\nbDbxr//6rygWiy/MhrtuL6Z9rhxAhUIB8XgcABCPx6VkZy6XmwJ7FhcXcXx8/By6ed1eZDOFZDKZ\nlEND5+zRngdtvGn6olbCTaDBzHivDwXSI5+H8DCFtT7YqIxSgaYhwAOJh8csQTrr/lf1Vwt/q9U6\nVdmBY0cQip5J5iDSnpFZ/fg8Tcdaa0PWVOSApxRvHRbFPum5Zlw/vXipVAp3796VZLxkBfD9mXCR\n+UJIj261WnC5XEin04jH46IsDQYDxGIxzM/PS+WedrsNv9+PO3fu4JVXXpH1BTxNUsk1ynfW7CZz\n/vSancUA4rWa1n/dPl/T9GxTcb0ITAaeep3oVQXOwWqPx4NsNiugotVqRa/XE2pzp9MRdgNLqRNk\noUFoVnIDnpZ+1eE48/Pzsv65T3m9xWKRMtnD4VCMX64p5pSw2WwIhUKIx+Ow2c6roTEc0u12i0eR\noTyhUEjyVtErnkgksLS0hFgsBpvNJuXn6/U60um05FMikMaKXgCmjLlsNiuUcIZqMjktcM7WWF1d\nhcViEQMxn8+jUCgIQEbDhuyFWq0Gi8UiYTYcYxpsen5pYJjK9Kw2y3jhXtT7l3NnsZznkllfX4fT\n6UQikcCjR4/w/vvvixymfKWxw9xBi4uLU3KaIVRHR0fo9/uyHgg608BjH2noe71eAbOj0ajkfCGw\nohlsZGVp8IeMHFZP8vv9CIfDCIVCCIVCU0wqm+1paW49Jp1ORwwp9oG5bpxOJyKRiACPrNi2v7+P\narUq4Wg0nDXwNJlMZP/RccF3d7lciEajiMViAtzqeeHc6/2mZQABhsFgICwzzYrQYR5s5jU803WY\niQZ+tf7B9+GzdZ4ezpff78eNGzemStHznRkKQkBQvydzVtGh4fV6EY/HxUmyurqKbDaLxcVFeL1e\nCcXs9/soFosoFovCniUAzVxcBJgzmQzu3buH9fV1JBIJAEA+n8fW1haOjo4EcNjZ2UG1WsXCwgJa\nrdZUaCpZMnSucCwoszXQR3CTOhPHg2uaYYEEYTkfZFtZrecJm/f29lAsFhGJRETONhoNHB4eolwu\nw2KxfKrSYzgcliIAzGfE0DAAiEQiiMfjiMfjsNvtKBQKU0atx+MR0IwyOJlMChikw8g7nc7UMwgY\naz3CdKJxrDQ7lCwe7kNTl9ZggM5TabLodGUy3p+yh/fUeXS07LxIjrJxT+kcMReFjel7al2fOqzO\nHcRrOD76OQTQ6BTVQJXJOpnVKLsJjBEE4nrUwBSdrFx7BKI060qPowZ1Zp07/B7Xgr6H7rvJ3uG4\ncN/xWn126X6Y83fVeGgQnWNL1tv6+jp+8IMf4Jvf/CYymYycFXyuCag/i31Tr9cFpNWff5Z+X7df\nT/vCSaD1Jrno79fty9n05qRAm5+fx9LSEiaTiZRBNUsLAtMAkL6XPlxMhVx7Bfh3KhAUxM+j6cPv\nIs+yTmCoFRj93VnMG3M9XyXgtJGrDyj2UwMXmk1gfvcy78RnaRp804Y3310Lf50EkP3mIUflnUng\naJwyIem9e/eQyWRgs50nvW00GmLgttttKZfNChP0ficSCdy+fVsqg1itVvE822zn1VZYwtnlcmFt\nbU2M/lnzwfebxeji+PI6bUSagCbn61qePZ+mWWXaEGPT1Gfz76YBybxOwFPvm91uRzAYFNp1NBpF\nq9VCu91GsViUakvtdlsMVCr9/D6NNYI3uhqdBsa5T9xutxh2VNAZUlAoFCSx6NraGm7duoV4PI7T\n01McHh5KVTDNBiEIkUwmpYKO0+mcAig4hgyx7PV68Hg8Um2HQNVkMhElm0pfNptFJBJBvV6HzWYT\n44cKKEObdLLdWCyGlZUVdLtd5HI5HBwcoN/vw+PxiJFLY5/7jiwVKr6m4XCZfNVzOgssvGg/ajnu\ndruxtrYmlPfd3V0BBG02myTodDgcEnZl3pdj0uv1UKvVYLfbJcREA0E69ImVo87OziSfC3OlMAyR\nBiEBhG63KyAO5W2/30er1UK5XBZQiYlsl5aWJGxNs4tMEGp+fl6qyDHXDMObaOyRmcLn0bFHI4es\nGJ6X9Cgz4T6vXVhYQCKRwM2bN7G8vAyv1ytjyIIHfDfTMKJeQUDs9PRUGMndbhe9Xg+TyUTWG4EI\nMvcIrDGETvd3PB5LvqXd3V24XC6kUil4vV4BPekwYN4bzhsrtcXj8U+xYpkf6eHDh6jVapifn59K\n4E1QMRqNwu12C5D0zW9+E3fu3EEymZwymAnCHBwcoFAooFKpoFqt4vj4WIA6zWK0Wq3IZDLIZDKy\nDinzOI48g2u1mrC4xuPznD0Ezrn2z87OBHxmKArDanlvhngSqOIeqtfrU3MXDAYRjUbh8XjkvgQU\nzs7OUC6XJacbdc79/X0J19TVnMgG8ng8WFlZwb1797CysiIyc2dnBzs7OxJezqqDh4eHqFaraDQa\nss5ZGTIWi8na1IwX6mKUE1pPNfU4DVpqmcQ+k4FBFhHzPVksFqmWyDOHBrsGcbUOxs91qJMOq2cf\nCHhwrDUjSBvnWhbz/maFuFnsn1ksF44LAMmzaObx4Vhw3c56PnNxala52fTnlF8acJvlGGDOGwLK\nvJayiHKTfb0ISNNsIg3QsS9k2mogSI8754TzRTnB9URw96L3vahpucD70fFDdvIbb7yB3//935fk\n9ToskLqPjlK46Hxl/ykvdNEDjivvf92+vO1zAUDxeBz5fB6JRAInJyciRNPpNA4PD+W6o6MjpNPp\n59PT6/ZCm2aAsCQnlTUtrE2Dmd+9zGs7q+l7XuX5/azNVNAuuq8Z7qMPA/b9KuPkqqaFvm4cSx4S\nJtijEfxn9YY8S9MG9UX303Orx5GHO40Hq9UqyV9p3DidToTDYXg8HlGkWd2IgBGNGSoppKhHIhHc\nunUL6+vrSKfTn0reZ7FYkEgkxLCnAXIZKKe9KzrsQI/HrO9fRBO+BoC+eDPL87LR+KIxfpnyQaVW\n/669/VzD8/PzSKfTSKfTso4ajQaOj4+xv7+PYrEoQHehUJAEo7wH1wwVeCqvrP4xGo0EJIlEIkgk\nEojH47BYLFI5yul04uHDhxgMBlheXsYrr7yCe/fuSRgGGQJOpxPRaFTCjrgvPB6PeGXJ5qFyTaV1\nfn4egUAAfr9/aq1SpnNsNdAJQHJ18Ho2vruW+Zwz5uVYWFiQcDyPxyMVh/Q8E0RiH7Ribq4JzQDT\nTcs/ygGtoJvnEhkc+jOr9bwyYDKZlJC6xcVFLCwsoNFoIJ/Po1arAQCKxSI++OADMVyZe4k5UMjK\nofzyeDyIxWKIRqOSx4WGOkNWadgToHA6nQImMFE0gRfODw3ZTqcjpdYJ4uiqQXSeWK1WmQN9fozH\nYwmz7Xa7kreIVb3YJ5/PJ2ua+4thTQCmDGC9V2nIhsNhxGIxLC4uYnl5Gevr64hEIp+aU55B2tGh\n1+twOESlUpG9SKCKY8EqbX6/X/YGmXBaXut1Q+OUYUcPHz6U92g2m1hfX0c8Hke/30e73RbZYLfb\nEQqFEIvFxGin5576AkGoXC6HZrOJbrcL4Jz5w3FJJBLC4otGo7h37x5+8zd/U5Iqk8nQbDaxu7uL\nnZ0d5HI5MQYHgwHK5fIUiOb3+5FKpfDSSy8hnU4jFAqJTCCg3e/3ZQ02m00BGrxeL2w2m1R6c7vd\niMViCIfD6PV6KBQKMg+tVgvNZlMMW+51sqM6nQ5arZYwZpgfjIAqjXvtdNKAQS6Xw87ODtxuN4rF\nIra3t1Gv12VdMEST486wt5deegl37tyBx+NBv99HOBzGZDLBxx9/jK2tLezt7aFSqWB/f1/m8+zs\nDPV6HQ6HA6lUCvF4HNFoFIVCAU+ePEE+nxewi4BBp9OZAtv0O/A99D/2mfKH8ohrn6CrdnhqJxtB\nBc1O5PdosFMWMScT17wGQ7RT4zL9hc/mHLHPGrwwdelZDkler3VpvoNmRxKw1bnMAEx9Rzt9LgMi\nAEzdW7+PZs9zDHSImH5HnncE1HROLNMBoe0Dyl4NGukUDxo80+uZf2PIH+Utf9YsJNNOuqwRpNVr\nxOVySfW7jY0NAX/YD5Ndxne9ysHC6xn+rPs5izF23b587XMBQN/97nfxox/9CH/1V3+FH/3oR/ij\nP/oj+fxP/uRP8Bd/8Rc4Pj7GkydP8MYbbzzXDl+359dM4aIPLVbO0ZVTNBhgeu70/2zaS6ArQugD\nhwoLmR7PIuSuaiZ7Qytr7KOmtmrQx/y+qZzO8gxf1vh83lcr5foQMp9pCl/TEPuiTSsvemy0EkOD\nWc87m8fjkSomoVAIgUBAwjxYkYuKY61WE+PK4XBIYlxWK/H7/VhZWcHq6io2NjaQTqdhs9nkcCTw\npMeO99IHNg9eU7EyWV18FxP8McFA3fRaML1C1+2zNT2fwPR+02vR3IPaU2bOnfkZ87loAJXPZnWc\nlZUVtFotKTFvtZ5X6KJSpBXstbU1KVnNst0s7c4k1dlsFtFoVPrv9Xpx9NCbVwAAIABJREFU48YN\nWK3n+VAGgwFee+01pNNp8ZzNz88jk8kgGo1Kbgv2mUoUx0t7LTX7iUYzlTAaAZqirpuWgTQICa6Q\nIq/p66b3kXPicrmQTCan7qnZJwSQLgJ9tDw1c3eZTZ8jF92PfTX/rtcSc97cu3cPt2/fxsLCAnK5\nHD766CNsbW1JRbnt7W1hRVmtVhwdHWE0GiGbzWJlZQV2ux2rq6tIpVJCr9dsEz7T7/cjk8mg0Wjg\n4OAAx8fH8nksFoPf78d4PEYwGJSQHIKOBGyYIJqgN89OJhcnIDQYDCTxLuc3n89LlTcahQRler0e\nyuWyhOTW63UxSk5PT1Gr1aZygGgvOvNUsHJdMBhENpvF2toalpeXkU6n4Xa7RYbT2GMYIA1BLUtp\nNLXbbakyxzOFQAirS52enkp/uXc0E/Ai49FuP09Avra2Ju/w7rvv4pNPPkEwGBQW3MHBAUqlEoLB\noICe8/Pz6HQ6YqhrJwFZC5ptxITMyWQSy8vLklsrk8ng/v374iTh3iVgSPZLJBJBLpdDu91GvV6X\nfWCz2RAIBIRxl06nZVwJwPZ6PRweHuL999/Hhx9+iFarJUngOX8cJ+bJYXWsVquF7e1tqfrHJPqT\nyTm7i0AE9SeuUVYwbDQaUxXzotGo9IksrG63K2yeR48e4ejoCJFIBM1mU3LjUCaRhTQajYSxze/y\nfefm5mT+xuPzkuv1eh3ValXCy7mW2+02/vM//xO5XA7//d//LZUlNzc3kcvlpnITksVJhthFgIQG\nubiOOcY6hw/TKpj3otzmeUJZSrCEzD4m4+bYc84JRHKNaB3O1N213m/uFZ47eo4va/r+lDnasdnr\n9aTP+hwhOEiWkk7Gf1GfzXOIz58FUhFEoR7A8ed5xzkiC4fOHTLSedaaQBH3u04rwPfW+osGQDTQ\nbTon2TcCLvpz8/2epVGfZ381MDM3NycgKfcCQ1/1s6gHXNZ4HcFvMgBNu/B52HPX7cW1KwGg73//\n+/jpT3+KcrmMTCaDv/mbv8Ff//Vf43vf+x7+8R//EcvL52XgAeDOnTv43ve+hzt37sBut+Mf/uEf\nLlXqrtuvt2mBqT15ZGcQANDhGLOa9raZBjUFLw8dTZmk4GdI0VVI/7M0E9TSnlBtcJJuqoEpemW0\n8WPe22xXgQEaZOHvvD/HQTMZ+DeOPQ+VZ/UAXNW0YsD5YCicfoaeGzZ6olhBJJFIYHV1FclkUhRk\nKvqkgFMJI3Wf78mQlnQ6jUQiIVVOmJ+C702FSsfZ0+uix5J9NoEBrejo32d5ta7ykrGZQKB5r2dd\nw9wzZk4Bc+3OAls/S9M5TmiE0esM/N+Hts1Snvm//ttl82LOsf7MNDQBTO0//s4QmXA4LElWHz16\nhHK5LPl6aHC//vrrEuY4Go3E680QM6/Xi2QyKV5xPovhR6lUCpPJRPJnaKVbe8pNj6j5Xvxdr0Hu\nj4ve9aK51deZDLmr7mH2CXjqEdXXXLauzPnUXkdzP/0/9r6suZHsuPoAJEHsOwFwb/Y+i6SRZNmS\n7HB8Cr/4yT9If8j/wOEHe8J2OKwIxciWxprRzHRPb1xBEBuxkwTqe2Cc5MHtAgmy2T0tDzKCQRJL\n1a1bt/Jmnsw8qWn3fsfxW8PuZwno/MM//IN1w6LjxEyely9fmrPNlueFQgF3797Fhx9+aKAg1w6f\npUljo+6mM392doZcLof79+8jn8/b97LZLObmzvme6JSUy2XLntSyAmadMaOFzi4Jdjc3NzE/P4+v\nvvoKv//97/H06VMcHx9jcXHR2oFzDbM0hg4bnR+W6xDoo05mWQhBIjoW4XAY9+/fx8cff4x8Pj8G\nhLj7FsEfYJyvjX+Tn4mZZcySoa7kvSWAxAi3H4Cvzs7p6Smy2Sx++ctf4uHDhwDOo9e1Wg3//d//\njc8++8yAOTYqIL8YX1PnUp9XOu8MRITDYWxubuLx48f4+OOPcf/+feRyOes8lk6nAVxw6ygQlMlk\njGy50+kYGbIShDMLjZxMnKN6vY6DgwO8evUKz549s/VM0CUUChlgxLIhlokwU4lADQmk2eaaWYal\nUsmy/pvNJrrdrt07XgvvO/mAMpmM2QcUgokAjItobm5uLLuNYJ/aAZFIxLIrFSxlFpJmsVC/suyH\n66zX6+HJkyeoVCqYm7voNqU2B/dI2qw8tupe5bZSx18zWDkXGtBSW1SDbto5UcfD9aEBORJiU4cx\nk8Mvg0aBFT4/+swpLx9tL8001UAhr4nPMp9L3XepKwgicc54fM0yVL+AomAEM72pE3g8zqPqXZ1L\nrm+OUzvj8dwMoPCalNdPfQfeA2b08P71ej07pgLDuvfxf64dDUa5QRteu5+44KNrF1JH8l5xfnTd\nEnDTIJt7bF4rr8XNatNAHMeh2fo83kzef7kSAPrHf/xH39f/5V/+xff1X//61/j1r3/9ZqOayTsV\n92FtNptWi6uKftJDzU1ANx1uIsqJoGmyqihcZXQb1+PnQLjjY72zGhdqvNyGEuOGp84br5nRLBoL\n3DCBiw2W372t8ejmoZubazDzNRr+yjfCiGihUMD6+jrW1taMGHM4HFqnkVqtZkZdoVAwUlMacMzE\nSCaTlkGkhog7Vr0GP1Dsso3zsv/1+9M4rBoxcd+7Lvij9+Cy8b7J/ee95FrSsbpRtu9KLtMt1/3u\nJCDJ73s02gqFgpEVHxwcYG5uDvl83hyXfD4/1vWL3cQ0o5HtmVXooPq9p/fBBVL8xn7ZNU1aP9cB\nYKb93mXHe5M1pLrZBUGvAyZNelYCgfNoealUGit3IJgdDJ63qmdLZjq8+XzeSqvcTMlprpeZC+l0\n2oDGXC5nZWLUsz/84Q+xvr5upPiff/45dnd3x/ZMDZz0ej3jNen3+1Z+89VXX8HzPLx8+RLPnz83\nMt1MJmMluSwBIycLy8ToNHU6HfT7fQMzRqORARy8F3SGyIFULBaRTqfNqXL1NEuENFCg9051It9n\nowDXqadDrtkzej7uo3qfaY8UCgWk02l4nmclWrVaDdvb2zg9PUWtVjP+Ge5TqVRqjGBb7z0dK5Yj\nLywsYHV1FT/96U/xi1/8Ah9//DGWl5eNF0X5ihiAojPKfbJUKqFSqaBareLw8NB4dhQsOzk5Ma4m\ntmp/+fIlXr16hb29Pct84XVqgI7n0zkjiEXSfP5mhjb5i7hPE5DitfNatH017y+z5Dj+VquFeDyO\nVCoFz/OMc0j3Q7Ur+RONRs2R57UMh0O0Wi3r4MVyMHZUK5fLVp7HOWB796Ojo7FgGDliNHMCgAGv\nmn2jwSQ3C0X3XK5birsHK9ein61DIMTl4tFyP91DOMdKSKwAAf92v0PbT8el9jPHxbFo4JYgg2ar\ncn9Ue0nng99R4IffUwCI41CQxd0b9H/qRy1R1WvjeRWU4b314ytS0EszoxRc1j1B7wF1kB7Htb35\nfF4lbpBc90hejwJJunaCweBYB1D9DI+lGcYUv3O445lU7nVb/txM3p68MQn0TP78RZUyCQjn5+fN\n4Jv2GOpgUmFwc3MzijSaqeVQt3U9rmHOzUM5PHQDoUGmxsttAkB6rrm5OUuxdCM8jL4B4zXVLthx\nU+E18bhMSebGrZufbmA0Wmn8MGK4tLSEbDZr0Rmmqx8cHKDRaCCTyRhIlMlkjGySJRMKNPE6dZx6\n7dcBZ/Rv13nwcyanOYeeS9er68BPI/y+pm270SUdp9+4pgVuaMy52VBqML3rLKD3SVhak81mLRtj\nNBpZeSKjsmrcEbx9m+DZ9+1+cC3yb2C8QyUzNKeZl0nPMu+hZkIGAgFzukmCzAABu86pg6bO3rRj\nAWAgIkk5Xb3OrKSzszMkEgnU63XjLun1erbugNezRQnQ9Ho97O7uotvtGgkuy5ZYKra3t2dlNQTj\nue8T2CQvkHbJIVik+4PneWNZKFpSoPfx7OwMlUoFh4eH5viTv0cBcNoMbEusoCuPp/uoa3Mws4n8\nWey6piUbwWBwDKSKRCJYW1tDqVRCp9Mx8CgSiRjYQcCE5ZK6fjgHpVLJ+LFWV1fx0Ucf4Qc/+AG2\ntraQSCTM7mi1WtbEgATUvE52KuL8/OEPfwAA4xzj3BK8+Oabb9Dv95FKpdBsNvH8+XNbL+rE0gnk\n/WPmL22eVquF3d1dHB0dGXcOy3fooDJQMzc3Z5lB/X5/zAFkltlgMDDgiCB4qVTCysoKVlZW0Gg0\nDHAiDxqzmlhudXZ2Zi3qh8OhBRO5Fsvlss3pq1ev8MUXX+DFixcYDAbI5XKWZcXsur29PcssYkmV\ncooxw5n3AYDZqUr6TzuC18fnjz8KPqgOcIFtjoO/NbjAY/I4tJn5jNBWYBkjnXHVcwpqEAii6J6v\n51FAQzPh1SZ1wR1es9/nXNBKz6/HoXCsnDPOPZ9tv2Coq2/c8fE7Ct6oT6Jgk46Zzw6fTc/zrAyP\ndrquH90b1K/hPqL8aXp901JfuD6AH1io9j3nk9fCNcD7pFm+1PvMjAbOEwFYZqslctT3utZc+/e2\n/KeZvF2ZAUDfc3GNyF6vh2q1agrD3TiuEkWKuYkSQOCGzh+tMdYN503FzynTCM/8/LxFtmhUkehz\nmrrnmwjHogYZ/+cmzo5BuiHeFvDjihup0mgW7x15PLR1ptYOx+NxRKNRi8YOBgMrjWm325ibm0Ox\nWMTjx49x9+5dxONxOxdT9nU8Gp26rrgRWf7W6JCfY3JTcSMm193w3OiTjs/vWO6a1nUxLWjl3mM3\nKvh9FTXG2aqZkUdNM1fQz80mfFtj+r6JGuiMcFNvTzsnfs+5C/wqKKxAAEv8PM97rWz4JvpDgQoS\ngmtGj+vIeN55RgXLxI6OjqyLHLNVG40G2u32GHeLZpLQseaxyCvFMihm+8zNnXeKWl1dNaJbAk0c\ns3LGMFjAeaMDnUwmDSTgfeMx6NB1Oh3s7e1he3vbggcsA1ZAjA54JBKxvVnnyXWyNBOLIBYBEHLR\ncY9lNiuAMedPbQPgwmmKRqMGKGUyGbNhNLODYyOpeCaTQSaTwfLysmXz0MkLhUIYjUY4Pj7G7u4u\nQqGQEce7JMkcD++nOqck9yWY1263DTDc3t5Gv99HIpGwUrNAIIBarYbRaGScTaenp7beGXzZ29vD\ncDjEwcEBzs7ObK3Ozc0hmUxiY2MDd+/etWvSbCxeGwBrmc6OdrxHqVQKmUwGa2tr1gn066+/tk5k\ndEpJ0t7tdi1LjeuX96BarRppc7fbxf7+vmW2pdNpRKNRA+9I/E9eKwK8bjYFgQOWajEgqp2S1DZ0\nA0nsZOZmA7m6Sfdb6hnqpGAwaKAXn1UFaFy9w9JAjlmvi+vYb9w6fo6X51Rw19V1LtBBXUAgRMEb\njt3P/gLGyZv1edKxaiaZn80zSS+7x+H18RnifOkx3Ovi8bi+qZ8457xuAmDMRuS4NTtYn2d+niCp\nH3+On0yyi/mMaiWFAnHuulDASj+vGVLdbhdff/01nj9/jk6ng3A4jGg0inQ6bR0HNfPML4D4Nuyi\nmdyuzACg77m4TjiNOnVOp32QXTSayDmjNkTziaCzznnSBvGmosekEQjA2nECsE2PKdSaAntb43GB\nKBrJwMX8M/Wa7RT1e29bkbobPAEglq5w41FDSNN9yU/BtUNHOhKJYGNjw8hxFWzQDWhSyZ274U8C\nWRQ0cjdvd026n9HXr+N007gBbkYKzXMxQs9oM0FTN9tAvzep3Gfac7qvacrvm8j7FPW5DjhG54UO\nCEtz/Mow1eh1DcibrAM/eV/m8LsQJZTs9XpotVpIpVJWonXdudE1qUCfu+/xfQU/NJqqn7nufVYe\nDV1r7rOnjkgsFsOdO3cwHA7NuY/FYmi1Wnj69ClevnyJ+fl5yzhhpg7Lq6i3SX5LIIilusy6TKVS\nBsQ0m01Uq1Xj/xgOh/adfr8/BqAwiMNnJJFIjJUBc47YhYrBjdPTU3Q6HRsDHSRG5HkPCMRpW2OC\nTnSemOXD0qJGo4Ht7W2Uy2Vz2pjd6jq9uv+cnp6iXq/b8cgN5XkXXdw0O0szAfk7HA4jk8kglUoZ\nuDU/P28letxTFYBstVpjhMy6FgnGkbzZXUfMHiKIA1yAp4lEAnfunHdhW1lZwfz8PMrlMp49e4av\nv/4a3W4X1WrVug6m02kEg+e8ff1+38oUNesunU5jc3MT6+vrVv5FsmZmyfDztVoNmUzG9rWlpaWx\n8m82f1hcXMTh4SGePXtmQSOux3A4PNaKntkJsVjM1tXe3h4875xn6OTkBOl0GisrK4hEIlbCSELb\nbDZr2UyNRsNK/GjjKligfDs8BmWSDaH2pX7O77Pu62pLcG1qZohr32gWh35/WnvJD1TVY+v33b8n\nXYcGLV1Ax50Tfc+1g/WZUhDpsmOpned+Vp91vfZJlQ0uSKJZX0qXwExABU8IdtNWoI1M8Ic6maDP\nycmJld2+ifA8DJy49iHXj16bay9r5s9gMMDh4SH++Mc/4k9/+hPa7baB8qVSCYFAAMvLy2PZ6352\nqh5/Ju+nzACgmYyJKr3rRDn1t3ssErmpQtQU1mnQ75tei/7N8WjJEcenaZi6Adz2OLjh0TDVaLAq\n8reRhQT43yP3OjlPHCMjWzTCmB3EaAaBH+CCgJcGH0lvgYu0X60t53VfNS4/Y8Y1Ai67ZgUZ/SJJ\n13Es3WPcZJ14nod+v4/t7W3UajXk83msrq6OpdO6AKzO001BAn5PI4p+KbzXvRb9/V0DGNe9HzrP\nahy55Ub6rOp5bgv4ue6Yv+t5flsyHA7R6/XQaDSsZXkul7uyMwllku7W+dL9zc+Z4N6g/1P0+dfy\npKuuCfBfKzSgGcXl2OPxuLUnH41GCIfDaDQa+PLLL/HHP/7RuPqYZcFxMjMoHo9b0IUABcthCOBw\nntWJ5N7DvZqgFbN0GPkmefC9e/fwwQcfIJlMjula8umUy2UDtTY3N8eySjQDhXPE8kryyVFPMXOJ\nZcaNRsOymAhg1Go1DAYDxONxnJycoNlsjhGi8j4ys2B+fh79fh+VSgXtdtu6finIsb+/j2w2a3ua\n6xwD43sbuYDa7TYqlQoAWHkpQYzl5WVUq1V4nodut2tE1jx2p9PB7u6udd+KRCLmKC4uLiKZTFoL\ncxLM7+/vI5/PI5FI4MGDB7h//z6KxaJ1L3v58qV13QoGg0in03jw4AHW1tYQDofHSluYzcWMkFgs\nZhk1tFGYScP7xtfZ+GEwGNgaYtdQ2gwArEtaLBZDo9Gway0UClYWx2vlfqUZeixjHAwGyGaz+NGP\nfoSPP/4YwWAQ+/v7lg2m95vldey412w2LRudtgifQRcY8tMDugYU3NM14uccq85xS6V036FjTmBV\nO1QpYM3nwwWrXUCF55wUDPK7tmmceT9AfdLx3f1zkp13mR7XYzGrPhC44D3S4yngpQC/e2wXLAEu\neJaAi26cLAcl4Kkle3oc2snsTkiglvayNlm5SWBDz6V2HPcY3RP85lDBRJZ4nZ6emh7d29tDtVq1\n57jf71v2H/cEgtJuooDuoTN5f2UGAM3kNYV7E+dYlSy/z1RJGgFUUhpJBC5SZ2/LoZm0ARGsAGBs\n+FTwfhvnbY3HnVca0Izs0Thl5BbAG0cFLhuL6/BM2oh5/7ixUuGzzp81+yw3CAaD6Ha7aLVaVpLA\n62RJjfI90LD3c66AC2fpKjBS3/P7HO+tOl9+0YppRQ0Jv+9Ps5aZBsxoEB0yJZrlsSYBTTcBrXi/\nWfIYDAaNwPMm693PkHof5DpglFvWxdc8zxszrjXy7xqq70L8gFrK/xUwiM6OtjYmge119ghXp7jf\nY7R0knPjOg2uQ6cAwnVEjXWeh7pAHU+uRYIsBB6ZXbK5uYnDw0NznLvdrgU3uMeSYLfZbJq+SqVS\nyGazaDQa2NvbM642AKjValZexpIlZttodgONepY9ffLJJ/jkk09sj+e96na7qFQqqFQqiMViWFlZ\nQS6XM+dBy4O4x/AahsMh6vU66vU6AoGLbGKWPh0fH1u79Hq9bg5MLBbD0tKScfyQ94WvcY41U0BL\nQtj8giVxwDkXxvb2thFd+9kGnU4Hh4eHZsscHx+jWq3aPAEYu6crKyuIxWLY29vDzs4Ojo6OjAeH\nZVgsj5qfn0csFjNHi2VUGxsbWF9fx+bmJgBga2sLvV7P5iCZTNr6TCaT+PjjjxGJRJDP5xEIBPDw\n4UPcvXsXiUTC5oSlgdwPeN+VwJbXEo1GbT3ocxKNRlEsFsdKyzVjkutjMBhYt1DeB841X+e6XlhY\nsP+Vj4frJZ1OY3V11YIo6XQavV4P3377Lba3t832IBB3cnJiJOnMZCLgQzuRx74q2KN7DUFczaDX\na9bP87gEA7h2lINofn7eQFKXQoF6krqS9hp1k2vPXQZ6TPIB3M9OskGuAtzdtTPtfLq2nXsdgUDA\n5oii5Uw8ngvOuOfWv9UuZYD67OzMgGkCQOTQ0uwrd60TUARg4BR5tzToPI0N5bf36Tn5nLgAEN93\ns8rceWCpbq1Ww/7+vu0L1K3kwqPwurRU3m8+Z/L+ygwA+p6LPrR+JS3XdfD0s8PhcIxQ091MJ0UE\n3oZ4nmeI+2AwMJJJAhsa6Xlb2TfuWLj5ayt2buQEpd7Gua8zRm5udMwYkWM0j2n9NEYajQbK5bIZ\nmolEwqKJbqnRYDAY27B43uuAb65R4jp73PwmAXw3dZwVyHIBtWmOOTd33jqcUVqWc9ApUoOCP2+S\nqaPRHho27XZ7LPrzJqVg1wFc3jeh0a7iGsRKpvhdyFXgz20C6N+VqPMTiUQsy4EOph9g7Cd+joe7\nPi8jk6Ze1o6Emg1APXgd0fIfV4LB4Gv8MxyrguCDwcB4GOjgVioVvHjxAjs7O5ZJwuwJgvPVatVI\ndzudjmXH8Pt0PtvtNtrt9ljpTDAYHOOo4L4AnIN1sVgMxWLRgBHVMXqvlL+GJL8qvV7Pum9lMhmz\nHdi+XiPp2WwW4XAY3W4X5XIZrVYLyWQShUIBm5ubyOVyGAwG2N/fR7VaNXCNoATnXCP1hUIBR0dH\nyGQyqNfr6HQ6CATOu1f1+33s7u4iFoshm82+di9Ho/OOai9fvjQOP2bPkIibAR1mAFHn7OzsYHd3\n1zhqeJ3dbhc7OzsAgGKxaHxCBHeWl5dRKpWwtLQEz/OsgxhwEVBSThvO68OHDw0E0SDOaDQysJVz\npKA3nwMVvs/ng9/Ta+Xn+KM8KcwUYgkX1ysz1vjcp1Ipa0rCjB060Ny/yCnUaDSQTCYRDAaRTCaR\nzWZxfHyMVqtl969QKGB+fh6ZTAaBQABHR0dGaM0SQwKStMOuokJQ3aXzr3uHftYFHHRe1Faibeja\nG/1+3wBLtkinTclnze+e+Y170v31GytlEpDC9/xE9bcfmOR3bjfIojpRS7bJQ9btdn0BIM3w9Ruz\nHzjHPYLrjDpRCZ1JUK1jVF3JzyjoyTWu/EbT2BWT/AE+W9Sveh06V36VHczI6/V6qNfrRtS/v7+P\nubk5bGxsjAUrPM/D9vY2nj9/jq2trbEOjAo4XddvnMl3IzMAaCYA/JW/lohM830/ZFk3JFeJa7Tk\nOiVnV4lf1EnHpOnLqihd5XVb41EFqcfX1Gl2PuFGo2nJt+nYuY6jvqYbM19T44eRyEwmg0gkgtFo\nZLwBBHl4jPn5edy7dw937941Ik7t5sEoimsIuGvNr6bbFXe9uZ/Ta+B9dsvOdMOeRt4EPOKYPM9D\nIpEwh4xOpkbIdPN9k/PxmnlsLTXxA0CmEXeT/3Pd8F3+Ar8MDRo6fiDfddfOTC4Xzjlw4QxdFxi+\n7Niq41T/6F5B7pGTkxPjZzs5OcHc3Bzi8bgRzU4r6hy4elbXjV6r7puaHUpAIxwOI5VKGZ/KcDjE\n8vIy7t27h0KhYMftdrt48eIFvv76a+zs7OBPf/qTEeIyGk3wx3V4Pc9Du90GACuB4jiVoFj3OGaP\nxONxFItFc9bb7Taq1epYN04CTAcHB3jy5Am63S7W19dRLBaRTCbtPJxzZtOwpT051BYXF5HP51Es\nFpFKpSySrY48cNECmhnAnndO9ryxsWE8S6enp2NZIZ53nkW1s7ODTCbzmuPjeeclW0dHR5bNQgLp\nw8NDBINBrK+vo1AomIPGOeS+2Gw2UavVEA6Hsba2hmKxaMTT5LYhHxMbLyhwyDXr8sIwU4drdTQa\nGfDBTBcGb1xd7temm6LOqBJ/c0y6rrnmGQQkKEXQqlAooN1uWyYs54ZrLp1O27ojMKf2I3mHnjx5\ngmAwaAAPS98Y+GMgi+uo0+mgUqn4Ehe7hLquraT2Az/DLDW/xgGq0/hdzWrktfB63IwpBfkJFPF7\nGjh0z6NjniSTwBi/vdD9uUouC1y4tqjfnuoCM65wbqg3J+3Bam/7Xaffa26WINeFZhPxfmuJL58d\nBWCZSUm7np8NBoPXonzw86/0dVZcABirtHCDoArYnp2dWcfJg4MD9Ho95HI55HI5PH782DKdyuUy\nvvnmG7x69cqAsGg0at0nr8MXO5P3Q2YA0PdcJgE/+r4faKDighuqIKk8GVGlUqIy0siqn/N+mULx\n+xwVoatQdaOg8amouKL2OvY3FT9QRSUYDI4RYHKuLotW3Ka4G/KkMRL4WVpaMjJMtoqdmzvv3BGN\nRlGtVrG2toaf/OQn+OCDDwz8USOH4oJzfqJgyFXOn5ZO6Gt0chhJ9it3ug3n8jqi46SD53aC0fG8\n6dj0eIHARdtrGk1K3HrZMdz/aeyQiyGRSFw7O+I2xc8AnkbUWQHGr/WyWnp+9jr3x3X82ZWH0X99\n9qmf6PBM88xcdl4d83W+4wKqwPj8ajkGjfF2u43hcIhwOGwZhJcJo63UFe5Y/cY8jW7we0/H4u4j\nvJ+1Ws1ABOVmCwQCtsY977xxgrb41THRaZh03kl6SB1Drmkenw4mOWq0LOjevXtIJpNjkeBEIoF7\n9+4hl8vh6dOn+O1vf2sZQcBFJF1BYd0jCRAQECGhaTQaRSqVQiJmqJNsAAAgAElEQVSRwOnpqZHq\n8ppYclYsFlEulw1Qa7ValrnA0h7tTkVQgpkcAJBOp41jaDQaIRaLYX19HfV6He12G3fu3MHm5iYS\niYTpt9XVVeNC0iwKBTf4bCcSCTx+/BjHx8eo1+sWFR8OhzbOZrOJ//mf/8H8/DzW1tZsrCx1I/jA\ncxPUbzQa2N3dtawdAMZPVC6X0Wg04HnnWTzxeNxK/EheHIvFjHOE62PS3uAGD1yH0f2suw9pWbM2\nJPA7j4r7vOpn3bIRricSWWezWXMymcWQTqextraGra0t3L17F7lczohpP/30UxwfH4/dm36/j8PD\nQ8RiMSMsj8ViVmLNjNtkMolYLGZk0+l0GgcHB0YYPhgMzCZTkMYVBWg4H71ez/if+EMgmUEXflf3\nAIKUzPDTrD8XCPErF9X3NNNlWlBhkn3pB/TcBPSZ5v1pASVgfO1RR2k79uvKtN8ZjUa2PhTEc++B\nzvtgMLBnV9+7bG1dZ7z6bHqeZ8AqbUnt1KXk1VxzBOB3dnbwxRdfoN1uY3V1FVtbWwbOElxcXl5G\nKpXCN998g2aziadPnyIajaJSqVj3SHcPnMn7LTMAaCbXFm7iaqy6CDMwHm3VTQu4aJPoKgpXoU2S\nSe9Pik5QOZGYTTdZpmO+DcIy1+jSv5WckmmmCo69TfFzPPhbASvOWTwex9raGu7evYuVlRUrJ6DR\nc3p6avwDW1tb+Oijj5BKpXwduTcdq5/Q6OPGxpIJRrYXFxdvzHPzNsVvM3fXzNs4ZyAQMC6O6641\nHR+NZQVY35X4AREasZx2DoPBiw4uLv+SH6h4G2PW/5mt4HmelfcwGyMcDlvJiBqNV82zX8RZr+u6\n5VQK7uscU3SOmFmgGQQ3Acg0C+iq8V4X3LrsM9zLCFLwHtDRYDcXAl4sTyEIwkxOzsu01+zuEzoP\nLNHR9Tg/P2+gSCaTQTgcRiKRsGwJzgnLjhhoiEQiePr0KV68eIFXr15ZlgjfV2468mvQQWZWKksv\n2PbcLT3g+OnMk9+FxLss7wuFQuY8kIA5lUqZ/mZZm84Hrz2fz+PBgwcYDocoFosG/hDY4nPDkjPN\n6nDXAUmy2dlMsyk6nY6Vap+dnWF7e3vMrmm328ZDtLm5iWKxiFgshqOjI/R6PXS7Xbx8+RK5XM6A\nnpOTE3z77bcGAGlZnQJrJO12176ujatARXdNTdr7/T5/G+LOueqwZDKJe/fuWXkNM8RWV1dx7949\nrKysIJlMIhQKWbbb9vY2PvvsMxwfHwM4v8+aOd3r9QCMt0hnZhK75hEEyuVyyGazY3sXv097YhLw\n7ILPansAF6V4Sh5Mnh/lVKKe43g5R3psDbRo9z0CkGzWoQTRt2VDvm/OvLuWqCcCgfPSuLdJPMys\nHt4D7Yzrp1tuA/DR4wH+JXc8P7l6mG1H4AkY9zeAC/CMWZ+0j8mnlUqlLDOQ+pkZdK1Wy/aaXq83\nliH9vq2XmUyWGQA0k6nFRZKJMmuKrft54EJJa9qhdjTgJqspsVcpksve84t8cIMn3wnP45Z6qIF0\nW5voJMSe4/E8z2q69ZxvQ5FO2kR0rDonTOlPJpNYXV3F/fv3sbS0hEqlgmazaZwH5XIZx8fHWF5e\nxoMHD5BOp98pGMB72Ww2cXh4aOTVNLgikYitV9eQ5vffpej6dte6roHbHpe7SV8XLNHj8HuM6Lp8\nTu9C/MbtOotXycnJCSqVCgKBgHX7uc73pxW/efe8c16ARqOBXq+HZrOJSqWCg4MDnJ6eGsmr28Vo\nGrlNJ4Bjde8vHRM6OjQoGSll5sU04+Z888dvLU0CSia9P805dS2rcUwSXQIgBARVr9GZIwjAzAKO\n6011IMEI3TcoBDfIZ+cCsDovJPYlOTB/j0ajMQJpZvkwE0MzaDqdjpEjM6M3lUpZlifPqfeEGZi9\nXg+dTsc6PTHbg6VrzKohaNNqtVCtVs3RYOkF+Y2Ac9BmY2PDro2cdN1u1/iJYrGYlXtR/AAQZoxo\naV+327XyMZYepVIpe4+R/Xa7jcPDw7FuXv1+30oHR6MRDg8P8fTpU+vcNhqNUC6XrdMmAAM5eI3c\ns7gOLnsm3OtxwV7V89cFS29D9LyaDUEg8cGDB0gmk+h0OgDOM75yuZzdC2Y2LC8vY3V1Fb/73e/Q\n7XatFIWfYWZbLBYzsIQcTMwUYqAqFAohl8thY2MD6XQajUYD9Xodx8fHxslUq9XGCNAvE8/zjBNG\ngUgFeXTPV/sYOH9W2u226UttmsFn7uTkxGwaXjPXJgFqPc//RXH1PUGzQCBg+us2xbUXyY1JbjQ/\njif+pr2pQP4kAOemovsmyanJKQrAMtCoAxiMV56pUqlkGZzsFqmZewz0scshQdVA4LxqQUH6mfz5\nyAwAmsm1hEYIO3tpWqv7OVUIjMS40XXXIeJ7Nx3bpO8reMUop0sI6X7er/b7TcV1nJkVRRDNdcjf\nlUL1i9ABsAwgRntZj08yUiUPZiRKHYJ3JdzsqtUqnj59ap3IEokECoWCOXOug3TbTv608l0Y4n4R\nmkmlMNMcS0Ud93clLjfBZU7SJKEzWq1WsbCwgEQiYVwrPMdtRsLdOSJ4RtJTAJZZp5/lM8n082nO\npb/9QI5pRXUBv6vOCLlxFhYWrMTn7OzMSpEIFlw1bo2WuuN09xP3Gq+rJ9VId7OL+DdBH80cceef\n+oO8MYFAYKz70pvqF50LBXg0u4uGuzsHGvnV3+SZYZv2VquFk5MTc5ZJpMqxu+S2CkrFYrGx58XV\nLYPBAIeHh9jb27NMl3g8biTN5DKi05FIJDAcDu0ZaLVaaLVaRtC8urqKpaWlMdCE97DT6WB/fx/N\nZhPpdBrr6+uIxWIAYCS76pDpuuZzmM1mUSqVsL29bd25NLOi0Whgfn7eiKcXFxeNjPr4+BhnZ2fG\nBcQyJXZHazQa9mzMzZ13pFpfX7dMglAoNJZRpeuOY73sGXF14CQb4rvY7/S8HLdSA7B0mPOsJYla\nOsP1HolE0O12jbia+rNcLuPw8NBI5JXLS4mSPe+CODsej6PT6aBaraJSqaBer+Po6Ainp6fWBnua\ngCTf12zNQCAwxsHU7/cto8ydF5YdKq+P8sToXKg+1I6x3xcH3AU4GfB7W9nz+hyS90zH4Pcscu9w\n9143wDvteCftcxowYXXD3NycAc3KZUa9zfEFg0GjA2DggoC666vofqC6lPrKBZ5n8v7LDACaybVE\nnUg/p8Lvwafi0fRU17BR8TNQXMduWiXqRiR5HN1Er9o0tK5+2rrqq0TBB1Wwt3X8acQ9t77mciYx\nsjAcDo3D4fHjx1azzuguI6TvWrhmSP5YrVbRbrexsLCAZDKJeDwOwN+h12v/Lsbsrk/3/bd5/puc\nwzW+/MDTdyFuFNUFB6Y1Qmg4MYvAfTZvEyTU0lOuxXg8jlKpZECK5523+w4Gg1bao/ryOmNx58d1\nDq8Sdy4AGNjBVs0sK+r3+2g2m6hWq2YosnTqOkYu58fzLtqs04F3gS393nXmRtetH+hOJ/2y55GZ\nFUyfJzE+uTyoR2+aBeTeK83kUAN+0jW7zwCfV5Yz5XI5K7sif1csFkOn07GMXZaYaBYvwbFYLDbG\n+eVm2XDfZykus3+y2SxCoRDa7TY6nQ4ikQjC4bCVzwQCgbFSOmZlkCdmYWEBmUwGAKz7EwGW/f19\nKz+kc6/3Tu+x35qMRCLIZrMWxOD65rE8zzPOo1qtZmBVs9kcI9DWeU6n00ilUlhcXLQyOpaLlUol\nm386Y9qVUQEdzifXgdt6mffAzyF923KdYIY79xwvQUYeg3PJa/U8zziv4vE4hsMhEomElaLofSBA\nFI1GzQkOBAJj5S7r6+solUpYWFhAp9PBwcGBdVpjJ7ajo6OxbmDK3+Paweokk7Q9l8vh/v372Nzc\nRDAYxIsXL/DkyRNsb29bK3deq/5MY5e6a9kFGP6vip+/4WeH3Na5eB6/eVWwjuLqSm0hrxlDt5Gp\nRN3AZ0Sz63S/p+4nVQKJxvk9LUlUn8n9W9c/wW0NQMw4gP68ZAYAzWRq0QefCDgNnWk2K0WQqaiU\n6G5SOQoVnEsiTIVEcGkSC72CRFSIzBjRTdcPTFJDS427myq4yzYRNQTelijQoJuX8m3oOJiKnkwm\njbtgOBxidXUVH3zwAVZXVzE3NzdGnsnI1bsUljAUi0VzHnZ2diy1H5iczfFdR0Sveu1tnfdNzuWC\nuN8FiKYOsas7aLQzNXmSEIBZXl62ch/lqKCOue1yRh6X3BQsBXX5PhTUcPXYVcd3P+en36a5X+4x\n2DKcJTLM3CCApanii4uLvo6qn+h8axtjEs6zREY7KU0a51UyGo1sXDwnnQiNlCqHkd9c8vOJRMIi\n+Fre4paMTRK/Y3NOdB44DvfZ83P+FNhXUQ4hlm7SKSZ/BPcjljloV8poNIqlpSXcuXMHxWJxLANI\nr4PrOx6PIx6P4/j42IA82gDM8CGnEpsJeJ5nmUL8DImWE4mEXfPBwYFlfLZaLfT7fUQiEcTjcbTb\nbeMFmmbNcC7ZsYclZ8vLy/joo4+wsbFhmSbNZhP7+/t49eqVgQ7BYNBKjNipi7wzzCxkuVIymXwN\nAOL95vqho8YMIo5L2z3zXroZP/rsTwp4+L13E7mu/tdsRl23Wial3CXKL9Ltdm1NeZ431rZen1va\nNcys5nPIzKxms4lSqYRMJmNlhyzPUzLulZUVhEIhy5JjRye1OZUXi/ZhJBLBvXv38Fd/9Vf427/9\nWzx8+BDz8/P44osv8B//8R/4p3/6J7x8+dKu37UpaWNr+Zlmf+m+pMTdnIOZA/7mMkkn8725uTl7\ntvmMEvBhZiUAy3JjxQTvLW1pXetXiTsOLc3ifsYyRK4N2uNnZ2c4Pj4e21+vysx1gVrNSGPmGa/J\nb+5m6/D9lhkANJNLxc/5YETFNZgvU2JUjnSySGjZ6/XGFImf0lXwh5FVvs8oJQ22SQpHjSJGrNnF\nw+97ChYxM4BKT1PCbyo8H1M1qaD587YzgXhdzC5g+RnHwtdY0lUqlZBKpWwTWF1dxdraGuLxOEaj\nEcLhMPL5PAaDgW2G71JoRC0sLKBYLI5FkhkFdB0x12maxrF+W/IugZ+bntPPyH/XkWaVSeUO5FI4\nPj42gPIyCQTOuT14DJdI87auT4FuXXfUSTp+LSNQR0nBqWnOpX/rXKkzcZko0EBR7hk6YwTOGPWe\nm5tDOp0eG891MgPo9J2cnKBer+PZs2cIh8N49OjRGOHwZY7uZaLgD3U69Z8CgTSO/UpvlEuOvFEs\nl7opyDrJmebeyc/QEHd111XPpgL8zDhhy3NypgQC56VdnBuCP7zOdDqNBw8e4Ec/+hE2Njbs2jXS\nzHXmNl3gnHEPpVP/4sUL7O3tIR6PW/eZ+fl54x8KBs+5BiuVCr755hscHR2Z035wcID9/X20Wi0s\nLCwgn8+j3W6j0WiYvcDMVC2r8VuTo9HIeLg6nQ6SySTu37+PTz75BHfv3rVSnuPjY3z11VeW+cp7\n0+/3bQ8kGEpgk/eQ4CjHpjpBOV/UMfQ8z+6POmHkUSKBq5+Od59fPxvptoCgaY7J1zWbTQFXrk8N\nDnIe2GHt6OjIuP7IJ8UyoHQ6bWUp9XrdeIUIdHqeh3K5bDxBqVQKnU4Hh4eHxjtFzqClpSXEYjHU\n63X0ej30+330+33TGTwnQSBeVzwex6NHj/CrX/0Kv/rVr4wIfWtrC/F4HN9++y0qlYo9b37zw2dO\nn3d9fnguPh+0T99WGdT7Iu561tffhi0yCUQPBs954nK5nPHEsYvn7u4unj9/bn4S7U8FawDY8zyN\nve8H/jCIy0ALsxZpw2gHTpan9vt9Oz8/pwE01Y1+Jed8j9c6Pz//Wsc66qK3Scg9kzeXGQA0k6lE\nHSwqNG7akwAU97sKAIXDYYvOUElqG2z3R49FMIgKhr+1rpqfdZ0sKmEaHfzepKiJsuq79bNvAtJQ\nedNhAGDRJc7H29jEFYjTtFRGl9UJYtQil8thc3MTmUwGzWbTNj7tDMPSK17XuzZACND1ej2EQiHE\n43HruuI3JtdRAr7baMV1IqhvIm87GvwuxS/q7XkXNfpKlnmZuKU16kjdNsClwK/qDwWHlHzRb6zT\nZCQpmEUODBqo170etxSDRqUaeQStSLzLa2JgwM3e9BN1As/Ozoxc98mTJ6hUKrh///7YfVYdSUN1\n2mvT+el2u2i32wBgjjmNWl6nX+mNRuE1Y5JZCdMAdZPGRXGdGgIn2hbczai47NgcM8GcZDKJnZ0d\nVKtV1Ot1cw6Y6aJ8efPz88jlclhbW8OjR4/w6NEjpNNpu886LxwPcM4Np4TO3PMjkQhWVlZQrVax\nv7+P7e1tzM3NoVQqodfroVgsIhqNWqkZS3xevHiBRqNhmRu8X9zHNVuJABP3fy0tc+eKa4gdotgF\nh2ua2TsAjEjbbTHPts+cM3amYuaKkuWr/aIlLAoycv0w645ZSew8d3x8jFgsZnxELrB1FfhzUwBV\nv+/aaXpuv2OqQ+l5nmW50A46OztDt9s1Mm/N8slms/joo4+QTCbx8uVLvHz5Eru7u2i32xgMBlhc\nXDQeJQbS+v3+azxknU7H1mMsFsPZ2RmazaYRRhNwm5s771QWDAato1u32zUwTrMDSa67uLiIeDyO\nbDZrpYOj0ch4o9hJNRwOW8cxzpuCe9qZMhKJWLkNQXGCP1wPdPwZzHvbQcT3UW7ThrvsWFzX9A+i\n0SgSiQSWlpZsfzw6OkK1WjVgjvqUJZ4aSL/JeGjHc12Q40rLFQHYe/1+3ziy1tbWbI/jsTW4pBmy\n7hjoK/EaVFcpEDST919mANBMrhQXSNGo42WfVxRZu30p6OGmqVMJuYqEDggNO0191jRHv1REHQcN\nWW6WbgczjpHfYYbO4uKiGZhKZn3TuaQjoSAMjSE/gsDL5vsmY9AsBLbRJBBEok5edzqdRj6fRzQa\nNX6PVqtlKfbkWWBGVzwef+dggpYWMPLMEoNOp2MRfV6/n7ytCNJlMulZuu1xTLo211m7TPyMATUa\n3vXc6fl0HMwwVJL6q46j0Sp1zphVdlvj1XmeBDRphw2/DI5pz6X8JCxLyefzY47sVaI6gbqTQtBY\ns5d4bhq9jFpPszb0epvNJr788kv88Y9/xLNnz5DL5fDLX/7Soo5aiqt70rSAC0lhCVyTnJV6kPwi\n7ESka4AgI+eHzmGr1bKuU3TEyI9zHXGDLXxGh8OhESmT10zLP1Rcp0L3NYI1/CF3TrfbNV2uexD3\n1nA4jFwuh+XlZSwvLyOZTNrx+v3+GN+OXsPJyQl6vZ45OycnJ0gmk9jY2EAoFMKrV6/QarWMv4dk\n7CyvuHv3roEp1WoV0WgUW1tbuHPnjnV2IlDVbrfheeddHwuFAvL5vJU99Ho9i1jT7lDSYY753r17\nlsWxs7MzRtyrpOa8zyT/ZkBECXwJ3NCOSCQSyOfzKBQK1s3Mfa50nfGeKSCiDiSvV7OA3PWgx/Hb\nZ971vkfbjnPOeQCAVquFer1ugA7LB2njce0/fvwYpVLJAEhm4rA5RTwet5Iutkjv9/sAzvXr8fEx\nGo2GjUG53yjdbtc4UzjfymVJUfuSQA0zdRjQC4fDiEajYy3m/QJldLxdnhhyCvE54DjC4TBisdhr\n2WL/V8XNyORr7xp0IPDBLDCCwKlUCrlczjrNHR0djfkyBKw1iH0ToM71Z6iPWJ7IigjtgDgYDFAu\nl/Gb3/wG/+///T8UCoWxPXpSViR9LupN7UzIFvC0ufn6DAT685AZADSTMVEnRQ1rNzPH/c6kKBM3\nMTUKdSOlAa2ItZ/ioDHcarVM2XFzp/LRFpB+GTR0ZgaDwVjWz6T6+VAoNHYOff+6c+o6+jSE6XBo\nyrN+z70HriGnn/GLwLnH8zu2ln6RzJR/P3z4ED//+c9x7949a33b6XSwt7eHpaUl42PQzc0lbL1K\nCMBxQ9TrnZZDg5+hMUmehGg0apFARmcDgYsWrRpxI9/Cu5Q3ib7e5DyuTAP8XHZMLW+4bSHwQIdV\nyRY18gS87vizPn+acU0yfOjEUUdVKhX0er0xo5ufm3bduM+g39/XeXYuk+FwiOPjYzx//hxnZ2f4\n4IMPxqJ+0zxXnU4HT58+xatXr5DNZvHgwQPTjYlEYmyv0DFTf/A9P/DVr6wKgJV1BAIB5HI55HI5\n3Llzx/hSGESYtA/xXvK8yiVCabfblolKpy2TyZjDzjbko9EImUxmLLuRZT3UMcwm0Ou6Dg+a7pG6\nvnlNzE7Y29vDwcEBRqMRHj9+PJYF5O6zk87NtU6eCF7rT37yEyvzHQwG2NnZwYsXL/DVV18BgJHY\n8rnSjFjqfWAcOPK8c76gDz/8EKurq9a1iVFrtuJmgGFjYwO7u7sAYOBPPp9HLBYzEODg4ABra2v4\n+OOPxzJDYrEYstksXr58CeC8VIhj0WdV1wlw8Qzoupybm8PKygrK5bIRPR8eHhq4kEqlUKvV8PXX\nX+Pw8NCAh2QyiWQyiW63i1qthna7jWQyaQBiMBhEPp/Ho0ePjNxd58rdv1V6vR4ODw/Hnqlms4mF\nhQXkcjnEYjEDfd1yKtWRfvaAZvj52RPunLkAH9fVtDpLwRYtFyRY4nmekWYTSKFQNwaDQaTTaTx8\n+ND+//bbb3F2doZWq2VgYKPRMBuL9gQzJAhgM5uBmV6hUGisjIZgIInSqUMbjQZqtZo52242z9HR\nEZ48eYKVlRVsbW1hYWEBtVoNv/vd73BwcDC2/lRf0NlmO/tSqWTrn003VlZW0G630W637by6H6st\nNQkM5r13wcI/F+H1TsqW57rQObjs865cBSzxnhEg9LwLkvJyuWxZpRStJtAyMALTCgTzmdQyd1dH\nMJtIAW1moHH8BKiHwyFisRjK5TI+++wzeJ6HR48emd4mwEg7hvx+BL8ZMInH40ilUohEIta9jyCQ\nVoUAf15r6fsqMwBoJiZqiLs/CmBQmU6rSPX7p6enVrOt3RX0+JOULiOvCiBFIhHjstFotXtuGsjs\ntkEFzFI091o0K0JLv1zxM7QnzYm70RKM4rk5FzyubuLqHOg5XLBHHVk1/Ah0EGTStFFNSeeGEg6H\nsbS0hNXVVRSLRSNdZLScrVLT6bQZnwqmTCvctPR/BcmuC1DwmhcWFiw9n5FRt1uBayBNm10xk3F5\nW+CVOrMuwOD+7yfXccB5TP3tnkeJHAksMMp2FZnideS25pMR6G63O1Yiq6DNVUKHan9/H5VKxSLw\nS0tLFsmcdBwXEFLdpPrV/WwoFLLymgcPHgA4BwQITPt9xz0vwR+Nzg+HQ9TrdVQqFSsfojHOiCyN\n9FgsZsDjaDQaa+lNziMFoxidJ1Ck9+CqeVbHjfsUyzi4T5F7p9PpoNPpmCPL77rAD/Wa33NCPc3S\nrLW1NXzyySdYW1tDJBLBcDjEnTt3kM1m7Xj37t3DycmJdYDUslruW5pxxz2YneDY5csFOllytbGx\ngVQqhcePHwPAWBYQOzkWi0UEAgFks1lEo1FzVgKBAFZXVxEMBlEsFi2DVfkCed3uvLvC+8V1X6vV\nxsAidv969eoVvvzySxwfHyOZTGJlZcU6SrXbbXzxxRfY29tDq9Uyx53ZU9lsdixbZ5LwHpIziOVz\nrVbL7l00GkWhULCuda6uVJCF16y2Fj/rPsd+II+fftT1dhmAdZmofiWhOJ9Dt7TTBaKSySTu3Llj\nnd/K5bJlaPN4LNeibcgSL6UEiMfjWFlZwdraGlKpFNrtNra3t7G3t2c8LeFwGMlkEqVSybKhnz9/\njnK5jE6nY+VZHMvR0RH+9Kc/wfM8fP755xiNRtje3saTJ09wdHSEUChk5WVuWaBmkyUSCaysrGB9\nfR1bW1tW/vbs2TMDvbrdLjqdDvr9/hgYPMmOdv9/W3v4bYvf2BUsd214P1t00rxcR9QHYdCAgYv5\n+Xns7Oyg3W6P2dQMKGu3O4LAWp7KLC+uJdXjaudrCTCz23Qt8Vq5TwSDQRwfH2NnZwfz8/M4PDy0\nrJ1wOIxUKoVkMom5uTnL2GRmK0EqEuyTJP3+/fsGftE34x7gUnLM5P2TmcfzPRdX8etG6xcNUmN1\n2pRTNThoGGoU5jrKWMEnGkdEo5nloQAQx0wFxWgJHQACSOQN0dIC4KIcg8CJu6FcJ+uGRgc/w/EM\nBoMxBQrgtWgAv6/3xW8M7v1TnqG5uTkzTtThYTRbNxpthwtgrO0zI9804N1MguveT93IgAtnkeeZ\nRlwjhsYVyRLZHcbNOlBOldty4GdyO0IHWI0dym0arK6DBPhzMsViMXNQuD7V6XrfRLMM+/0+KpWK\ntTlWYt7LhCTwhUJhzLHifdHn9ypxP6P6jP/zhxme+l0FdK46j+phdawajQZ2d3fx6NGj1zKtqJ+5\n7vgenUd9X6+HulDByusKxwfgtTKTYDBokf/RaIR6vY5QKDQGbvntOZwDNxuArzNLYmVlBSsrK0in\n0wY8pdNpJJNJy57JZDJjPFDcL7kWNPuo1+uhVqthbu6cCJyBBt0nNPMXgHHNZbNZmwPNTmCGViAQ\nsGAE3+N+xq5O3P+Z7akgwrQBq6OjI7x69Qp7e3tWJri8vIzBYIBOp2OOfzAYRKlUwr1793Dnzh3E\n43ErOY5Go3jx4gXOzs4QjUaxsrIyVvrlJ3qPgIsMkXg8jmKxiMFgYJ2s2EDj8PAQmUwG6XR6bC/n\nvZr0nPsBOq6tpzag+zm/59k93lXz7Op0JbOdBFRroIsZUA8fPkQ4HMbh4SF6vZ6VeHHPZ7biYDAw\nYmhmPrO7IDPLCoUC6vU6hsMhqtWq6c/FxUUkk0msr68jl8uhVqvZ+mImULPZtEymZrOJZ8+eoV6v\nmyNOvrFut2u2mTrtfMZCoZB1UVxfX8df/uVf4sc//jHW1tYQjUZRr9fx6aefYm9vz55jdiBkNvxl\n4gek/DmIuzavym5y16kLplxX9HsE3prNppVics3WajUrTaAsS5UAACAASURBVKX+UOBHKxWAC1uH\nvgz3G2ZA83lWna+6n75LJBKxvVPtW/4w6Ly7uztWnra4uGi8Z/SHCCyxNJr2ODmPc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DFr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ayWTS5iUYuHn/g++ixApNkFqtZmVeAOysB7p9kjAKh8NWotZoNOz/1WrVxLQzmYwKhcKY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\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VfzwzhfPO50lSJpPR48ePtby8rFAo9KVyo1gspo2NDes+iS6XJOVyOft8RM9rtZqBkbx3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\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aXwIdxgNgEvcAYB5MKs4SYWk+X9PQ0JDJSn/mYKlFIhFbBxhZ/lygvLN/R0ZGdHh4qFKp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TDa7TYOf1y3zy+JoY/4HnX3tt3HJByWYfm5yhAA+g0XN/ilfi0f3wwHMXd8Q0tTrSSFMeO6HRRP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dYGINuHM3Kvjm45s6454KI2loLwWQ4n35vr9n6lR7zSpfM94nvs+aZj78PR0UGwXmOHjvwBP9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\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qDg8PQ+MGQXHS7GB6cB3pDjTz9CnYifgTADivyMY4e+qf6744U9GvT7UsB2BhIk0mk+hH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\nRiIR91mba8WKmvaaQe1gn4/fDapn631CscY3SFv1OTsuIpEIkskk+vv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2PrnQ5wLU/o6C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"text": [ "<matplotlib.figure.Figure at 0x483ed10>" ] } ], "prompt_number": 16 }, { "cell_type": "code", "collapsed": false, "input": [], "language": "python", "metadata": {}, "outputs": [] } ], "metadata": {} } ] }
apache-2.0
adam2392/paremap
paremap_nih_rotation/notebooks/.ipynb_checkpoints/word block and group analysis-checkpoint.ipynb
1
946829
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyzing Word Pairs With Temporal Drift\n", "By: Adam Li\n", "\n", "Now that I've analyzed word pairs between various groups of word pairs (e.g. different, reverse, probe overlap, target overlap and same group), it seems that I need to:\n", "1. reduce the dimensionality of my frequency, time and channel domain\n", "2. manage the temporal drift effect of my data across experimental blocks" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import Necessary Libraries\n", "import numpy as np\n", "import scipy.io\n", "\n", "import matplotlib\n", "from matplotlib import *\n", "from matplotlib import pyplot as plt\n", "\n", "from sklearn.decomposition import PCA\n", "import scipy.stats as stats\n", "from scipy.spatial import distance as Distance\n", "\n", "# pretty charting\n", "import seaborn as sns\n", "sns.set_palette('muted')\n", "sns.set_style('darkgrid')\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "There were 49 number of incorrect events.\n", "The list of incorrect probe words: \n", "{\"[u'PANTS']\": 7, \"[u'JUICE']\": 8, \"[u'BRICK']\": 12, \"[u'CLOCK']\": 13, \"[u'GLASS']\": 9}\n", "\n", "This is the length of the events struct with only correct responses: 1431\n" ] } ], "source": [ "######## Load in EVENTS struct to find correct events\n", "eventsDir = '../NIH034/behavioral/paRemap/' + 'events.mat'\n", "\n", "events = scipy.io.loadmat(eventsDir)\n", "events = events['events']\n", "\n", "# print number of incorrect events and which words they belonged to\n", "incorrectIndices = events['isCorrect'] == 0\n", "incorrectEvents = events[incorrectIndices]\n", "incorrectWords = []\n", "wordList = {}\n", "for i in range(0, len(incorrectEvents)):\n", " incorrectWords.append(incorrectEvents['probeWord'][i][0])\n", "\n", "for word in np.unique(incorrectEvents['probeWord']):\n", " wordList[str(word)] = sum(incorrectWords == word)\n", " \n", "print \"There were \",len(incorrectEvents), \" number of incorrect events.\"\n", "print \"The list of incorrect probe words: \\n\", wordList\n", "# \n", "# get only correct events\n", "correctIndices = events['isCorrect'] == 1\n", "events = events[correctIndices]\n", "\n", "print \"\\nThis is the length of the events struct with only correct responses: \", len(events)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Different Word Groups\n", "\n", "The struct from MATLAB has\n", "\n", "* data.powerMatZ = thisPowMat;\n", "* data.chanNum = thisChan;\n", "* data.chanStr = thisChanStr;\n", "* data.probeWord = THIS_TRIGGER;\n", "* data.targetWord = targetWord;\n", "* data.timeZero = 45; \n", "* data.vocalization = data.timeZero + round([metaEvents.responseTime]/Overlap);\n", " \n", "Input:\n", "A list of directories that correspond to each session and block #. Within each directory there is a list of word pairs available from that sessionBlock and structs that represent the data from each channel. \n", "\n", "Either the words are completely different, reversed, probe overlap, target overlap, or completely the same\n", "\n", "Algorithm:\n", "0. Analyze block 0/1, 1/2, 2/3, 3/4, 4/5 (5 total)\n", "1. Loop through each channel:\n", " - extract probewords, targetwords, Z scored power matrix, channel #, channel string, time zero(probe on), vocalization\n", "2. Create Feature Vectors\n", " - extract delta, theta and high gamma frequencies\n", " - run ANOVA on each time/freq window\n", " - compute a threshold to include channel for feature vector\n", "3. Plot Histogram of Distances From the Other's centroid" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Blocks are: \n", "['BLOCK_0', 'BLOCK_1', 'BLOCK_2', 'BLOCK_3', 'BLOCK_4', 'BLOCK_5']\n" ] } ], "source": [ "######## Get list of files (.mat) we want to work with ########\n", "filedir = '../condensed_data/blocks/'\n", "sessions = os.listdir(filedir)\n", "sessions = sessions[2:]\n", "print \"Blocks are: \\n\", os.listdir(filedir+sessions[0])\n", "\n", "# functions for finding the different groups of word pairings\n", "def find_reverse(wordpair, groups):\n", " # split wordpair and reverse\n", " wordsplit = wordpair.split('_')\n", " wordsplit.reverse()\n", " reverseword = '_'.join(wordsplit)\n", " \n", " # find index of reversed word index\n", " try:\n", " reverseword_index = groups.index(reverseword)\n", " except:\n", " reverseword_index = -1\n", " \n", " return reverseword_index\n", "def find_different(wordpair, groups):\n", " # split wordpair and reverse\n", " wordsplit = wordpair.split('_') \n", " \n", " differentword_index = []\n", " \n", " for idx, group in enumerate(groups):\n", " groupsplit = group.split('_')\n", " \n", " if not any(x in groupsplit for x in wordsplit):\n", " differentword_index.append(idx)\n", " \n", " # convert to single number if a list\n", " if len(differentword_index) == 1:\n", " differentword_index = differentword_index[0]\n", " \n", " return differentword_index\n", "\n", "def find_probe(wordpair, groups):\n", " # split wordpair and reverse\n", " wordsplit = wordpair.split('_') \n", " probeword_index = []\n", " \n", " # loop through group of words to check word pair in\n", " for idx, group in enumerate(groups):\n", " groupsplit = group.split('_')\n", " \n", " # check if probe word overlaps\n", " if wordsplit[0] == groupsplit[0] and wordsplit[1] != groupsplit[1]:\n", " probeword_index.append(idx)\n", " \n", " # convert to single number if a list\n", " if len(probeword_index) != 1 and probeword_index:\n", " print probeword_index\n", " print \"problem in find probe\"\n", " elif not probeword_index: # if no probe words overlap\n", " probeword_index = -1\n", " else:\n", " probeword_index = probeword_index[0]\n", " \n", " return probeword_index\n", "\n", "def find_target(wordpair, groups):\n", " # split wordpair and reverse\n", " wordsplit = wordpair.split('_') \n", " targetword_index = []\n", " \n", " # loop through group of words to check word pair in\n", " for idx, group in enumerate(groups):\n", " groupsplit = group.split('_')\n", " \n", " # check if target word overlaps\n", " if wordsplit[1] == groupsplit[1] and wordsplit[0] != groupsplit[0]:\n", " targetword_index.append(idx)\n", " \n", " # convert to single number if a list\n", " if len(targetword_index) != 1 and targetword_index:\n", " print targetword_index\n", " print \"problem in find target\"\n", " elif not targetword_index: # if no target words overlap\n", " targetword_index = -1\n", " else:\n", " targetword_index = targetword_index[0]\n", " \n", " return targetword_index\n", "\n", "def find_same(wordpair, groups):\n", " # split wordpair and reverse\n", " wordsplit = wordpair.split('_') \n", " \n", " try:\n", " sameword_index = groups.index(wordpair)\n", " except:\n", " sameword_index = -1\n", " return sameword_index\n", "\n", "def inGroup(group, names):\n", " for i in range(0, len(group)):\n", " if cmpT(group[i],names):\n", " return True\n", " return False\n", "\n", "def cmpT(t1, t2): \n", " return sorted(t1) == sorted(t2)" ] }, { "cell_type": "code", "execution_count": 93, "metadata": { "collapsed": false }, "outputs": [], "source": [ "### Functions to help extract features and plot histogram of distances\n", "# loops through each wordpairing group and extract features\n", "def extractFeatures(wordgroup, session, blockone, blocktwo):\n", " fig = plt.figure(figsize=(7.5*len(wordgroup), 3))\n", " \n", " for idx, pair in enumerate(wordgroup):\n", " # load in data\n", " first_wordpair_dir = firstblock_dir + '/' + pair[0]\n", " second_wordpair_dir = secondblock_dir + '/' + pair[1]\n", "\n", " # initialize np arrays for holding feature vectors for each event\n", " first_pair_features = []\n", " second_pair_features = []\n", "\n", " # load in channels\n", " first_channels = os.listdir(first_wordpair_dir)\n", " second_channels = os.listdir(second_wordpair_dir)\n", " # loop through channels\n", " for jdx, chans in enumerate(first_channels):\n", " first_chan_file = first_wordpair_dir + '/' + chans\n", " second_chan_file = second_wordpair_dir + '/' + chans\n", "\n", " # load in data\n", " data_first = scipy.io.loadmat(first_chan_file)\n", " data_first = data_first['data']\n", " data_second = scipy.io.loadmat(second_chan_file)\n", " data_second = data_second['data']\n", "\n", " ## 06: get the time point for probeword on\n", " first_timeZero = data_first['timeZero'][0][0][0]\n", " second_timeZero = data_second['timeZero'][0][0][0]\n", "\n", " ## 07: get the time point of vocalization\n", " first_vocalization = data_first['vocalization'][0][0][0]\n", " second_vocalization = data_second['vocalization'][0][0][0]\n", "\n", " ## Power Matrix\n", " first_matrix = data_first['powerMatZ'][0][0]\n", " second_matrix = data_second['powerMatZ'][0][0]\n", " first_matrix = first_matrix[:, freq_bands,:]\n", " second_matrix = second_matrix[:, freq_bands,:]\n", "\n", " ### 02: get only the time point before vocalization\n", " first_mean = []\n", " second_mean = []\n", " for i in range(0, len(first_vocalization)):\n", " # either go from timezero -> vocalization, or some other timewindow\n", " first_mean.append(np.mean(first_matrix[i,:,first_timeZero:first_vocalization[i]], axis=1))\n", "# first_mean.append(np.ndarray.flatten(first_matrix[i,:,first_vocalization[i]-num_time_windows:first_vocalization[i]]))\n", " for i in range(0, len(second_vocalization)):\n", " second_mean.append(np.mean(second_matrix[i,:,second_timeZero:second_vocalization[i]], axis=1))\n", "# second_mean.append(np.ndarray.flatten(second_matrix[i,:,second_vocalization[i]-num_time_windows:second_vocalization[i]]))\n", "\n", " # create feature vector for each event\n", " if jdx == 0:\n", " first_pair_features.append(first_mean)\n", " second_pair_features.append(second_mean)\n", " first_pair_features = np.squeeze(np.array(first_pair_features))\n", " second_pair_features = np.squeeze(np.array(second_pair_features))\n", " else:\n", " first_pair_features = np.concatenate((first_pair_features, first_mean), axis=1)\n", " second_pair_features = np.concatenate((second_pair_features, second_mean), axis=1)\n", " # end of loop through channels\n", " \n", " # compute paired distances between each feature matrix\n", " first_hist, second_hist = computePairDistances(first_pair_features, second_pair_features)\n", " \n", " ## Plotting Paired Distances\n", " plt.subplot(1, len(wordgroup), idx+1)\n", "# plt.subplot(1, 5, )\n", " plt.hist(first_hist, label=pair[0], lw=3, alpha = 0.75)\n", " plt.hist(second_hist, label=pair[1], lw=3, alpha = 0.75)\n", " plt.ylim([0.0, 6.0])\n", " plt.xlim([0.0, 1.6])\n", " plt.legend()\n", " plt.title(session + ' comparing ' + pair[0] + '(' + str(len(first_pair_features)) +') vs '+ pair[1] + '(' + str(len(second_pair_features)) +')')\n", "\n", "def computePairDistances(first_mat, second_mat):\n", " first_centroid = np.mean(first_mat, axis=0)\n", " second_centroid = np.mean(second_mat, axis=0)\n", " \n", " # compute pairwise distance to other centroid\n", " first_distances = np.array([distances(x, second_centroid) for x in first_mat])\n", " second_distances = np.array([distances(x, first_centroid) for x in second_mat])\n", " \n", "# first_distances = []\n", "# second_distances = []\n", "# for idx in range(0, first_mat.shape[0]):\n", "# first_distances.append([distances(x, first_mat[idx,:]) for x in second_mat])\n", "# for idx in range(0, second_mat.shape[0]):\n", "# second_distances.append([distances(x, second_mat[idx,:]) for x in first_mat])\n", " \n", "# first_distances = np.array(first_distances)\n", "# second_distances = np.array(second_distances)\n", " return first_distances, second_distances " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Processing\n", "Go through and collect lists of word_pairs that are associated with one of the groups defined: same, reverse, probe, target, different. \n", "\n", "Then I can go through each group listing and analyze the data correspondingly. Obviously this depends on the matlab dirs being saved in a certain manner: '/blocks/sessions/blocks/wordpairs/channels'" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 6]\n", "['delta', 'theta', 'HFO']\n", "The length of the feature vector for each channel will be: 15\n" ] } ], "source": [ "##### HYPER-PARAMETERS TO TUNE\n", "anova_threshold = 90 # how many channels we want to keep\n", "distances = Distance.cosine # define distance metric to use\n", "num_time_windows = 5\n", "freq_bands = [0, 1, 6]\n", "# freq_bands = np.arange(0,7,1)\n", "\n", "freq_labels = ['delta', 'theta', 'alpha', 'beta', 'low gamma', 'high gamma', 'HFO']\n", "print freq_bands\n", "print [freq_labels[i] for i in freq_bands]\n", "\n", "print \"The length of the feature vector for each channel will be: \", num_time_windows*len(freq_bands)" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Analyzing session session_1\n", "Analyzing block BLOCK_0 and BLOCK_1\n", "['BRICK_CLOCK', 'CLOCK_BRICK', 'GLASS_JUICE', 'JUICE_GLASS']\n", "['BRICK_CLOCK', 'CLOCK_BRICK', 'GLASS_PANTS', 'PANTS_GLASS']\n", "[['BRICK_CLOCK', 'BRICK_CLOCK'], ['CLOCK_BRICK', 'CLOCK_BRICK']]\n", "[['BRICK_CLOCK', 'CLOCK_BRICK']]\n", "[['GLASS_JUICE', 'GLASS_PANTS']]\n", "[['JUICE_GLASS', 'PANTS_GLASS']]\n", "[['BRICK_CLOCK', 'GLASS_PANTS'], ['BRICK_CLOCK', 'PANTS_GLASS']]\n", "Analyzing block BLOCK_1 and BLOCK_2\n", "['BRICK_CLOCK', 'CLOCK_BRICK', 'GLASS_PANTS', 'PANTS_GLASS']\n", "['BRICK_JUICE', 'GLASS_PANTS', 'JUICE_BRICK', 'PANTS_GLASS']\n", "[['GLASS_PANTS', 'GLASS_PANTS'], ['PANTS_GLASS', 'PANTS_GLASS']]\n", "[['GLASS_PANTS', 'PANTS_GLASS']]\n", "[['BRICK_CLOCK', 'BRICK_JUICE']]\n", "[['CLOCK_BRICK', 'JUICE_BRICK']]\n", "[['BRICK_CLOCK', 'GLASS_PANTS'], ['BRICK_CLOCK', 'PANTS_GLASS']]\n", "Analyzing block BLOCK_2 and BLOCK_3\n", "['BRICK_JUICE', 'GLASS_PANTS', 'JUICE_BRICK', 'PANTS_GLASS']\n", "['BRICK_JUICE', 'CLOCK_GLASS', 'GLASS_CLOCK', 'JUICE_BRICK']\n", "[['BRICK_JUICE', 'BRICK_JUICE'], ['JUICE_BRICK', 'JUICE_BRICK']]\n", "[['BRICK_JUICE', 'JUICE_BRICK']]\n", "[['GLASS_PANTS', 'GLASS_CLOCK']]\n", "[['PANTS_GLASS', 'CLOCK_GLASS']]\n", "[['BRICK_JUICE', 'CLOCK_GLASS'], ['BRICK_JUICE', 'GLASS_CLOCK']]\n", "Analyzing block BLOCK_3 and BLOCK_4\n", "['BRICK_JUICE', 'CLOCK_GLASS', 'GLASS_CLOCK', 'JUICE_BRICK']\n", "['BRICK_PANTS', 'CLOCK_GLASS', 'GLASS_CLOCK', 'PANTS_BRICK']\n", "[['CLOCK_GLASS', 'CLOCK_GLASS'], ['GLASS_CLOCK', 'GLASS_CLOCK']]\n", "[['CLOCK_GLASS', 'GLASS_CLOCK']]\n", "[['BRICK_JUICE', 'BRICK_PANTS']]\n", "[['JUICE_BRICK', 'PANTS_BRICK']]\n", "[['BRICK_JUICE', 'CLOCK_GLASS'], ['BRICK_JUICE', 'GLASS_CLOCK']]\n", "Analyzing block BLOCK_4 and BLOCK_5\n", "['BRICK_PANTS', 'CLOCK_GLASS', 'GLASS_CLOCK', 'PANTS_BRICK']\n", "['BRICK_PANTS', 'GLASS_JUICE', 'JUICE_GLASS', 'PANTS_BRICK']\n", "[['BRICK_PANTS', 'BRICK_PANTS'], ['PANTS_BRICK', 'PANTS_BRICK']]\n", "[['BRICK_PANTS', 'PANTS_BRICK']]\n", "[['GLASS_CLOCK', 'GLASS_JUICE']]\n", "[['CLOCK_GLASS', 'JUICE_GLASS']]\n", "[['BRICK_PANTS', 'GLASS_JUICE'], ['BRICK_PANTS', 'JUICE_GLASS']]\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/Users/adam2392/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:48: DeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n", "/Users/adam2392/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:51: DeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future\n" ] }, { "data": { "image/png": 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MmZOaNcPo0uU5rrnmmotaX0Zxw3e/b9/eicAwa+3MlJY1xhQDngKaA2WAXDgD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\n7dqZaPqGDesoUKAgxYoVZ8u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"text/plain": [ "<matplotlib.figure.Figure at 0x11ff37c10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x12392a050>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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E0KHDWLbsM958cymjRm2N7/ssWDCP0tIydt/9u5SX92PIkKEMGTKUww47gssv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\n4YX/x5ZbbrJW01u1aiXlJeXEUv3Wajr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"text/plain": [ "<matplotlib.figure.Figure at 0x128ae65d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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spLY2SSqVJplMUVubbHIaiUSKVDpNIpGkqqq2Xhl0jDq2rlYe6Hpl6orlaY6C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\naZ9sFq3Q8fkU6O2cGxR+3h3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"text/plain": [ "<matplotlib.figure.Figure at 0x1392497d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x139252250>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x13e4be650>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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piU8//RhjXMyXX3411dXfMHPmDHzfp76+nl13/RmjRh2bd72JiEjf6Y8cOXfu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\nEUzWevoIeDESzZ0o0yfn+L7vTsAdlRs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"text/plain": [ "<matplotlib.figure.Figure at 0x13d02fc10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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i8nq9PPlkO0JDr+fttwdd0rrSghu++/37900CRlhrZyW1rDGmIPAk0BQoBmTH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\nc3YC0+J6GmNu59Chg/z115F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"text/plain": [ "<matplotlib.figure.Figure at 0x13e4a5150>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x11da0d750>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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4Yc8IsfVxknXNx9BWW/K4r89F9a9rG2szEskY8UqfDdUx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\nIsxtntRntiqROzJyP0Lsjozcj3DidmTqM9tRgLn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"text/plain": [ "<matplotlib.figure.Figure at 0x11da0d890>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x120ae2ad0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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KTK/1lSs/DENSXek1ir2QZF2CVFeaMBdDqncMfZFKZViRWcnyTO8y4k1xYiEk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\nIkkfyhCeAAAAAElFTkSuQmCC\n", "text/plain": [ "<matplotlib.figure.Figure at 0x12492e6d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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RERFACVFERARQQhQREQGUEEVERAAlRBEREUAJUUREBFBCFBERAZQQRUREACVE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\nqFvkzxUuZyiV+s3W24mu+Rx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"text/plain": [ "<matplotlib.figure.Figure at 0x11da0f890>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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5PP74owwbdgIPP/wgl1/+KwBee+2fzJ8/j/HjJwHwyiv/YOLE2xg79hbuvvsO\njjpqWEuiuOKKS3j55RfZddc98i5j2LATWiqGX375BddccyVTp/4RgKeeeoyjjz6Ohx+e3lI589ZF\nBTfccE27H/Hcd98D2HffA5g79xuuvvoKbr/9rpZxo0ZdzOjRY1h33cGkUimGDz+FbbbZnh/96Mcl\nrcOeTvlRRFakFZkjAbbZZjuuvvp6wGso/PnPj2S//Q7ks89mM2TIhrz11pssXbqUXr16AVBVVcWk\nSbex+eZb0Ldvv5ZyhgzZsOVNjEcffSi//vWdxGJeNeTOO29nu+12aMl5EyeO59FHH+bss3N/1/nz\n5zFmzFWMHXsL66yzLgAvv/wikyffzujR1xb8Pk888QhHHTWMRx99mFNO8cp/6qnHGTBgYEs+nz59\nGvfddy/nnXcx48fftMJzZNEXghhjKgGstXv5/5R4RGSl8MorL7PNNtu3VMwAhg7dmNtvvws3R0sj\nwNKlS3nnnTc55ZTTee+9/1Bf712Xr7LKKnz00Yc8//xzLFy4gF122Z1rrx0HwKqrDuCppx7nvff+\nQzKZZMyYcQUrZkCr5S9cuIDevXu3fH722b8ybNjxJBIJPv/8s5bhW2+9LX379uUvf3mw5HUwYMAA\nHn74Qaz9CMdxmDz5N6qYlUj5UURWdtm5aMmSJUSjUaLRGI8//gh77rk3u+22B0899XjLNL1792bY\nsOO5+eaxhUptVe6qq67Kiy8+z5tvvkFTUxNnnXU+Rx89LO/cTz/9JIccclhLxQxg1133KFox++ab\nr1m0aBEnnHAizzzzFKlUqmX5b7zxGjNnvkxDwxKOPPIYzjnnQqB7cmQpd862AKqNMc8AUeAKa+3r\nXRqViADL9xBwR7zyyksrZDlB8803c1h77bVbPo8adTGLFy9m/vx5bLHF1i1dHrI9//wz7LbbnsTj\ncfbaax8ef/wRjj/+RIYO3ZjLLruCRx99mAkTbmHQoNU455wL2HLLrTnnnAuYMeMh7r77Dj77bDY7\n7rgzF154KX369Mkb25///Cf+/vfncJwINTU1XHbZlQC8+eYbDBmyIf369efAAw/mL395kBEjRgLg\nOA4XXTSS008/kR122KmkdXDVVdcxffoD3HLLWL75Zg57770f55xzQUuLphSk/Cgrvc7IQ5m7N3r5\nRPi8/fabnHfecBzHIRaLc+GFl5JOp3j33X8zcuRoBg9ej8svH8GRRx7jz+Fw2GFH8dJL/+C5555u\ndfdsGadFOcnDAAAgAElEQVTVp2OPPZ6+fftx//1/4MMPR7LFFlty0UWXMWhQ35wxffPN1+y44y6A\n1yVxxIjzAKir+45p02bk/S5PPPEoBx54CNXVfdh00834xz9eYK+99mb33ffCcbzu/zfccDUbbPAj\nLrhgBEOGbNgtObKUkhuAm621vzHG/Aj4qzHmx9ba9k8X+mpr8/cRDbowxw7hjj/MsUPXxB+PR1na\nlKaxOf+blpZHVUWUXpXeDfTuXP/xeJQYESJph3g8Wtb82SKOQywaIR6PFvxeG264HrNmzWqZ5t57\nva4exx57LPG4Q01NVbv5n376CWKxGJdffhGNjY3MnTuXCy44B2stW265MT/72R4AzJw5k0svvZSZ\nM2fy8ssvc9ZZp3HWWaexdOlSxo0bx4MP/p7LLrsMaL/uq6srOe20Uzn22GPbxfzss09QVzeXyy+/\niObmZqy1jB49iv79e1NVFWeDDdbiyiuv4MYbx7DNNtvQt2+vlvKbm+tbrZPm5mY+//xDRoy4gBEj\nLqC+vp6RI0fy978/xfHHH1/q6u/JelR+hHDHH+bYofvij8ejNKWbaEo1ll1GOl1FZbwytNugu+Pu\nrhzZv39vdt55J2699dZWw++//34iEYcrrxyB67r88MP3zJ79PjvssAORiENtbQ233HIjxx9/PGee\neSZVVfFWy4lGHQYO7ENFRQUAr776Kr/4xTBOPPHnJBIJpkyZwt13387tt9+eM77111+X+vp5/rga\npk27H4BddtmF2toaqqri9OvXq9W86XSav/3tadZZZx3eeGMm9fX1PP74Xzj22MP597//zb777smR\nRx6M67o88sgj3HjjtUybNq1bcmQplbOPgU8BrLWfGGPmA2sAc/LNENZf8w77L5GHOf4wxw5dF38i\nkWLx0iQLl3RN5axfdZpYxDsNdOf6TyRSJFNp0q7LxF9M7tC8ESf3w87JVJoEqYLfa4stfsLkyXez\n006vtnRt/Oqr//H119+w1lrrUl+/tNX8s2d/SlNTgkmT7m0ZdtFF5zBjxpPMmfM/vvjicy699Aoc\nx2GVVVansrKKurpFjB17I42NabbccmsAamvXYOHChdTVLcq57yxZ0kRVVWO74QsWLOCdd/7N9OmP\ntQy76abr+cMfHmCDDX5EY2OCurpFbLrptqyxxlM89NBfOOus81rK+f77JTQ3J1s+J5NJLr54BBMm\nTPa7hzisskotTU3pkvaH7r5gCYAekx8h3OfpMMcO3Rt/IpFiSaqhrJdRZPSvgEgqFsptEIR9p7ty\n5IIFDS15Jdu0aQ8ydux4Bg9eD4Bnn32aqVPvY4MNNiGd9vJHJNKbk046jZtvvoUdd9y5VRmpVJp5\n8xYTj8cBuPfeqXz66Zfst593l3bQoLX58MOPgdznzF133ZtLLjmfzTbblrXXXgeAjz76kCVLGqir\nW0RjY4IFCxpazfvKKy9hzMaMGbOsu+XPf34kr732Do8//gj9+/fnpJNOBaC2dm0ikRjz5y/plhxZ\nSuXsFGAz4GxjzJp4r075pqyliUjZjj7/d51a3vQJJ3ZqecurwqmkT8cbBIlF878muJhevXpx4423\nMXny7Xz//XySySTRaJTzzruIzz6bzZ/+9DuefPJRAHr3rmbIkA3Zb78DWpVx0EGH8fDD07nllglM\nmnQbJ530c/r06YPjOC3936+9diy33XYzd9zxa2KxOGuuuRYjRozKG5fjODmHP/PMk+y++16thh18\n8GFcf/3VXHzxyFbDzz//Yt5++82CZcdiMcaMGcfYsWNIpVI4jsPQoRtz4IGHFFhrkkX5UXqUjr6M\nAjr28gnJrztyZC4ff/wRQEvFDGCPPfZi0qTb+O67b8nusrjvvgfw0ksv5CildY675JLLueWWcTz4\n4ANUVlbSv/8qLd31cxk0aDWuuuo6Jk4cz9KlS2lqaqK6ug/jxi27wzdhwi1UV1cDsO66g2loaOCg\ngw5rVY6Xvx/knHMuZPz4GznllOOpqupFVVUvRo0a3W050sn30HuGMSYO/BYYDKSBy6y1rxWYxe3u\nFoZyBaF1ZHmEOf4wxw5dF//hhx/IgiXenbOuqJz1q47SvzrGK6+81K3rvyf/wGaY9/3a2prctcge\noiflRwj9vhra2KF74z/88ANZlKpncWpR2ZWz/hX96EWfwJ+PcwnCvtNTc2QQ1v3yKDdHFr1zZq1N\nAPrRGxHpMsuTHMJ68k4mk1x44dlUVMRaJc511x1c8K6aBIfyo4isCD0xRwKce+65zJv3fctn13Xp\n06eGsWNv6caoup5exyUi0g1isRgTJ94d6sQpIiLSVSZOnNgj82PR3zkTERERERGRrqfKmYiIiIiI\nSACociYiIiIiIhIAqpyJiIiIiIgEgCpnIiIiIiIiAaDKmYiIiIiISACociYiIiIiIhIAqpyJiIiI\niIgEgCpnIiIiIiIiAaDKmYiIiIiISACociYiIiIiIhIAqpyJiIiIiIgEgCpnIiIiIiIiAaDKmYiI\niIiISACociYiIiIiIhIAqpyJiIiIiIgEgCpnIiIiIiIiAaDKmYiIiIiISACociYiIiIiIhIAsVIm\nMsYMAt4E9rbWfty1IYmIiISHcqSIiHSWonfOjDEx4C6goevDERERCQ/lSBER6Uyl3Dm7BZgMjOri\nWERERMJGOVK61OGHH0g8HiWRSC13WTNmPNkJEYlIVypYOTPGnAR8Z619zhhzeamF1tbWLG9c3SbM\nsUO44w9z7NA18cfjUWLRNI6TJl5RUi/kkjmOQywaIR6PAuFe/2GOHcIff09VTo4M+7YOc/xhjT0e\nj9KUbqKJxrLLqIxWURmpLGsdxONRYkSIpJ2WfNEREcdpKSes2yCscWeEOf4wx16uYld7JwNpY8w+\nwJbA740xh1hrvys0U13dos6Kb4Wqra0JbewQ7vjDHDt0XfyJRIpkKo3ruiSak51atuu6JFPpltbY\nsK5/7TvdpycmzTY6nCPDuq0h/PtqWGNPJFI00ciC5oVll9EnmiYSjZW1DjJ5KO26Zd29S7tuSzlh\n3AZh3ncg3PGHOXYoP0cWrJxZa3fP/G2MeQE4o1jFTEREpCdQjpQVbfjU0zs8z12n3NMFkYhIV+nI\nq/TdLotCREQk3JQjRURkuZX8EIu1dq+uDERERCSslCNFRKQz6EeoRUREREREAkCVMxERERERkQBQ\n5UxERERERCQAVDkTEREREREJAFXOREREREREAkCVMxERERERkQBQ5UxERERERCQAVDkTEREREREJ\nAFXOREREREREAkCVMxERERERkQBQ5UxERERERCQAVDkTEREREREJAFXOREREREREAkCVMxERERER\nkQBQ5UxERERERCQAVDkTEREREREJAFXOREREREREAkCVMxERERERkQBQ5UxERERERCQAYsUmMMZE\ngCmAAdLAcGvtB10dmIiISJApP4qISGcr5c7ZwYBrrd0FGA3c0LUhiYiIhILyo4iIdKqid86stY8a\nYx73P64H/NClEUmn+eSTj/n889lduoxIJMLee+/bpcsQEQki5ceV35IlS5g586Wy5//gg/dpamrM\nOW7gwEGss846Rcv4/vv5pCoSuHG37DhEJDyKVs4ArLVpY8x9wGHAUV0akXSaN954jYce+nOXLqOi\noqJV5ezwww8sq5x4PEoikQLgvffebRm+2Wablx1b23KyP5ejUCzZ8c+Y8eRyLUdEwkP5ceW2cOEC\nJk+eVPb8H39saWpqyjluwIABrLnmWkXLmDNnDhX9YvQZVFNWDF9/9DVRJ0qUWNEcnStPLlmymF6r\nVlG1ai9mzepYHl28eAn19fV89/23RJqKL7+Yzro+yEW5W4KipMoZgLX2JGPMIOANY8xG1tql+aat\nrS3vBBIEYY4dWsdfU1NFPB7ly29zt9otr1X6xOjTJ9ZqmfF4lKZ0E02pji1zaWLZ32lSxKpixCqj\nLGVx2fG1LaeiX4y0C67bsdbHVFOaVGOaxY3p/BM1pqmqiNKrMtKp+1A8HiUWTeM4aeIVJR+uJXEc\nh1g0QjweBcK974c5dgh//D1dT8mPEO74y4m9qakP8XiUeQsTLGlMdXj+pc1piDtUrRpvNTwacUhV\nJqhLflu0jBRJIIbj0HK+7ggHiFVGiVfFiubUXHnSaXS8QnBJFUiDubj45ThRUmm3cB4tQSrtEo1X\nEYlVLndZGaXk7jDv9xDu+MMce7lKeSHICcDa1tpxQCOQwnvwOa+6ukWdE90KVltbE9rYoX38ixY1\n0tycIpFMs8qg9Vhl0Hqdspx0OsUXH7xMIpmiuTnZapmJRIolqQYWpzq2HiOOQ9pPBkk3SbwiRrQm\nyoLmhWXH2bacaE2UiAsdrJuRSidILnGZX5/IO43jOPTtnSYWiXXqPpRIpEim0riuS6I52Wnlgpd8\nk6l0yx2/sO77K9txGyY9MWlm60n5EcK/r5YT+/z5i0kkUiSSKYhUsM6Pd+jQ/EsWLyLpLiRakWa1\njdckVumQSiaIRR2qq6up6du34Pyfvfl5S0XJdWk5X3eEC0QqIyXl1Fx50vnBAccBFxLJjiXQlnKc\nCMlUqmAeLUUy5RKpqMCJ913usjL6VRfO3WHe7yHc8Yc5dig/R5bSFP8w8FtjzD/86c+31ua+Ry+B\nteYGW7Px9od2SlnJRDNffPBy0emGTz295DKzuwVetdPVZZXRVttyZs16l2TKJZmC2rWHllTGIxf9\nlopVqunbp5qjz/9d3ukeuv2ksuMUkdBSfuxBKnv3Y5ufntyheermWBYt+hw3Vs8Wh+9KvNpl8YJv\nqYw7DBw4kDXWWLPw/J/XLU/I7RTLqbny5JRDxhKJxHAi0ZJzZ8ac2W+1/F279kYF82gp7rxk+5a/\nl7csgOkTTlzuMkQ6WykvBGkAjl0BsYiIiISG8qOIiHQ2/Qi1iIiIiIhIAKhyJiIiIiIiEgCqnImI\niIiIiASAKmciIiIiIiIBoMqZiIiIiIhIAKhyJiIiIiIiEgCqnImIiIiIiASAKmciIiIiIiIBoMqZ\niIiIiIhIAKhyJiIiIiIiEgCqnImIiIiIiASAKmciIiIiIiIBoMqZiIiIiIhIAKhyJiIiIiIiEgCq\nnImIiIiIiASAKmciIiIiIiIBoMqZiIiIiIhIAKhyJiIiIiIiEgCqnImIiIiIiASAKmciIiIiIiIB\nECs00hgTA6YC6wEVwPXW2sdXQFwiIiKBphwpIiKdrdidsxOAedba3YD9gUldH5KIiEgoKEeKiEin\nKnjnDHgQmO7/HQESXRuOdIeGRfP5/P2XSp4+nUqypL4OmiIkllYwbdqfWsZ9++23JCsSOL3crghV\nRCRIlCNFRKRTFaycWWsbAIwxNXgJ6IoVEZSsWA2L5vPB64+UPL3rpmmon0dz1GFxfYQ///n+lnHf\nffct8b4x+lT1KTueVCpFIpEkmooxa9a7nVbO4sVLIBLFcYq1SSxT9+lccCM4bpTpE07MO92c2W/h\nOOAAG264TsEyN9ts85KX/9577+JU9CVS0bfkeYrJfI+6rz7k+6hDLOKw5557kkikOhRXRke+TyEz\nZjzZKeWIrCjKkVKO5qYlJJsdmpubmT9/XsFpv//+exKJBI2NDtGGaNGcuOmmnXM+7kwL/7uQdNIl\nUf9hwTwqIp6iV6nGmHWAh4FJ1to/l1JobW3N8sbVbcIcO7SOv6aminhFFMdxiEWjxCtyb+5YPIbj\nOLiVP+BGm0taTt/BvYg44EQcvk183TI8SYIKxysvHo92KPbM9E7LEJdUukNF5OGV4+K2lB2JOAXn\naOFArDJKrDIOFQ15J+u9ahU4Do4TIZrne6ea0qQa0yxuLP1LpdJuy0Gab/t1lOM4EG2msn+caASi\nEYf6xMIOlZEmRawqRqwyylIWL1c8ldEqKiOVy3XsrUzHrYRLR3Nk2Ld1mOMvJ/ampj7E41GikRRE\nnA6fhyPRCJmWu4jj4Dh+rxLHwSVSNMe5Lrgs+5dv+ojjEImQM+86/n8diudlx3FwHAB3WZ50Wgop\nPXe2EesVpbK6cB4tGFc6DqkKP0ZvWGfkRO/6KEI8Hi24f4R5v4dwxx/m2MtV7IUgqwHPAGdba18o\ntdC6ukXLG1e3qK2tCW3s0D7+RYsaSTSncF2XZCpFojmZc75kIonrurhAVb9qBm//oyJLcln0w1z/\nwj7CWmutBcDH//wEXD+ZuG6H7sTE49GW6Vs6RLqQSHZC90i/HNcFxwUcSKdLLNf1KmeV/WK4sfyV\nkF4tlbOsLNZGKp0gucRlfn3pPZ+SKZdI2vX6S+XZfh3lui6u00xlv5h3zeBAfaKetFv6uk66SeIV\nMaI1URY0d6xi11afaJpINFb2sbeyHbdh0hOTZrZycmRYtzWEf18tJ/b58xeTSKRIpdM4abfD5+F0\nKt1Sw0q7Xh4CwImQxima49IuLfO7BXJiLOriOE7OvOv6/3Upnpdd18X1g2zJk5maIR3InW3j6xUr\nmkcLxpWoxnHjfozesM7Iid71UZpEIpV3/wjzfg/hjj/MsUP5ObJYs8MooD8w2hhzFd7hub+1tqms\npUng9e5fzUb7bVVwGtd1mTfHEotCPBZlk002BeCbT+Z2ejy1aw8te17HiRKJxHAiUWrXHsqc2W8t\nVyyHjT8577g5s9/ylxVj9cGbtBv/yEW/pWKVavr2qebo839X8jLvvGT7smIt1d5X/JRY1GGrrbbq\nUGX6qp2ubvl7+NTTy17+XafcU/a8IgGgHCnLpViO61VticTiROOVxCuqck5f99VHXRVepyuUR/N5\n5KLfdkEkIsFV7JmzC4ALVlAsIiIioaEcKSIinU0/Qi0iIiIiIhIAqpyJiIiIiIgEgCpnIiIiIiIi\nAaDKmYiIiIiISACociYiIiIiIhIAqpyJiIiIiIgEgCpnIiIiIiIiAaDKmYiIiIiISACociYiIiIi\nIhIAqpyJiIiIiIgEgCpnIiIiIiIiAaDKmYiIiIiISACociYiIiIiIhIAqpyJiIiIiIgEgCpnIiIi\nIiIiAaDKmYiIiIiISACociYiIiIiIhIAqpyJiIiIiIgEgCpnIiIiIiIiAaDKmYiIiIiISACUVDkz\nxvzEGPNCVwcjIiISNsqRIiLSWWLFJjDGXAL8Aljc9eGIiIiEh3KkiIh0plLunH0KHN7VgYiIiISQ\ncqSIiHSaonfOrLUzjDGDV0QwEh7NTUtIONDkOMya9S4A8+fNI5FIEGlyiC2NtgwvheM4uK7rfXBZ\n9vdKou7TueBGcNwo0yecWPJ8zU0NJBPNOIu/585Ltm8ZXrv2RuXH8tWHVPaPUVHT/Y+cfv3R10Sd\nKFFiHH74gWWVEY9HSSRSAMyY8WRZZZS77I4qNz4JLuVI6W6ZfOzg5My7qVSKRCJJNBUrmpcXL14C\nkSiOU/TyMDQK5dw5s9/iawccYMMN18k5TUPDkpZrkurqPiUtc7PNNs85vJwcsLz5KZMj33vv3bxx\nLS/lts7VJUdfbW1NVxS7QoQ5dmgdf01NFfGKKI7jEItGiVfk3tyxeAzHcbwPDkQiTsFlZOpNjhMB\nJ0oq7X1OL6tb4ULL8NK4Of8uFktBjv8vu5y2n8spo9C0+aZzIFYZJVYZh4qG0pYN9F61CifirefE\nogTRyiixqliHymgfy7IN4zhOy7aPx6OlF+H/18Hp0Hy5yolVRolXxVhaZq+wpQmojFZRGaks+/iN\nx6MsbUrT2Jwqa/5iqiqi9KqM5I0v7OcdKV3Yt3WY4y8n9qamPsTjUaKRFEScvHk0n0g0Ao539R9x\nHBzHy2+ZLFE8rzhkT5FveseJ4DiRInnXLZqXXdz2sXUkDxZR1vxO61yVuVwpdVs4jgPRZtxIot24\n3qtWeevYiRDNl8saIVYZI1YVI1pRON85jkPEoV0+W54cFY9HaUo30ZRq7PC8sCxHRiIOybTTqXmu\nWG7rDGE+55SrI2eZko+ourpFZYTS/Wpra0IbO7SPf9GiRhLNKVzXJZlKkWhO5pwvmUjiuq5XJXIh\nnS5812rZXS2/0pD0PqezynBdt2X48igWS0GZWmJ2OS7gdKDcXGUUmjZf2a5XCansF8ONlV4J6dWS\nOByaFzcSq4p3uIx2oTgpMl/Kdd2Wynbm7lNJZfj/dXE7NF+uciKVEaI1URY0LyyrjIjj0DuVJhKN\nlX38JhIpFi9NsnBJ11TO+lWniUVyxxfm805PTJoFlJQjw7qtIfz7ajmxz5+/mEQiRSqdxkm7efNo\nPulU2mvRdP0c2ToVlZBX3FZNl/mnj4ATK5x3XYrmZdcFp20u60geLKKs+TO9adxM3vIGl7otXNfF\ndZohtqTduOwcm+8Qdn5wiPeOU9WvEidSrHLm/Wubz/pEy89RiUSKJakGFqfKO/YyOTKddlm8NNGp\nea5QbusMYT7nQPk5siOVs5Wrn5l0mtq1hwLQq88XRKL/JRqvIFZR1TK8FJGIs9wn/bA4bPzJJU87\nZ/ZbRCIxnEiMx899rKwy2ppyyNiy5+1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"text/plain": [ "<matplotlib.figure.Figure at 0x120b31b90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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GtRs5fvw4+fKFULr0LTz00CNUrx6W7LQHDuxn4sRfWbRoIXv27OHcubMULnwT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\n3EHJkjf7N3LVqtXA6/WyYkX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"text/plain": [ "<matplotlib.figure.Figure at 0x13cbb0090>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x120838d50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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pj9WXH1BERCTklCNHDhVnIhIJ3X4X7en+b5yKZTwyA/hJkDFxqKd04pk//3lm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\nF3X1oysYL0W6J0Vj4xg6OtoZs8oYGia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"text/plain": [ "<matplotlib.figure.Figure at 0x120838ed0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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oqVN3Kzm+1D4qNmdO12218cabVLzOQtnFixcxftXxpMYma9r2ixcvYszYpqrL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\niIgASogiIiIA/H85bbyOgL7ezwAAAABJRU5ErkJggg==\n", "text/plain": [ "<matplotlib.figure.Figure at 0x11f40f6d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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WyWSJJwcQS1S2XG89DaiIM7Ay1iXHR3895vqT/lan/lafchTrIZ4KZMzsy8Au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\ndiGvPtgmFNIX24RCymoTyk2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"text/plain": [ "<matplotlib.figure.Figure at 0x12b0352d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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WpS/md9V9lE+b/dqFmaWAk4spXESkq1pXf+0inU5z0UVj1/iKx2abDVrtbtXu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\nzk0E5gKlDd7vku1CE/Xpim1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"text/plain": [ "<matplotlib.figure.Figure at 0x12829ef10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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PItJf+itHtrQ0M2XKOUyZMrWtiHnnnbe55ZYb2GqrbbKO9/jjj3LUUcfy8MMP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\nLYH7jTEHWmu/zDVSbe2K3oq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"text/plain": [ "<matplotlib.figure.Figure at 0x13d026f50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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XPXr0CDNm/MDy5cs4cOAA58+fo2jRm6hTpx6PPvp4gnEDn3vuKTZujOD772f6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\nfPEVypUrz7p1a3jrrdcu+TM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"text/plain": [ "<matplotlib.figure.Figure at 0x121ef3610>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x11e56bb90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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iIvI99803y2pyEMDVV1/B8OGD+cUvBmbMYS+99AJHH30c0WiU44//KQsXPg24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\nPuvXWjpsK81tagbtyH4uocwBU23blZLiASimp6d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"text/plain": [ "<matplotlib.figure.Figure at 0x11e56b090>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x13dd4e990>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x120b25790>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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g+VCyohU1Arxkw5++H8TgeT6e55NINp04dzJsOm0iCb7n4fvBCUIxfM8jGvGJ\nxSIdsn90932utXpbfaD31am31acYhdxUcySwkZldAawEEgQ312RVWVnVPtF1AxUV5d2+PrW19cTr\nE8Trk3zfjSQaK806rR/xWfT1x9SsWE60NEnftfuw1tCKhvH1dTUkEnV4XiRMiPlVL1rBki8WE+0T\npXRQCZ4XdLNGkh54XtCCiy5vVZ1SXoJ0oksm0wnPBy9KvL41CbBpwRCvTxGNpPA8j3g8UVQxyVSK\n+kSSOIl23z96wj7XGr2tPtD76tQb61OMQlqIDwJ3Oef+EU5/qpnVFrU06XDDdz2Msr4Ds46PlURZ\nMP9FFn/zMamSRayzyfcY8evdGsYv/NKoq12O50eJRkup2OhHeZf5ycvGZ//8jEi0hEi0hINmjgPg\nywVv4PtRPD/KeoOHtaoes351edZxTWPyfS8jabYsSPBRvAKTvIisfgq5qWYFcGgnxCIiItJl9GC+\niIgISogiIiKAEqKIiAighCgiIgIoIYqIiABKiCIiIoASooiICKCEKCIiAighioiIAEqIIiIigBKi\niIgIoIQoIiICKCGKiIgASogiIiKAEqKIiAighCgiIgIoIYqIiABKiCIiIoASooiICKCEKCIiAkA0\n10jnXBS4ExgClACXmtkjnRCXiIhIp8rXQjwSWGhmuwL7Ajd2fEgiIiKdL2cLEZgD3B/+7QPxjg1H\nRESka+RMiGa2AsA5V06QGM/tjKBEeor5899m9Oj92q0sAN/3GDZs83YpE2Du3L+3W1kivVm+FiLO\nue8BDwI3mtmfCym0oqK8rXF1K929PqWlUaIRH8/ziMWixEpyb9ZoLILneaTw8LzgANxUekhL45pN\n63sNM3hN5/EKL6dZAE3nzVFW3vIzygPwPC9cX5HWxZVenucRjfj4vkd90mNlXaKocjIlkikisTKI\nlrJ8ZbLN5ZWVROhT6neL/bc7xNDeeludelt9ipHvppp1gSeAk8zs2UILraysamtc3UZFRXm3r09t\nbT31iSSpVIp4vJ5IXX3WaWMlUerjCVKpFJAilYJkMtVsuvSQlsY1mzaZapgh1XSeFOAVVk6zAFJN\nYshSlu97+cvPKA8glQrqHo8Xl8iSqRT1iSTJZIrlNXGWVrc9IdYnUvglJXixASxa1varEwP7JYn6\n0S7ff3vCd6i1eludemN9ipGvhTgVGASc75ybRnBI2dfMaotamkgvdvCp/69N8980Zbt2K+v+649u\n0/wiq6MyhcAkAAAI20lEQVR81xBPA07rpFhERES6jB7MFxERQQlRREQEUEIUEREBlBBFREQAJUQR\nERFACVFERARQQhQREQGUEEVERAAlRBEREUAJUUREBFBCFBERAZQQRUREACVEERERQAlRREQEUEIU\nEREBlBBFREQAJUQRERFACVFERARQQhQREQGUEEVERIACE6Jzbnvn3LMdHYyIiEhXieabwDk3BTgK\nWN7x4YiIiHSNQlqIHwKjOzoQERGRrpS3hWhmc51zgzsjGJHOUFdbTdwDD4933nm7qDI+f/dzUokU\n9bX1/OfdN/G8CPdff3TRMVV+8T61NVXUrlxONPZdm8oC+HLBG/zPAw8YPXq/NpXV1Ny5fy963nQs\n8+cXt94LsfnmW7RreW2pr/QseRNiMSoqyjui2C7T3etTWholGvHxPI9YLEqsJPdmjcYieJ5HCg/P\nA9/3mk2THtLSuGbT+l7DDF7TebzCy2kWQNN5c5SVt/yM8gA8z8fzfBLJ1oWVKVoWpaR/DD8SA8+H\nkhXFF+YlKelXQrRPFD9S0rayAD/qES2NECuLUtNOVztKI2WU+qVFfR/S88RiEWpqkySSKSJlPpHS\ntt3X53kevgcrl9YSLYsSLY10Wn27+3GhtXpbfYrRmoRY8BGtsrKqiFC6p4qK8m5fn9raeuoTSVKp\nFPF4PZG6+qzTxkqi1McTpFIpIEUqBclkqtl06SEtjWs2bTLVMEOq6TwpwCusnGYBpJrEkKUs3/fy\nl59RXjgXeFHi9a2MK0O0LErZwFLwvOAEI1r8gTjlJYj1i9FnUBme77epLAgSYqxvjLJBZSypW9qm\nstL6R5L4kWirvw+Z36F4PMHymnrqEykiMY9oedvOyT0v+Fe/pJ5YSZRIeaRT6tsTjgut0RvrU4zW\n7I3FHzlEuqmKjX5U1HyeF8H3g6/PrlN2xfOjrDd4WNFxzPrV5Q1/73rm7m0qC+C2/S9p+Hv8nSe0\nqSyAW467rc1lNFW+xgYQq2bUNccWNX/lF/8hGoFoxGPOCQ82DO+u9ZXur6CEaGafAjt2cCwiIiJd\nRg/mi4iIoIQoIiICKCGKiIgASogiIiKAEqKIiAighCgiIgIoIYqIiABKiCIiIoASooiICKCEKCIi\nAighioiIAEqIIiIigBKiiIgIoIQoIiICKCGKiIgASogiIiKAEqKIiAighCgiIgIoIYqIiABKiCIi\nIgBE803gnPOAm4DhwErgt2b2UUcHJiIi0pkKaSGOAkrNbEdgKnBNx4YkIiLS+fK2EIGdgccBzOxV\n59y2HRuStMVrT9yGH8m+WSMRn8Xffs7ShZ9TVgFf/+cTnrthbsP4RH0dyUQcPA/fj9K3/Iu8y6xZ\nvLxdYhcR6UpeKpXKOYFzbhbwgJk9EX7+BBhqZskss6QqK6vaM8YuVVFRTnevz9FHH87X337H0hWJ\nvNP6vk911WIWL/ySPhUl+FGv2TTJRD3g4/mFX2Je9r+llJaX0GfNPnheMF8qmQDPw/M8oPlyclny\n2WJKy0spG1iK50faVFbT8oA2ldWsvDaWlS6vpH8JfQaVheu9+LJWxVdC2cAyItFIm8oC8P0Iy75c\nxsplK+nXr38r5/VIJoPjTHX1cqKxUurqaulX0Y8+a5S1KS7PC/4t/XwZfQaWUTKgpNXxZdM/Uk55\nZABz5/692biecFxojV5Yn6K+QIW0EJcB5Rmf/RzJEMCrqCjPMbrn6e71efTRR7o6BJHVTnc/LrRW\nb6tPMQppArwE/ALAObcDML9DIxIREekChbQQ5wI/c869FH4+tgPjERER6RJ5ryGKiIisDvRgvoiI\nCEqIIiIigBKiiIgIoIQoIiICFHaXaTP53m/qnNsfOB+IA3eZ2e3tEGuHKqBOhwGnEtRpvplN6JJA\nC1ToO2idc7cCi8zsnE4OsVUK2D4jgKvDj18DR5pZXacHWqAC6nMEMAmoJ/gO3dIlgRbBObc9cIWZ\n7d5keI87LkDO+vSoY0JatvpkjO8Rx4S0HNun1ceEYluIWd9v6pyLhp/3AnYDTnDOVRS5nM6Uq05l\nwO+An5rZLsAg59wvuybMguV9B61zbhywWWcHVqR89bkNOMbMdiV41eDgTo6vtfLVZzqwB8GrE89w\nzg3s5PiK4pybAswCSpsM75HHhRz16YnHhKz1yRjfk44J+erT6mNCsQmx0ftNgcz3m/4Y+K+ZLTOz\nOPAisGuRy+lMuepUC+xoZrXh5yjBWX13lqs+OOdGAiOAWzs/tKJkrY9z7ofAImCSc+45YE0z+29X\nBNkKObcP8G9gDaBP+LmnPB/1ITC6heE99biQrT498ZgA2evTE48JkKU+xR4Tik2IA4ClGZ/rnXN+\nlnFVQE84u81aJzNLmVklgHNuItDPzJ7qghhbI2t9nHPrARcAJ9PWl2Z2nlz73NrASGAmQQtkL+fc\nbp0bXqvlqg/Au8AbBG+G+puZLevM4IplZnMJunmb6pHHhWz16aHHhKz16aHHhFz7W1HHhGITYq73\nmy4j2PnTyoElRS6nM+V8Z6tzznPOTQf2BA7s7OCKkKs+BwNrAY8CZwOHO+d+3cnxtVau+iwCPjSz\nD8ysnqDl1d1/lSVrfZxzmwP7EXTxDAHWdc6N6fQI21dPPS5k1QOPCbn0xGNCLkUdE4pNiLneb/o+\nsKlzbpBzroSgW+TlIpfTmfK9s/U2gms+ozK6SbqzrPUxsxvMbISZ7QFcAcw2s3u6JsyC5do+HwH9\nnXNDw8+7ELSwurNc9VkKrABqzSwFfEvQfdqTNG1l9NTjQlpLraaedkzI1Kg+PfSYkKnp9inqmFDU\nXaa08H7T8I6rfmZ2u3NuEvBkGOTtZvZVkcvpTFnrRNB1dSzwgnPuWYLrOdeb2UNdE2pBcm6jLoyr\nWPn2ud8A9zrnAP5pZo91VaAFylef24AXnXO1wALg7i6Ks1gpaLgTsycfF9Ia1YeeeUzI1Gz7dHE8\nbdXS/tbqY4LeZSoiIoIezBcREQGUEEVERAAlRBEREUAJUUREBFBCFBERAZQQRUREACVEERERAP4/\nYif96A/JDf4AAAAASUVORK5CYII=\n", "text/plain": [ "<matplotlib.figure.Figure at 0x13d53f450>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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UKVOprKyirm4xJ574czba6L8A8ubuO++8jUwmwy23TAfgm2++Yfz407n22t+w\nxhprMmHC2YwffwGbbLIZAFOn/po777yNE08cC7jxffrpHCZOPI+JEy9vXR49qWRxZoypBLDW7tHj\n0YiIrCQvvfQC22wzinXXHQ64nfpFF11Obe23PPFEx/vwZ8z4MyeffBr9+/cHoKKigrFjz2DKlKvY\needdeemlv7X5LZZddtmNrbbauvXzkiVLuPLKSzj44J+0Xm0r5OGH/4djj/1Fa2EGcNhhR5acp7fe\neoPhw4dz4IGHcPnlF7UWZ8OGDeOFF55l3XWHM3LklpxyyunEYu59UNXVg3jwwfvZddc92GijEdx7\n7wwSCd1UUQ7lRxFZFb388ktss812bQqRTTbZjBtvvJW77spf3C1btoy33nqdP/zhAX72s8Opq1vM\noEGDGTp0KB9++AHPPvs02247ip133pUddtgJgGHDVuMvf5lFv3792HTTzbn88quJx+MF43r00Zkc\ndtiRVFZWATBo0GCmT7+HgQMH8v777+Zt8+STf2HGjFmtn9daay0OPvgwHn/8UbbZZhRrrrlWa2EG\ncMopp5HJLL8N9JNPPmbixPOYNOlaRozYuIylt+LKycBbAgOMMU8CceBCa+1rPRuWSO955523O/0A\nbqEHbvWwbXjNmzePddZZt023qqqq1lsB2/vqqy9bC7msddZZl2+++YbFixe1KaSyBg0a1Pr3FVdM\nZLXVVqe2trZkbF9//RXDhw9v/Xvy5MvwfR/f91vP/uXz2GMPs+++B7LeeuuTTFbwwQfvsemmm3P4\n4UcxaNBg7rvvD3zwwQS23HIrzjrrPNZYY01uuOFm7r//Xi699EIWLVrIAQcczPHHd/7sbB+l/Cgi\nq5yvv/6yNQcBnH/+2SxdupT58+ex5ZZb5813zz77JLvssjvJZJI99tiTWbMe5qijjmWTTTbjvPMu\n5JFHHmLq1CmsscaanHrqGWy11daceuoZzJw5g9tuu4VPP53D6NE7ceaZ4xk4cGDeuFzebpuHCw0L\nsHDhQgYPHtx6MjJrnXXW5f3332XevNoOxwG5xwANDfVMnnwZiUSCJUtW3u+tlVOcNQDXWWvvNMb8\nN/BXY8x3rLWZQg1qaqq7LcCVLcqxQ7Tj747Yk8k4CWLEMh7JZOGzL/nEPI9EPEYs5pHKeDQ253+7\nUV6NbTeHqoo4/Spjkfo+eivWFfnOcseRlf0ek8l40Xn6znc24r333mszzNy5c2lqqsvbdvjwdWhq\nWsxGG60Oe+jjAAAf7ElEQVTd2u2jjz5ivfXWZeON16OhYWmHNrNmzWLMmDEkk3EmTDiP0aNHc8gh\nh7DLLqPZdtvl96y3b7fhhuuxdOkCamqqqakx3H//fTQ3NzNmzBhqaqpJJuMMGzagTbu6ujpee202\n9fVLePTRGTQ1LePxx2eyyy47MHv2bI455giOPfantLS0MH36dG677UYmTZpEY+NiLr74AgC+/fZb\nxo0bx/bbb8Nuu+1WxpLv8/pUfoRoxx/l2EHx96bejL03cuTGG2/Iu+++29r/jjvc1bLDDz+cZNKj\nurqqQ9snnniMRCLBBRecRWNjI9988w1nnHEq1lq22moz9tprNwBeeeUVxo8fzyuvvMJLL73EKaf8\nilNO+RXLli3j6quv5oEH7uG8885rHW/udDbaaH2amuradHvzzTdZffXVqa6uon//ijb9hgypor5+\nKautNqBNgbZo0beMGLEBm266MbNnv9imzaJFi3jrrbfYfffd8TyP6dNvY8GCBYwbN44HH3yQYcOG\nlb3cu6qc4uwj4BMAa+3Hxpj5wNrAl4UaRPXXvKP+S+RRjr+7Ym9pSZNKZ8j4fsFXxxaS8X3XNuOz\ndFkLi+vLb+95Hn7OA7eDB2RIxBKR+T56c93J/c5uOmZap9vHvI4PO6fSGVpIF52nkSO35be/ncaP\nfrQ/6647nFQqxWWXXcmoUdvT3Jzq0Ha//Q7hyisnM2nStfTvP4CGhgYmTbqK/fY7mIULl7Httjsw\nbdp0Dj30CMC9POT++//IDjvsRktLmmHD1mbZMp/zz7+Us846mzvv/CNDhgzJu+z32ms/pky5inXX\nHdF6hnL27FfIZHxqa5fQ0pJmwYKlDBy4vN2MGX9mn33255RTTgPcMwOHHXYAH3/8BXfccReffPJ5\n6+2Ua6wxnA8++IivvprPaaedzu23/56hQ4fh+5VUVw+hoaHj/OcT5YOtbtJn8iMox/Qmxd97ejv2\n3siRW265PdOm3caOO85uvbVx7twv+Oqrr1l33fWpq1vWpu2cOZ/Q1NTCzTff0drtrLNOZebMx/ny\nyy/47LN/MX78hXiex9Cha1FZWUVt7RKuuuoaGhszrY8A1NSszeLFi1vH3X7Z77773kybdjMjRmxG\nVVUVCxcuYPx4d8vhkiWN1Nc3dZinXXf9AZMmXcOJJ47F8zy+/HIuf/jDH7nuuqmsueZafP75F7z0\n0mtssslm+L7P1KnXU1lZxRZbbEu/fv2IxwdQUzOAAw88lNNPP7P12blydDVHllOcHQ+MBMYaY9YB\nqoGvuzQ1kYj5yel3lzVcsiJBS3MKgAenFn4Dn+RX4VUysGsnBEnE878muJT+/Qdw4YWXcu21k/B9\nn4aGBnbeeRd22GFHpk27iV/96mf4PngenHrqmey00/dpaKjn7LPHEYvFyWTS7LffQey++w8BGDfu\nDG666QZOPvl4wGPQoEFMnnwd0PYh5c0334IDDjiYyy67kBtuuCVvbMZswtixpzNp0qWk02kaGhpY\nY401mDTp2tZhJk6cQEVFBQBbbbUNr7/+GhMnXt7av7Kyil133YNZsx7m3HMvYMqUq3nggT9RWVnJ\nkCFDOeecCQwbthpnnHEu48efQSKRIJ3OsOOOOzNq1PblLXxRfhSRHreyc2S/fv245pobmDbtRhYs\nmE8qlSIej3PaaWfx6adzuPfeu3n88UcAl0tHjNiYvffep8049t33QB566EGmTJnKzTffwHHH/ZSB\nAwfieR4TJ14BwBVXXMUNN1zHLbf8hkQiyTrrrNvm2e32tthiJPvvfxBnnnkK8XiC5uZmTj75NEaM\n2BhrP+SJJx7njTf+0Zq7b7rpdk466VTuuut2TjjhOCoqKkgmk0yYcDFrrbV2EMPVXH/9NTQ2NtLY\nuIzNNx+Z8+Kt5bn7iCOO5h//eI3f//4Ojjvul2Ut+67y/HbVdHvGmCTwO2ADIAOcZ619tUgTX2dH\nekeU4++u2A866McsSdexNL2k0281uvX42xkYr+az9z9jvRGbsbg+3eXibPCAOEMGJCLzzFlvrjt9\n/Qc2I77ddnw1Vh/Sl/IjRH5djWzsoPh7U2/H3pdzZG8v+xXV1RxZ8sqZtbYFOLorIxcRKWVFk0NU\nd96pVIozzxxLRUWiTeJcf/0Nip45lPBQfhSRntbXcmQ2N3qe16aw7Eu5Ue9LFhHpBYlEgptuui1y\niVNERKSnZHMjRK+w7C6x0oOIiIiIiIhIT1NxJiIiIiIiEgIqzkREREREREJAxZmIiIiIiEgIqDgT\nEREREREJARVnIiIiIiIiIaDiTEREREREJARUnImIiIiIiISAijMREREREZEQUHEmIiIiIiISAirO\nREREREREQkDFmYiIiIiISAioOBMREREREQkBFWciIiIiIiIhoOJMREREREQkBFSciYiIiIiIhICK\nMxERERERkRBQcSYiIiIiIhICKs5ERERERERCIFHOQMaYNYDXgR9aaz/q2ZBERESiQzlSRES6S8kr\nZ8aYBHAr0NDz4YiIiESHcqSIiHSncm5rnAJMA77q4VhERESiRjlSRES6TdHbGo0xxwHfWmufNsZc\nUO5Ia2qqVzSuXhPl2CHa8XdH7MlknAQxYhmPZDLeqbYxzyMRjxGLuX89L0Oyoqw7f920g2G9YDzJ\nZDxS30eUYm0vyrFD9OPvq7qSI6P+XUc5/ijHDoq/N0U5doh2/FGOvatKHXn+HMgYY/YEtgLuMcbs\nb639tlij2tol3RXfSlVTUx3Z2CHa8XdX7C0taVLpDBnfp6Ul3am2Gd93bTPuX9/3aWlOldU2WZFo\nHdYPxtPSko7M96F1p/dEOf6+mDTb6XSOjOp3DdFfV6MaOyj+3hTl2CHa8Uc5duh6jixanFlrd83+\nbYx5HjixVGEmIiLSFyhHiohId+vMq/T9HotCREQk2pQjRURkhZX9QI21do+eDERERCSqlCNFRKQ7\n6EeoRUREREREQkDFmYiIiIiISAioOBMREREREQkBFWciIiIiIiIhoOJMREREREQkBFSciYiIiIiI\nhICKMxERERERkRBQcSYiIiIiIhICKs5ERERERERCQMWZiIiIiIhICKg4ExERERERCQEVZyIiIiIi\nIiGg4kxERERERCQEVJyJiIiIiIiEgIozERERERGREFBxJiIiIiIiEgIqzkREREREREJAxZmIiIiI\niEgIqDgTEREREREJgUSpAYwxMWA6YIAMcJK19v2eDkxERCTMlB9FRKS7lXPlbD/At9buDEwEJvds\nSCIiIpGg/CgiIt2q5JUza+0jxphZwccNgYU9GpGIiEgEKD9KV73++j9YsGB+2cMPHtyfxYsb2nTz\nPI8999y7u0MTkV5WsjgDsNZmjDG/Bw4EDu3RiKTXHXTQj7tlPO+88zYjR363rGGTyTgtLek2bbPK\nHUe2XXJQgsSgePmBhkx3LX+AmTMf77ZxiUhHyo/SFbNmPcLbb/9v2cO3z5EA8XiiW4qzcnPOO++8\nTX390tbPAwYMLHsasZjH5puPBDqXl1YkH3b1OKK9l19+scttRbqirOIMwFp7nDFmDeAfxphNrbXL\nCg1bU1PdLcH1hijHDt0TfzIZZ1lThsbmdOmBC6iqiBOLeWTiKZrSjSWHX9bS9nOGNImqBInKOMtY\nmr9RHhnS+F4cD49ksnMFWszzSMRjxGLuX8/LkKwoexNpHdYLxpNMxrv0fXTX8u9XGevU9KO87kc5\ndoh+/H1dX8mPEO34wxR7//4V+PE085rmldcg1fbjoOQghlQMWak5P53xSfRLkOyfBGLEy8yxnucR\n8yATT1EZq+xUzMlknKZMU1nHEe119TgiqzJeRWWsEgjXutMVUY4/yrF3VTkvBDkaGG6tvRpoBNK4\nB58Lqq1d0j3RrWQ1NdWRjR26L/6WljRLl6VYXN/14mDwgAyZjE99cwNL06VjinkeGd9v/ZzyUyQr\nEsSr4yxqXlz2dFN+iqSfwMfvcJaxlIzvk0q7uFPpDL7v09KcKt0QV5hlh/WD8bS0pLv0fXTX8k/E\nEmVPP8rrfpRjh2jH3xeTZq6+lB8h+utqmGJvaGimpSVFKpNi/ZHrMWDYgKLDx2Mx0pkMftrnw5ct\nLakUzc1dyzHtlZtzUmmfxIAEVUOr8DwP8Moav+e5HF/f3EAsXn5eysZWny7vOKJDvF08jsgaGM8Q\ni7vD5DCtO50VtnW/M6IcO3Q9R5ZzWeAh4HfGmL8Fw59urW3q0tQkcn5y+t2dbvPg1GM7dDvprhOK\ntml/y8bFO15adttcue1WBd21/EWkRyg/ygobuddI1tt8eNFhsjky3ZLmw5dtj8VSLOc8OPVYFi58\nD4Bdxu/OWhtsXnJ8tXM/JBGH5yY/t8KxdeZYALp+HAFw6/G3d2p4ke5UzgtBGoDDV0IsIiIikaH8\nKCIi3U0/Qi0iIiIiIhICKs5ERERERERCQMWZiIiIiIhICKg4ExERERERCQEVZyIiIiIiIiGg4kxE\nRERERCQEVJyJiIiIiIiEgIozERERERGREFBxJiIiIiIiEgIqzkREREREREJAxZmIiIiIiEgIqDgT\nEREREREJARVnIiIiIiIiIaDiTEREREREJARUnImIiIiIiISAijMREREREZEQUHEmIiIiIiISAirO\nREREREREQkDFmYiIiIiISAioOBMREREREQmBRLGexpgEcBewIVABTLLWzloJcYmIiISacqSIiHS3\nUlfOjgbmWWt3AcYAN/d8SCIiIpGgHCkiIt2q6JUz4AHgweDvGNDSs+FI2Lz36sxOt6mvqyXTGKOl\npZm6ujqoyvRAZCIivU45Urrk00/nsGDBfFqqmlm4YD6V3yaLDh+Px0inM1RWVK2kCEWktxQtzqy1\nDQDGmGpcArpwZQQl4fH+aw93us2i2n+z2INMOs282m/pv1p/3n337aJtPM/D9/3Wz+l0mpaWFBVU\ndHr6LS0p4ulEyWm2V1+/lMV1i6mvX8rcuXOJVQzq9LRXlgenHluw35dz3uArDzxg443XKzqekSO/\nC0AyGaelJd2h/8yZj69QnCKrMuVI6apPP53D/EXzqaypYMGCBcS+9YoOX19fj+9DIhbni/e+wE/5\n+On8+/j6+qWtfw8YMLBkv/r6pRBL4Hnxormldu4HxPtnICdXl9LcVE+LB/P/tYB5mfn4Kb9kXoLl\nuemdd94mOShBYlC8Tf9y8nv2OCKeTvDqq7Nbuw8cOKBk2+zxwCYbbFZyWJHuVurKGcaY9YCHgJut\ntX8uZ6Q1NdUrGleviXLs0D3xJ5NxEvEMnueuePnJevzk0hKtlltt4yHgeSz+92J8wAfSJS+eddzZ\ne4CHRzIZ7zh4AV7wH2VNs2MIGd9vE0myouQm0mFYz/NIxGMkk/EufR+5y7/Q9D3Pg3gzfqzjifr+\nw6rA8/C8GPECyy7dlCHdmGFpY7CQGtsurKqKOP0qY5HZHqISZyFRj78v62yOjPp3HeX4wxR7IhHD\n9yGTgeaUT2Nz8YInnfGJx2L4wdMoiaoE8co48YqO+/iqyiqXI+iYA7xGj3hl3LUP2rrh4+DFoKKh\ncBBeBmLLi8hYrHhB2drMy4m5In/My4f1iHmwDHfMkSGN78U7HAt4nkcm43J2aT4+Pp4Xw/NiZR4b\neHgerdMM07rTFVGOP8qxd1WpF4KsCTwJjLXWPl/uSGtrl6xoXL2ipqY6srFD98Xf0pImlc60Xsny\nfR98MHttVVb7hbWfUTd3CYu/WEy2OmtJlX+mLcs19fNe0Snexv3V2WlmfJcAXcJ0KbClOVVW22RF\nonVY3/dJpTO0tKS79H3kLv9C0/d9H99rhkR9h379Wouz3FK1rXSmhVS9z/w6V9y1v3I5eECGRCwR\nie1B223v6YtJM1dXcmRUv2uI/roapthTqUzrBajKfoPoP2j1gsO2NDXQ3FSP78VI+26fHu+XoN/g\nSrxYx0InnvEK5gBvoUeyf5KqnLa5w/uJwidifS/tirhAJlNujo2B54qzqgIxt8bnQmFR82IAUn6K\npJ/ocCzg+z7pjE+qnMMD313s87wYeImyjg183x0LZKcZpnWns8K27ndGlGOHrufIUpcFzgeGABON\nMRfjjnvHWGubujQ1ibRN9/5eWcN9OSfDF7O/5N/8m3iygniygprhmxRtE4t5rTv62rkfrnCsWaWm\nm6uicjYVQwew5D913Tb9leHA63/e5vOXc94gFkvgxRKstcHmHYZ/+KzfUTF0AIMGDuAnp98NtC0u\ni93WIiJtKEfKCqvsX82AIsVZw5L51NfVtn72vDjxWAIvHuewm8d2GL5YDpi+/1VBvziH3TK25PDt\n266I9tNtr3buhyTikIh7bLGFu63x4h0vLTneQnne8+Kt0yxn+Nw4RHpTqWfOzgDOWEmxiIiIRIZy\npIiIdDf9CLWIiIiIiEgIqDgTEREREREJARVnIiIiIiIiIaDiTEREREREJARUnImIiIiIiISAijMR\nEREREZEQUHEmIiIiIiISAirOREREREREQkDFmYiIiIiISAioOBMREREREQkBFWciIiIiIiIhoOJM\nREREREQkBFSciYiIiIiIhICKMxERERERkRBQcSYiIiIiIhICKs5ERERERERCQMWZiIiIiIhICKg4\nExERERERCQEVZyIiIiIiIiGg4kxERERERCQEyirOjDHbG2Oe7+lgREREokY5UkREukui1ADGmHOB\nY4ClPR+OiIhIdChHiohIdypZnAGfAAcBf+jhWKQbzJ37BY8++vAKjePLL+fS7FeS9vqRTCa7KTIR\nkVWScqSIiHSbksWZtXamMWaDlRGMrLiFCxfw9NNPkM74+H7XxjF/wQJiFYNIVCV7pThrbqoH38f3\nfdLpDO+++3bZbdPpNHE/Dr77uzNqP/kG/Bh+Jk1DXS3e0gU8OPXY0u3mftD6d83wTamd+wEL4h6J\nmMdBB/247OnPnPl4p+LtKYXif+ed8r+HrJEjv9uhW1jmU6Q7KEeKdI/mpnpaPPDwWvN+Op2mpSVF\nPJ1ocyywdGk9xOJ4XjnXGDofx8LPFpBJZXj1678zdOhQMpnSB1Tt811nc11njheKUY6Nvu5fq4Ga\nmuqeGO1KEeXYAYYOHUAyGad2fhPLmjJdGkdzS4aqCvA88Nz/8D2IxbzyR+K1/aOctm2Hyf7tk+7a\nbJQ93dxJJirjJPr1IxZPgBeHioYy2mWIV8ZJVCWgooHKIUniMYjHPJaVcadTZbyKylhl67qXTMZJ\nxDN4XoZkRf5NtOT3EnQq1M/zPDzPazP+3L9j8UpiFVUsbVy+8NMZn3hVjHhl6UdVPc8j5tFm/tvP\nZ3eK+nYb9filfFH/rqMcf5hiTyRieMHu2fO8ormqdTjAz/7huX8KtiuUA7wC/YrljHbDZJWdX722\n/xaf1xieF8uT99seC/j4raMtugzyHFaUF7dHol+SRGUCryJGvNiQ7fJdV3NdMhmnKdNEU7qxU+2y\nik03TOt+Z0U59q7qTHFW9lFube2SLoTS+2pqqiMbO7j4Fy6sp6UlTTrtM2DwGpitx3RqHEsW/Ydv\nv3gf33dX3vzgChY+ZZ05auW3/aNU21jM6zhMkIlaUl28BFjGdNvwXXFWUe0qU8/z8BOliyvfS5Po\nl6RyUAI/sZTKwYmgsIVFzYtLth8YzxCLJ1rXvZaWNKl0Bt/3aWlO5Z9mqe/FB7zC/Vzb5eNPViTa\nTMuLV+IlBzG/rqW1WyrtE096JKpL7zbyzX/7+ewuq8J2G9X4+2LSLKKsHBnV7xqiv66GKfZUKtN6\nd4vv+0VzVetwrR3cfz5FclyhHOAvH1GbfsVyRu4wOcrOr37bf4u3i4GX6Jj32x0L+D54pWLOmdfW\nz6XmMUeiX4J+g6vwYsVKs475rqu5rqUlTX26gaXprq2nhaYbtnW/M6IcO3Q9R3amOOv6EbL0in4D\nhzJi5O6dajPvy496KJqu8WJxaoZvUv7wXvGdaLl2OXcXvFiCtTbYvOSw0/e/qvXvA6//ObVzPyQR\nh0TcY4stOt7Wl+vW429f4Vh70k9Ov7v17wenHoufrIdkPQde//OCbfLNf9jnU6QbKEeKdJNs3ve8\nOLFYosOxwJdz3lgpcXixOEdMO7VgQdc+33VXrjvprhM6Nbxy7KqlrOLMWvs5sGMPxyIiIhI5ypEi\nItJd9CPUIiIiIiIiIaDiTEREREREJARUnImIiIiIiISAijMREREREZEQUHEmIiIiIiISAirORERE\nREREQkDFmYiIiIiISAioOBMREREREQkBFWciIiIiIiIhoOJMREREREQkBFSciYiIiIiIhICKMxER\nERERkRBQcSYiIiIiIhICKs5ERERERERCQMWZiIiIiIhICKg4ExERERERCQEVZyIiIiIiIiGg4kxE\nRERERCQEVJyJiIiIiIiEgIozERERERGREEiUGsAY4wG/BbYEGoFfWms/7enAREREwkz5UUREuls5\nV84OBCqttTsC5wPX92xIIiIikaD8KCI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"text/plain": [ "<matplotlib.figure.Figure at 0x11febd350>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Vk8vM0aNH6NXreV5//RWuuOJKihQperGrJKJTf1537tw5AHbs2E758hUTjC9btgJvvPEW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\nb+Duu1tQrVpYstM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"text/plain": [ "<matplotlib.figure.Figure at 0x11f218250>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [ "<matplotlib.figure.Figure at 0x13d821190>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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Zs+9l/vxnqanZGIBYLMbZZ4/kRz/aDYDFixczYsRQJk++nMGDD26Jf9Kkcdx/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\nJ5QxtwK45Jdj01nbnJhNXnlnLXJxBK8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"text/plain": [ "<matplotlib.figure.Figure at 0x13d821810>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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RWNT2xREREekYxfQQrwBuACa0cVlkBTBs2H6tks6MGY+2SjoiIlkFA6Jz7mjg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\nmVloZpUAzrkxQC8z+0cHlLE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"text/plain": [ "<matplotlib.figure.Figure at 0x12ea50750>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x1406fbe10>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x12088d050>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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PHnvszkknncbIkSNXKv6evZiNjctyxpxJigCXXXYxVVVVXUNKZs68krq60SsV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\nSvvgOfXuyTmHBc6f/ybJlEs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"text/plain": [ "<matplotlib.figure.Figure at 0x14417c150>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "######## Get list of files (.mat) we want to work with ########\n", "filedir = '../condensed_data/blocks/'\n", "sessions = os.listdir(filedir)\n", "sessions = sessions[2:]\n", "\n", "# loop through each session\n", "for session in sessions:\n", " print \"Analyzing session \", session\n", " sessiondir = filedir + session\n", " \n", " # get all blocks for this session\n", " blocks = os.listdir(sessiondir)\n", " \n", " if len(blocks) != 6: # error check on the directories\n", " print blocks\n", " print(\"Error in the # of blocks. There should be 5.\")\n", " break\n", " \n", " # loop through each block one at a time, analyze\n", " for i in range(0, 5):\n", " print \"Analyzing block \", blocks[i], ' and ', blocks[i+1]\n", " firstblock = blocks[i]\n", " secondblock = blocks[i+1]\n", " \n", " firstblock_dir = sessiondir+'/'+firstblock\n", " secondblock_dir = sessiondir+'/'+secondblock\n", " # in each block, get list of word pairs from first and second block\n", " first_wordpairs = os.listdir(sessiondir+'/'+firstblock)\n", " second_wordpairs = os.listdir(sessiondir+'/'+secondblock)\n", " \n", " diff_word_group = []\n", " reverse_word_group = []\n", " probe_word_group = []\n", " target_word_group = []\n", " same_word_group = []\n", " \n", " print first_wordpairs\n", " print second_wordpairs\n", " \n", " #### plot meta information about which session and blocks we're analyzing\n", " fig=plt.figure()\n", " axes = plt.gca()\n", " ymin, ymax = axes.get_ylim()\n", " xmin, xmax = axes.get_xlim()\n", " plt.text((xmax-xmin)/4.5, (ymax-ymin)/2, r'Session %s %scomparing %s vs. %s'%(session, '\\n',firstblock, secondblock), fontsize=20)\n", " plt.title(session + ' comparing blocks: ' + firstblock + ' vs. ' + secondblock)\n", " plt.grid(False)\n", " \n", " # go through first block and assign pairs to different groups\n", " for idx, pair in enumerate(first_wordpairs):\n", "# print \"Analyzing \", pair\n", " # obtain indices of: sameword, reverseword, differentwords, probeoverlap, targetoverlap\n", " same_word_index = find_same(pair, second_wordpairs)\n", " reverse_word_index = find_reverse(pair, second_wordpairs)\n", " diff_word_index = find_different(pair, second_wordpairs)\n", " probe_word_index = find_probe(pair, second_wordpairs)\n", " target_word_index = find_target(pair, second_wordpairs)\n", " \n", " # append to list groupings holding pairs of these word groupings\n", " if same_word_index != -1 and not inGroup(same_word_group, [pair, second_wordpairs[same_word_index]]):\n", " same_word_group.append([pair, second_wordpairs[same_word_index]])\n", " if reverse_word_index != -1 and not inGroup(reverse_word_group, [pair, second_wordpairs[reverse_word_index]]): \n", " reverse_word_group.append([pair, second_wordpairs[reverse_word_index]])\n", " if diff_word_index != -1:\n", " if isinstance(diff_word_index, list): # if list, break it down and one pairing at a time\n", " for diffI in diff_word_index: # loop through each different word index\n", " if not inGroup(diff_word_group, [pair, second_wordpairs[diffI]]):\n", " diff_word_group.append([pair, second_wordpairs[diffI]])\n", " else:\n", " diff_word_group.append([pair, second_wordpairs[diff_word_index]])\n", " if probe_word_index != -1 and not inGroup(probe_word_group, [pair, second_wordpairs[probe_word_index]]): \n", " probe_word_group.append([pair, second_wordpairs[probe_word_index]])\n", " if target_word_index != -1 and not inGroup(target_word_group, [pair, second_wordpairs[target_word_index]]):\n", " target_word_group.append([pair, second_wordpairs[target_word_index]])\n", " # end of loop through word pairs\n", " # end of loop through block\n", " print same_word_group\n", " print reverse_word_group\n", " print probe_word_group\n", " print target_word_group\n", " print diff_word_group[0:2]\n", " \n", " #### Go through each group and extract the feature data per event\n", " ## 01: Same Word Group\n", " fig = plt.figure(figsize=(15, 3))\n", " extractFeatures(same_word_group, session, firstblock, secondblock)\n", " extractFeatures(reverse_word_group, session, firstblock, secondblock)\n", " extractFeatures(probe_word_group, session, firstblock, secondblock)\n", " extractFeatures(target_word_group, session, firstblock, secondblock)\n", " extractFeatures(diff_word_group[0:2], session, firstblock, secondblock)\n", " \n", " break" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Classification Analysis\n", "Since the distributions of distances don't seem to separate, I was going to analyze the accuracy of classification analysis on the distributions.\n", "\n", "Electrode layout:\n", "\n", " r=[\n", " 1,32;\t\t% G\t\t(left temporal grid)\n", " 33,38;\t\t% TT\t\t(left temporal tip)\n", " 39,42;\t\t% OF\t\t(left orbitofrontal)\n", " 43,46;\t\t% AST\t\t(left anterior subtemporal)\n", " 47,50;\t\t% MST\t\t(left middle subtemporal)\n", " 51,54;\t\t% PST\t\t(left posterior subtemporal)\n", " 55,60;\t\t% IO\t\t(left inferior occipital)\n", " 61,66;\t\t% MO\t\t(left left middle occipital)\n", " 67,72;\t\t% SO\t\t(left superior occipital)\n", " 73,80;\t\t% PP\t\t(left posterior parietal)\n", " 81,86;\t\t% LP\t\t(left lateral parietal)\n", " 87,90;\t\t% PPST\t\t(left posterior posterior subtemporal)\n", " 91,96;\t\t% LF\t\t(left lateral frontal)\n", " ];\n", " \n", "Use sklearn to perform recursive feature selection" ] }, { "cell_type": "code", "execution_count": 98, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 6]\n", "['delta', 'theta', 'HFO']\n", "The length of the feature vector for each channel will be: 15\n" ] } ], "source": [ "##### HYPER-PARAMETERS TO TUNE\n", "anova_threshold = 90 # how many channels we want to keep\n", "distances = Distance.cosine # define distance metric to use\n", "num_time_windows = 5\n", "freq_bands = [0, 1, 6]\n", "# freq_bands = np.arange(0,7,1)\n", "\n", "channels_we_want = np.arange(38,72,1)\n", "# channels_we_want = np.arange(0,96,1)\n", "\n", "freq_labels = ['delta', 'theta', 'alpha', 'beta', 'low gamma', 'high gamma', 'HFO']\n", "print freq_bands\n", "print [freq_labels[i] for i in freq_bands]\n", "\n", "print \"The length of the feature vector for each channel will be: \", num_time_windows*len(freq_bands)" ] }, { "cell_type": "code", "execution_count": 99, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn import cross_validation\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.cross_validation import LeaveOneOut\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n", "from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis\n", "\n", "from sklearn.svm import LinearSVC\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.feature_selection import SelectFromModel\n", "from sklearn.feature_selection import RFECV\n", "from sklearn.cross_validation import StratifiedKFold" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": true }, "outputs": [], "source": [ "np.random.seed(12345678) # for reproducibility, set random seed\n", "\n", "names = [\"Nearest Neighbors\", \"Linear SVM\", \"Random Forest\",\n", " \"Linear Discriminant Analysis\", \"Quadratic Discriminant Analysis\",\n", " \"Logistic Regression\"]\n", "\n", "classifiers = [\n", " KNeighborsClassifier(3),\n", " SVC(kernel=\"linear\", C=0.5),\n", " RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),\n", " LinearDiscriminantAnalysis(),\n", " QuadraticDiscriminantAnalysis(),\n", " LogisticRegression()]" ] }, { "cell_type": "code", "execution_count": 101, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# loops through each wordpairing group and extract features\n", "def extractFeatures(wordgroup, session, blockone, blocktwo):\n", " # load in data\n", " first_wordpair_dir = firstblock_dir + '/' + wordgroup[0]\n", " second_wordpair_dir = secondblock_dir + '/' + wordgroup[1]\n", "\n", " # initialize np arrays for holding feature vectors for each event\n", " first_pair_features = []\n", " second_pair_features = []\n", "\n", " # load in channels\n", " first_channels = os.listdir(first_wordpair_dir)\n", " second_channels = os.listdir(second_wordpair_dir)\n", " index = 0\n", " # loop through channels\n", " for jdx, chans in enumerate(first_channels):\n", " if jdx in channels_we_want:\n", "# print chans\n", " first_chan_file = first_wordpair_dir + '/' + chans\n", " second_chan_file = second_wordpair_dir + '/' + chans\n", "\n", " # load in data\n", " data_first = scipy.io.loadmat(first_chan_file)\n", " data_first = data_first['data']\n", " data_second = scipy.io.loadmat(second_chan_file)\n", " data_second = data_second['data']\n", "\n", " ## 06: get the time point for probeword on\n", " first_timeZero = data_first['timeZero'][0][0][0]\n", " second_timeZero = data_second['timeZero'][0][0][0]\n", "\n", " ## 07: get the time point of vocalization\n", " first_vocalization = data_first['vocalization'][0][0][0]\n", " second_vocalization = data_second['vocalization'][0][0][0]\n", "\n", " ## Power Matrix\n", " first_matrix = data_first['powerMatZ'][0][0]\n", " second_matrix = data_second['powerMatZ'][0][0]\n", " first_matrix = first_matrix[:, freq_bands,:]\n", " second_matrix = second_matrix[:, freq_bands,:]\n", "\n", " ### 02: get only the time point before vocalization\n", " first_mean = []\n", " second_mean = []\n", " for i in range(0, len(first_vocalization)):\n", " # either go from timezero -> vocalization, or some other timewindow\n", " # first_mean.append(np.mean(first_matrix[i,:,first_timeZero:first_vocalization[i]], axis=1))\n", " first_mean.append(np.mean(first_matrix[i,:,first_vocalization[i]-num_time_windows:first_vocalization[i]], axis=1))\n", " for i in range(0, len(second_vocalization)):\n", " # second_mean.append(np.mean(second_matrix[i,:,second_timeZero:second_vocalization[i]], axis=1))\n", " second_mean.append(np.mean(second_matrix[i,:,second_vocalization[i]-num_time_windows:second_vocalization[i]], axis=1))\n", "\n", " # create feature vector for each event\n", " if index == 0:\n", " first_pair_features.append(first_mean)\n", " second_pair_features.append(second_mean)\n", " first_pair_features = np.squeeze(np.array(first_pair_features))\n", " second_pair_features = np.squeeze(np.array(second_pair_features))\n", " index += 1\n", " else:\n", " first_pair_features = np.concatenate((first_pair_features, first_mean), axis=1)\n", " second_pair_features = np.concatenate((second_pair_features, second_mean), axis=1)\n", " \n", " # end of loop through channels\n", " return first_pair_features, second_pair_features" ] }, { "cell_type": "code", "execution_count": 102, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Analyzing session session_1\n", "Analyzing block BLOCK_0 and BLOCK_1\n", "-------------------------------------------------\n", "Analyzing groups: ['BRICK_CLOCK', 'GLASS_PANTS']\n", "('Accuracy for determining pair: ', ['BRICK_CLOCK', 'GLASS_PANTS'], ' with #events: ', 20, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.50 (+/- 1.00)\n", "Accuracy of Linear SVM: 0.55 (+/- 0.99)\n", "Accuracy of Random Forest: 0.42 (+/- 0.99)\n", "Accuracy of Linear Discriminant Analysis: 0.62 (+/- 0.97)\n", "Accuracy of Quadratic Discriminant Analysis: 0.45 (+/- 0.99)\n", "Accuracy of Logistic Regression: 0.60 (+/- 0.98)\n", "\n", "\n", "Analyzing groups: ['CLOCK_BRICK', 'GLASS_PANTS']\n", "('Accuracy for determining pair: ', ['CLOCK_BRICK', 'GLASS_PANTS'], ' with #events: ', 20, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.55 (+/- 0.99)\n", "Accuracy of Linear SVM: 0.53 (+/- 1.00)\n", "Accuracy of Random Forest: 0.38 (+/- 0.97)\n", "Accuracy of Linear Discriminant Analysis: 0.57 (+/- 0.99)\n", "Accuracy of Quadratic Discriminant Analysis: 0.47 (+/- 1.00)\n", "Accuracy of Logistic Regression: 0.60 (+/- 0.98)\n", "\n", "\n", "\n", "\n", "\n", "\n", "Analyzing block BLOCK_1 and BLOCK_2\n", "-------------------------------------------------\n", "\n", "\n", "\n", "\n", "Analyzing groups: ['BRICK_CLOCK', 'GLASS_PANTS']\n", "('Accuracy for determining pair: ', ['BRICK_CLOCK', 'GLASS_PANTS'], ' with #events: ', 17, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.57 (+/- 0.99)\n", "Accuracy of Linear SVM: 0.49 (+/- 1.00)\n", "Accuracy of Random Forest: 0.51 (+/- 1.00)\n", "Accuracy of Linear Discriminant Analysis: 0.62 (+/- 0.97)\n", "Accuracy of Quadratic Discriminant Analysis: 0.59 (+/- 0.98)\n", "Accuracy of Logistic Regression: 0.62 (+/- 0.97)\n", "\n", "\n", "Analyzing groups: ['BRICK_CLOCK', 'PANTS_GLASS']\n", "('Accuracy for determining pair: ', ['BRICK_CLOCK', 'PANTS_GLASS'], ' with #events: ', 17, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.38 (+/- 0.97)\n", "Accuracy of Linear SVM: 0.38 (+/- 0.97)\n", "Accuracy of Random Forest: 0.59 (+/- 0.98)\n", "Accuracy of Linear Discriminant Analysis: 0.46 (+/- 1.00)\n", "Accuracy of Quadratic Discriminant Analysis: 0.51 (+/- 1.00)\n", "Accuracy of Logistic Regression: 0.38 (+/- 0.97)\n", "\n", "\n", "Analyzing block BLOCK_2 and BLOCK_3\n", "-------------------------------------------------\n", "Analyzing groups: ['BRICK_JUICE', 'CLOCK_GLASS']\n", "('Accuracy for determining pair: ', ['BRICK_JUICE', 'CLOCK_GLASS'], ' with #events: ', 19, ' and ', 18)\n", "Accuracy of Nearest Neighbors: 0.65 (+/- 0.95)\n", "Accuracy of Linear SVM: 0.46 (+/- 1.00)\n", "Accuracy of Random Forest: 0.38 (+/- 0.97)\n", "Accuracy of Linear Discriminant Analysis: 0.70 (+/- 0.91)\n", "Accuracy of Quadratic Discriminant Analysis: 0.49 (+/- 1.00)\n", "Accuracy of Logistic Regression: 0.54 (+/- 1.00)\n", "\n", "\n", "\n", "\n", "Analyzing groups: ['GLASS_PANTS', 'JUICE_BRICK']\n", "('Accuracy for determining pair: ', ['GLASS_PANTS', 'JUICE_BRICK'], ' with #events: ', 20, ' and ', 19)\n", "Accuracy of Nearest Neighbors: 0.54 (+/- 1.00)\n", "Accuracy of Linear SVM: 0.59 (+/- 0.98)\n", "Accuracy of Random Forest: 0.54 (+/- 1.00)\n", "Accuracy of Linear Discriminant Analysis: 0.67 (+/- 0.94)\n", "Accuracy of Quadratic Discriminant Analysis: 0.36 (+/- 0.96)\n", "Accuracy of Logistic Regression: 0.62 (+/- 0.97)\n", "\n", "\n", "\n", "\n", "Analyzing block BLOCK_3 and BLOCK_4\n", "-------------------------------------------------\n", "\n", "\n", "Analyzing groups: ['BRICK_JUICE', 'CLOCK_GLASS']\n", "('Accuracy for determining pair: ', ['BRICK_JUICE', 'CLOCK_GLASS'], ' with #events: ', 20, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.38 (+/- 0.97)\n", "Accuracy of Linear SVM: 0.53 (+/- 1.00)\n", "Accuracy of Random Forest: 0.60 (+/- 0.98)\n", "Accuracy of Linear Discriminant Analysis: 0.60 (+/- 0.98)\n", "Accuracy of Quadratic Discriminant Analysis: 0.47 (+/- 1.00)\n", "Accuracy of Logistic Regression: 0.50 (+/- 1.00)\n", "\n", "\n", "Analyzing groups: ['BRICK_JUICE', 'GLASS_CLOCK']\n", "('Accuracy for determining pair: ', ['BRICK_JUICE', 'GLASS_CLOCK'], ' with #events: ', 20, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.55 (+/- 0.99)\n", "Accuracy of Linear SVM: 0.65 (+/- 0.95)\n", "Accuracy of Random Forest: 0.60 (+/- 0.98)\n", "Accuracy of Linear Discriminant Analysis: 0.45 (+/- 0.99)\n", "Accuracy of Quadratic Discriminant Analysis: 0.47 (+/- 1.00)\n", "Accuracy of Logistic Regression: 0.65 (+/- 0.95)\n", "\n", "\n", "\n", "\n", "Analyzing block BLOCK_4 and BLOCK_5\n", "-------------------------------------------------\n", "Analyzing groups: ['BRICK_PANTS', 'GLASS_JUICE']\n", "('Accuracy for determining pair: ', ['BRICK_PANTS', 'GLASS_JUICE'], ' with #events: ', 20, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.53 (+/- 1.00)\n", "Accuracy of Linear SVM: 0.47 (+/- 1.00)\n", "Accuracy of Random Forest: 0.45 (+/- 0.99)\n", "Accuracy of Linear Discriminant Analysis: 0.62 (+/- 0.97)\n", "Accuracy of Quadratic Discriminant Analysis: 0.60 (+/- 0.98)\n", "Accuracy of Logistic Regression: 0.47 (+/- 1.00)\n", "\n", "\n", "\n", "\n", "\n", "\n", "Analyzing groups: ['CLOCK_GLASS', 'PANTS_BRICK']\n", "('Accuracy for determining pair: ', ['CLOCK_GLASS', 'PANTS_BRICK'], ' with #events: ', 20, ' and ', 19)\n", "Accuracy of Nearest Neighbors: 0.54 (+/- 1.00)\n", "Accuracy of Linear SVM: 0.51 (+/- 1.00)\n", "Accuracy of Random Forest: 0.56 (+/- 0.99)\n", "Accuracy of Linear Discriminant Analysis: 0.54 (+/- 1.00)\n", "Accuracy of Quadratic Discriminant Analysis: 0.54 (+/- 1.00)\n", "Accuracy of Logistic Regression: 0.51 (+/- 1.00)\n", "\n", "\n", "Analyzing session session_2\n", "Analyzing block BLOCK_0 and BLOCK_1\n", "-------------------------------------------------\n", "Analyzing groups: ['BRICK_CLOCK', 'GLASS_PANTS']\n", "('Accuracy for determining pair: ', ['BRICK_CLOCK', 'GLASS_PANTS'], ' with #events: ', 20, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.40 (+/- 0.98)\n", "Accuracy of Linear SVM: 0.38 (+/- 0.97)\n", "Accuracy of Random Forest: 0.45 (+/- 0.99)\n", "Accuracy of Linear Discriminant Analysis: 0.47 (+/- 1.00)\n", "Accuracy of Quadratic Discriminant Analysis: 0.53 (+/- 1.00)\n", "Accuracy of Logistic Regression: 0.38 (+/- 0.97)\n", "\n", "\n", "Analyzing groups: ['CLOCK_BRICK', 'GLASS_PANTS']\n", "('Accuracy for determining pair: ', ['CLOCK_BRICK', 'GLASS_PANTS'], ' with #events: ', 19, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.62 (+/- 0.97)\n", "Accuracy of Linear SVM: 0.54 (+/- 1.00)\n", "Accuracy of Random Forest: 0.38 (+/- 0.97)\n", "Accuracy of Linear Discriminant Analysis: 0.56 (+/- 0.99)\n", "Accuracy of Quadratic Discriminant Analysis: 0.54 (+/- 1.00)\n", "Accuracy of Logistic Regression: 0.54 (+/- 1.00)\n", "\n", "\n", "\n", "\n", "\n", "\n", "Analyzing block BLOCK_1 and BLOCK_2\n", "-------------------------------------------------\n", "\n", "\n", "\n", "\n", "Analyzing groups: ['BRICK_CLOCK', 'GLASS_PANTS']\n", "('Accuracy for determining pair: ', ['BRICK_CLOCK', 'GLASS_PANTS'], ' with #events: ', 20, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.65 (+/- 0.95)\n", "Accuracy of Linear SVM: 0.70 (+/- 0.92)\n", "Accuracy of Random Forest: 0.55 (+/- 0.99)\n", "Accuracy of Linear Discriminant Analysis: 0.68 (+/- 0.94)\n", "Accuracy of Quadratic Discriminant Analysis: 0.55 (+/- 0.99)\n", "Accuracy of Logistic Regression: 0.70 (+/- 0.92)\n", "\n", "\n", "Analyzing groups: ['BRICK_CLOCK', 'PANTS_GLASS']\n", "('Accuracy for determining pair: ', ['BRICK_CLOCK', 'PANTS_GLASS'], ' with #events: ', 20, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.45 (+/- 0.99)\n", "Accuracy of Linear SVM: 0.55 (+/- 0.99)\n", "Accuracy of Random Forest: 0.57 (+/- 0.99)\n", "Accuracy of Linear Discriminant Analysis: 0.72 (+/- 0.89)\n", "Accuracy of Quadratic Discriminant Analysis: 0.35 (+/- 0.95)\n", "Accuracy of Logistic Regression: 0.57 (+/- 0.99)\n", "\n", "\n", "Analyzing block BLOCK_2 and BLOCK_3\n", "-------------------------------------------------\n", "Analyzing groups: ['BRICK_JUICE', 'CLOCK_GLASS']\n", "('Accuracy for determining pair: ', ['BRICK_JUICE', 'CLOCK_GLASS'], ' with #events: ', 20, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.40 (+/- 0.98)\n", "Accuracy of Linear SVM: 0.42 (+/- 0.99)\n", "Accuracy of Random Forest: 0.47 (+/- 1.00)\n", "Accuracy of Linear Discriminant Analysis: 0.42 (+/- 0.99)\n", "Accuracy of Quadratic Discriminant Analysis: 0.50 (+/- 1.00)\n", "Accuracy of Logistic Regression: 0.42 (+/- 0.99)\n", "\n", "\n", "\n", "\n", "Analyzing groups: ['GLASS_PANTS', 'JUICE_BRICK']\n", "('Accuracy for determining pair: ', ['GLASS_PANTS', 'JUICE_BRICK'], ' with #events: ', 20, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.70 (+/- 0.92)\n", "Accuracy of Linear SVM: 0.62 (+/- 0.97)\n", "Accuracy of Random Forest: 0.60 (+/- 0.98)\n", "Accuracy of Linear Discriminant Analysis: 0.53 (+/- 1.00)\n", "Accuracy of Quadratic Discriminant Analysis: 0.50 (+/- 1.00)\n", "Accuracy of Logistic Regression: 0.65 (+/- 0.95)\n", "\n", "\n", "\n", "\n", "Analyzing block BLOCK_3 and BLOCK_4\n", "-------------------------------------------------\n", "\n", "\n", "Analyzing groups: ['BRICK_JUICE', 'CLOCK_GLASS']\n", "('Accuracy for determining pair: ', ['BRICK_JUICE', 'CLOCK_GLASS'], ' with #events: ', 19, ' and ', 18)\n", "Accuracy of Nearest Neighbors: 0.51 (+/- 1.00)\n", "Accuracy of Linear SVM: 0.43 (+/- 0.99)\n", "Accuracy of Random Forest: 0.43 (+/- 0.99)\n", "Accuracy of Linear Discriminant Analysis: 0.57 (+/- 0.99)\n", "Accuracy of Quadratic Discriminant Analysis: 0.49 (+/- 1.00)\n", "Accuracy of Logistic Regression: 0.51 (+/- 1.00)\n", "\n", "\n", "Analyzing groups: ['BRICK_JUICE', 'GLASS_CLOCK']\n", "('Accuracy for determining pair: ', ['BRICK_JUICE', 'GLASS_CLOCK'], ' with #events: ', 19, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.59 (+/- 0.98)\n", "Accuracy of Linear SVM: 0.62 (+/- 0.97)\n", "Accuracy of Random Forest: 0.59 (+/- 0.98)\n", "Accuracy of Linear Discriminant Analysis: 0.56 (+/- 0.99)\n", "Accuracy of Quadratic Discriminant Analysis: 0.44 (+/- 0.99)\n", "Accuracy of Logistic Regression: 0.67 (+/- 0.94)\n", "\n", "\n", "\n", "\n", "Analyzing block BLOCK_4 and BLOCK_5\n", "-------------------------------------------------\n", "Analyzing groups: ['BRICK_PANTS', 'GLASS_JUICE']\n", "('Accuracy for determining pair: ', ['BRICK_PANTS', 'GLASS_JUICE'], ' with #events: ', 20, ' and ', 19)\n", "Accuracy of Nearest Neighbors: 0.46 (+/- 1.00)\n", "Accuracy of Linear SVM: 0.44 (+/- 0.99)\n", "Accuracy of Random Forest: 0.44 (+/- 0.99)\n", "Accuracy of Linear Discriminant Analysis: 0.59 (+/- 0.98)\n", "Accuracy of Quadratic Discriminant Analysis: 0.49 (+/- 1.00)\n", "Accuracy of Logistic Regression: 0.46 (+/- 1.00)\n", "\n", "\n", "\n", "\n", "\n", "\n", "Analyzing groups: ['CLOCK_GLASS', 'PANTS_BRICK']\n", "('Accuracy for determining pair: ', ['CLOCK_GLASS', 'PANTS_BRICK'], ' with #events: ', 18, ' and ', 20)\n", "Accuracy of Nearest Neighbors: 0.42 (+/- 0.99)\n", "Accuracy of Linear SVM: 0.66 (+/- 0.95)\n", "Accuracy of Random Forest: 0.37 (+/- 0.96)\n", "Accuracy of Linear Discriminant Analysis: 0.32 (+/- 0.93)\n", "Accuracy of Quadratic Discriminant Analysis: 0.37 (+/- 0.96)\n", "Accuracy of Logistic Regression: 0.55 (+/- 0.99)\n", "\n", "\n" ] } ], "source": [ "######## Get list of files (.mat) we want to work with ########\n", "filedir = '../condensed_data/blocks/'\n", "sessions = os.listdir(filedir)\n", "sessions = sessions[2:]\n", "\n", "accuracy=np.zeros((len(sessions),len(range(0,5)),len(classifiers),2))\n", "\n", "# loop through each session\n", "for sdx, session in enumerate(sessions):\n", " print \"Analyzing session \", session\n", " sessiondir = filedir + session\n", " \n", " # get all blocks for this session\n", " blocks = os.listdir(sessiondir)\n", " \n", " if len(blocks) != 6: # error check on the directories\n", " print blocks\n", " print(\"Error in the # of blocks. There should be 5.\")\n", " break\n", " \n", " # loop through each block one at a time, analyze\n", " for i in range(0, 5):\n", " print \"Analyzing block \", blocks[i], ' and ', blocks[i+1]\n", " print \"-------------------------------------------------\"\n", " firstblock = blocks[i]\n", " secondblock = blocks[i+1]\n", " \n", " firstblock_dir = sessiondir+'/'+firstblock\n", " secondblock_dir = sessiondir+'/'+secondblock\n", " # in each block, get list of word pairs from first and second block\n", " first_wordpairs = os.listdir(sessiondir+'/'+firstblock)\n", " second_wordpairs = os.listdir(sessiondir+'/'+secondblock)\n", " \n", " diff_word_group = []\n", " reverse_word_group = []\n", " probe_word_group = []\n", " target_word_group = []\n", " same_word_group = []\n", " \n", " # go through first block and assign pairs to different groups\n", " for idx, pair in enumerate(first_wordpairs):\n", "# print \"Analyzing \", pair\n", " # obtain indices of: sameword, reverseword, differentwords, probeoverlap, targetoverlap\n", " same_word_index = find_same(pair, second_wordpairs)\n", " reverse_word_index = find_reverse(pair, second_wordpairs)\n", " diff_word_index = find_different(pair, second_wordpairs)\n", " probe_word_index = find_probe(pair, second_wordpairs)\n", " target_word_index = find_target(pair, second_wordpairs)\n", " \n", " # append to list groupings holding pairs of these word groupings\n", " if same_word_index != -1 and not inGroup(same_word_group, [pair, second_wordpairs[same_word_index]]):\n", " same_word_group.append([pair, second_wordpairs[same_word_index]])\n", " if reverse_word_index != -1 and not inGroup(reverse_word_group, [pair, second_wordpairs[reverse_word_index]]): \n", " reverse_word_group.append([pair, second_wordpairs[reverse_word_index]])\n", " if diff_word_index != -1:\n", " if isinstance(diff_word_index, list): # if list, break it down and one pairing at a time\n", " for diffI in diff_word_index: # loop through each different word index\n", " if not inGroup(diff_word_group, [pair, second_wordpairs[diffI]]):\n", " diff_word_group.append([pair, second_wordpairs[diffI]])\n", " else:\n", " diff_word_group.append([pair, second_wordpairs[diff_word_index]])\n", " if probe_word_index != -1 and not inGroup(probe_word_group, [pair, second_wordpairs[probe_word_index]]): \n", " probe_word_group.append([pair, second_wordpairs[probe_word_index]])\n", " if target_word_index != -1 and not inGroup(target_word_group, [pair, second_wordpairs[target_word_index]]):\n", " target_word_group.append([pair, second_wordpairs[target_word_index]])\n", " # end of loop through word pairs\n", " # end of loop through block\n", " \n", " # try some classifiers to see if we can determine diff vs. same, \n", " \n", " ###### 01: Check same vs. different groups\n", " # loop through each word in first_wordpairs\n", " for word_pair in first_wordpairs:\n", " group_pairings = []\n", " \n", " check_diff = [item for item in diff_word_group if word_pair in item]\n", " check_same = [item for item in same_word_group if word_pair in item]\n", " \n", " # check if there is a match in diff_words_groups, reverse_words, probe_words and target_words\n", " if len(check_diff) > 0:\n", " if len(check_same) > 0:\n", " # add group pairing for all 4 groups\n", "# group_pairings.append(check_same[0])\n", " group_pairings.append(check_diff[0])\n", " \n", " ## loop through the new pairing to perform classification analysis\n", " for jdx, group in enumerate(group_pairings):\n", " # extract features \n", " first_feature_mat, second_feature_mat = extractFeatures(group, session, firstblock, secondblock)\n", " \n", " \n", " # Create classes and feature vects\n", " features = np.append(first_feature_mat, second_feature_mat, axis=0)\n", " y = np.ones((first_feature_mat.shape[0],))\n", " y = np.concatenate((y, np.zeros((second_feature_mat.shape[0],))))\n", " \n", " print \"Analyzing groups: \", group\n", " print(\"Accuracy for determining pair: \", group, \" with #events: \", first_feature_mat.shape[0], ' and ',second_feature_mat.shape[0])\n", " ## LOOP THROUGH EACH CLASSIFIER\n", " for idx, cla in enumerate(classifiers):\n", " X_train, X_test, y_train, y_test = cross_validation.train_test_split(features, y, test_size=0.5, random_state=0)\n", " \n", "# try:\n", "# pipe_cla = Pipeline([\n", "# ('feature_selection', SelectFromModel(LinearSVC(penalty=\"l1\"))),\n", "# ('classification', cla)\n", "# ])\n", "# clf = pipe_cla.fit(X_train, y_train)\n", "# except:\n", "# try:\n", "# rfecv = RFECV(estimator=cla, step=1, scoring='accuracy')\n", "# clf = rfecv.fit(X_train, y_train)\n", "# except:\n", "# clf = cla.fit(X_train, y_train)\n", "\n", " # no feature selection\n", " clf = cla.fit(X_train, y_train)\n", " loo = LeaveOneOut(len(features))\n", " scores = cross_validation.cross_val_score(clf, features, y, cv=loo)\n", " accuracy[sdx,i,idx,] = [float(scores.mean()), float(scores.std())]\n", " print(\"Accuracy of %s: %0.2f (+/- %0.2f)\" % (names[idx], scores.mean(), scores.std() * 2))\n", " \n", " print \"\\n\"\n", "# break # to only analyze 1 block\n", "# break # to only analyze 1 session" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2, 5, 6, 2)\n" ] }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11f41fcd0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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PpNqwGDYEgL8AO+OuxJqqqouj6g/DXR7bCNytqnN70JbzgKnAKq/oNFVdlCp7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\nAP4CnJtSXVS/ZLEFiv9+SYjIz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"text/plain": [ "<matplotlib.figure.Figure at 0x11f3a85d0>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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"text/plain": [ "<matplotlib.figure.Figure at 0x11cf09c50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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J67n+OMjJVfUB4IHEbSLyFC1V1NqtkiYiBf5xa4FzAlwqlEvVlCC3qjvF5VpM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cAAAg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\ndMkJINvkIttEDJ8LhVi1vrreC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"text/plain": [ "<matplotlib.figure.Figure at 0x11f43f610>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print accuracy.shape\n", "for sessI in range(0, accuracy.shape[0]):\n", " sesh_accuracy = np.squeeze(accuracy[sessI,:,:,:])\n", " # plot text indicating new session\n", " plt.figure()\n", " axes = plt.gca()\n", " ymin, ymax = axes.get_ylim()\n", " xmin, xmax = axes.get_xlim()\n", " plt.text((xmax-xmin)/4.5, (ymax-ymin)/2, r'Session %i'%(sessI), fontsize=20)\n", " plt.grid(False)\n", " \n", " fig = plt.figure(figsize=(5, 12))\n", " for class_idx, cla in enumerate(classifiers):\n", " class_accuracy = np.squeeze(accuracy[sessI,:,class_idx,:])\n", "\n", " # plot errorbars for each classifier on all blocks within this session\n", " plt.subplot(len(classifiers), 1, class_idx+1)\n", " plt.errorbar(range(0, accuracy.shape[1]), class_accuracy[:,0], yerr = class_accuracy[:,1], hold=True, label=names[class_idx])\n", " plt.xlabel('block #')\n", " plt.ylabel('classification accuracy')\n", " plt.axhline(0.5, color='red', linestyle='--')\n", " plt.legend()\n", "# print class_accuracy.shape\n", "# break # only plot for 1 classifier\n", "# print sesh_accuracy.shape\n", " \n", "# break # only do it for 1 session\n", "\n", "plt.tight_layout()" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[False True True True]\n", "[2 1 1 1]\n" ] } ], "source": [ "from sklearn import datasets\n", "from sklearn.feature_selection import RFE\n", "from sklearn.linear_model import LogisticRegression\n", "# load the iris datasets\n", "dataset = datasets.load_iris()\n", "# create a base classifier used to evaluate a subset of attributes\n", "model = LogisticRegression()\n", "# create the RFE model and select 3 attributes\n", "rfe = RFE(model, 3)\n", "rfe = rfe.fit(dataset.data, dataset.target)\n", "# summarize the selection of the attributes\n", "print(rfe.support_)\n", "print(rfe.ranking_)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0.10329852 0.08443241 0.31908967 0.4931794 ]\n" ] } ], "source": [ "# Feature Importance\n", "from sklearn import datasets\n", "from sklearn import metrics\n", "from sklearn.ensemble import ExtraTreesClassifier\n", "# load the iris datasets\n", "dataset = datasets.load_iris()\n", "# fit an Extra Trees model to the data\n", "model = ExtraTreesClassifier()\n", "model.fit(dataset.data, dataset.target)\n", "# display the relative importance of each attribute\n", "print(model.feature_importances_)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Plotting Spectrograms\n" ] }, { "cell_type": "code", "execution_count": 292, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "\n", "def plotSpect(wordgroup, chans_we_want, session, blockone, blocktwo):\n", " fig = plt.figure(figsize=(len(wordgroup)*6, 3.5))\n", " \n", " for idx, pair in enumerate(wordgroup):\n", " # load in data\n", " first_wordpair_dir = firstblock_dir + '/' + pair[0]\n", " second_wordpair_dir = secondblock_dir + '/' + pair[1]\n", "\n", " # initialize np arrays for holding feature vectors for each event\n", " first_pair_features = []\n", " second_pair_features = []\n", "\n", " # load in channels\n", " first_channels = os.listdir(first_wordpair_dir)\n", " second_channels = os.listdir(second_wordpair_dir)\n", " \n", " first_spect = []\n", " second_spect = []\n", " # loop through channels\n", " for jdx, chans in enumerate(first_channels):\n", " if jdx in chans_we_want:\n", " first_chan_file = first_wordpair_dir + '/' + chans\n", " second_chan_file = second_wordpair_dir + '/' + chans\n", "\n", " # load in data\n", " data_first = scipy.io.loadmat(first_chan_file)\n", " data_first = data_first['data']\n", " data_second = scipy.io.loadmat(second_chan_file)\n", " data_second = data_second['data']\n", "\n", " ## 06: get the time point for probeword on\n", " first_timeZero = data_first['timeZero'][0][0][0]\n", " second_timeZero = data_second['timeZero'][0][0][0]\n", "\n", " ## 07: get the time point of vocalization\n", " first_vocalization = data_first['vocalization'][0][0][0]\n", " second_vocalization = data_second['vocalization'][0][0][0]\n", "\n", " ## Power Matrix\n", " first_matrix = data_first['powerMatZ'][0][0]\n", " second_matrix = data_second['powerMatZ'][0][0]\n", " \n", " first_spect.append(first_matrix)\n", " second_spect.append(second_matrix)\n", "\n", "# break\n", " first_numevents = np.array(first_spect).shape[1]\n", " second_numevents = np.array(second_spect).shape[1]\n", " # average along events and channels\n", " \n", " first_spect = np.mean(np.array(first_spect),axis=(0,1))\n", " second_spect = np.mean(np.array(second_spect), axis=(0,1))\n", " spect = second_spect-first_spect\n", " \n", " ## For plotting spectrogram separately\n", "# fig =plt.figure(figsize=(12,4))\n", "# fig1= plt.subplot(1,2,1)\n", "# ax1 = plt.gca()\n", "# im1 = plt.imshow(first_spect, interpolation='none', cmap='jet', aspect='auto')\n", "# plt.yticks(np.arange(0,7,1), freq_labels)\n", "# plt.legend()\n", "# plt.axvline(first_timeZero, color='k')\n", "# plt.gca().invert_yaxis()\n", "# plt.title(session + pair[0] + '(' + str(first_numevents) +')')\n", "# vmin1, vmax1 = plt.gci().get_clim()\n", "# divider = make_axes_locatable(ax1)\n", "# cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", "# plt.colorbar(im1, cax=cax)\n", " \n", "# fig2 = plt.subplot(1,2,2)\n", "# ax2 = plt.gca()\n", "# im2 = plt.imshow(second_spect, interpolation='none', cmap='jet', aspect='auto')\n", "# plt.yticks(np.arange(0,7,1), freq_labels)\n", "# plt.legend()\n", "# plt.axvline(first_timeZero, color='k')\n", "# plt.gca().invert_yaxis()\n", "# plt.title(session + pair[1] + '(' + str(second_numevents) +')')\n", "# vmin2, vmax2 = plt.gci().get_clim()\n", "# divider = make_axes_locatable(ax2)\n", "# cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", "# plt.colorbar(im2, cax=cax)\n", "\n", "# ## set to compare spectrograms fairly\n", "# im1.set_clim([-0.2, 0.15])\n", "# im2.set_clim([-0.2, 0.15])\n", "# im1.set_clim([min(vmin1,vmin2), max(vmax1,vmax2)])\n", "# im2.set_clim([min(vmin1,vmin2), max(vmax1,vmax2)])\n", " \n", " \n", " ## Create spectrogram \n", " ## Plotting Paired Distances\n", " plt.subplot(1, len(wordgroup), idx+1)\n", "# fig =plt.figure(figsize=(10, 20))\n", " ax = plt.gca()\n", " im = plt.imshow(spect, interpolation='none', cmap='jet', aspect='auto')\n", " plt.yticks(np.arange(0,7,1), freq_labels)\n", " plt.legend()\n", " plt.axvline(first_timeZero, color='k')\n", " plt.gca().invert_yaxis()\n", " plt.title(session + ' comparing ' + pair[0] + '(' + str(first_numevents) +') vs '+ pair[1] + '(' + str(second_numevents) +')')\n", " vmin, vmax = plt.gci().get_clim()\n", " divider = make_axes_locatable(ax)\n", " cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n", " plt.colorbar(im, cax=cax)\n", " im.set_clim([-0.2, 0.2])\n", " \n", " yline = np.arange(1,7,1)\n", " for i in range(0, len(yline)):\n", " plt.axvline(x=yline[i], color='k')\n", " ax.grid(False)\n", " \n", " \n", " \n", " plt.tight_layout()\n", "# break" ] }, { "cell_type": "code", "execution_count": 293, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "96\n", "[0 1 2 3 4 5 6]\n", "['delta', 'theta', 'alpha', 'beta', 'low gamma', 'high gamma', 'HFO']\n", "The length of the feature vector for each channel will be: 35\n" ] } ], "source": [ "##### HYPER-PARAMETERS TO TUNE\n", "anova_threshold = 90 # how many channels we want to keep\n", "distances = Distance.cosine # define distance metric to use\n", "num_time_windows = 5\n", "freq_bands = np.arange(0,7,1)\n", "\n", "# channels_we_want = np.arange(38,72,1)\n", "channels_we_want = np.arange(0,96,1)\n", "print len(channels_we_want)\n", "\n", "freq_labels = ['delta', 'theta', 'alpha', 'beta', 'low gamma', 'high gamma', 'HFO']\n", "print freq_bands\n", "print [freq_labels[i] for i in freq_bands]\n", "\n", "print \"The length of the feature vector for each channel will be: \", num_time_windows*len(freq_bands)" ] }, { "cell_type": "code", "execution_count": 294, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Analyzing session session_1\n", "Analyzing block BLOCK_0 and BLOCK_1\n", "['BRICK_CLOCK', 'CLOCK_BRICK', 'GLASS_JUICE', 'JUICE_GLASS']\n", "['BRICK_CLOCK', 'CLOCK_BRICK', 'GLASS_PANTS', 'PANTS_GLASS']\n", "[['BRICK_CLOCK', 'BRICK_CLOCK'], ['CLOCK_BRICK', 'CLOCK_BRICK']]\n", "[['BRICK_CLOCK', 'CLOCK_BRICK']]\n", "[['GLASS_JUICE', 'GLASS_PANTS']]\n", "[['JUICE_GLASS', 'PANTS_GLASS']]\n", "[['BRICK_CLOCK', 'GLASS_PANTS'], ['BRICK_CLOCK', 'PANTS_GLASS']]\n" ] }, { "data": { "image/png": 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OXbj5ZhOv3PLlS/nuu9Fs27aV06dPUaJESRo2vIc2bdqSLVu2NJWLPQJs3boN3br18C+7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\n7dqZaPqGDesoUKAgxYoVZ8u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"text/plain": [ "<matplotlib.figure.Figure at 0x11ee9a590>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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ikgc1jFWtGqdUwBIRyaFuddVNV6hv0WgLgV4lv9cGrXWYCzzn7quB6Wa23Mz6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\n4Ohrel7s44HhN4SueD0jyt44Pn3DZU9MPxQhjRznuYMLKlt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"text/plain": [ "<matplotlib.figure.Figure at 0x11d069490>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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3uT9fYRAAC7DnY07Wnps+GSvHUt8vL7m+89yU5iLsPunl5Uye12l8GYCnMgda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\nlM6e/6Z+PpvC2tFNH7b2isnQzc/yksl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"text/plain": [ "<matplotlib.figure.Figure at 0x11fdd0c50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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arPe6Elu+Pi0Vfgy2GKkSmKyqpzWWpjlEDyoIgiCoE5/eOExVTxT7tGaMqibz8R2wBUzb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\nE4GzUpEz2D57i/x4CTWXCwBVrQY+8/R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"text/plain": [ "<matplotlib.figure.Figure at 0x128b15a90>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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TwXfWqJcnM3m9yAd+B2bNzdarpkTkKOBw369vOLaN0pHu1xZ4D9vDbxW25dLX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\nuWU83rqqlS5/Ve30k3zk1uPk/5A8/6R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"text/plain": [ "<matplotlib.figure.Figure at 0x127953b50>" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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w+YfzhZBmFHZK3Z5D0+/00pLNUu/ecwHYhHcBOHyHfwEwh96r0wywMs34eYYD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\niBSk0FylDpaISIkKHXYhIiIFKTRXqYMlIlKiQs8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"text/plain": [ "<matplotlib.figure.Figure at 0x11dde9d10>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "######## Get list of files (.mat) we want to work with ########\n", "filedir = '../condensed_data/blocks/'\n", "sessions = os.listdir(filedir)\n", "sessions = sessions[2:]\n", "\n", "# loop through each session\n", "for session in sessions:\n", " print \"Analyzing session \", session\n", " sessiondir = filedir + session\n", " \n", " # get all blocks for this session\n", " blocks = os.listdir(sessiondir)\n", " \n", " if len(blocks) != 6: # error check on the directories\n", " print blocks\n", " print(\"Error in the # of blocks. There should be 5.\")\n", " break\n", " \n", " # loop through each block one at a time, analyze\n", " for i in range(0, 5):\n", " print \"Analyzing block \", blocks[i], ' and ', blocks[i+1]\n", " firstblock = blocks[i]\n", " secondblock = blocks[i+1]\n", " \n", " firstblock_dir = sessiondir+'/'+firstblock\n", " secondblock_dir = sessiondir+'/'+secondblock\n", " # in each block, get list of word pairs from first and second block\n", " first_wordpairs = os.listdir(sessiondir+'/'+firstblock)\n", " second_wordpairs = os.listdir(sessiondir+'/'+secondblock)\n", " \n", " diff_word_group = []\n", " reverse_word_group = []\n", " probe_word_group = []\n", " target_word_group = []\n", " same_word_group = []\n", " \n", " print first_wordpairs\n", " print second_wordpairs\n", " \n", " #### plot meta information about which session and blocks we're analyzing\n", " fig=plt.figure()\n", " axes = plt.gca()\n", " ymin, ymax = axes.get_ylim()\n", " xmin, xmax = axes.get_xlim()\n", " plt.text((xmax-xmin)/4.5, (ymax-ymin)/2, r'Session %s %scomparing %s vs. %s'%(session, '\\n',firstblock, secondblock), fontsize=20)\n", " plt.title(session + ' comparing blocks: ' + firstblock + ' vs. ' + secondblock)\n", " plt.grid(False)\n", " \n", " # go through first block and assign pairs to different groups\n", " for idx, pair in enumerate(first_wordpairs):\n", "# print \"Analyzing \", pair\n", " # obtain indices of: sameword, reverseword, differentwords, probeoverlap, targetoverlap\n", " same_word_index = find_same(pair, second_wordpairs)\n", " reverse_word_index = find_reverse(pair, second_wordpairs)\n", " diff_word_index = find_different(pair, second_wordpairs)\n", " probe_word_index = find_probe(pair, second_wordpairs)\n", " target_word_index = find_target(pair, second_wordpairs)\n", " \n", " # append to list groupings holding pairs of these word groupings\n", " if same_word_index != -1 and not inGroup(same_word_group, [pair, second_wordpairs[same_word_index]]):\n", " same_word_group.append([pair, second_wordpairs[same_word_index]])\n", " if reverse_word_index != -1 and not inGroup(reverse_word_group, [pair, second_wordpairs[reverse_word_index]]): \n", " reverse_word_group.append([pair, second_wordpairs[reverse_word_index]])\n", " if diff_word_index != -1:\n", " if isinstance(diff_word_index, list): # if list, break it down and one pairing at a time\n", " for diffI in diff_word_index: # loop through each different word index\n", " if not inGroup(diff_word_group, [pair, second_wordpairs[diffI]]):\n", " diff_word_group.append([pair, second_wordpairs[diffI]])\n", " else:\n", " diff_word_group.append([pair, second_wordpairs[diff_word_index]])\n", " if probe_word_index != -1 and not inGroup(probe_word_group, [pair, second_wordpairs[probe_word_index]]): \n", " probe_word_group.append([pair, second_wordpairs[probe_word_index]])\n", " if target_word_index != -1 and not inGroup(target_word_group, [pair, second_wordpairs[target_word_index]]):\n", " target_word_group.append([pair, second_wordpairs[target_word_index]])\n", " # end of loop through word pairs\n", " # end of loop through block\n", " print same_word_group\n", " print reverse_word_group\n", " print probe_word_group\n", " print target_word_group\n", " print diff_word_group[0:2]\n", " \n", " #### Go through each group and extract the feature data per event\n", " ## 01: Same Word Group\n", " plotSpect(same_word_group, channels_we_want, session, firstblock, secondblock)\n", " plotSpect(reverse_word_group, channels_we_want, session, firstblock, secondblock)\n", " plotSpect(probe_word_group,channels_we_want, session, firstblock, secondblock)\n", " plotSpect(target_word_group,channels_we_want, session, firstblock, secondblock)\n", " plotSpect(diff_word_group[0:2],channels_we_want, session, firstblock, secondblock)\n", " break" ] }, { "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
austinjalexander/sandbox
python/py/TextAnalytics/02_Language_Identification.ipynb
1
2461
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##Lang Detect" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from langdetect import detect\n", "print detect(\"War doesn't show who's right, just who's left.\")\n", "print detect(\"Ein, zwei, drei, vier\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##More Languages" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import unicodecsv \n", "with open('7languages.txt', 'rb') as input_file:\n", " row_reader = unicodecsv.reader(input_file)\n", " result = []\n", " for row in row_reader:\n", " lang = detect(row[0])\n", " result = result + [lang.encode('ascii','ignore')]\n", " print row[0] , \"|\" , lang" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Confusion Matrix" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#cerate truth set\n", "truth = ['ko','de','zh','ar','es','ja']\n", "print truth\n", "print result" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Display Confusion Matrix\n", "import nltk\n", "print nltk.ConfusionMatrix(truth, result)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----------------\n", "##You Try It\n", "1. Put Lang Detect to the test on the 8 Genesis texts\n", "2. Output the detected language for each\n", "3. Did it get them all right? " ] }, { "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 }
mit